AI-powered telecommunications infrastructure aging index calculation system and method.

Abstract

It is an AI-powered telecommunications infrastructure aging index calculation system, and its features include: a data collection module (1) that collects age, fault, traffic and environmental data of the infrastructure and transfers this data to the infrastructure inventory database (2), an infrastructure inventory database (2) that stores the infrastructure data collected by the data collection module (1) in time series and structured format and enables the use of this data in analysis processes, an environmental risk analysis module (3) that matches the geographical location information of the infrastructure components with disaster and environmental risk layers and produces location-based risk parameters as a result of this matching, and a physical structure analysis module (4) that converts fiber splice points, manhole numbers and physical infrastructure characteristics into numerical parameters and provides input to the aging analysis with these parameters.Usage and traffic density analysis module (5), which normalizes traffic and usage statistics of infrastructure components and thus determines operational load profiles; AI-based aging analysis engine (6), which analyzes different types of infrastructure, usage, failure and environmental data with machine learning algorithms and learns the operational aging behavior of infrastructure components; aging index calculation unit (7), which produces a numerical and comparable aging index for each infrastructure component based on the analysis outputs of the AI-based aging analysis engine (6); risk classification and prioritization module (8), which divides the calculated aging indices into risk levels using threshold values ​​and statistical methods and creates a priority ranking according to these risk levels;It includes a reporting and decision support module (9) that visualizes the aging index and risk analysis results and provides decision support outputs for maintenance and investment planning.

Description

1 TARIFF AI-powered telecommunications infrastructure aging index calculation system and method Technical Area The invention is an AI-powered telecommunications infrastructure aging index calculation system and It is related to the method. State of the Art In current telecommunications infrastructure management systems, fiber optic lines and transmission systems are used. The condition of field equipment is usually determined by chronological age, date of last failure, or number of failures. It is evaluated based on limited and singular indicators such as those mentioned above. NMS and OSS platforms, 15 These systems provide real-time status information and alarm generation for infrastructure components; however, capable of holistically analyzing the long-term wear and tear and aging levels of the infrastructure. It does not offer a mechanism. In current practices, the environmental conditions to which infrastructure components are exposed (earthquake risk, 20 physical factors such as flooding, temperature changes, usage intensity, and the number of manholes and extensions. Factors and past failure behaviors are not evaluated together; these data are not considered in conjunction with each other. No meaningful relationship is established. Therefore, two infrastructure components of the same age are completely... Although they have different risk profiles, they are handled similarly in current techniques. is being taken. 25 The resulting technical problem is the actual operational aspect of infrastructure assets. a numerical, comparable and objective aging indicator that reflects the condition The inability to produce it. This deficiency leads to reactive maintenance and investment decisions, critical the failure to identify infrastructure components in a timely manner and the inefficient use of resources 30 This leads to its use. The application with registration number US9311615B2 was revealed as a result of technical investigations. Summary: “An approach to infrastructure asset management is presented. This approach focuses on physical capable of receiving asset-related data and additional external data, and applying advanced analytics to that data. 35 an end-to-end analytics-based system that generates business insights, forecasts, and planning information through implementation. 2 It includes a maintenance approach. More specifically, this approach involves a physical aspect of an infrastructure. to obtain data about a set of entities and about that set of physical entities Predicting maintenance requirements for each physical asset in the cluster by analyzing data. It uses a maintenance analysis tool structured accordingly. The maintenance analysis tool also includes physical Based on the projected maintenance requirements for each asset in the asset set, a 5 It includes an output component structured for generating a maintenance plan. As can be seen, the invention relates to a method for infrastructure asset management, and in addition to this... a structure that can provide a solution to the disadvantages mentioned above It does not mention. 10 In conclusion, due to the negative aspects described above and the current solutions, the subject matter... Due to its shortcomings, it has become necessary to make improvements in the relevant technical field. Purpose of the Invention 15 The invention represents a new breakthrough in this field, unlike the structures used in existing technology. The aim is to create a structure with different technical specifications that bring these elements together. The system and method described in the invention relate to the operation and monitoring of telecommunication infrastructures and 20 It is used in the field of investment planning, especially in fiber optic infrastructures and transmission. analyzing the risks of aging, wear and tear, and failure of systems and field equipment It is an AI-powered decision support system. The primary aim of the invention is to analyze historical failure data beyond the chronological age of infrastructure assets. by considering usage intensity, environmental conditions and physical structure information together The aim is to quantitatively determine the operational aging level of the infrastructure. This enables data-driven, objective, and efficient prioritization of maintenance, renovation, and investment processes. Comparative analyses are presented. Another aim of the invention is to determine the chronological age of telecommunications infrastructure components. Instead of evaluating based on a single factor, a comprehensive analysis of numerous technical and environmental parameters is used. an AI-powered infrastructure that enables the calculation of an aging index system to present. 35 3 The invention covers fiber optic lines, transmission systems, and field equipment. chronological age information, past breakdown records, frequency and types of breakdowns, traffic density, physical Building information (connection points, number of manholes, etc.) and environmental risk data are considered together. These diverse data are normalized and brought to a common ground for evaluation, and It is analyzed through machine learning-based algorithms. 5 Artificial intelligence algorithms analyze the nonlinear relationships between these parameters. by learning, a numerical and comparable aging index for each infrastructure component. This index represents the actual operational wear and tear level of the infrastructure. and reflects not only the calendar age, but also the conditions of use and environmental impacts. 10 The invention analyzes the results without any automated intervention in the infrastructure. reporting, classifying risk levels and planning for maintenance and investment. It provides decision support outputs. Thus, it offers objective and holistic solutions that are not available in current technology. An infrastructure aging assessment mechanism is provided. 15 The invention relates to fiber optic lines located within large-scale telecommunications infrastructures, transmission systems, field equipment and services affected by this infrastructure It is implemented within a holistic analysis framework. In this context, the general structure of the system is as follows: A multi-layered system consisting of data sources, a central analysis infrastructure, and decision support layers. It represents an architecture. At the lowest layer of the structure, fiber infrastructure elements and transmission systems are located. systems, field equipment and their operational, physical and environmental data This layer constitutes the core infrastructure components that the system analyzes. At the top layer, technical, environmental and usage data are obtained from different institutions and systems. There is a central data management structure into which the data is integrated. This structure comprises 25 components of the infrastructure. It is possible to evaluate age, malfunction, traffic, geographical location and risk data together. It makes it possible. At the central analytics layer, the infrastructure is built using artificial intelligence and advanced data analysis algorithms. The aging behaviors of its components are being modeled, and numerical aging indices are being created. Risk levels are calculated and determined. This layer enables the system to make decisions. This forms the basis of its capabilities. At the highest level are reporting, visualization, and decision-making. Support components are included, assessing the overall condition of the infrastructure, risk distribution, and Outputs are produced that present the prioritization results in a holistic manner. 4 This structure enables the system to perform its analysis and recommendation generation functions. It provides this without directly interfering with the existing infrastructure, covering the entire infrastructure. It allows for predictive and comparable assessments to be made. To achieve the objectives described above, the invention enables AI-powered telecommunications 5. Its underlying infrastructure is an aging index calculation system, and its characteristic feature is;  Collecting age, fault, traffic and environmental data related to the infrastructure and using that data Data collection module that transfers data to infrastructure inventory database,  Time series and infrastructure data collected by the data collection module storing this data in a structured format and making it available for use in analysis processes. infrastructure inventory database providing  Geographic location information of infrastructure components combined with disaster and environmental risk layers matching and generating location-based risk parameters as a result of this matching. environmental risk analysis module,  Fiber optic splice points, manhole numbers, and physical infrastructure characteristics are converted into numerical parameters. 15 physical structure that transforms and provides input to aging analysis with these parameters analysis module,  normalizing traffic and usage statistics for infrastructure components and thereby Usage and traffic density analysis module that determines operational load profiles,  Machine learning analyzes different types of infrastructure, usage, malfunction, and environmental data. 20 analyzes the operational aging of infrastructure components using algorithms. AI-based aging analysis engine that learns behavior,  Each one based on the analysis outputs of the AI-based aging analysis engine aging that produces a numerical and comparable aging index for the infrastructure component The unit of index calculation is 25.  Calculating aging indices using threshold values ​​and statistical methods risk is categorized into risk levels and prioritized according to these risk levels. classification and prioritization module,  Visualizing aging index and risk analysis results, and managing maintenance and investment Reporting and decision support module 30, which provides decision support outputs for planning. It includes. The structural and characteristic features and all the advantages of the invention are given in the figures below. Thanks to the detailed explanation written with references to the figures, it becomes clearer. This will be understood, and therefore the evaluation should also take these figures and detailed explanations into account. 35 It must be done by taking it. Figures that will help understand the invention. Figure 1 is a schematic representation of the system that is the subject of the invention. Figure 2 shows a schematic representation of the method described in the invention. 5 The drawings do not necessarily need to be scaled and are necessary for understanding the invention. Details that are not present may have been overlooked. Furthermore, at least to a large extent... Elements that are identical or at least have substantially identical functions are numbered the same. It is shown. 10 Explanation of Part References 1. Data collection module 2. Infrastructure inventory database 15 3. Environmental risk analysis module 4. Physical structure analysis module 5. Usage and traffic density analysis module 6. AI-based aging analysis engine 7. Aging index calculation unit: 20 8. Risk classification and prioritization module 9. Reporting and decision support module 1001. Age information and fault data for fiber infrastructure, transmission systems, and field equipment. its history, traffic data, physical structure information and environmental data, data collection 25 collected by module (1), 1002. Infrastructure inventory for use in analysis processes of collected infrastructure data. (2) time series and stored in structured format in the database, 1003. Geographic location information of infrastructure components, environmental risk analysis module (3) Location-based risk assessment by matching disaster and environmental risk data. 30 creation of parameters, 1004. Fiber optic splice points, manhole numbers, and similar physical infrastructure elements, physical structure The numerical evaluation parameters are analyzed by the analysis module (4). transformation, 6 1005. Traffic and usage data for infrastructure components, usage and traffic density. long-term operational load profiles are analyzed by the analysis module (5) determination, Environmental, physical and Technical and fault parameters stored in the infrastructure inventory database (2) data by artificial intelligence based aging analysis engine (6) Analyzing the operational aging of the infrastructure together using algorithms modeling behavior, 1007. Based on artificial intelligence analysis results, the aging index calculation unit (7) a numerical and comparable aging index for each infrastructure component by 10 calculation, 1008. Risk classification and prioritization using calculated aging indices. Risk of infrastructure components by threshold values ​​and statistical methods by module (8) separating and prioritizing them into levels, 1009. Reporting of analysis results by the reporting and decision support module (9), 15 visualization and decision support outputs for maintenance and investment planning presentation. Detailed Description of the Invention In this detailed explanation, the preferred configurations of the invention are not merely for better understanding the subject. in order to facilitate understanding and without imposing any limiting effects It is explained. The invention; an AI-powered telecommunications infrastructure aging index calculation system and 25 It is related to the method. The elements and functions used in the system and method that are the subject of the invention are as follows: Data collection module (1) collects age, fault, traffic and environmental data of the infrastructure from NMS, OSS, Cacti, 30 collecting and integrating field inventory and environmental data sources, and the said data It is the module that transfers to the infrastructure inventory database (2). The infrastructure inventory database (2) contains the infrastructure data collected by the data collection module (1). storing time series and structured format data and using this data in analysis processes 35 It is the database that enables its use. 7 Environmental risk analysis module (3) provides geographical location information of infrastructure components for disasters and matching environmental risk layers and, as a result of this matching, location-based risk It is the module that generates the parameters. Physical structure analysis module (4), fiber splice points, manhole numbers and physical infrastructure converting its characteristics into numerical parameters and using these parameters for aging analysis. It is the module that provides this. Usage and traffic density analysis module (5), traffic and usage of infrastructure components 10 This module normalizes statistics and thereby determines operational load profiles. AI-based aging analysis engine (6) analyzes different types of infrastructure, usage, failures and Analyzing environmental data with machine learning algorithms and infrastructure components It is the engine that learns operational aging behavior. 15 Aging index calculation unit (7), artificial intelligence based aging analysis engine (6) Numerical and comparable aging for each infrastructure component based on analysis outputs. It is the unit that generates the index. The risk classification and prioritization module (8) uses the calculated aging indices as thresholds. using values ​​and statistical methods to categorize risk levels and assign these risk levels to... It is a module that creates a priority ranking based on criteria. The reporting and decision support module (9) displays the aging index and risk analysis results. visualizes and provides decision support outputs for maintenance and investment planning. It is a module. The invention works on the following principle: The invention relates to the age of the system, fiber infrastructure, transmission systems and field equipment. A database that collects fault history, traffic, physical structure, and environmental data from various sources. It includes the collection module (1). The collected data are time series and structured. It is transferred to a central infrastructure inventory database (2) to be stored in the format. The system combines the geographic location information of infrastructure components with disaster and environmental risk data. 35 using the matching location-based environmental risk analysis module (3) for each component 8 It creates location-based risk parameters. Digital and numerical data related to physical infrastructure elements. The structural data can be measured by analyzing the physical structure analysis module (4). Traffic and usage data are converted into parameters such as usage and traffic density. in deriving long-term usage profiles through the analysis module (5) Technical, environmental and operational data are used in AI-based aging analysis. 5 Aging behavior of infrastructure components evaluated together by engine (6) is being modeled. Based on these analysis results, numerical and quantitative models are created for each infrastructure component. Comparable infrastructure aging index, aging index calculation unit (7) It is produced by [company name]. The calculated aging indices, risk classification, and Using the prioritization module (8), infrastructure components are 10 according to their critical levels. Classification is provided. Finally, the system includes a reporting and decision support module (9) through this system, it presents the analysis results to the user and provides guidance for maintenance and investment planning. It produces decision support outputs. The steps involved in the process of implementing the system described in this invention are listed below: 15  Age information and fault data for fiber infrastructure, transmission systems, and field equipment. its history, traffic data, physical structure information and environmental data, data collection Collection by module (1) (1001), (NMS, OSS, Cacti, field inventory and environmental (It is collected in an integrated manner with data sources.) 20  The collected infrastructure data will be used in the infrastructure inventory for analysis processes. (2) storing time series and structured format in the database (1002),  Geographic location information of infrastructure components by the environmental risk analysis module (3) Location-based risk parameters are determined by matching them with disaster and environmental risk data. creation (1003), 25  Physical infrastructure elements such as fiber optic splice points, manhole numbers, and similar features, physical structure The numerical evaluation parameters are analyzed by the analysis module (4). conversion (1004),  Analysis of traffic and usage data for infrastructure components, and usage and traffic density analysis. long-term operational load profiles are analyzed by module (5) 30 determination (1005),  Environmental, physical and Technical and fault parameters stored in the infrastructure inventory database (2) data by artificial intelligence based aging analysis engine (6) Analyzing the operational aging of the infrastructure together using algorithms 35 modeling of behavior (1006) 9  Based on artificial intelligence analysis results, the aging index calculation unit (7) a numerical and comparable aging index for each infrastructure component by calculation (1007),  Risk classification and prioritization using calculated aging indices Risk assessment of infrastructure components by threshold values ​​and statistical methods by module (8) 5 separating and prioritizing into levels (1008),  Reporting of analysis results by the reporting and decision support module (9), visualization and decision support outputs for maintenance and investment planning presentation (1009).

Claims

REQUESTS 1. It is an AI-powered telecommunications infrastructure aging index calculation system, and its feature is;  Collecting age, fault, traffic and environmental data related to the infrastructure and using that data Data collection module (1), which transfers to the infrastructure inventory database (2), 5  Time series and infrastructure data collected by the data collection module (1) storing this data in a structured format and using this data in analysis processes. infrastructure inventory database (2),  Geographic location information of infrastructure components combined with disaster and environmental risk layers 10 that matches and generates location-based risk parameters as a result of this matching. environmental risk analysis module (3),  Fiber optic splice points, manhole numbers, and physical infrastructure characteristics converted into numerical parameters physical structure that transforms and provides input to aging analysis with these parameters analysis module (4),  normalizing traffic and usage statistics for infrastructure components and thus 15 Usage and traffic density analysis module (5) which determines operational load profiles.  Machine learning of diverse infrastructure, usage, malfunction, and environmental data. analyzes the operational aging of infrastructure components using algorithms. behavior-learning AI-based aging analysis engine (6),  Each of the 20 analysis outputs from the AI-based aging analysis engine (6) aging that produces a numerical and comparable aging index for the infrastructure component Unit of index calculation (7),  Calculating aging indices using threshold values ​​and statistical methods risk is categorized into risk levels and prioritized according to these risk levels. Classification and prioritization module (8), 25  Visualizing aging index and risk analysis results, and managing maintenance and investment reporting and decision support module that provides decision support outputs for planning. (9) It includes.

2. The system complies with Claim 1 and features include: NMS, OSS, Cacti, field inventory and environmental data. It includes a data collection module (1) that collects data in an integrated manner with its sources.

3. This is an artificial intelligence-supported method for calculating the telecommunications infrastructure aging index. Feature; 35 11  Age information and fault data for fiber infrastructure, transmission systems, and field equipment. its history, traffic data, physical structure information and environmental data, data collection Collection by module (1) (1001),  The collected infrastructure data will be used in the infrastructure inventory for analysis processes. (2) time series and structured format storage in the database (1002), 5  Geographic location information of infrastructure components, environmental risk analysis module (3) Location-based risk assessment is performed by matching disaster and environmental risk data. creation of parameters (1003),  Physical infrastructure elements such as fiber optic splice points, manhole numbers, and similar features, physical structure The numerical evaluation parameters are 10, analyzed by the analysis module (4). conversion (1004),  Traffic and usage data for infrastructure components, usage and traffic density. long-term operational load analyzed by analysis module (5) Determination of profiles (1005),  Environmental, physical and 15 obtained in process steps 1003, 1004 and 1005 Technical and infrastructure inventory database (2) stored with usage parameters The fault data is artificially analyzed by the AI-based aging analysis engine (6). Analyzing the infrastructure together using intelligence algorithms and making it operational. Modeling aging behavior (1006)  Based on artificial intelligence analysis results, the aging index calculation unit (7) 20 numerical and comparable aging for each infrastructure component by Calculation of the index (1007),  Risk classification and prioritization using calculated aging indices The infrastructure components are determined by the threshold values ​​and statistical methods of the module (8). separating and prioritizing risk levels (1008), 25  Reporting of analysis results by the reporting and decision support module (9), visualization and decision support outputs for maintenance and investment planning presentation (1009) It includes the steps of the process.

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