Internet multicast protocol driven IPTV server management and control method and system

Abstract

The invention relates to the technical field of network communication, and discloses an internet protocol television (IPTV) server management and control method and system driven by an internet multicast protocol, which comprises the following steps of: firstly obtaining multicast data flow and analyzing protocol characteristics of the multicast data flow, identifying multicast nodes and then fragmenting the flow, constructing a flow on-demand framework, extracting flow load data in the framework and grading and marking; calculating a bandwidth allocation value to detect a traffic abnormal point, determining a congestion area corresponding to the abnormal point and marking a speed limit, calculating a conflict threshold after data is obtained through protocol analysis, identifying a redundancy parameter based on the threshold, optimizing the parameter in combination with a preset QoS standard, and finally making a multicast control strategy according to the protocol analysis data. According to the invention, the resource scheduling efficiency of the IPTV server can be improved.

Description

Technical Field

[0001] The present invention relates to a method and system for controlling an IPTV server driven by an Internet multicast protocol, belonging to the field of network communication technology. Background Art

[0002] IPTV (Internet Protocol Television) is a technology for transmitting multimedia content based on an IP network, widely used in live broadcast, on-demand, and interactive video services. Its multicast protocols (such as IGMP, PIM) are the core transmission mechanisms of IPTV, reducing network load through one-to-many distribution.

[0003] Current technologies usually adopt fixed multicast routing strategies or manually adjust multicast group member relationships, lacking the ability to perceive network status (such as congestion, node failure) and user needs (such as channel switching frequency) in real time. In addition, traditional methods have weak coordination ability in cross-domain multicast scenarios, easily causing data redundancy or packet loss problems, affecting the quality of service (QoS). Therefore, an intelligent control method based on the Internet multicast protocol is needed to improve the resource scheduling efficiency of IPTV servers. Summary of the Invention

[0004] The present invention provides a method and system for controlling an IPTV server driven by an Internet multicast protocol, and its main purpose is to improve the resource scheduling efficiency of the IPTV server.

[0005] To achieve the above object, a method for controlling an IPTV server driven by an Internet multicast protocol provided by the present invention includes: Obtain the multicast data stream corresponding to the target IPTV server, perform protocol parsing on the multicast data stream to obtain multicast protocol features, and based on the multicast protocol features, identify the multicast nodes in the multicast data stream; Based on the multicast nodes, perform traffic sharding on the multicast data stream to obtain sharded traffic units, and based on the sharded traffic units, construct a traffic on-demand framework corresponding to the target IPTV server; Extract the traffic load data in the traffic on-demand framework, perform hierarchical marking on the traffic load data to obtain a hierarchical load queue, calculate the bandwidth allocation value corresponding to the hierarchical load queue, and based on the bandwidth allocation value, detect traffic anomaly points in the target IPTV server; Determine the congestion area corresponding to the traffic anomaly points, perform speed limit marking on the congestion area to obtain an area control label, perform protocol analysis on the area control label to obtain protocol analysis data, and calculate the conflict threshold corresponding to the protocol analysis data; Based on the conflict threshold, identify redundant parameters in the protocol analysis data, control and optimize the redundant parameters with a preset QoS standard to obtain control and optimization parameters, and formulate a multicast control strategy corresponding to the target IPTV server based on the protocol analysis data.

[0006] Optionally, the identifying of multicast nodes in the multicast data stream based on the multicast protocol characteristics includes: Analyze the protocol fields in the multicast protocol; Based on the protocol fields, traverse the node routing table in a preset topology database; Extract the timing communication records of active nodes in the node routing table; Filter candidate record points in the timing communication records; Based on the candidate record points, identify multicast nodes in the multicast data stream.

[0007] Optionally, the traffic sharding of the multicast data stream based on the multicast nodes to obtain sharded traffic units includes: Query the multicast traffic sequence corresponding to the multicast data stream; Extract the traffic sequence vector in the multicast traffic sequence; Divide the dynamic sharding interval corresponding to the traffic sequence vector; Identify the sharding granularity in the dynamic sharding interval; Based on the sharding granularity, perform traffic sharding on the multicast data stream to obtain sharded traffic units.

[0008] Optionally, the constructing of a traffic on-demand framework corresponding to the target IPTV server based on the sharded traffic units includes: Analyze the traffic peak and time period distribution in the sharded traffic units; Fit the traffic fluctuation curve corresponding to the traffic peak and the time period distribution; Extract the time period load segment and buffer interval segment in the traffic fluctuation curve; Based on the time period load threshold, dynamically divide the node resource pool corresponding to the target IPTV server; Based on the buffer interval segment and the node resource pool, construct a traffic on-demand framework corresponding to the target IPTV server.

[0009] Optionally, the calculating of the bandwidth allocation value corresponding to the hierarchical load queue includes: Calculate the bandwidth allocation value corresponding to the hierarchical load queue using the following formula: ; Where represents the bandwidth allocation value corresponding to the hierarchical load queue, represents the total number of levels corresponding to the hierarchical load queue, represents the level index corresponding to the hierarchical load queue, represents the priority weight of the i-th level load, represents the normalized load value of the i-th level load, and respectively represent the start time and end time of the bandwidth calculation period, represents the cumulative load of the target node in the hierarchical load queue during the bandwidth calculation period, represents the maximum load value in the hierarchical load queue, represents the minimum load value in the hierarchical load queue.

[0010] Optionally, detecting traffic anomaly points in the target IPTV server based on the bandwidth allocation value includes: Parsing the bandwidth traffic packet corresponding to the bandwidth allocation value; Based on the bandwidth traffic packet, extracting the peak-valley fluctuation data corresponding to the transmission unit in the target IPTV server; Determining the dynamic threshold baseline corresponding to the peak-valley fluctuation data; Analyzing the instantaneous transmission rate of the traffic unit in the target IPTV server according to the dynamic threshold baseline; Based on the instantaneous transmission rate, determining the traffic anomaly points in the target IPTV server.

[0011] Optionally, performing speed limit marking on the congested area to obtain an area control label includes: Extracting the traffic peak data in the congested area; Dividing the dynamic speed limit level corresponding to the congested area according to the traffic peak data; Matching the protocol speed limit rules corresponding to the dynamic speed limit level; Based on the protocol speed limit rules, marking the congestion boundary corresponding to the congested area; According to the congestion boundary, performing speed limit marking on the congested area to obtain an area control label.

[0012] Optionally, calculating the conflict threshold corresponding to the protocol analysis data includes: Calculating the conflict threshold corresponding to the protocol analysis data using the following formula: ; where, represents the conflict threshold corresponding to the protocol analysis data, Represents the current parameter vector corresponding to the protocol analysis data, Represents the reference parameter vector, Represents the total number of data items corresponding to the protocol analysis data, Represents the data item index corresponding to the protocol analysis data, Represents the conflict-sensitive weight corresponding to the th data item, Represents the load fluctuation coefficient, Represents the end time of the load analysis period, Represents the load change rate at time t, Represents the average load within the load analysis period, Represents the historical conflict factor, Represents the historical number of conflicts, Represents the conflict statistics period.

[0013] Optionally, identifying redundant parameters in the protocol analysis data based on the conflict threshold includes: Analyzing the threshold baseline corresponding to the conflict threshold; Based on the threshold baseline, performing conflict marking on the protocol analysis data to obtain a marked data set; Filtering redundant nodes in the marked data set; Based on the redundant nodes, identifying redundant parameters in the protocol analysis data.

[0014] To solve the above problems, the present invention also provides an IPTV server management and control system driven by an Internet multicast protocol, the system includes: A node identification module, configured to obtain a multicast data stream corresponding to a target IPTV server, perform protocol parsing on the multicast data stream to obtain multicast protocol features, and identify multicast nodes in the multicast data stream based on the multicast protocol features; A framework construction module, configured to perform traffic sharding on the multicast data stream based on the multicast nodes to obtain sharded traffic units, and construct a traffic on-demand framework corresponding to the target IPTV server based on the sharded traffic units; An anomaly detection module, configured to extract traffic load data in the traffic on-demand framework, perform hierarchical marking on the traffic load data to obtain a hierarchical load queue, calculate a bandwidth allocation value corresponding to the hierarchical load queue, and detect traffic anomaly points in the target IPTV server based on the bandwidth allocation value; A threshold calculation module, configured to determine a congestion area corresponding to the traffic anomaly point, perform speed limit marking on the congestion area to obtain a regional management label, perform protocol analysis on the regional management label to obtain protocol analysis data, and calculate a conflict threshold corresponding to the protocol analysis data; A policy-making module, configured to identify redundant parameters in the protocol analysis data based on the conflict threshold, perform control and optimization on the redundant parameters and a preset QoS standard to obtain control and optimization parameters, and formulate a multicast control policy for the target IPTV server based on the protocol analysis data.

[0015] Compared with the problems described in the background art, the present invention can accurately identify multicast nodes and construct a topological structure through protocol parsing by obtaining the multicast data stream corresponding to the target IPTV server, providing a data basis for traffic sharding and the construction of an on-demand framework, effectively guaranteeing the quality of IPTV services and reducing network load. Based on the multicast nodes, the present invention can split the data stream into sharded traffic units adapted to the processing capabilities of different nodes according to the node positions (such as edge routers and core servers) and load differences, improving the transmission efficiency and ensuring the fluency and stability of IPTV services. Further, by extracting the traffic load data in the traffic on-demand framework, the present invention can accurately grasp the traffic pressure of each node at different times, helping operation and maintenance personnel anticipate potential network congestion in advance, reasonably allocate resources, and can also mine traffic patterns through historical load data to guide server expansion or upgrade, improving the overall performance and user experience of IPTV services. Further, by determining the congestion area corresponding to the traffic anomaly point, the present invention helps analyze the cause of congestion, whether it is insufficient device performance, link failure or traffic burst, etc., providing a basis for subsequent optimization; it can also assist in reasonably planning network resources, expanding or adjusting the configuration of congestion-prone areas in advance, improving the overall reliability and stability of the IPTV network. Finally, based on the conflict threshold, the present invention identifies redundant parameters in the protocol analysis data, can accurately locate parameters that have no substantial impact or minimal impact on the conflict risk, avoid interference from invalid configurations, simplify protocol rules, and enhance network stability and reliability. Therefore, the IPTV server control method and system driven by the Internet multicast protocol provided by the embodiments of the present invention can improve the resource scheduling efficiency of IPTV servers. Description of the Drawings

[0016] Figure 1 It is a schematic flowchart of an IPTV server control method driven by the Internet multicast protocol provided by an embodiment of the present invention; Figure 2 It is a schematic architecture diagram of a traffic on-demand framework in an IPTV server control method driven by the Internet multicast protocol provided by an embodiment of the present invention; Figure 3 It is a schematic module diagram of an IPTV server control system driven by the Internet multicast protocol provided by an embodiment of the present invention.

[0017] The implementation, functional features and advantages of the present invention will be further described in conjunction with embodiments with reference to the drawings. Specific Embodiments

[0018] It should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0019] An embodiment of the present application provides an IPTV server management and control method driven by an Internet multicast protocol. The execution subject of the IPTV server management and control method driven by the Internet multicast protocol includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the IPTV server management and control method driven by the Internet multicast protocol can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0020] Embodiment 1: Referring to Figure 1 As shown, it is a flowchart of an IPTV server management and control method driven by an Internet multicast protocol provided by an embodiment of the present invention. In this embodiment, the IPTV server management and control method driven by the Internet multicast protocol includes: S1. Obtain the multicast data stream corresponding to the target IPTV server, perform protocol parsing on the multicast data stream to obtain multicast protocol features, and identify the multicast nodes in the multicast data stream based on the multicast protocol features.

[0021] By obtaining the multicast data stream corresponding to the target IPTV server, the present invention can accurately identify multicast nodes through protocol parsing, construct a topological structure, provide a data basis for traffic sharding and on-demand framework building, effectively ensure the quality of IPTV services, and reduce network load.

[0022] Among them, the target IPTV server refers to the specific IPTV service provider device to be controlled, usually a server deployed in the operator's computer room or content delivery network (CDN), responsible for receiving, processing, and pushing audio and video content through the multicast protocol. For example, a 4K live server provided by an operator for users in the northern region, which undertakes the multicast stream sending task of channels such as CCTV-5, is a typical target IPTV server; the multicast data stream refers to a collection of multimedia data transmitted based on the IP multicast protocol (such as IGMP, PIM), which distributes live streams, on-demand content, etc. from the source end to multiple receivers in a "one-to-many" manner, reducing network bandwidth occupancy. For example, when a user watches a Premier League live broadcast through an IPTV set-top box, the continuous video frames, audio streams, and control signaling sent by the server to the multicast group address 239.1.1.1 together constitute the multicast data stream of this channel. Optionally, obtaining the multicast data stream corresponding to the target IPTV server can be achieved through a network protocol parsing tool. For example, use Wireshark to capture IGMP protocol packets, parse the multicast group address and port, and then input the multicast address through the VLC player to receive the data stream in real time, thereby obtaining the multicast data stream.

[0023] Furthermore, the present invention performs protocol parsing on the multicast data stream to obtain multicast protocol characteristics, which can accurately identify key information such as multicast group addresses, membership relationships, and routing paths in the data stream, quickly construct a multicast topology structure, and help achieve intelligent control and resource optimization configuration of IPTV server multicast transmission.

[0024] Among them, the multicast protocol characteristics refer to the key parameters and behavioral characteristics parsed from the multicast data stream that reflect the execution status of the multicast protocol (such as IGMP, PIM), including but not limited to multicast group addresses (such as 239.0.0.1), join / leave message types of member hosts, query election status (IGMPv3), flood-prune status of the PIM routing protocol, RP (rendezvous point) address, multicast tree type (SPT / RPT), and protocol message interaction delay, etc. For example, IGMP protocol characteristics can reflect changes in the membership relationship of user-subscribed channels, and PIM protocol characteristics can reflect the routing optimization path during cross-domain multicast. Optionally, performing protocol parsing on the multicast data stream can be achieved through a network protocol analysis tool. For example, use Wireshark to capture multicast packets, parse their IP headers, UDP / TCP ports, and payload content, thereby extracting multicast protocol characteristics.

[0025] Furthermore, based on the multicast protocol features, the present invention identifies the multicast nodes in the multicast data stream, can quickly locate the positions and roles of topology components such as data sources, routers, and receivers, clarify the data transmission path, can assist in discovering abnormal nodes (such as failed routers or illegal receivers), and facilitate timely optimization of the multicast tree structure, improving the stability and security of IPTV transmission.

[0026] Among them, the multicast nodes refer to all network entities participating in multicast data transmission, including data source nodes (IPTV servers), forwarding nodes (multicast routers, layer-3 switches), receiving nodes (user set-top boxes, terminal devices), and query nodes responsible for protocol control, etc.

[0027] As an embodiment of the present invention, the identifying the multicast nodes in the multicast data stream based on the multicast protocol features includes: parsing the protocol fields in the multicast protocol; traversing the node routing tables in a preset topology database based on the protocol fields; extracting the timing communication records of active nodes in the node routing tables; filtering the candidate record points in the timing communication records; and identifying the multicast nodes in the multicast data stream based on the candidate record points.

[0028] Among them, the protocol field refers to the fields with specific functions in multicast protocol (such as IGMP, PIM) messages. For example, the type field of IGMP (identifying member join / leave messages), the group address field, the message type field of PIM (such as Join / Prune messages), the source address field, etc., which are used to carry the interaction logic and status information between multicast nodes; the preset topology database refers to the pre-stored IPTV network topology structure database, which contains the physical addresses, logical groupings, connection relationships and protocol configuration information of devices such as routers, servers, switches, etc. in the network, and is used to match with the multicast protocol characteristics parsed in real time; the node routing table refers to the multicast routing entries maintained by each network node (such as routers) recorded in the topology database, which contains information such as multicast group addresses, outbound interface lists, routing protocol types (such as PIM-SM), next-hop addresses, etc., and reflects the forwarding path of multicast data in the node; the active node refers to the node that is currently participating in multicast data transmission and is in a normal communication state, which is confirmed by the alive message (such as IGMP general query response) in the protocol field or real-time traffic interaction, and is different from the nodes in a dormant, faulty or not joined multicast group state; the chronological communication record refers to the protocol message interaction log between active nodes recorded in chronological order. For example, the sending time of IGMP member reports, the receiving chronological order of PIM Join messages, etc., which are used to analyze the continuity and behavior patterns of node communication; the candidate record point refers to the key interaction events related to the target multicast data stream selected from the chronological communication record, such as the first join request for a specific multicast group address, the time point of re-negotiation of the routing protocol between nodes, etc., which are used as the characteristic markers for identifying multicast nodes.

[0029] Further, the parsing of the protocol fields in the multicast protocol can be implemented through a network protocol analysis tool. For example, the protocol parsing engine of Wireshark is used to deconstruct the IP header, UDP / TCP packets, and RTP / RTCP payloads layer by layer to extract protocol fields such as source address, destination address, port number, and timestamp. The traversal of the node routing tables in the preset topology database can be implemented through a graph database query language. For example, the MATCH path query is executed through the Cypher statement of Neo4j to traverse the next-hop routing entries of all nodes, thereby obtaining the complete node routing table. The extraction of the time-series communication records of the active nodes in the node routing table can be implemented through a time-series database. For example, the tx_bytes / rx_bytes counters of a specified node are queried from InfluxDB according to the time range, and an aggregated communication record sequence with timestamps is generated. The filtering of the candidate record points in the time-series communication records can be implemented through a sliding window algorithm. For example, the rolling function of Pandas is used to calculate the 5-minute traffic average value, and the abnormal peak points exceeding the threshold of 3σ are retained as candidate record points. The identification of the multicast nodes in the multicast data stream can be implemented through a machine learning classifier. For example, the random forest model of Scikit-learn is used to classify and identify the multicast member nodes according to the node communication frequency, packet TTL characteristics, and IGMP protocol tags.

[0030] S2. Based on the multicast nodes, perform traffic sharding on the multicast data stream to obtain sharded traffic units, and based on the sharded traffic units, construct a traffic on-demand framework corresponding to the target IPTV server.

[0031] Based on the multicast nodes, the present invention can split the data stream into sharded traffic units adapted to the processing capabilities of different nodes according to the node positions (such as edge routers and core servers) and load differences, improve the transmission efficiency, and ensure the fluency and stability of IPTV services.

[0032] Among them, the sharded traffic unit refers to an independent data unit split from the multicast data stream according to the dynamic sharding interval and sharding granularity, which can be used as the basic unit of node scheduling. For example, a 30-second live stream is split into 30 shards at a 1-second granularity, and each shard contains video frames, audio streams, and multicast control messages within that second, which is the sharded traffic unit and can be independently transmitted and cached among multicast nodes.

[0033] As an embodiment of the present invention, performing traffic fragmentation on the multicast data stream based on the multicast node to obtain fragmented traffic units includes: querying the multicast traffic sequence corresponding to the multicast data stream; extracting the traffic sequence vector in the multicast traffic sequence; dividing the dynamic fragmentation interval corresponding to the traffic sequence vector; identifying the fragmentation granularity in the dynamic fragmentation interval; and performing traffic fragmentation on the multicast data stream based on the fragmentation granularity to obtain fragmented traffic units.

[0034] Among them, the multicast traffic sequence refers to the ordered arrangement in the time dimension of the continuous data stream sent by the target IPTV server through the multicast protocol, including audio and video data, control signaling, etc., forming a traffic set with temporal characteristics in the transmission order. For example, in a live stream of a sports channel, 50 video frames sent per second and the corresponding RTP / RTCP control packets constitute the multicast traffic sequence of this channel in chronological order; the traffic sequence vector refers to converting the multicast traffic sequence into a numerical feature vector, including quantization values in dimensions such as traffic size, packet interval time, data type (such as I-frame / P-frame), protocol type (IGMP / PIM), etc., used to characterize the dynamic characteristics of the traffic. For example, splitting the video stream within 1 second into a vector composed of parameters such as "traffic peak 10Mbps, average packet interval 20ms, I-frame ratio 10%" is a traffic sequence vector; the dynamic fragmentation interval refers to the traffic segmentation range dynamically divided according to the change trend of the traffic sequence vector (such as bandwidth fluctuation, data type switching), used to adapt to the processing capabilities of different nodes. For example, when it is detected that the live stream switches from the advertisement period (low bitrate) to the event screen (high bitrate), the switching point is automatically used as the fragmentation boundary to form two front and back dynamic fragmentation intervals; the fragmentation granularity refers to the size of the traffic fragmentation granularity, usually in units of time length (such as 500ms / segment) or data volume (such as 10MB / segment), and needs to be dynamically adjusted in combination with node performance (such as the cache capacity of the edge router). For example, in an area with weak processing capabilities of the edge node, the fragmentation granularity is set to 500ms, so that each fragmented traffic unit contains about 5MB of data, facilitating fast forwarding by the node.

[0035] Further, querying the multicast traffic sequence corresponding to the multicast data stream can be implemented through a time series database. For example, use the SELECT statement of InfluxDB to query the byte_count metric of a specified multicast group within a time range, and aggregate to generate a traffic sequence with timestamps; extracting the traffic sequence vector in the multicast traffic sequence can be implemented through a numerical processing library. For example, use the array function of NumPy to convert the time series into a two-dimensional vector matrix of [timestamp, traffic value]; dividing the dynamic sharding interval corresponding to the traffic sequence vector can be implemented through a clustering algorithm. For example, apply the KMeans algorithm of Scikit-learn to automatically divide high / medium / low load intervals according to traffic fluctuation characteristics; identifying the sharding granularity in the dynamic sharding interval can be implemented through a statistical analysis tool. For example, use the describe function of Pandas to calculate the standard deviation of the traffic within each interval, and determine the optimal sharding size according to the 3σ principle; performing traffic sharding on the multicast data stream can be implemented through a stream processing framework. For example, based on the window operator of Apache Flink, dynamically divide the data stream according to the identified sharding granularity to generate equal-length sharded traffic units.

[0036] Based on the sharded traffic units, the present invention constructs a traffic on-demand framework for the target IPTV server. By associating the sharded units with multicast node resources (such as edge server caches) through the framework, it can achieve proximity distribution and fast response, reduce the pressure on the core network, and improve the playback fluency and system concurrency processing ability in the on-demand scenario.

[0037] Among them, the traffic on-demand framework refers to an intelligent scheduling system constructed based on sharded traffic units, a node resource pool, and a buffering mechanism, which realizes the on-demand allocation and flexible control of multicast streams. For example, when a user requests a live segment, the framework calls the pre-cached corresponding sharded unit from the node resource pool and quickly pushes it to the terminal through an optimized path, supporting operations such as pause and fast forward.

[0038] As an embodiment of the present invention, constructing the traffic on-demand framework for the target IPTV server based on the sharded traffic units includes: parsing the traffic peak and period distribution in the sharded traffic units; fitting the traffic fluctuation curve corresponding to the traffic peak and the period distribution; extracting the period load segment and the buffer interval segment in the traffic fluctuation curve; dynamically dividing the node resource pool corresponding to the target IPTV server based on the period load threshold; and constructing the traffic on-demand framework for the target IPTV server based on the buffer interval segment and the node resource pool.

[0039] Among them, the traffic peak refers to the maximum data transmission rate (such as Mbps) of the sharded traffic unit per unit time, reflecting the burst load situation of the multicast stream. For example, during a live broadcast of a sports event, when the video coding bitrate suddenly increases to 15 Mbps at the moment of a goal, this is the traffic peak for that period; the time period distribution refers to the distribution law of the traffic peak and various types of data (such as I-frames, audio streams) on the time axis, usually statistically measured in minutes or seconds. For example, an advertisement switch occurs every 10 minutes in a certain TV drama, corresponding to the law that the bitrate is lower during the advertisement period and higher during the plot period in the time period distribution; the traffic fluctuation curve refers to the traffic rate curve that changes with time drawn in the coordinate system by fitting the traffic peak and the time period distribution, visually showing the dynamic fluctuations of the data stream. For example, connecting the average bitrate per minute of a 2-hour live stream into a curve presents a wavy fluctuation of "stable - peak - stable"; the time period load segment refers to the time period in the traffic fluctuation curve where the load exceeds the preset threshold, and node resources need to be allocated with emphasis. For example, when the curve shows that the bitrate continuously exceeds 8 Mbps (the threshold is 5 Mbps) during the period from 19:00 to 19:30, this time period is the time period load segment, and edge server cache resources need to be increased; the buffer interval segment refers to the time period in the traffic fluctuation curve where the load is lower than the threshold and there are redundant resources, which can be used for pre-caching or traffic smoothing. For example, when the bitrate of the advertisement period drops to 2 Mbps (lower than the threshold), the sharded traffic units in this period can be pre-cached to the edge nodes to provide buffering for subsequent high-load periods; the node resource pool refers to the set of multicast node resources dynamically divided according to the time period load segment, including edge servers, router caches, bandwidth quotas, etc., used to respond to real-time traffic demands. For example, 10 edge servers close to users are divided into a resource pool and preferentially allocated to the sharded transmission of popular channels during high-load periods.

[0040] Further, the analysis of the traffic peak and time period distribution in the sharded traffic unit can be achieved through time series analysis tools. For example, the resample and max functions of Pandas are used to count the maximum traffic values in each time period, and the Matplotlib is combined to draw a 24-hour traffic heat map, so as to extract the peak time period and distribution characteristics. The fitting of the traffic fluctuation curve corresponding to the traffic peak and the time period distribution can be achieved through regression algorithms. For example, the SVR model of Scikit-learn is applied to perform non-linear fitting on the peak points to generate a smooth traffic fluctuation trend curve. The extraction of the time period load segment and buffer interval segment in the traffic fluctuation curve can be achieved through inflection point detection algorithms. For example, the Kneedle algorithm is used to identify the mutation points of the curve slope, and the intervals higher than the threshold are marked as the load segment, and the rest are used as the buffer interval segment. The dynamic partitioning of the node resource pool corresponding to the target IPTV server can be achieved through a resource scheduling framework. For example, the HPA component of Kubernetes automatically scales the Pod instances according to the load segment requirements to form an elastic resource pool. The construction of the traffic on-demand framework corresponding to the target IPTV server can be achieved through a streaming media architecture. For example, an edge node cluster is built based on the Nginx-RTMP module, and a hierarchical caching policy is configured according to the buffer interval segment, and finally an on-demand framework supporting dynamic resource scheduling is formed.

[0041] Specifically, to further intuitively understand the on-demand logic architecture corresponding to the traffic on-demand framework in this application, reference can be made to Figure 2 the image shown, which is the on-demand logic architecture diagram provided by the present invention. It should be noted that in the present invention, Figure 2 the presented architecture schematic diagram is only used to display the on-demand logic architecture, which is a hierarchical on-demand distribution structure starting from the CP / SP (content provider / service provider), passing through the national content center, nine distribution centers, provincial nodes to city nodes, and involves components such as LVS (load balancer), and Figure 2 in the content of constructing the traffic on-demand framework, it involves the analysis and processing of traffic-related parameters (such as traffic peak, time period distribution, etc.), and then partitions the node resource pool and constructs the on-demand framework. The various levels of nodes and distribution paths presented in this architecture diagram are the physical or logical architecture basis on which the sharded traffic unit actually performs distribution, scheduling, and resource allocation after the above traffic analysis and processing, providing a landing node layout and transmission path support for the traffic on-demand framework, and not limiting the relationship analysis of the traffic on-demand framework in actual different application scenarios.

[0042] S3. Extract the traffic load data in the traffic on-demand framework, perform hierarchical marking on the traffic load data to obtain a hierarchical load queue, calculate the bandwidth allocation value corresponding to the hierarchical load queue, and based on the bandwidth allocation value, detect the traffic anomaly points in the target IPTV server.

[0043] By extracting the traffic load data in the traffic on-demand framework, the present invention can accurately grasp the traffic pressure of each node at different times, helping the operation and maintenance personnel to anticipate potential network congestion in advance, rationally allocate resources, and can also mine traffic patterns through historical load data to guide server expansion or upgrade, improving the overall performance and user experience of IPTV services.

[0044] Among them, the traffic load data refers to a data set in the traffic on-demand framework that reflects the data transmission load conditions of network nodes, links, etc. within a specific time period, including indicators such as traffic rate (e.g., the amount of data transmitted per second, unit Mbps), concurrent connection number, and bandwidth occupancy rate. For example, for a specific edge server from 8 pm to 9 pm, the average traffic rate is 8 Mbps, the concurrent connection number reaches 500, and the bandwidth occupancy rate is 70%. These values are the traffic load data for that time period. Optionally, the extraction of the traffic load data in the traffic on-demand framework can be achieved through a streaming media monitoring tool. For example, use the stub_status module of Nginx to collect indicators such as connection number and request rate in real time, and combine Telegraf for data collection, and finally output load data including QPS and bandwidth utilization rate.

[0045] Furthermore, by classifying and marking the traffic load data, the present invention obtains a classified load queue, which can clearly distinguish different levels of traffic pressure, facilitating the operation and maintenance personnel to quickly locate high-load nodes, prioritize the handling of potential risks, and can also provide an intuitive basis for the traffic scheduling strategy, dynamically adjusting the content distribution path according to the load level to ensure the stable and smooth IPTV service.

[0046] Among them, the classified load queue refers to a queue formed by arranging network nodes, links, etc. in order of level according to the size or severity of the traffic load data. Usually, it is divided into multiple levels from light to heavy (or from low to high), such as low load, medium load, and high load. For example, nodes with a bandwidth occupancy rate lower than 30% are marked as the low-load level and arranged at the head of the queue, 30%-70% is the medium-load level and arranged in the middle, and higher than 70% is the high-load level and arranged at the end of the queue, thus forming an ordered queue for convenient management and scheduling. Optionally, the classification and marking of the traffic load data can be achieved through a clustering algorithm. For example, use the KMeans algorithm of Scikit-learn to automatically divide the load data into three levels of high / medium / low according to features such as bandwidth occupancy rate and request concurrency, generating a classified load queue.

[0047] Furthermore, by calculating the bandwidth allocation value corresponding to the hierarchical load queue, the present invention can achieve differential allocation of bandwidth resources according to the load level, dynamically increase the bandwidth quota for high-load nodes to relieve congestion, avoid waste of resources for low-load nodes, and optimize the QoS metrics of IPTV services.

[0048] Among them, the bandwidth allocation value refers to the bandwidth quota allocated to network nodes (such as servers, routers, etc.) for data transmission, which is calculated through the above formula according to relevant parameters of the hierarchical load queue (such as load level, weight, etc.). The unit is usually Mbps (megabits per second), which is used to ensure that nodes can transmit data normally under corresponding load conditions, avoiding congestion or resource waste.

[0049] As an embodiment of the present invention, calculating the bandwidth allocation value corresponding to the hierarchical load queue includes: Calculating the bandwidth allocation value corresponding to the hierarchical load queue by using the following formula: ; Among them, represents the bandwidth allocation value corresponding to the hierarchical load queue, represents the total number of levels corresponding to the hierarchical load queue, represents the level index corresponding to the hierarchical load queue, represents the priority weight of the i-th level load, represents the normalized load value of the i-th level load, and respectively represent the start time and end time of the bandwidth calculation period, represents the cumulative load of the target node in the hierarchical load queue during the bandwidth calculation period, represents the maximum load value in the hierarchical load queue, represents the minimum load value in the hierarchical load queue.

[0050] Specifically, the priority weight refers to a value used to measure the importance of different levels of loads in the hierarchical load queue. The value range is generally in (0, 1]. The higher the weight, the more priority and urgency the resource requirements of the corresponding level of load are. For example, in IPTV services, the priority weight of live service loads can be set higher than that of ordinary video-on-demand service loads to ensure the smoothness of live broadcasts; the normalized load value refers to the value obtained by mapping the actual load of the node (such as bandwidth occupancy rate, number of concurrent connections, etc.) to the range of [0, 1] through a certain calculation method. For example, the load value of a node with a bandwidth occupancy rate of 80% is normalized to 0.8; the bandwidth calculation period refers to the time interval selected for calculating the node bandwidth allocation value, which consists of the start time and the end time The time period can be flexibly set according to actual needs, such as hourly or daily statistics, to count the load of the node in this time period and provide a basis for bandwidth allocation; the cumulative load refers to the cumulative value of the load of the target node at each time point (such as bandwidth occupancy rate) during the bandwidth calculation period, which is generally calculated by the time interval [ , ]Internal load variation function over time It is obtained by performing an integral operation, which reflects the total load pressure of the node during the period; the maximum load value refers to the maximum value of the normalized load values corresponding to all levels of load in the hierarchical load queue, which represents the heaviest load in the current load queue and can be used to measure the load difference; the minimum load value refers to the minimum value of the normalized load values corresponding to all levels of load in the hierarchical load queue, which is relative to the maximum load value and is used to reflect the lowest load level in the load queue.

[0051] Furthermore, the present invention detects traffic anomalies in the target IPTV server based on the bandwidth allocation value, can promptly discover situations where actual traffic does not match the allocated bandwidth, quickly locate problems such as congestion caused by a sudden increase in traffic or idle resources caused by a sudden decrease in traffic, effectively identify the source of abnormal traffic, and facilitate the investigation of hidden dangers such as malicious attacks or software failures.

[0052] Among them, the traffic anomaly point refers to the time point or data segment when the instantaneous transmission rate deviates significantly from the dynamic threshold baseline, which may be caused by network attacks, equipment failures or sudden traffic. For example, if the traffic in a period suddenly soars to 25Mbps (exceeding the threshold baseline of 14Mbps), analysis shows that it is caused by a large number of live stream requests from malicious crawlers. This time point is marked as a traffic anomaly point.

[0053] As an embodiment of the present invention, detecting traffic anomalies in the target IPTV server based on the bandwidth allocation value includes: parsing bandwidth traffic packets corresponding to the bandwidth allocation value; extracting peak-valley fluctuation data corresponding to transmission units in the target IPTV server based on the bandwidth traffic packets; determining a dynamic threshold baseline corresponding to the peak-valley fluctuation data; analyzing the instantaneous transmission rate of the traffic units in the target IPTV server based on the dynamic threshold baseline; and determining traffic anomalies in the target IPTV server based on the instantaneous transmission rate.

[0054] Among them, the bandwidth traffic packet refers to a set of network data packets carrying IPTV data streams, including data units such as video frames, audio streams, and control signaling. Its size and transmission frequency directly affect the bandwidth allocation value. For example, if the bandwidth traffic packet of a 4K live channel consists of 25 video GOPs (groups of pictures) per second and related RTSP control packets, and the size of each GOP is approximately 1.2MB; the transmission unit refers to the smallest data unit that can be independently transmitted and scheduled in the IPTV data stream, usually corresponding to audio and video segments within a specific time window. For example, a transmission unit can contain 1 second of video data (about 5 - 10MB) and a synchronized audio stream, encapsulated as a continuous sequence of data packets through the RTP protocol; the peak-valley fluctuation data refers to the bandwidth usage change curve of the transmission unit in the time series, reflecting the dynamic fluctuation characteristics of the traffic. For example, during a live sports event, the bandwidth peak at the moment of a goal can reach 15Mbps, while it drops to 3Mbps during an advertisement period. This alternating high and low fluctuation forms the peak-valley data; the dynamic threshold baseline refers to an anomaly detection baseline that is adaptively adjusted according to historical traffic data and the current bandwidth allocation value, and is dynamically updated as the network load changes. For example, if the historical traffic average of a certain server is 8Mbps and the standard deviation is 2Mbps, the dynamic threshold baseline can be set to the average plus 3 times the standard deviation (14Mbps), and any value exceeding this is determined to be abnormal; the traffic unit refers to a continuous data stream transmitted at a specific time point or time period, which can be regarded as a collection of transmission units. For example, a traffic unit can contain all video frames and audio packets within 5 seconds, with a total size of approximately 50MB, and is used to evaluate traffic bursts within a short period; the instantaneous transmission rate refers to the real-time data transmission speed of the traffic unit at a certain moment, usually measured in Mbps. For example, if the instantaneous rate during the transmission of a video key frame can reach 20Mbps, while the transmission rate of an ordinary P frame is about 5Mbps, anomalies can be quickly identified by comparing the instantaneous rate with the threshold baseline.

[0055] Further, the parsing of the bandwidth traffic packet corresponding to the bandwidth allocation value can be implemented through a network protocol analysis tool. For example, use Wireshark to capture TCP / IP packets, and extract the DSCP field and payload size through the tshark command-line tool to generate a bandwidth traffic packet containing QoS markings; the extraction of the peak-valley fluctuation data corresponding to the transmission unit in the target IPTV server can be implemented through a time series analysis tool. For example, calculate the bandwidth range within a 5-minute sliding window through the rolling function of Pandas, and combine Matplotlib to draw a bandwidth fluctuation curve to output a peak-valley difference sequence; the determination of the dynamic threshold baseline corresponding to the peak-valley fluctuation data can be implemented through a statistical modeling method. For example, apply the Facebook Prophet algorithm to perform seasonal decomposition on historical fluctuation data to generate a dynamic baseline threshold containing a confidence interval; the analysis of the instantaneous transmission rate of the traffic unit in the target IPTV server can be implemented through a streaming computing framework. For example, based on the window function of Apache Flink, calculate the number of bytes per second in real time and output an instantaneous rate sequence with timestamps; the determination of the traffic anomaly points in the target IPTV server can be implemented through an anomaly detection algorithm. For example, adopt the Isolation Forest model to perform unsupervised learning on the rate sequence, and mark the data points with a deviation exceeding 3σ as traffic anomaly points.

[0056] S4. Determine the congestion area corresponding to the traffic anomaly point, perform speed limit marking on the congestion area to obtain a regional control label, perform protocol analysis on the regional control label to obtain protocol analysis data, and calculate the conflict threshold corresponding to the protocol analysis data.

[0057] By determining the congestion area corresponding to the traffic anomaly point, the present invention helps to analyze the reasons for congestion, whether it is insufficient device performance, link failure, or traffic burst, etc., providing a basis for subsequent optimization; it can also assist in reasonably planning network resources, expanding or adjusting the configuration of congestion-prone areas in advance, and improving the overall reliability and stability of the IPTV network.

[0058] Among them, the congestion area refers to a specific range area in the IPTV network where data transmission is blocked and network performance deteriorates due to abnormal traffic (such as sudden traffic growth, too high transmission rate). It covers several servers, some links, or a subnet composed of multiple nodes and their connections. For example, when the instantaneous traffic of an individual edge server far exceeds its processing capacity, the range where the surrounding switches and links connected to it are located forms a congestion area, and phenomena such as video stuttering and slow loading will occur. Optionally, determining the congestion area corresponding to the traffic anomaly point can be achieved through a network topology analysis tool. For example, by combining the Gephi visualization tool to perform graph analysis on the connection relationship of network nodes where the anomaly point is located, and identifying a device cluster with high-density connections as the congestion area.

[0059] Furthermore, the present invention performs speed limit marking on the congestion area to obtain a regional control label, which can quickly relieve local network pressure by restricting the transmission rate of abnormal traffic, avoid congestion spreading to the core link, can provide a clear routing avoidance signal for the traffic scheduling system, guide subsequent data streams to bypass to a low-load path, and improve the overall transmission efficiency.

[0060] Among them, the regional control label refers to a metadata label carrying the speed limit level, boundary range, and protocol rules of the congestion area, which is used to notify upstream nodes to adjust the traffic scheduling strategy. For example, the label content can be expressed as "[area ID: EDGE-01, speed limit level: medium, boundary port: GE0 / 1, protocol rule: DSCP=AF41, speed limit value: 9Mbps]" for the core router to identify and bypass this area.

[0061] As an embodiment of the present invention, performing speed limit marking on the congestion area to obtain a regional control label includes: extracting the traffic peak data in the congestion area; dividing the dynamic speed limit level corresponding to the congestion area according to the traffic peak data; matching the protocol speed limit rules corresponding to the dynamic speed limit level; marking the congestion boundary corresponding to the congestion area based on the protocol speed limit rules; and performing speed limit marking on the congestion area according to the congestion boundary to obtain a regional control label.

[0062] Among them, the traffic peak data refers to the maximum data transmission rate (such as Mbps) and duration in the congestion area during the abnormal period, reflecting the intensity and duration of traffic bursts. For example, if the peak rate of the edge server reaches 22 Mbps (normal threshold 15 Mbps) during 20:00 - 20:05, the peak value of this period and the record of lasting for 5 minutes are the traffic peak data; the dynamic speed limit level refers to dividing the speed limit requirements of the congestion area into different levels (such as mild, moderate, and severe speed limits) according to the deviation degree between the traffic peak data and the normal threshold. For example, when the peak exceeds the threshold by 30%, it is divided into mild speed limit (speed limited to 110% of the threshold), when it exceeds 50% it is moderate (speed limited to 90% of the threshold), and when it exceeds 80% it is severe (speed limited to 70% of the threshold); the protocol speed limit rule refers to the specific rule for implementing traffic speed limit through network protocols (such as IP precedence, DiffServ) or device configurations (such as QoS policies), including speed limit thresholds, priority adjustment, traffic discard policies, etc. For example, based on the DiffServ protocol, set the DSCP mark of non-critical traffic (such as standard-definition video stream) in the congestion area to EF and limit its rate not to exceed 5 Mbps; the congestion boundary refers to the logical or physical demarcation between the congestion area and the non-congestion area in the network topology, usually in units of router interfaces, switch ports, or IP network segments. For example, if the congestion area consists of 3 edge servers and the aggregation switch they are connected to, its boundary is the uplink port of the aggregation switch, and the speed limit rule only acts on the traffic within this port.

[0063] Further, the extraction of the traffic peak data in the congested area can be achieved through a time series analysis tool. For example, the historical peak traffic of each node in the congested area can be obtained using the SELECT MAX() query statement of InfluxDB, and a peak distribution heat map can be plotted in combination with Grafana to extract key peak data. The division of the dynamic speed limit levels corresponding to the congested area can be achieved through an adaptive clustering algorithm. For example, the OPTICS density clustering algorithm is applied to automatically divide three speed limit level intervals of severe / moderate / mild according to the distribution characteristics of the peak data. The matching of the protocol speed limit rules corresponding to the dynamic speed limit levels can be achieved through a rule engine. For example, based on the Drools rule engine, QoS policies are configured (such as severe speed limit: TCP 50%, UDP 80%; moderate speed limit: TCP 30%, etc.) to generate a differentiated protocol speed limit rule set. The marking of the congestion boundary corresponding to the congested area can be achieved through a network topology analysis tool. For example, the Cytoscape.js visualization library is used to analyze the node connectivity degree, and the BGP AS numbers of the border routers are identified to divide the logical congestion boundary. The speed limit marking of the congested area can be achieved through an SDN controller. For example, a DSCP speed limit marking is added to the border switch ports through the Flow Mod message of the OpenFlow protocol to generate an identifiable area control label.

[0064] By performing protocol analysis on the area control label, the present invention obtains protocol analysis data, which can deeply interpret the speed limit rules and topology information carried by the label (such as DiffServ marking, border ports), ensuring the compatibility of the speed limit policy between different protocol layers, thereby improving the control efficiency and stability of the IPTV network in a complex protocol environment.

[0065] Among them, the protocol analysis data refers to the quantitative results of the label structure, speed limit rule parameters, topology positioning information, and protocol execution status extracted by parsing the protocol fields of the area control label (such as the DSCP value in the IP header, QoS policy parameters, border port identifiers, etc.). For example, from the label "[DSCP=AF41, port = GE0 / 1, speed limit value = 9Mbps]", the following data can be parsed: the protocol type is DiffServ, the priority marking AF41 corresponds to the moderate speed limit level, the acting port is GE0 / 1, and the consistency status of the actual speed limit value with the rule configuration (such as whether there is an error within 5%). These data are used to evaluate the effectiveness and protocol compatibility of the speed limit policy. Optionally, the protocol analysis of the area control label can be achieved through a network protocol parsing tool. For example, Wireshark is used to capture the packets carrying the DSCP marking, and the TShark command-line tool is combined to extract the QoS fields, protocol type, and speed limit policy parameters to generate structured protocol analysis data.

[0066] Furthermore, by calculating the conflict threshold corresponding to the protocol analysis data, the present invention can quantify the potential conflict risks in the execution of protocol rules. By setting a reasonable threshold (such as the critical value of priority mark difference), it can quickly identify the policy conflict points and give early warnings, ensuring the coordination consistency and execution efficiency of the regional control policies in the IPTV system.

[0067] Among them, the conflict threshold refers to a quantitative index used to determine whether there are conflict risks in the parameter configurations in the protocol analysis data. When the relevant calculated value exceeds this threshold, it means that there are conflicts in the protocol parameter configurations, and further investigation and adjustment are required to ensure the normal data transmission and service operation in the network.

[0068] As an embodiment of the present invention, calculating the conflict threshold corresponding to the protocol analysis data includes: Calculating the conflict threshold corresponding to the protocol analysis data by using the following formula: ; Among them, represents the conflict threshold corresponding to the protocol analysis data, represents the current parameter vector corresponding to the protocol analysis data, represents the reference parameter vector, represents the total number of data items corresponding to the protocol analysis data, represents the data item index corresponding to the protocol analysis data, represents the th conflict sensitivity weight corresponding to the data item, represents the load fluctuation coefficient, represents the end time of the load analysis period, represents the load change rate at time t, represents the average load within the load analysis period, represents the historical conflict factor, represents the historical conflict times, represents the conflict statistical period.

[0069] Specifically, the current parameter vector refers to the vector composed of the current actually measured parameter values in the protocol analysis data. For example, when analyzing the IPTV network protocol, it may include multiple parameters such as DSCP value, speed limit value, TTL value, etc., and is represented in vector form P = , ,..., to reflect the actual state of the current protocol parameters; the reference parameter vector refers to the pre-set parameter vector considered to be in a normal or standard situation, which can be determined based on network design specifications, parameter values in the historical good operating state, etc., and is in the form of = , ,..., , as a comparison benchmark to measure whether the current parameter vector deviates from the normal range; the conflict-sensitive weight refers to a weight value set for each data item (parameter) in the protocol analysis data, reflecting the sensitivity and impact degree of the parameter on the occurrence of conflicts, and its value range is generally in [0, 1]. Parameters with high sensitivity (such as parameters related to traffic priority and key routes) have weights closer to 1, and vice versa closer to 0; the load fluctuation coefficient refers to a dimensionless coefficient, and its value range is usually in [0, 1], which is used to measure the impact degree of load fluctuation on the protocol conflict risk. The larger this coefficient is, the higher the weight of load fluctuation in calculating the conflict threshold, that is, the more likely the load fluctuation is to cause an evaluation change in the conflict risk; the load analysis period refers to the time interval selected for calculating load-related indicators (such as load change rate, average load), from the start time to the end time T. This time period can be set according to actual network monitoring and analysis requirements, such as by hour, by day, etc., and is used to statistically analyze the dynamic changes of network load during this period; the load change rate refers to at a certain moment , the degree of change of network load (such as bandwidth occupancy rate, data traffic rate, etc.), usually expressed by the change amount of load per unit time, and it is a function that changes with time , reflecting the instantaneous fluctuation characteristics of network load at that moment; the historical conflict factor refers to the degree of influence of historical conflict situations on the calculation of the current conflict threshold. The larger this factor is, the higher the weight of historical conflict situations in calculating the current conflict threshold, that is, the more emphasis is placed on the role of the frequency and situation of historical conflicts in the current risk assessment; the historical conflict count refers to the cumulative number of conflicts in the protocol parameter configuration in the network within a certain past statistical period (with a duration of ), which is a statistical value used to measure the frequency of protocol conflicts occurring in the network during the historical time period; the conflict statistical period refers to the time length used to statistically analyze the historical conflict count, represented by , for example, to count the number of protocol conflicts occurring in the network in the past week ( = 7×24×3600 seconds), and this one-week duration is the conflict statistical period.

[0070] S5. Based on the conflict threshold, identify the redundant parameters in the protocol analysis data, perform control and optimization on the redundant parameters in combination with the preset QoS standard to obtain control and optimization parameters, and formulate a multicast control strategy for the target IPTV server based on the protocol analysis data.

[0071] Based on the conflict threshold, the present invention identifies redundant parameters in the protocol analysis data, can accurately locate the parameters that have no substantial impact or minimal impact on the conflict risk, avoid interference from invalid configurations, simplify protocol rules, and enhance network stability and reliability.

[0072] Among them, the redundant parameter refers to a parameter in the protocol analysis data that makes no actual contribution to the realization of the protocol function or increases complexity and causes conflicts. For example, in several network traffic control protocols, multiple duplicate traffic priority parameters are set, and the redundant part among them is the redundant parameter. Removing it can optimize the protocol configuration and reduce the possibility of conflicts.

[0073] As an embodiment of the present invention, the identifying the redundant parameters in the protocol analysis data based on the conflict threshold includes: analyzing the threshold baseline corresponding to the conflict threshold; performing conflict marking on the protocol analysis data based on the threshold baseline to obtain a marked data set; screening redundant nodes in the marked data set; and identifying redundant parameters in the protocol analysis data based on the redundant nodes.

[0074] Among them, the threshold baseline is a reference standard line determined according to historical data, network normal operation indicators, etc. for measuring whether the conflict threshold is reasonable. It is a quantitative value. For example, by statistically analyzing the conflict threshold during the normal operation of the network in the past month and taking the average value as the threshold baseline. If the current conflict threshold is much higher than this line, it indicates the risk of parameter conflicts. The marked data set refers to the set formed by integrating the data with conflict markings (such as marked as conflict, potential conflict, no conflict) after performing conflict marking on the protocol analysis data. For example, when analyzing IPTV network protocol data, each parameter group is marked with the corresponding conflict status according to the comparison result with the threshold baseline, and these parameter groups with markings together constitute the marked data set. The redundant node refers to a node in the marked data set that has no substantial effect on the normal operation of the protocol or repeats the function and increases the conflict risk. For example, in the network routing protocol configuration, there are two routing configuration nodes with exactly the same function, and one of them may be a redundant node, which not only occupies resources but also may cause conflicts due to configuration differences.

[0075] Further, the threshold baseline corresponding to the analyzed conflict threshold can be implemented through statistical modeling methods. For example, the Facebook Prophet time series prediction model is used to perform seasonal decomposition on historical threshold data to generate a dynamic baseline including confidence intervals. The conflict marking of the protocol analysis data can be achieved through a rule engine. For example, protocol conflict detection rules (such as TCP / UDP rate mismatch, QoS marking conflict, etc.) are configured based on the Drools rule engine to output a dataset with conflict markings. The screening of redundant nodes in the marked dataset can be realized through graph theory algorithms. For example, the PageRank algorithm is applied to calculate the importance scores of network nodes, and redundant nodes with a connection degree lower than the threshold and no critical services are screened out. The identification of redundant parameters in the protocol analysis data can be achieved through feature selection methods. For example, the SelectKBest algorithm of Scikit-learn is used based on the chi-square test to identify protocol parameters with low contribution to traffic classification.

[0076] By controlling and optimizing the redundant parameters with the preset QoS standards, the present invention obtains control and optimization parameters, which can accurately allocate network resources, avoid resource waste caused by parameter redundancy, and improve resource utilization rate. It can also simplify network management, reduce operation and maintenance complexity, quickly locate and solve network performance problems, and enhance network stability.

[0077] Among them, the preset QoS standards refer to a series of indicators and specifications set in advance for measuring and guaranteeing network service quality, which cover the specific thresholds and requirements of parameters such as bandwidth, latency, jitter, and packet loss rate. For example, for IPTV high-definition video services, the preset QoS standards stipulate that the bandwidth is not less than 5 Mbps, the latency is less than 100 ms, and the packet loss rate is lower than 1%, so as to ensure smooth video playback, clear pictures, and meet the user experience requirements. The control and optimization parameters refer to the parameters obtained by processing the redundant parameters and adjusting and optimizing them according to the preset QoS standards. These parameters can make the network protocol configuration more reasonable and efficient, and guarantee network service quality. For example, after removing redundant traffic control parameters, according to the bandwidth requirements in the QoS standards, appropriate speed limit parameters are reset, etc., to make the network resource allocation more scientific, reduce the conflict risk, and improve the overall network performance. Optionally, the control and optimization of the redundant parameters with the preset QoS standards can be achieved through a parameter optimization framework. For example, the optimizer module of TensorFlow is used to perform gradient descent calculation on the redundant parameters, minimize the parameter dimension under the condition of meeting the minimum bandwidth guarantee, and finally obtain the control and optimization parameters.

[0078] Furthermore, based on the protocol analysis of the data, the present invention formulates the multicast control strategy corresponding to the target IPTV server, which can accurately match the multicast traffic characteristics by parsing protocol parameters (such as IGMP group address, PIM hello interval), dynamically optimize the forwarding tree structure and bandwidth quota, reduce the cross-segment forwarding delay, and improve the live stream transmission efficiency.

[0079] Among them, the multicast control strategy refers to a set of traffic scheduling and resource management rules formulated according to the multicast service characteristics of the target IPTV server through protocol analysis of data (such as IGMPv3 membership report, PIM-SM signaling parameters, multicast stream bandwidth distribution). Its content includes but is not limited to: multicast group address planning, forwarding tree construction strategy (such as SPT / RPT switching threshold), bandwidth quota allocation (such as the maximum allowable rate for each multicast group), QoS priority marking (such as allocating DSCP = 46 for 4K live multicast stream), member relationship aging time configuration (such as setting the IGMP leave delay to 10 seconds), etc. For example, if the number of users in the multicast group in the strategy exceeds 500, the forwarding tree is automatically switched to the shortest path tree (SPT), and a dedicated bandwidth of 10 Mbps is allocated for the group stream. At the same time, the optimal forwarder is elected through the PIM assert mechanism to avoid loops and bandwidth waste. This strategy aims to optimize the multicast transmission efficiency, ensure service quality, and reduce network resource consumption. Optionally, the formulation of the multicast control strategy corresponding to the target IPTV server can be implemented through a policy orchestration framework, such as: using the policy engine of ONAP to automatically generate rules including bandwidth reservation, multicast tree optimization, etc. according to the network topology and QoS requirements, and finally obtain the multicast control strategy.

[0080] Compared with the problems described in the background art, the present invention can accurately delimit the lighting range by obtaining the deployment location corresponding to the explosion-proof single lamp controller and determining the target lighting area corresponding to the deployment location, avoiding overlapping or omission of the lighting area and reducing energy waste. At the same time, it can achieve refined and intelligent control of lighting, effectively improving the safety and energy-saving efficiency of the lighting system in a dangerous environment. By monitoring the real-time energy consumption data in the distributed control link, the present invention can optimize the working mode of lighting equipment, achieve accurate energy saving on the premise of meeting the lighting requirements, reduce unnecessary energy waste, and improve energy utilization efficiency. Further, based on the optimal dimming coefficient, the present invention collects the signal response frequency band in the target lighting area, can accurately master the interaction feedback between the lighting system and the environment, and can timely detect potential faults of lighting equipment, such as abnormal frequency bands indicating lamp aging or electrical problems, which is convenient for early maintenance. Further, based on the harmonic distortion rate, the present invention performs light compensation on the defect response section to obtain a light compensation threshold, which can effectively correct the light abnormality caused by harmonic interference, thereby accurately compensating for problems such as light intensity deviation and color temperature fluctuation, and ensuring uniform and stable lighting. Finally, by correcting the power of the high-load node to obtain a load correction parameter, the present invention can significantly improve the operating stability of the explosion-proof lighting system, avoid the risk of equipment overload by dynamically adjusting the load parameters, and can achieve accurate energy consumption reallocation, optimizing the overall energy efficiency while ensuring the lighting quality of key areas. Therefore, the IPTV server management and control method and system driven by the Internet multicast protocol provided by the embodiments of the present invention can improve the resource scheduling efficiency of the IPTV server. The IPTV server management and control method and system driven by the Internet multicast protocol provided by the embodiments of the present invention can improve the resource scheduling efficiency of the IPTV server.

[0081] Embodiment 2: As Figure 3 shown, it is a functional module diagram of an IPTV server management and control system driven by the Internet multicast protocol of the present invention.

[0082] The IPTV server management and control system 200 driven by the Internet multicast protocol of the present invention can be installed in an electronic device. According to the functions achieved, the IPTV server management and control system driven by the Internet multicast protocol can include a node identification module 201, a framework construction module 202, an anomaly detection module 203, a threshold calculation module 204, and a policy formulation module 205. The modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0083] In the embodiments of the present invention, the functions of each module / unit are as follows: The node recognition module 201 is configured to obtain the multicast data stream corresponding to the target IPTV server, parse the protocol of the multicast data stream to obtain multicast protocol features, and recognize the multicast nodes in the multicast data stream based on the multicast protocol features; The framework construction module 202 is configured to perform traffic fragmentation on the multicast data stream based on the multicast nodes to obtain fragmented traffic units, and construct a traffic on-demand framework corresponding to the target IPTV server based on the fragmented traffic units; The anomaly detection module 203 is configured to extract the traffic load data in the traffic on-demand framework, perform hierarchical marking on the traffic load data to obtain a hierarchical load queue, calculate the bandwidth allocation value corresponding to the hierarchical load queue, and detect traffic anomaly points in the target IPTV server based on the bandwidth allocation value; The threshold calculation module 204 is configured to determine the congestion area corresponding to the traffic anomaly point, perform speed limit marking on the congestion area to obtain a regional control label, perform protocol analysis on the regional control label to obtain protocol analysis data, and calculate the conflict threshold corresponding to the protocol analysis data; The policy formulation module 205 is configured to identify redundant parameters in the protocol analysis data based on the conflict threshold, perform control optimization on the redundant parameters and a preset QoS standard to obtain control optimization parameters, and formulate a multicast control policy corresponding to the target IPTV server based on the protocol analysis data.

[0084] Specifically, each module in the IPTV server control system 200 driven by the Internet multicast protocol in the embodiments of the present invention adopts the same technical means as those in the above-mentioned Figure 1 Internet multicast protocol-driven IPTV server control method described, and can produce the same technical effects, which will not be elaborated here.

[0085] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An IPTV server management and control method driven by an Internet multicast protocol, characterized in that, The method includes: Obtain the multicast data stream corresponding to the target IPTV server, perform protocol analysis on the multicast data stream to obtain multicast protocol features, and identify multicast nodes in the multicast data stream based on the multicast protocol features; Based on the multicast nodes, perform traffic sharding on the multicast data stream to obtain sharded traffic units, and construct a traffic on-demand framework corresponding to the target IPTV server based on the sharded traffic units; Extract traffic load data from the traffic on-demand framework, perform hierarchical marking on the traffic load data to obtain a hierarchical load queue, calculate the bandwidth allocation value corresponding to the hierarchical load queue, and detect traffic anomaly points in the target IPTV server based on the bandwidth allocation value; Determine the congestion area corresponding to the traffic anomaly point, perform speed limit marking on the congestion area to obtain an area control label, perform protocol analysis on the area control label to obtain protocol analysis data, and calculate the conflict threshold corresponding to the protocol analysis data; Based on the conflict threshold, identify redundant parameters in the protocol analysis data, perform control optimization on the redundant parameters and a preset QoS standard to obtain control optimization parameters, and formulate a multicast control strategy corresponding to the target IPTV server based on the protocol analysis data.

2. The IPTV server control method driven by the Internet multicast protocol according to claim 1, wherein The identifying the multicast nodes in the multicast data stream based on the multicast protocol features includes: Analyze the protocol fields in the multicast protocol; Based on the protocol fields, traverse the node routing table in the preset topology database; Extract the timing communication records of active nodes in the node routing table; Filter candidate record points in the timing communication records; Identify the multicast nodes in the multicast data stream based on the candidate record points.

3. The IPTV server control method driven by the Internet multicast protocol according to claim 1, characterized in that The performing traffic sharding on the multicast data stream based on the multicast nodes to obtain sharded traffic units includes: Query the multicast traffic sequence corresponding to the multicast data stream; Extract the traffic sequence vector in the multicast traffic sequence; Divide the dynamic sharding interval corresponding to the traffic sequence vector; Identify the sharding granularity in the dynamic sharding interval; Based on the sharding granularity, perform traffic sharding on the multicast data stream to obtain sharded traffic units.

4. The IPTV server control method driven by the Internet multicast protocol according to claim 1, characterized in that, The constructing a traffic on-demand framework corresponding to the target IPTV server based on the sharded traffic units includes: Analyze the traffic peak value and time period distribution in the sharded traffic units; Fit the traffic fluctuation curve corresponding to the traffic peak value and the time period distribution; Extract the time period load segment and buffer interval segment in the traffic fluctuation curve; Based on the time period load threshold, dynamically divide the node resource pool corresponding to the target IPTV server; Based on the buffer interval segment and the node resource pool, construct a traffic on-demand framework corresponding to the target IPTV server.

5. The IPTV server management and control method driven by an Internet multicast protocol according to claim 1, wherein The calculating the bandwidth allocation value corresponding to the hierarchical load queue includes: Calculate the bandwidth allocation value corresponding to the hierarchical load queue using the following formula: ； Among them, represents the bandwidth allocation value corresponding to the hierarchical load queue, represents the total number of levels corresponding to the hierarchical load queue, represents the level index corresponding to the hierarchical load queue, represents the priority weight of the load at the i-th level, represents the normalized load value of the load at the i-th level, and respectively represent the start time and end time of the bandwidth calculation period, represents the cumulative load of the target node in the hierarchical load queue during the bandwidth calculation period, represents the maximum load value in the hierarchical load queue, represents the minimum load value in the hierarchical load queue.

6. The IPTV server management and control method driven by the Internet multicast protocol according to claim 1, characterized in that The detecting traffic anomaly points in the target IPTV server based on the bandwidth allocation value includes: Analyze the bandwidth traffic packet corresponding to the bandwidth allocation value; Extract the peak-valley fluctuation data corresponding to the transmission unit in the target IPTV server based on the bandwidth traffic packet; Determine the dynamic threshold baseline corresponding to the peak-valley fluctuation data; Analyze the instantaneous transmission rate of the traffic unit in the target IPTV server according to the dynamic threshold baseline; Determine the traffic anomaly points in the target IPTV server based on the instantaneous transmission rate.

7. The IPTV server control method driven by the Internet multicast protocol according to claim 1, characterized in that, The speed limit marking of the congestion area to obtain the area control label includes: Extract the traffic peak data in the congestion area; Divide the dynamic speed limit levels corresponding to the congestion area according to the traffic peak data; Match the protocol speed limit rules corresponding to the dynamic speed limit levels; Mark the congestion boundary corresponding to the congestion area based on the protocol speed limit rules; Perform speed limit marking on the congestion area according to the congestion boundary to obtain the area control label.

8. The method for controlling an IPTV server driven by an Internet multicast protocol according to claim 1, wherein The calculation of the conflict threshold corresponding to the protocol analysis data includes: Calculate the conflict threshold corresponding to the protocol analysis data using the following formula: ； Among them, represents the conflict threshold corresponding to the protocol analysis data, represents the current parameter vector corresponding to the protocol analysis data, represents the reference parameter vector, represents the total number of data items corresponding to the protocol analysis data, represents the data item index corresponding to the protocol analysis data, represents the conflict sensitivity weight corresponding to the th data item, represents the load fluctuation coefficient, represents the end time of the load analysis period, represents the load change rate at time t, represents the historical conflict factor, represents the historical conflict times, represents the conflict statistics period.

9. The IPTV server control method driven by the Internet multicast protocol according to claim 1, characterized in that, Based on the conflict threshold, identify the redundant parameters in the protocol analysis data, including: Analyze the threshold baseline corresponding to the conflict threshold; Perform conflict marking on the protocol analysis data based on the threshold baseline to obtain a marked data set; Filter the redundant nodes in the marked data set; Identify the redundant parameters in the protocol analysis data based on the redundant nodes.

10. An IPTV server management and control system driven by an Internet multicast protocol, characterized in that, The system includes: A node identification module, configured to obtain the multicast data stream corresponding to the target IPTV server, perform protocol parsing on the multicast data stream to obtain multicast protocol characteristics, and identify the multicast nodes in the multicast data stream based on the multicast protocol characteristics; A framework construction module, configured to perform traffic sharding on the multicast data stream based on the multicast nodes to obtain sharded traffic units, and construct a traffic on-demand framework corresponding to the target IPTV server based on the sharded traffic units; An anomaly detection module, configured to extract the traffic load data in the traffic on-demand framework, perform hierarchical marking on the traffic load data to obtain a hierarchical load queue, calculate the bandwidth allocation value corresponding to the hierarchical load queue, and detect the traffic anomaly points in the target IPTV server based on the bandwidth allocation value; A threshold calculation module, configured to determine the congestion area corresponding to the traffic anomaly point, perform speed limit marking on the congestion area to obtain the area control label, perform protocol analysis on the area control label to obtain protocol analysis data, and calculate the conflict threshold corresponding to the protocol analysis data; A policy formulation module, configured to identify the redundant parameters in the protocol analysis data based on the conflict threshold, perform control optimization on the redundant parameters and the preset QoS standard to obtain control optimization parameters, and formulate a multicast control policy corresponding to the target IPTV server based on the protocol analysis data.

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