Gateway service comprehensive management method and system based on fttr demand

Abstract

The application relates to the technical field of data processing, and discloses a gateway service comprehensive management method and system based on FTTR demand. The method comprises the following steps: obtaining a room-level FTTR topology mapping table through topology scanning of an optical time domain reflectometer test signal, extracting features of service traffic of each room based on deep packet detection to obtain a room function identification label library, compensating attenuation of corresponding room optical ports by using an optical power meter to obtain an FTTR link quality evaluation matrix, performing bandwidth allocation by using a reinforcement learning algorithm to obtain a room-level QoS strategy configuration set, and obtaining an FTTR gateway service comprehensive management scheme based on MQTT protocol building data interaction. The application improves the intelligent level and resource allocation efficiency of room-level management of the FTTR network. The application improves the real-time performance of optical fiber link quality monitoring and the collaboration of building network management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a gateway service comprehensive management method and system based on FTTR demand. BACKGROUND

[0002] The existing FTTR (Fiber to the Room) network management technology mainly adopts a traditional centralized gateway management mode, and all the fiber access ports of the rooms are configured and monitored uniformly through a single network management protocol. These technologies usually collect the basic running state information of the network equipment based on the SNMP protocol or a dedicated network management interface, and adopt a static bandwidth allocation strategy to configure the network resources of the rooms fixedly. The existing scheme mainly relies on periodic manual inspection and simple connectivity testing in terms of fiber link monitoring, lacks the ability of automatic identification of the room function features in terms of service identification, and adopts a coarse-grained QoS strategy configuration based on the port in terms of service quality guarantee.

[0003] However, the existing technology has significant deficiencies, mainly manifested as the lack of room-level refined network topology awareness ability, the inability to accurately identify the specific function types and service characteristics of the rooms, and the lack of a differentiated service quality guarantee mechanism based on the room functions. The traditional fiber link monitoring method cannot detect the optical power attenuation and transmission quality changes in real time, the static bandwidth allocation strategy cannot be dynamically adjusted according to the actual service demand of the rooms, and the centralized configuration management mode lacks effective integration ability with the intelligent building management system. These technical limitations result in low management efficiency of the FTTR network in a multi-room complex environment, and the network service demand of different rooms cannot be met individually.

[0004] Due to the lack of automatic construction ability of the room-level fiber network topology in the existing technology, intelligent service traffic identification and classification based on the room function features cannot be achieved, which directly leads to the lack of targeted fiber link quality evaluation and dynamic compensation mechanism, and finally the network resource allocation cannot be intelligently adjusted according to the actual demand and priority of the rooms, and the integrated data interaction ability with the building management system is also lacking. Therefore, there is an urgent need for a comprehensive solution that can realize room-level FTTR network topology automatic discovery, intelligent identification of service characteristics based on room functions, adaptive compensation of fiber link quality, intelligent bandwidth resource allocation, and building-level data collaborative management. SUMMARY

[0005] The present application provides a gateway service comprehensive management method and system based on FTTR demand, which is used to improve the intelligent level and resource allocation efficiency of the room-level management of the FTTR network. The present application improves the real-time performance of the fiber link quality monitoring and the collaboration performance of the building network management.

[0006] In a first aspect, the application provides a gateway service comprehensive management method based on FTTR demand, which comprises: performing topology scanning processing on optical ports of each room through optical time domain reflectometer test signals to obtain a room-level FTTR topology mapping table; performing feature extraction processing on service traffic of each room in the room-level FTTR topology mapping table according to deep packet inspection to obtain a room function identification tag library; performing attenuation compensation processing on optical ports of rooms corresponding to the room function identification tag library through an optical power meter to obtain an FTTR link quality evaluation matrix; performing bandwidth allocation processing on each room according to the FTTR link quality evaluation matrix through a reinforcement learning algorithm to obtain a room-level QoS policy configuration set; and performing building data interaction processing on the room-level QoS policy configuration set based on an MQTT protocol to obtain an FTTR gateway service comprehensive management scheme.

[0007] In a second aspect, the application provides a gateway service comprehensive management system based on FTTR demand, which comprises:

[0008] A scanning module, configured to perform topology scanning processing on optical ports of each room through optical time domain reflectometer test signals to obtain a room-level FTTR topology mapping table;

[0009] An extraction module, configured to perform feature extraction processing on service traffic of each room in the room-level FTTR topology mapping table according to deep packet inspection to obtain a room function identification tag library;

[0010] A compensation module, configured to perform attenuation compensation processing on optical ports of rooms corresponding to the room function identification tag library through an optical power meter to obtain an FTTR link quality evaluation matrix;

[0011] A distribution module, configured to perform bandwidth allocation processing on each room according to the FTTR link quality evaluation matrix through a reinforcement learning algorithm to obtain a room-level QoS policy configuration set;

[0012] An interaction module, configured to perform building data interaction processing on the room-level QoS policy configuration set based on an MQTT protocol to obtain an FTTR gateway service comprehensive management scheme.

[0013] In a third aspect, a gateway service comprehensive management device based on FTTR demand is provided, which comprises a memory and at least one processor, the memory storing instructions; the at least one processor invokes the instructions in the memory to enable the gateway service comprehensive management device based on FTTR demand to perform the gateway service comprehensive management method based on FTTR demand described above.

[0014] In a fourth aspect, a computer readable storage medium is provided, which stores instructions that, when executed on a computer, cause the computer to perform the FTTR demand-based gateway service integrated management method described above.

[0015] In the technical solution provided in the present application, the optical time domain reflectometer test signal is used to perform topology scanning processing on the optical ports of each room and obtain a room-level FTTR topology mapping table, thereby solving the problem that the room-level network topology cannot be automatically constructed in the prior art, and realizing accurate identification of the FTTR network physical connection relationship and accurate positioning of the spatial position, laying a foundation for subsequent personalized management. The deep packet inspection is used to perform feature extraction processing on the service traffic of each room in the room-level FTTR topology mapping table and obtain a room function identification tag library, thereby breaking through the technical bottleneck of lack of room function automatic identification capability in traditional network management, and realizing accurate classification of conference rooms, offices, public areas and other rooms with different functions through intelligent service traffic analysis and clustering algorithms, thereby providing a scientific basis for differentiated services. The optical power meter is used to perform attenuation compensation processing on the optical ports of the rooms corresponding to the room function identification tag library and obtain an FTTR link quality evaluation matrix, thereby effectively solving the problems of signal attenuation and quality degradation in the optical fiber transmission process, realizing dynamic optimization of the link quality through real-time optical power monitoring and automatic gain adjustment, and ensuring the signal transmission stability of rooms with different functions. The reinforcement learning algorithm is used to perform bandwidth allocation processing on each room according to the FTTR link quality evaluation matrix and obtain a room-level QoS policy configuration set, thereby overcoming the limitation that the static bandwidth allocation strategy cannot adapt to dynamic service requirements, realizing intelligent resource allocation based on room function and link quality, and significantly improving the network resource utilization efficiency and user experience satisfaction.

[0016] The present application realizes optimal allocation of bandwidth resources through a state-action-reward decision framework, and the self-learning and adaptive characteristics of the algorithm enable the network management strategy to be continuously optimized as the environment changes. The room-level QoS policy configuration set is processed through building data interaction based on the MQTT protocol and an FTTR gateway service integrated management scheme is obtained, thereby realizing seamless integration of the gateway device and the building intelligent management system. Through a standardized message transmission and real-time configuration synchronization mechanism, the problem of information silos in traditional network management is solved, and a unified building-level network management platform is established. The entire technical solution forms a complete technical chain from physical layer topology discovery, service layer function identification, transmission layer quality guarantee, network layer resource allocation to management layer collaborative control, thereby realizing all-around intelligent management of the FTTR network and providing an efficient and reliable network infrastructure management solution for intelligent buildings and digital office environments. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0018] Figure 1 An embodiment of the gateway service comprehensive management method based on FTTR demand in the present application;

[0019] Figure 2 An embodiment of the gateway service comprehensive management system based on FTTR demand in the present application;

[0020] Figure 3 An embodiment of the gateway service comprehensive management device based on FTTR demand in the present application. DETAILED DESCRIPTION

[0021] The present application provides a gateway service comprehensive management method and system based on FTTR demand. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily mean a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "comprising" or "having" and any variation thereof is intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0022] For the sake of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 An embodiment of the gateway service comprehensive management method based on FTTR demand in the present application includes:

[0023] Step S101, performing topology scanning processing on each room optical port by optical time domain reflectometer test signal to obtain a room-level FTTR topology mapping table;

[0024] Step S102, performing feature extraction processing on the service traffic of each room in the room-level FTTR topology mapping table according to deep packet inspection to obtain a room function recognition tag library;

[0025] In step S103, the optical power meter performs attenuation compensation processing on the optical port of the room corresponding to the room function identification tag library, to obtain an FTTR link quality evaluation matrix.

[0026] In step S104, a bandwidth allocation process is performed on each room according to the FTTR link quality evaluation matrix through a reinforcement learning algorithm, to obtain a room-level QoS policy configuration set.

[0027] In step S105, the room-level QoS policy configuration set is processed through building data interaction based on the MQTT protocol, to obtain an FTTR gateway service comprehensive management scheme.

[0028] It can be understood that the execution subject of the present application can be a gateway service comprehensive management system based on FTTR requirements, and can also be a terminal or a server, which is not limited here. The server is taken as an example for illustration in the embodiments of the present application.

[0029] Specifically, the working principle of the optical time domain reflectometer test signal is to emit light pulses into the optical fiber. When the optical signal encounters a joint, a bend, a breakpoint or impedance mismatch during transmission in the optical fiber, it will produce backscattered light. The physical properties of the optical fiber are analyzed by detecting the time delay and intensity of these reflected light signals. The optical fiber physical link parameter set includes reflection delay parameters and reflection intensity parameters. The reflection delay parameters directly reflect the propagation time of the optical signal in the optical fiber, and the actual length of the optical fiber is calculated in combination with the propagation speed of light in the optical fiber. The room optical fiber link length data table records the optical fiber length information of each room corresponding to the optical port. The transmission distance parameter considers the bending radius and actual wiring path of the optical fiber. The room space distribution coordinate matrix converts the transmission distance into relative coordinate positions, and establishes a spatial relationship model of the rooms in the building. The port-room mapping relationship database binds the physical identifiers of the optical ports and the room numbers one by one, forming a lookup index structure. The topology structure construction process integrates the connection relationship, distance information and port mapping relationship of each room into a network topology graph, and finally generates a room-level FTTR topology mapping table containing room positions, connection paths and network levels.

[0030] The deep packet inspection technology identifies the service type by analyzing the protocol header information of each layer of the network data packet and the application layer load content. The protocol analysis process extracts the network layer information of the data packet, such as the source address, target address, port number, and protocol type. The application layer load extraction process analyzes the content characteristics of the data packet, including HTTP request type, video encoding format, and file transfer protocol application characteristics. The room service traffic feature vector set converts the traffic data of each room into a multi-dimensional feature vector, including traffic type distribution vector, bandwidth occupation mode vector, and time distribution vector. The statistical analysis process calculates the traffic statistical indicators of each room in different time windows, such as average bandwidth usage, peak traffic time, and service type proportion, forming the room network usage behavior pattern data. The clustering calculation process uses a distance-based clustering method to classify rooms with similar traffic characteristics into the same category, and the cluster center represents the typical room function characteristics. The room function type classification result divides the rooms into function categories such as conference room type, office type, and public area type according to the clustering result. The function label dictionary contains predefined room function labels and their corresponding traffic feature thresholds. The matching mapping process determines the room function label by calculating the similarity between the room traffic features and the label features. The room function recognition label sequence records the corresponding function label and confidence score of each room, and the label library construction establishes an index structure for the label information of all rooms according to the room number.

[0031] The optical power level real-time detection process measures the received optical power value of each room optical port using an optical power meter, forming the room optical port power monitoring data set. The deviation calculation process compares the measured optical power value with the standard optical power threshold of the type of optical port, and calculates the absolute value and relative value of the power deviation. The optical power deviation parameter table records the power deviation value, deviation direction, and out-of-limit state of each room optical port. The optical transmission power gain automatic adjustment process calculates the required power compensation based on the size and direction of the power deviation, and adjusts the driving current of the optical transmitter to change the optical output power. The optical power compensation link parameters include the adjusted optical power value, optical signal-to-noise ratio, and bit error rate. The comprehensive quality score calculation process weights and sums the optical power parameters, fiber insertion loss value, and transmission loss value according to the weight coefficients, to obtain the comprehensive quality score of each room optical port. The room optical port quality score data records the quality score and quality level classification of each room optical port. The matrix arrangement process arranges the quality scores of each room according to the room row index and port column index in a two-dimensional array, and the index construction process establishes a fast lookup mechanism for the matrix elements.

[0032] The reinforcement learning algorithm adopts a state-action-reward decision framework to optimize the bandwidth allocation strategy. The correlation mapping process multiplies the room quality score in the FTTR link quality evaluation matrix with the priority weight value in the room function identification tag library to obtain a room bandwidth demand state vector that considers quality and function. The reinforcement learning environment state space includes current network total bandwidth capacity, historical bandwidth usage of each room, real-time business load state, and other environmental parameters. The value function calculation process evaluates the influence of different bandwidth allocation actions on the overall network performance, and measures the pros and cons of the allocation strategy through cumulative reward values. The room bandwidth allocation strategy decision sequence determines the bandwidth quota value and service priority level of each room. The QoS parameter configuration process sets the corresponding service quality parameters according to the room function type and bandwidth allocation result, including minimum guaranteed bandwidth, maximum allowed bandwidth, packet priority, delay tolerance, etc. The room individual service quality parameter set records the specific QoS configuration parameters of each room. The reward function evaluation process calculates the reward score of the current allocation strategy according to network performance monitoring data and user experience feedback, generates policy optimization adjustment instructions to improve the allocation decision in the next round. The configuration integration process integrates the QoS parameters of each room into a unified configuration data structure, and the strategy packaging process packages the configuration parameters according to the standard format.

[0033] The JSON format serialization packaging process converts the room-level QoS strategy parameters into a standard JSON data format, including room identification, bandwidth quota, priority setting, service parameters, and other information fields. The MQTT message payload packet constructs the message header and payload part according to the MQTT protocol specification, and the message header includes topic name, message type, service quality level, and other control information. The building network topic subscription mechanism establishes a topic-based publish-subscribe communication mode, and the gateway device acts as a publisher to send configuration messages to a specific topic, and the building management center acts as a subscriber to receive messages related to the topic. The publish-subscribe process realizes message routing and state management through the MQTT broker server. The building data interaction communication link establishes a bidirectional communication channel between the gateway and the management center, supporting configuration delivery and state reporting. The real-time synchronization process ensures that the gateway local configuration is consistent with the configuration information in the management center database, and detects configuration changes through version number and timestamp mechanism. The gateway configuration state feedback data includes configuration execution results, error information, device state, and other feedback information. The consistency check process verifies the data integrity by comparing the local configuration hash value with the remote configuration hash value, and the strategy execution state verification result records the correctness and validity of the configuration. The scheme integration process integrates the network management strategy, service quality configuration, monitoring data, and other information of each room, and the management strategy generation process generates a network management scheme document according to the integration result.

[0034] In a specific embodiment, the process of performing step S101 can specifically include the following steps:

[0035] sending an optical pulse test signal to each room optical port, detecting the reflection time delay and reflection intensity parameters of the optical signal, and obtaining a set of optical fiber physical link parameters;

[0036] calculating the optical fiber length and transmission distance according to the reflection time delay parameter in the set of optical fiber physical link parameters, and obtaining a room optical fiber link length data table;

[0037] performing geographical location mapping processing on each room based on the transmission distance in the room optical fiber link length data table, and obtaining a room spatial distribution coordinate matrix;

[0038] associating and binding the room spatial distribution coordinate matrix with the optical port identifier, and obtaining a port-room mapping relationship database;

[0039] performing topology structure construction processing on the port-room mapping relationship database, and obtaining a room-level FTTR topology mapping table.

[0040] Specifically, the sending process of the optical pulse test signal is that the optical time domain reflectometer in the gateway device injects narrow pulse laser signals to each room optical port. When these optical pulses propagate in the optical fiber, Rayleigh scattering and Fresnel reflection will occur at positions such as optical fiber joints, connectors, bending points, or impedance mismatches, and the reflected light signals return to the receiving end of the optical time domain reflectometer along the original path. The reflection time delay parameter is obtained by measuring the time difference between the time of emitting the optical pulse and the time of receiving the reflected light signal, and the reflection intensity parameter is determined by converting the reflected light signal into an electrical signal by a photodetector and measuring its amplitude value. The set of optical fiber physical link parameters stores the reflection time delay value, reflection intensity value, optical fiber type identifier, connector type, and other information corresponding to each room optical port in a structured manner, forming a multi-dimensional data set containing room number, port identifier, time delay data, and intensity data.

[0041] The optical fiber length calculation process is based on the product relationship between the propagation speed of light in the optical fiber and the reflection time delay. Since the optical signal needs to propagate back and forth, the actual optical fiber length is equal to the product of the speed of light and the reflection time delay divided by twice the refractive index of the optical fiber. The calculation of the transmission distance needs to consider the actual layout path of the optical fiber, including the bending, detour, and redundant length of the optical fiber in the pipeline. By multiplying the calculated optical fiber length by a path correction coefficient, the actual transmission distance is obtained. The room optical fiber link length data table arranges the optical fiber length, transmission distance, path loss, connection loss, and other parameters of each room according to the room number, and establishes a corresponding relationship table between the room identifier and the optical fiber physical parameters.

[0042] The geographical position mapping process converts the transmission distance data into the relative spatial positions of the rooms in the building, by establishing a coordinate system with the gateway device as the origin, and determining the coordinate positions of each room according to the main path and branch path of the fiber cabling. Rooms with shorter transmission distances are determined to be closer to the gateway device, and rooms with longer transmission distances are determined to be farther away from the gateway device, while the coordinate calculation results are corrected in combination with the building structure information and fiber cabling drawings of the building. The room spatial distribution coordinate matrix adopts a two-dimensional coordinate system, with the X-axis representing the horizontal position of the room and the Y-axis representing the vertical position or floor information of the room. Each room occupies a coordinate point in the matrix, and the coordinate value is calculated according to the transmission distance and cabling path.

[0043] The association binding process performs one-to-one data association between the coordinate information in the room spatial distribution coordinate matrix and the optical port identifier, which includes information such as the physical number of the port, the port type, and the interface specification. The binding process is implemented by establishing a lookup table, with the room coordinate as the primary key and the optical port identifier as the foreign key, while recording the timestamp and data source of the binding. The port-room mapping relationship database adopts a relational data structure, including three basic tables: the room table, the port table, and the mapping relationship table. The room table stores information such as room number, coordinate position, and room type, the port table stores information such as port identifier, port status, and technical specification, and the mapping relationship table stores the corresponding relationship between room number and port identifier.

[0044] The topology structure construction process generates a network topology graph based on the association information in the port-room mapping relationship database, with the topology graph adopting a node-edge graph structure to represent the connection relationship between rooms. Each room is a node in the graph, with node attributes including room number, coordinate position, and port information, and the fiber connection is an edge in the graph, with edge attributes including transmission distance, link quality, and bandwidth capacity. The topology construction algorithm traverses all records in the mapping relationship database, determines the parent-child relationship and sibling relationship between rooms according to the main-branch structure of the fiber, and establishes a hierarchical topology structure. The room-level FTTR topology mapping table stores the constructed topology structure in a standardized format, including information such as node list, edge list, topology level, and connection path, forming a room-level fiber network topology description.

[0045] In a specific embodiment, the process of performing step S102 can specifically include the following steps:

[0046] Performing protocol analysis and application layer load extraction processing on the network data packets of each room in the room-level FTTR topology mapping table to obtain a room service traffic feature vector set;

[0047] According to statistical analysis processing of the traffic type distribution and bandwidth occupation mode in the room service traffic feature vector set, room network use behavior mode data is obtained.

[0048] Based on the time window traffic change curve in the room network use behavior mode data, clustering calculation processing is performed to obtain room function type classification results.

[0049] The room function type classification results are matched and mapped with the preset function label dictionary to obtain a room function recognition label sequence.

[0050] The room function recognition label sequence is processed for label library construction and index establishment to obtain a room function recognition label library.

[0051] Specifically, the protocol analysis processing extracts key information by stripping the protocol header of the network data packet layer by layer. First, the Ethernet frame header is analyzed to obtain the source MAC address and the target MAC address, and the sending device and the receiving device of the data packet are determined. Then, the IP header is analyzed to obtain the source IP address, the target IP address, the protocol type, the data packet length and other network layer information. Next, the TCP or UDP header is analyzed to obtain the source port number, the target port number, the sequence number, the window size and other transport layer information. The application layer load extraction processing analyzes the application layer content of the data packet. The client type is determined by identifying the User-Agent field in the HTTP request header. The website type accessed is identified by analyzing the URL path of the HTTP request. The encoding format of the audio and video stream is identified by analyzing the load type of the RTP data packet. The room service traffic feature vector set converts the network traffic data of each room into a structured multi-dimensional vector. Each dimension of the vector represents a service feature, including the HTTP traffic proportion dimension, the video traffic proportion dimension, the file transfer traffic proportion dimension, the real-time communication traffic proportion dimension, the average data packet size dimension, the connection concurrency dimension, etc. Each room corresponds to a feature vector, and the values in the vector are calculated by statistically analyzing the traffic data of the room in a specific time window. The statistical analysis processing performs mathematical calculations on the data in the room service traffic feature vector set to identify the traffic pattern. The traffic type distribution analysis determines the main service type of the room by calculating the percentage of various service type traffic in the total traffic. The bandwidth occupation mode analysis identifies the use peak and low peak of the room by calculating the bandwidth usage in different time periods. The room network use behavior mode data records the typical use characteristics of each room, including the weekday traffic pattern, the weekend traffic pattern, the daytime traffic change trend, the nighttime traffic change trend and other time-related behavior characteristics. At the same time, the service type preference, the bandwidth demand level, the network access habit and other static characteristics are recorded.

[0052] The clustering calculation process adopts an unsupervised learning method based on distance measurement to classify rooms with similar network usage behaviors into the same category. The time window traffic variation curve describes the traffic variation trajectory of a room in a continuous time period. The horizontal axis of the curve represents the time point, and the vertical axis represents the traffic size at the time point. The clustering algorithm first calculates the Euclidean distance between the traffic variation curves of any two rooms, which reflects the similarity of the network usage patterns of the two rooms. Then, through an iterative optimization process, rooms with close distances are classified into the same cluster center, and finally a number of clustering clusters are formed, each of which represents a typical room function type. The room function type classification result records the cluster number to which each room belongs and the distance value of the room to the cluster center. The smaller the distance value, the more the room conforms to the typical characteristics of the function type.

[0053] The matching mapping process corresponds the room function types obtained by the clustering calculation to the preset function label dictionary. The function label dictionary includes predefined room function categories such as conference room label, office label, public area label, and server room label, each of which corresponds to a set of typical network usage feature descriptions. The mapping process determines the best matching relationship by comparing the traffic features in the clustering result with the feature descriptions in the label dictionary. When the traffic features of a certain cluster have the highest similarity with the feature descriptions of the conference room label, all rooms in the cluster are labeled as conference room type. The room function recognition label sequence records the function label corresponding to each room and the matching confidence score in the order of room number. The confidence score is obtained by calculating the similarity between the room features and the label features. The higher the score, the more accurate the label assignment. The label library construction process converts the room function recognition label sequence into a database structure convenient for querying and managing. The label library stores in the form of key-value pairs, with the primary key being the room identifier and the value being a data record containing function label, confidence, assignment time, update time, and other information. The index establishment process creates various query indexes for the label library, including the primary index for querying by room number, the auxiliary index for querying by function label, the filter index for querying by confidence range, and the like. The index structure uses B-tree or hash table to support fast retrieval. The room function recognition label library integrates the function label information of all rooms to form a room function mapping data set, while recording the historical change records and statistical information of the label assignment.

[0054] In a specific embodiment, the process of performing step S103 can specifically include the following steps:

[0055] Based on the function labels of each room in the room function recognition label library, real-time optical power level detection processing is performed on the corresponding optical port to obtain a room optical port power monitoring data set;

[0056] According to the received optical power value in the room optical port power monitoring data set and the standard optical power threshold, deviation calculation processing is performed to obtain an optical power deviation parameter table;

[0057] The deviation value exceeding the preset range in the optical power deviation parameter table is subjected to optical transmission power gain automatic adjustment processing to obtain a link parameter after optical power compensation;

[0058] The link parameter after optical power compensation is subjected to comprehensive quality score calculation processing in combination with fiber insertion loss and transmission loss to obtain room optical port quality score data;

[0059] Based on the room optical port quality score data, matrix arrangement and index construction processing are performed to obtain an FTTR link quality evaluation matrix.

[0060] Specifically, the optical power level real-time detection processing determines the corresponding optical power monitoring strategy according to the room function type recorded in the room function identification tag library. For a room marked as a conference room type, the detection frequency is set to 10 times per second to ensure the stability of the video conference service. For a room marked as an office type, the detection frequency is set to 5 times per second to balance the monitoring accuracy and resource consumption. For a room marked as a public area type, the detection frequency is set to 2 times per second to meet the basic monitoring requirements. The optical power detection measures the optical signal strength received by the optical port through an optical power meter. The optical power meter converts the optical signal into a corresponding electrical signal, and then converts the electrical signal into a digital signal through an analog-to-digital converter for processing. The room optical port power monitoring dataset records the optical power measurement values of each room at different time points. The data structure includes fields such as room identifier, port number, timestamp, optical power value, signal quality indicator, and records the change trend and abnormal fluctuation of the optical power. The deviation calculation processing compares the measured optical power value with the standard optical power threshold corresponding to the room function type. The standard optical power threshold is set according to the service requirements of different room types. The standard threshold for a conference room type is set to -12 dBm, the standard threshold for an office type is set to -15 dBm, and the standard threshold for a public area type is set to -18 dBm. The deviation value is obtained by subtracting the standard threshold from the measured optical power value. A positive deviation indicates that the optical power is higher than the standard value, and a negative deviation indicates that the optical power is lower than the standard value. The optical power deviation parameter table records the deviation value, deviation direction, deviation amplitude, and deviation duration of each room optical port, and marks the abnormal ports exceeding the preset deviation range. The preset deviation range is determined according to the fault tolerance requirements of the room function type. The allowed deviation range for a conference room type is ±1 dB, the allowed deviation range for an office type is ±2 dB, and the allowed deviation range for a public area type is ±3 dB.

[0061] The optical transmitter power gain automatic adjustment process compensates the optical port power when the deviation exceeds the preset range. The adjustment algorithm calculates the required power adjustment amount according to the size and direction of the deviation value. When the optical power is low, the driving current of the optical transmitter is increased to increase the output power. When the optical power is high, the driving current is reduced to reduce the output power. The step value of power adjustment is determined according to the deviation amplitude. Small deviations use a fine adjustment step of 0.5 dB, medium deviations use a standard step of 1 dB, and large deviations use a coarse adjustment step of 2 dB. The link parameters after optical power compensation include the adjusted optical power value, optical signal-to-noise ratio, bit error rate, link stability index, and other data. These parameters are obtained by re-measuring the signal quality of the optical port to ensure that the power adjustment achieves the expected effect. The comprehensive quality score calculation process analyzes the link parameters after optical power compensation, fiber insertion loss, and transmission loss data. Fiber insertion loss data comes from physical measurements of fiber connectors, including connector insertion loss, fusion loss, and bending loss. Transmission loss data is calculated based on fiber length and fiber type attenuation coefficient. The quality score algorithm uses a weighted sum method, with optical power parameters assigned a weight of 40%, fiber insertion loss assigned a weight of 30%, transmission loss assigned a weight of 20%, and signal stability assigned a weight of 10%. After normalization, each parameter value is weighted and calculated. The room optical port quality score data records the comprehensive quality score, quality level classification, and quality change trend of each room optical port. The quality level is divided into excellent, good, general, and poor, corresponding to a score interval of 90 points or more, 70 to 90 points, 50 to 70 points, and 50 points or less, respectively.

[0062] The matrix arrangement process arranges the quality scores of the optical ports in each room in a two-dimensional array according to the room number and port number. The row index of the matrix represents the room number, the column index represents the port number, and the matrix element stores the quality score value of the corresponding room port. The index construction process establishes a multi-dimensional query index for the quality assessment matrix, including a row index for room number query, a column index for port number query, a classification index for quality level query, and an interval index for score range query. The FTTR link quality assessment matrix integrates the quality information of all room optical ports, forming a network quality state description. The matrix structure supports fast retrieval of the quality status of a specific room or port, while recording the timestamp and historical change trajectory of the quality assessment.

[0063] In a specific embodiment, the process of performing optical transmitter power gain automatic adjustment on the deviation values in the optical power deviation parameter table that exceed the preset range can specifically include the following steps:

[0064] Numerical comparison is performed on the deviation values in the optical power deviation parameter table and the preset optical power deviation threshold range to obtain a list of out-of-limit deviation values;

[0065] The light emission power gain compensation amount calculation process is performed according to the deviation degree of each room in the out-of-limit deviation value marking list, and room-level power gain adjustment parameters are obtained;

[0066] The room-level power gain adjustment parameters are input into the light emitter for power output level dynamic adjustment processing, and the compensated light emission power value is obtained;

[0067] Based on the compensated light emission power value, the signal transmission quality of each room optical port is re-measured, and the power-compensated optical link transmission parameters are obtained;

[0068] The power-compensated optical link transmission parameters are integrated and structured, and the optical power-compensated link parameters are obtained.

[0069] Specifically, the numerical comparison processing compares each room optical port deviation value recorded in the optical power deviation parameter table with the preset optical power deviation threshold range of the corresponding room function type one by one. The preset optical power deviation threshold range is set according to the business characteristics and service quality requirements of the room. The threshold range of the conference room type is ±1dB, the threshold range of the office type is ±2dB, and the threshold range of the public area type is ±3dB. The comparison algorithm traverses all records in the deviation parameter table to determine whether each deviation value exceeds the threshold range boundary. When the absolute value of the deviation value is greater than the upper limit of the threshold range, the record is marked as out-of-limit. The out-of-limit deviation value marking list records all room optical port information that exceeds the preset range, including room identifier, port number, actual deviation value, threshold range, out-of-limit degree, etc. The out-of-limit degree is quantified by calculating the numerical size of the deviation value exceeding the threshold range, providing accurate data basis for subsequent compensation calculation. The light emission power gain compensation amount calculation process determines the required power adjustment amount according to the specific deviation degree of each room in the out-of-limit deviation value marking list. The calculation algorithm uses linear mapping to directly convert the deviation degree into power gain adjustment value. When the room optical port power is low, the compensation amount is equal to the absolute value of the negative deviation value. When the optical power is high, the compensation amount is equal to the negative value of the positive deviation value. This calculation method ensures that the adjusted optical power value is close to the standard threshold. The room-level power gain adjustment parameters include the specific adjustment amount, adjustment direction, and adjustment priority of each room that needs to be adjusted. The adjustment priority is determined according to the room function type. The conference room type has the highest priority, the office type has medium priority, and the public area type has lower priority, ensuring that the signal quality of critical business rooms is prioritized.

[0070] The power output level dynamic adjustment process inputs the room-level power gain adjustment parameter as a control instruction into the light emitter device of the corresponding room. After receiving the adjustment parameter, the light emitter changes the driving current of the laser diode according to the adjustment amount and adjustment direction in the parameter. The adjustment of the driving current is realized through a digital-to-analog converter. The adjustment amount is converted into a corresponding current change value. A positive adjustment amount increases the driving current, and a negative adjustment amount reduces the driving current. The change in current directly affects the optical output power of the laser diode. The compensated light emission power value is obtained by real-time measurement through the optical power monitoring module. The monitoring module compares the adjusted optical power value with the value before adjustment to verify whether the adjustment effect reaches the expected target, and records the power change trajectory and stabilization time during the adjustment process. The signal transmission quality re-measurement process comprehensively detects the transmission performance of each room optical port based on the compensated light emission power value. The measurement content includes key indicators such as optical power stability, optical signal-to-noise ratio, bit error rate, and signal jitter. The optical power stability is evaluated by continuously monitoring the power fluctuation range within a certain time period. The smaller the fluctuation range, the more stable the signal. The optical signal-to-noise ratio is calculated by analyzing the ratio of the useful signal power to the noise power in the received signal. The higher the ratio, the better the signal quality. The bit error rate is measured by sending known test data packets and counting the number of error bits at the receiving end. The ratio of the number of error bits to the total number of transmitted bits is the bit error rate. The power-compensated optical link transmission parameters are summarized to form a complete data set describing the transmission quality of each room optical port.

[0071] The parameter integration and data structuring process organizes and stores the power-compensated optical link transmission parameters in a standardized data format. The integration process collects data scattered in different measurement modules and establishes an index relationship according to room numbers and port numbers. The data structuring process uses a hierarchical data model. The top layer is room-level data, including room identification and overall quality assessment. The middle layer is port-level data, including specific transmission parameters for each port. The bottom layer is parameter-level data, including detailed values and measurement timestamps for each performance indicator. The power-compensated link parameters form a complete link quality description document. The document format supports fast query and data analysis, while retaining the history and trends of parameter adjustment.

[0072] In a specific embodiment, the process of performing step S104 can specifically include the following steps:

[0073] The room quality score in the FTTR link quality evaluation matrix is associated and mapped with the priority weight in the room function identification tag library to obtain a room bandwidth demand state vector.

[0074] A reinforcement learning environment state space is constructed based on a room bandwidth demand state vector, a value function calculation process is performed on bandwidth allocation actions, and a room bandwidth allocation strategy decision sequence is obtained.

[0075] A QoS parameter configuration process is performed on each room according to the bandwidth quota in the room bandwidth allocation strategy decision sequence, and a room individualized service quality parameter set is obtained.

[0076] A reward function evaluation process is performed on the room individualized service quality parameter set and network load feedback information, and a policy optimization adjustment instruction is obtained.

[0077] A configuration integration and policy packaging process is performed on the policy optimization adjustment instruction, and a room-level QoS policy configuration set is obtained.

[0078] Specifically, the correlation mapping process performs numerical multiplication operation on the quality score of each room in the FTTR link quality evaluation matrix and the corresponding priority weight in the room function identification tag library. The priority weight is pre-set according to the room function type, the weight of the conference room type is 0.9, the weight of the office type is 0.7, the weight of the public area type is 0.5, and the weight of the server room type is 0.8. The mapping calculation obtains a normalized room importance index by multiplying the room quality score divided by 100 and the priority weight, which reflects the priority degree of the room in network resource allocation. The room bandwidth demand state vector combines the importance index, historical bandwidth usage peak, current business load, and expected bandwidth demand of each room into a multi-dimensional vector. Each dimension of the vector represents a key factor affecting bandwidth allocation decision, the vector length is equal to the total number of rooms multiplied by the number of parameter dimensions, and a complete data structure describing the entire network bandwidth demand state is formed. The reinforcement learning environment state space construction process establishes a decision environment model based on the room bandwidth demand state vector. The state space includes current network total bandwidth capacity, real-time bandwidth occupation of each room, business type distribution, network congestion degree, and other environmental variables. The value function calculation process uses the core idea of Q-learning algorithm to quantitatively evaluate the long-term benefits of bandwidth allocation actions. The action space is defined as a combination scheme of allocating a specific bandwidth quota to each room, and each allocation scheme corresponds to a Q value. Q value calculation determines the impact of allocation scheme on network overall performance, including user satisfaction index, network utilization index, service quality compliance rate index, and other evaluation dimensions. The higher the Q value, the better the overall effect of the allocation scheme. The room bandwidth allocation strategy decision sequence records the optimal allocation scheme selected by the algorithm. Each element in the sequence contains room identifier, allocated bandwidth value, allocation priority, execution time, and other information. The decision sequence is sorted according to room priority to ensure that the bandwidth demand of key rooms is satisfied first.

[0079] The QoS parameter configuration process sets the corresponding quality of service parameters for each room according to the bandwidth quota determined in the room bandwidth allocation strategy decision sequence. The configuration parameters include minimum guaranteed bandwidth, maximum allowed bandwidth, packet priority, delay tolerance, packet loss retransmission mechanism, etc. The minimum guaranteed bandwidth is set according to the basic demand of the room function type. The minimum guaranteed bandwidth of the conference room type is 80% of the allocated bandwidth, the office type is 60%, and the public area type is 40%, which ensures the basic quality of service of critical business. The packet priority uses a hierarchical marking method. The data packets of high-priority rooms are marked as emergency level, the data packets of medium-priority rooms are marked as important level, and the data packets of low-priority rooms are marked as ordinary level. Network devices perform differential processing according to the priority marking. The room individualized quality of service parameter set stores all room QoS configuration parameters in a structured manner according to room number, forming a complete quality of service configuration database. The database supports multiple retrieval methods such as room query, parameter type query, and priority query.

[0080] The reward function evaluation process comprehensively analyzes the actual execution effect of the room individualized quality of service parameter set and the network load feedback information, including real-time data such as actual bandwidth usage, service quality compliance, and user experience evaluation of each room. The reward function evaluates the execution quality of the strategy by calculating the deviation between the expected effect and the actual effect. When the actual bandwidth usage is close to the allocated value and the service quality indicators meet the standards, a positive reward is given. When there is bandwidth waste or service quality does not meet the standards, a negative reward is given. The policy optimization adjustment instruction generates specific adjustment suggestions based on the evaluation results of the reward function, including increasing or decreasing the bandwidth quota of a specific room, adjusting the priority setting, modifying the QoS parameters, etc. The execution priority of the adjustment instruction is determined according to the severity and impact of the problem. The configuration integration process integrates the policy optimization adjustment instruction with the existing room individualized quality of service parameters. The integration process uses a version control mechanism to record information such as the time, reason, and impact range of each configuration change, ensuring traceability and rollback of configuration changes. The policy encapsulation process packages the updated configuration parameters in a standardized format, including configuration header information, parameter data area, and checksum. This ensures the integrity and correctness of the configuration data. The room-level QoS strategy configuration integrates all the final configuration parameters of the rooms to form a complete network service quality management scheme. The configuration set supports a hot update mechanism, allowing dynamic adjustment of network parameters without interrupting business, meeting the real-time management needs in the FTTR network environment.

[0081] In a specific embodiment, the process of performing step S105 can specifically include the following steps:

[0082] The policy parameters in the room-level QoS policy configuration set are serialized and packaged in JSON format to obtain an MQTT message payload data packet;

[0083] A building network topic subscription mechanism is constructed according to the MQTT message payload data packet, and a room configuration update message is published and subscribed to obtain a building data interaction communication link;

[0084] The room-level policy configuration information is synchronized in real time with the building management center based on the building data interaction communication link to obtain gateway configuration state feedback data;

[0085] The gateway configuration state feedback data is consistency checked with the room-level QoS policy configuration set to obtain a policy execution state verification result;

[0086] The policy execution state verification result is integrated and managed to generate a policy to obtain an FTTR gateway business comprehensive management scheme.

[0087] Specifically, the JSON serialization and packaging process converts each policy parameter in the room-level QoS policy configuration set into a standard JSON data format. The encapsulation process first extracts key information such as room identifiers, bandwidth quotas, priority settings, and quality of service parameters in the configuration set, and then constructs a nested data structure according to the JSON syntax specification. The configuration information of each room forms a JSON object, which includes a roomId field to store the room number, a bandwidth field to store the allocated bandwidth value, a priority field to store the priority level, and a qosParams field to store the detailed quality of service parameter array. All room objects are organized into a JSON array, and the array is added with message header information including timestamp, version number, configuration type, and other metadata. The MQTT message payload data packet takes the JSON format configuration data as the message payload, and adds the message header required by the MQTT protocol, including the topic name, quality of service level, reserved flag, and repeat flag control fields. The payload data is converted to byte stream format through UTF-8 encoding to meet the transmission requirements of the MQTT protocol.

[0088] The building network theme subscription mechanism constructs a hierarchical theme structure based on an MQTT message payload data packet. The theme naming adopts a hierarchical format such as "building / floor / room / config" to represent configuration information at different levels. The gateway device acts as a publisher to send configuration update messages to a specific theme. The building management center acts as a subscriber to listen to the message changes of the related theme. The publish-subscribe processing implements message routing and forwarding through an MQTT broker server. The broker server maintains a theme subscription list, records the theme and quality of service requirements subscribed by each client, and pushes the message to the corresponding subscriber according to the theme matching rule when the gateway publishes the message. The building data interaction communication link establishes a bidirectional communication channel between the gateway and the management center. The communication link adopts a TCP connection as the underlying transmission protocol to ensure the reliability of message transmission. The link supports a heartbeat detection mechanism to monitor the connection state and automatically reconnects when a connection exception is detected.

[0089] The real-time synchronization processing synchronizes the room-level policy configuration information of the gateway with the database of the building management center based on the building data interaction communication link. The synchronization process adopts a version control mechanism to handle concurrent updates and conflict resolution. The gateway device periodically sends a configuration snapshot to the management center. The snapshot contains the current configuration state and version number of all rooms. The management center receives the snapshot, compares it with the local database, identifies the configuration differences, and generates a synchronization instruction. The gateway configuration state feedback data includes feedback information such as configuration execution results, error information, device state, and performance indicators. The feedback data is encoded in a structured format, including fields such as status code, message description, timestamp, and impact range. The management center updates the device state record and configuration execution log based on the feedback data.

[0090] The consistency verification processing verifies the correctness and integrity of the configuration by comparing the gateway configuration state feedback data with the room-level QoS policy configuration set item by item. The verification algorithm uses a hash value comparison method. The MD5 hash value is calculated for each room configuration parameter, and then the hash values at the gateway and the management center are compared for consistency. When the hash values are found to be inconsistent, the verification program further analyzes the specific parameter differences to locate the inconsistent configuration items and difference contents. The policy execution state verification result records all problems found in the verification process, including configuration missing, parameter error, version conflict, insufficient permissions, and other abnormal situations. At the same time, the number of configuration items that pass the verification and the overall consistency degree are recorded.

[0091] The scheme integration processing combines and optimizes the policy execution state verification result with the existing network management policy, the integration process classifies and processes the problems according to the severity of the verification result, and automatically corrects the slight configuration deviation, generates an alarm information for the serious configuration error and requires manual intervention. The management policy generation processing formulates a complete network management scheme according to the integration result, and the scheme includes room-level configuration policy, network monitoring policy, fault processing policy, performance optimization policy and the like. The FTTR gateway service comprehensive management scheme integrates all policy components into a unified management framework, the framework supports dynamic loading and hot updating of the policy, allows adjusting the management policy without interrupting the service, and provides policy execution monitoring and effect evaluation functions, to ensure continuous optimization and improvement of the management scheme.

[0092] The gateway service comprehensive management method based on the FTTR demand in the embodiments of the present application is described above, and the gateway service comprehensive management system based on the FTTR demand in the embodiments of the present application is described below. Please refer to Figure 2 The gateway service comprehensive management system based on the FTTR demand in the embodiments of the present application includes one embodiment:

[0093] The scanning module is configured to perform topology scanning processing on the optical ports of each room by an optical time domain reflectometer test signal, to obtain a room-level FTTR topology mapping table.

[0094] The extraction module is configured to perform feature extraction processing on the service traffic of each room in the room-level FTTR topology mapping table according to deep packet inspection, to obtain a room function recognition tag library.

[0095] The compensation module is configured to perform attenuation compensation processing on the optical ports of the room corresponding to the room function recognition tag library by an optical power meter, to obtain an FTTR link quality evaluation matrix.

[0096] The distribution module is configured to perform bandwidth distribution processing on each room according to the FTTR link quality evaluation matrix by a reinforcement learning algorithm, to obtain a room-level QoS policy configuration set.

[0097] The interaction module is configured to perform building data interaction processing on the room-level QoS policy configuration set based on an MQTT protocol, to obtain an FTTR gateway service comprehensive management scheme.

[0098] The gateway service comprehensive management method based on the FTTR demand in the embodiments of the present application is described above, and the gateway service comprehensive management system based on the FTTR demand in the embodiments of the present application is described below. Please refer to Figure 2 The gateway service comprehensive management system based on the FTTR demand in the embodiments of the present application is described above, and the gateway service comprehensive management system based on the FTTR demand in the embodiments of the present application is described below. Please refer to

[0099] Please refer to Figure 3The embodiment of the present application also provides a gateway service comprehensive management device based on FTTR demand, which can be a server, and the internal structure of the server can be as shown in the figure. Figure 3 The gateway service comprehensive management device based on FTTR demand comprises a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. The processor of the computer is used for providing computing and control capabilities. The memory of the gateway service comprehensive management device based on FTTR demand comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The database of the gateway service comprehensive management device based on FTTR demand is used for storing corresponding data in the embodiment. The network interface of the gateway service comprehensive management device based on FTTR demand is used for communicating with external terminals through network connection. The computer program is executed by the processor to implement the above method.

[0100] Those skilled in the art can understand that, Figure 3 The structure shown in the figure is only a block diagram of part of the structure related to the present application, and does not constitute a limitation on the gateway service comprehensive management device based on FTTR demand to which the present application is applied.

[0101] The present application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium or a volatile computer readable storage medium, and the computer readable storage medium stores instructions, and when the instructions are run on a computer, the computer executes the steps of the gateway service comprehensive management method based on FTTR demand.

[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system, system and unit can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0103] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a FTTR demand-based gateway service integrated management device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0104] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A gateway service integrated management method based on FTTR requirements, characterized in that, The method includes: A room-level FTTR topology mapping table is obtained by performing topology scanning on the optical ports of each room using optical time domain reflectometer (OTDR) test signals. This includes: sending optical pulse test signals to the optical ports of each room, detecting the reflection delay and reflection intensity parameters of the optical signals to obtain a set of optical fiber physical link parameters; calculating the fiber length and transmission distance based on the reflection delay parameters in the set of optical fiber physical link parameters to obtain a room optical fiber link length data table; mapping the geographical locations of each room based on the transmission distances in the room optical fiber link length data table to obtain a room spatial distribution coordinate matrix; associating and binding the room spatial distribution coordinate matrix with optical port identifiers to obtain a port-room mapping relationship database; and constructing a topology structure for the port-room mapping relationship database to obtain a room-level FTTR topology mapping table. Based on deep packet inspection, feature extraction processing is performed on the service traffic of each room in the room-level FTTR topology mapping table to obtain a room function identification tag library. This includes: performing protocol parsing and application layer payload extraction processing on the network data packets of each room in the room-level FTTR topology mapping table to obtain a set of room service traffic feature vectors; performing statistical analysis processing on the traffic type distribution and bandwidth usage patterns in the set of room service traffic feature vectors to obtain room network usage behavior pattern data; performing clustering calculation processing on the time window traffic change curves in the room network usage behavior pattern data to obtain room function type classification results; matching and mapping the room function type classification results with a preset function tag dictionary to obtain a room function identification tag sequence; and constructing and indexing the room function identification tag sequence to obtain the room function identification tag library. The optical power meter performs attenuation compensation processing on the optical ports of the corresponding rooms in the room function identification tag library to obtain an FTTR link quality assessment matrix. This includes: real-time detection of optical power levels of the corresponding optical ports based on the function tags of each room in the room function identification tag library to obtain a room optical port power monitoring dataset; calculation of the deviation between the received optical power value and the standard optical power threshold in the room optical port power monitoring dataset to obtain an optical power deviation parameter table; automatic adjustment of optical transmit power gain for deviation values ​​exceeding the preset range in the optical power deviation parameter table to obtain link parameters after optical power compensation; and comprehensive quality score calculation of the link parameters after optical power compensation combined with fiber insertion loss and transmission loss to obtain room optical port quality score data. Based on the room optical port quality score data, a matrix arrangement and index construction process is performed to obtain the FTTR link quality assessment matrix. The matrix arrangement process arranges the optical port quality scores of each room into a two-dimensional array according to room number and port number. The row index of the matrix represents the room number, the column index represents the port number, and the matrix elements store the corresponding room port quality score values. The index construction process establishes a multi-dimensional query index for the quality assessment matrix, including a row index for querying by room number, a column index for querying by port number, a category index for querying by quality level, and a range index for querying by score range. The FTTR link quality assessment matrix integrates the quality information of all room optical ports to form a network quality status description. The matrix structure supports quick retrieval of the quality status of a specific room or port, while also recording the timestamp and historical change trajectory of the quality assessment. The room-level QoS policy configuration set is obtained by performing bandwidth allocation processing on each room based on the FTTR link quality assessment matrix using a reinforcement learning algorithm. The room-level QoS policy configuration set is processed through building data interaction based on the MQTT protocol to obtain the FTTR gateway service integrated management solution.

2. The gateway service integrated management method based on FTTR requirements according to claim 1, characterized in that, The step of automatically adjusting the optical transmit power gain of the deviation values ​​in the optical power deviation parameter table that exceed the preset range to obtain the link parameters after optical power compensation includes: The deviation values ​​in the optical power deviation parameter table are compared with the preset optical power deviation threshold range to obtain a list of out-of-limit deviation values. Based on the deviation degree of each room in the list of out-of-limit deviation values, the optical emission power gain compensation amount is calculated to obtain the room-level power gain adjustment parameters; The room-level power gain adjustment parameters are input into the optical transmitter for dynamic power output level adjustment to obtain the compensated optical emission power value. Based on the compensated optical transmission power value, the signal transmission quality of the optical ports in each room is re-measured and processed to obtain the power-compensated optical link transmission parameters. The power-compensated optical link transmission parameters are integrated and data structured to obtain the power-compensated link parameters.

3. The gateway service integrated management method based on FTTR requirements according to claim 1, characterized in that, The process of allocating bandwidth to each room using a reinforcement learning algorithm based on the FTTR link quality assessment matrix yields a room-level QoS policy configuration set, including: The room quality score in the FTTR link quality assessment matrix is ​​correlated and mapped with the priority weight in the room function identification tag library to obtain the room bandwidth demand state vector. Based on the room bandwidth demand state vector, a reinforcement learning environment state space is constructed. Value function calculations are then performed on bandwidth allocation actions to obtain a room bandwidth allocation strategy decision sequence. The reinforcement learning environment state space construction process establishes a decision environment model based on the room bandwidth demand state vector. The state space includes the current total network bandwidth capacity, real-time bandwidth occupancy of each room, service type distribution, and network congestion level. The value function calculation process uses the Q-learning algorithm to quantify and evaluate the long-term benefits of bandwidth allocation actions. The action space is defined as a combination of schemes for allocating specific bandwidth quotas to each room, with each allocation scheme corresponding to a Q-value. The Q-value is determined by evaluating the impact of the allocation schemes on overall network performance, including user satisfaction indicators, network utilization indicators, and service quality compliance rate indicators. Based on the bandwidth quota in the room bandwidth allocation strategy decision sequence, QoS parameter configuration is performed on each room to obtain a set of personalized service quality parameters for the room. The set of personalized room service quality parameters and network load feedback information are evaluated using a reward function to obtain strategy optimization and adjustment instructions. The policy optimization and adjustment instructions are configured, integrated, and encapsulated to obtain a room-level QoS policy configuration set.

4. The gateway service integrated management method based on FTTR requirements according to claim 1, characterized in that, The process of interacting with building data based on the room-level QoS policy configuration set using the MQTT protocol to obtain the FTTR gateway service integrated management solution includes: The policy parameters in the room-level QoS policy configuration set are serialized and encapsulated in JSON format to obtain MQTT message payload data packets; Based on the MQTT message payload data packet, a building network topic subscription mechanism is constructed, and the room configuration update message is published and subscribed to, thereby obtaining the building data interaction communication link; Based on the building data interaction communication link, the room-level policy configuration information is synchronized with the building management center in real time to obtain gateway configuration status feedback data. The gateway configuration status feedback data is compared with the room-level QoS policy configuration set to obtain the policy execution status verification result. The policy execution status verification results are integrated and managed through policy generation to obtain a comprehensive FTTR gateway service management solution.

5. A gateway service integrated management system based on FTTR requirements, characterized in that, For implementing the gateway service integrated management method based on FTTR requirements as described in any one of claims 1-4, the gateway service integrated management system based on FTTR requirements includes: The scanning module is used to perform topology scanning processing on the optical ports of each room using optical time domain reflectometer test signals to obtain a room-level FTTR topology mapping table. This includes: sending optical pulse test signals to the optical ports of each room, detecting the reflection delay and reflection intensity parameters of the optical signals to obtain a set of optical fiber physical link parameters; calculating the fiber length and transmission distance based on the reflection delay parameters in the set of optical fiber physical link parameters to obtain a room optical fiber link length data table; performing geographical location mapping processing on each room based on the transmission distance in the room optical fiber link length data table to obtain a room spatial distribution coordinate matrix; associating and binding the room spatial distribution coordinate matrix with optical port identifiers to obtain a port-room mapping relationship database; and performing topology structure construction processing on the port-room mapping relationship database to obtain a room-level FTTR topology mapping table. The extraction module is used to perform feature extraction processing on the service traffic of each room in the room-level FTTR topology mapping table based on deep packet inspection to obtain a room function identification tag library. This includes: performing protocol parsing and application layer payload extraction processing on the network data packets of each room in the room-level FTTR topology mapping table to obtain a set of room service traffic feature vectors; performing statistical analysis processing on the traffic type distribution and bandwidth usage patterns in the set of room service traffic feature vectors to obtain room network usage behavior pattern data; performing clustering calculation processing on the time window traffic change curves in the room network usage behavior pattern data to obtain room function type classification results; matching and mapping the room function type classification results with a preset function tag dictionary to obtain a room function identification tag sequence; and constructing and indexing the room function identification tag sequence to obtain the room function identification tag library. The compensation module is used to perform attenuation compensation processing on the optical ports of the corresponding rooms in the room function identification tag library using an optical power meter to obtain an FTTR link quality assessment matrix. This includes: real-time detection of optical power levels on the corresponding optical ports based on the function tags of each room in the room function identification tag library to obtain a room optical port power monitoring dataset; calculation of the deviation between the received optical power value and the standard optical power threshold in the room optical port power monitoring dataset to obtain an optical power deviation parameter table; automatic adjustment of optical transmit power gain for deviation values ​​exceeding a preset range in the optical power deviation parameter table to obtain link parameters after optical power compensation; and comprehensive quality score calculation processing of the link parameters after optical power compensation combined with fiber insertion loss and transmission loss to obtain room optical port quality score data. Based on the room optical port quality score data, a matrix arrangement and index construction process is performed to obtain the FTTR link quality assessment matrix. The matrix arrangement process arranges the optical port quality scores of each room into a two-dimensional array according to room number and port number. The row index of the matrix represents the room number, the column index represents the port number, and the matrix elements store the corresponding room port quality score values. The index construction process establishes a multi-dimensional query index for the quality assessment matrix, including a row index for querying by room number, a column index for querying by port number, a category index for querying by quality level, and a range index for querying by score range. The FTTR link quality assessment matrix integrates the quality information of all room optical ports to form a network quality status description. The matrix structure supports quick retrieval of the quality status of a specific room or port, while also recording the timestamp and historical change trajectory of the quality assessment. The allocation module is used to perform bandwidth allocation processing on each room according to the FTTR link quality assessment matrix using a reinforcement learning algorithm, so as to obtain a room-level QoS policy configuration set. The interaction module is used to perform building data interaction processing on the room-level QoS policy configuration set based on the MQTT protocol to obtain the FTTR gateway service comprehensive management solution.

6. A gateway service integrated management device based on FTTR requirements, characterized in that, It includes a memory and a processor, the memory storing a computer program that can run on the processor, and the processor executing the computer program to implement the gateway service integrated management method based on FTTR requirements as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it causes the processor to execute the gateway service integrated management method based on FTTR requirements as described in any one of claims 1 to 4.

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