A telecommunications service information processing system

Abstract

The present invention discloses a telecommunication business information processing system, which relates to the technical field of wireless communication networks. The system comprises the following components: a data collection module: responsible for collecting telecommunication business data in real time, including call data, text message data, and network flow data, and sending the data to a distributed computing module for processing. The present invention decomposes the data into multiple subtasks through distributed computing technology, and distributes the data to multiple computing nodes for parallel processing. The computing nodes share and coordinate data through high-speed network communication to ensure the efficiency and accuracy of processing. A virtualized computing resource pool is constructed through cloud computing technology. The scale and configuration of computing resources are dynamically adjusted according to the resource requirements of the distributed computing modules. The provision of such elastic computing resources ensures that the distributed computing modules can efficiently process data of different scales and complexities, thereby improving the flexibility and scalability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication networks, and in particular to a telecommunication service information processing system. Background Art

[0002] With the rapid development of communication technology, the amount of telecommunications business data has shown an explosive growth trend. These data include call records, text message content, network traffic information and other types. They are not only huge in quantity, but also real-time, diverse and complex. The processing and analysis of such massive data is crucial for telecom operators, as it can help them better understand user needs, optimize network resource allocation and improve service quality.

[0003] However, traditional data processing methods are unable to cope with such a huge amount of data. The traditional centralized data processing architecture has problems such as poor scalability, slow processing speed, and high cost. It cannot meet the real-time and high efficiency requirements of telecommunications business data processing. Therefore, a more advanced data processing technology is needed to solve these problems. Summary of the invention

[0004] The purpose of the present invention is to make up for the shortcomings of the prior art and provide a telecommunications business information processing system. Through the flexible resource allocation of the cloud computing module, the system can be dynamically expanded according to business needs to meet the growing data processing needs. Through the distributed computing module, parallel processing and collaborative computing are adopted to greatly improve the concurrent processing capability of the system, and a large amount of telecommunications business data can be processed simultaneously.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a telecommunication service information processing system, the system comprising the following components:

[0006] Data collection module: responsible for collecting telecommunications business data in real time, including call data, SMS data, and network traffic data, and sending the data to the distributed computing module for processing;

[0007] Distributed computing module: Distributed computing technology is used to decompose telecommunication service data into multiple subtasks, which are assigned to multiple computing nodes for parallel processing. The computing nodes communicate through high-speed networks to achieve data sharing and collaborative computing, thereby improving data processing speed and concurrent processing capabilities.

[0008] Cloud computing module: Using cloud computing technology, a virtualized computing resource pool is constructed to provide flexible computing resources for the distributed computing module. According to data processing requirements, the cloud computing module can dynamically adjust the scale and configuration of computing resources to meet the performance requirements in different scenarios.

[0009] Data storage module: Distributed storage technology is used to achieve efficient storage and management of telecommunications business data. The data storage module has high reliability, high scalability and high concurrency, and can support rapid reading and writing and real-time analysis of massive data.

[0010] Furthermore, the data collection module implements the steps:

[0011] (1) Define the types of data that need to be collected based on business needs, such as call data, SMS data, and network traffic data;

[0012] (2) Collect data from telecommunications business systems in real time through API interfaces or data capture technology;

[0013] (3) Preliminary cleaning and formatting of the collected data to remove invalid and duplicate data and ensure data quality;

[0014] (4) Send the cleaned data to the distributed computing module for processing.

[0015] Furthermore, the distributed computing module implements the steps of:

[0016] (1) Select an appropriate distributed computing framework based on the characteristics and processing requirements of the data;

[0017] (2) Decompose telecommunication business data into multiple subtasks, each of which corresponds to the processing of a portion of the data;

[0018] (3) Assign subtasks to multiple computing nodes for parallel processing;

[0019] (4) Computing nodes communicate through high-speed networks, share data and computing results, and achieve collaborative computing;

[0020] (5) Summarize the processing results of each computing node to form the final data processing results.

[0021] Furthermore, the algorithm of the distributed computing framework adopts Hadoop and Spark. MapReduce is a programming model and computing framework, which divides the computing process into two stages: mapping (Map) and reduction (Reduce). In the Map stage, the input data is divided into multiple independent parts and processed in parallel by different nodes. In the Reduce stage, the processing results of each node are summarized and integrated. Spark supports large-scale data processing and analysis, and provides rich data operation functions and conversion operations. It uses RDD (Resilient Distributed Dataset) or DataFrame as the basic unit of data processing, and optimizes the execution plan of tasks through the DAG (Directed Acyclic Graph) computing model.

[0022] Furthermore, the cloud computing module implements the following steps:

[0023] (1) Based on the resource requirements of the distributed computing module, a virtualized computing resource pool is constructed using cloud computing technology;

[0024] (2) Dynamically adjust the scale and configuration of computing resources according to data processing requirements, such as increasing or decreasing the number of virtual machines and adjusting CPU and memory resources;

[0025] (3) Monitor the usage of computing resources to ensure effective resource utilization and load balancing.

[0026] Furthermore, the cloud computing module implements dynamic resource adjustment using an adaptive scheduling algorithm, where the key variables in the adaptive scheduling algorithm are node In time The load, :node The length of the task queue, :node The current resource allocation of The adjustment coefficient is a positive number used to control the magnitude of resource adjustments. The average load of all nodes, The average task queue length of all nodes, the resource adjustment formula of the adaptive scheduling algorithm is: Specifically, if and ,Description node The load and task queue length are higher than average, so the resource allocation needs to be increased by an amount equal to and The product of is proportional to the adjustment coefficient Control, if and ,Description node The load and task queue length are both below average, so it may be necessary to reduce the amount of resource allocation to avoid resource waste. If one of the load and task queue length is above average and the other is below average, the direction and magnitude of resource adjustment will depend on the relative difference between the two factors and the adjustment coefficient α. The cloud computing module uses a weighted polling scheduling algorithm to achieve load balancing. The key variables of the weighted polling scheduling algorithm are node The weight is a positive number, which indicates the processing capacity or performance index of the node. The total number of nodes, that is, the number of all available nodes in the system, node The probability of being selected, the node selection probability formula of the weighted polling scheduling algorithm is: , this formula represents the node Probability of being selected is its weight Divided by the sum of all node weights, the sum of weights is calculated by the weights of all nodes (in From 1 to This formula ensures that nodes with higher weights have a higher probability of being selected to perform tasks. This is because nodes with higher weights contribute more to the denominator, making them account for a larger proportion in the probability calculation.

[0027] Furthermore, the implementation steps of the data storage module are:

[0028] (1) Choose a suitable distributed storage system, such as HDFS or Ceph;

[0029] (2) Design the data structure and indexing method for data storage to support efficient data reading, writing and query;

[0030] (3) Store the processed telecommunications business data in the distributed storage system according to the designed data structure

[0031] (4) Provide data backup and recovery mechanism to ensure data security and reliability.

[0032] Furthermore, the system also includes a data display module: through a visual interface, the processing results and analysis reports of telecommunication business data are displayed, and users can understand the operating status of telecommunication business, find problems and optimize them through the data display module.

[0033] Furthermore, the implementation steps of the data display module are:

[0034] (1) Design a visual interface for data display, including charts and tables;

[0035] (2) Obtain corresponding data from the data storage module according to the user's request;

[0036] (3) Conduct necessary statistics and analysis on the data to form intuitive data display results;

[0037] (4) Display the data display results to the user through a visual interface.

[0038] Furthermore, the overall implementation steps of the system are:

[0039] a) Collect telecommunication service data in real time through the data collection module, such as call data, SMS data and network traffic data, and send the collected raw data to the distributed computing module for processing;

[0040] b) Use the distributed computing module to receive the raw data from the data collection module, use distributed computing technology to decompose the data into multiple subtasks, and assign them to multiple computing nodes for parallel processing. The computing nodes share data and perform collaborative computing through high-speed network communication. After the processing is completed, the results are sent to the data storage module for storage, or part of the results are sent to the data display module for visual display as needed;

[0041] c) The cloud computing module uses cloud computing technology to build a virtualized computing resource pool. According to the resource requirements of the distributed computing module, it dynamically adjusts the scale and configuration of computing resources, provides flexible computing resources for the distributed computing module, and ensures that it can efficiently process data of different scales and complexities.

[0042] d) The data storage module receives the processing results from the distributed computing module, adopts distributed storage technology, realizes efficient storage and management of telecommunications business data, provides data backup and recovery mechanism, ensures data security and reliability, and provides data query and reading services for the data display module as needed;

[0043] e) The data display module retrieves the corresponding data from the data storage module according to the user's request, visualizes the data, generates charts and reports to display the results, and displays the processing results and analysis reports to the user through a visual interface to help the user understand the operation status of the telecommunications business, identify problems and optimize them.

[0044] Compared with the prior art, the telecommunication service information processing system has the following beneficial effects:

[0045] 1. The present invention uses distributed computing technology to decompose data into multiple subtasks and assign them to multiple computing nodes for parallel processing. The computing nodes achieve data sharing and collaborative computing through high-speed network communication to ensure processing efficiency and accuracy.

[0046] 2. The present invention constructs a virtualized computing resource pool through cloud computing technology, and dynamically adjusts the scale and configuration of computing resources according to the resource requirements of distributed computing modules. The provision of such elastic computing resources ensures that the distributed computing modules can efficiently process data of different scales and complexities, thereby improving the flexibility and scalability of the system.

[0047] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 The present invention is a flow chart of a telecommunication business information processing system. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Example 1

[0052] A telecommunications service information processing system, characterized in that the system comprises the following components:

[0053] Data collection module: responsible for collecting telecommunications business data in real time, including call data, SMS data, and network traffic data, and sending the data to the distributed computing module for processing;

[0054] Distributed computing module: Distributed computing technology is used to decompose telecommunication service data into multiple subtasks, which are assigned to multiple computing nodes for parallel processing. The computing nodes communicate through high-speed networks to achieve data sharing and collaborative computing, thereby improving data processing speed and concurrent processing capabilities.

[0055] Cloud computing module: Using cloud computing technology, a virtualized computing resource pool is constructed to provide flexible computing resources for the distributed computing module. According to data processing requirements, the cloud computing module can dynamically adjust the scale and configuration of computing resources to meet the performance requirements in different scenarios.

[0056] Data storage module: Distributed storage technology is used to achieve efficient storage and management of telecommunications business data. The data storage module has high reliability, high scalability and high concurrency, and can support rapid reading and writing and real-time analysis of massive data.

[0057] The distributed computing module decomposes the telecommunications business data into multiple subtasks, each subtask corresponds to the processing of a part of the data, and these subtasks are then assigned to multiple computing nodes for parallel processing, parallel processing and collaborative computing. On multiple computing nodes, data is processed in parallel. These computing nodes communicate through high-speed networks, share data and computing results, and achieve collaborative computing. This parallel processing method can significantly improve the speed and efficiency of data processing. Map and Reduce stage processing: Map stage: The input data is divided into multiple independent parts and processed in parallel by different nodes. Each node executes the Map function, processes the input data, and outputs a set of key-value pairs. Reduce stage: The processing results of each node are summarized in the Reduce stage. The Reduce function merges and integrates these key-value pairs to form the final data processing result. The distributed computing module summarizes the processing results of each computing node to form the final data processing result.

[0058] Example 2

[0059] The system consists of the following components:

[0060] Data collection module: responsible for collecting telecommunications business data in real time, including call data, SMS data, and network traffic data, and sending the data to the distributed computing module for processing;

[0061] Distributed computing module: Distributed computing technology is used to decompose telecommunication service data into multiple subtasks, which are assigned to multiple computing nodes for parallel processing. The computing nodes communicate through high-speed networks to achieve data sharing and collaborative computing, thereby improving data processing speed and concurrent processing capabilities.

[0062] Cloud computing module: Using cloud computing technology, a virtualized computing resource pool is constructed to provide flexible computing resources for the distributed computing module. According to data processing requirements, the cloud computing module can dynamically adjust the scale and configuration of computing resources to meet the performance requirements in different scenarios.

[0063] Data storage module: Distributed storage technology is used to achieve efficient storage and management of telecommunications business data. The data storage module has high reliability, high scalability and high concurrency, and can support rapid reading and writing and real-time analysis of massive data.

[0064] The cloud computing module uses an adaptive scheduling algorithm to achieve dynamic resource adjustment. The key variables in the adaptive scheduling algorithm are node In time The load, :node The length of the task queue, :node The current resource allocation of The adjustment coefficient is a positive number used to control the magnitude of resource adjustments. The average load of all nodes, The average task queue length of all nodes, the resource adjustment formula of the adaptive scheduling algorithm is: Specifically, if and ,Description node The load and task queue length are higher than average, so the resource allocation needs to be increased by an amount equal to and The product of is proportional to the adjustment coefficient Control, if and ,Description node The load and task queue length are both below average, so it may be necessary to reduce the amount of resource allocation to avoid resource waste. If one of the load and task queue length is above average and the other is below average, the direction and magnitude of resource adjustment will depend on the relative difference between the two factors and the adjustment coefficient α. The cloud computing module uses a weighted polling scheduling algorithm to achieve load balancing. The key variables of the weighted polling scheduling algorithm are node The weight is a positive number, which indicates the processing capacity or performance index of the node. The total number of nodes, that is, the number of all available nodes in the system, node The probability of being selected, the node selection probability formula of the weighted polling scheduling algorithm is: , this formula represents the node Probability of being selected is its weight Divided by the sum of all node weights, the sum of weights is calculated by the weights of all nodes (in From 1 to This formula ensures that nodes with higher weights have a higher probability of being selected to perform tasks. This is because nodes with higher weights contribute more to the denominator, making them account for a larger proportion in the probability calculation.

[0065] The cloud computing module will dynamically adjust the scale and configuration of computing resources according to key variables such as the node load, task queue length, and current resource allocation. If the node load and task queue length are both higher than the average level, the cloud computing module will increase the resource allocation of the node to cope with higher processing requirements. If the node load and task queue length are both lower than the average level, the cloud computing module may reduce the resource allocation of the node to avoid resource waste. If one of the load and task queue length is higher than the average level and the other is lower than the average level, the cloud computing module will determine the direction and magnitude of resource adjustment based on the relative difference and adjustment coefficient of these two factors. By dynamically adjusting computing resources, the cloud computing module can ensure that the system can maintain high performance in different scenarios and effectively respond to various data processing requirements. The cloud computing module also uses a weighted polling scheduling algorithm to achieve load balancing. The algorithm allocates tasks according to the weight of the node to ensure that nodes with higher weights have a higher probability of being selected to perform tasks. When scheduling tasks, the cloud computing module selects nodes according to the weight and probability of the nodes to ensure that tasks can be evenly distributed to each node, thereby achieving load balancing.

[0066] Example 3

[0067] The working principle of the system based on Example 1 and Example 2 is as follows:

[0068] The working principle of the whole system is based on the synergy of distributed computing and cloud computing technologies. By collecting telecommunications business data in real time, using distributed computing modules to decompose and process the data in parallel, and relying on the cloud computing module to provide flexible computing resource support, the efficient processing, storage and display of telecommunications business data are finally realized. Specifically, the data collection module collects telecommunications business data in real time, such as calls, text messages and network traffic data, and sends these raw data to the distributed computing module. After receiving the data, the distributed computing module uses distributed computing technology to decompose the data into multiple subtasks and assigns them to multiple computing nodes for parallel processing. The computing nodes share data and perform collaborative computing through high-speed network communication to ensure the efficiency and accuracy of processing. After the processing is completed, the results will be sent to the data storage module for storage, or sent to the data display module according to the demand. The cloud computing module plays a key role in the whole process. It uses cloud computing technology to build a virtualized computing resource pool, and dynamically adjusts the scale and configuration of computing resources according to the resource requirements of the distributed computing module. The provision of such elastic computing resources ensures that the distributed computing module can efficiently process data of different scales and complexities, and improves the flexibility and scalability of the system. The data storage module adopts distributed storage technology to efficiently store and manage telecommunications business data. It provides data backup and recovery mechanisms to ensure data security and reliability. When the data display module needs data, the data storage module will provide corresponding data query and reading services.

[0069] Finally, the data display module retrieves data from the data storage module according to user requests, performs visualization processing, and displays the processing results and analysis reports in the form of charts, reports, etc. Through the visual interface, users can intuitively understand the operating status of telecommunications services, identify problems and optimize them. The core of the working principle of the entire system lies in the combination of distributed computing and cloud computing technologies. Through efficient data processing, storage and display, it provides users with comprehensive telecommunications business data analysis and optimization support.

[0070] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A telecommunications service information processing system, characterized in that: The system consists of the following components: Data collection module: collects telecommunication business data in real time, including call data, SMS data, and network traffic data, and sends the data to the distributed computing module for processing; Distributed computing module: Distributed computing is used to decompose telecommunication service data into multiple subtasks, which are assigned to multiple computing nodes for parallel processing. The computing nodes communicate with each other through the network to achieve data sharing and collaborative computing. Cloud computing module: Using cloud computing technology, a virtualized computing resource pool is built to provide flexible computing resources for the distributed computing module. According to data processing requirements, the cloud computing module can dynamically adjust the scale and configuration of computing resources to meet the performance requirements in different scenarios; Data storage module: uses distributed storage technology to achieve storage and management of telecommunications business data; The implementation steps of the cloud computing module are: (1) Based on the resource requirements of the distributed computing module, a virtualized computing resource pool is constructed using cloud computing technology; (2) Dynamically adjust the scale and configuration of computing resources according to data processing requirements, increase or decrease the number of virtual machines, and adjust CPU and memory resources; (3) Monitor the usage of computing resources to ensure effective resource utilization and load balancing; The cloud computing module implements dynamic resource adjustment by using an adaptive scheduling algorithm, and the resource adjustment formula of the adaptive scheduling algorithm is: , For Node In time The load, For Node The length of the task queue, For Node The current resource allocation of is the adjustment coefficient, which is a positive number. is the average load of all nodes, is the average task queue length of all nodes; if and , increase the amount of resource allocation, the increase is and The product of is proportional to control; if and , reduce the amount of resource allocation. If one of the load and task queue length is higher than the average level and the other is lower than the average level, the direction and magnitude of resource adjustment will depend on the relative difference between the two factors and the adjustment coefficient α; the cloud computing module uses a weighted polling scheduling algorithm to achieve load balancing. The node selection probability formula of the weighted polling scheduling algorithm is: ,in For Node The weight of is the total number of nodes, For Node The probability of being selected, is the sum of weights, which is obtained by summing the weights of all nodes, where From 1 to .

2. The telecommunication service information processing system according to claim 1, characterized in that: The data collection module implements the following steps: (1) Define the types of data that need to be collected based on business needs, including call data, SMS data, and network traffic data; (2) Capture data through API interfaces and collect data from telecommunications business systems in real time; (3) Clean and format the collected data to remove invalid and duplicate data; (4) Send the cleaned data to the distributed computing module for processing.

3. The telecommunication service information processing system according to claim 2, characterized in that: The distributed computing module implements the following steps: (1) Select a distributed computing framework based on the characteristics and processing requirements of the data; (2) Decompose telecommunication business data into multiple subtasks, each of which corresponds to the processing of a portion of the data; (3) Assign subtasks to multiple computing nodes for parallel processing; (4) Computing nodes communicate over the network, share data and computing results, and achieve collaborative computing; (5) Summarize the processing results of each computing node to form the data processing results.

4. The telecommunication service information processing system according to claim 1, characterized in that: The implementation steps of the data storage module are: (1) Select a distributed storage system; (2) Set the data structure and indexing method for data storage to support efficient data reading, writing and query; (3) storing the processed telecommunications service data in a distributed storage system according to the set data structure; (4) Data backup and recovery to ensure data security and reliability.

5. The telecommunication service information processing system according to claim 1, characterized in that: The system also includes a data display module: a processing result and an analysis report of the telecommunication business data are displayed through a visual interface.

6. The telecommunication service information processing system according to claim 5, characterized in that , the implementation steps of the data display module: (1) Design a visual interface for data display, including charts and tables; (2) Obtain corresponding data from the data storage module according to the user's request; (3) Conduct statistics and analysis on the data to form data display results; (4) Display the data display results to the user through a visual interface.

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