A method and system for wireless base station signal detection and remote early warning

Abstract

The present invention discloses a method and system for signal detection and remote early warning of wireless base stations, comprising the following steps: collecting environmental factors and signal quality data of multiple base stations in real time through a dynamic environment perception system, combining historical signal data, comprehensively evaluating the signal health status, and generating a multi-dimensional analysis report on the signal health status, using a fuzzy logic algorithm to accurately predict signal failures and attenuation trends, and dynamically adjusting the signal early warning threshold to avoid false alarms and missed alarms caused by fixed thresholds, realizing signal sharing and collaborative analysis between base stations through an intelligent collaborative detection mechanism, optimizing signal health management through cross-base station linkage, identifying potential signal failure areas in advance and optimizing response strategies, using a remote monitoring and repair platform, creating an intelligent operation and maintenance system through virtualization technology and intelligent algorithms, simulating fault repair scenarios and optimizing repair paths, and the system can adaptively learn and continuously optimize signal quality prediction and risk control capabilities.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and system for wireless base station signal detection and remote early warning. Background Art

[0002] With the rapid development of wireless communication technology, especially the popularization of 5G and future 6G networks, base stations play an increasingly important role in modern communication networks. As the core infrastructure of wireless communications, base stations are responsible for providing data transmission and communication services for large-scale users. With the increase in the number of base stations and the complexity of network architecture, the signal quality of base stations is affected by many factors. How to effectively monitor and predict signal failures and improve fault response efficiency has become a technical problem that needs to be solved urgently.

[0003] Existing signal monitoring technologies mainly rely on signal quality detection of a single base station or a local area. These traditional systems usually perform fault prediction and response processing based on static warning thresholds and fixed rules. Since the working environment of base stations is complex and changeable, signal quality is not only related to the performance of a single base station, but also affected by the surrounding environment, network load, interference sources and other factors. The existing monitoring system fails to take into account the spatial correlation and dynamic changes of signal quality, and cannot effectively identify potential fault risks and changes in network status, especially before signal anomalies occur. This limitation causes the system to react slowly when facing sudden failures, and it is unable to respond to emergencies and schedule resources in a timely manner, affecting the overall stability and service quality of the network.

[0004] Traditional signal warning systems generally use fixed thresholds to determine whether the signal quality is normal. Fixed thresholds are difficult to adapt to changes under different environmental conditions, especially when network load changes, environmental factors fluctuate, or base station fault history data is updated, fixed thresholds are prone to false alarms or missed alarms. The existing technology lacks a flexible and adaptive warning threshold adjustment mechanism, and cannot be optimized in a timely manner according to real-time data and environmental changes. This problem has led to the inability of traditional systems to provide accurate and timely signal quality assessments in complex dynamic environments, which in turn affects the early prediction and response of faults.

[0005] Most existing fault prediction technologies rely on historical data or simple rule matching, and lack intelligent learning and adaptive optimization mechanisms. When signal anomalies occur, existing repair strategies often rely on manual intervention, and decisions during the repair process are often based on fixed processes and plans, lacking dynamic adjustments based on real-time data. This not only leads to low repair efficiency and long recovery time, but also fails to effectively optimize resource scheduling in the case of multiple base station failures, affecting the fault recovery speed of the entire network.

[0006] Therefore, how to provide a method and system for wireless base station signal detection and remote early warning is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0007] One purpose of the present invention is to propose a method and system for wireless base station signal detection and remote early warning. The present invention makes full use of technologies such as dynamic environment perception, adaptive algorithm, fuzzy logic control, cross-base station signal sharing and collaborative analysis, combines the real-time signal quality, environmental data, historical fault data and network load of the base station, and realizes comprehensive monitoring and accurate early warning of the signal quality of the wireless base station through multi-dimensional data analysis and prediction models. Through intelligent signal prediction and dynamic adjustment of early warning thresholds, the present invention effectively improves the sensitivity and accuracy of the system and avoids the false alarm and missed alarm problems caused by fixed thresholds in traditional technologies. The present invention also automatically optimizes resource allocation and repair strategies when signal anomalies occur through cross-base station linkage and intelligent optimization mechanisms, significantly improving emergency response efficiency and fault recovery speed. The method has the advantages of strong real-time performance, accurate early warning, efficient response and strong system optimization capabilities.

[0008] According to an embodiment of the present invention, a method and system for wireless base station signal detection and remote early warning includes the following steps:

[0009] S1. Through the dynamic environment perception system, perception modules are deployed at multiple base stations to collect environmental factors related to wireless signals in real time. Combined with historical signal data, the adaptive weighted model is used to comprehensively evaluate the signal quality and interference risk of each base station, and a multi-dimensional analysis report on the signal health status is generated;

[0010] S2. Based on the signal health status analysis report, the fuzzy logic control algorithm is used to accurately predict the signal failure and attenuation trend, and the signal warning threshold is adaptively adjusted. The threshold is dynamically adjusted according to the geographical location of the base station, historical fault data and real-time environmental changes to optimize the sensitivity and accuracy of the signal warning and avoid false alarms and missed alarms caused by fixed thresholds;

[0011] S3. Before signal anomalies occur, the intelligent collaborative detection mechanism is used to achieve dynamic signal sharing and collaborative analysis between base stations. By combining local data exchange with remote signal models, regional and global signal failures can be identified in advance, and early warning signals can be issued across base stations to optimize the response delay caused by a single base station failure and the response speed and accuracy of overall signal health management.

[0012] S4. When a signal anomaly occurs, the adaptive emergency response mechanism is used to intelligently schedule alternative base stations or other network resources according to the real-time load of the base station, network conditions and nearby maintenance resources, and the system configuration is optimized in real time, so that the processing path and resource scheduling of abnormal signals are optimal, and the emergency response efficiency and fault recovery time are optimized;

[0013] S5. By integrating the remote monitoring and repair platform, combining virtualization technology and intelligent algorithms, an intelligent operation and maintenance system based on a simulated environment is created, allowing operation and maintenance personnel to simulate different fault repair scenarios in a virtual environment, automatically generate the optimal repair plan, and optimize and verify the plan before actual operation, so as to optimize the fault handling path and optimize manual intervention and repair time;

[0014] S6. After each signal anomaly and repair, the system automatically collects data from the anomaly handling process, environmental data and operation and maintenance feedback through the automatic optimization mechanism to form an intelligent optimization model. Based on the model, the signal detection strategy is automatically adjusted to achieve system adaptive learning and continuous optimization, and optimize signal quality prediction and risk control capabilities.

[0015] Optionally, the S1 specifically includes:

[0016] S11. Deploy environmental sensing modules at multiple base stations through a dynamic environmental sensing system to collect environmental factors related to wireless signals in real time, including temperature, humidity, air pressure, electromagnetic interference, geographic location, weather conditions, building structure and other factors that affect signal quality;

[0017] S12. Combining the environmental factors with the historical signal data of each base station, using an adaptive weighted model to comprehensively evaluate the signal quality and interference risk of each base station, wherein the adaptive weighted model calculates the comprehensive signal health status of each base station:

[0018]

[0019] Among them, Q i represents the comprehensive signal health status of the i-th base station, X ij represents the jth evaluation index of the i-th base station, w j is the dynamic weight of the jth evaluation indicator, and n is the total number of evaluation indicators;

[0020] S13. Generate a multi-dimensional analysis report based on the comprehensive signal health status assessment results, the report includes the signal quality score, interference risk level, signal health status and its change trend of each base station, and dynamically update the report content according to the geographical location of the base station, historical fault data and real-time environmental changes, so as to improve the timeliness and accuracy of the signal health status assessment;

[0021] S14, automatically adjusting the weight value w in the adaptive weighting model according to the correlation between the historical signal data and the current environment data j , update the weights in a timely manner to reflect the impact of the latest environmental conditions on signal quality, and optimize the accuracy of signal quality assessment and the reliability of risk prediction.

[0022] Optionally, the S2 specifically includes:

[0023] S21. Based on the signal health status analysis report, accurately predict signal failure and attenuation trends through a fuzzy logic control algorithm. In the fuzzy logic control algorithm, the real-time signal quality, environmental data, historical failure data, network load, etc. of each base station are input to form a multi-dimensional input variable set;

[0024] S22. Mapping each input variable to its corresponding fuzzy value through a fuzzy rule base to generate a signal attenuation trend prediction value for each base station, wherein the fuzzy rule base includes multiple rules for signal attenuation, fault trend, and environmental interference, and inferring the future development trend of the signal health status for each combination of input variables;

[0025] S23. Use a fuzzy inference engine to perform fuzzy inference operations on input variables to generate a fault prediction value for each base station, where the fault prediction value reflects the risk of signal attenuation and potential faults:

[0026]

[0027] Among them, F i (t) is the fault prediction value of the i-th base station at time t, X i is the real-time signal quality of the i-th base station, E i is the set of environmental factors of the ith base station, T i is historical data, L i is the network load of the current base station, w m is the weight of the mth fuzzy rule, Φ m represents the mth fuzzy rule;

[0028] S24, based on the fault prediction value F generated by the fuzzy inference engine i (t), dynamically adjust the signal warning threshold according to the size of the predicted value:

[0029]

[0030] Among them, Θ i (t) is the signal warning threshold of the i-th base station, E ik is the kth environmental factor of the ith base station, α ik Environmental factor E ik The weighting factor, Fi (t) is the fault prediction value of the i-th base station, β is the adjustment coefficient, S i is the signal quality stability of the i-th base station;

[0031] S25, dynamically adjusted signal warning threshold Θ i (t) Make adjustments based on real-time environmental data, signal quality predictions, and fault trends to optimize the response sensitivity and accuracy of the signal monitoring system and avoid false alarms and missed alarms that may be caused by fixed thresholds.

[0032] Optionally, the S3 specifically includes:

[0033] S31. Before a signal anomaly occurs, dynamic signal sharing and collaborative analysis between base stations are realized through an intelligent collaborative detection mechanism, wherein the mechanism includes exchanging real-time signal quality data, environmental factor data, and historical fault data between multiple base stations to obtain the dynamic change trend of global signal quality, and evaluating and predicting the change trend and potential faults of each base station signal based on the signal quality data, environmental factor data, and historical data in a combination of local and remote data;

[0034] S32. Through the signal sharing and collaborative analysis mechanism, a spatiotemporal model based on weighted collaborative analysis is used to dynamically monitor the signal quality. The model calculates the signal health assessment value of each base station in real time based on the environmental data, historical fault data, signal quality and network load of each base station:

[0035]

[0036] Among them, P i (t) is the signal health assessment value of the i-th base station at time t, S ij (t) is the signal quality between the i-th base station and the j-th base station at time t, Θ ij (t) is the signal health warning threshold between the i-th base station and the j-th base station, ρ ij (t) is the signal correlation between the i-th base station and the j-th base station, R i (t) is the network load of the i-th base station, α ij is the weight coefficient, γ is the adjustment coefficient;

[0037] S33. Based on the weighted collaborative analysis model, generate a signal health prediction value for each base station, wherein the prediction value integrates the influence of multiple dimensions of signal quality, environmental factors, and network load of each base station;

[0038] S34, dynamically adjusting the warning threshold of the signal using a weighted signal prediction algorithm according to the signal health prediction value;

[0039] S35. According to the dynamically adjusted warning threshold and signal health prediction value, warning signals are issued across base stations to identify potential signal fault areas in advance and optimize fault response strategies. When signal anomalies occur, the response delay caused by a single base station failure and the real-time, response speed and accuracy of the overall signal health management can be optimized to the greatest extent.

[0040] Optionally, the S4 specifically includes:

[0041] S41. When a signal anomaly occurs, an adaptive emergency response mechanism is used to collect the operating status information of the base station in real time, including real-time load, network bandwidth, signal quality, fault data and nearby available maintenance resources;

[0042] S42. According to the operation status information of the base station, the emergency response priority of each base station is evaluated by an adaptive algorithm, and the signal quality of the base station is dynamically weighted. The weighting coefficient is adjusted according to the real-time load, network load, resource availability and fault history data to optimize the priority of the emergency response of each base station;

[0043] S43. Based on the evaluated priority and resource availability, a dynamic scheduling algorithm is used to select alternative base stations and other network resources, and the system configuration is adjusted in real time to optimize the fault recovery path:

[0044]

[0045] Among them, R opt (t) is the optimal resource scheduling solution, C k (t) is the signal quality of the kth candidate base station, L k (t) is the real-time load of the kth candidate base station, α k ,β k is the adjustment coefficient, δ k is the priority coefficient of the resource;

[0046] S44. According to the optimal resource solution of the dynamic scheduling, the resource allocation of the base station is adjusted in real time through the intelligent scheduling mechanism, so that the processing path and resource scheduling of abnormal signals are optimized, and the emergency response efficiency and fault recovery time are optimized.

[0047] Optionally, the S5 specifically includes:

[0048] S51. When a signal anomaly occurs, an intelligent operation and maintenance system based on a simulation environment is created through an integrated remote monitoring and repair platform, combined with virtualization technology and intelligent algorithms. The system can remotely simulate, analyze and generate repair strategies for signal faults;

[0049] S52. In the intelligent operation and maintenance system, a virtual environment model is constructed based on the real-time collected base station operation data, environmental factors, historical fault data and network status. The virtual environment model includes the current signal quality, network load, fault history and environmental influencing factors of the base station, and can reflect the operation status of the base station and its interaction with the surrounding environment in real time;

[0050] S53, simulating different types of signal faults and their possible impacts through a virtual environment model, wherein the simulation results include predictions of the time of occurrence of the fault, the scope of impact, the timeliness of repair, and resource requirements, and generating multiple alternative repair paths for each potential fault;

[0051] S54. Based on the simulation results, each candidate repair path is evaluated by an intelligent optimization algorithm, wherein the evaluation takes into account the timeliness, resource consumption, repair efficiency and overall impact on the system of the repair path, and finally generates an optimal repair path;

[0052] S56. Based on the optimal repair path, dynamically adjust the resource configuration of the base station, and implement remote repair through virtualization technology. The repair process will be automatically scheduled according to the priority in the repair path to minimize the fault recovery time and optimize the system stability.

[0053] Optionally, the S6 specifically includes:

[0054] S61. After each signal anomaly and repair, the system automatically collects feedback data, environmental data and operation and maintenance information during the anomaly handling process through an automatic optimization mechanism. The data includes signal quality, network load, environmental factors, fault repair timeliness and resource usage;

[0055] S62. Based on the feedback data, an intelligent optimization model is constructed. The model combines historical data of signal anomaly processing, environmental changes, and repair paths to generate a signal detection strategy optimization function. The optimization function can dynamically adjust the signal quality prediction and risk control capabilities according to multi-dimensional factors such as environmental changes, repair efficiency, resource consumption, and network load;

[0056] S63, using an adaptive learning algorithm to adjust the signal detection strategy in real time based on an intelligent optimization model. The algorithm learns the feedback data after each signal anomaly processing through an online learning mechanism, so that the model can continuously optimize signal quality prediction, anomaly identification and repair timeliness, and optimize the system's adaptability to changes in different network environments;

[0057] S64. During each feedback data feedback process, by analyzing the actual effect of each repair process, the various parameters in the intelligent optimization model are adjusted, including the signal quality prediction weight, the repair path priority and the fault risk warning threshold. During the next signal anomaly processing, the system can adjust the strategy according to the latest feedback information;

[0058] S65. Through dynamically adjusted signal detection strategies, the signal health monitoring and risk control capabilities are optimized, system adaptive learning and continuous optimization are achieved, the accuracy of signal quality prediction and the sensitivity of fault warning are optimized, and the system operation and maintenance costs and fault recovery time are effectively optimized.

[0059] Optional modules include:

[0060] Signal quality assessment module: collects and analyzes the signal quality and interference risk of each base station in real time, and generates a signal health status and multi-dimensional analysis report of the base station based on environmental factors and historical signal data;

[0061] Dynamic environment perception module: Deploy environment perception modules on multiple base stations to monitor environmental factors related to wireless signals in real time, and combine environmental data with historical signal data to optimize the accuracy of the signal quality assessment model;

[0062] Signal fault prediction module: Based on the signal health status analysis report, the fuzzy logic control algorithm is used to accurately predict signal faults and attenuation trends, and the signal warning threshold is automatically adjusted to avoid false alarms and missed alarms caused by fixed thresholds;

[0063] Intelligent collaborative detection and early warning module: realizes dynamic signal sharing and collaborative analysis between base stations, identifies regional and global signal failures in advance, issues early warning signals, and optimizes the response speed and accuracy of signal health management;

[0064] Adaptive emergency response module: When a signal anomaly occurs, it intelligently dispatches alternative base stations and other network resources based on the base station's real-time load, network conditions, and nearby maintenance resources, optimizes signal processing paths and resource scheduling, and optimizes emergency response efficiency and fault recovery time;

[0065] Remote monitoring and repair platform module: Combines virtualization technology and intelligent algorithms to create an intelligent operation and maintenance system based on a simulated environment. It achieves efficient fault repair management by simulating fault scenarios and optimizing repair paths.

[0066] Repair path optimization module: Based on the fault repair simulation results, it uses intelligent optimization algorithms to evaluate the repair path, select the path with the best timeliness, resource consumption and repair efficiency, and perform real-time repairs;

[0067] Automatic optimization and learning module: Through automatic learning and optimization mechanism, based on the feedback data of signal anomalies and repair process, the signal detection strategy is dynamically adjusted to achieve adaptive learning and continuous optimization;

[0068] Resource scheduling and configuration optimization module: intelligently schedule network resources according to the real-time load of the base station, network status and resource availability, and optimize the system configuration in real time to optimize the fault recovery path, optimize system stability and response speed.

[0069] The beneficial effects of the present invention are:

[0070] Based on a dynamic environment perception system and an adaptive weighted model, the present invention can collect environmental factors related to wireless signals in real time, and combine historical signal data to accurately evaluate the signal health status of each base station. This multi-dimensional, dynamic evaluation method avoids excessive reliance on static signal thresholds in traditional technologies, making signal quality prediction more accurate and effectively reducing the occurrence of false alarms and missed alarms. Compared with traditional base stations that rely on a single monitoring method, the present invention can more accurately predict signal failures and attenuation trends by comprehensively analyzing multiple influencing factors, thereby providing a more scientific basis for signal early warning and fault diagnosis.

[0071] The present invention utilizes a fuzzy logic control algorithm to accurately predict the signal failure and attenuation trends of each base station, and dynamically adjusts the signal warning threshold in combination with an adaptive adjustment mechanism. Unlike the traditional fixed threshold method, the threshold adjustment of the present invention can be dynamically adjusted according to the real-time operating status, environmental changes and historical data of each base station, thereby improving the sensitivity and accuracy of the signal warning system, enabling the system to better adapt to changes in different network environments. This technology effectively eliminates the impact of unstable signal quality and complex environmental changes, ensures that signal failures can be warned in advance, reduces the frequency of base station failures, and optimizes the stability and reliability of the entire wireless network.

[0072] The present invention realizes dynamic signal sharing and collaborative analysis across base stations through an intelligent collaborative detection mechanism, can obtain the dynamic change trend of global signal quality in real time, and predict signal health through a weighted collaborative analysis model. This innovation enables multiple base stations to share signal quality data, environmental factor data and historical fault data, effectively identify regional and global signal failures, and then optimize early warning strategies across base stations. Compared with the traditional method of operating alone by base stations, the present invention greatly improves the prediction accuracy of signal failures, reduces the response delay caused by a single base station failure, and effectively improves the overall response speed and accuracy of the system.

[0073] The adaptive emergency response mechanism of the present invention optimizes resource scheduling and system configuration by evaluating the load, network status and maintenance resources of the base station in real time, and intelligently scheduling alternative base stations or other network resources, thereby improving fault recovery efficiency and reducing fault handling time. Compared with traditional manual scheduling, this intelligent scheduling method based on real-time load and resource availability significantly improves the automation level of emergency response and shortens the recovery time of the system. By integrating virtualization technology and intelligent algorithms, an intelligent operation and maintenance system based on a simulation environment is also constructed, allowing operation and maintenance personnel to simulate different fault repair scenarios in a virtual environment and verify the effectiveness of the repair plan in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0075] Figure 1 A flowchart of a method and system for wireless base station signal detection and remote early warning proposed by the present invention;

[0076] Figure 2 It is a workflow diagram of the cross-base station signal sharing and collaborative analysis mechanism in the present invention. DETAILED DESCRIPTION

[0077] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0078] refer to Figure 1-2 , a method and system for wireless base station signal detection and remote early warning, comprising the following steps:

[0079] S1. Through the dynamic environment perception system, perception modules are deployed at multiple base stations to collect environmental factors related to wireless signals in real time. Combined with historical signal data, the adaptive weighted model is used to comprehensively evaluate the signal quality and interference risk of each base station, and a multi-dimensional analysis report on the signal health status is generated;

[0080] S2. Based on the signal health status analysis report, the fuzzy logic control algorithm is used to accurately predict the signal failure and attenuation trend, and the signal warning threshold is adaptively adjusted. The threshold is dynamically adjusted according to the geographical location of the base station, historical fault data and real-time environmental changes to optimize the sensitivity and accuracy of the signal warning and avoid false alarms and missed alarms caused by fixed thresholds;

[0081] S3. Before signal anomalies occur, the intelligent collaborative detection mechanism is used to achieve dynamic signal sharing and collaborative analysis between base stations. By combining local data exchange with remote signal models, regional and global signal failures can be identified in advance, and early warning signals can be issued across base stations to optimize the response delay caused by a single base station failure and the response speed and accuracy of overall signal health management.

[0082] S4. When a signal anomaly occurs, the adaptive emergency response mechanism is used to intelligently schedule alternative base stations or other network resources according to the real-time load of the base station, network conditions and nearby maintenance resources, and the system configuration is optimized in real time, so that the processing path and resource scheduling of abnormal signals are optimal, and the emergency response efficiency and fault recovery time are optimized;

[0083] S5. By integrating the remote monitoring and repair platform, combining virtualization technology and intelligent algorithms, an intelligent operation and maintenance system based on a simulated environment is created, allowing operation and maintenance personnel to simulate different fault repair scenarios in a virtual environment, automatically generate the optimal repair plan, and optimize and verify the plan before actual operation, so as to optimize the fault handling path and optimize manual intervention and repair time;

[0084] S6. After each signal anomaly and repair, the system automatically collects data from the anomaly handling process, environmental data and operation and maintenance feedback through the automatic optimization mechanism to form an intelligent optimization model. Based on the model, the signal detection strategy is automatically adjusted to achieve system adaptive learning and continuous optimization, and optimize signal quality prediction and risk control capabilities.

[0085] In this implementation manner, the S1 specifically includes:

[0086] S11. Deploy environmental sensing modules at multiple base stations through a dynamic environmental sensing system to collect environmental factors related to wireless signals in real time, including temperature, humidity, air pressure, electromagnetic interference, geographic location, weather conditions, building structure and other factors that affect signal quality;

[0087] S12. Combining the environmental factors with the historical signal data of each base station, using an adaptive weighted model to comprehensively evaluate the signal quality and interference risk of each base station, wherein the adaptive weighted model calculates the comprehensive signal health status of each base station:

[0088]

[0089] Among them, Q i represents the comprehensive signal health status of the i-th base station, X ij represents the jth evaluation index of the i-th base station, w j is the dynamic weight of the jth evaluation indicator, and n is the total number of evaluation indicators;

[0090] S13. Generate a multi-dimensional analysis report based on the comprehensive signal health status assessment results, the report includes the signal quality score, interference risk level, signal health status and its change trend of each base station, and dynamically update the report content according to the geographical location of the base station, historical fault data and real-time environmental changes, so as to improve the timeliness and accuracy of the signal health status assessment;

[0091] S14, automatically adjusting the weight value w in the adaptive weighting model according to the correlation between the historical signal data and the current environment data j , update the weights in a timely manner to reflect the impact of the latest environmental conditions on signal quality, and optimize the accuracy of signal quality assessment and the reliability of risk prediction.

[0092] In this implementation, S2 specifically includes:

[0093] S21. Based on the signal health status analysis report, accurately predict signal failure and attenuation trends through a fuzzy logic control algorithm. In the fuzzy logic control algorithm, the real-time signal quality, environmental data, historical failure data, network load, etc. of each base station are input to form a multi-dimensional input variable set;

[0094] S22. Mapping each input variable to its corresponding fuzzy value through a fuzzy rule base to generate a signal attenuation trend prediction value for each base station, wherein the fuzzy rule base includes multiple rules for signal attenuation, fault trend, and environmental interference, and inferring the future development trend of the signal health status for each combination of input variables;

[0095] S23. Use a fuzzy inference engine to perform fuzzy inference operations on input variables to generate a fault prediction value for each base station, where the fault prediction value reflects the risk of signal attenuation and potential faults:

[0096]

[0097] Among them, F i (t) is the fault prediction value of the i-th base station at time t, X i is the real-time signal quality of the i-th base station, E i is the set of environmental factors of the ith base station, T i is historical data, L i is the network load of the current base station, w m is the weight of the mth fuzzy rule, Φ m represents the mth fuzzy rule;

[0098] S24, based on the fault prediction value F generated by the fuzzy inference engine i (t), dynamically adjust the signal warning threshold according to the size of the predicted value:

[0099]

[0100] Among them, Θ i (t) is the signal warning threshold of the i-th base station, E ik is the kth environmental factor of the ith base station, α ik Environmental factor E ik The weighting factor, F i (t) is the fault prediction value of the i-th base station, β is the adjustment coefficient, S i is the signal quality stability of the i-th base station;

[0101] S25, dynamically adjusted signal warning threshold Θ i (t) Make adjustments based on real-time environmental data, signal quality predictions, and fault trends to optimize the response sensitivity and accuracy of the signal monitoring system and avoid false alarms and missed alarms that may be caused by fixed thresholds.

[0102] In this implementation, S3 specifically includes:

[0103] S31. Before a signal anomaly occurs, dynamic signal sharing and collaborative analysis between base stations are realized through an intelligent collaborative detection mechanism, wherein the mechanism includes exchanging real-time signal quality data, environmental factor data, and historical fault data between multiple base stations to obtain the dynamic change trend of global signal quality, and evaluating and predicting the change trend and potential faults of each base station signal based on the signal quality data, environmental factor data, and historical data in a combination of local and remote data;

[0104] S32. Through the signal sharing and collaborative analysis mechanism, a spatiotemporal model based on weighted collaborative analysis is used to dynamically monitor the signal quality. The model calculates the signal health assessment value of each base station in real time based on the environmental data, historical fault data, signal quality and network load of each base station:

[0105]

[0106] Among them, P i (t) is the signal health assessment value of the i-th base station at time t, S ij (t) is the signal quality between the i-th base station and the j-th base station at time t, Θ ij (t) is the signal health warning threshold between the i-th base station and the j-th base station, ρ ij (t) is the signal correlation between the i-th base station and the j-th base station, R i (t) is the network load of the i-th base station, α ij is the weight coefficient, γ is the adjustment coefficient;

[0107] S33. Based on the weighted collaborative analysis model, generate a signal health prediction value for each base station, wherein the prediction value integrates the influence of multiple dimensions of signal quality, environmental factors, and network load of each base station;

[0108] S34, dynamically adjusting the warning threshold of the signal using a weighted signal prediction algorithm according to the signal health prediction value;

[0109] S35. According to the dynamically adjusted warning threshold and signal health prediction value, warning signals are issued across base stations to identify potential signal fault areas in advance and optimize fault response strategies. When signal anomalies occur, the response delay caused by a single base station failure and the real-time, response speed and accuracy of the overall signal health management can be optimized to the greatest extent.

[0110] In this implementation manner, the S4 specifically includes:

[0111] S41. When a signal anomaly occurs, an adaptive emergency response mechanism is used to collect the operating status information of the base station in real time, including real-time load, network bandwidth, signal quality, fault data and nearby available maintenance resources;

[0112] S42. According to the operation status information of the base station, the emergency response priority of each base station is evaluated by an adaptive algorithm, and the signal quality of the base station is dynamically weighted. The weighting coefficient is adjusted according to the real-time load, network load, resource availability and fault history data to optimize the priority of the emergency response of each base station;

[0113] S43. Based on the evaluated priority and resource availability, a dynamic scheduling algorithm is used to select alternative base stations and other network resources, and the system configuration is adjusted in real time to optimize the fault recovery path:

[0114]

[0115] Among them, R opt (t) is the optimal resource scheduling solution, C k (t) is the signal quality of the kth candidate base station, L k (t) is the real-time load of the kth candidate base station, α k ,β k is the adjustment coefficient, δ k is the priority coefficient of the resource;

[0116] S44. According to the optimal resource solution of the dynamic scheduling, the resource allocation of the base station is adjusted in real time through the intelligent scheduling mechanism, so that the processing path and resource scheduling of abnormal signals are optimized, and the emergency response efficiency and fault recovery time are optimized.

[0117] In this implementation manner, S5 specifically includes:

[0118] S51. When a signal anomaly occurs, an intelligent operation and maintenance system based on a simulation environment is created through an integrated remote monitoring and repair platform, combined with virtualization technology and intelligent algorithms. The system can remotely simulate, analyze and generate repair strategies for signal faults;

[0119] S52. In the intelligent operation and maintenance system, a virtual environment model is constructed based on the real-time collected base station operation data, environmental factors, historical fault data and network status. The virtual environment model includes the current signal quality, network load, fault history and environmental influencing factors of the base station, and can reflect the operation status of the base station and its interaction with the surrounding environment in real time;

[0120] S53, simulating different types of signal faults and their possible impacts through a virtual environment model, wherein the simulation results include predictions of the time of occurrence of the fault, the scope of impact, the timeliness of repair, and resource requirements, and generating multiple alternative repair paths for each potential fault;

[0121] S54. Based on the simulation results, each candidate repair path is evaluated by an intelligent optimization algorithm, wherein the evaluation takes into account the timeliness, resource consumption, repair efficiency and overall impact on the system of the repair path, and finally generates an optimal repair path;

[0122] S56. Based on the optimal repair path, dynamically adjust the resource configuration of the base station, and implement remote repair through virtualization technology. The repair process will be automatically scheduled according to the priority in the repair path to minimize the fault recovery time and optimize the system stability.

[0123] In this implementation manner, S6 specifically includes:

[0124] S61. After each signal anomaly and repair, the system automatically collects feedback data, environmental data and operation and maintenance information during the anomaly handling process through an automatic optimization mechanism. The data includes signal quality, network load, environmental factors, fault repair timeliness and resource usage;

[0125] S62. Based on the feedback data, an intelligent optimization model is constructed. The model combines historical data of signal anomaly processing, environmental changes, and repair paths to generate a signal detection strategy optimization function. The optimization function can dynamically adjust the signal quality prediction and risk control capabilities according to multi-dimensional factors such as environmental changes, repair efficiency, resource consumption, and network load;

[0126] S63, using an adaptive learning algorithm to adjust the signal detection strategy in real time based on an intelligent optimization model. The algorithm learns the feedback data after each signal anomaly processing through an online learning mechanism, so that the model can continuously optimize signal quality prediction, anomaly identification and repair timeliness, and optimize the system's adaptability to changes in different network environments;

[0127] S64. During each feedback data feedback process, by analyzing the actual effect of each repair process, the various parameters in the intelligent optimization model are adjusted, including the signal quality prediction weight, the repair path priority and the fault risk warning threshold. During the next signal anomaly processing, the system can adjust the strategy according to the latest feedback information;

[0128] S65. Through dynamically adjusted signal detection strategies, the signal health monitoring and risk control capabilities are optimized, system adaptive learning and continuous optimization are achieved, the accuracy of signal quality prediction and the sensitivity of fault warning are optimized, and the system operation and maintenance costs and fault recovery time are effectively optimized.

[0129] In this embodiment, the following modules are included:

[0130] Signal quality assessment module: collects and analyzes the signal quality and interference risk of each base station in real time, and generates a signal health status and multi-dimensional analysis report of the base station based on environmental factors and historical signal data;

[0131] Dynamic environment perception module: Deploy environment perception modules on multiple base stations to monitor environmental factors related to wireless signals in real time, and combine environmental data with historical signal data to optimize the accuracy of the signal quality assessment model;

[0132] Signal fault prediction module: Based on the signal health status analysis report, the fuzzy logic control algorithm is used to accurately predict signal faults and attenuation trends, and the signal warning threshold is automatically adjusted to avoid false alarms and missed alarms caused by fixed thresholds;

[0133] Intelligent collaborative detection and early warning module: realizes dynamic signal sharing and collaborative analysis between base stations, identifies regional and global signal failures in advance, issues early warning signals, and optimizes the response speed and accuracy of signal health management;

[0134] Adaptive emergency response module: When a signal anomaly occurs, it intelligently dispatches alternative base stations and other network resources based on the base station's real-time load, network conditions, and nearby maintenance resources, optimizes signal processing paths and resource scheduling, and optimizes emergency response efficiency and fault recovery time;

[0135] Remote monitoring and repair platform module: Combines virtualization technology and intelligent algorithms to create an intelligent operation and maintenance system based on a simulated environment. It achieves efficient fault repair management by simulating fault scenarios and optimizing repair paths.

[0136] Repair path optimization module: Based on the fault repair simulation results, it uses intelligent optimization algorithms to evaluate the repair path, select the path with the best timeliness, resource consumption and repair efficiency, and perform real-time repairs;

[0137] Automatic optimization and learning module: Through automatic learning and optimization mechanism, based on the feedback data of signal anomalies and repair process, the signal detection strategy is dynamically adjusted to achieve adaptive learning and continuous optimization;

[0138] Resource scheduling and configuration optimization module: intelligently schedule network resources according to the real-time load of the base station, network status and resource availability, and optimize the system configuration in real time to optimize the fault recovery path, optimize system stability and response speed.

[0139] Embodiment 1:

[0140] In order to verify the feasibility of the present invention in implementation, the present invention is applied to the signal monitoring and early warning system of a wireless communication base station in a certain city as an application scenario. It is intended to solve the problems of slow prediction of existing base station signal failures and slow emergency response speeds through the wireless base station signal detection and remote early warning method proposed by the present invention, terrain characteristics, electromagnetic interference, etc. In order to ensure the normal operation of the communication network, it is necessary to monitor the signal health status of the base station in real time and respond to possible signal failures in a timely manner to avoid communication interruptions and reduced service quality.

[0141] In this embodiment, 20 wireless communication base stations are deployed in the city, covering the main commercial areas, residential areas, transportation hubs and industrial areas of the city. Each base station is equipped with a signal quality monitoring module, an environmental perception module and a historical fault data module, which evaluates the signal quality of the base station through an adaptive weighted model and transmits real-time data to a centralized management platform.

[0142] In the process of base station signal monitoring and early warning, the environmental perception module is used to collect environmental factor data related to wireless signal quality in real time, including temperature, humidity, air pressure, electromagnetic interference, weather conditions, etc. In specific applications, the system monitors that the weather in the area where the base station is located suddenly turns bad, with wind speeds reaching 40km / h and humidity increasing to 85%. These changes in environmental factors may cause the base station signal quality to deteriorate. Through real-time acquisition of historical fault data, the system analyzes that the signal quality of base stations in this area has declined under similar weather conditions, which in turn caused disconnection and communication quality problems for some users. Based on these data, the system begins to use an adaptive weighted model to evaluate signal quality and predict signal attenuation trends.

[0143] Through the fuzzy logic control algorithm, the system predicts faults based on the real-time collected signal quality data, environmental data and historical fault data. In this process, the system uses the fuzzy rule base to accurately predict the real-time signal quality of each base station, and generates a signal attenuation trend prediction value for each base station in a certain period of time in the future based on different input variables. Through the fuzzy reasoning engine, the system further generates a fault prediction value for each base station. For example, in the fault prediction of base station A, the real-time signal quality of the base station is 0.8, the ambient temperature is 30°C, the humidity is 85%, and signal attenuation problems in similar environments have appeared in historical fault data. Based on these data, the system calculates that the probability of signal quality degradation in base station A in the next hour is 65%.

[0144] Based on this predicted value, the system automatically adjusts the signal warning threshold and dynamically adjusts the warning sensitivity to avoid false alarms or missed alarms caused by fixed thresholds. For example, when the system predicts an increased risk of signal attenuation, the system increases the signal warning threshold of base station A from 0.7 to 0.75 to ensure that a warning is issued in time before the signal quality deteriorates. After receiving the warning notification, the management personnel of base station A begin to monitor and evaluate the changes in the signal quality of the base station.

[0145] In the event of a signal anomaly, the system starts resource scheduling of alternative base stations through an intelligent emergency response mechanism. Assuming that base station A fails, the system automatically identifies that adjacent base stations B, C and other alternative base stations can support the traffic of some users, and transfers the traffic to these base stations through an adaptive scheduling mechanism to ensure that communication is not interrupted after the failure. The system dynamically optimizes the configuration of emergency resources based on the real-time load, network bandwidth and environmental data of each base station to minimize the impact on users.

[0146] Within 1 minute after the failure of base station A, the system had activated the emergency response mechanism of base stations B and C, transferred part of the traffic to these alternative base stations, and adjusted the network resource configuration in time. Under the system's scheduling, the load of base stations B and C increased by 10% and 8% respectively, while the recovery time of base station A was 3 minutes, and finally the normal signal quality was restored. The total time from the failure to the recovery was only 4 minutes, which was significantly lower than the 10-minute response time of the traditional system, and the user's service interruption time was only 2 minutes, ensuring the continuity of communication.

[0147] Virtualization technology and intelligent operation and maintenance systems are also applied in this embodiment. The system simulates different fault repair scenarios through a virtual environment. Before the actual repair, the operation and maintenance personnel can view the effects of different repair paths and select the best solution. In the simulated repair, the system generated the fault repair path of base station A and evaluated the repair time, resource consumption and repair effect of each repair path. Finally, the system recommended an optimal repair path, enabling the operation and maintenance personnel to implement fault recovery efficiently and shorten the repair time.

[0148] Through this embodiment, the system realizes the comprehensive optimization of signal quality prediction, fault warning, emergency response and intelligent repair. Data show that the system based on the method of the present invention reduces the average response time by 60%, shortens the fault recovery time by 50%, and improves the accuracy of signal quality prediction by 30% in the signal monitoring and management of major base stations in the city. These optimizations not only improve the stability of the communication network, but also greatly enhance the efficiency of signal monitoring and fault handling, effectively improving the user's communication experience.

[0149] Table 1 Base station signal health status and prediction results

[0150]

[0151]

[0152] Table 2 Signal warning threshold adjustment and sensitivity change

[0153]

[0154] According to the data in the table, it can be seen that the signal quality, fault prediction value and warning threshold of the base station have changed during the dynamic adjustment process. The signal quality of base station A increased from 0.80 to 0.75, and the fault prediction value was 65%. The adjusted warning threshold was increased to 0.75, and the sensitivity changed by 10%, thereby enhancing the response speed and warning accuracy of the system. Due to the high signal quality of base station B, the adjusted warning threshold remained unchanged, and the sensitivity changed by 0%, indicating that its failure risk was low and did not require excessive adjustment. Other base stations such as base station C and base station D showed obvious threshold adjustment, especially base station D, whose adjusted threshold was increased to 0.80 and the sensitivity increased by 6.67%, effectively avoiding false alarms and missed alarms. Dynamic adjustment of the warning threshold helps to optimize signal quality monitoring and fault warning, and improve the overall reliability of the system.

[0155] In summary, the present invention realizes efficient monitoring and fault warning of wireless base station signals through a series of innovative technical means, and has significant advantages such as strong real-time performance, high accuracy, and rapid emergency response. It provides a new intelligent solution for the operation and maintenance management of wireless base stations, significantly improves the reliability and stability of the system, reduces the operation and maintenance costs, and optimizes the overall performance of the wireless network.

[0156] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for detecting and remotely warning wireless base station signals, characterized in that: The steps include: S1. Through the dynamic environment perception system, perception modules are deployed at multiple base stations to collect environmental factors related to wireless signals in real time. Combined with historical signal data, the adaptive weighted model is used to comprehensively evaluate the signal quality and interference risk of each base station, and a multi-dimensional analysis report on the signal health status is generated; S2. Based on the signal health status analysis report, the fuzzy logic control algorithm is used to accurately predict the signal failure and attenuation trend, and the signal warning threshold is adaptively adjusted. The threshold is dynamically adjusted according to the geographical location of the base station, historical fault data and real-time environmental changes to optimize the sensitivity and accuracy of the signal warning and avoid false alarms and missed alarms caused by fixed thresholds; S3. Before signal anomalies occur, the intelligent collaborative detection mechanism is used to achieve dynamic signal sharing and collaborative analysis between base stations. By combining local data exchange with remote signal models, regional and global signal failures can be identified in advance, and early warning signals can be issued across base stations to optimize the response delay caused by a single base station failure and the response speed and accuracy of overall signal health management. S4. When a signal anomaly occurs, the adaptive emergency response mechanism is used to intelligently schedule alternative base stations or other network resources according to the real-time load of the base station, network conditions and nearby maintenance resources, and the system configuration is optimized in real time, so that the processing path and resource scheduling of abnormal signals are optimal, and the emergency response efficiency and fault recovery time are optimized; S5. By integrating the remote monitoring and repair platform, combining virtualization technology and intelligent algorithms, an intelligent operation and maintenance system based on a simulated environment is created, allowing operation and maintenance personnel to simulate different fault repair scenarios in a virtual environment, automatically generate the optimal repair plan, and optimize and verify the plan before actual operation, so as to optimize the fault handling path and optimize manual intervention and repair time; S6. After each signal anomaly and repair, the system automatically collects data from the anomaly handling process, environmental data and operation and maintenance feedback through the automatic optimization mechanism to form an intelligent optimization model. Based on the model, the signal detection strategy is automatically adjusted to achieve system adaptive learning and continuous optimization, and optimize signal quality prediction and risk control capabilities.

2. A method for detecting and remotely warning wireless base station signals according to claim 1, characterized in that: The S1 specifically includes: S11. Deploy environmental sensing modules at multiple base stations through a dynamic environmental sensing system to collect environmental factors related to wireless signals in real time, including temperature, humidity, air pressure, electromagnetic interference, geographic location, weather conditions, building structure and other factors that affect signal quality; S12. Combining the environmental factors with the historical signal data of each base station, using an adaptive weighted model to comprehensively evaluate the signal quality and interference risk of each base station, wherein the adaptive weighted model calculates the comprehensive signal health status of each base station: Among them, Q i represents the comprehensive signal health status of the i-th base station, X ij represents the jth evaluation index of the i-th base station, w j is the dynamic weight of the jth evaluation indicator, and n is the total number of evaluation indicators; S13. Generate a multi-dimensional analysis report based on the comprehensive signal health status assessment results, the report includes the signal quality score, interference risk level, signal health status and its change trend of each base station, and dynamically update the report content according to the geographical location of the base station, historical fault data and real-time environmental changes, so as to improve the timeliness and accuracy of the signal health status assessment; S14, automatically adjusting the weight value w in the adaptive weighting model according to the correlation between the historical signal data and the current environment data j , update the weights in a timely manner to reflect the impact of the latest environmental conditions on signal quality, and optimize the accuracy of signal quality assessment and the reliability of risk prediction.

3. The method for wireless base station signal detection and remote early warning according to claim 1, characterized in that: The S2 specifically includes: S21. Based on the signal health status analysis report, accurately predict signal failure and attenuation trends through a fuzzy logic control algorithm. In the fuzzy logic control algorithm, the real-time signal quality, environmental data, historical failure data, network load, etc. of each base station are input to form a multi-dimensional input variable set; S22. Mapping each input variable to its corresponding fuzzy value through a fuzzy rule base to generate a signal attenuation trend prediction value for each base station, wherein the fuzzy rule base includes multiple rules for signal attenuation, fault trend, and environmental interference, and inferring the future development trend of the signal health status for each combination of input variables; S23. Use a fuzzy inference engine to perform fuzzy inference operations on input variables to generate a fault prediction value for each base station, where the fault prediction value reflects the risk of signal attenuation and potential faults: Among them, F i (t) is the fault prediction value of the i-th base station at time t, X i is the real-time signal quality of the i-th base station, E i is the set of environmental factors of the ith base station, T i is historical data, L i is the network load of the current base station, w m is the weight of the mth fuzzy rule, Φ m represents the mth fuzzy rule; S24, based on the fault prediction value F generated by the fuzzy inference engine i (t), dynamically adjust the signal warning threshold according to the size of the predicted value: Among them, Θ i (t) is the signal warning threshold of the i-th base station, E ik is the kth environmental factor of the ith base station, α ik Environmental factor E ik The weighting factor, F i (t) is the fault prediction value of the i-th base station, β is the adjustment coefficient, S i is the signal quality stability of the i-th base station; S25, dynamically adjusted signal warning threshold Θ i (t) Make adjustments based on real-time environmental data, signal quality predictions, and fault trends to optimize the response sensitivity and accuracy of the signal monitoring system and avoid false alarms and missed alarms that may be caused by fixed thresholds.

4. The method for wireless base station signal detection and remote early warning according to claim 1, characterized in that: The S3 specifically includes: S31. Before a signal anomaly occurs, dynamic signal sharing and collaborative analysis between base stations are realized through an intelligent collaborative detection mechanism, wherein the mechanism includes exchanging real-time signal quality data, environmental factor data, and historical fault data between multiple base stations to obtain the dynamic change trend of global signal quality, and evaluating and predicting the change trend and potential faults of each base station signal based on the signal quality data, environmental factor data, and historical data in a combination of local and remote data; S32. Through the signal sharing and collaborative analysis mechanism, a spatiotemporal model based on weighted collaborative analysis is used to dynamically monitor the signal quality. The model calculates the signal health assessment value of each base station in real time based on the environmental data, historical fault data, signal quality and network load of each base station: Among them, P i (t) is the signal health assessment value of the i-th base station at time t, S ij (t) is the signal quality between the i-th base station and the j-th base station at time t, Θ ij (t) is the signal health warning threshold between the i-th base station and the j-th base station, ρ ij (t) is the signal correlation between the i-th base station and the j-th base station, R i (t) is the network load of the i-th base station, α ij is the weight coefficient, γ is the adjustment coefficient; S33. Based on the weighted collaborative analysis model, generate a signal health prediction value for each base station, wherein the prediction value integrates the influence of multiple dimensions of signal quality, environmental factors, and network load of each base station; S34, dynamically adjusting the warning threshold of the signal using a weighted signal prediction algorithm according to the signal health prediction value; S35. According to the dynamically adjusted warning threshold and signal health prediction value, warning signals are issued across base stations to identify potential signal fault areas in advance and optimize fault response strategies. When signal anomalies occur, the response delay caused by a single base station failure and the real-time, response speed and accuracy of the overall signal health management can be optimized to the greatest extent.

5. The method for wireless base station signal detection and remote early warning according to claim 1, characterized in that: The S4 specifically includes: S41. When a signal anomaly occurs, an adaptive emergency response mechanism is used to collect the operating status information of the base station in real time, including real-time load, network bandwidth, signal quality, fault data and nearby available maintenance resources; S42. According to the operation status information of the base station, the emergency response priority of each base station is evaluated by an adaptive algorithm, and the signal quality of the base station is dynamically weighted. The weighting coefficient is adjusted according to the real-time load, network load, resource availability and fault history data to optimize the priority of the emergency response of each base station; S43. Based on the evaluated priority and resource availability, a dynamic scheduling algorithm is used to select alternative base stations and other network resources, and the system configuration is adjusted in real time to optimize the fault recovery path: Among them, R opt (t) is the optimal resource scheduling solution, C k (t) is the signal quality of the kth candidate base station, L k (t) is the real-time load of the kth candidate base station, α k ,β k is the adjustment coefficient, δ k is the priority coefficient of the resource; S44. According to the optimal resource solution of the dynamic scheduling, the resource allocation of the base station is adjusted in real time through the intelligent scheduling mechanism, so that the processing path and resource scheduling of abnormal signals are optimized, and the emergency response efficiency and fault recovery time are optimized.

6. The method for wireless base station signal detection and remote early warning according to claim 1, characterized in that: The S5 specifically includes: S51. When a signal anomaly occurs, an intelligent operation and maintenance system based on a simulation environment is created through an integrated remote monitoring and repair platform, combined with virtualization technology and intelligent algorithms. The system can remotely simulate, analyze and generate repair strategies for signal faults; S52. In the intelligent operation and maintenance system, a virtual environment model is constructed based on the real-time collected base station operation data, environmental factors, historical fault data and network status. The virtual environment model includes the current signal quality, network load, fault history and environmental influencing factors of the base station, and can reflect the operation status of the base station and its interaction with the surrounding environment in real time; S53, simulating different types of signal faults and their possible impacts through a virtual environment model, wherein the simulation results include predictions of the time of occurrence of the fault, the scope of impact, the timeliness of repair, and resource requirements, and generating multiple alternative repair paths for each potential fault; S54. Based on the simulation results, each candidate repair path is evaluated by an intelligent optimization algorithm, wherein the evaluation takes into account the timeliness, resource consumption, repair efficiency and overall impact on the system of the repair path, and finally generates an optimal repair path; S56. Based on the optimal repair path, dynamically adjust the resource configuration of the base station, and implement remote repair through virtualization technology. The repair process will be automatically scheduled according to the priority in the repair path to minimize the fault recovery time and optimize the system stability.

7. The method for wireless base station signal detection and remote early warning according to claim 1, characterized in that: The S6 specifically includes: S61. After each signal anomaly and repair, the system automatically collects feedback data, environmental data and operation and maintenance information during the anomaly handling process through an automatic optimization mechanism. The data includes signal quality, network load, environmental factors, fault repair time and resource usage; S62. Based on the feedback data, an intelligent optimization model is constructed. The model combines historical data of signal anomaly processing, environmental changes, and repair paths to generate a signal detection strategy optimization function. The optimization function can dynamically adjust the signal quality prediction and risk control capabilities according to multi-dimensional factors such as environmental changes, repair efficiency, resource consumption, and network load; S63, using an adaptive learning algorithm to adjust the signal detection strategy in real time based on an intelligent optimization model. The algorithm learns the feedback data after each signal anomaly processing through an online learning mechanism, so that the model can continuously optimize signal quality prediction, anomaly identification and repair timeliness, and optimize the system's adaptability to changes in different network environments; S64. During each feedback data feedback process, by analyzing the actual effect of each repair process, the various parameters in the intelligent optimization model are adjusted, including the signal quality prediction weight, the repair path priority and the fault risk warning threshold. During the next signal anomaly processing, the system can adjust the strategy according to the latest feedback information; S65. Through dynamically adjusted signal detection strategies, the signal health monitoring and risk control capabilities are optimized, system adaptive learning and continuous optimization are achieved, the accuracy of signal quality prediction and the sensitivity of fault warning are optimized, and the system operation and maintenance costs and fault recovery time are effectively optimized.

8. A wireless base station signal detection and remote warning system, characterized in that: Includes the following modules: Signal quality assessment module: collects and analyzes the signal quality and interference risk of each base station in real time, and generates a signal health status and multi-dimensional analysis report of the base station based on environmental factors and historical signal data; Dynamic environment perception module: Deploy environment perception modules on multiple base stations to monitor environmental factors related to wireless signals in real time, and combine environmental data with historical signal data to optimize the accuracy of the signal quality assessment model; Signal fault prediction module: Based on the signal health status analysis report, the fuzzy logic control algorithm is used to accurately predict signal faults and attenuation trends, and the signal warning threshold is automatically adjusted to avoid false alarms and missed alarms caused by fixed thresholds; Intelligent collaborative detection and early warning module: realizes dynamic signal sharing and collaborative analysis between base stations, identifies regional and global signal failures in advance, issues early warning signals, and optimizes the response speed and accuracy of signal health management; Adaptive emergency response module: When a signal anomaly occurs, it intelligently dispatches alternative base stations and other network resources based on the base station's real-time load, network conditions, and nearby maintenance resources, optimizes signal processing paths and resource scheduling, and optimizes emergency response efficiency and fault recovery time; Remote monitoring and repair platform module: Combines virtualization technology and intelligent algorithms to create an intelligent operation and maintenance system based on a simulated environment. It achieves efficient fault repair management by simulating fault scenarios and optimizing repair paths. Repair path optimization module: Based on the fault repair simulation results, it uses intelligent optimization algorithms to evaluate the repair path, select the path with the best timeliness, resource consumption and repair efficiency, and perform real-time repairs; Automatic optimization and learning module: Through automatic learning and optimization mechanism, based on the feedback data of signal anomalies and repair process, the signal detection strategy is dynamically adjusted to achieve adaptive learning and continuous optimization; Resource scheduling and configuration optimization module: intelligently schedule network resources according to the real-time load of the base station, network status and resource availability, and optimize the system configuration in real time to optimize the fault recovery path, optimize system stability and response speed.

Images