Intelligent communication data management system and method based on 5G communication

Abstract

The invention discloses an intelligent communication data management system and method based on 5G communication, and relates to the technical field of data management. The system comprises a data acquisition and analysis module, an abnormal event identification module, a load index trend analysis module, a real-time monitoring and early warning module and a path adjustment and optimization module. The data acquisition and analysis module collects communication and log data and constructs an abnormal event database; the abnormal event identification module extracts an edge calculation node path of an abnormal event and calculates a radiation area and a load index of the edge calculation node path; the load index trend analysis module analyzes the load index trend curve, calculates a difference index, and matches an adjustment scheme according to the difference index; the real-time monitoring and early warning module is used for collecting real-time data, calculating an abnormal evaluation index and outputting early warning information; and the path adjustment and optimization module calculates a difference vector according to the matched adjustment scheme, performs similarity calculation with the difference vector of the current path, and finally outputs the adjustment scheme.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and specifically to an intelligent communication data management system and method based on 5G communication. Background Art

[0002] With the rapid development of information technology and the increasing demand for data communications, traditional data management systems are gradually exposing their shortcomings in handling large amounts of communication data. These shortcomings include slow processing speeds, high latency, and uneven resource allocation, which seriously impact the efficiency of communication data and the intelligence level of the system. Especially in the 5G era, the high speed and low latency of communication networks make traditional data management methods even more unsuitable, requiring new solutions to fully leverage the advantages of 5G technology.

[0003] 5G technology, with its significantly increased bandwidth and extremely low latency, has become a key driver of modern communications networks. It not only supports higher data rates but also offers greater network reliability and lower latency, meeting the demands of large-scale data transmission and real-time processing. However, effectively managing and utilizing this high-speed, high-volume data presents new challenges for communications network systems.

[0004] Existing communication data management systems mostly use static resource allocation strategies and traditional data processing methods, making them unable to meet the real-time, flexibility, and intelligence requirements of the 5G network environment. Traditional systems often lack the ability to monitor and dynamically adjust data traffic in real time, as well as effective intelligent analysis and automated scheduling mechanisms. This results in a system's inability to respond quickly to changes in network load, inefficient resource utilization, and insufficient resilience to fluctuations in data traffic. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent communication data management system and method based on 5G communication to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A method for intelligent communication data management based on 5G communication, the method comprising the following steps:

[0008] Step S100: Deploy edge computing nodes at the edge of the network, configure the edge computing nodes to connect to the 5G network, and collect communication data from the edge computing nodes; analyze the collected communication data in combination with log data to identify corresponding abnormal events, and build an abnormal event database based on the identification results;

[0009] Step S200. Obtain the edge computing node paths corresponding to all abnormal events based on the abnormal event database, and obtain the radiation area of the abnormal event based on the edge computing node paths; based on the radiation area of the abnormal event, evaluate the load index of the edge computing nodes in the radiation area of the edge computing node paths corresponding to the abnormal event;

[0010] Step S300: Analyze the load index trend curve of the edge computing node corresponding to the abnormal event, and combine it with the log data to obtain the load index trend curve of the edge computing node corresponding to the normal event; analyze the difference index between the load index trend curves corresponding to the abnormal event and the normal event, and match the difference index with the edge computing node path adjustment plan corresponding to the abnormal event;

[0011] Step S400. Collect real-time communication data corresponding to the edge computing node and obtain the corresponding current edge computing node path; match the abnormal events in the abnormal event database with the real-time communication data and the current edge computing node path, and calculate the difference index of the current edge computing node path, and make corresponding adjustments to the current edge computing node path based on the difference index.

[0012] Furthermore, step S100 includes:

[0013] S101. The communication data includes traffic data, user behavior data, and device status data; the log data refers to a detailed record of the edge computing node's operating status, event information, and system operations; the communication data of the edge computing nodes within a selected time period is obtained, and the communication data of each edge computing node is discretely analyzed, so that each timestamp should be a communication data segment;

[0014] S102. Obtain log data within the selected time period, and according to the time when the log data is recorded, correspond the communication data of each edge computing node with the corresponding log data, and according to the corresponding timestamp, correspond the communication data segment of the communication data of each edge computing node with the corresponding log data segment, and treat the corresponding communication data segment and log data segment as a data unit;

[0015] S103. Compare and analyze all data units of each edge computing node with the corresponding standard data units to obtain the corresponding abnormality assessment index Y, and the specific calculation formula is:

[0016] Y=(1 / n)Σ i∈[1,n] di / si,

[0017] Among them, n represents the number of data units corresponding to the edge computing node, di represents the similarity between the i-th data unit and the corresponding standard data unit feature, and si represents the standard feature value of the i-th standard data unit; the anomaly evaluation index Y is compared with the preset threshold Y0, and the edge computing nodes in the data units with anomaly evaluation index Y greater than or equal to the preset threshold Y0 are regarded as abnormal nodes, and the corresponding abnormal events are identified according to the corresponding log data, thereby constructing an abnormal event database.

[0018] Furthermore, step S200 includes:

[0019] S201. According to the abnormal event database, for the abnormal events in the abnormal event database, the corresponding abnormal event records are extracted, and the edge computing node path corresponding to the abnormal event is obtained based on the edge computing node identifier associated with the corresponding abnormal event in the abnormal event record; for each abnormal event, the abnormal node Pj and the corresponding abnormal evaluation index Yj are extracted from the corresponding edge computing node path, and the corresponding influence range radius Rj is calculated based on the abnormal evaluation index of the abnormal node Pj, and Rj = k × Yj, where k represents the influence range coefficient; Yj represents the abnormal evaluation index corresponding to the j-th abnormal node;

[0020] S202. For each abnormal node Pj and its corresponding impact radius Rj, a circular region is generated. This circular region is denoted as C(Pj, Rj), which represents the circular region with Pj as the center and Rj as the radius. For all circular regions in the same edge computing node path, their union area is calculated to obtain the radiation area of the abnormal event.

[0021] S203. For each edge computing node Nt in the radiation area, calculate its coverage CDt in the radiation area. The specific calculation formula is: CDt = S(Nt, C) / C(Nt, Rt), where S(Nt, C) represents the intersection area of the influence range of the edge computing node Nt and the corresponding radiation area, and C(Nt, Rt) represents the influence range area of the edge computing node Nt. The calculation process of C(Nt, Rt) is consistent with the calculation process of C(Pj, Rj). For the edge computing node Nt in the radiation area, calculate the corresponding load index FLIt. The specific calculation formula is:

[0022] FLIt=LIt×(α×CDt+β×Y),

[0023] Where LIt represents the initial load index of the edge computing node, and α and β represent weight factors.

[0024] Furthermore, step S300 includes:

[0025] S301. Extract the edge computing nodes in the radiation area corresponding to the abnormal event and obtain the load index trend curve A of the corresponding edge computing nodes in the selected time period; based on the log data, obtain the initial load index of the same edge computing node path in different time periods, thereby forming the load index trend curve B of the edge computing node for normal events;

[0026] S302. Calculate the difference indicators between the load index trend curves of abnormal events and normal events, where the difference indicators include the mean square error (MSE), the mean absolute error (MAE), and the correlation coefficient (ρ); construct a corresponding difference vector (V) based on the above difference indicators, and V = [MSE, MAE, ρ], and match the difference vector (V) with the edge computing node path adjustment plan of the corresponding abnormal event, and save it in the abnormal event database.

[0027] Furthermore, step S400 includes:

[0028] S401. Every selected time period, collect the real-time communication data corresponding to the edge computing node, analyze the real-time communication data, and thus obtain the current edge computing node path; based on the current edge computing node path, calculate the abnormality evaluation index Y1 of each edge computing node in the current edge computing node path, and compare the abnormality evaluation index Y1 with the preset threshold value Y0. If the abnormality evaluation index Y1 of the edge computing nodes in the current edge computing node path is less than the preset threshold value Y0, no warning information is output;

[0029] S402. If the abnormality evaluation index Y1 of an edge computing node in the current edge computing node path is greater than or equal to the preset threshold Y0, a warning message is output; and based on the current edge computing node path and the corresponding abnormal node, a search is performed in the abnormal event database to match the corresponding abnormal event; based on the abnormal node in the current edge computing node path, the corresponding load index is calculated and the corresponding load index trend curve A1 is obtained. Based on the load index trend curve A1, the corresponding difference index is calculated to form a difference vector V1, where V1 = [MSE1, MAE1, ρ1];

[0030] S403. Obtain the difference vector V of the matched abnormal event, calculate the similarity between the difference vector V and the difference vector V1, take the one with the greatest similarity as the final matching event, and output the edge computing node path adjustment plan corresponding to the matching event as the reference adjustment plan. The system makes corresponding adjustments to the current edge computing node path based on the reference adjustment plan.

[0031] An intelligent communication data management system based on 5G communication, the system includes: a data acquisition and analysis module, an abnormal event identification module, a load index trend analysis module, a real-time monitoring and early warning module, and a path adjustment and optimization module;

[0032] The data collection and analysis module deploys computing nodes at the edge of the network, collects communication data and log data, and performs discrete analysis to identify abnormal events and build an abnormal event database; the abnormal event identification module extracts the edge computing node path of abnormal events based on the abnormal event database, and calculates the radiation area and load index of abnormal events; the load index trend analysis module analyzes the load index trend curves of abnormal events and normal events, calculates the difference index, and matches the adjustment plan of the edge computing node path based on the difference index; the real-time monitoring and early warning module collects real-time communication data, calculates the anomaly evaluation index, compares it with the preset threshold, and outputs early warning information, as well as matches abnormal events in the abnormal event database through real-time data; the path adjustment and optimization module calculates the difference vector based on the adjustment plan of the matched abnormal event, performs similarity calculation with the difference vector of the current edge computing node path, and finally outputs the adjustment plan to optimize the edge computing node path.

[0033] Furthermore, the data acquisition and analysis module includes a data acquisition unit and a data analysis unit;

[0034] The data collection unit is responsible for collecting communication data from edge computing nodes. The communication data includes traffic data, user behavior data and device status data, and collecting operation logs from edge computing nodes, including node operation status, event occurrence information and system operation records; the data analysis unit divides the collected communication data into multiple data segments according to timestamps, and pairs them with corresponding log data to form data units. It compares all data units of each edge computing node with standard data units, calculates the anomaly assessment index, and compares the anomaly assessment index with the preset threshold to identify abnormal nodes and build an abnormal event database.

[0035] Furthermore, the abnormal event identification module includes an abnormal event record extraction unit, an abnormal node impact range calculation unit, and a node load calculation unit within the radiation area;

[0036] The abnormal event record extraction unit extracts abnormal event records from the abnormal event database and extracts the associated edge computing node identifier to obtain the edge computing node path corresponding to the abnormal event; the abnormal node impact range calculation unit calculates the impact range radius according to the abnormal evaluation index of the abnormal node, generates a circular area to represent the impact range, and calculates the union of all circular areas to obtain the radiation area of the abnormal event; the node load calculation unit in the radiation area calculates the coverage and load index of each edge computing node in the radiation area, and updates the abnormal event database based on these calculation results.

[0037] Furthermore, the load index trend analysis module includes a load index trend curve extraction unit and a load index difference analysis unit;

[0038] The load index trend curve extraction unit extracts the load index trend curves of the edge computing nodes corresponding to abnormal events and normal events, and obtains the load index trend curves of abnormal events and normal events respectively; the load index difference analysis unit calculates the difference indicators between the load index trend curves of abnormal events and normal events, including the mean square error, mean absolute error and correlation coefficient, generates a difference vector and matches it with the adjustment plan of the abnormal event.

[0039] Furthermore, the real-time monitoring and warning module includes a real-time data acquisition unit, a real-time anomaly assessment unit, and a real-time warning and matching unit;

[0040] The real-time data acquisition unit regularly collects real-time communication data of edge computing nodes and obtains the current edge computing node path; the real-time anomaly assessment unit calculates the anomaly assessment index based on the current edge computing node path, compares it with the preset threshold, and determines whether to output warning information based on the comparison result; when the real-time warning and matching unit detects that the anomaly assessment index is greater than or equal to the preset threshold, it outputs a warning message, matches the abnormal event based on the current path and the records in the abnormal event database, calculates the corresponding load index trend curve and difference index, and generates the corresponding difference vector;

[0041] The path adjustment and optimization module includes an adjustment scheme matching unit and a path optimization execution unit;

[0042] The adjustment scheme matching unit obtains the difference vector of the abnormal event, calculates the similarity with the difference vector obtained by real-time monitoring, and selects the one with the highest similarity as the final matching event; the path optimization execution unit outputs the edge computing node path adjustment scheme corresponding to the matching event as a reference adjustment scheme, and the system makes corresponding adjustments to the current edge computing node path based on the reference adjustment scheme.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention can dynamically monitor the network status by deploying edge computing nodes at the edge of the network and collecting communication data and log data in real time. This real-time data collection and analysis mechanism significantly improves the response speed to changes in network load, enabling the system to promptly identify and handle abnormal events, thereby improving the real-time and agility of the network. The present invention introduces an abnormal event database and an intelligent analysis mechanism, which can intelligently identify and predict potential problems by analyzing information such as load index trends and difference indicators of abnormal events. This intelligent analysis can not only effectively improve the detection accuracy of abnormal events, but also provide targeted adjustment solutions to optimize the allocation and use of network resources. Unlike traditional static resource allocation strategies, the present invention is based on a dynamic adjustment mechanism of real-time communication data and load index, which can automatically adjust resource allocation according to the current network status. This dynamic adjustment capability improves the flexibility of the network and ensures that the system can respond quickly when load fluctuations or abnormal events occur, thereby improving the stability and reliability of the overall network. By calculating the load index and coverage, the present invention can more accurately assess the load of edge computing nodes. Combining the impact range and radiation area of abnormal events, it can comprehensively analyze the load of edge computing nodes and optimize resource allocation. This optimization method can effectively avoid resource waste and improve the efficiency of network resource utilization. By continuously monitoring and analyzing real-time communication data, the present invention can provide accurate early warnings at the early stages of abnormal events, preventing potential problems from escalating. At the same time, based on a matching mechanism between historical data and real-time data, it can provide the most suitable adjustment plan for the current edge computing node path, thereby effectively improving network performance and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0046] Figure 1 This is a schematic diagram of a module of an intelligent communication data management system based on 5G communication in the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] See also Figure 1 , the present invention provides a technical solution:

[0049] An intelligent communication data management system based on 5G communication, the system includes: a data acquisition and analysis module, an abnormal event identification module, a load index trend analysis module, a real-time monitoring and early warning module, and a path adjustment and optimization module;

[0050] The data collection and analysis module deploys computing nodes at the edge of the network, collects communication data and log data, and performs discrete analysis to identify abnormal events and build an abnormal event database; the abnormal event identification module extracts the edge computing node path of abnormal events based on the abnormal event database, and calculates the radiation area and load index of abnormal events; the load index trend analysis module analyzes the load index trend curves of abnormal events and normal events, calculates the difference index, and matches the adjustment plan of the edge computing node path based on the difference index; the real-time monitoring and early warning module collects real-time communication data, calculates the anomaly evaluation index, compares it with the preset threshold, and outputs early warning information, as well as matches abnormal events in the abnormal event database through real-time data; the path adjustment and optimization module calculates the difference vector based on the adjustment plan of the matched abnormal event, performs similarity calculation with the difference vector of the current edge computing node path, and finally outputs the adjustment plan to optimize the edge computing node path.

[0051] The data acquisition and analysis module includes a data acquisition unit and a data analysis unit;

[0052] The data collection unit is responsible for collecting communication data from edge computing nodes. The communication data includes traffic data, user behavior data and device status data, and collecting operation logs from edge computing nodes, including node operation status, event occurrence information and system operation records; the data analysis unit divides the collected communication data into multiple data segments according to timestamps, and pairs them with corresponding log data to form data units. It compares all data units of each edge computing node with standard data units, calculates the anomaly assessment index, and compares the anomaly assessment index with the preset threshold to identify abnormal nodes and build an abnormal event database.

[0053] The abnormal event identification module includes an abnormal event record extraction unit, an abnormal node impact range calculation unit, and a node load calculation unit within the radiation area;

[0054] The abnormal event record extraction unit extracts abnormal event records from the abnormal event database and extracts the associated edge computing node identifier to obtain the edge computing node path corresponding to the abnormal event; the abnormal node impact range calculation unit calculates the impact range radius according to the abnormal evaluation index of the abnormal node, generates a circular area to represent the impact range, and calculates the union of all circular areas to obtain the radiation area of the abnormal event; the node load calculation unit in the radiation area calculates the coverage and load index of each edge computing node in the radiation area, and updates the abnormal event database based on these calculation results.

[0055] The load index trend analysis module includes a load index trend curve extraction unit and a load index difference analysis unit;

[0056] The load index trend curve extraction unit extracts the load index trend curves of the edge computing nodes corresponding to abnormal events and normal events, and obtains the load index trend curves of abnormal events and normal events respectively; the load index difference analysis unit calculates the difference indicators between the load index trend curves of abnormal events and normal events, including the mean square error, mean absolute error and correlation coefficient, generates a difference vector and matches it with the adjustment plan of the abnormal event.

[0057] The real-time monitoring and early warning module includes a real-time data acquisition unit, a real-time anomaly assessment unit, and a real-time early warning and matching unit;

[0058] The real-time data acquisition unit regularly collects real-time communication data of edge computing nodes and obtains the current edge computing node path; the real-time anomaly assessment unit calculates the anomaly assessment index based on the current edge computing node path, compares it with the preset threshold, and determines whether to output warning information based on the comparison result; when the real-time warning and matching unit detects that the anomaly assessment index is greater than or equal to the preset threshold, it outputs a warning message, matches the abnormal event based on the current path and the records in the abnormal event database, calculates the corresponding load index trend curve and difference index, and generates the corresponding difference vector;

[0059] The adjustment scheme matching unit obtains the difference vector of the abnormal event, calculates the similarity with the difference vector obtained by real-time monitoring, and selects the one with the highest similarity as the final matching event; the path optimization execution unit outputs the edge computing node path adjustment scheme corresponding to the matching event as a reference adjustment scheme, and the system makes corresponding adjustments to the current edge computing node path based on the reference adjustment scheme.

[0060] A method for intelligent communication data management based on 5G communication, the method comprising the following steps:

[0061] Step S100: Deploy edge computing nodes at the edge of the network, configure the edge computing nodes to connect to the 5G network, and collect communication data from the edge computing nodes; analyze the collected communication data in combination with log data to identify corresponding abnormal events, and build an abnormal event database based on the identification results;

[0062] Step S200. Obtain the edge computing node paths corresponding to all abnormal events based on the abnormal event database, and obtain the radiation area of the abnormal event based on the edge computing node paths; based on the radiation area of the abnormal event, evaluate the load index of the edge computing nodes in the radiation area of the edge computing node paths corresponding to the abnormal event;

[0063] Step S300: Analyze the load index trend curve of the edge computing node corresponding to the abnormal event, and combine it with the log data to obtain the load index trend curve of the edge computing node corresponding to the normal event; analyze the difference index between the load index trend curves corresponding to the abnormal event and the normal event, and match the difference index with the edge computing node path adjustment plan corresponding to the abnormal event;

[0064] Step S400. Collect real-time communication data corresponding to the edge computing node and obtain the corresponding current edge computing node path; match the abnormal events in the abnormal event database with the real-time communication data and the current edge computing node path, and calculate the difference index of the current edge computing node path, and make corresponding adjustments to the current edge computing node path based on the difference index.

[0065] Step S100 includes:

[0066] S101. The communication data includes traffic data, user behavior data, and device status data; the log data refers to a detailed record of the edge computing node's operating status, event information, and system operations; the communication data of the edge computing nodes within a selected time period is obtained, and the communication data of each edge computing node is discretely analyzed, so that each timestamp should be a communication data segment;

[0067] S102. Obtain log data within the selected time period, and according to the time when the log data is recorded, correspond the communication data of each edge computing node with the corresponding log data, and according to the corresponding timestamp, correspond the communication data segment of the communication data of each edge computing node with the corresponding log data segment, and treat the corresponding communication data segment and log data segment as a data unit;

[0068] S103. Compare and analyze all data units of each edge computing node with the corresponding standard data units to obtain the corresponding abnormality assessment index Y, and the specific calculation formula is:

[0069] Y=(1 / n)Σ i∈[1,n] di / si,

[0070] Among them, n represents the number of data units corresponding to the edge computing node, di represents the similarity between the i-th data unit and the corresponding standard data unit feature, and si represents the standard feature value of the i-th standard data unit; the anomaly evaluation index Y is compared with the preset threshold Y0, and the edge computing nodes in the data units with anomaly evaluation index Y greater than or equal to the preset threshold Y0 are regarded as abnormal nodes, and the corresponding abnormal events are identified according to the corresponding log data, thereby constructing an abnormal event database.

[0071] Step S200 includes:

[0072] S201. According to the abnormal event database, for the abnormal events in the abnormal event database, the corresponding abnormal event records are extracted, and the edge computing node path corresponding to the abnormal event is obtained based on the edge computing node identifier associated with the corresponding abnormal event in the abnormal event record; for each abnormal event, the abnormal node Pj and the corresponding abnormal evaluation index Yj are extracted from the corresponding edge computing node path, and the corresponding influence range radius Rj is calculated based on the abnormal evaluation index of the abnormal node Pj, and Rj = k × Yj, where k represents the influence range coefficient; Yj represents the abnormal evaluation index corresponding to the j-th abnormal node;

[0073] S202. For each abnormal node Pj and its corresponding impact radius Rj, a circular region is generated. This circular region is denoted as C(Pj, Rj), which represents the circular region with Pj as the center and Rj as the radius. For all circular regions in the same edge computing node path, their union area is calculated to obtain the radiation area of the abnormal event.

[0074] S203. For each edge computing node Nt in the radiation area, calculate its coverage CDt in the radiation area. The specific calculation formula is: CDt = S(Nt, C) / C(Nt, Rt), where S(Nt, C) represents the intersection area of the influence range of the edge computing node Nt and the corresponding radiation area, and C(Nt, Rt) represents the influence range area of the edge computing node Nt. The calculation process of C(Nt, Rt) is consistent with the calculation process of C(Pj, Rj). For the edge computing node Nt in the radiation area, calculate the corresponding load index FLIt. The specific calculation formula is:

[0075] FLIt=LIt×(α×CDt+β×Y),

[0076] Where LIt represents the initial load index of the edge computing node, and α and β represent weight factors.

[0077] In this embodiment, it is assumed that there are two abnormal nodes in a certain edge computing node path, and the corresponding abnormal node identifiers and their abnormality evaluation indices are:

[0078] Node P1: Y = 1.5, Node P2: Y = 2.0;

[0079] Assume k = 4, including nodes Nt 1, Nt 2, and Nt 3, whose initial load index (LIt) and influence range data are known;

[0080] Calculate the impact radius Rj (assuming the unit is km):

[0081] Abnormal evaluation index of node P1 (Y = 1.5), R_1 = 4 × 1.5 = 6 (km);

[0082] Abnormal evaluation index of node P2 (Y2 = 2.0, R2 = 4 × 2.0 = 8 (km);

[0083] Generate a circular region:

[0084] The circular area C(P1, R1) of node P1 and the circular area C(P2, R2) of node P2 are calculated; the union area of C(P1, R1) and C(P2, R2) is calculated, and the coverage and load index are calculated according to the formula.

[0085] Step S300 includes:

[0086] S301. Extract the edge computing nodes in the radiation area corresponding to the abnormal event and obtain the load index trend curve A of the corresponding edge computing nodes in the selected time period; based on the log data, obtain the initial load index of the same edge computing node path in different time periods, thereby forming the load index trend curve B of the edge computing node for normal events;

[0087] S302. Calculate the difference indicators between the load index trend curves of abnormal events and normal events, where the difference indicators include the mean square error (MSE), the mean absolute error (MAE), and the correlation coefficient (ρ); construct a corresponding difference vector (V) based on the above difference indicators, and V = [MSE, MAE, ρ], and match the difference vector (V) with the edge computing node path adjustment plan of the corresponding abnormal event, and save it in the abnormal event database.

[0088] Step S400 includes:

[0089] S401. Every selected time period, collect the real-time communication data corresponding to the edge computing node, analyze the real-time communication data, and thus obtain the current edge computing node path; based on the current edge computing node path, calculate the abnormality evaluation index Y1 of each edge computing node in the current edge computing node path, and compare the abnormality evaluation index Y1 with the preset threshold value Y0. If the abnormality evaluation index Y1 of the edge computing nodes in the current edge computing node path is less than the preset threshold value Y0, no warning information is output;

[0090] S402. If the abnormality evaluation index Y1 of an edge computing node in the current edge computing node path is greater than or equal to the preset threshold Y0, a warning message is output; and based on the current edge computing node path and the corresponding abnormal node, a search is performed in the abnormal event database to match the corresponding abnormal event; based on the abnormal node in the current edge computing node path, the corresponding load index is calculated and the corresponding load index trend curve A1 is obtained. Based on the load index trend curve A1, the corresponding difference index is calculated to form a difference vector V1, where V1 = [MSE1, MAE1, ρ1];

[0091] S403. Obtain the difference vector V of the matched abnormal event, calculate the similarity between the difference vector V and the difference vector V1, take the one with the greatest similarity as the final matching event, and output the edge computing node path adjustment plan corresponding to the matching event as the reference adjustment plan. The system makes corresponding adjustments to the current edge computing node path based on the reference adjustment plan.

[0092] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0093] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An intelligent communication data management method based on 5G communication, characterized by: The method comprises the following steps: Step S100: Deploy edge computing nodes at the edge of the network, configure the edge computing nodes to connect to the 5G network, and collect communication data from the edge computing nodes; analyze the collected communication data in combination with log data to identify corresponding abnormal events, and build an abnormal event database based on the identification results; Step S200. Obtain the edge computing node paths corresponding to all abnormal events based on the abnormal event database, and obtain the radiation area of the abnormal event based on the edge computing node paths; based on the radiation area of the abnormal event, evaluate the load index of the edge computing nodes in the radiation area of the edge computing node paths corresponding to the abnormal event; Step S300: Analyze the load index trend curve of the edge computing node corresponding to the abnormal event, and combine it with the log data to obtain the load index trend curve of the edge computing node corresponding to the normal event; analyze the difference index between the load index trend curves corresponding to the abnormal event and the normal event, and match the difference index with the edge computing node path adjustment plan corresponding to the abnormal event; Step S400. Collect real-time communication data corresponding to the edge computing node and obtain the corresponding current edge computing node path; match the abnormal events in the abnormal event database with the real-time communication data and the current edge computing node path, and calculate the difference index of the current edge computing node path, and make corresponding adjustments to the current edge computing node path based on the difference index.

2. The intelligent communication data management method based on 5G communication according to claim 1, characterized in that: The step S100 includes: S101. The communication data includes traffic data, user behavior data, and device status data; the log data refers to a detailed record of the edge computing node's operating status, event information, and system operations; the communication data of the edge computing nodes within a selected time period is obtained, and the communication data of each edge computing node is discretely analyzed, so that each timestamp should be a communication data segment; S102. Obtain log data within the selected time period, and according to the time when the log data is recorded, correspond the communication data of each edge computing node with the corresponding log data, and according to the corresponding timestamp, correspond the communication data segment of the communication data of each edge computing node with the corresponding log data segment, and treat the corresponding communication data segment and log data segment as a data unit; S103. Compare and analyze all data units of each edge computing node with the corresponding standard data units to obtain the corresponding abnormality assessment index Y, and the specific calculation formula is: Y=(1 / n)Σ i∈[1,n] di / si, Among them, n represents the number of data units corresponding to the edge computing node, di represents the similarity between the i-th data unit and the corresponding standard data unit feature, and si represents the standard feature value of the i-th standard data unit; the anomaly evaluation index Y is compared with the preset threshold Y0, and the edge computing nodes in the data units with anomaly evaluation index Y greater than or equal to the preset threshold Y0 are regarded as abnormal nodes, and the corresponding abnormal events are identified according to the corresponding log data, thereby constructing an abnormal event database.

3. The intelligent communication data management method based on 5G communication according to claim 2, characterized in that: The step S200 includes: S201. According to the abnormal event database, for the abnormal events in the abnormal event database, the corresponding abnormal event records are extracted, and the edge computing node path corresponding to the abnormal event is obtained based on the edge computing node identifier associated with the corresponding abnormal event in the abnormal event record; for each abnormal event, the abnormal node Pj and the corresponding abnormal evaluation index Yj are extracted from the corresponding edge computing node path, and the corresponding influence range radius Rj is calculated based on the abnormal evaluation index of the abnormal node Pj, and Rj = k × Yj, where k represents the influence range coefficient; Yj represents the abnormal evaluation index corresponding to the j-th abnormal node; S202. For each abnormal node Pj and its corresponding impact radius Rj, a circular region is generated. This circular region is denoted as C(Pj, Rj), which represents the circular region with Pj as the center and Rj as the radius. For all circular regions in the same edge computing node path, their union area is calculated to obtain the radiation area of the abnormal event. S203. For each edge computing node Nt in the radiation area, calculate its coverage CDt in the radiation area. The specific calculation formula is: CDt = S(Nt, C) / C(Nt, Rt), where S(Nt, C) represents the intersection area of the influence range of the edge computing node Nt and the corresponding radiation area, and C(Nt, Rt) represents the influence range area of the edge computing node Nt. For the edge computing node Nt in the radiation area, calculate the corresponding load index FLIt. The specific calculation formula is: FLIt=LIt×(α×CDt+β×Y), Where LIt represents the initial load index of the edge computing node, and α and β represent weight factors.

4. The intelligent communication data management method based on 5G communication according to claim 3, characterized in that: The step S300 includes: S301. Extract the edge computing nodes in the radiation area corresponding to the abnormal event and obtain the load index trend curve A of the corresponding edge computing nodes in the selected time period; based on the log data, obtain the initial load index of the same edge computing node path in different time periods, thereby forming the load index trend curve B of the edge computing node for normal events; S302. Calculate the difference indicators between the load index trend curves of abnormal events and normal events, where the difference indicators include the mean square error (MSE), the mean absolute error (MAE), and the correlation coefficient (ρ); construct a corresponding difference vector (V) based on the above difference indicators, and V = [MSE, MAE, ρ], and match the difference vector (V) with the edge computing node path adjustment plan of the corresponding abnormal event, and save it in the abnormal event database.

5. The intelligent communication data management method based on 5G communication according to claim 4, characterized in that: The step S400 includes: S401. Every selected time period, collect the real-time communication data corresponding to the edge computing node, analyze the real-time communication data, and thus obtain the current edge computing node path; based on the current edge computing node path, calculate the abnormality evaluation index Y1 of each edge computing node in the current edge computing node path, and compare the abnormality evaluation index Y1 with the preset threshold value Y0. If the abnormality evaluation index Y1 of the edge computing nodes in the current edge computing node path is less than the preset threshold value Y0, no warning information is output; S402. If the abnormality evaluation index Y1 of an edge computing node in the current edge computing node path is greater than or equal to the preset threshold Y0, a warning message is output; and based on the current edge computing node path and the corresponding abnormal node, a search is performed in the abnormal event database to match the corresponding abnormal event; based on the abnormal node in the current edge computing node path, the corresponding load index is calculated and the corresponding load index trend curve A1 is obtained. Based on the load index trend curve A1, the corresponding difference index is calculated to form a difference vector V1, where V1 = [MSE1, MAE1, ρ1]; S403. Obtain the difference vector V of the matched abnormal event, calculate the similarity between the difference vector V and the difference vector V1, take the one with the greatest similarity as the final matching event, and output the edge computing node path adjustment plan corresponding to the matching event as the reference adjustment plan. The system makes corresponding adjustments to the current edge computing node path based on the reference adjustment plan.

6. An intelligent communication data management system based on 5G communication, applied to the intelligent communication data management method based on 5G communication according to any one of claims 1 to 5, characterized in that: The system includes: a data acquisition and analysis module, an abnormal event identification module, a load index trend analysis module, a real-time monitoring and early warning module, and a path adjustment and optimization module; The data acquisition and analysis module deploys computing nodes at the edge of the network, collects communication data and log data, and performs discrete analysis to identify abnormal events and build an abnormal event database; the abnormal event identification module extracts the edge computing node path of the abnormal event based on the abnormal event database, and calculates the radiation area and load index of the abnormal event; the load index trend analysis module analyzes the load index trend curves of abnormal events and normal events, calculates the difference index, and matches the adjustment plan of the edge computing node path based on the difference index; the real-time monitoring and early warning module collects real-time communication data, calculates the abnormal evaluation index, compares it with the preset threshold, and outputs early warning information, and matches abnormal events in the abnormal event database through real-time data; the path adjustment and optimization module calculates the difference vector based on the adjustment plan of the matched abnormal event, performs similarity calculation with the difference vector of the current edge computing node path, and finally outputs the adjustment plan to optimize the edge computing node path.

7. The intelligent communication data management system based on 5G communication according to claim 6, characterized in that: The data acquisition and analysis module includes a data acquisition unit and a data analysis unit; The data collection unit is responsible for collecting communication data from edge computing nodes, and the communication data includes traffic data, user behavior data and device status data, and collecting operation logs from edge computing nodes, including node operation status, event occurrence information and system operation records; the data analysis unit divides the collected communication data into multiple data segments according to timestamps, and pairs them with corresponding log data to form data units, compares all data units of each edge computing node with standard data units, calculates the anomaly assessment index, and identifies abnormal nodes and builds an abnormal event database based on the comparison of the anomaly assessment index with the preset threshold.

8. The intelligent communication data management system based on 5G communication according to claim 6, characterized in that: The abnormal event identification module includes an abnormal event record extraction unit, an abnormal node impact range calculation unit, and a node load calculation unit within the radiation area; The abnormal event record extraction unit extracts abnormal event records from the abnormal event database and extracts the associated edge computing node identifier to obtain the edge computing node path corresponding to the abnormal event; the abnormal node impact range calculation unit calculates the impact range radius according to the abnormal evaluation index of the abnormal node, generates a circular area to represent the impact range, and calculates the union of all circular areas to obtain the radiation area of the abnormal event; The node load calculation unit in the radiation area calculates the coverage and load index of each edge computing node in the radiation area, and updates the database of abnormal events according to these calculation results.

9. The intelligent communication data management system based on 5G communication according to claim 6, characterized in that: The load index trend analysis module includes a load index trend curve extraction unit and a load index difference analysis unit; The load index trend curve extraction unit extracts the load index trend curves of the edge computing nodes corresponding to abnormal events and normal events, and obtains the load index trend curve of the abnormal event and the load index trend curve of the normal event respectively; The load index difference analysis unit calculates difference indicators between the load index trend curves of abnormal events and normal events, including mean square error, mean absolute error and correlation coefficient, generates a difference vector and matches it with the adjustment plan of the abnormal event.

10. The intelligent communication data management system based on 5G communication according to claim 6, characterized in that: The real-time monitoring and early warning module includes a real-time data acquisition unit, a real-time anomaly assessment unit, and a real-time early warning and matching unit; The real-time data acquisition unit regularly collects real-time communication data of the edge computing node and obtains the current edge computing node path; The real-time anomaly assessment unit calculates an anomaly assessment index based on the current edge computing node path, compares it with a preset threshold, and determines whether it is necessary to output warning information based on the comparison result; When the real-time warning and matching unit detects that the abnormality evaluation index is greater than or equal to a preset threshold, it outputs a warning message, matches the abnormal event based on the current path and the records in the abnormal event database, calculates the corresponding load index trend curve and difference index, and generates a corresponding difference vector; The path adjustment and optimization module includes an adjustment scheme matching unit and a path optimization execution unit; The adjustment scheme matching unit obtains a difference vector of the abnormal event, performs similarity calculation on the difference vector obtained by real-time monitoring, and selects the one with the highest similarity as the final matching event; The path optimization execution unit outputs the edge computing node path adjustment plan corresponding to the matching event as a reference adjustment plan, and the system makes corresponding adjustments to the current edge computing node path according to the reference adjustment plan.

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