Communication pipeline resource intelligent management method and system based on multi-source data fusion

Abstract

The invention relates to the technical field of communication engineering, in particular to a communication pipeline resource intelligent management method and system based on multi-source data fusion, and the method comprises the following steps: multi-source data collection and integration, multi-source data fusion, communication pipeline resource modeling, intelligent analysis and decision making, fault prediction and diagnosis, and optimal scheduling. The method has the beneficial effects that by fusing multi-source data, an intelligent communication pipeline resource management system is constructed, accurate modeling, dynamic monitoring, intelligent planning and efficient operation and maintenance of communication pipeline resources are realized, scientificity, accuracy and real-time performance of communication pipeline resource management are improved, operation and maintenance cost is reduced, stable and reliable operation of a communication network is guaranteed, and the communication pipeline resource management system is suitable for popularization and application. And the requirement of rapid development of the communication industry on pipeline resource management is met.

Description

Technical Field

[0001] This invention relates to the field of communication engineering technology, specifically to an intelligent management method and system for communication pipeline resources based on multi-source data fusion. Background Technology

[0002] With the rapid development of the telecommunications industry, the scale and complexity of communication pipeline resources are constantly increasing. As the infrastructure of communication networks, the accuracy and efficiency of communication pipeline management are crucial to ensuring the stable operation of communication networks. However, current communication pipeline resource management still faces many problems.

[0003] In terms of data management, the existing management of communication pipeline resources involves diverse data sources, including GIS data, manual surveying data, and engineering construction drawings. However, these data are often scattered across different systems and departments, lacking effective integration and sharing, resulting in data silos. For example, the GIS system records the geographical location information of the pipelines, while the engineering construction data contains the pipeline construction parameters. Because the data cannot be shared, it is difficult to obtain comprehensive and accurate information when conducting pipeline resource analysis and decision-making.

[0004] From a management perspective, traditional communication pipeline resource management mainly relies on manual inspections and paper-based records. This approach is not only inefficient but also prone to errors and omissions in information recording. Furthermore, there is a lack of real-time and effective means to monitor the operational status of pipelines, making it impossible to promptly detect potential faults. Often, reactive measures are taken only after a fault has occurred, severely impacting the reliability and stability of the communication network.

[0005] In terms of resource planning and optimization, the lack of a comprehensive understanding and precise analysis of communication pipeline resources makes it difficult to formulate scientific and reasonable planning schemes, leading to unreasonable resource allocation and resulting in resource waste or insufficient supply. Furthermore, the inability to quickly and accurately obtain relevant information when responding to emergencies or carrying out pipeline modifications affects the timeliness and accuracy of decision-making.

[0006] While some existing technologies attempt to utilize information technology for communication pipeline resource management, most only offer simple applications of single-type data, failing to fully leverage the synergistic advantages of multi-source data and thus unable to meet the increasingly complex needs of communication pipeline resource management. Therefore, there is an urgent need for an intelligent management method based on multi-source data fusion to improve the efficiency and effectiveness of communication pipeline resource management. Summary of the Invention

[0007] The purpose of this invention is to provide an intelligent management method and system for communication pipeline resources based on multi-source data fusion, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an intelligent management method for communication pipeline resources based on multi-source data fusion, comprising the following steps:

[0009] Multi-source data acquisition and integration: Collect multi-source data such as geographic information system data, pipeline detection data, engineering construction data, and operation and maintenance record data, classify and store them, and carry out data cleaning, data transformation and data standardization operations;

[0010] Multi-source data fusion: Extract the geometric features of spatial data and the attribute features of attribute data from the collected multi-source data, use feature matching algorithms to find the feature correlations between different data sources, unify the data timestamps in the time dimension, and use coordinate transformation and projection transformation methods in the spatial dimension to transform the data to a unified geographic coordinate system. Then, use one or more combinations of weighted average algorithm, Bayesian network algorithm, and DS evidence theory algorithm for fusion processing.

[0011] Communication pipeline resource modeling: Using BIM technology, a three-dimensional model of the communication pipeline resources is constructed based on the fused data, and a dynamic update mechanism is established;

[0012] Intelligent analysis and decision-making: Utilizing one or more combinations of genetic algorithms and simulated annealing algorithms, and combining communication service requirements with urban planning, intelligent planning of communication pipeline resources is carried out.

[0013] By using one or more combinations of neural network algorithms and support vector machine algorithms, a fault prediction model is established based on historical operation and maintenance data and real-time monitoring data to perform fault prediction and diagnosis.

[0014] Scheduling strategies are developed based on the operational status, fault conditions, and service requirements of communication pipeline resources to achieve optimized scheduling.

[0015] Preferably, in the multi-source data acquisition and integration step, the acquired multi-source data covers geographic information system data, pipeline detection data, engineering construction data, and operation and maintenance record data, and is classified and stored in different databases or data storage areas for subsequent targeted processing and retrieval.

[0016] Preferably, in the multi-source data fusion step, the feature extraction process extracts geometric features such as the geometric shape and positional relationship of spatial data, and extracts material, specification, and usage attribute features of attribute data; the feature matching algorithm determines the correspondence between features in different data sources by calculating the similarity or correlation between features.

[0017] Preferably, in the communication pipeline resource modeling step, when using BIM technology to construct a 3D model, the fused data is mapped and converted according to the specifications and standards of the BIM model to ensure that the model can accurately reflect the actual condition of the communication pipeline resources. The dynamic update mechanism adjusts the 3D model in real time according to the newly collected and fused data to ensure the timeliness and accuracy of the model.

[0018] Preferably, in the intelligent analysis and decision-making steps, during intelligent planning, genetic algorithms and simulated annealing algorithms search for the optimal communication pipeline resource planning scheme that meets the needs of communication services and urban planning constraints by simulating the natural selection and annealing process; during fault prediction and diagnosis, neural network algorithms and support vector machine algorithms learn patterns and rules in historical operation and maintenance data and real-time monitoring data to establish fault prediction models, and provide early warning and accurate diagnosis of potential faults in communication pipeline resources.

[0019] A system for an intelligent management method of communication pipeline resources based on multi-source data fusion includes:

[0020] Multi-source data acquisition and integration module: used to collect multi-source data such as geographic information system data, pipeline detection data, engineering construction data, and operation and maintenance record data, and to classify and store the collected data. At the same time, it performs data cleaning, data transformation and data standardization operations to complete data preprocessing.

[0021] Multi-source data fusion module: Extracts geometric features of spatial data and attribute features of attribute data from the collected multi-source data, uses a specific feature matching algorithm to find the feature correlation between different data sources, unifies data timestamps in the time dimension, and uses coordinate transformation and projection transformation methods in the spatial dimension to transform the data to a unified geographic coordinate system for spatiotemporal calibration. Then, it uses one or more combinations of weighted average algorithm, Bayesian network algorithm, and DS evidence theory algorithm for fusion processing.

[0022] Communication pipeline resource modeling module: Based on the fused data, a three-dimensional model of communication pipeline resources is constructed using BIM technology, and a dynamic update mechanism is established to reflect resource changes in real time;

[0023] Intelligent Analysis and Decision Module: Utilizes one or more combinations of genetic algorithms and simulated annealing algorithms to intelligently plan communication pipeline resources in conjunction with communication service requirements and urban planning; Employs one or more combinations of neural network algorithms and support vector machine algorithms to establish a fault prediction model based on historical operation and maintenance data and real-time monitoring data for fault prediction and diagnosis; Formulates scheduling strategies to achieve optimized scheduling based on the operating status, fault conditions, and service requirements of communication pipeline resources.

[0024] Preferably, in the multi-source data acquisition and integration module, the data acquisition methods include, but are not limited to, automatic acquisition through sensors, manual entry, and import from other related systems; the data is classified and stored according to the type, source, and time dimension, and stored in different databases or data storage areas to facilitate subsequent data retrieval and management.

[0025] Preferably, in the multi-source data fusion module, the feature extraction process extracts geometric features such as shape, positional relationship, and topological structure from spatial data, and extracts attribute features such as material, specifications, purpose, and service life from attribute data; the feature matching algorithm determines the correspondence between features in different data sources by calculating similarity indices between features, such as Euclidean distance and cosine similarity, providing an accurate basis for subsequent data fusion.

[0026] Preferably, in the communication pipeline resource modeling module, when using BIM technology to construct a 3D model, the fused data is mapped and converted according to the specifications and standards of the BIM model to ensure that the model accurately reflects the actual physical characteristics and spatial relationships of the communication pipeline resources; the dynamic update mechanism obtains the latest information on the communication pipeline resources through real-time monitoring data collection and periodic data synchronization, and automatically updates the 3D model to ensure the timeliness and accuracy of the model.

[0027] Preferably, in the intelligent analysis and decision-making module, during intelligent planning, genetic algorithms and simulated annealing algorithms search for the optimal communication pipeline resource planning scheme by simulating the natural selection and annealing process, under the conditions of meeting communication service requirements and urban planning constraints; during fault prediction and diagnosis, neural network algorithms and support vector machine algorithms learn patterns and rules from historical operation and maintenance data and real-time monitoring data to establish a high-precision fault prediction model, which can provide early warning and accurate diagnosis of potential faults in communication pipeline resources, providing decision support for operation and maintenance personnel; during optimized scheduling, scientific and reasonable scheduling strategies are formulated based on the real-time operating status of communication pipeline resources, fault occurrence, and the priority of service requirements to achieve efficient resource utilization.

[0028] Compared with the prior art, the beneficial effects of the present invention are:

[0029] The present invention proposes an intelligent management method and system for communication pipeline resources based on multi-source data fusion. By integrating multi-source data, an intelligent communication pipeline resource management system is constructed, enabling accurate modeling, dynamic monitoring, intelligent planning, and efficient operation and maintenance of communication pipeline resources. This improves the scientific nature, accuracy, and real-time performance of communication pipeline resource management, reduces operation and maintenance costs, ensures the stable and reliable operation of communication networks, and meets the needs of the rapidly developing communication industry for pipeline resource management.

[0030] By integrating multi-source data, data from different channels and of different types is combined, eliminating data silos and improving the accuracy and completeness of communication pipeline resource data. Actual testing showed that after adopting this method, the accuracy of pipeline resource data increased from 80% to over 95%, providing a reliable data foundation for subsequent management and decision-making.

[0031] This system automates and intelligentizes communication pipeline resource management, reducing manual operations and paper-based record management, and significantly improving management efficiency. For example, in pipeline inspection, real-time monitoring and fault prediction reduce unnecessary manual inspections, increasing inspection efficiency by more than 30%. In fault handling, fault location time has been reduced from an average of 2 hours to less than 30 minutes, significantly improving fault handling speed.

[0032] Intelligent planning and optimized scheduling functions can rationally allocate communication pipeline resources according to actual needs, avoiding resource waste and insufficient supply. Through the planning and application of communication pipeline resources in a certain region, the utilization rate of pipeline resources in that region has increased by 20%, while reducing construction and maintenance costs. Attached Figure Description

[0033] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the present invention clear and complete, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only some, not all, embodiments of the present invention, and are merely illustrative of the embodiments of the present invention. They are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Example 1: This invention provides a technical solution: an intelligent management method for communication pipeline resources based on multi-source data fusion, comprising the following steps:

[0036] 1. Multi-source data acquisition and integration

[0037] Data Acquisition: Multi-source data related to communication pipelines is collected through various methods. Spatial data such as the geographical location and topography of the pipelines are obtained using Geographic Information Systems (GIS); physical attribute data such as burial depth, direction, and material of underground communication pipelines are collected using pipeline detection equipment, such as ground-penetrating radar and electromagnetic induction detectors; engineering construction data such as construction time, construction technology, and design drawings are obtained from engineering construction departments; maintenance records such as fault records, maintenance logs, and inspection data are collected from operation and maintenance departments; and external data such as meteorological data and urban planning data can also be integrated to provide richer information support for communication pipeline resource management.

[0038] Data storage and preprocessing: The collected multi-source data is classified and stored to establish a comprehensive database of communication pipeline resources. Before storage, the data undergoes preprocessing operations, including data cleaning to remove duplicate, erroneous, and incomplete data; data conversion to unify data of different formats into a standard format for subsequent processing; and data standardization to normalize numerical data and eliminate the influence of data units.

[0039] 2. Multi-source data fusion

[0040] Feature extraction and matching: For different types of data, feature information is extracted. For spatial data, geometric features such as pipeline coordinates, length, and nodes are extracted; for attribute data, attribute features such as material, pipe diameter, and service life are extracted. By establishing a feature matching algorithm, features from different data sources are matched to find the relationships between the data. For example, by using the geographical coordinates of pipelines, GIS data and pipeline detection data are correlated to determine the corresponding information of the same pipeline in different data sets.

[0041] Spatiotemporal calibration: Since the acquisition time of multi-source data may differ from the spatial reference, spatiotemporal calibration is required. In the time dimension, the timestamps of the data are unified, adjusting data collected at different times to the same time series; in the spatial dimension, methods such as coordinate transformation and projection transformation are used to transform data from different spatial references to a unified geographic coordinate system, ensuring the spatiotemporal consistency of the data.

[0042] Data fusion algorithm: A data fusion algorithm based on weighted average, Bayesian network, and DS evidence theory is employed to fuse the data after feature extraction, matching, and spatiotemporal calibration. Appropriate weights are assigned based on the reliability and importance of different data points to generate more accurate and comprehensive communication pipeline resource data. For example, pipeline detection data is given higher weights for pipeline data from important nodes to improve data accuracy.

[0043] 3. Communication pipeline resource modeling

[0044] 3D Modeling: Based on the fused multi-source data, 3D modeling technology is used to construct a 3D model of communication pipeline resources. This visually displays the spatial location, physical attributes, and connection relationships of the pipelines, facilitating viewing and analysis by management personnel. During the modeling process, BIM (Building Information Modeling) technology can be employed to achieve detailed modeling of communication pipeline resources, accurately reflecting the actual condition of the pipelines.

[0045] Dynamic Updates: A dynamic data update mechanism is established to promptly collect relevant data and update the comprehensive database and 3D model of communication pipeline resources when new engineering construction or pipeline maintenance occurs. By monitoring data changes in real time, it ensures that the model accurately reflects the latest status of communication pipeline resources.

[0046] 4. Intelligent Analysis and Decision Making

[0047] Intelligent planning: Utilizing artificial intelligence algorithms, such as genetic algorithms and simulated annealing algorithms, combined with factors such as communication service requirements and urban planning, intelligent planning of communication pipeline resources is performed. By analyzing existing pipeline resources, the layout and routing of pipelines are optimized, and the construction location and scale of new pipelines are rationally planned to improve resource utilization efficiency and avoid resource waste.

[0048] Fault Prediction and Diagnosis: A fault prediction model is established based on historical operation and maintenance data and real-time monitoring data. Machine learning algorithms, such as neural networks and support vector machines, are used to analyze the correlation between pipeline operating status and faults, predict potential pipeline faults, and issue early warnings. When a fault occurs, fault diagnosis algorithms are used to quickly locate the fault location and cause, providing accurate maintenance guidance for maintenance personnel.

[0049] Optimized scheduling: Based on the operational status, fault conditions, and service demands of communication pipeline resources, optimized scheduling strategies are formulated. By rationally allocating resources, priority is given to ensuring the communication needs of critical services, thereby improving the overall performance and reliability of the communication network. For example, in the event of a partial failure, communication routes are adjusted promptly to divert service traffic to other normal pipelines, reducing the impact of the failure on services.

[0050] Example 2, based on Example 1, proposes a system for intelligent management of communication pipeline resources based on multi-source data fusion, comprising:

[0051] Multi-source data acquisition and integration module: This module is used to collect multi-source data, including geographic information system data, pipeline detection data, engineering construction data, and operation and maintenance record data. It also classifies and stores the collected data, and performs data cleaning, data transformation, and data standardization to complete data preprocessing. Data acquisition methods include, but are not limited to, automatic acquisition through sensors, manual entry, and import from other related systems. The data is classified and stored according to its type, source, and time dimension, and stored in different databases or data storage areas to facilitate subsequent data retrieval and management.

[0052] The multi-source data fusion module extracts geometric features from spatial data and attribute features from attribute data collected from multiple sources. It employs specific feature matching algorithms to identify feature relationships between different data sources, unifies data timestamps in the time dimension, and uses coordinate transformation and projection transformation methods in the spatial dimension to convert the data to a unified geographic coordinate system for spatiotemporal calibration. Then, it uses one or more combinations of weighted average algorithms, Bayesian network algorithms, and DS evidence theory algorithms for fusion processing. The feature extraction process extracts geometric features such as shape, positional relationships, and topological structure from spatial data, and material, specifications, usage, and service life attributes from attribute data. The feature matching algorithm determines the correspondence between features in different data sources by calculating similarity indices such as Euclidean distance and cosine similarity, providing an accurate basis for subsequent data fusion.

[0053] Communication pipeline resource modeling module: Based on the fused data, a 3D model of communication pipeline resources is constructed using BIM technology, and a dynamic update mechanism is established to reflect resource changes in real time. When constructing the 3D model using BIM technology, the fused data is mapped and converted according to BIM model specifications and standards to ensure that the model accurately reflects the actual physical characteristics and spatial relationships of the communication pipeline resources. The dynamic update mechanism obtains the latest information on communication pipeline resources through real-time monitoring data collection and periodic data synchronization, and automatically updates the 3D model to ensure the timeliness and accuracy of the model.

[0054] The intelligent analysis and decision-making module utilizes one or more combinations of genetic algorithms and simulated annealing algorithms, combined with communication service requirements and urban planning, to intelligently plan communication pipeline resources. It employs one or more combinations of neural network algorithms and support vector machine algorithms to establish a fault prediction model based on historical operation and maintenance data and real-time monitoring data for fault prediction and diagnosis. It formulates scheduling strategies based on the operating status, fault conditions, and service requirements of communication pipeline resources to achieve optimized scheduling. During intelligent planning, genetic algorithms and simulated annealing algorithms simulate natural selection and annealing processes to search for the optimal communication pipeline resource planning scheme under the constraints of communication service requirements and urban planning. During fault prediction and diagnosis, neural network algorithms and support vector machine algorithms learn patterns and rules from historical operation and maintenance data and real-time monitoring data to establish a high-precision fault prediction model, enabling early warning and accurate diagnosis of potential faults in communication pipeline resources, providing decision support for operation and maintenance personnel. During optimized scheduling, it formulates scientific and reasonable scheduling strategies based on the real-time operating status of communication pipeline resources, fault occurrence, and the priority of service requirements to achieve efficient resource utilization.

[0055] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent management of communication pipeline resources based on multi-source data fusion, characterized in that: Includes the following steps: Multi-source data acquisition and integration: Collect multi-source data such as geographic information system data, pipeline detection data, engineering construction data, and operation and maintenance record data, classify and store them, and carry out data cleaning, data transformation and data standardization operations; Multi-source data fusion: Extract the geometric features of spatial data and the attribute features of attribute data from the collected multi-source data, use feature matching algorithms to find the feature correlations between different data sources, unify the data timestamps in the time dimension, and use coordinate transformation and projection transformation methods in the spatial dimension to transform the data to a unified geographic coordinate system. Then, use one or more combinations of weighted average algorithm, Bayesian network algorithm, and DS evidence theory algorithm for fusion processing. Communication pipeline resource modeling: Using BIM technology, a three-dimensional model of the communication pipeline resources is constructed based on the fused data, and a dynamic update mechanism is established; Intelligent analysis and decision-making: Utilizing one or more combinations of genetic algorithms and simulated annealing algorithms, combined with communication service requirements and urban planning, intelligent planning of communication pipeline resources is carried out; By using one or more combinations of neural network algorithms and support vector machine algorithms, a fault prediction model is established based on historical operation and maintenance data and real-time monitoring data to perform fault prediction and diagnosis. Scheduling strategies are developed based on the operational status, fault conditions, and service requirements of communication pipeline resources to achieve optimized scheduling.

2. The intelligent management method for communication pipeline resources based on multi-source data fusion according to claim 1, characterized in that: In the multi-source data acquisition and integration step, the acquired multi-source data covers geographic information system data, pipeline detection data, engineering construction data, and operation and maintenance record data, and is classified and stored in different databases or data storage areas for subsequent targeted processing and retrieval.

3. The intelligent management method for communication pipeline resources based on multi-source data fusion according to claim 2, characterized in that: In the multi-source data fusion step, the feature extraction process extracts geometric features such as geometric shape and positional relationship for spatial data, and extracts material, specification and usage attribute features for attribute data; feature Matching algorithms determine the correspondence between features from different data sources by calculating the similarity or correlation between features.

4. The intelligent management method for communication pipeline resources based on multi-source data fusion according to claim 3, characterized in that: In the communication pipeline resource modeling process, when using BIM technology to construct a 3D model, the fused data is mapped and converted according to the specifications and standards of the BIM model to ensure that the model can accurately reflect the actual condition of the communication pipeline resources. The dynamic update mechanism adjusts the 3D model in real time based on newly collected and fused data to ensure the timeliness and accuracy of the model.

5. The intelligent management method for communication pipeline resources based on multi-source data fusion according to claim 4, characterized in that: In the intelligent analysis and decision-making process, during intelligent planning, genetic algorithms and simulated annealing algorithms search for the optimal communication pipeline resource planning scheme that meets the needs of communication services and urban planning constraints by simulating the natural selection and annealing process; during fault prediction and diagnosis, neural network algorithms and support vector machine algorithms learn patterns and rules from historical operation and maintenance data and real-time monitoring data to establish fault prediction models, and provide early warning and accurate diagnosis of potential faults in communication pipeline resources.

6. A system for the intelligent management method of communication pipeline resources based on multi-source data fusion as described in claim 5, characterized in that: include: Multi-source data acquisition and integration module: used to collect multi-source data such as geographic information system data, pipeline detection data, engineering construction data, and operation and maintenance record data, and to classify and store the collected data. At the same time, it performs data cleaning, data transformation and data standardization operations to complete data preprocessing. Multi-source data fusion module: Extracts geometric features of spatial data and attribute features of attribute data from the collected multi-source data, uses a specific feature matching algorithm to find the feature correlation between different data sources, unifies data timestamps in the time dimension, and uses coordinate transformation and projection transformation methods in the spatial dimension to transform the data to a unified geographic coordinate system for spatiotemporal calibration. Then, it uses one or more combinations of weighted average algorithm, Bayesian network algorithm, and DS evidence theory algorithm for fusion processing. Communication pipeline resource modeling module: Based on the fused data, a three-dimensional model of communication pipeline resources is constructed using BIM technology, and a dynamic update mechanism is established to reflect resource changes in real time; Intelligent Analysis and Decision Module: Utilizes one or more combinations of genetic algorithms and simulated annealing algorithms to intelligently plan communication pipeline resources in conjunction with communication service requirements and urban planning; Employs one or more combinations of neural network algorithms and support vector machine algorithms to establish a fault prediction model based on historical operation and maintenance data and real-time monitoring data for fault prediction and diagnosis; Formulates scheduling strategies to achieve optimized scheduling based on the operating status, fault conditions, and service requirements of communication pipeline resources.

7. The system according to claim 6, characterized in that: In the multi-source data acquisition and integration module, data acquisition methods include, but are not limited to, automatic acquisition through sensors, manual entry, and import from other related systems; Categorized storage divides data according to type, source, and time dimension, storing it in different databases or data storage areas to facilitate subsequent data retrieval and management.

8. The system according to claim 7, characterized in that: In the multi-source data fusion module, the feature extraction process extracts geometric features such as shape, positional relationship, and topological structure from spatial data, and extracts attribute features such as material, specifications, purpose, and service life from attribute data. feature Matching algorithms determine the correspondence between features from different data sources by calculating similarity metrics between features, such as Euclidean distance and cosine similarity, thus providing an accurate basis for subsequent data fusion.

9. A system according to claim 8, characterized in that: In the communication pipeline resource modeling module, when using BIM technology to construct a 3D model, the merged data is mapped and converted according to the specifications and standards of the BIM model to ensure that the model accurately reflects the actual physical characteristics and spatial relationships of the communication pipeline resources. The dynamic update mechanism obtains the latest information on the communication pipeline resources through real-time monitoring data collection and periodic data synchronization, and automatically updates the 3D model to ensure the timeliness and accuracy of the model.

10. A system according to claim 9, characterized in that: In the intelligent analysis and decision-making module, during intelligent planning, genetic algorithms and simulated annealing algorithms simulate natural selection and annealing processes to search for the optimal communication pipeline resource planning scheme under the conditions of meeting communication service requirements and urban planning constraints. During fault prediction and diagnosis, neural network algorithms and support vector machine algorithms learn patterns and rules from historical operation and maintenance data and real-time monitoring data to establish a high-precision fault prediction model, which can provide early warning and accurate diagnosis of potential faults in communication pipeline resources, providing decision support for operation and maintenance personnel. During optimized scheduling, scientific and reasonable scheduling strategies are formulated based on the real-time operating status of communication pipeline resources, fault occurrence, and the priority of service requirements to achieve efficient resource utilization.

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