A remote monitoring platform based on 5G communication

Abstract

The present invention relates to the field of remote monitoring technology, specifically a remote monitoring platform based on 5G communication, which includes a network data monitoring module, a spectrum resource adjustment module, a data transmission optimization module, and a data synchronization processing module. In the present invention, the utilization of network bandwidth is optimized through advanced data capture technology to achieve accurate monitoring of network traffic and state fluctuations. At the same time, the system has the ability to adjust spectrum resources in real time, respond quickly to changes in network conditions, significantly improve the stability and response speed of the network, automatically identify high-demand periods and adjust spectrum allocation, accurately evaluate and optimize data transmission paths, effectively reduce network congestion and transmission delays, ensure the continuity and reliability of data streams, enhance the efficiency and integrity of data synchronization processing, and especially in real-time data interaction between multiple nodes, ensure efficient and accurate data synchronization.

Description

Technical Field

[0001] The present invention relates to the field of remote monitoring technology, and in particular to a remote monitoring platform based on 5G communication. Background Art

[0002] The field of remote monitoring technology involves the use of various sensors, cameras, and surveillance equipment to remotely collect data and images over a network, enabling users to monitor and control physical environments such as factories, public spaces, homes, and offices from remote locations. This technology typically includes data collection, transmission, and analysis capabilities, aiming to improve efficiency and safety through real-time feedback. Furthermore, these systems are often equipped with automated features such as motion detection, event-triggered recording, and alarm notifications to respond to unusual situations.

[0003] A remote monitoring platform is a software or hardware platform that implements remote monitoring capabilities. It is specifically designed to receive, analyze, and display data transmitted from remote monitoring devices. It is typically highly integrated and customizable, allowing users to adjust system settings based on specific monitoring needs. Key applications include, but are not limited to, security monitoring, environmental monitoring, and production process control. Through a remote monitoring platform, users can receive real-time alerts, surveillance video, or other sensor data, enabling timely responses to various situations, effectively improving the monitoring system's response speed and decision-making quality.

[0004] When handling large amounts of data and high-demand monitoring scenarios, traditional remote monitoring technologies often face network congestion and transmission delays, significantly impacting monitoring effectiveness and system response speed. These technologies are inadequate in network bandwidth management and latency optimization, and are particularly lacking in flexibility and adaptability under dynamic network conditions. For example, in critical scenarios such as security or environmental monitoring, the slow response speed and limited data processing capabilities of traditional systems lead to delayed responses to emergencies, impacting decision-making quality and execution effectiveness. They fail to fully realize the potential of high-speed data transmission and big data technologies, limiting the overall performance and scalability of monitoring systems. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose a remote monitoring platform based on 5G communication.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: A remote monitoring platform based on 5G communication includes:

[0007] The network data monitoring module deploys sensors and receivers to capture network traffic and latency data in real time, record 5G network bandwidth usage, analyze traffic patterns within different time periods based on the recorded results, evaluate network status fluctuations, and generate real-time network data snapshots;

[0008] The spectrum resource adjustment module analyzes spectrum occupancy based on the real-time network data snapshot, identifies key traffic demand periods, adjusts spectrum allocation parameters based on the identification results, implements dynamic spectrum management to match changes in network conditions, and generates an adjusted spectrum configuration;

[0009] The data transmission optimization module uses the adjusted spectrum configuration to evaluate the existing data transmission path, calculate the network delay index of the transmission path, optimize the path based on the calculation results, avoid network congestion and transmission delay, and generate the optimal data path solution;

[0010] The data synchronization processing module uses the optimal data path solution to adjust and update the data transmission configuration between monitoring nodes, performs monitoring data synchronization processing between all locations, and generates remote monitoring data synchronization records.

[0011] As a further solution of the present invention, the steps of recording the 5G network bandwidth usage are:

[0012] Deploy sensors and receivers at 5G network exchange points to capture packet flows and signal delays per second, obtaining preliminary network traffic and latency data;

[0013] Cleaning the preliminary network traffic data and latency data to remove noise and outliers, ensuring data consistency through data synchronization, and obtaining accurate network usage data;

[0014] Based on the accurate network usage data, the time series changes of the data are analyzed and the formula is used.

[0015]

[0016] Calculate the overall bandwidth utilization T and obtain the 5G network bandwidth usage record, where N represents the number of measurements, B i Represents the bandwidth usage at each time point, L i Represents the corresponding delay.

[0017] As a further solution of the present invention, the steps of obtaining the real-time network data snapshot are:

[0018] Based on the 5G network bandwidth usage records, perform time series analysis to determine the periodic changes in data traffic, identify traffic peaks and valleys within different time periods, and obtain time period traffic pattern data;

[0019] Analyzing the traffic pattern data in the time period to obtain network status fluctuation data by measuring the fluctuation of the network status in the different time periods;

[0020] The network status fluctuation data is integrated to calculate the total network load, integrate the delay change and bandwidth utilization, and use dynamic data visualization to generate a real-time network data snapshot.

[0021] As a further solution of the present invention, the step of identifying the critical flow demand period is:

[0022] receiving the real-time network data snapshot, collating network traffic, number of user connections, and device type information to generate a real-time network status data set;

[0023] Based on the real-time network status data set, the formula is adopted:

[0024]

[0025] Calculate the comprehensive spectrum occupancy index F(t) at time point t, identify network traffic peaks and spectrum occupancy patterns, and obtain peak traffic period data. N(t) represents network traffic, and D(t) represents the number of device connections.

[0026] Using the traffic peak period data, set the traffic threshold T and compare the traffic density of the time window. If F(t) is greater than T, mark the target time window as the critical traffic demand period and generate the critical traffic demand period result.

[0027] As a further solution of the present invention, the step of acquiring the adjusted spectrum configuration is:

[0028] Analyze spectrum usage, traffic density distribution, and service quality requirements based on the results of the key traffic demand period, and generate a network status analysis record;

[0029] Based on the network status analysis record, dynamic spectrum allocation is performed using the formula:

[0030]

[0031] Calculate the priority P(f) of frequency band f and adjust the spectrum allocation parameters to obtain the optimized spectrum allocation parameters, where μ and σ are the mean and standard deviation obtained from the network status data.

[0032] Apply the optimized spectrum allocation parameters, update the spectrum configuration database, promote the new configuration to base stations and access points, monitor the implementation effect of the new configuration, and generate an adjusted spectrum configuration.

[0033] As a further solution of the present invention, the steps for calculating the transmission path network delay index are:

[0034] Using the adjusted spectrum configuration, evaluating existing data transmission paths, analyzing key nodes and data traffic distribution, and generating path status evaluation records;

[0035] Based on the path status evaluation record, the formula is used:

[0036]

[0037] Calculate the total delay D and generate the network delay index, where L i is the length of the i-th path, is the average length of all paths, v i is the signal speed of the i-th path, and m represents the number of paths;

[0038] Based on the network delay index, the network performance is evaluated, whether the delay index matches the network operation standard is confirmed, and a network performance record is generated.

[0039] As a further solution of the present invention, the steps of obtaining the optimal data path solution are:

[0040] Using the network latency metrics, analyze the current data transmission path, simulate network performance under different load conditions, compare latency data under different configurations, record the best performing configuration, and generate a draft of the optimized path plan;

[0041] Based on the draft of the optimized path plan, and with reference to the network performance data of the path plan, formulate network reconstruction and adjustment measures and generate a network adjustment proposal;

[0042] Based on the network adjustment proposal, an implementation plan is developed, including technical implementation details, resource allocation and time schedule. Regular evaluation points are set to monitor the optimization progress and results to obtain the optimal data path solution.

[0043] As a further solution of the present invention, the steps for obtaining the synchronous record of the remote monitoring data are:

[0044] Using the optimal data path solution, perform configuration analysis on the monitoring nodes, adjust the transmission configuration parameters of each node to match the optimal path requirements, meet the current data flow and network stability requirements, and obtain the adjusted monitoring node transmission configuration;

[0045] Initiate a synchronization protocol adjustment process based on the adjusted monitoring node transmission configuration, analyze delays and data loss during the synchronization process, dynamically adjust transmission parameters, monitor the synchronization status in real time, and obtain a record of data synchronization efficiency improvement;

[0046] According to the data synchronization efficiency improvement record, a comprehensive synchronization record is made for data transmission, and a unified recording protocol is used to collect and store the data synchronization content of all monitoring points to generate a remote monitoring data synchronization record.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, the utilization of network bandwidth is optimized through advanced data capture technology, and accurate monitoring of network traffic and status fluctuations is achieved. At the same time, the system has the ability to adjust spectrum resources in real time, respond quickly to changes in network conditions, significantly improve network stability and response speed, automatically identify high-demand periods and adjust spectrum allocation, accurately evaluate and optimize data transmission paths, effectively reduce network congestion and transmission delays, ensure the continuity and reliability of data streams, enhance the efficiency and integrity of data synchronization processing, and especially in real-time data interaction between multiple nodes, ensure efficient and accurate data synchronization. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a system flow chart of the present invention;

[0050] Figure 2 This is a flow chart for recording 5G network bandwidth usage in the present invention;

[0051] Figure 3 This is a flowchart of obtaining a real-time network data snapshot according to the present invention;

[0052] Figure 4 This is a flow chart for identifying key flow demand periods of the present invention;

[0053] Figure 5 A flowchart of obtaining the adjusted spectrum configuration according to the present invention;

[0054] Figure 6 Flowchart for calculating the transmission path network delay index of the present invention;

[0055] Figure 7 A flowchart for obtaining the optimal data path solution of the present invention;

[0056] Figure 8 This is a flowchart for obtaining synchronous records of remote monitoring data according to the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0059] See also Figure 1 , a remote monitoring platform based on 5G communication includes:

[0060] The network data monitoring module deploys sensors and receivers to capture network traffic and latency data in real time, record 5G network bandwidth usage, analyze traffic patterns within different time periods based on the recorded results, evaluate network status fluctuations, and generate real-time network data snapshots;

[0061] The spectrum resource adjustment module analyzes spectrum occupancy based on real-time network data snapshots, identifies key traffic demand periods, adjusts spectrum allocation parameters based on the identified results, implements dynamic spectrum management to match changing network conditions, and generates adjusted spectrum configurations.

[0062] The data transmission optimization module uses the adjusted spectrum configuration to evaluate existing data transmission paths, calculate the network delay indicators of the transmission paths, and optimize the paths based on the calculation results to avoid network congestion and transmission delays and generate the optimal data path solution.

[0063] The data synchronization processing module uses the optimal data path solution to adjust and update the data transmission configuration between monitoring nodes, synchronizes the monitoring data between all locations, and generates remote monitoring data synchronization records.

[0064] Real-time network data snapshots include bandwidth utilization data, latency measurement results, and traffic change graphs. Adjusted spectrum configurations include spectrum utilization rate, spectrum allocation strategy, and adjustment response time. Optimal data path solutions include path latency data, path selection lists, and congestion avoidance measures. Remote monitoring data synchronization records specifically refer to synchronization frequency, synchronization success rate, and data consistency status records.

[0065] See also Figure 2 , the steps for recording 5G network bandwidth usage are as follows:

[0066] Deploy sensors and receivers at 5G network exchange points to capture packet flows and signal delays per second, obtaining preliminary network traffic and latency data;

[0067] High-precision sensors and receivers are deployed at the exchange points of the 5G network. The specific installation locations are determined based on the geographical and network traffic distribution characteristics. For example, nodes with high network traffic and frequent data transmission are selected as monitoring points, which are usually located in the core position of the network architecture or in areas with dense users. Next, the equipment is installed according to technical specifications to ensure that all sensors and receivers can operate normally and capture data in real time. During this process, the sensitivity of the sensors also needs to be set so that the flow of data packets and signal delays per second can be accurately captured, from which preliminary network traffic data and delay data can be obtained.

[0068] Clean preliminary network traffic and latency data to remove noise and outliers, ensure data consistency through data synchronization, and obtain accurate network usage data.

[0069] During the data cleaning process for network traffic data and latency data, we first perform an initial screening to identify and eliminate anomalies in the data, such as signal mutations or packet loss. Next, we perform format verification to ensure that all data conforms to the preset data format specifications. The logical verification step checks the timestamps and sequences in the data to eliminate errors caused by time misalignment or data duplication. In addition, data synchronization processing is an essential step. By comparing data collected by different sensors, the deviation problem of the data timeline is resolved, ensuring that data collected from multiple sources can be analyzed within the same time frame. This ensures the quality and consistency of the data before entering the analysis, which is a key step in obtaining accurate network usage data.

[0070] Based on accurate network usage data, we analyze the time series changes of the data and use the formula:

[0071]

[0072] Calculate the overall bandwidth utilization T and obtain the 5G network bandwidth usage record, where N represents the number of measurements, B i Represents the bandwidth usage at each time point, L i Represents the corresponding delay;

[0073] There are 10 measurement points. The bandwidth usage at each point is 50, 55, 60, 65, 70, 75, 80, 85, 90, and 95 Mbps, respectively. The corresponding delays are 10, 9, 8, 7, 6, 5, 4, 3, 2, and 1 ms. The calculation process is:

[0074]

[0075] The results show that the average bandwidth utilization is 357.5Mbps. The number reflects the overall usage of network bandwidth and explains the bandwidth usage of the network in actual operation.

[0076] See also Figure 3 ,The steps to obtain the real-time network data snapshot are:

[0077] Based on the 5G network bandwidth usage records, perform time series analysis to determine the periodic changes in data traffic, identify traffic peaks and valleys within different time periods, and obtain time period traffic pattern data;

[0078] When conducting time series analysis to identify cyclical changes in network traffic, we first select an appropriate time window, such as hourly or daily, based on bandwidth usage records to collect data points. Time series analysis tools such as ARIMA or Fourier transform are then used to analyze these data points to help identify seasonal fluctuations and trends in the data. The analysis process includes normalizing the data to eliminate the impact of inconsistent units. Autocorrelation and partial autocorrelation functions are then used to examine the inherent correlations in the data and identify key cyclical patterns, such as increased traffic on weekends or traffic spikes caused by specific events. The resulting traffic pattern data for each segmented time period not only reflects cyclical changes but also reveals the specific periods of peak and trough traffic, providing critical information for subsequent network management and capacity planning.

[0079] Using the traffic pattern data of different time periods, we analyze the statistical indicators of the traffic data, measure the fluctuation of the network status in different time periods, and obtain the network status fluctuation data;

[0080] Traffic pattern data is used to further calculate statistical indicators of the data to assess the volatility of network status. This process first determines the mean of traffic data for each time period. Next, the deviation of each data point from the mean is calculated. The squares of all deviations are summed and divided by the total number of data points to obtain the variance. Finally, the square root of the variance is calculated to obtain the standard deviation. The standard deviation provides a quantified measure of fluctuation, allowing network engineers to assess the stability of the network over different time periods. In addition, the coefficient of variation, which is the ratio of the standard deviation to the mean, can be calculated to provide a relative measure of fluctuations in network performance. Through detailed statistical analysis, time periods that may require additional attention or improvement, such as network bottlenecks caused by sudden increases in traffic, can be identified. Specific network optimization or capacity increases can then be performed for these time periods to ensure optimal network performance.

[0081] Integrate network status fluctuation data, calculate total network load, integrate delay changes and bandwidth utilization, and use dynamic data visualization to generate real-time network data snapshots;

[0082] When calculating the total network load, use the formula: Calculate, where n is the number of measured time points, P i is the number of packets at time point i, V iis the data rate at the corresponding time point. In a measurement, n = 5, and the number of packets and the data rate at each time point are (100, 0.5 Mbps), (150, 0.75 Mbps), (200, 1.0 Mbps), (250, 1.25 Mbps), and (300, 1.5 Mbps), respectively. The calculation is as follows:

[0083] R=(100×0.5)+(150×0.75)+(200×1.0)+(250×1.25)+(300×1.5)=50

[0084] +112.5+200+312.5+450=1125

[0085] The data shows that the total network load during the observation period was 1125 Mbps. The results show the network load during this period and provide network administrators with a real-time data snapshot to help monitor and adjust network configuration to adapt to changing network needs.

[0086] See also Figure 4 ,The steps for identifying the critical traffic demand period are:

[0087] Receive real-time network data snapshots, organize network traffic, number of user connections, and device type information, and generate a real-time network status dataset;

[0088] Automatically receive data on network traffic and device types, and upload the data to a central server through real-time monitoring devices. The server analyzes the data in real time to generate a real-time network status dataset. This process involves not only data collection but also preliminary data cleaning and formatting. For example, the data collected from various network devices is not in a uniform format and needs to be converted into a unified format through a conversion program. At the same time, the data quality is assessed to eliminate those data points with obvious anomalies, such as abnormal data with sudden increases or decreases in traffic, to ensure the accuracy of subsequent analysis. The final dataset will be used for further traffic analysis and spectrum occupancy analysis.

[0089] Based on the real-time network status data set, the formula is adopted.

[0090]

[0091] Calculate the comprehensive spectrum occupancy index F(t) at time point t, identify network traffic peaks and spectrum occupancy patterns, and obtain peak traffic period data. N(t) represents network traffic, and D(t) represents the number of device connections.

[0092] At a specific time t, the monitored network traffic N(t) is 5000 MB / s, and the number of connected devices D(t) is 800 devices. Considering that the time unit for real-time data monitoring is usually set to seconds, t is set to 1 second. Substituting this into the formula, the calculation steps are as follows:

[0093] ln(N(t))=ln(5000)≈8.517193191

[0094]

[0095]

[0096] The result 28.43 indicates that at time point t = 1 second, taking into account network traffic and the number of device connections, the comprehensive index of spectrum occupancy is 28.43, which can be used to analyze the peak pattern of spectrum occupancy and help identify peak traffic periods.

[0097] Using the traffic peak period data, set the traffic threshold T and compare the traffic density of the time window. If F(t) is greater than T, mark the target time window as the critical traffic demand period and generate the critical traffic demand period result.

[0098] Using the traffic peak period data obtained from the analysis, the traffic density within each time window is further screened and compared. By setting different traffic thresholds T, critical traffic demand periods are identified. For example, if the threshold T is set to 25, the relationship between the F(t) value of each time window and T is compared. If F(t) is greater than T, the time window is marked as a critical traffic demand period. Through this method, the threshold T can be dynamically adjusted to adapt to different network conditions and needs. Ultimately, through these calculations, those time windows that exceed the threshold T are accurately screened to determine the final critical traffic demand period. This process is dynamic and iterative, and the threshold T needs to be continuously adjusted according to actual conditions to ensure that critical periods can be accurately identified, thereby providing a basis for subsequent network management and spectrum allocation.

[0099] See also Figure 5 , the steps to obtain the adjusted spectrum configuration are:

[0100] Based on the results of key traffic demand periods, analyze spectrum utilization, traffic density distribution, and service quality requirements, and generate network status analysis records;

[0101] Based on data from identified key traffic demand periods, the system analyzes current spectrum occupancy and network load in real time. First, real-time data, including quality of service requirements, device types, and user behavior patterns, is obtained from the network monitoring system. This data analysis helps determine spectrum utilization efficiency and traffic density distribution. The analysis process includes data clustering, spectrum utilization calculation, and network load prediction. Each calculation step is based on actual monitoring data, ensuring the accuracy of the results. Furthermore, different quality of service requirements are classified to provide decision support for the next step of spectrum adjustment. Finally, a detailed network status analysis report is generated, providing a basis for adjusting spectrum allocation parameters.

[0102] Based on the network status analysis record, dynamic spectrum allocation is performed using the formula:

[0103]

[0104] Calculate the priority P(f) of frequency band f and adjust the spectrum allocation parameters to obtain the optimized spectrum allocation parameters, where μ and σ are the mean and standard deviation obtained from the network status data.

[0105] In a specific example, μ=3 GHz and σ=0.5 GHz.

[0106] A specific frequency band f=3.2 GHz is selected.

[0107] Calculate the exponential part in the numerator and get:

[0108] (3.2-3) / 0.5=0.4

[0109] Exponential function calculation:

[0110] e -0.4 ≈0.67032

[0111] Substituting this value into the formula, we get:

[0112]

[0113] The results show that for the 3.2 GHz frequency band, its priority is approximately 0.5987, which means that this frequency band has a medium to high priority compared to other frequency bands and is used to optimize spectrum resource allocation.

[0114] Apply the optimized spectrum allocation parameters, update the spectrum configuration database, roll out the new configuration to base stations and access points, monitor the implementation effect of the new configuration, and generate an adjusted spectrum configuration;

[0115] The optimized spectrum allocation parameters are applied to the network management system. First, the obtained spectrum allocation parameters are imported into the network management system's database. The system updates the spectrum allocation strategy and rules based on the parameters, including adjustments to the spectrum allocation width and access rights. Next, the system automatically sends the updated configuration to each base station and access point through the network. The base stations and access points adjust their equipment settings based on the new spectrum configuration to ensure that each node operates according to the latest configuration. During this process, the network operation support system is also responsible for monitoring the implementation effect of the configuration update and collecting performance data after implementation for subsequent evaluation and adjustment to ensure that the entire network can efficiently respond to changes in spectrum configuration and optimize network performance and user experience.

[0116] See also Figure 6 ,The calculation steps of the transmission path network delay index are:

[0117] Using the adjusted spectrum configuration, evaluate existing data transmission paths, analyze key nodes and data traffic distribution, and generate path status assessment records;

[0118] When evaluating data transmission paths using the adjusted spectrum configuration, the first step is to determine the data traffic distribution of key nodes through actual network monitoring data. High-precision spectrum analyzers are then used to capture the data traffic of each node at different time periods. Cluster analysis is then performed on the data to identify peaks and troughs in data traffic. This information is then used to further evaluate the data load capacity of the entire transmission path and determine which nodes may become bottlenecks for data transmission. Various network traffic scenarios, such as high load, low load, and sudden high traffic, are then simulated to verify whether the adjusted spectrum configuration meets the expected performance standards in each situation, generating a path evaluation status.

[0119] Based on the path status evaluation record, the formula is used.

[0120]

[0121] Calculate the total delay D and generate the network delay index, where L i is the length of the i-th path, is the average length of all paths, v i is the signal speed of the i-th path, and m represents the number of paths;

[0122] There are three paths of different lengths, 10km, 12km and 8km, with an average length of Assuming the signal speed of each segment is 100 Mbps, 120 Mbps, and 80 Mbps, substitute them into the formula to calculate the delay contribution of each segment. Then add up the values ​​and take the square root to get the total delay D.

[0123] First paragraph:

[0124]

[0125] Second paragraph:

[0126]

[0127] Paragraph 3:

[0128]

[0129] Total delay:

[0130]

[0131] The results show that the total delay of the three transmission paths is 0.205ms.

[0132] Evaluate network performance based on network latency indicators, confirm whether the latency indicators match network operation standards, and generate network performance records;

[0133] Based on the calculated network latency indicators, the process of comprehensively evaluating network performance involves multiple steps, including collecting latency data through a real-time network monitoring system, performing statistical analysis on the data, and calculating the average latency and its standard deviation to determine whether network performance meets the set threshold. In addition, network simulation tests are required to simulate the latency conditions under different data flows and network loads to verify the adaptability and stability of the network configuration, confirm whether the latency indicators meet the network operation standards, and finally generate a network performance report to ensure that the network can operate in an optimal state.

[0134] See also Figure 7 , the steps to obtain the optimal data path solution are:

[0135] Use network latency metrics to analyze the current data transmission path, simulate network performance under varying load conditions, compare latency data under varying configurations, record the optimal configuration, and generate a draft optimized path plan.

[0136] When using network latency metrics for analysis, we first collect data on data flow and response time by setting up multiple network performance monitoring points. These monitoring points record the passing data packets, as well as the send and receive times, in real time. This data enables accurate simulations under varying network loads and conditions. Furthermore, by simulating different traffic configurations, we can visualize the network's performance in detail under each configuration and identify potential congestion points. Finally, by combining simulation results with actual data, we can compare and select the network path with the best latency performance, providing a basis for further network optimization and generating a detailed optimization plan for the existing network structure.

[0137] Based on the draft of the optimized path plan, and referring to the network performance data of the path plan, formulate network reconstruction and adjustment measures and generate network adjustment proposals;

[0138] After determining the specific network path optimization solution, further planning steps include a comprehensive performance analysis of the selected solution to ensure that the proposed solution can maintain network performance under various traffic conditions. For example, Wireshark and SolarWinds are used to capture and analyze detailed data packets at each node in the network. This analysis provides a detailed understanding of how data flows in the network and potential bottlenecks. Next, by simulating different network traffic scenarios, the performance of the selected path solution in actual applications is verified, ensuring that the selected optimization solution is not only feasible in theory but also achieves the expected delay reduction effect in actual operation. The generated report contains detailed solutions and recommendations for each situation.

[0139] Based on the network adjustment proposal, develop an implementation plan, including technical implementation details, resource allocation, and time schedule. Set regular evaluation points to monitor the optimization progress and results, and obtain the optimal data path solution.

[0140] Implementing a network optimization solution is a complex process, involving the detailed planning and execution of multiple key phases. First, the technical planning phase involves designing the overall network architecture, selecting appropriate hardware and software, and determining the optimal configuration and installation location of network equipment. Next, the resource allocation phase ensures that all necessary technical equipment and personnel are in place and deployed as planned. Furthermore, a detailed project timeline includes the start and end dates for each key task and how project progress will be assessed over time. The establishment of monitoring mechanisms, including performance monitoring and problem response systems, is crucial for ensuring the project progresses as planned and enabling rapid response to issues.

[0141] See also Figure 8 ,The steps for obtaining remote monitoring data synchronization records are:

[0142] Using the optimal data path solution, we perform configuration analysis on the monitoring nodes, adjust the transmission configuration parameters of each node to match the optimal path requirements, meet the current data flow and network stability requirements, and obtain the adjusted monitoring node transmission configuration;

[0143] Using the generated optimal data path solution, we perform configuration analysis on each monitoring node, adjust the transmission configuration parameters of each node to match the optimal path requirements, and ensure that the configuration update meets the current data traffic and network stability requirements. Based on the network topology and traffic data, we evaluate the data traffic and load between each node to determine the configuration update required for each node. According to the specific needs of each node and the overall network optimization goals, we adjust the parameters to match these needs to ensure that each node can effectively transmit data on the optimal data path. Each step of parameter adjustment is based on real-time network conditions and predicted data. It involves not only basic data collection and analysis, but also in-depth understanding and simulation of network behavior to obtain the adjusted monitoring node transmission configuration.

[0144] Based on the adjusted monitoring node transmission configuration, the synchronization protocol adjustment process is started, the delay and data loss during the synchronization process are analyzed, the transmission parameters are dynamically adjusted, the synchronization status is monitored in real time, and a record of the improvement in data synchronization efficiency is obtained;

[0145] Based on the adjusted transmission configuration of the monitoring nodes, the synchronization protocol adjustment process is started to ensure that data is efficiently synchronized along the optimal path. In response to possible delays or data loss problems during the synchronization process, the transmission parameters are dynamically adjusted, and the synchronization status is monitored in real time. Based on the latest network configuration and path optimization results, the data synchronization protocol is deployed, including precise setting and real-time adjustment of protocol parameters to adapt to changes in network conditions and fluctuations in data traffic. During the monitoring process, each stage of data transmission is tracked, any anomalies or delays are recorded, and the transmission settings are immediately adjusted to resolve these problems, ensuring efficient and stable synchronization of data between all monitoring nodes, and obtaining a record of improved data synchronization efficiency.

[0146] According to the data synchronization efficiency improvement record, comprehensive synchronization record of data transmission is carried out, and the data synchronization content of all monitoring points is collected and stored using a unified recording protocol to generate remote monitoring data synchronization records;

[0147] Comprehensive synchronization records are made for data transmission from each node to the central monitoring system. A unified recording protocol is used to collect and store data synchronization details of all monitoring points. The data transmission details of each node are collected through the network monitoring system, including the start and end time of the transmission, the size of the data packet, and any errors or interruptions during the transmission process. The data is recorded in a central database. Each record contains a timestamp, node ID, data volume, and transmission status. During this process, the system automatically analyzes the efficiency and integrity of data transmission, detects possible problem areas such as network congestion or node failure, and then generates reports. These reports provide decision support for network operators, helping them optimize network operations and maintenance plans, thereby ensuring efficient and stable synchronization of data between all monitoring nodes and generating detailed remote monitoring data synchronization records.

[0148] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A remote monitoring platform based on 5G communication, characterized in that: The platform includes: The network data monitoring module deploys sensors and receivers to capture network traffic and latency data in real time, record 5G network bandwidth usage, analyze traffic patterns within different time periods based on the recorded results, evaluate network status fluctuations, and generate real-time network data snapshots; The spectrum resource adjustment module analyzes spectrum occupancy based on the real-time network data snapshot, identifies key traffic demand periods, adjusts spectrum allocation parameters based on the identification results, implements dynamic spectrum management to match changes in network conditions, and generates an adjusted spectrum configuration; The data transmission optimization module uses the adjusted spectrum configuration to evaluate the existing data transmission path, calculate the network delay index of the transmission path, optimize the path based on the calculation results, avoid network congestion and transmission delay, and generate the optimal data path solution; The data synchronization processing module uses the optimal data path solution to adjust and update the data transmission configuration between monitoring nodes, performs monitoring data synchronization processing between all locations, and generates remote monitoring data synchronization records.

2. The remote monitoring platform based on 5G communication according to claim 1 is characterized in that: The steps for recording the 5G network bandwidth usage are as follows: Deploy sensors and receivers at 5G network exchange points to capture packet flows and signal delays per second, obtaining preliminary network traffic and latency data; Cleaning the preliminary network traffic data and latency data to remove noise and outliers, ensuring data consistency through data synchronization, and obtaining accurate network usage data; Based on the accurate network usage data, the time series changes of the data are analyzed and the formula is used. Calculate the overall bandwidth utilization T and obtain the 5G network bandwidth usage record, where N represents the number of measurements, B i Represents the bandwidth usage at each time point, L i Represents the corresponding delay.

3. The remote monitoring platform based on 5G communication according to claim 2 is characterized in that: The steps for obtaining the real-time network data snapshot are: Based on the 5G network bandwidth usage records, perform time series analysis to determine the periodic changes in data traffic, identify traffic peaks and valleys within different time periods, and obtain time period traffic pattern data; Analyzing the traffic pattern data in the time period to obtain network status fluctuation data by measuring the fluctuation of the network status in the different time periods; The network status fluctuation data is integrated to calculate the total network load, integrate the delay change and bandwidth utilization, and use dynamic data visualization to generate a real-time network data snapshot.

4. The remote monitoring platform based on 5G communication according to claim 3 is characterized in that: The steps for identifying the critical traffic demand period are: receiving the real-time network data snapshot, collating network traffic, number of user connections, and device type information to generate a real-time network status data set; Based on the real-time network status data set, the formula is adopted: Calculate the comprehensive spectrum occupancy index F(t) at time point t, identify network traffic peaks and spectrum occupancy patterns, and obtain peak traffic period data. N(t) represents network traffic, and D(t) represents the number of device connections. Using the traffic peak period data, set the traffic threshold T and compare the traffic density of the time window. If F(t) is greater than T, mark the target time window as the critical traffic demand period and generate the critical traffic demand period result.

5. The remote monitoring platform based on 5G communication according to claim 4 is characterized in that: The steps for obtaining the adjusted spectrum configuration are: Analyze spectrum usage, traffic density distribution, and service quality requirements based on the results of the key traffic demand period, and generate a network status analysis record; Based on the network status analysis record, dynamic spectrum allocation is performed using the formula: Calculate the priority P(f) of frequency band f and adjust the spectrum allocation parameters to obtain the optimized spectrum allocation parameters, where μ and σ are the mean and standard deviation obtained from the network status data. Apply the optimized spectrum allocation parameters, update the spectrum configuration database, promote the new configuration to base stations and access points, monitor the implementation effect of the new configuration, and generate an adjusted spectrum configuration.

6. The remote monitoring platform based on 5G communication according to claim 5, characterized in that: The calculation steps of the transmission path network delay index are as follows: Using the adjusted spectrum configuration, evaluating existing data transmission paths, analyzing key nodes and data traffic distribution, and generating path status evaluation records; Based on the path status evaluation record, the formula is used: Calculate the total delay D and generate the network delay index, where L i is the length of the i-th path, is the average length of all paths, v i is the signal speed of the i-th path, and m represents the number of paths; Based on the network delay index, the network performance is evaluated, whether the delay index matches the network operation standard is confirmed, and a network performance record is generated.

7. The remote monitoring platform based on 5G communication according to claim 6, characterized in that: The steps for obtaining the optimal data path solution are: Using the network latency metrics, analyze the current data transmission path, simulate network performance under different load conditions, compare latency data under different configurations, record the best performing configuration, and generate a draft of the optimized path plan; Based on the draft of the optimized path plan, and with reference to the network performance data of the path plan, formulate network reconstruction and adjustment measures and generate a network adjustment proposal; Based on the network adjustment proposal, an implementation plan is developed, including technical implementation details, resource allocation and time schedule. Regular evaluation points are set to monitor the optimization progress and results to obtain the optimal data path solution.

8. The remote monitoring platform based on 5G communication according to claim 7, characterized in that: The steps for obtaining the remote monitoring data synchronization record are: Using the optimal data path solution, perform configuration analysis on the monitoring nodes, adjust the transmission configuration parameters of each node to match the optimal path requirements, meet the current data flow and network stability requirements, and obtain the adjusted monitoring node transmission configuration; Initiate a synchronization protocol adjustment process based on the adjusted monitoring node transmission configuration, analyze delays and data loss during the synchronization process, dynamically adjust transmission parameters, monitor the synchronization status in real time, and obtain a record of data synchronization efficiency improvement; According to the data synchronization efficiency improvement record, a comprehensive synchronization record is made for data transmission, and a unified recording protocol is used to collect and store the data synchronization content of all monitoring points to generate a remote monitoring data synchronization record.

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