A method and system for integrated monitoring of wind power towers and communication base stations

Abstract

This invention relates to the field of wind power generation technology, specifically disclosing a comprehensive monitoring method and system for wind turbine towers and communication base stations. The system includes a data fusion preprocessing module, a collaborative health assessment module, a co-located fault reasoning module, a collaborative monitoring and control module, an intelligent decision-making and planning module, a maintenance execution and feedback module, and a cloud monitoring and analysis platform. This invention achieves comprehensive monitoring of the co-located system of wind turbine towers and communication base stations through data fusion, collaborative assessment, fault location, precise monitoring, and intelligent decision-making. It overcomes the drawbacks of traditional independent operation and maintenance models, proactively identifying cascading communication faults caused by tower structural problems. By formulating collaborative maintenance plans, it significantly reduces the number of times maintenance personnel need to climb the tower, improving operation and maintenance efficiency and safety. Furthermore, by utilizing encrypted uploads and file updates, it can continuously improve the system's health management level.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, and more specifically to a method and system for integrated monitoring of wind power generation towers and communication base stations. Background Technology

[0002] With the synergistic development of clean energy and the digital economy, a comprehensive application model that co-locates communication base stations on wind turbine towers is gradually emerging. This model can effectively save land resources, reduce base station construction costs, and utilize the abundant power supply of wind farms, aligning with the direction of green and low-carbon new infrastructure construction.

[0003] Currently, the industry generally adopts an independent management model for the operation and maintenance of wind turbine towers and communication base stations. In existing technologies, the structural health monitoring of wind turbine towers usually relies on a separate system. This system collects structural status data through sensors deployed on the tower and sets thresholds for safety warnings. Its focus is on the structural integrity, stability, and fatigue life of the tower itself, aiming to prevent serious safety accidents such as tower overturning and fracture.

[0004] Meanwhile, the operation and maintenance of communication base stations fall under another independent network management system. This system mainly monitors the operating performance indicators of base stations, such as transmit power, receive sensitivity, bit error rate, equipment temperature, and traffic load. Its core objective is to ensure the coverage quality, signal strength, and transmission stability of the wireless communication network.

[0005] However, this fragmented operation and maintenance management model has gradually revealed its inherent limitations in co-located application scenarios. First, because the two systems are independent and their data is not shared, it is impossible to understand the overall operating status of the co-located system. Structural anomalies in the tower are often the root cause of stress concentration on the base station equipment installation platform and slight shifts in antenna azimuth angle. These physical changes directly degrade the communication performance of the base station.

[0006] In existing technologies, base station management systems can only monitor the phenomenon of declining base station signal quality, but cannot trace it back to the root cause of changes in the tower structure. This can easily lead maintenance personnel to misjudge the problem as a fault in the base station's own equipment, resulting in ineffective maintenance or component replacement.

[0007] Secondly, existing technologies lack the ability to collaboratively predict and prevent potential risks. Because a functional dependency model between the tower and the base station has not been established, it is impossible to deduce the chain reactions that a single component failure might trigger. In terms of maintenance execution, independent operation and maintenance plans lead to inefficiency and resource waste. Operation and maintenance teams belonging to the wind power and communications sectors must plan and climb the towers separately, which not only significantly increases the safety risks and high operation and maintenance costs of high-altitude operations, but may also cause delays in problem handling due to asynchronous maintenance schedules, failing to achieve the collaborative and efficient goal of resolving all related issues in a single tower climb.

[0008] Therefore, how to provide a comprehensive monitoring method and system for wind power towers and communication base stations is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0009] In view of this, the present invention provides a method and system for integrated monitoring of wind power towers and communication base stations to solve the problems mentioned in the background section above.

[0010] To achieve the above objectives, the present invention adopts the following technical solution: A method for integrated monitoring of wind power generation towers and communication base stations includes: S1. Obtain structural status monitoring data of wind turbine towers and operational status data of co-located communication base stations. By performing spatiotemporal alignment and standardization fusion processing on the heterogeneous data of wind turbine towers and communication base stations that are asynchronous in time and space, a comprehensive status dataset that can be used for joint analysis is generated. S2. Based on the comprehensive status dataset, accurately assess the overall status of the co-located system, construct a first health index reflecting the safety of the tower structure and a second health index reflecting the communication performance of the base station, and establish a joint assessment model based on the physical coupling relationship between the tower and the base station. Output a comprehensive assessment index to characterize the overall health of the co-located system through the joint assessment model. S3. Based on the comprehensive evaluation index, locate the chain faults caused by tower structure problems, identify the abnormal transmission path between the tower and the base station, locate the co-located fault area where the base station performance is degraded due to tower structure changes, and infer the joint fault warning points that need to be monitored in coordination based on the functional dependency network describing the dependencies between system components. S4. Obtain more accurate diagnostic data, adjust the monitoring strategy of the multi-source sensor group deployed at the joint fault early warning point, and perform enhanced collaborative acquisition of tower structure deformation, vibration spectrum, base station signal quality, and equipment temperature to obtain high-precision real-time collaborative monitoring results. S5. Analyze the real-time collaborative monitoring results, make scientific maintenance decisions, determine whether collaborative maintenance conditions are triggered, and if triggered, integrate the current operation and maintenance resource status, meteorological environment information and maintenance task priority to formulate an integrated collaborative maintenance plan that simultaneously covers tower structure reinforcement and base station equipment maintenance. S6. The scheduling and maintenance unit executes the integrated collaborative maintenance plan, collects maintenance process data during the maintenance process to generate a structured maintenance report, encrypts the report and uploads it to the cloud monitoring platform, and updates the health status file of the co-located system.

[0011] This invention achieves comprehensive supervision of the co-located system of wind power towers and communication base stations by integrating data, collaborative assessment, fault location, precise monitoring and intelligent decision-making. It overcomes the drawbacks of the traditional independent operation and maintenance mode, can proactively identify cascading communication failures caused by tower structural problems, and significantly reduces the number of times operation and maintenance personnel need to climb the tower by formulating collaborative maintenance plans, thereby improving operation and maintenance efficiency and safety. At the same time, it can continuously improve the health management level of the system by using encrypted upload and file updates.

[0012] Preferably, in the above-mentioned integrated monitoring method for wind power towers and communication base stations, the method for spatiotemporal alignment and standardized fusion of heterogeneous data from wind power towers and communication base stations that are asynchronous in time and space is as follows: The system receives structural condition monitoring data from tower tilt sensors, vibration accelerometers, and strain gauges, as well as operational status data from the base station baseband processing unit, antenna system, and environmental monitoring unit. All data is timestamped and mapped to the same three-dimensional coordinate system. Wavelet denoising is applied to vibration signal data, outlier removal is performed on communication performance data, and all data is normalized to the same dimension for standardized fusion. Spatiotemporal alignment resolves the issue of using data from different sources and at different times within the same analytical framework, providing a reliable data foundation for subsequent collaborative analysis. Targeted processing such as wavelet denoising and outlier removal effectively improves the signal-to-noise ratio and quality of vibration signals and communication performance data. Data normalization enables effective weighted fusion and comparative analysis of structural health and communication performance—two originally different dimensional indicators—ensuring the accuracy and reliability of the joint evaluation model.

[0013] Preferably, in the above-mentioned integrated monitoring method for wind power generation towers and communication base stations, the method for establishing a joint evaluation model based on the physical coupling relationship between the tower and the base station is as follows: A mapping knowledge base is established between tower vibration modes and base station antenna azimuth offset, tower top sway and stress distribution on the base station main equipment frame mounting platform, and tower natural frequency and base station radio frequency signal phase noise. Based on this mapping knowledge base, a fuzzy logic reasoning method is used to weight and fuse a first health indicator and a second health indicator for comprehensive system health assessment. The weight allocation is dynamically adjusted according to the strength of the physical coupling relationship to generate the comprehensive assessment index. By constructing a specific "mapping knowledge base," the abstract physical coupling relationship between the tower and the base station is transformed into a concrete and quantifiable knowledge system, making the joint assessment based on evidence. The use of fuzzy logic reasoning and dynamic weight adjustment flexibly handles the uncertainty and complexity of the system state, making the final comprehensive assessment index more accurately reflect the overall health status of the co-located system and avoiding the one-sidedness of single indicator assessment.

[0014] Preferably, in the above-mentioned integrated monitoring method for wind power towers and communication base stations, the method for locating the co-located fault area where base station performance degradation is caused by changes in tower structure is as follows: When the comprehensive evaluation index falls below a preset threshold, the abnormal components in the first and second health indicators are traced back. If an increase in the vibration amplitude of the tower in a specific direction is found, accompanied by a decrease in signal quality in the coverage area of ​​the base station in that direction, then that direction is determined to be an abnormal conduction path, and the tower section and base station antenna unit involved in that path are jointly marked as the co-located fault area. By tracing the abnormal components back and confirming the accompanying phenomena, it is possible to effectively distinguish whether the base station performance degradation is caused by a tower problem or whether the base station itself has independently failed, directly locating the source of the fault and the affected related equipment, improving the efficiency and accuracy of fault diagnosis, and providing a clear target for subsequent precise maintenance.

[0015] Preferably, in the above-mentioned integrated monitoring method for wind power generation towers and communication base stations, the method for inferring joint fault early warning points requiring collaborative monitoring based on the functional dependency network describing the dependencies between system components is as follows: A directed graph model is constructed with towers as basic nodes and base station equipment as functional nodes. Within a co-located fault region, the critical node with the highest out-degree in the directed graph model is searched. This critical node and its directly connected functional nodes carrying core functions are collectively identified as the joint fault early warning point. By constructing a functional dependency network, the complex physical system is abstracted into a computable and analyzable graph model, making the reasoning process for system vulnerabilities quantifiable and automated. Searching for the critical node with the highest out-degree accurately identifies the core components whose failure would have the greatest cascading impact on the system, ensuring that the points posing the greatest threat to system stability are under continuous monitoring and achieving preventative maintenance.

[0016] Preferably, in the above-mentioned integrated monitoring method for wind power towers and communication base stations, adjusting the monitoring strategy of the multi-source sensor group deployed at the joint fault early warning point includes: For tower sections marked as joint fault early warning points, the sampling frequency of vibration sensors is increased from the conventional frequency to a high-frequency mode to capture more detailed vibration characteristics; For base station antenna units marked as joint fault warning points, the signal probes around them are switched from intermittent sampling to continuous monitoring mode. Simultaneously triggering the panoramic camera deployed on the top of the tower to capture images of the warning point area, and coordinating the collection of vibration, signal and visual data from multiple dimensions to form a multi-dimensional chain of evidence.

[0017] Increasing the sampling frequency and switching to continuous monitoring for early warning points can obtain more accurate and continuous fault characteristic data. The collaborative acquisition of multimodal data can cross-verify the fault status from different dimensions, reducing false alarms and missed alarms.

[0018] Preferably, in the above-mentioned integrated monitoring method for wind power towers and communication base stations, an integrated collaborative maintenance plan is formulated, including: Analyze the real-time collaborative monitoring results to determine the type of maintenance task, such as tower structure maintenance, base station equipment maintenance, or a combination of both, and clarify the scope of maintenance. Obtain the location, skills and qualifications list, and current load of available operation and maintenance teams; obtain spare parts inventory information and logistics status; and access real-time weather forecast data. For collaborative maintenance tasks, priority should be given to dispatching composite teams with dual qualifications in wind power structure maintenance and communication equipment maintenance to generate a single collaborative maintenance plan that integrates all maintenance operations, personnel, equipment, materials, and time windows.

[0019] This invention develops a reasonable maintenance plan by comprehensively considering fault types, human resources, material inventory, and even weather conditions. In particular, for co-located maintenance scenarios, it optimizes by scheduling composite teams and focusing on reducing tower climbing, which can merge independent maintenance tasks into one completion, thereby reducing the risks of high-altitude operations, labor costs, and maintenance time.

[0020] Preferably, in the above-mentioned integrated monitoring method for wind power generation towers and communication base stations, the method by which the dispatch and maintenance unit executes the integrated collaborative maintenance plan is as follows: The collaborative maintenance plan is broken down into executable work order instructions, which are then sent to the designated mobile terminals of the maintenance team via a secure link. The maintenance team receives the work order instructions and equipment drawings using equipped augmented reality glasses and performs maintenance operations based on the instructions. The mobile terminals and augmented reality glasses automatically record key steps of the maintenance process, the numbers of replaced parts, and completed photos, generating a structured maintenance report. Through the precise issuance of work order instructions and the assistance of AR technology, the error rate of on-site operations is reduced, and the success rate and efficiency of repairs are improved. The automatic recording of the maintenance process and the generation of structured reports overcome the shortcomings of traditional paper records, which are prone to errors and difficult to archive, establishing a complete and accurate electronic maintenance archive. This provides a high-quality data source for equipment lifecycle management, accountability, and model optimization.

[0021] Preferably, in the above-mentioned integrated monitoring method for wind power towers and communication base stations, the report is encrypted before being uploaded to the cloud monitoring platform, including: The structured maintenance report is encrypted using an asymmetric encryption algorithm. The encrypted report is then transmitted back to the cloud monitoring platform via the wireless transmission channel provided by the communication base station. After decrypting the report, the cloud platform updates the device files and performs self-learning optimization on the mapping knowledge base in the joint evaluation model based on the maintenance data. Utilizing the base station's own channel for data transmission demonstrates a clever concept of resource reuse and reduces the cost of deploying additional communication facilities. Feeding the maintenance results back to the cloud and using them to optimize the mapping knowledge base enables the entire system to learn, making the evaluation model increasingly accurate.

[0022] A comprehensive monitoring system for wind power towers and communication base stations, wherein the system performs comprehensive monitoring through the coordinated operation of various modules, the system comprising: The data fusion preprocessing module acquires and merges heterogeneous data from towers and base stations to provide unified data for subsequent analysis; The collaborative health assessment module is connected to the data fusion preprocessing module, receives comprehensive status data and constructs a joint assessment model, and calculates a comprehensive assessment index. The co-location fault reasoning module is connected to the collaborative health assessment module, and identifies co-location fault areas and infers joint fault early warning points based on the comprehensive assessment index; The collaborative monitoring and control module is connected to the co-located fault reasoning module, receives early warning point information and adjusts sensor strategies to obtain real-time collaborative monitoring results; The intelligent decision-making and planning module is connected to the collaborative monitoring and control module, analyzes the monitoring results, and generates an integrated collaborative maintenance plan. The maintenance execution and feedback module is connected to the intelligent decision-making and planning module to execute maintenance plans and generate and encrypt maintenance reports for uploading. The cloud monitoring and analysis platform connects to the maintenance execution and feedback module, stores all data, updates models, and provides a human-computer interaction and centralized management interface for the entire system.

[0023] As can be seen from the above technical solution, compared with the prior art, this invention discloses a comprehensive monitoring method and system for wind power generation towers and communication base stations. This invention breaks down the technical barriers between the two independent systems of wind power generation and communication base stations, integrating the previously isolated monitoring and maintenance processes. Through spatiotemporal alignment and fusion processing of heterogeneous data, it solves the analytical obstacles caused by inconsistent data sources. Furthermore, by establishing a joint evaluation model based on physical coupling, it scientifically quantifies and comprehensively evaluates the overall health status of the co-located system, overcoming the one-sidedness of single-indicator evaluation. Therefore, this invention can accurately locate cascading communication failures caused by tower structural problems.

[0024] This invention also generates an integrated collaborative maintenance plan by integrating resource, environmental, and priority information, and issues the plan to be executed in a digital and command-based manner. Finally, it optimizes the knowledge model through maintenance feedback data, thereby optimizing the allocation of operation and maintenance resources, merging independent high-altitude operations into one, significantly reducing operational risks and operation and maintenance costs, and improving safety and economy. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0026] Figure 1 The attached figure is a flowchart of a method for integrated monitoring of wind power generation towers and communication base stations according to the present invention.

[0027] Figure 2 The attached figure is a framework diagram of a wind power generation tower and communication base station integrated monitoring system according to the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] This invention discloses a method for integrated monitoring of wind power generation towers and communication base stations, including: S1. Obtain structural status monitoring data of wind turbine towers and operational status data of co-located communication base stations. By performing spatiotemporal alignment and standardization fusion processing on the heterogeneous data of wind turbine towers and communication base stations that are asynchronous in time and space, a comprehensive status dataset that can be used for joint analysis is generated. S2. Based on the comprehensive status dataset, accurately assess the overall status of the co-located system, construct a first health index reflecting the safety of the tower structure and a second health index reflecting the communication performance of the base station, and establish a joint assessment model based on the physical coupling relationship between the tower and the base station. Output a comprehensive assessment index to characterize the overall health of the co-located system through the joint assessment model. S3. Based on the comprehensive evaluation index, locate the chain faults caused by tower structure problems, identify the abnormal transmission path between the tower and the base station, locate the co-located fault area where the base station performance is degraded due to tower structure changes, and infer the joint fault warning points that need to be monitored in coordination based on the functional dependency network that describes the dependencies between system components. S4. Obtain more accurate diagnostic data, adjust the monitoring strategy of the multi-source sensor group deployed at the joint fault early warning point, and perform enhanced collaborative acquisition of tower structure deformation, vibration spectrum, base station signal quality, and equipment temperature to obtain high-precision real-time collaborative monitoring results. S5. Analyze the real-time collaborative monitoring results, make scientific maintenance decisions, determine whether the collaborative maintenance conditions are triggered, and if triggered, integrate the current operation and maintenance resource status, meteorological environment information and maintenance task priority to formulate an integrated collaborative maintenance plan that covers both tower structure reinforcement and base station equipment maintenance. S6. The scheduling and maintenance unit executes the integrated collaborative maintenance plan, collects maintenance process data during the maintenance process to generate a structured maintenance report, encrypts the report and uploads it to the cloud monitoring platform, and updates the health status file of the co-located system.

[0030] This invention achieves comprehensive supervision of the co-located system of wind power towers and communication base stations by integrating data, collaborative assessment, fault location, precise monitoring and intelligent decision-making. It overcomes the drawbacks of the traditional independent operation and maintenance mode, can proactively identify cascading communication failures caused by tower structural problems, and significantly reduces the number of times operation and maintenance personnel need to climb the tower by formulating collaborative maintenance plans, thereby improving operation and maintenance efficiency and safety. At the same time, it can continuously improve the health management level of the system by using encrypted upload and file updates.

[0031] To further optimize the above technical solution, the method for spatiotemporal alignment and standardized fusion of heterogeneous data from wind turbine towers and communication base stations that are asynchronous in time and space is as follows: The system receives structural condition monitoring data from tower tilt sensors, vibration accelerometers, and strain gauges, as well as operational status data from the base station baseband processing unit, antenna system, and environmental monitoring unit. All data is timestamped and mapped to the same three-dimensional coordinate system. Wavelet denoising is applied to vibration signal data, outlier removal is performed on communication performance data, and all data is normalized to the same dimension for standardized fusion. Spatiotemporal alignment resolves the issue of using data from different sources and at different times within the same analytical framework, providing a reliable data foundation for subsequent collaborative analysis. Targeted processing such as wavelet denoising and outlier removal effectively improves the signal-to-noise ratio and quality of vibration signals and communication performance data. Data normalization enables effective weighted fusion and comparative analysis of structural health and communication performance—two originally different dimensional indicators—ensuring the accuracy and reliability of the joint evaluation model.

[0032] To further optimize the above technical solution, the method for establishing a joint evaluation model based on the physical coupling relationship between the tower and the base station is as follows: A mapping knowledge base was established between tower vibration modes and base station antenna azimuth offset, tower top sway and stress distribution on the base station main equipment frame mounting platform, and tower natural frequency and base station radio frequency signal phase noise. Based on this mapping knowledge base, a fuzzy logic reasoning method was used to weight and fuse the first and second health indicators for comprehensive system health assessment. The weight allocation was dynamically adjusted according to the strength of the physical coupling relationship to generate a comprehensive assessment index. By constructing a specific "mapping knowledge base," the abstract physical coupling relationship between the tower and the base station was transformed into a concrete and quantifiable knowledge system, making the joint assessment based on evidence. The use of fuzzy logic reasoning and dynamic weight adjustment flexibly handled the uncertainty and complexity of the system state, making the final comprehensive assessment index more accurately reflect the overall health status of the co-located system and avoiding the one-sidedness of single indicator assessment.

[0033] To further optimize the above technical solution, the method for locating co-located fault areas where base station performance degradation is caused by changes in tower structure is as follows: When the comprehensive evaluation index falls below a preset threshold, the abnormal components in the first and second health indicators are traced back. If an increase in vibration amplitude in a specific direction of the tower is found, accompanied by a decrease in signal quality in the coverage area of ​​the base station in that direction, then that direction is determined to be an abnormal conduction path, and the tower section and base station antenna unit involved in this path are jointly marked as a co-located fault area. By tracing the abnormal components back and confirming the accompanying phenomena, it is possible to effectively distinguish whether the base station performance degradation is caused by a tower problem or whether the base station itself has independently failed. This directly locates the source of the fault and the affected related equipment, improving the efficiency and accuracy of fault diagnosis and providing a clear target for subsequent precise maintenance.

[0034] To further optimize the above technical solution, a method for inferring joint fault early warning points requiring collaborative monitoring based on the functional dependency network describing the dependencies between system components is as follows: A directed graph model is constructed with towers as basic nodes and base station equipment as functional nodes. Within a co-located fault area, the critical node with the highest out-degree in the directed graph model is searched. The critical node and its directly connected functional nodes carrying core functions are jointly identified as joint fault early warning points. By constructing a functional dependency network, the complex physical system is abstracted into a computable and analyzable graph model, making the reasoning process for system vulnerabilities quantifiable and automated. Searching for the critical node with the highest out-degree can accurately identify the core components whose failure would have the greatest cascading impact on the system, ensuring that the points that pose the greatest threat to system stability are under continuous monitoring, thus achieving preventative maintenance.

[0035] To further optimize the above technical solution, the monitoring strategy of the multi-source sensor group deployed at the joint fault early warning point will be adjusted, including: For tower sections marked as joint fault early warning points, the sampling frequency of vibration sensors is increased from the conventional frequency to a high-frequency mode to capture more detailed vibration characteristics; For base station antenna units marked as joint fault warning points, the signal probes around them are switched from intermittent sampling to continuous monitoring mode. Simultaneously triggering the panoramic camera deployed on the top of the tower to capture images of the warning point area, and coordinating the collection of vibration, signal and visual data from multiple dimensions to form a multi-dimensional chain of evidence.

[0036] Increasing the sampling frequency and switching to continuous monitoring for early warning points can obtain more accurate and continuous fault characteristic data. The collaborative acquisition of multimodal data can cross-verify the fault status from different dimensions, reducing false alarms and missed alarms.

[0037] To further optimize the above technical solution, an integrated collaborative maintenance plan has been developed, including: Analyze the real-time collaborative monitoring results to determine the type of maintenance task, such as tower structure maintenance, base station equipment maintenance, or a combination of both, and clarify the scope of maintenance. Obtain the location, skills and qualifications list, and current load of available operation and maintenance teams; obtain spare parts inventory information and logistics status; and access real-time weather forecast data. For collaborative maintenance tasks, priority should be given to dispatching composite teams with dual qualifications in wind power structure maintenance and communication equipment maintenance to generate a single collaborative maintenance plan that integrates all maintenance operations, personnel, equipment, materials, and time windows.

[0038] This invention develops a reasonable maintenance plan by comprehensively considering fault types, human resources, material inventory, and even weather conditions. In particular, for co-located maintenance scenarios, it optimizes by scheduling composite teams and focusing on reducing tower climbing, which can merge independent maintenance tasks into one completion, thereby reducing the risks of high-altitude operations, labor costs, and maintenance time.

[0039] To further optimize the above technical solution, the method for the scheduling and maintenance unit to execute the integrated collaborative maintenance plan is as follows: The collaborative maintenance plan is broken down into executable work orders, which are then sent to designated maintenance team mobile terminals via a secure link. The maintenance team receives the work orders and equipment drawings using equipped augmented reality (AR) glasses and performs maintenance operations accordingly. Key steps of the maintenance process, replaced component numbers, and completed photos are automatically recorded via mobile terminals and AR glasses, generating a structured maintenance report. Through precise work order issuance and AR technology, the error rate of on-site operations is reduced, and the success rate and efficiency of repairs are improved. The automatic recording of the maintenance process and generation of structured reports overcome the shortcomings of traditional paper records, which are prone to errors and difficult to archive, establishing a complete and accurate electronic maintenance archive. This provides a high-quality data source for equipment lifecycle management, accountability, and model optimization.

[0040] To further optimize the above technical solution, the report is encrypted before being uploaded to the cloud monitoring platform, including: Structured maintenance reports are encrypted using an asymmetric encryption algorithm. The encrypted reports are then transmitted back to the cloud monitoring platform via the wireless transmission channel provided by the communication base station. After decryption, the cloud platform updates the device profile and performs self-learning optimization of the mapping knowledge base in the joint evaluation model based on the maintenance data. Utilizing the base station's own transmission channel demonstrates a clever resource reuse concept and reduces the cost of deploying additional communication facilities. Feeding the maintenance results back to the cloud and using them to optimize the mapping knowledge base enables the entire system to learn, making the evaluation model increasingly accurate.

[0041] A comprehensive monitoring system for wind power generation towers and communication base stations, the system performs comprehensive monitoring through the coordinated operation of various modules, and the system includes: The data fusion preprocessing module acquires and merges heterogeneous data from towers and base stations to provide unified data for subsequent analysis; The collaborative health assessment module connects to the data fusion and preprocessing module, receives comprehensive status data, constructs a joint assessment model, and calculates a comprehensive assessment index. The co-location fault reasoning module is connected to the collaborative health assessment module. It identifies co-location fault areas and infers joint fault early warning points based on the comprehensive assessment index. The collaborative monitoring and control module connects to the co-located fault reasoning module, receives early warning point information, adjusts sensor strategies, and obtains real-time collaborative monitoring results. The intelligent decision-making and planning module connects to the collaborative monitoring and control module, analyzes monitoring results, and generates an integrated collaborative maintenance plan. The maintenance execution and feedback module connects to the intelligent decision-making and planning module to execute maintenance plans and generate and encrypt maintenance reports for uploading. The cloud-based monitoring and analysis platform connects the maintenance execution and feedback modules, stores all data, updates models, and provides a human-computer interaction and centralized management interface for the entire system.

[0042] Technical principle: The core principle of this invention lies in constructing a collaborative monitoring scheme and system based on data fusion and causal reasoning. By collecting and spatiotemporally aligning the structural state data of wind turbine towers and the operational state data of communication base stations in real time, a unified comprehensive state dataset is formed, providing a data foundation for addressing system heterogeneity. Furthermore, by establishing a mapping knowledge base representing the physical coupling relationship between the tower and the base station, a joint evaluation model is constructed. This model deeply integrates and weights previously isolated structural health indicators and communication performance indicators to generate a comprehensive evaluation index, achieving a unified quantitative understanding of the overall state of the co-located system. Based on this, causal reasoning and source tracing are further performed through functionally dependent networks, locating specific co-located fault areas and joint fault early warning points from system-level anomalies, thus achieving a leap from phenomenon to root cause. Based on the precise location results, the monitoring strategy of multi-source sensors is dynamically adjusted, implementing enhanced collaborative acquisition to provide high-precision data support for decision-making.

[0043] This invention utilizes real-time data, resource status, and environmental information for optimization calculations to generate an integrated collaborative maintenance plan. It then uses digital tools to schedule and execute the plan. By feeding maintenance results data back to a knowledge base, it drives the model to continuously self-optimize, thereby forming an intelligent monitoring system with learning and evolution capabilities.

[0044] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0045] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for integrated monitoring of wind power generation towers and communication base stations, characterized in that, include: S1. Obtain structural status monitoring data of wind turbine towers and operational status data of co-located communication base stations. By performing spatiotemporal alignment and standardization fusion processing on the heterogeneous data of wind turbine towers and communication base stations that are asynchronous in time and space, a comprehensive status dataset that can be used for joint analysis is generated. S2. Based on the comprehensive status dataset, accurately assess the overall status of the co-located system by constructing a first health index reflecting the safety of the tower structure and a second health index reflecting the communication performance of the base station. A joint evaluation model is then established based on the physical coupling relationship between the tower and the base station. The method for establishing the joint evaluation model based on the physical coupling relationship between the tower and the base station is as follows: Establish a knowledge base that maps tower vibration modes to base station antenna azimuth offset, tower top sway to stress distribution on base station main equipment frame mounting platform, and tower natural frequency to base station radio frequency signal phase noise. Based on the mapping knowledge base, in order to comprehensively evaluate the health of the system, a fuzzy logic reasoning method is used to weight and fuse the first health indicator and the second health indicator. The weight allocation is dynamically adjusted according to the strength of the physical coupling relationship. The joint evaluation model outputs a comprehensive evaluation index to characterize the overall health of the co-located system. S3. Based on the comprehensive evaluation index, locate the chain faults caused by tower structure problems, identify the abnormal transmission path between the tower and the base station, locate the co-located fault area where the base station performance is degraded due to tower structure changes, and infer the joint fault warning points that need to be monitored in coordination based on the functional dependency network describing the dependencies between system components. The method for locating co-located fault areas where base station performance degradation is caused by changes in tower structure is as follows: When the comprehensive evaluation index is lower than a preset threshold, the abnormal components in the first health indicator and the second health indicator are traced back in reverse. If an increase in the vibration amplitude of the tower in a specific direction is found, accompanied by a decrease in the signal quality of the base station coverage area in that direction, then the spatial location corresponding to that specific direction is determined to be an abnormal conduction path, and the tower section and base station antenna unit involved in that path are jointly marked as the co-located fault area. The method for inferring joint fault early warning points requiring collaborative monitoring based on the functional dependency network describing the dependencies between system components is as follows: Construct a directed graph model with towers as basic nodes and base station equipment as functional nodes. Within the co-located fault area, search for the key node with the highest out-degree in the directed graph model. The key node and its directly connected functional nodes that carry core functions are jointly identified as the joint fault early warning point. S4. Obtain more accurate diagnostic data, adjust the monitoring strategy of the multi-source sensor group deployed at the joint fault early warning point, and perform enhanced collaborative acquisition of tower structure deformation, vibration spectrum, base station signal quality, and equipment temperature to obtain high-precision real-time collaborative monitoring results. S5. Analyze the real-time collaborative monitoring results, make scientific maintenance decisions, determine whether collaborative maintenance conditions are triggered, and if triggered, integrate the current operation and maintenance resource status, meteorological environment information and maintenance task priority to formulate an integrated collaborative maintenance plan that simultaneously covers tower structure reinforcement and base station equipment maintenance. S6. The scheduling and maintenance unit executes the integrated collaborative maintenance plan, collects maintenance process data during the maintenance process to generate a structured maintenance report, encrypts the report and uploads it to the cloud monitoring platform, and updates the health status file of the co-located system.

2. The integrated monitoring method for wind power generation towers and communication base stations according to claim 1, characterized in that, The method for spatiotemporal alignment and standardization fusion of heterogeneous data from wind turbine towers and communication base stations that are asynchronous in time and space is as follows: It receives structural status monitoring data from tower tilt sensors, vibration accelerometers, and strain gauges, as well as operational status data from base station baseband processing units, antenna systems, and environmental monitoring units; it assigns a unified timestamp to all data and maps it to the same three-dimensional spatial coordinate system; it performs wavelet noise reduction processing on vibration signal data, outlier removal processing on communication performance data, and normalizes all data to the same dimension for standardized fusion.

3. The integrated monitoring method for wind power generation towers and communication base stations according to claim 1, characterized in that, Adjusting the monitoring strategy of the multi-source sensor group deployed at the joint fault early warning point includes: For tower sections marked as joint fault early warning points, the sampling frequency of vibration sensors is increased from the conventional frequency to a high-frequency mode to capture more detailed vibration characteristics; For base station antenna units marked as joint fault warning points, the signal probes around them are switched from intermittent sampling to continuous monitoring mode. Simultaneously triggering the panoramic camera deployed on the top of the tower to capture images of the warning point area, and coordinating the collection of vibration, signal and visual data from multiple dimensions to form a multi-dimensional chain of evidence.

4. The integrated monitoring method for wind power generation towers and communication base stations according to claim 1, characterized in that, Develop an integrated collaborative maintenance plan, including: Analyze the real-time collaborative monitoring results to determine the type of maintenance task, such as tower structure maintenance, base station equipment maintenance, or a combination of both, and clarify the scope of maintenance. Obtain the location, skills and qualifications list, and current load of available operation and maintenance teams; obtain spare parts inventory information and logistics status; and access real-time weather forecast data. For collaborative maintenance tasks, priority should be given to dispatching composite teams with dual qualifications in wind power structure maintenance and communication equipment maintenance to generate a single collaborative maintenance plan that integrates all maintenance operations, personnel, equipment, materials, and time windows.

5. The integrated monitoring method for wind power generation towers and communication base stations according to claim 1, characterized in that, The method by which the scheduling and maintenance unit executes the integrated collaborative maintenance plan is as follows: The collaborative maintenance plan is broken down into executable work order instructions and sent to the designated mobile terminals of the maintenance team via a secure link. The maintenance team uses equipped augmented reality glasses to receive the work order instructions and equipment drawings, and performs maintenance operations based on the instructions. The key steps of the maintenance process, the numbers of the replaced parts, and the completed photos are automatically recorded through the mobile terminals and augmented reality glasses to generate the structured maintenance report.

6. The integrated monitoring method for wind power generation towers and communication base stations according to claim 5, characterized in that, The report is encrypted and then uploaded to the cloud monitoring platform, including: The structured maintenance report is encrypted using an asymmetric encryption algorithm. The encrypted maintenance report is then transmitted back to the cloud monitoring platform via the wireless transmission channel provided by the communication base station. After the cloud platform decrypts the report, it updates the device files and performs self-learning optimization on the mapping knowledge base in the joint evaluation model based on the maintenance data.

7. A comprehensive monitoring system for wind power generation towers and communication base stations, characterized in that, The system is used to implement the method described in any one of claims 1-6, and the system performs comprehensive supervision through the coordinated operation of various modules. The system includes: The data fusion preprocessing module acquires and merges heterogeneous data from towers and base stations to provide unified data for subsequent analysis; The collaborative health assessment module is connected to the data fusion preprocessing module, receives comprehensive status data and constructs a joint assessment model, and calculates a comprehensive assessment index. The co-location fault reasoning module is connected to the collaborative health assessment module, and identifies co-location fault areas and infers joint fault early warning points based on the comprehensive assessment index; The collaborative monitoring and control module is connected to the co-located fault reasoning module, receives early warning point information and adjusts sensor strategies to obtain real-time collaborative monitoring results; The intelligent decision-making and planning module is connected to the collaborative monitoring and control module, analyzes the monitoring results, and generates an integrated collaborative maintenance plan. The maintenance execution and feedback module is connected to the intelligent decision-making and planning module to execute maintenance plans and generate and encrypt maintenance reports for uploading. The cloud monitoring and analysis platform connects to the maintenance execution and feedback module, stores all data, updates models, and provides a human-computer interaction and centralized management interface for the entire system.

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