Motorized network situation data dynamic convergence method based on bandwidth awareness

Abstract

The invention discloses a mobile network situation data dynamic convergence method based on bandwidth awareness, and belongs to the technical field of mobile communication network operation and maintenance. The method comprises the following steps: acquiring running state data of equipment, links, subnets and services in the maneuvering network through distributed acquisition nodes; measuring and predicting the available bandwidth of each communication link in real time; dynamically adjusting the acquisition frequency, the transmission priority and the aggregation node of the data to realize the aggregation of the situation data; and performing fusion processing on the converged situation data to generate a maneuvering communication comprehensive situation view. According to the method, real-time acquisition, dynamic convergence and visual presentation of multi-dimensional situation data of equipment, links, subnets, services and the like in a maneuvering network can be realized, the acquisition frequency and the data priority are dynamically adjusted based on a real-time bandwidth sensing result, key data transmission is ensured when the bandwidth is insufficient, the data granularity is improved when the bandwidth is sufficient, and the data transmission efficiency is improved. And efficient utilization of resources and dynamic adjustment of comprehensive situation views are realized, and network situation support is provided for command decision making.

Description

Technical Field

[0001] This invention relates to the field of mobile communication network operation and maintenance technology, and in particular to a method for dynamic aggregation of mobile network situational data based on bandwidth awareness. Background Technology

[0002] The technology in the field of mobile communication network operation and maintenance is rapidly developing towards diversification, simplified operation, and unmanned operation. How to comprehensively display the overall network situation of multiple domains and levels on a single view has become an urgent problem to be solved. In mobile communication scenarios, the network environment is characterized by large bandwidth fluctuations, dynamic topology changes, and diverse communication link types, encompassing various communication methods such as scatter communication, satellite communication, fiber optic communication, shortwave communication, VHF radio communication, 5G communication, and WiFi, involving various equipment such as scattering devices, satellite communication equipment, and 5G base stations. To ensure smooth and stable mobile communication, it is necessary to collect real-time operational status data of equipment, links, subnets, and services to generate a comprehensive situation view, providing support for command and decision-making.

[0003] However, existing data aggregation methods mostly adopt fixed collection frequencies and transmission strategies, without fully considering the bandwidth characteristics and dynamic changes of different communication links. When bandwidth is tight, it can easily lead to delays or loss of critical data transmission, while when bandwidth is sufficient, it can cause resource waste. At the same time, in the face of dynamic changes in network topology, there is a lack of effective adaptive adjustment mechanisms for aggregation nodes, making it difficult to guarantee the reliability of data aggregation. Summary of the Invention

[0004] In view of this, this invention proposes a method for dynamic aggregation of mobile network situational data based on bandwidth awareness. This invention dynamically adjusts the acquisition frequency and data priority based on real-time bandwidth awareness results, ensuring the transmission of critical data when bandwidth is limited and increasing data granularity when bandwidth is sufficient. This achieves efficient resource utilization and dynamic adjustment of the comprehensive situational view, providing network situational support for command and decision-making.

[0005] The technical solution adopted in this invention is as follows:

[0006] A method for dynamic aggregation of mobile network situational data based on bandwidth awareness includes the following steps:

[0007] S1: Collect operational status data of devices, links, subnets and services in the mobile network through distributed collection nodes;

[0008] S2: Real-time measurement and prediction of available bandwidth for each communication link;

[0009] S3: Based on the bandwidth sensing results obtained in step S2, dynamically adjust the data acquisition frequency, transmission priority, and aggregation node to achieve the aggregation of situational data;

[0010] S4: Perform fusion processing on the converged situational data to generate a comprehensive situational view of mobile communications.

[0011] Furthermore, in step S1, a hybrid mode of active detection and passive reception is used to collect data. Active detection obtains the real-time status of the link through link status acquisition nodes, while passive reception receives operating parameters reported by the device through the device management interface. Specifically:

[0012] a) Collect equipment operating parameters: For scattering equipment, satellite communication equipment, 5G base stations, VHF radios, HF radios, and microwave network radios, collect transmit power, receive signal-to-noise ratio, bit error rate, equipment temperature, and power status through the equipment's built-in management interface; for routing and switching equipment, collect routing table update frequency, port traffic, forwarding latency, and packet loss rate; for BeiDou equipment, collect positioning information, time synchronization accuracy, and message transmission and reception success rate.

[0013] b) Collect link operating parameters: Deploy link probes at both ends of the scatter communication, satellite communication, fiber optic communication, shortwave / ultra-shortwave communication, 5G, and WiFi links to collect bandwidth utilization, real-time available bandwidth, transmission latency, and jitter; among them, the satellite communication link is collected once every 5 seconds, and the fiber optic communication link is collected once every 1 second;

[0014] c) Collect subnet operating parameters: collect the number of nodes in the subnet, the topology connection relationship obtained through routing and switching equipment, and the gateway load;

[0015] d) Data collection service operation parameters: For command information transmission and video conferencing services, data collection response time, packet loss rate, and service availability.

[0016] Furthermore, the specific method of step S2 is as follows:

[0017] a) Identify the link type label based on the link identification information;

[0018] b) Determine the real-time available bandwidth based on the link type label and probe packet parameters;

[0019] c) Based on the historical bandwidth sequence and environmental parameter weights, obtain the bandwidth prediction value for the next 10 seconds.

[0020] Furthermore, the specific method of step S3 is as follows:

[0021] a) Adaptive adjustment of collection frequency: When the available bandwidth idle rate of the link is >50%, the collection frequency of equipment fault alarm and link interruption information is increased from 5s / time to 1s / time; when the available bandwidth idle rate of the link is <20%, the collection frequency of equipment temperature and historical traffic statistics is reduced from 1s / time to 10s / time, and the LZ77 algorithm is used for compression.

[0022] b) The situational data is divided into five priority levels, in descending order of priority:

[0023] P0, equipment fault alarm;

[0024] P1, a sudden change in the link state;

[0025] P2, Service Availability;

[0026] P3, Subnet Topology Change;

[0027] P4, Historical Statistical Data;

[0028] A priority queue mechanism is adopted. When bandwidth is sufficient, all data is transmitted in the order of the queue. When bandwidth is insufficient, P0-P2 level data is transmitted first, P3 level data is transmitted with a delay, and P4 level data is temporarily stored in the local cache and uploaded in batches after bandwidth is restored.

[0029] c) Based on the subnet topology connection relationship, a temporary aggregation node is elected in each subnet; when the available bandwidth of the link of the temporary aggregation node is less than 10% or a device failure occurs, it is automatically switched to the backup node.

[0030] Furthermore, the specific method of step S4 is as follows:

[0031] a) Based on the time synchronization information provided by BeiDou devices, the status data of different devices and links are unified to the same time axis; when there is a conflict between multi-source data, a weighted fusion algorithm is used to resolve the conflict, in which the weight of the data reported by the device is 0.7 and the weight of the data actively detected is 0.3.

[0032] b) Display the connection relationships of each subnet, device, and link in a graphical manner, use color to mark the status, and display dynamic curves of the average latency of the entire network, the available bandwidth ratio of each link, and service availability in real time. Green indicates normal, red indicates fault, and yellow indicates bandwidth shortage. Highlight P0-P1 level alarms with pop-up windows or flashing icons, and associate them with historical data of relevant devices / links to assist in fault location.

[0033] The beneficial effects achieved by this invention are as follows:

[0034] 1. This invention constructs a bandwidth-aware dynamic adaptive aggregation framework, breaking through the limitations of traditional fixed strategies.

[0035] 2. This invention proposes a differentiated bandwidth sensing algorithm, which can achieve accurate measurement and prediction of multiple types of links.

[0036] 3. This invention constructs a four-level architecture consisting of a data acquisition layer, a bandwidth awareness layer, a dynamic aggregation layer, and a situation presentation layer. This enables real-time acquisition, dynamic aggregation, and visualization of multi-dimensional situational data of devices, links, subnets, and services in mobile networks. It can perform dynamic data aggregation of mobile network situation based on bandwidth awareness.

[0037] 4. The bandwidth sensing layer of this invention senses the available bandwidth of each link in real time and makes predictions. The dynamic aggregation layer adaptively adjusts the data acquisition frequency, transmission priority and aggregation node based on the sensing results, which effectively solves the problems of real-time and reliability of data aggregation in scenarios such as large bandwidth fluctuations and dynamic topology changes in mobile networks, and provides accurate network situation support for mobile communication command and decision-making. Attached Figure Description

[0038] Figure 1 This is a schematic diagram illustrating the principle of a bandwidth-aware dynamic aggregation method for mobile network situational data.

[0039] Figure 2 This is a schematic diagram illustrating the mobile network situation. Detailed Implementation

[0040] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0041] A method for dynamic aggregation of mobile network situational data based on bandwidth awareness includes the following steps:

[0042] S1: Data acquisition, which involves collecting operational status data of devices, links, subnets and services in the mobile network through distributed acquisition nodes;

[0043] S2: Bandwidth awareness, which measures and predicts the available bandwidth of each communication link in real time;

[0044] S3: Dynamic aggregation. Based on the bandwidth sensing results obtained in step S2, the data acquisition frequency, transmission priority and aggregation node are dynamically adjusted to achieve efficient aggregation of situational data.

[0045] S4: Situation Presentation. This function integrates and processes the aggregated situation data to generate a comprehensive situation view of mobile communications.

[0046] Specifically, step S1 includes:

[0047] S11: Equipment Operation Parameter Collection: For scattering equipment, satellite communication equipment, 5G base stations, VHF radios, HF radios, and microwave network radios, collect data such as transmit power, receive signal-to-noise ratio, bit error rate, equipment temperature, and power status through the equipment's built-in management interface (such as SNMP, Telnet, or proprietary protocols); for routing and switching equipment, collect data such as routing table update frequency, port traffic, forwarding latency, and packet loss rate; for BeiDou equipment, collect data such as positioning information, time synchronization accuracy, and message transmission and reception success rate.

[0048] S12: Link operation parameter collection: Deploy link status collection nodes at both ends of links such as scatter communication, satellite communication, fiber optic communication, shortwave / ultra-shortwave communication, 5G, and WiFi to collect bandwidth utilization, real-time available bandwidth, transmission latency, and jitter; among them, satellite communication links are collected once every 2 seconds, and fiber optic communication links are collected once every 1 second.

[0049] S13: Subnet operation parameter collection: Collect the number of nodes in the subnet, the topology connection relationship obtained through the routing and switching equipment, and the gateway load.

[0050] S14: Service operation parameter collection: For critical services such as command information transmission and video conferencing, collect response time, packet loss rate, and service availability.

[0051] S15: Data acquisition mode: adopts a hybrid mode of "active detection + passive reception". Active detection obtains the real-time status of the link through the status acquisition node, and passive reception receives the operating parameters reported by the device through the device management interface.

[0052] Step S2 specifically includes:

[0053] S21: Link Type Identification Algorithm. Input: Link identification information (including communication method type field, such as "satellite", "fiber optic", "shortwave", etc.). Output: Link type label (LType). Specific steps are as follows:

[0054] 1) Read the communication method type field from the link identification information;

[0055] 2) If the field is "Satellite" or "Scattering", then LType is marked as "High Latency - High Jitter".

[0056] 3) If the field is "Fiber Optic" or "5G", then the LType is marked as "High Stability - High Bandwidth".

[0057] 4) If the field is “shortwave” or “ultra-shortwave”, then the LType is marked as “narrow bandwidth - easily interfered”.

[0058] S22: Bandwidth Measurement Algorithm. Input: Link Type Label (LType), probe packet parameters (size S, sending interval T). Output: Real-time available bandwidth (B_measured). Specific steps are as follows:

[0059] 1) If LType is "high latency - high jitter": a) Send two probe data packets of size S consecutively with an interval of T; b) Record the timestamps t1 and t2 of the two data packets received by the receiver; c) Calculate the time difference Δt = t2 - t1; d) B_measured = S / Δt;

[0060] 2) If LType is "High Stability - High Bandwidth": a) Establish a temporary TCP connection and send a data stream with a duration of T (total size S_total); b) Record the actual transmission completion time t_total; c) B_measured = S_total / t_total;

[0061] 3) If LType is “narrow bandwidth - easily interfered”: a) Send a small data packet of size S (S≤128 bytes) every T interval; b) Send N times consecutively (N≥5), and record the successful reception time of each transmission; c) Calculate the average transmission rate as B_measured = (S×N) / (Σ reception time difference).

[0062] S23: Bandwidth Prediction Algorithm. Input: Historical bandwidth sequence {B(t-5), B(t-4), ..., B(t-1)} (t is the current time, in seconds), environmental parameter weights W (satellite elevation angle influence factor 0.3, weather condition influence factor 0.2). Output: Bandwidth prediction value for the next 10 seconds {B(t+10)}. Specific steps are as follows:

[0063] 1) Sliding window mean calculation: Take the mean of the historical sequence with a window size of 3 to obtain the smoothed sequence {M(t-3), M(t-2), M(t-1)}, where M(tk) = [B(tk-2)+B(tk-1)+B(tk)] / 3;

[0064] 2) Exponential smoothing: Give more weight to recent data and calculate the trend value T(t) = α×M(t-1) + (1-α)×M(t-2) (α=0.7);

[0065] 3) Environmental parameter correction: If the satellite elevation angle is <30°, then B_corr = T(t) × (1 - 0.3×W); if the weather is heavy rain, then B_corr = T(t) × (1 - 0.2×W);

[0066] 4) Output predicted value: B(t+10) = B_corr.

[0067] Step S3 specifically includes:

[0068] S31: Adaptive adjustment of acquisition frequency:

[0069] 1) When the available bandwidth idle rate of the link is >50%, the collection frequency of key data such as equipment fault alarms and link interruption information will be increased from 5 seconds / time to 1 second / time;

[0070] 2) When the available bandwidth idle rate of the link is less than 20%, the collection frequency of non-critical data such as device temperature and historical traffic statistics is reduced from 1 second / time to 10 seconds / time, and the LZ77 algorithm is used to compress the non-critical data with a compression rate of ≥60%.

[0071] S32: Transmission priority scheduling:

[0072] 1) Priority Classification: The situational data is divided into five priority levels, in descending order of priority: P0 (device failure alarm), P1 (link status change), P2 (service availability), P3 (subnet topology change), and P4 (historical statistics).

[0073] 2) Scheduling mechanism: A priority queue (PQ) mechanism is adopted. When bandwidth is sufficient, all data is transmitted in the order of the queue. When bandwidth is insufficient, P0-P2 level data is transmitted first, P3 level data is transmitted with a delay, and P4 level data is temporarily stored in the local cache and uploaded in batches after bandwidth is restored.

[0074] S33: Dynamic selection of aggregation nodes:

[0075] 1) Temporary aggregation node election: Based on the subnet topology connection relationship, temporary aggregation nodes are elected in each subnet, giving priority to devices with low load and stable links (such as 5G base stations or routing and switching equipment).

[0076] 2) Node switching mechanism: When the available bandwidth of the link of the temporary aggregation node is less than 10% or a device failure occurs (such as a power outage), it will automatically switch to the backup node (such as a microwave network radio in an adjacent subnet).

[0077] Step S4 specifically includes:

[0078] S41: Data Fusion

[0079] 1) Spatiotemporal alignment: Based on the time synchronization information provided by BeiDou devices, the status data of different devices and links are unified to the same time axis;

[0080] 2) Conflict resolution: When there is a conflict between multi-source data (such as the active detection results and the device-reported results of the same link), a weighted fusion algorithm is adopted, with the weight of the device-reported data being 0.7 and the weight of the active detection data being 0.3.

[0081] S42: View Generation:

[0082] 1) Topology view: Displays the connection relationships of subnets, devices, and links in a graphical way, and uses colors to indicate status (green indicates normal, red indicates fault, and yellow indicates bandwidth shortage);

[0083] 2) Performance View: Real-time display of dynamic curves for key indicators such as average latency across the entire network, available bandwidth percentage of each link, and service availability;

[0084] 3) Alarm View: High-priority alarms at levels P0-P1 are highlighted in a pop-up window or with a flashing icon, and historical data of related devices / links are associated to assist in fault location.

[0085] Complete the dynamic aggregation process of mobile network situational data based on bandwidth awareness.

[0086] Here is a specific example:

[0087] A method for dynamic aggregation of mobile network situational data based on bandwidth awareness, such as Figure 1 As shown, it includes the following steps:

[0088] Step S1: Data Acquisition Implementation

[0089] In this scenario, three distributed acquisition nodes are deployed, corresponding to the coverage areas of the satellite link, 5G link, and ultra-shortwave link, respectively. The specific acquisition process and results are as follows:

[0090] S11: Equipment operating parameter acquisition.

[0091] 1) Satellite communication equipment (a certain type of mobile communication terminal): Data is collected every 2 seconds through the management interface, showing a transmit power of 15W, a receive signal-to-noise ratio of 18dB, and a bit error rate of 3×10⁻. 7 Equipment temperature 42℃;

[0092] 2) 5G base station: Data was collected every 1 second via the SNMP protocol, showing a sector load of 45%, 28 user connections, and downlink port traffic of 35Mbps;

[0093] 3) VHF radio: Data is collected every 1 second via SNMP protocol, showing a transmit power of 2W, a receive signal-to-noise ratio of 12dB, and a power supply voltage of 13.8V;

[0094] 4) Routing and switching equipment: Data is collected every 1 second via SNMP protocol, resulting in a routing table update frequency of 0.5 times / second, a port packet loss rate of 0.3%, and a forwarding latency of 12ms;

[0095] 5) Beidou equipment: It collects data every 1 second to obtain positioning coordinates (39.9°N, 116.3°E), time synchronization accuracy of 50ns, and message transmission and reception success rate of 99.8%.

[0096] S12: Collection of link operation parameters.

[0097] 1) Satellite link (Ku band, nominal bandwidth 8Mbps): The status acquisition node collects data every 2 seconds, obtaining a bandwidth utilization rate of 60%, a real-time available bandwidth of 3.2Mbps, a transmission latency of 450ms, and a jitter of 35ms;

[0098] 2) 5G link (Sub-6GHz, nominal bandwidth 200Mbps): The status collection node collects data every 2 seconds, obtaining a bandwidth utilization rate of 30%, a real-time available bandwidth of 140Mbps, a transmission latency of 25ms, and a jitter of 5ms;

[0099] 3) Ultra-shortwave link (30-88MHz, nominal bandwidth 1Mbps): The status acquisition section collects data every 1 second, obtaining a bandwidth utilization rate of 70%, a real-time available bandwidth of 0.3Mbps, a transmission delay of 8ms, and a jitter of 12ms.

[0100] S13: Subnet operation parameter collection.

[0101] 1) Subnet 1 (Satellite Coverage Area): 12 nodes, star topology, gateway load 35%;

[0102] 2) Subnet 2 (5G coverage area): 25 nodes, tree topology, gateway load 20%.

[0103] S14: Service operation parameter collection.

[0104] 1) Command information transmission service: response time 80ms, packet loss rate 0.1%, service availability 99.9%;

[0105] 2) Video conferencing service: Response time 200ms, packet loss rate 0.5%, service availability 99.5%.

[0106] S15: Results of hybrid acquisition mode.

[0107] 1) The real-time available bandwidth of the satellite link measured by active detection (status acquisition node pair) was 3.1 Mbps, with an error of 3.1% compared to the value reported by the device (3.2 Mbps);

[0108] 2) The error between the passively received 5G base station load data (45%) and the active scanning results (46%) was 2.2%, which verified the data consistency.

[0109] Step S2: Bandwidth-aware implementation.

[0110] Based on the link data collected by S1, a bandwidth-aware algorithm is executed. The process and results are as follows:

[0111] S21: Link type identification. Satellite link: The identifier field is "Satellite", i.e., LType = "High latency - High jitter"; 5G link: The identifier field is "5G", i.e., LType = "High stability - High bandwidth"; UHF link: The identifier field is "UHF", i.e., LType = "Narrow bandwidth - Easily interfered".

[0112] S22: Bandwidth measurement algorithm execution.

[0113] 1) Satellite link (LType = High latency - High jitter): Two 1024-byte data packets are sent with a 10ms interval. The receiving timestamps are t1 = 10:00:00.000 and t2 = 10:00:00.004. Therefore, Δt = 4ms and B_measured = 1024 × 8 bits / 0.004s = 2.048Mbps (the difference from 3.2Mbps in S1.2 is due to real-time fluctuations).

[0114] 2) 5G link (LType = High Stability - High Bandwidth): Sends 1MB (1024×1024 bytes) of data stream within 1 second, with a transmission completion time of 0.007s. Therefore, B_measured = 1×8Mbps / 0.007 ≈ 1142.8Mbps (the actual bandwidth is the sub-link aggregation bandwidth, and the difference from the nominal 200Mbps is due to multi-carrier aggregation).

[0115] 3) Ultra-shortwave link (LType = narrow bandwidth - susceptible to interference): 5 64-byte data packets are sent every 1 second, with successful reception time differences of 1.2s, 1.1s, 1.3s, 1.2s, and 1.2s respectively. Therefore, the total data volume = 5 × 64 × 8 = 2560 bits, the total time = 5.8s, and B_measured = 2560 / 5.8 ≈ 441.4bps ≈ 0.44Mbps (the difference from 0.3Mbps in S112 is due to interference fluctuations).

[0116] S23: Bandwidth prediction algorithm execution

[0117] 1) Input: Satellite link historical bandwidth over the past 5 seconds [2.2, 2.1, 2.0, 1.9, 1.8] Mbps, satellite elevation angle 25° (W1=0.3), weather light rain (W2=0.2);

[0118] 2) Sliding window mean (window = 3): M(t-3) = [2.2 + 2.1 + 2.0] / 3 ≈ 2.1, M(t-2) = [2.1 + 2.0 + 1.9] / 3 ≈ 2.0, M(t-1) = [2.0 + 1.9 + 1.8] / 3 ≈ 1.9;

[0119] 3) Exponential smoothing (α=0.7): T(t)=0.7×1.9 + 0.3×2.0=1.93Mbps;

[0120] 4) Environmental correction: If the elevation angle is less than 30° and it is raining lightly, then B_corr = 1.93 × (1 - 0.3 × 0.5 - 0.2 × 0.5) = 1.93 × 0.75 ≈ 1.45 Mbps;

[0121] 5) Output: Predicted bandwidth for the next 10 seconds B(t+10) = 1.45Mbps.

[0122] Step S3: Dynamic aggregation implementation.

[0123] Based on the bandwidth awareness results of S2 (satellite link available bandwidth 1.45Mbps, idle rate = 1.45 / 8 ≈ 18% < 20%; 5G link idle rate > 50%; UHF link idle rate ≈ 44%), dynamic adjustments are performed:

[0124] S31: Adaptive sampling frequency

[0125] 1) Satellite link (bandwidth constraints): The collection frequency of critical data (equipment fault alarms) is maintained at 5 seconds / time; the collection frequency of non-critical data (equipment temperature) is reduced from 1 second / time to 10 seconds / time. After LZ77 compression, the data volume is reduced from 200 bytes / time to 70 bytes / time (compression rate 65%).

[0126] 2) 5G Link (Sufficient Bandwidth): The frequency of collecting critical data (service availability) has been increased from 5 seconds / time to 1 second / time, and the data update latency has been reduced from 5 seconds to 1 second.

[0127] S32: Transmission Priority Scheduling

[0128] 1) Data priority queue: P0 (equipment fault alarm): 1 alarm / 500ms (e.g., "WeChat equipment temperature exceeds threshold"); P1 (link status change): 2 alarms / 1s (e.g., "UHF link jitter increases to 20ms"); P2 (service availability): 5 alarms / 1s; P3 (subnet topology change): 1 alarm / 10s; P4 (historical statistics): 10 alarms / 60s;

[0129] 2) When satellite link bandwidth is insufficient: P0-P2 level data is transmitted first (occupying 1.2Mbps of bandwidth), P3 level data is delayed until bandwidth is restored, and P4 level data is temporarily stored locally (cumulative cache of 100 records / 5min).

[0130] S33: Dynamic selection of aggregation nodes

[0131] 1) Subnet 1 (Satellite Coverage Area): Candidate device load rate: satellite communication equipment 25%, routing and switching equipment 35%. At this time, the satellite communication equipment is elected as the temporary aggregation node (with lower load).

[0132] 2) Subnet 2 (5G coverage area): Candidate device link stability: 5G base station jitter 5ms, microwave radio jitter 10ms. At this time, the 5G base station is elected as the temporary aggregation node (the link is more stable).

[0133] 3) Switching mechanism verification: When the temporary aggregation node (satellite communication equipment) of subnet 1 suddenly fails, it will switch to the backup node (routing and switching equipment) within 300ms, and the data interruption time is <500ms.

[0134] Step S4: Situation presentation implementation.

[0135] The aggregated data is then fused and processed to generate a comprehensive situational view:

[0136] S41: Data Fusion

[0137] 1) Spatiotemporal alignment: Data of each node is calibrated based on BeiDou time (10:00:00.000), with a time synchronization error of ≤30ms;

[0138] 2) Conflict resolution: The active detection bandwidth of the satellite link is 1.45Mbps and the device reported bandwidth is 1.5Mbps. According to the weighted formula, the result is: 1.5×0.7 + 1.45×0.3≈1.485Mbps (as the final display value).

[0139] S42: View Generation

[0140] 1) Topology view: Satellite links are displayed in yellow (bandwidth is tight), 5G links are displayed in green (normal), and VHF links are displayed in yellow; satellite communication equipment icons are flashing (due to temperature exceeding the threshold), and 5G base station icons are normal (low load).

[0141] 2) Performance View: The available bandwidth of the satellite link decreased from 1.45Mbps to 1.3Mbps within 10 seconds (7% error from the predicted value of 1.45Mbps); the 5G link service availability curve remained at 99.9%.

[0142] 3) Alarm View: A pop-up window displays the P0 level alarm "Weitong equipment temperature 45℃ (threshold 40℃)", associated with historical data (temperature trend in the past 5 minutes: 38→42→45℃) to help locate heat dissipation faults.

[0143] The process of dynamically aggregating mobile network situational awareness data based on bandwidth awareness is completed. The results of the mobile network situational awareness data are presented as follows: Figure 2 As shown.

[0144] This invention addresses the problem that existing data aggregation methods often employ fixed acquisition frequencies and transmission strategies, failing to fully consider the bandwidth characteristics and dynamic changes of different communication links. This can lead to delays or losses in critical data transmission when bandwidth is limited, while causing resource waste when bandwidth is sufficient. The invention realizes a dynamic aggregation scheme for mobile network situational data based on bandwidth awareness.

[0145] This invention constructs a four-level architecture consisting of a data acquisition layer, a bandwidth awareness layer, a dynamic aggregation layer, and a situation presentation layer. This enables real-time acquisition, dynamic aggregation, and visualization of multi-dimensional situational data on devices, links, subnets, and services in mobile networks. Based on real-time bandwidth awareness results, the acquisition frequency and data priority are dynamically adjusted. This ensures the transmission of critical data when bandwidth is limited and improves data granularity when bandwidth is sufficient. This achieves efficient resource utilization and dynamic adjustment of the comprehensive situational view, providing network situational support for command and decision-making.

Claims

1. A method for dynamic aggregation of mobile network situational data based on bandwidth awareness, characterized in that, Includes the following steps: S1: Collect operational status data of devices, links, subnets and services in the mobile network through distributed collection nodes; S2: Real-time measurement and prediction of available bandwidth for each communication link; S3: Based on the bandwidth sensing results obtained in step S2, dynamically adjust the data acquisition frequency, transmission priority, and aggregation node to achieve the aggregation of situational data; S4: Perform fusion processing on the converged situational data to generate a comprehensive situational view of mobile communications.

2. The method for dynamic aggregation of mobile network situational data based on bandwidth awareness according to claim 1, characterized in that, In step S1, a hybrid mode of active detection and passive reception is used to collect data. Active detection obtains the real-time status of the link through link status acquisition nodes, while passive reception receives operating parameters reported by the device through the device management interface. Specifically: a) Collect equipment operating parameters: For scattering equipment, satellite communication equipment, 5G base stations, VHF radios, HF radios, and microwave network radios, collect transmit power, receive signal-to-noise ratio, bit error rate, equipment temperature, and power status through the equipment's built-in management interface; for routing and switching equipment, collect routing table update frequency, port traffic, forwarding latency, and packet loss rate; for BeiDou equipment, collect positioning information, time synchronization accuracy, and message transmission and reception success rate. b) Collect link operating parameters: Deploy link probes at both ends of the scatter communication, satellite communication, fiber optic communication, shortwave / ultra-shortwave communication, 5G, and WiFi links to collect bandwidth utilization, real-time available bandwidth, transmission latency, and jitter; among them, the satellite communication link is collected once every 5 seconds, and the fiber optic communication link is collected once every 1 second; c) Collect subnet operating parameters: collect the number of nodes in the subnet, the topology connection relationship obtained through routing and switching equipment, and the gateway load; d) Data collection service operation parameters: For command information transmission and video conferencing services, data collection response time, packet loss rate, and service availability.

3. The method for dynamic aggregation of mobile network situational data based on bandwidth awareness according to claim 1, characterized in that, The specific method of step S2 is as follows: a) Identify the link type label based on the link identification information; b) Determine the real-time available bandwidth based on the link type label and probe packet parameters; c) Based on the historical bandwidth sequence and environmental parameter weights, obtain the bandwidth prediction value for the next 10 seconds.

4. The method for dynamic aggregation of mobile network situational data based on bandwidth awareness according to claim 1, characterized in that, The specific method of step S3 is as follows: a) Adaptive adjustment of collection frequency: When the available bandwidth idle rate of the link is >50%, the collection frequency of equipment fault alarm and link interruption information is increased from 5s / time to 1s / time; when the available bandwidth idle rate of the link is <20%, the collection frequency of equipment temperature and historical traffic statistics is reduced from 1s / time to 10s / time, and the LZ77 algorithm is used for compression. b) The situational data is divided into five priority levels, in descending order of priority: P0, equipment fault alarm; P1, a sudden change in the link state; P2, Service Availability; P3, Subnet Topology Change; P4, Historical Statistical Data; A priority queue mechanism is adopted. When bandwidth is sufficient, all data is transmitted in the order of the queue. When bandwidth is insufficient, P0-P2 level data is transmitted first, P3 level data is transmitted with a delay, and P4 level data is temporarily stored in the local cache and uploaded in batches after bandwidth is restored. c) Based on the subnet topology connection relationship, a temporary aggregation node is elected in each subnet; when the available bandwidth of the link of the temporary aggregation node is less than 10% or a device failure occurs, it is automatically switched to the backup node.

5. The method for dynamic aggregation of mobile network situational data based on bandwidth awareness according to claim 1, characterized in that, The specific method for step S4 is as follows: a) Based on the time synchronization information provided by BeiDou devices, the status data of different devices and links are unified to the same time axis; when there is a conflict between multi-source data, a weighted fusion algorithm is used to resolve the conflict, in which the weight of the data reported by the device is 0.7 and the weight of the data actively detected is 0.

3. b) Display the connection relationships of each subnet, device, and link in a graphical manner, use color to mark the status, and display dynamic curves of the average latency of the entire network, the available bandwidth ratio of each link, and service availability in real time. Green indicates normal, red indicates fault, and yellow indicates bandwidth shortage. Highlight P0-P1 level alarms with pop-up windows or flashing icons, and associate them with historical data of relevant devices / links to assist in fault location.

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