A tower safety monitoring system

Abstract

This invention relates to the field of communication tower technology, specifically to a tower safety monitoring system. The system includes: a data acquisition module for real-time monitoring of the tower's operational status; a data processing module; a fault location module that, based on the preliminary status judgment results output by the data processing module and a pre-trained tower performance evaluation model, uses spatial positioning algorithms and data analysis methods to locate the tower fault location and identifies the fault type by comparing changes in monitoring data before and after the fault; and a maintenance strategy generation module that, based on the fault location results and the tower's real-time operational status, combined with the tower's maintenance history, environmental factor data, and equipment lifespan model, generates an optimal maintenance strategy. This invention provides a tower safety monitoring system that monitors towers in real-time, determines tower status, predicts potential fault points, identifies fault types, and generates optimal maintenance strategies, improving the timeliness, accuracy, and operational efficiency of tower safety monitoring.

Description

Technical Field

[0001] This invention relates to the field of communication tower technology, and in particular to a tower safety monitoring system. Background Technology

[0002] With the rapid development of the telecommunications industry, towers, as a crucial infrastructure of communication networks, are directly related to communication quality in terms of security and stability. However, traditional manual inspection methods suffer from low efficiency, high costs, and difficulty in detecting potential safety hazards in real time. Manual inspections are particularly challenging in inclement weather or remote areas.

[0003] The current traditional maintenance method is to manually monitor the tower and repair any loose bolts, issues with verticality, or potential corrosion.

[0004] For example, Chinese patent publication number CN116517370A, entitled "A Communication Tower with Monitoring Function," includes a communication tower body, a mounting plate, and a monitoring mechanism. The monitoring mechanism includes a controller, an alarm module, a tilt sensor, a high-voltage grounding sensor, an infrared sensor, and a displacement sensor. By setting up the tilt sensor, high-voltage grounding sensor, infrared sensor, and displacement sensor, various parameters of the communication tower body are monitored. When an abnormality is detected, a signal is sent to the controller, which sends a signal to the alarm module. The alarm module then sends an alarm signal to the monitoring station or the staff's mobile phone, allowing staff to promptly grasp the abnormal situation of the communication tower body and carry out timely maintenance, achieving real-time monitoring of the communication tower body. The disadvantages are: it cannot identify the fault type and cannot automatically generate the optimal maintenance strategy. Summary of the Invention

[0005] To address the problem that existing technologies cannot identify fault types in communication towers and cannot automatically generate optimal maintenance strategies, this invention provides a tower safety monitoring system that monitors towers in real time, determines tower status and predicts potential fault points, identifies fault types, and generates optimal maintenance strategies, thereby improving the timeliness, accuracy, and operational efficiency of tower safety monitoring.

[0006] To achieve the above-mentioned technical objectives, the present invention provides a technical solution: a tower safety monitoring system, comprising: The data acquisition module monitors the operating status of the tower in real time and transmits the data to the data processing module in real time. The data processing module is located near the tower and receives data from the data acquisition module. It performs a preliminary state assessment on the processed data using a pre-trained state assessment model, establishes a tower performance assessment model, and predicts potential fault points of the tower using machine learning algorithms. The fault location module, based on the preliminary state judgment results output by the data processing module and the pre-trained tower performance evaluation model, uses spatial positioning algorithms and data analysis methods to locate the fault location of the tower and identifies the fault type by comparing the changes in monitoring data before and after the fault. The maintenance strategy generation module generates the optimal maintenance strategy based on the fault location results and the real-time operating status of the tower, combined with the tower's maintenance history, environmental factor data, and equipment life model.

[0007] In this technical solution, the data acquisition module utilizes accelerometers and stress patch multi-devices to capture tower operating status data in real time from all angles, collecting and rapidly transmitting data on vibration, stress changes, and tilt. The data processing module, located near the tower, can quickly receive data and, using a pre-trained state assessment model, rapidly make a preliminary state judgment, clarifying whether the tower is operating normally or experiencing anomalies. Simultaneously, the established performance evaluation model, leveraging machine learning algorithms, can accurately predict potential fault points and issue early warnings. The fault location module, based on the data processing results, employs spatial positioning algorithms and data analysis methods to quickly and accurately locate the fault position. By comparing data changes before and after the fault, it accurately identifies the fault type. The maintenance strategy generation module, based on fault location and real-time operating status, combined with maintenance history, environmental factors, and equipment lifespan models, uses optimization algorithms to generate the optimal maintenance strategy. This strategy, while ensuring tower safety, reasonably controls maintenance costs and time, improves operational efficiency, and keeps the tower in good operating condition at all times.

[0008] The present invention is further configured such that: the real-time monitoring of the tower's operating status includes: The vibration acceleration data of the tower in the X, Y, and Z directions are monitored in real time using accelerometers. Distributed fiber optic stress patches are evenly arranged along the main load-bearing components of the tower to monitor changes in stress parameters inside the components. The tilt angle of the tower is monitored in real time by a dual-axis tilt sensor, which monitors the tilt data of the tower in two mutually perpendicular directions. Several cameras are installed at various heights and locations on the tower to collect real-time images of the tower's appearance and identify defects such as rust, cracks, and loose bolts on the tower's surface.

[0009] In this technical solution, the accelerometer can capture the vibration acceleration data of the tower in the X, Y, and Z directions in real time, capturing both wind-induced vibration and dynamic responses caused by external forces such as earthquakes. Distributed fiber optic stress patches are uniformly arranged along the main load-bearing components of the tower, enabling precise monitoring of stress parameter changes within the components and timely detection of stress anomalies caused by long-term loads and environmental corrosion. Dual-axis tilt sensors monitor the tower's tilt data in two mutually perpendicular directions in real time, detecting foundation settlement and structural deformation. Cameras installed at multiple heights and orientations on the tower acquire real-time images of its appearance, identifying defects such as rust, cracks, and loose bolts, providing intuitive and detailed visual information for timely maintenance and repair.

[0010] The present invention is further configured such that the establishment of the state assessment model includes: Based on the acceleration, stress, tilt angle, and image data acquired by the data acquisition module, as well as environmental factor data, the data is fused using a data fusion algorithm. Data on iron towers with the same operational characteristics are grouped together using clustering algorithms; A model of the relationship between tower performance indicators and various influencing factors was established based on regression analysis algorithms.

[0011] In this technical solution, a data fusion algorithm integrates acceleration, stress, tilt angle, and image data acquired by the data acquisition module, along with environmental factor data. This breaks the limitations of a single data source, and the fused data more comprehensively and accurately reflects the tower's operational status, covering aspects from structural dynamic response and appearance to environmental impact, providing an information foundation for subsequent analysis. The clustering algorithm groups tower data with similar operational characteristics, enabling categorized management of tower operational status. This helps identify common characteristics and potential problems among different types of towers, facilitating the development of targeted maintenance strategies. The regression analysis algorithm establishes a relationship model between tower performance indicators and various influencing factors, accurately quantifying the impact of each factor on tower performance and predicting performance trends under different conditions. Maintenance personnel can anticipate potential performance degradation or failure risks and take timely preventative measures.

[0012] The present invention is further configured such that: the method of locating the fault location of the iron tower using spatial positioning algorithms and data analysis methods includes: Based on positioning data from the Global Positioning System and the BeiDou Navigation Satellite System, the location data of key parts of the tower are obtained. Based on the three-dimensional model data of the tower, the data collected by sensors and cameras are matched with the three-dimensional model to determine the area where the fault occurred. By analyzing acceleration, stress, and tilt angle using signal processing methods, and using machine learning-based pattern recognition methods, the monitoring data before and after the fault are classified and identified, thus narrowing down the fault location range. The fault location results are corrected by iterative algorithms, and the location parameters are adjusted based on the difference between the actual measurement data and the model prediction data.

[0013] This technical solution utilizes dual-mode positioning data from both the Global Positioning System (GPS) and the BeiDou Navigation Satellite System to acquire the three-dimensional coordinate information of key nodes on the tower. Combined with a digital three-dimensional model of the tower, acceleration, stress, and tilt angle data collected by sensors, along with appearance images captured by cameras, are matched to the corresponding locations on the model using a spatial coordinate mapping method, enabling rapid localization of the fault's macroscopic location. Based on macroscopic location, the system employs time-domain analysis, frequency-domain analysis, and wavelet transform signal processing methods to extract features from the vibration signals captured by the acceleration sensors. Machine learning pattern recognition algorithms are then used to dynamically compare monitoring data before and after the fault. In practical applications, when the tower tilts due to foundation settlement, the system continuously corrects the positioning parameters through iterative algorithms, gradually approximating the true fault location. This fault localization achieves full-process coverage from macroscopic area locking to microscopic fault point localization.

[0014] The present invention is further configured such that: identifying the fault type by comparing changes in monitoring data before and after the fault includes: Compare the acceleration, stress, tilt angle, and image monitoring data before and after the fault; The feature changes obtained from comparative analysis are matched with a pre-established fault feature pattern library, and the fault type is determined by the pattern matching algorithm.

[0015] This technical solution compares acceleration, stress, tilt angle, and image monitoring data before and after a fault, enabling comprehensive and multi-dimensional capture of changes in the tower's operating status. Acceleration data reflects the tower's response changes under dynamic loads, stress data reveals changes in the internal stress of components, tilt angle data visually presents foundation settlement or structural deformation, and image monitoring data clearly displays the tower's surface condition, such as rust, cracks, and loose bolts. Matching the feature changes obtained from the comparative analysis with a pattern library, and using a pattern matching algorithm, the fault type can be quickly and accurately determined. This matching method, based on actual data and experience, improves the accuracy of fault type identification and avoids misjudgments caused by subjective judgment or insufficient experience.

[0016] The present invention is further configured such that the acceleration, stress, tilt angle, and image monitoring data before and after the fault include: Compare the changes in amplitude, frequency, and phase before and after the fault; By comparing the distribution of stress parameters before and after the failure, it can be determined whether there is stress concentration or abnormal changes. By comparing the changes in tilt angle data before and after the fault, the degree of structural deformation of the tower can be assessed. By comparing image data, we can identify changes in the data regarding defects such as rust, cracks, and loose bolts on the tower surface before and after the failure.

[0017] In this technical solution, the system can accurately identify abnormal dynamic response of the tower by comparing the changes in amplitude, frequency, and phase parameters collected by the accelerometer before and after the fault. The system can also establish a three-dimensional stress cloud map to locate damage by comparing the distribution changes of stress parameters before and after the fault, and establish a deformation development curve by comparing the changes in tilt angle data before and after the fault.

[0018] The present invention is further configured such that: the step of generating the optimal maintenance strategy based on the fault location results and the real-time operating status of the tower, combined with the tower's maintenance history records, environmental factor data, and equipment life model, includes: Based on a multi-objective optimization algorithm, the optimal maintenance strategy that meets the requirements for safe operation of the tower is determined with objectives such as maintenance cost, maintenance time, and spare parts inventory. Based on the results of the optimization algorithm, an optimal maintenance strategy is generated, which includes regular inspections, emergency repairs, component replacements, maintenance schedules, and spare parts usage plans.

[0019] In this technical solution, a multi-objective optimization algorithm considers maintenance cost, maintenance time, and spare parts inventory as key optimization objectives to construct a model. Maintenance cost encompasses labor costs, spare parts procurement costs, and indirect losses from equipment downtime. Through meticulous consideration of these factors, the algorithm avoids unnecessary expenses and achieves efficient resource utilization. It comprehensively considers factors such as the urgency of the fault, the impact of weather conditions on construction, and the scheduling of personnel and equipment to ensure maintenance work is carried out at the most appropriate time, avoiding both premature resource idleness and delayed fault exacerbation. By combining equipment lifespan models to predict the service life and replacement frequency of spare parts, and considering the response speed of the supply chain, it achieves precise control of spare parts inventory, avoiding inventory backlogs or shortages. Under the premise of meeting the mandatory condition of safe tower operation, through complex calculations and trade-offs, it can select the optimal maintenance strategy from a vast number of possible solutions, greatly improving the accuracy and rationality of decision-making.

[0020] The present invention is further configured such that the preliminary state includes the normal operation state of the tower, the minor abnormal state, and the serious fault state.

[0021] The present invention is further configured such that: the system also includes a monitoring and control module, which receives the real-time operating status data and fault location data of the tower transmitted by the data processing module, and displays the tower's vibration acceleration curve, stress distribution cloud map, tilt angle value, appearance image and fault location mark in a graphical manner on the monitoring interface; when the tower's abnormal status or potential fault is detected, an early warning is automatically triggered.

[0022] This technical solution presents various key information about the tower in a graphical and innovative way on the monitoring interface, greatly improving the intuitiveness and convenience of information acquisition. When abnormal tower status or potential faults are detected, the module automatically triggers the early warning function. Through dynamic curve displays, maintenance personnel can clearly observe the vibration changes of the tower under different time periods and operating conditions.

[0023] The present invention is further configured such that: the system also includes a new antenna mounting module, the new antenna mounting module comprising: The data acquisition simulation unit collects acceleration and stress data at the expected mounting location under simulated mounting conditions by installing sensors at the expected mounting location. Using a 3D modeling method, the parameters of the new antenna are integrated into the 3D model of the tower to perform virtual mounting simulation. Simulation software is used to calculate the stress and vibration characteristics of various parts of the tower after the new antenna is mounted. The structural strength assessment unit, based on the collected simulation data and 3D model analysis results, combined with the original structural design and material performance parameters of the tower, uses the finite element analysis method to evaluate the structural strength of the tower. The feasibility assessment unit automatically generates a feasibility report for mounting new antennas based on the results of the structural strength assessment.

[0024] In this technical solution, the data acquisition and simulation unit collects acceleration and stress data at the expected mounting location by installing sensors, simulating the mounting state. This data is then integrated into the tower's 3D model to create a virtual mounting scenario. Simulation software calculates the stress and vibration characteristics of various parts of the tower after the new antenna is mounted, allowing maintenance personnel to clearly predict the tower's future state before actual antenna mounting. The structural strength assessment unit, based on the rich simulation data and in-depth analysis of the 3D model obtained by the data acquisition and simulation unit, and closely combined with the tower's original structural design and material performance parameters, discretizes the tower structure into numerous micro-units. By calculating the stress and deformation of each unit, it comprehensively and accurately assesses the structural safety of the entire tower after the new antenna is mounted. The feasibility assessment unit, based on the assessment results from the structural strength assessment unit, quickly and automatically generates a feasibility report for the new antenna mounting. This accurate assessment of the impact of the new antenna mounting ensures the safe and efficient use of the tower.

[0025] The beneficial effects of the present invention are: (1) Real-time monitoring of the tower, determination of the tower status and prediction of potential fault points, identification of fault types, generation of optimal maintenance strategies, and improvement of the timeliness, accuracy and operation and maintenance efficiency of tower safety monitoring; (2) Accurate assessment of the impact of adding new antennas, ensuring the safety and efficient use of the tower. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the structure of the tower safety monitoring system of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only one preferred embodiment of this invention and are only used to explain this invention. They do not limit the scope of protection of this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0028] like Figure 1 As shown in the figure, as an embodiment of the present invention, a tower safety monitoring system includes: The data acquisition module monitors the operating status of the tower in real time and transmits the data to the data processing module in real time. The data processing module is located near the tower and receives data from the data acquisition module. It performs a preliminary state assessment on the processed data using a pre-trained state assessment model, establishes a tower performance assessment model, and predicts potential fault points of the tower using machine learning algorithms. The fault location module, based on the preliminary state judgment results output by the data processing module and the pre-trained tower performance evaluation model, uses spatial positioning algorithms and data analysis methods to locate the fault location of the tower and identifies the fault type by comparing the changes in monitoring data before and after the fault. The maintenance strategy generation module generates the optimal maintenance strategy based on the fault location results and the real-time operating status of the tower, combined with the tower's maintenance history, environmental factor data, and equipment life model.

[0029] In this embodiment, the data acquisition module utilizes an accelerometer and stress patch multi-device to capture tower operating status data in real time from all angles, collecting and rapidly transmitting data on vibration, stress changes, and tilt. The data processing module, located near the tower, can quickly receive data and, using a pre-trained state assessment model, rapidly make a preliminary state judgment, clarifying whether the tower is operating normally or experiencing an anomaly. Simultaneously, the established performance evaluation model, leveraging machine learning algorithms, can accurately predict potential fault points and issue early warnings. The fault location module, based on the data processing results, employs spatial positioning algorithms and data analysis methods to quickly and accurately locate the fault position. By comparing data changes before and after the fault, it accurately identifies the fault type. The maintenance strategy generation module, based on fault location and real-time operating status, combined with maintenance history, environmental factors, and equipment lifespan models, uses optimization algorithms to generate the optimal maintenance strategy. This strategy, while ensuring tower safety, reasonably controls maintenance costs and time, improves operational efficiency, and keeps the tower in good operating condition at all times.

[0030] Understandably, the state assessment model is based on a deep learning algorithm and is trained using a large amount of historical tower operation data and failure cases.

[0031] Understandably, the data analysis methods include various signal processing techniques such as time domain analysis, frequency domain analysis, and wavelet analysis.

[0032] In one embodiment of the present invention, the real-time monitoring of the tower's operating status includes: The vibration acceleration data of the tower in the X, Y, and Z directions are monitored in real time using accelerometers. Distributed fiber optic stress patches are evenly arranged along the main load-bearing components of the tower to monitor changes in stress parameters inside the components. The tilt angle of the tower is monitored in real time by a dual-axis tilt sensor, which monitors the tilt data of the tower in two mutually perpendicular directions. Several cameras are installed at various heights and locations on the tower to collect real-time images of the tower's appearance and identify defects such as rust, cracks, and loose bolts on the tower's surface.

[0033] In this technical solution, the accelerometer can capture the vibration acceleration data of the tower in the X, Y, and Z directions in real time, capturing dynamic responses caused by wind-induced vibration or seismic forces. Distributed fiber optic stress patches are uniformly arranged along the main load-bearing components of the tower, enabling precise monitoring of stress parameter changes within the components and timely detection of stress anomalies caused by long-term loads and environmental corrosion. Dual-axis tilt sensors monitor the tower's tilt data in two mutually perpendicular directions in real time, detecting foundation settlement and structural deformation. Cameras installed at multiple heights and orientations on the tower acquire real-time images of its appearance, identifying surface defects such as rust, cracks, and loose bolts, providing intuitive and detailed visual information for timely maintenance and repair.

[0034] In one embodiment of the present invention, the establishment of the state assessment model includes: Based on the acceleration, stress, tilt angle, and image data acquired by the data acquisition module, as well as environmental factor data, the data is fused using a data fusion algorithm. Data on iron towers with the same operational characteristics are grouped together using clustering algorithms; A model of the relationship between tower performance indicators and various influencing factors was established based on regression analysis algorithms.

[0035] In this technical solution, a data fusion algorithm integrates acceleration, stress, tilt angle, and image data acquired by the data acquisition module, along with environmental factor data. This breaks the limitations of a single data source, and the fused data more comprehensively and accurately reflects the tower's operational status, covering aspects from structural dynamic response and appearance to environmental impact, providing an information foundation for subsequent analysis. The clustering algorithm groups tower data with similar operational characteristics, enabling categorized management of tower operational status. This helps identify common characteristics and potential problems among different types of towers, facilitating the development of targeted maintenance strategies. The regression analysis algorithm establishes a relationship model between tower performance indicators and various influencing factors, accurately quantifying the impact of each factor on tower performance and predicting performance trends under different conditions. Maintenance personnel can anticipate potential performance degradation or failure risks and take timely preventative measures.

[0036] In one embodiment of the present invention, the method of locating the fault location of the iron tower using spatial positioning algorithms and data analysis methods includes: Based on positioning data from the Global Positioning System and the BeiDou Navigation Satellite System, the location data of key parts of the tower are obtained. Based on the three-dimensional model data of the tower, the data collected by sensors and cameras are matched with the three-dimensional model to determine the area where the fault occurred. By analyzing acceleration, stress, and tilt angle using signal processing methods, and using machine learning-based pattern recognition methods, the monitoring data before and after the fault are classified and identified, thus narrowing down the fault location range. The fault location results are corrected by iterative algorithms, and the location parameters are adjusted based on the difference between the actual measurement data and the model prediction data.

[0037] This technical solution utilizes dual-mode positioning data from the Global Positioning System (GPS) and the BeiDou Navigation Satellite System to acquire the three-dimensional coordinate information of key nodes on the tower. Combined with a digital three-dimensional model of the tower, acceleration, stress, and tilt angle data collected by sensors, along with appearance images captured by cameras, are matched to the corresponding locations on the model using a spatial coordinate mapping method, enabling rapid localization of the fault's macroscopic location. Based on macroscopic location, the system employs time-domain analysis, frequency-domain analysis, and wavelet transform signal processing methods to extract features from the vibration signals captured by the acceleration sensors. Machine learning pattern recognition algorithms are then used to dynamically compare monitoring data before and after the fault. In practical applications, when the tower tilts due to foundation settlement, the system continuously corrects the positioning parameters through iterative algorithms, gradually approximating the true fault location. This fault localization achieves full-process coverage from macroscopic area locking to microscopic fault point localization.

[0038] Understandably, this involves identifying vibration mode changes caused by different excitation sources such as wind vibration and mechanical impact, performing vibration characteristic analysis; detecting abnormal abrupt changes in stress distribution within components, performing stress gradient analysis; and tracking the development trajectory and rate of structural deformation, performing tilt angle time-series analysis.

[0039] The method of identifying fault types by comparing changes in monitoring data before and after a fault includes: Compare the acceleration, stress, tilt angle, and image monitoring data before and after the fault; The feature changes obtained from comparative analysis are matched with a pre-established fault feature pattern library, and the fault type is determined by the pattern matching algorithm.

[0040] This technical solution compares acceleration, stress, tilt angle, and image monitoring data before and after a fault, enabling comprehensive and multi-dimensional capture of changes in the tower's operating status. Acceleration data reflects the tower's response changes under dynamic loads, stress data reveals changes in the internal stress of components, tilt angle data visually presents foundation settlement or structural deformation, and image monitoring data clearly displays the tower's surface condition, such as rust, cracks, and loose bolts. Matching the feature changes obtained from the comparative analysis with a pattern library, and using a pattern matching algorithm, the fault type can be quickly and accurately determined. This matching method, based on actual data and experience, improves the accuracy of fault type identification and avoids misjudgments caused by subjective judgment or insufficient experience.

[0041] Preferably, the comparison of acceleration, stress, tilt angle, and image monitoring data before and after the fault includes: Compare the changes in amplitude, frequency, and phase before and after the fault; By comparing the distribution of stress parameters before and after the failure, it can be determined whether there is stress concentration or abnormal changes. By comparing the changes in tilt angle data before and after the fault, the degree of structural deformation of the tower can be assessed. By comparing image data, we can identify changes in the data regarding defects such as rust, cracks, and loose bolts on the tower surface before and after the failure.

[0042] In this technical solution, the system can accurately identify abnormal dynamic response of the tower by comparing the changes in amplitude, frequency, and phase parameters collected by the accelerometer before and after the fault. The system can also establish a three-dimensional stress cloud map to locate damage by comparing the distribution changes of stress parameters before and after the fault, and establish a deformation development curve by comparing the changes in tilt angle data before and after the fault.

[0043] Understandably, when the tower foundation settles, the vertical vibration amplitude will increase significantly, and the system can automatically trigger an early warning by setting a threshold. Loose bolts will cause a decrease in the structure's natural frequency, and the system can identify frequency shifts within 0.5Hz through spectrum analysis. Component fractures will cause a phase difference of more than 15 degrees in vibrations at multiple measuring points, and the system can locate the fracture location through phase relationship analysis. By comparing changes in image data before and after the fault, the system can establish a defect development database.

[0044] Understandably, by comparing the stress gradients under normal and fault conditions, abnormal areas with stress concentration factors exceeding 2.5 at the main material connections can be identified; when corrosion causes a cross-sectional loss rate exceeding 10%, a characteristic change of stress level increase exceeding 30% can be detected; by comparing the stress change ratios at different height measuring points, it can be determined whether the structural load transfer path has changed.

[0045] Understandably, pixel grayscale analysis can quantify and identify severely corroded areas with a rust coverage exceeding 15%; edge detection algorithms can measure crack width changes, triggering an alert when the expansion rate exceeds 0.1 mm / month; and template matching technology can detect loosening of bolt heads with displacement exceeding 3 mm.

[0046] The process of generating the optimal maintenance strategy based on fault location results and the real-time operating status of the tower, combined with the tower's maintenance history, environmental factor data, and equipment lifespan model, includes: Based on a multi-objective optimization algorithm, the optimal maintenance strategy that meets the requirements for safe operation of the tower is determined with objectives such as maintenance cost, maintenance time, and spare parts inventory. Based on the results of the optimization algorithm, an optimal maintenance strategy is generated, which includes regular inspections, emergency repairs, component replacements, maintenance schedules, and spare parts usage plans.

[0047] The multi-objective optimization algorithm uses maintenance cost, maintenance time, and spare parts inventory as key optimization objectives to build a model. Maintenance cost encompasses labor costs, spare parts procurement costs, and indirect losses from equipment downtime. Through meticulous consideration of these factors, the algorithm avoids unnecessary expenses and achieves efficient resource utilization. It comprehensively considers factors such as the urgency of the fault, the impact of weather conditions on construction, and the scheduling of personnel and equipment to ensure maintenance work is carried out at the most appropriate time, avoiding both premature resource idleness and delayed fault exacerbation. By combining equipment lifespan models to predict the service life and replacement frequency of spare parts, and considering the responsiveness of the supply chain, it achieves precise control of spare parts inventory, avoiding inventory backlogs or shortages. Under the premise of meeting the mandatory condition of safe tower operation, it can select the optimal maintenance strategy from a vast number of possible solutions through complex calculations and trade-offs, greatly improving the accuracy and rationality of decision-making.

[0048] The optimal maintenance strategy generated by the optimization algorithm is comprehensive and highly targeted. Regular inspection plans are formulated based on the importance of different tower components, historical failure frequencies, and current operating status, enabling timely detection of potential safety hazards. Emergency repair plans are quickly initiated for sudden failures, clearly defining repair procedures, required spare parts, and personnel arrangements to ensure the tower's normal operation is restored in the shortest possible time, reducing power outages and other accidents. Component replacement strategies are based on equipment lifespan models and real-time monitoring data to accurately determine the remaining service life of components and replace them at the appropriate time, avoiding serious failures caused by component aging. Maintenance scheduling fully considers power grid maintenance plans and weather conditions, selecting the best construction windows to improve the efficiency and safety of maintenance work. Spare parts usage plans are closely integrated with spare parts inventory management, rationally arranging the allocation and use of spare parts according to maintenance needs to ensure effective utilization of spare parts.

[0049] Understandably, the preliminary status includes the tower's normal operating status, minor abnormal status, and serious fault status. A tiered response mechanism improves operational efficiency.

[0050] In one embodiment of the present invention, the system further includes a monitoring and control module. After receiving the real-time operating status data and fault location data of the tower transmitted by the data processing module, the module displays the tower's vibration acceleration curve, stress distribution cloud map, tilt angle value, appearance image and fault location mark on the monitoring interface in a graphical manner. When an abnormal tower status or potential fault is detected, an early warning is automatically triggered.

[0051] This technical solution presents various key information about the tower in a graphical and innovative way on the monitoring interface, greatly improving the intuitiveness and convenience of information acquisition. When abnormal tower status or potential faults are detected, the module automatically triggers the early warning function. Through dynamic curve displays, maintenance personnel can clearly observe the vibration changes of the tower under different time periods and operating conditions.

[0052] In one embodiment of the present invention, the system further includes a new antenna mounting module, the new antenna mounting module comprising: The data acquisition simulation unit collects acceleration and stress data at the expected mounting location under simulated mounting conditions by installing sensors at the expected mounting location. Using a 3D modeling method, the parameters of the new antenna are integrated into the 3D model of the tower to perform virtual mounting simulation. Simulation software is used to calculate the stress and vibration characteristics of various parts of the tower after the new antenna is mounted. The structural strength assessment unit, based on the collected simulation data and 3D model analysis results, combined with the original structural design and material performance parameters of the tower, uses the finite element analysis method to evaluate the structural strength of the tower. The feasibility assessment unit automatically generates a feasibility report for mounting new antennas based on the results of the structural strength assessment.

[0053] In this technical solution, the data acquisition and simulation unit collects acceleration and stress data at the expected mounting location by installing sensors, simulating the mounting state. It integrates the parameters of the new antenna into the tower's 3D model, constructing a virtual mounting scenario. Simulation software calculates the stress and vibration characteristics of various parts of the tower after the new antenna is mounted, allowing maintenance personnel to clearly predict the tower's future state before the actual antenna is installed. The structural strength assessment unit, based on the rich simulation data and in-depth analysis results of the 3D model obtained by the data acquisition and simulation unit, and closely combined with the tower's original structural design and material performance parameters, discretizes the tower structure into numerous micro-units. By calculating the stress and deformation of each unit, it comprehensively and accurately assesses the structural safety of the entire tower after the new antenna is mounted. The feasibility assessment unit, based on the assessment results from the structural strength assessment unit, quickly and automatically generates a feasibility report for the new antenna mounting.

[0054] The above embodiments, which describe the specific features of the present invention, are only used to further illustrate the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-essential improvements and adjustments made to the present invention by those skilled in the art based on the above description of the invention shall fall within the scope of protection of the present invention.

Claims

1. A tower safety monitoring system, characterized in that, include: The data acquisition module monitors the operating status of the tower in real time and transmits the data to the data processing module in real time. The data processing module is located near the tower and receives data from the data acquisition module. It performs a preliminary state assessment on the processed data using a pre-trained state assessment model, establishes a tower performance assessment model, and predicts potential fault points of the tower using machine learning algorithms. The fault location module, based on the preliminary state judgment results output by the data processing module and the pre-trained tower performance evaluation model, uses spatial positioning algorithms and data analysis methods to locate the fault location of the tower and identifies the fault type by comparing the changes in monitoring data before and after the fault. The maintenance strategy generation module generates the optimal maintenance strategy based on the fault location results and the real-time operating status of the tower, combined with the tower's maintenance history, environmental factor data, and equipment life model.

2. The tower safety monitoring system according to claim 1, characterized in that, The real-time monitoring of the tower's operating status includes: The vibration acceleration data of the tower in the X, Y, and Z directions are monitored in real time using accelerometers. Distributed fiber optic stress patches are evenly arranged along the main load-bearing components of the tower to monitor changes in stress parameters inside the components. The tilt angle of the tower is monitored in real time by a dual-axis tilt sensor, which monitors the tilt data of the tower in two mutually perpendicular directions. Several cameras are installed at various heights and locations on the tower to collect real-time images of the tower's appearance and identify defects such as rust, cracks, and loose bolts on the tower's surface.

3. The tower safety monitoring system according to claim 1, characterized in that, The establishment of the state assessment model includes: Based on the acceleration, stress, tilt angle, and image data acquired by the data acquisition module, as well as environmental factor data, the data is fused using a data fusion algorithm. Data on iron towers with the same operational characteristics are grouped together using clustering algorithms; A model of the relationship between tower performance indicators and various influencing factors was established based on regression analysis algorithms.

4. The tower safety monitoring system according to claim 1, characterized in that, The method of locating the fault location of the iron tower using spatial positioning algorithms and data analysis methods includes: Based on positioning data from the Global Positioning System and the BeiDou Navigation Satellite System, the location data of key parts of the tower are obtained. Based on the three-dimensional model data of the tower, the data collected by sensors and cameras are matched with the three-dimensional model to determine the area where the fault occurred. By analyzing acceleration, stress, and tilt angle using signal processing methods, and using machine learning-based pattern recognition methods, the monitoring data before and after the fault are classified and identified, thus narrowing down the fault location range. The fault location results are corrected by iterative algorithms, and the location parameters are adjusted based on the difference between the actual measurement data and the model prediction data.

5. The tower safety monitoring system according to claim 1 or 4, characterized in that, The method of identifying fault types by comparing changes in monitoring data before and after a fault includes: Compare the acceleration, stress, tilt angle, and image monitoring data before and after the fault; The feature changes obtained from comparative analysis are matched with a pre-established fault feature pattern library, and the fault type is determined by the pattern matching algorithm.

6. The tower safety monitoring system according to claim 5, characterized in that, The acceleration, stress, tilt angle, and image monitoring data before and after the fault include: Compare the changes in amplitude, frequency, and phase before and after the fault; By comparing the distribution of stress parameters before and after the failure, it can be determined whether there is stress concentration or abnormal changes. By comparing the changes in tilt angle data before and after the fault, the degree of structural deformation of the tower can be assessed. By comparing image data, we can identify changes in the data regarding defects such as rust, cracks, and loose bolts on the tower surface before and after the failure.

7. The tower safety monitoring system according to claim 1, characterized in that, The process of generating the optimal maintenance strategy based on fault location results and the real-time operating status of the tower, combined with the tower's maintenance history, environmental factor data, and equipment lifespan model, includes: Based on a multi-objective optimization algorithm, the optimal maintenance strategy that meets the requirements for safe operation of the tower is determined with objectives such as maintenance cost, maintenance time, and spare parts inventory. Based on the results of the optimization algorithm, an optimal maintenance strategy is generated, which includes regular inspections, emergency repairs, component replacements, maintenance schedules, and spare parts usage plans.

8. The tower safety monitoring system according to claim 1, characterized in that, The preliminary status includes the normal operation status, minor abnormal status, and serious fault status of the tower.

9. The tower safety monitoring system according to claim 1, 2, 3, or 4, characterized in that, The system also includes a monitoring and control module. After receiving the real-time operating status data and fault location data of the tower transmitted by the data processing module, the module displays the tower's vibration acceleration curve, stress distribution cloud map, tilt angle value, appearance image and fault location mark on the monitoring interface in a graphical manner. When the tower's abnormal status or potential fault is detected, an early warning is automatically triggered.

10. The tower safety monitoring system according to claim 1, 2, 3, or 4, characterized in that, The system also includes a new antenna mounting module, which includes: The data acquisition simulation unit collects acceleration and stress data at the expected mounting location under simulated mounting conditions by installing sensors at the expected mounting location. Using a 3D modeling method, the parameters of the new antenna are integrated into the 3D model of the tower to perform virtual mounting simulation. Simulation software is used to calculate the stress and vibration characteristics of various parts of the tower after the new antenna is mounted. The structural strength assessment unit, based on the collected simulation data and 3D model analysis results, combined with the original structural design and material performance parameters of the tower, uses the finite element analysis method to evaluate the structural strength of the tower. The feasibility assessment unit automatically generates a feasibility report for mounting new antennas based on the results of the structural strength assessment.

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