A method and system for predicting fatigue life of a tower

Abstract

The application provides a tower fatigue life prediction method and system, which is suitable for the technical field of data processing. The method comprises the following steps: based on a preset communication tower stress monitoring information cutting model, a plurality of communication tower stress monitoring information cutting segment quantity information is obtained by cutting calculation on the plurality of communication tower stress monitoring information, and a plurality of communication tower stress monitoring cutting information is obtained; and based on a preset tower fatigue life prediction model, a plurality of tower fatigue life prediction information is obtained by calculation according to the plurality of communication tower stress monitoring cutting information. The application greatly improves the accuracy and timeliness of tower fatigue life prediction, helps operation and maintenance personnel to discover potential safety risks of tower structure in advance, timely formulates targeted maintenance and reinforcement scheme, thereby prolongs the service cycle of the communication tower, reduces the operation and maintenance cost, and guarantees the long-term stable operation of the communication network.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, and in particular relates to a method and system for predicting the fatigue life of iron towers. Background Technology

[0002] With the rapid development of the communications industry, the number and scale of communication towers have continued to expand. They are exposed to natural environments such as wind, rain, ice and snow for a long time. Their structural safety is directly related to the stable operation of the communication network and public safety. Therefore, the structural health monitoring of communication towers has become a key focus of the industry.

[0003] In existing technologies, stress sensors such as strain gauges and fiber optic gratings are installed at key parts of the tower to collect tower stress data in real time. The data is then uploaded to a cloud platform for analysis and processing using wireless or wired transmission technologies to analyze the fatigue life of the tower.

[0004] However, the existing technology directly uses raw continuous data for analysis, which leads to high data redundancy and insufficient accuracy and reliability of fatigue life prediction results, making it difficult to accurately reflect the actual service status of the tower. Summary of the Invention

[0005] In view of this, the present application provides a method and system for predicting the fatigue life of iron towers, aiming to solve the problems of low accuracy, reliability and efficiency in predicting the fatigue life of iron towers in the prior art.

[0006] The first aspect of this application provides a method for predicting the fatigue life of a steel tower, including:

[0007] Based on the preset communication tower stress monitoring information cutting model, the multiple communication tower stress monitoring information is cut and calculated according to the number of cutting segments of the multiple communication tower stress monitoring information to obtain multiple communication tower stress monitoring cutting information.

[0008] Based on the preset tower fatigue life prediction model, multiple tower fatigue life prediction information is calculated according to the stress monitoring and cutting information of the multiple communication towers.

[0009] A second aspect of this application provides a tower fatigue life prediction system, comprising:

[0010] The information acquisition module is used to acquire stress monitoring information from multiple communication towers and the number of segments cut from the stress monitoring information of multiple communication towers.

[0011] The communication tower stress monitoring cutting information generation module is used to perform cutting calculations on the multiple communication tower stress monitoring information based on a preset communication tower stress monitoring information cutting model and according to the number of cutting segments of the multiple communication tower stress monitoring information, to obtain multiple communication tower stress monitoring cutting information.

[0012] The tower fatigue life prediction information calculation module is used to calculate multiple tower fatigue life prediction information based on a preset tower fatigue life prediction model and the stress monitoring and cutting information of the multiple communication towers.

[0013] A third aspect of this application provides a terminal device, which includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the tower fatigue life prediction method described in the first aspect above.

[0014] A fourth aspect of this application provides a computer-readable storage medium, comprising: storing a computer program, wherein when executed by a processor, the computer program implements the steps of the tower fatigue life prediction method described in the first aspect above.

[0015] Compared with the prior art, the beneficial effects of this application are: this application significantly improves the adaptability to stress monitoring data of different communication towers, accurately captures the key characteristics of stress changes in tower structures, efficiently mines the structural damage information hidden in stress data, reduces the dependence on human experience judgment, provides efficient support for building a more comprehensive and targeted communication tower operation and maintenance solution, and helps improve the reliability of communication tower structural safety assessment and the scientific nature of operation and maintenance decisions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating the implementation process of the tower fatigue life prediction method provided in Embodiment 1 of this application;

[0018] Figure 2 This is a schematic diagram of the implementation process of the tower fatigue life prediction method provided in Embodiment 2 of this application;

[0019] Figure 3 This is a schematic diagram illustrating the implementation process of the tower fatigue life prediction method provided in Embodiment 3 of this application;

[0020] Figure 4 This is a schematic diagram of the implementation process of the tower fatigue life prediction method provided in Embodiment 4 of this application;

[0021] Figure 5 This is a schematic diagram of the implementation process of the tower fatigue life prediction method provided in Embodiment 5 of this application;

[0022] Figure 6 This is a schematic diagram of the implementation process of the tower fatigue life prediction method provided in Embodiment Six of this application;

[0023] Figure 7 This is a schematic diagram of the structure of the tower fatigue life prediction system provided in the embodiments of this application;

[0024] Figure 8 This is a schematic diagram of the terminal device provided in the embodiments of this application. Detailed Implementation

[0025] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0026] To illustrate the technical solution described in this application, specific embodiments are provided below.

[0027] Figure 1 The implementation flowchart of the tower fatigue life prediction method provided in Embodiment 1 of this application is shown, and is described in detail below:

[0028] Step S101: Obtain stress monitoring information for multiple communication towers and the number of segments cut from the stress monitoring information for multiple communication towers.

[0029] In this embodiment, the stress monitoring information of multiple communication towers refers to various parameter information related to the stress of the tower structure collected by relevant equipment and algorithms during the long-term service of the communication tower, with key structural parts such as the tower body, tower legs, and crossarms as monitoring targets. Specifically, this includes information such as stress peak values, stress valley values, and stress fluctuation amplitude at key parts, reflecting the dynamic changes in structural stress of the tower under environmental factors such as wind loads, temperature changes, and external vibrations. Stress-related parameters can be collected by installing monitoring equipment such as strain gauges and fiber optic grating sensors at key parts of the tower, and then the raw data collected by the sensors is processed using a data acquisition module and signal processing algorithms. The number of segments in the stress monitoring information of multiple communication towers refers to the specific number of segments used to divide the continuous stress monitoring data into several data fragments when processing the stress monitoring information of multiple communication towers. This number can be obtained by manual input.

[0030] In this embodiment, the communication tower stress monitoring information may include communication tower structural node stress monitoring information, communication tower stress change rate monitoring information, and communication tower stress cycle number monitoring information.

[0031] Step S102: Based on the preset communication tower stress monitoring information cutting model, the multiple communication tower stress monitoring information is cut and calculated according to the number of cutting segments of the multiple communication tower stress monitoring information to obtain multiple communication tower stress monitoring cutting information.

[0032] In this embodiment, the preset communication tower stress monitoring information segmentation model can be manually preset. This model can be set based on stress data feature clustering algorithms and segmentation optimization strategies, or it can be a greedy Gaussian segmentation algorithm model. First, stress monitoring information of communication tower structural nodes, stress change rate monitoring information, and stress cycle count monitoring information can be selected as core segmentation features from multiple communication tower stress monitoring information sources. Then, auxiliary information such as stress data acquisition time and ambient temperature is added to construct a feature set. The data within the feature set is then normalized to eliminate dimensional differences between different dimensions. Next, combining the number of segmentation information from multiple communication tower stress monitoring information sources, a feature clustering algorithm is used to identify abrupt changes and stable intervals in the stress data. The abrupt changes are used as the boundary points for data segmentation. Then, continuous stress monitoring data is segmented according to the preset number of segmentation segments. Simultaneously, the integrity of each segmented data fragment is verified, and invalid fragments with missing or abnormal data are removed, thereby generating multiple communication tower stress monitoring segmentation information.

[0033] Step S103: Based on the preset tower fatigue life prediction model, and according to the stress monitoring and cutting information of the multiple communication towers, calculate the fatigue life prediction information of multiple towers.

[0034] In this embodiment, the preset tower fatigue life prediction model can be manually preset. This model can be constructed based on the material fatigue cumulative damage theory and machine learning algorithms, or it can be an LSTM model. First, stress monitoring and cutting information from multiple communication towers can be input into the model. Key damage factors such as the number of stress cycles and the magnitude of stress changes in each cutting information are extracted. Then, based on the material fatigue cumulative damage theory, the structural damage value of the tower corresponding to each cutting information is calculated. Next, the correspondence between historical stress monitoring data and the actual fatigue life of the tower is trained using machine learning algorithms to optimize the mapping model parameters between damage values ​​and fatigue life. Then, the structural damage values ​​corresponding to each cutting information are substituted into the optimized mapping model to calculate the fatigue life prediction value of the data segment corresponding to each communication tower. Finally, the fatigue life prediction values ​​of multiple data segments from the same tower are weighted and fused to eliminate prediction bias caused by a single data segment, thereby generating multiple tower fatigue life prediction information.

[0035] The tower fatigue life prediction method provided in this application significantly improves the adaptability to stress monitoring data of different communication towers, accurately captures the key characteristics of stress changes in tower structures, efficiently mines the structural damage information hidden in stress data, reduces the dependence on human experience judgment, provides efficient support for building more comprehensive and targeted communication tower operation and maintenance solutions, and helps improve the reliability of communication tower structural safety assessment and the scientific nature of operation and maintenance decisions.

[0036] Figure 2 The flowchart illustrating the implementation of the tower fatigue life prediction method provided in Embodiment 2 of this application is shown. The difference between this method and Embodiment 1 described above is that:

[0037] The number of segments of stress monitoring information for multiple communication towers includes the number of segments of stress monitoring information for the first communication tower and the number of segments of stress monitoring information for the second communication tower.

[0038] Step S102 specifically includes:

[0039] Step S201: Based on the preset communication tower stress monitoring information cutting model, according to the number of cutting segments of the first communication tower stress monitoring information and the number of cutting segments of the second communication tower stress monitoring information, the multiple communication tower stress monitoring information is cut and calculated to obtain multiple first communication tower stress monitoring cutting information and multiple second communication tower stress monitoring cutting information.

[0040] In this embodiment, the preset communication tower stress monitoring information segmentation model can be pre-set manually. First, stress monitoring information of communication tower structural nodes, stress change rate, and stress cycle count can be selected from multiple communication tower stress monitoring information sets as core segmentation features. Auxiliary information such as stress data acquisition time and ambient temperature is added to construct a feature set. Then, the data within the feature set is normalized to eliminate dimensional differences between different dimensions. Next, combined with the number of segments in the first communication tower stress monitoring information segmentation, a feature clustering algorithm is used to identify abrupt changes and stable intervals in the stress data. The abrupt changes are used as the dividing points for data segmentation. Then, continuous stress monitoring data is segmented according to the number of segments. Simultaneously, the integrity of each segment is verified, and invalid segments with missing or abnormal data are removed, thereby generating multiple first communication tower stress monitoring segmentation information sets. Then, based on the same model processing logic, the number of segments in the second communication tower stress monitoring information segmentation is combined to perform segmentation calculations and verifications on the multiple communication tower stress monitoring information sets, thereby generating multiple second communication tower stress monitoring segmentation information sets.

[0041] Step S202: Based on the stress monitoring and cutting information of the plurality of first communication towers and the preset tower stress cycle identification function, the first tower stress cycle identification information is calculated.

[0042] In this embodiment, the preset tower stress cycle identification function can be manually preset. This function can be used to quantitatively evaluate the clarity and identifiability of stress cycle features in each cutting information. First, key parameters such as stress peak value, stress valley value, and stress change cycle within each cutting segment can be extracted from multiple first communication tower stress monitoring cutting information. These parameters are then substituted into the preset tower stress cycle identification function. The function calculates the stress cycle identification score corresponding to each cutting segment. A weighted average of the identification scores for all cutting segments is then calculated to eliminate the random bias of individual segment scores. Finally, the calculated average score is defined as an indicator that can comprehensively characterize the quality of the first type of cutting results, thereby generating the first tower stress cycle identification information.

[0043] Step S203: Calculate the stress cycle identification information of the second communication tower based on the stress monitoring and cutting information of the plurality of second communication towers and the preset tower stress cycle identification function.

[0044] In this embodiment, the preset tower stress cycle identification function can be manually preset. Key parameters such as stress peak value, stress valley value, and stress change cycle corresponding to each cut segment can be extracted from the stress monitoring and cutting information of multiple second communication towers. These parameters are then substituted into the function for calculation to obtain the stress cycle identification score corresponding to each cut segment. A weighted average of the identification scores of all cut segments is then calculated to avoid the limitations of a single segment score. The calculated average score is then defined as an indicator that can comprehensively characterize the quality of the second type of cutting results, thereby generating the second tower stress cycle identification information.

[0045] Step S204: Based on the first tower stress cycle identification information, the second tower stress cycle identification information, the stress monitoring and cutting information of multiple first communication towers, and the stress monitoring and cutting information of multiple second communication towers, multiple communication tower stress monitoring and cutting information are obtained.

[0046] In this embodiment, the stress cycle identification information of the first and second towers is first numerically compared. The segmentation information corresponding to the higher value, i.e., the better stress cycle characteristic identification, is selected as the core reference. For the overlapping data acquisition time in the two types of segmentation information, the actual characteristics of the communication tower stress change are considered, such as whether the segmentation completely covers the rising, peak, and falling phases of the stress cycle, and whether it accurately captures the key abrupt change nodes of the stress change rate. The overlapping parts are verified and screened, retaining segments that accurately reflect the stress cycle law of the tower. For the non-overlapping parts in the two types of segmentation information, the completeness and rationality of the data are verified one by one. Segments with abnormal stress data or missing cycle characteristics caused by deviations in the segmentation parameter settings are eliminated. Finally, the screened data segments are integrated into multiple continuous information sets that can comprehensively characterize the stress change law of the tower, i.e., multiple communication tower stress monitoring segmentation information.

[0047] The tower fatigue life prediction method provided in this application effectively reduces the error caused by the selection of the number of single cutting segments, significantly improves the accuracy of cutting stress monitoring information of multiple communication towers, and combines the actual characteristics of stress changes in communication towers for screening and verification to ensure the rationality and completeness of stress monitoring cutting information of multiple communication towers. This allows the fatigue life prediction information of multiple towers to more comprehensively and accurately reflect the actual service status of the towers, greatly enhances the adaptability to different communication tower stress monitoring datasets, reduces the dependence on human experience judgment, and provides more efficient and reliable technical support for building a more comprehensive and targeted communication tower operation and maintenance solution. This helps to improve the reliability of communication tower structural safety assessment and the scientific nature of operation and maintenance decisions.

[0048] Figure 3The flowchart illustrating the implementation of the tower fatigue life prediction method provided in Embodiment 3 of this application is shown. The difference between this method and Embodiment 2 is that step S204 specifically includes:

[0049] Step S301: Calculate the difference between the first tower stress cycle identification information and the second tower stress cycle identification information to obtain the tower stress cycle identification difference information.

[0050] In this embodiment, the specific values ​​of the first tower stress cycle identification information and the second tower stress cycle identification information can be determined first. Then, the identification information with the higher value is used as the minuend and the identification information with the lower value is used as the subtrahend. The absolute difference between the two identification information is obtained through subtraction. This absolute difference is then defined as an index that can quantify the degree of difference in quality between the two types of cutting results, thereby generating the tower stress cycle identification difference information.

[0051] Step S302: Determine whether the tower stress cycle identification difference information is less than or equal to the preset tower stress cycle identification difference threshold; if yes, proceed to step S303; if no, proceed to step S304.

[0052] In this embodiment, the preset threshold for the difference in tower stress cycle identification can be manually set. This threshold is used to determine whether the quality difference between the two types of cutting results is within an acceptable range. If the difference in tower stress cycle identification is less than or equal to the threshold, it indicates that the quality difference between the two types of cutting results is small, and both can effectively characterize the stress cycle characteristics of the tower. If the difference in tower stress cycle identification is greater than the threshold, it indicates that the quality difference between the two types of cutting results is large, and further adjustment of the cutting parameters is needed to optimize the cutting effect.

[0053] Step S303: Based on the first tower stress cycle identification information and the second tower stress cycle identification information, select the multiple first communication tower stress monitoring cutting information and the multiple second communication tower stress monitoring cutting information to obtain multiple communication tower stress monitoring cutting information.

[0054] In this embodiment, the identification information of the first tower stress cycle and the second tower stress cycle are first numerically compared, and the segmentation information with the higher identification value is selected as the basic data set. Then, the parts of the other segmentation information that do not overlap with the basic data set are verified one by one to check whether they can supplement and reflect the special laws of tower stress change. Then, the verified segments are integrated into the basic data set, and segments with duplicate data or conflicting features are removed. Finally, multiple communication tower stress monitoring segmentation information that can comprehensively and accurately characterize the tower stress cycle features are generated.

[0055] Step S304: Based on the preset incremental information for adjusting the number of communication tower stress monitoring information segments, adjust and calculate the number of communication tower stress monitoring information segments for the first and second communication towers to obtain the adjusted number of communication tower stress monitoring information segments for the first and second communication towers.

[0056] In this embodiment, the preset adjustment increment information for the number of communication tower stress monitoring information segments can be manually preset. This increment information is a fixed parameter used to adjust the number of segments. The number of communication tower stress monitoring information segments can be summed with this adjustment increment information to generate the adjusted number of communication tower stress monitoring information segments; then, the number of communication tower stress monitoring information segments can be summed with this adjustment increment information to generate the adjusted number of communication tower stress monitoring information segments.

[0057] Step S305: Use the adjusted number of segments of the first communication tower stress monitoring information as the number of segments of the first communication tower stress monitoring information, use the adjusted number of segments of the second communication tower stress monitoring information as the number of segments of the second communication tower stress monitoring information, and return to step S201.

[0058] In this embodiment, the original number of segments in the first communication tower stress monitoring information is replaced with the adjusted number of segments in the first communication tower stress monitoring information, and the original number of segments in the second communication tower stress monitoring information is replaced with the adjusted number of segments in the second communication tower stress monitoring information. Then, by iteratively adjusting the number of segments, performing cutting processing and judging the identification difference, the quality difference between the two types of cutting results is gradually reduced until the identification difference of tower stress cycle is less than or equal to the preset identification difference threshold of tower stress cycle, ensuring that the final multiple communication tower stress monitoring cutting information has higher accuracy and reliability.

[0059] The tower fatigue life prediction method provided in this application improves the accuracy and stability of communication tower stress monitoring information cutting, effectively avoids the problem of stress cycle feature loss caused by unreasonable cutting parameter settings, and enables the generated multiple communication tower stress monitoring cutting information to more accurately reflect the actual stress change law of the tower, improve the reliability and accuracy of multiple tower fatigue life prediction information, and greatly enhance the adaptability to different communication tower stress monitoring datasets. It provides more rigorous and scientific technical support for communication tower structural health monitoring and fatigue life assessment, and helps operation and maintenance personnel to formulate more targeted tower maintenance and reinforcement plans.

[0060] Figure 4 The flowchart illustrating the implementation of the tower fatigue life prediction method provided in Embodiment 4 of this application is shown. The difference between this method and Embodiment 3 above is that step S303 specifically includes:

[0061] Step S401: Determine whether the stress cycle identification information of the first iron tower is greater than or equal to the stress cycle identification information of the second iron tower; if yes, proceed to step S402; if no, proceed to step S403.

[0062] In this embodiment, the specific values ​​of the first tower stress cycle identification information and the second tower stress cycle identification information can be directly compared to determine whether the stress cycle feature identification of the first type of cutting result is better than or equal to the identification of the second type of cutting result.

[0063] Step S402: The stress monitoring and cutting information of the plurality of first communication towers is used as the stress monitoring and cutting information of the plurality of communication towers.

[0064] In this embodiment, since the stress cycle identification information of the first tower is greater than or equal to that of the stress cycle identification information of the second tower, it indicates that the stress monitoring and cutting information of multiple first communication towers can more accurately and comprehensively characterize the stress cycle characteristics of the communication tower. Therefore, this type of cutting information is directly determined as the input data for subsequent tower fatigue life prediction calculation, without the need for additional segment screening and integration operations.

[0065] Step S403: The stress monitoring and cutting information of the plurality of second communication towers is used as the stress monitoring and cutting information of the plurality of communication towers.

[0066] In this embodiment, since the stress cycle identification information of the first tower is less than that of the second tower, it indicates that multiple second communication tower stress monitoring cutting information can more accurately and comprehensively characterize the stress cycle characteristics of the communication tower. Therefore, this type of cutting information is directly determined as the input data for subsequent tower fatigue life prediction calculation, without the need for additional segment screening and integration operations.

[0067] The tower fatigue life prediction method provided in this application improves the efficiency of processing stress monitoring information segmentation for communication towers, while ensuring the quality and stability of the segmentation results. It avoids data redundancy or feature deviation caused by complex segment integration operations, making the tower fatigue life prediction information both accurate and efficient. This significantly improves the overall execution efficiency of tower fatigue life prediction work, providing more convenient and reliable technical support for the efficient operation and maintenance of communication towers. It also helps maintenance personnel quickly grasp the structural health status of the towers and formulate timely response strategies.

[0068] Figure 5The flowchart illustrating the implementation of the tower fatigue life prediction method provided in Embodiment 5 of this application is shown. Its difference from Embodiment 1 described above lies in:

[0069] The preset tower fatigue life prediction model includes multiple preset tower fatigue life prediction sub-models, multiple preset tower fatigue life prediction sub-model matching functions, and preset tower fatigue life prediction calculation weight information; among them, the preset tower fatigue life prediction sub-models and the preset tower fatigue life prediction sub-model matching functions correspond one-to-one.

[0070] Step S103 specifically includes:

[0071] Step S501: Based on the stress monitoring and cutting information of the multiple communication towers and the matching function of the multiple preset tower fatigue life prediction sub-models, calculate the matching degree information of the multiple tower fatigue life prediction sub-models.

[0072] In this embodiment, the multiple preset tower fatigue life prediction sub-model matching functions can be manually preset. Each matching function corresponds to a specific tower fatigue life prediction sub-model and is used to quantitatively evaluate the fit between multiple communication tower stress monitoring cutting information and the corresponding sub-model. Key feature parameters such as stress cycle count, stress variation amplitude, and stress peak distribution can be extracted from the multiple communication tower stress monitoring cutting information. These feature parameters are then substituted into each preset tower fatigue life prediction sub-model matching function. The function calculation yields a preliminary matching score between each cutting information and the corresponding sub-model. Consistency checks are then performed on the preliminary matching scores of all cutting information corresponding to the same sub-model, eliminating abnormally deviating scores. The average of the valid scores is then taken as the matching degree index between the sub-model and the corresponding cutting information set, thereby generating multiple tower fatigue life prediction sub-model matching degree information.

[0073] Step S502: Based on the preset weight information for predicting the fatigue life of the iron tower, the matching degree information of the multiple iron tower fatigue life prediction sub-models corresponding to the multiple preset iron tower fatigue life prediction sub-model matching functions is weighted and summed to obtain the weighted matching degree information of the multiple iron tower fatigue life prediction sub-models.

[0074] In this embodiment, the preset weight information for predicting the fatigue life of the tower can be manually preset. This weight information is set according to factors such as the prediction accuracy and applicable scenario range of different tower fatigue life prediction sub-models, and is used to distinguish the importance of the matching degree information of different sub-models. First, the weight coefficients corresponding to the matching function of each preset tower fatigue life prediction sub-model can be determined. Then, the matching degree information of multiple tower fatigue life prediction sub-models corresponding to each sub-model matching function is multiplied by the corresponding weight coefficient to obtain the weighted value of each matching degree information. Finally, all weighted values ​​corresponding to the matching function of the same sub-model are summed to eliminate the limitations of a single matching degree information, thereby generating multiple weighted matching degree information for tower fatigue life prediction sub-models.

[0075] Step S503: Calculate the maximum value of the weighted information of the matching degree of the multiple tower fatigue life prediction sub-models corresponding to the multiple preset tower fatigue life prediction sub-model matching functions, and obtain the values ​​of the multiple tower fatigue life prediction sub-model matching functions.

[0076] In this embodiment, the weighted information of the matching degree of multiple tower fatigue life prediction sub-models can be grouped according to the category of the preset tower fatigue life prediction sub-model matching function to ensure that each group of data corresponds to the same matching function. Then, the maximum value is retrieved for each group of data, and the weighted information value that best represents the matching degree in each group is selected. Then, the maximum value is defined as the core representation value of the corresponding matching function, thereby generating multiple tower fatigue life prediction sub-model matching function values.

[0077] Step S504: Based on the matching function values ​​of the multiple tower fatigue life prediction sub-models and the multiple preset tower fatigue life prediction sub-models, multiple tower fatigue life prediction information is calculated.

[0078] In this embodiment, the multiple preset tower fatigue life prediction sub-models can be manually preset, including sub-models based on material fatigue cumulative damage theory, sub-models based on machine learning, and other types. First, based on the matching function values ​​of the multiple tower fatigue life prediction sub-models, the preset tower fatigue life prediction sub-model with the best matching degree for each function value is selected as the target sub-model. Then, the stress monitoring and cutting information of multiple communication towers is input into the corresponding target sub-models. Key damage factors in the cutting information are extracted through the target sub-models, and preliminary fatigue life calculations are performed to obtain preliminary prediction values ​​corresponding to each cutting information. Then, all preliminary prediction values ​​for the same communication tower are weighted and fused to eliminate prediction biases between different sub-models. Finally, the rationality of the fused prediction values ​​is verified, and outliers exceeding the normal service life range are removed, thereby generating multiple tower fatigue life prediction information.

[0079] The tower fatigue life prediction method provided in this application significantly improves the adaptability to stress monitoring data of communication towers with different characteristics, accurately matches the optimal prediction sub-model to improve prediction accuracy, and effectively reduces the limitations of single model prediction by using weighted summation and maximum value screening to optimize the matching evaluation logic. This makes the fatigue life prediction information of multiple towers more consistent with the actual service status of the towers, greatly enhances the robustness and reliability of prediction calculation, provides more comprehensive technical support for the structural health assessment of communication towers, and helps maintenance personnel to formulate more scientific maintenance and reinforcement strategies.

[0080] Figure 6 The flowchart illustrating the implementation of the tower fatigue life prediction method provided in Embodiment Six of this application is shown. Its difference from Embodiment Five described above lies in:

[0081] Multiple preset tower fatigue life prediction sub-model matching functions include preset tower stress cycle characteristic matching sub-function, preset tower environmental load coupling characteristic matching sub-function, and preset tower structural performance degradation matching sub-function.

[0082] Step S501 specifically includes:

[0083] Step S601: Based on the stress monitoring and cutting information of the multiple communication towers and the preset tower stress cycle feature matching sub-function, calculate the matching degree information of the multiple tower stress cycle features.

[0084] In this embodiment, the preset tower stress cycle feature matching sub-function can be manually preset. This function is used to quantitatively evaluate the degree of agreement between the stress cycle features in multiple communication tower stress monitoring cutting information and the corresponding preset tower fatigue life prediction sub-model adaptation features. Cyclic feature parameters such as stress cycle period, stress peak-to-valley difference, and stress change rate fluctuation law can be extracted from multiple communication tower stress monitoring cutting information. These parameters are then substituted into the preset tower stress cycle feature matching sub-function. The function calculation yields the cycle feature matching score corresponding to each cutting information. The matching scores of multiple cutting information for the same tower are then integrated, and the average value is taken as the cycle feature matching index for that tower, thereby generating multiple tower stress cycle feature matching degree information.

[0085] Step S602: Based on the stress monitoring and cutting information of the multiple communication towers and the preset tower environmental load coupling feature matching sub-function, calculate the matching degree information of the multiple tower environmental load coupling features.

[0086] In this embodiment, the preset tower environmental load coupling feature matching sub-function can be manually preset. This function is used to evaluate the degree of matching between the coupling features of environmental load and stress change in multiple communication tower stress monitoring cutting information and the corresponding sub-model adaptation features. First, the correlation features between stress change and load parameters such as ambient temperature and wind force can be extracted from multiple communication tower stress monitoring cutting information. Then, these correlation feature parameters are substituted into the preset tower environmental load coupling feature matching sub-function to perform coupling matching degree calculation to obtain the coupling feature matching score for each cutting information. Subsequently, the validity of the coupling feature matching scores for all cutting information is screened, eliminating abnormal scores caused by missing environmental data. Finally, the average of the valid scores is taken as the coupling feature matching index for the corresponding tower, thereby generating multiple tower environmental load coupling feature matching degree information.

[0087] Step S603: Based on the stress monitoring and cutting information of the multiple communication towers and the preset tower structure performance attenuation matching sub-function, calculate the multiple tower structure performance attenuation matching degree information.

[0088] In this embodiment, the preset tower structure performance degradation matching sub-function can be manually preset. This function is used to quantify the degree of fit between the structural performance degradation-related features in multiple communication tower stress monitoring cutting information and the corresponding sub-model adaptation features. First, stress cycle accumulation number and stress residual deformation-related feature parameters can be extracted from multiple communication tower stress monitoring cutting information. These parameters can indirectly reflect the tower structure performance degradation state. Then, these parameters are substituted into the preset tower structure performance degradation matching sub-function, and the performance degradation matching score corresponding to each cutting information is obtained through function calculation. Subsequently, trend analysis is performed on multiple performance degradation matching scores for the same tower, retaining the scores that conform to the structural degradation law and taking the average value as the performance degradation matching index for that tower, thereby generating multiple tower structure performance degradation matching degree information.

[0089] Step S604: The matching degree information of multiple tower fatigue life prediction sub-models is calculated by summing the matching degree information of multiple tower stress cycle characteristics, multiple tower environmental load coupling characteristics, and multiple tower structural performance degradation.

[0090] In this embodiment, the correspondence between multiple tower stress cycle characteristic matching degree information, multiple tower environmental load coupling characteristic matching degree information, and multiple tower structural performance degradation matching degree information can be clarified first to ensure that the three types of matching degree information of the same tower correspond one-to-one. Then, the three types of matching degree information of each group are summed to obtain the comprehensive matching score of each tower. Then, all comprehensive matching scores are normalized to eliminate the dimensional differences of matching degree information in different dimensions. Then, the normalized comprehensive matching score is defined as the overall matching degree index between the corresponding tower and the sub-model, thereby generating multiple tower fatigue life prediction sub-model matching degree information.

[0091] The tower fatigue life prediction method provided in this application enables accurate matching and evaluation of multi-dimensional characteristics of tower stress cycle, environmental load coupling, and structural performance degradation. It effectively improves the accuracy and scientific nature of fatigue life prediction information for multiple towers, enhances the adaptability of tower fatigue life prediction models to communication towers in complex service environments, provides more rigorous technical support for communication tower structural health monitoring and life assessment, and helps maintenance personnel accurately grasp the structural status of towers and carry out maintenance work in a timely manner.

[0092] Corresponding to the method in the above embodiments, Figure 7 The diagram shows a structural block diagram of the tower fatigue life prediction system provided in the embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown. Figure 7 The example tower fatigue life prediction system can be the execution subject of the tower fatigue life prediction method provided in the aforementioned embodiment 1.

[0093] Reference Figure 7 The tower fatigue life prediction system includes:

[0094] The information acquisition module 710 is used to acquire stress monitoring information of multiple communication towers and the number of cutting segments of stress monitoring information of multiple communication towers;

[0095] The communication tower stress monitoring cutting information generation module 720 is used to perform cutting calculations on the multiple communication tower stress monitoring information based on a preset communication tower stress monitoring information cutting model and according to the number of cutting segments of the multiple communication tower stress monitoring information, to obtain multiple communication tower stress monitoring cutting information.

[0096] The tower fatigue life prediction information calculation module 730 is used to calculate multiple tower fatigue life prediction information based on a preset tower fatigue life prediction model and the stress monitoring and cutting information of the multiple communication towers.

[0097] The process of each module in the tower fatigue life prediction system provided in this application implementing its respective function can be found in the foregoing. Figure 1The description of Embodiment 1 shown will not be repeated here.

[0098] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0099] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0100] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0101] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0102] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. It should also be understood that although the terms "first," "second," etc., are used in the text to describe various elements in some embodiments of this application, these elements should not be limited by these terms. These terms are merely used to distinguish one element from another. For example, a first table may be named a second table, and similarly, a second table may be named a first table, without departing from the scope of the various described embodiments. Both the first table and the second table are tables, but they are not the same table.

[0103] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0104] The method for predicting the fatigue life of iron towers provided in this application can be applied to terminal devices such as mobile phones, tablets, wearable devices, vehicle-mounted devices, laptops, super mobile personal computers, netbooks, and personal digital assistants. This application does not impose any restrictions on the specific type of terminal device.

[0105] For example, the terminal device may be a station in a WLAN, a cellular phone, a cordless phone, a session initiation protocol phone, a wireless local loop station, a personal digital processing device, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, an in-vehicle device, a vehicle-to-everything (V2X) terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a user premises equipment, and / or other devices for communication over a wireless system, as well as next-generation communication systems, such as mobile terminals in 5G networks or mobile terminals in future evolved public terrestrial mobile networks.

[0106] Figure 8 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. For example... Figure 8 As shown, the terminal device 8 of this embodiment includes: at least one processor 80 ( Figure 8 Only one is shown in the image), and a memory 81 is stored in which a computer program 82 that can run on the processor 80 is stored. When the processor 80 executes the computer program 82, it implements the steps in the above embodiments of the tower fatigue life prediction method, for example... Figure 1 The steps S101 to S103 are shown. Alternatively, when the processor 80 executes the computer program 82, it implements the functions of each module / unit in the above system embodiments, for example... Figure 7 The functions of modules 710 to 730 are shown.

[0107] The terminal device 8 can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will understand that... Figure 8 This is merely an example of terminal device 8 and does not constitute a limitation on terminal device 8. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal device may also include input transmission devices, network access devices, buses, etc.

[0108] The processor 80 may be a central processing unit, or it may be other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0109] In some embodiments, the memory 81 may be an internal storage unit of the terminal device 8, such as a hard disk or memory of the terminal device 8. The memory 81 may also be an external storage device of the terminal device 8, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc., equipped on the terminal device 8. Furthermore, the memory 81 may include both internal and external storage units of the terminal device 8. The memory 81 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 81 can also be used to temporarily store data that has been sent or will be sent.

[0110] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0111] This application also provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor. When the processor executes the computer program, it causes the terminal device to implement the steps in any of the above method embodiments.

[0112] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0113] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0114] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0115] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0116] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0117] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0118] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for predicting the fatigue life of iron towers, characterized in that, include: Obtain stress monitoring information from multiple communication towers and the number of segments cut from the stress monitoring information of multiple communication towers; Based on the preset communication tower stress monitoring information cutting model, the multiple communication tower stress monitoring information is cut and calculated according to the number of cutting segments of the multiple communication tower stress monitoring information to obtain multiple communication tower stress monitoring cutting information. Based on the preset tower fatigue life prediction model, multiple tower fatigue life prediction information is calculated according to the stress monitoring and cutting information of the multiple communication towers.

2. The method for predicting the fatigue life of iron towers as described in claim 1, characterized in that, The number of segments of stress monitoring information for multiple communication towers includes the number of segments of stress monitoring information for the first communication tower and the number of segments of stress monitoring information for the second communication tower. The step of calculating the segmentation of multiple communication tower stress monitoring information based on a preset communication tower stress monitoring information segmentation model, and obtaining multiple communication tower stress monitoring segmentation information, specifically includes: Based on the preset communication tower stress monitoring information cutting model, the multiple communication tower stress monitoring information is cut and calculated according to the number of cutting segments of the first communication tower stress monitoring information and the number of cutting segments of the second communication tower stress monitoring information, to obtain multiple first communication tower stress monitoring cutting information and multiple second communication tower stress monitoring cutting information. Based on the stress monitoring and cutting information of the multiple first communication towers and the preset tower stress cycle identification function, the first tower stress cycle identification information is calculated. Based on the stress monitoring and cutting information of the multiple second communication towers and the preset tower stress cycle identification function, the stress cycle identification information of the second tower is calculated. Based on the stress cycle identification information of the first tower, the stress cycle identification information of the second tower, the stress monitoring and cutting information of multiple first communication towers, and the stress monitoring and cutting information of multiple second communication towers, multiple communication tower stress monitoring and cutting information are obtained.

3. The method for predicting the fatigue life of iron towers as described in claim 2, characterized in that, The step of obtaining multiple communication tower stress monitoring and cutting information based on the first tower stress cycle identification information, the second tower stress cycle identification information, multiple first communication tower stress monitoring and cutting information, and multiple second communication tower stress monitoring and cutting information specifically includes: Calculate the difference between the stress cycle identification information of the first iron tower and the stress cycle identification information of the second iron tower to obtain the tower stress cycle identification difference information; Determine whether the tower stress cycle identification difference information is less than or equal to a preset tower stress cycle identification difference threshold; If so, then based on the first tower stress cycle identification information and the second tower stress cycle identification information, the plurality of first communication tower stress monitoring cutting information and the plurality of second communication tower stress monitoring cutting information are selected to obtain plurality of communication tower stress monitoring cutting information. If not, then based on the preset incremental information for adjusting the number of communication tower stress monitoring information segments, the number of communication tower stress monitoring information segments and the number of communication tower stress monitoring information segments are adjusted and calculated to obtain the adjusted number of communication tower stress monitoring information segments and the adjusted number of communication tower stress monitoring information segments. The steps involve taking the adjusted number of segments of the first communication tower stress monitoring information as the first communication tower stress monitoring information segment number information, taking the adjusted number of segments of the second communication tower stress monitoring information as the second communication tower stress monitoring information segment number information, and returning to the preset communication tower stress monitoring information segmentation model. Based on the first and second communication tower stress monitoring information segmentation numbers, the steps involve performing segmentation calculations on the multiple communication tower stress monitoring information to obtain multiple first communication tower stress monitoring segmentation information and multiple second communication tower stress monitoring segmentation information.

4. The method for predicting the fatigue life of iron towers as described in claim 3, characterized in that, The step of selecting from the plurality of first communication tower stress monitoring cutting information and the plurality of second communication tower stress monitoring cutting information based on the first tower stress cycle identification information and the second tower stress cycle identification information to obtain plurality of communication tower stress monitoring cutting information specifically includes: Determine whether the stress cycle identification information of the first iron tower is greater than or equal to the stress cycle identification information of the second iron tower; If so, the stress monitoring and cutting information of the plurality of first communication towers shall be used as the stress monitoring and cutting information of the plurality of communication towers. If not, then the stress monitoring and cutting information of the multiple second communication towers shall be used as the stress monitoring and cutting information of multiple communication towers.

5. The method for predicting the fatigue life of iron towers as described in claim 1, characterized in that, The preset tower fatigue life prediction model includes multiple preset tower fatigue life prediction sub-models, multiple preset tower fatigue life prediction sub-model matching functions, and preset tower fatigue life prediction calculation weight information; among them, the preset tower fatigue life prediction sub-models and the preset tower fatigue life prediction sub-model matching functions correspond one-to-one. The step of calculating multiple tower fatigue life prediction information based on the preset tower fatigue life prediction model and the stress monitoring and cutting information of multiple communication towers specifically includes: Based on the stress monitoring and cutting information of the multiple communication towers and the matching function of the multiple preset tower fatigue life prediction sub-models, the matching degree information of the multiple tower fatigue life prediction sub-models is calculated. Based on the preset weight information for predicting the fatigue life of iron towers, the matching degree information of the multiple iron tower fatigue life prediction sub-models corresponding to the multiple preset iron tower fatigue life prediction sub-model matching functions is weighted and summed to obtain the weighted matching degree information of the multiple iron tower fatigue life prediction sub-models. The maximum value of the weighted information of the matching degree of multiple tower fatigue life prediction sub-models corresponding to multiple preset tower fatigue life prediction sub-model matching functions is calculated to obtain the values ​​of multiple tower fatigue life prediction sub-model matching functions. Based on the matching function values ​​of the multiple tower fatigue life prediction sub-models and the multiple preset tower fatigue life prediction sub-models, multiple tower fatigue life prediction information is calculated.

6. The method for predicting the fatigue life of iron towers as described in claim 5, characterized in that, Multiple preset tower fatigue life prediction sub-model matching functions include preset tower stress cycle characteristic matching sub-function, preset tower environmental load coupling characteristic matching sub-function, and preset tower structural performance degradation matching sub-function. The step of calculating the matching degree information of multiple tower fatigue life prediction sub-models based on the stress monitoring and cutting information of multiple communication towers and multiple preset tower fatigue life prediction sub-model matching functions specifically includes: Based on the stress monitoring and cutting information of the multiple communication towers and the preset tower stress cycle feature matching sub-function, the matching degree information of the multiple tower stress cycle features is calculated. Based on the stress monitoring and cutting information of the multiple communication towers and the preset tower environmental load coupling feature matching sub-function, the matching degree information of the multiple tower environmental load coupling features is calculated. Based on the stress monitoring and cutting information of the multiple communication towers and the preset tower structure performance attenuation matching sub-function, the matching degree information of the multiple tower structure performance attenuation is calculated. The matching degree information of multiple tower fatigue life prediction sub-models is calculated by summing the matching degree information of multiple tower stress cycle characteristics, multiple tower environmental load coupling characteristics, and multiple tower structural performance degradation.

7. The method for predicting the fatigue life of iron towers as described in claim 1, characterized in that, The communication tower stress monitoring information includes communication tower structural node stress monitoring information, communication tower stress change rate monitoring information, and communication tower stress cycle number monitoring information.

8. A fatigue life prediction system for iron towers, characterized in that, include: The information acquisition module is used to acquire stress monitoring information from multiple communication towers and the number of segments cut from the stress monitoring information of multiple communication towers. The communication tower stress monitoring cutting information generation module is used to perform cutting calculations on the multiple communication tower stress monitoring information based on a preset communication tower stress monitoring information cutting model and according to the number of cutting segments of the multiple communication tower stress monitoring information, to obtain multiple communication tower stress monitoring cutting information. The tower fatigue life prediction information calculation module is used to calculate multiple tower fatigue life prediction information based on a preset tower fatigue life prediction model and the stress monitoring and cutting information of the multiple communication towers.

9. A terminal device, characterized in that, The terminal device includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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