Emergency information service system and method based on fusion of satellite communication and ground mobile communication

Abstract

The invention provides an emergency information service system and method based on fusion of satellite communication and ground mobile communication, and the system comprises a high-throughput satellite fixed station terminal which employs a Ku wave band 0.6 m VSAT terminal, integrates an ACU and an MODEM, and is provided with a three-axis structure and a carbon fiber antenna. The ground mobile communication subsystem comprises a 4G skin base station, a security gateway and a signaling gateway; the multi-WAN port routing equipment realizes dynamic load balancing of a satellite and a ground broadband; the emergency power supply system adopts a generator and a charge pal to form three-stage power supply; the network management monitoring system supports remote configuration and A I optimization. The method comprises the steps of disaster monitoring, system activation, network establishment, signal coverage, communication service and management maintenance. Through satellite and ground communication fusion and in combination with A I-driven network optimization, a reliable communication network is quickly established in an emergency scene, the method has the characteristics of wind load resistance, low time delay, intelligent scheduling and the like, and the emergency communication guarantee capability is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the field of emergency communication technology, and specifically relates to an emergency information service system and method based on the integration of satellite communication and terrestrial mobile communication, which is particularly suitable for emergency communication guarantee of village-level units in areas prone to natural disasters. Background Art

[0002] Natural disasters such as earthquakes, floods, and mudslides often result in power outages: power outages, internet outages, and power outages, crippling traditional terrestrial communication networks. This is especially true in remote mountainous and rural areas, where weak communication infrastructure makes it difficult to obtain timely disaster information and communicate with the outside world. While existing satellite communications can provide emergency communication services, they suffer from complex terminal equipment, high costs, and insufficient integration with terrestrial mobile communication networks, making them unable to meet the actual needs of village-level emergency communications.

[0003] The existing technology has the following problems: insufficient coverage of conventional base stations in remote areas and poor disaster resistance; power supply interruptions during disasters cause communication equipment to stop working; emergency communication systems are expensive to build and difficult to maintain; there is a lack of effective satellite and terrestrial communication integration solutions; emergency communication system management is decentralized and response speeds are slow.

[0004] Therefore, designing an emergency information service system that can provide reliable communication services in emergency situations such as disasters, achieve efficient communication guarantee in disaster scenarios, and meet the needs of broadband and real-time communication in emergency situations is an urgent task to ensure emergency communications under village-level disaster conditions. Summary of the Invention

[0005] In view of this, the present invention provides an emergency information service method and system based on the integration of satellite communication and terrestrial mobile communication. Through the organic combination of high-throughput satellite fixed terminals and 4G pico base stations, a stable, reliable and low-cost village-level emergency communication network is constructed. The network performance is comprehensively evaluated through the AI ​​big model, and the network is adaptively adjusted and optimized to solve the communication guarantee problem under disaster conditions.

[0006] The present application provides an emergency information service method and system based on the integration of satellite communication and terrestrial mobile communication, constructs a distributed cloud-edge collaborative power transmission line defect identification platform, deploys an inference and semi-automatic annotation system on the edge, and constructs a multi-model incremental training system on the cloud through digital twin technology; the central cloud and the edge nodes cooperate with each other, and the annotation efficiency and model accuracy can be improved through incremental learning. At the same time, samples are assisted by manual or other systems, and then the model is incrementally trained again. If the incrementally trained model has a significant improvement in accuracy over the original model, it can be deployed to the edge node to update the original model. The pipeline task implementation mechanism is implemented through crowdsourcing, and the semi-automatic annotation task is divided into multiple small pipeline tasks. The pipeline operation not only makes the annotation work more efficient, but also integrates the characteristics of different models to make the obtained annotation results more accurate.

[0007] In a first aspect, an embodiment of the present application provides an emergency information service system based on the integration of satellite communications and terrestrial mobile communications, the system comprising:

[0008] High-throughput satellite fixed station terminal: This terminal uses a Ku-band 0.6-meter terrestrial automatic VSAT terminal, integrating an antenna control unit (ACU) and a modem. It features a three-axis structure and a high-strength carbon fiber composite antenna surface, enabling satellite broadband access.

[0009] Ground mobile communication subsystem: includes 4G pico base stations, pico base station gateways, and network management systems. The pico base station gateways include security gateways and signaling gateways, providing LTE terminal access services.

[0010] Multi-WAN port routing device: configured to access satellite broadband and terrestrial broadband simultaneously and achieve dynamic load balancing;

[0011] Emergency power supply system: including small generators and high-power power banks, forming a three-level power supply guarantee structure to ensure power supply to equipment in the event of a power outage;

[0012] Network management and monitoring system: This includes satellite fixed station network management and pico base station network management, supports remote parameter configuration and status monitoring, and uses AI large models to comprehensively evaluate network performance and adaptively adjust and optimize the network.

[0013] Optionally, in an implementation of the first aspect of the present invention, the high-throughput satellite fixed station terminal includes:

[0014] The three-axis stabilization structure consists of a crossed roller bearing on the azimuth axis, a harmonic reducer on the pitch axis, and a stepper motor on the polarization axis. The pointing error is ≤0.3° under wind load conditions of level 6. The antenna surface is made of a 2.5mm thick carbon fiber composite material with a tensile strength of ≥800MPa, and the total weight is less than 15kg. The ACU and MODEM are integrated into the antenna feed through a co-aperture design, with signal insertion loss less than 0.5dB. It supports adaptive coding modulation from QPSK to 64APSK and has a built-in TCP acceleration engine optimized for the SCPS-TP protocol, ensuring end-to-end transmission latency of less than 800ms in GEO satellite scenarios.

[0015] The 4G pico base station in the ground mobile communication subsystem adopts a software-defined radio (SDR) architecture, supports dynamic spectrum sharing (DSS) technology, can simultaneously access LTE Band 3 / 5 / 8 frequency bands within a 10MHz bandwidth, has an adjustable transmission power of 0.1W-5W, and a coverage radius of 70-100 meters; the security gateway adopts a hardware encryption module, supports the IPSec protocol and the national secret SM4 algorithm, and has a throughput of ≥200Mbps; the signaling gateway dynamically selects the MME node through the S1-flex interface, and the signaling processing capacity is ≥200EPS / s.

[0016] Optionally, in an implementation of the first aspect of the present invention, the emergency power supply system includes:

[0017] The small generator has a rated power of 2kVA, uses variable frequency technology, has a fuel consumption of less than 0.3L / kWh, and supports 48 hours of continuous operation. The high-power power bank uses a lithium iron phosphate battery pack with a capacity of 20kWh and an operating temperature range of -20°C to 60°C. When the mains power is interrupted, the UPS switches power in less than 10ms, and the generator starts within 30 seconds. The multi-WAN port routing device:

[0018] Built-in multi-path transport protocol MPTCP dynamically allocates traffic weights based on satellite link latency and terrestrial broadband packet loss rate; uses the DiffServ QoS mechanism to assign DSCP 46 priority to voice services, ensuring a MOS value of ≥3.5; and integrates an 8GB SSD local cache for storing emergency command information.

[0019] The network management monitoring system adopts dual-channel monitoring: satellite network management and base station network management;

[0020] The satellite network management system monitors EIRP and G / T value radio frequency parameters in real time and supports remote adjustment of symbol rates. The base station network management system visualizes user distribution and traffic heat maps and supports one-click triggering of the SON self-organizing network function. Alarm linkage automatically triggers base station power reduction and pushes work orders to the operation and maintenance app when the satellite link packet loss rate exceeds 5% for 5 minutes.

[0021] Optionally, in an implementation of the first aspect of the present invention, comprehensively evaluating network performance using a large AI model and adaptively adjusting and optimizing the network include:

[0022] S1, set different priorities for satellite network management and base station network management;

[0023] S2, the network management with high priority is used as the first communication link, and the network management with low priority is used as the second communication link;

[0024] S3, monitoring parameter data of the network management performance of the first communication link, the parameter data including: signal strength, transmission delay, and packet loss rate;

[0025] S4, performing time series performance prediction on the parameter data using an AI big model;

[0026] S5, comparing the time series performance prediction with each corresponding preset reference value, and comprehensively evaluating each comparison result to obtain a first evaluation value;

[0027] S6, when the evaluation value is greater than or equal to the preset threshold, return to S3;

[0028] S7, otherwise, switch the network management priority, return to steps S2-S5, and use the obtained result as the second evaluation value;

[0029] S8, comparing the first evaluation value with the second evaluation value, and when the first evaluation value is less than the second evaluation value, optimizing the communication link of the network management;

[0030] S9, when the first evaluation value is greater than or equal to the second evaluation value, switch the network management priority and optimize the network management communication link.

[0031] Optionally, in an implementation of the first aspect of the present invention, the AI ​​large model adopts a hybrid neural network architecture with multi-module fusion, combining time series prediction and reinforcement learning optimization capabilities, and the network architecture includes: an input layer, a feature extraction module, a performance prediction module, an evaluation and decision module, and an output layer;

[0032] The input layer is used to receive network management performance parameters monitored in real time, and the data format is the sliding window data of the past 60 seconds;

[0033] The feature extraction module is a CNN-LSTM hybrid layer combined with an attention mechanism, wherein the CNN branch is used to extract the spatial local features of the parameters, such as the mutation pattern of the signal strength; the LSTM branch is used to capture the temporal dependencies, including the periodic fluctuations of the packet loss rate; and the attention mechanism is used to dynamically weight the importance of different parameters.

[0034] The performance prediction module includes a time series prediction sub-network, which uses Transformer to predict the performance trend in the next 5-10 seconds and outputs the predicted values ​​of delay and packet loss rate;

[0035] The evaluation and decision module is used for reinforcement learning agents, including state, action, and reward function attributes. The state attributes include: current predicted value, historical evaluation value, and link priority; the action attributes include: switching priority, adjusting link weight, and triggering optimization strategy; and the reward function attributes are dynamically generated based on the objective function.

[0036] The output layer is used to generate evaluation values ​​and optimization instructions;

[0037] The objective function needs to balance performance stability and resource utilization, and is divided into two parts: prediction and optimization:

[0038] Among them, the time series prediction target loss function is:

[0039] L pred =α·MSE(y delay )+β·MAE(y loss )+γ·Huber(y signal ),

[0040]

[0041] Among them, the weighted mean square error MSE(y delay ) represents the delay y delay Indicator, mean absolute error MAE(y loss ) represents the packet loss rate y loss Indicator, Huber(y signal ) represents the loss smoothing signal strength y signal Outliers, α, β, and γ represent the adjustment factors of the corresponding indicators, L and N are the ordinal and total numbers of the sampled data, respectively;

[0042] The optimization decision objective function is:

[0043]

[0044] in, It represents the ratio of the current link quality to the threshold, SwitchCost represents the switching cost, Fairness represents the fairness item to prevent low-priority links from being idle for a long time, and w1, w2, and w3 represent the corresponding weights respectively.

[0045] Optionally, in an implementation of the first aspect of the present invention, the AI ​​large model includes a data processing module, a model training module, an inference module, and an optimization control module, and the modules work together to achieve real-time monitoring, prediction, and optimization of network performance;

[0046] The data processing module is used to collect and pre-process network performance parameters and input them into the AI ​​model;

[0047] The model training module uses historical data to perform model training to learn the relationship between network performance and parameters and generate optimization suggestions;

[0048] The inference module predicts the current network status based on the trained model and outputs an optimization strategy;

[0049] The optimization control module dynamically adjusts network parameters according to the inference results, including priority allocation and link switching, to improve network performance.

[0050] In a second aspect, an embodiment of the present application provides an emergency information service method based on the integration of satellite communication and terrestrial mobile communication, which is applied to an emergency information service system based on the integration of satellite communication and terrestrial mobile communication as described in the first aspect, including:

[0051] Disaster monitoring steps: The operator's monitoring system detects an out-of-service alarm for a village-level conventional base station;

[0052] System activation steps: Automatically or manually activate the village-level broadband satellite emergency communication station;

[0053] Network establishment steps: High-throughput satellite fixed terminals establish satellite links, and pico base stations access the core network through the satellite network;

[0054] Signal coverage steps: Pico base stations provide 4G network signal coverage to the disaster-stricken areas;

[0055] Communication service steps: The mobile phones of the affected people are automatically connected to the emergency network to communicate with the outside world;

[0056] Management and maintenance steps: Remotely monitor and manage equipment through the network management system, and use AI large models to comprehensively evaluate network performance and adaptively adjust and optimize the network.

[0057] Optionally, in an implementation of the second aspect of the present invention, establishing a satellite communication link includes automatically aligning with the Asia-Pacific 6D high-throughput satellite, establishing a 50Gbps communication bandwidth, and providing regional 4G coverage through pico base stations, including using external antennas to achieve 70-100 meters of precise coverage.

[0058] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0059] processor;

[0060] a memory for storing processor-executable instructions;

[0061] Wherein, the processor is configured to implement an emergency information service method based on the integration of satellite communication and terrestrial mobile communication as described in the second aspect when executing the instructions.

[0062] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a program, and the program instructs a device to execute an emergency information service method based on the integration of satellite communication and terrestrial mobile communication as described in the second aspect.

[0063] The present application provides an emergency information service system and method based on the integration of satellite communication and terrestrial mobile communication. The system includes: a high-throughput satellite fixed station terminal, using a Ku-band 0.6-meter VSAT terminal, integrating ACU and MODEM, with a three-axis structure and a carbon fiber antenna; a terrestrial mobile communication subsystem, including a 4G pico base station, a security gateway, and a signaling gateway; multi-WAN port routing equipment to achieve dynamic load balancing between satellite and terrestrial broadband; an emergency power supply system using a generator and a power bank to form a three-level power supply; and a network management monitoring system that supports remote configuration and AI optimization. The method includes disaster monitoring, system activation, network establishment, signal coverage, communication services, and management and maintenance steps. The present invention integrates satellite and terrestrial communications, combined with AI-driven network optimization, to quickly establish a reliable communication network in emergency scenarios, with the characteristics of wind load resistance, low latency, and intelligent scheduling, significantly improving the emergency communication guarantee capability.

[0064] Beneficial effects:

[0065] (1) Highly reliable communication guarantee: Satellite and ground dual links are integrated, and dynamic load balancing is achieved through multi-WAN port routing equipment to ensure uninterrupted communication in extreme disaster environments.

[0066] (2) The high-throughput satellite terminal adopts a three-axis stable structure and a carbon fiber antenna, which can withstand level 6 wind loads, with a pointing error of ≤0.3°, and can adapt to harsh environments.

[0067] (3) Rapid deployment and flexible coverage: 4G pico base stations support dynamic spectrum sharing (DSS) with a coverage radius of 70-100 meters, enabling rapid establishment of emergency networks to meet communication needs in disaster areas.

[0068] (4) Automatically align with satellites, 50Gbps high-speed bandwidth, and external antennas of Pico base stations to achieve precise coverage and improve rescue efficiency.

[0069] (5) Intelligent management and optimization: The AI ​​big model monitors network performance (signal strength, latency, packet loss rate) in real time, predicts trends in the next 5-10 seconds, dynamically adjusts priorities and link weights, and optimizes communication quality.

[0070] (6) The network management system supports remote monitoring and automatically triggers alarm linkage (such as reducing power when the packet loss rate is greater than 5%), thereby improving operation and maintenance efficiency.

[0071] (7) Efficient energy guarantee: The three-level power supply architecture (generator + large-capacity lithium iron battery + UPS) supports 48 hours of continuous operation and ensures stable power supply to the equipment in the event of a power outage.

[0072] (8) Security and adaptability: The security gateway supports national SM4 encryption, and the throughput is ≥

[0073] 200Mbps, ensuring data transmission security. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A schematic diagram of an emergency information service system based on the integration of satellite communication and terrestrial mobile communication provided in one embodiment of the present application.

[0075] Figure 2 A schematic diagram of the process of evaluating network performance using an AI large model provided in one embodiment of the present application.

[0076] Figure 3 This is a diagram of the AI ​​large model network architecture provided for one embodiment of the present application.

[0077] Figure 4 A schematic diagram of an emergency information service system method based on the integration of satellite communication and terrestrial mobile communication is provided in one embodiment of the present application.

[0078] Figure 5 A schematic diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0079] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0080] It should be noted that, in the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art in the art to which this application relates. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0081] It should be noted that, in the embodiments of the present application, words such as "first" and "second" are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying an order. Features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way.

[0082] Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0083] Example 1

[0084] Figure 1 A schematic diagram of an emergency information service system based on the integration of satellite communication and terrestrial mobile communication provided in one embodiment of the present application.

[0085] like Figure 1 As shown, an emergency information service system based on the integration of satellite communication and terrestrial mobile communication includes:

[0086] High-throughput satellite fixed station terminal: This terminal uses a Ku-band 0.6-meter terrestrial automatic VSAT terminal, integrating an antenna control unit (ACU) and a modem. It features a three-axis structure and a high-strength carbon fiber composite antenna surface, enabling satellite broadband access.

[0087] Ground mobile communication subsystem: includes 4G pico base stations, pico base station gateways, and network management systems. The pico base station gateways include security gateways and signaling gateways, providing LTE terminal access services.

[0088] Multi-WAN port routing device: configured to access satellite broadband and terrestrial broadband simultaneously and achieve dynamic load balancing;

[0089] Emergency power supply system: including small generators and high-power power banks, forming a three-level power supply guarantee structure to ensure power supply to equipment in the event of a power outage;

[0090] Network management and monitoring system: This includes satellite fixed station network management and pico base station network management, supports remote parameter configuration and status monitoring, and uses AI large models to comprehensively evaluate network performance and adaptively adjust and optimize the network.

[0091] It is understandable that, in this embodiment, the high-throughput satellite fixed station terminal includes:

[0092] The three-axis stabilization structure consists of a crossed roller bearing on the azimuth axis, a harmonic reducer on the pitch axis, and a stepper motor on the polarization axis. The pointing error is ≤0.3° under wind load conditions of level 6. The antenna surface is made of a 2.5mm thick carbon fiber composite material with a tensile strength of ≥800MPa, and the total weight is less than 15kg. The ACU and MODEM are integrated into the antenna feed through a co-aperture design, with signal insertion loss less than 0.5dB. It supports adaptive coding modulation from QPSK to 64APSK and has a built-in TCP acceleration engine optimized for the SCPS-TP protocol, ensuring end-to-end transmission latency of less than 800ms in GEO satellite scenarios.

[0093] The 4G pico base station in the ground mobile communication subsystem adopts a software-defined radio (SDR) architecture, supports dynamic spectrum sharing (DSS) technology, can simultaneously access LTE Band 3 / 5 / 8 frequency bands within a 10MHz bandwidth, has an adjustable transmission power of 0.1W-5W, and a coverage radius of 70-100 meters; the security gateway adopts a hardware encryption module, supports the IPSec protocol and the national secret SM4 algorithm, and has a throughput of ≥200Mbps; the signaling gateway dynamically selects the MME node through the S1-flex interface, and the signaling processing capacity is ≥200EPS / s.

[0094] The emergency power supply system includes:

[0095] The small generator has a rated power of 2kVA, uses variable frequency technology, has a fuel consumption of less than 0.3L / kWh, and supports 48 hours of continuous operation. The high-power power bank uses a lithium iron phosphate battery pack with a capacity of 20kWh and an operating temperature range of -20°C to 60°C. When the mains power is interrupted, the UPS switches power in less than 10ms, and the generator starts within 30 seconds. The multi-WAN port routing device:

[0096] Built-in multi-path transport protocol MPTCP dynamically allocates traffic weights based on satellite link latency and terrestrial broadband packet loss rate; uses the DiffServ QoS mechanism to assign DSCP 46 priority to voice services, ensuring a MOS value of ≥3.5; and integrates an 8GB SSD local cache for storing emergency command information.

[0097] The network management monitoring system adopts dual-channel monitoring: satellite network management and base station network management;

[0098] The satellite network management system monitors EIRP and G / T value radio frequency parameters in real time and supports remote adjustment of symbol rates. The base station network management system visualizes user distribution and traffic heat maps and supports one-click triggering of the SON self-organizing network function. Alarm linkage automatically triggers base station power reduction and pushes work orders to the operation and maintenance app when the satellite link packet loss rate exceeds 5% for 5 minutes.

[0099] Figure 2 This is a flow chart of evaluating network performance using an AI big model provided in one embodiment of the present application. Figure 2 As shown, the AI ​​big model is used to comprehensively evaluate network performance and adaptively adjust and optimize the network, including:

[0100] S1, set different priorities for satellite network management and base station network management;

[0101] S2, the network management with high priority is used as the first communication link, and the network management with low priority is used as the second communication link;

[0102] S3, monitoring parameter data of the network management performance of the first communication link, the parameter data including: signal strength, transmission delay, and packet loss rate;

[0103] S 4, performing time series performance prediction on the parameter data using the AI ​​big model;

[0104] S5, comparing the time series performance prediction with each corresponding preset reference value, and comprehensively evaluating each comparison result to obtain a first evaluation value;

[0105] S6: When the evaluation value is greater than or equal to the preset threshold, return to S3;

[0106] S7, otherwise, switch the network management priority, return to steps S2-S5, and use the obtained result as the second evaluation value;

[0107] S8, comparing the first evaluation value and the second evaluation value, and when the first evaluation value is smaller than the second evaluation value, optimizing the communication link of the network management;

[0108] S9: When the first evaluation value is greater than or equal to the second evaluation value, switch the network management priority and optimize the network management communication link.

[0109] Figure 3 This is a diagram of the AI ​​large model network architecture provided for one embodiment of the present application.

[0110] like Figure 3As shown, the AI ​​large model adopts a hybrid neural network architecture with multi-module fusion, combining time series prediction and reinforcement learning optimization capabilities. The network architecture includes: input layer, feature extraction module, performance prediction module, evaluation and decision module, and output layer;

[0111] The input layer is used to receive network management performance parameters monitored in real time, and the data format is the sliding window data of the past 60 seconds.

[0112] Specifically, the input layer receives real-time monitoring of network management performance parameters in the form of a sliding window of data over the past 60 seconds. This design captures dynamic trends in time series data and provides high-quality input data for subsequent modules.

[0113] The feature extraction module is a CNN-LSTM hybrid layer combined with an attention mechanism, wherein the CNN branch is used to extract the spatial local features of the parameters, which include the mutation pattern of the signal strength; the LSTM branch is used to capture temporal dependencies, including periodic fluctuations in the packet loss rate; and the attention mechanism is used to dynamically weight the importance of different parameters.

[0114] Specifically, the feature extraction module uses a CNN-LSTM hybrid layer combined with an attention mechanism. The CNN branch extracts spatially localized parameter features, such as the mutation pattern of signal strength, while the LSTM branch captures temporal dependencies, such as periodic fluctuations in packet loss rate. The attention mechanism further dynamically weights the importance of different parameters, thereby improving the model's ability to focus on key features.

[0115] The performance prediction module includes a time series prediction sub-network, which uses Transformer to predict the performance trend in the next 5-10 seconds and outputs the predicted values ​​of delay and packet loss rate.

[0116] Specifically, the performance prediction module contains a time series prediction sub-network, which uses the Transformer model to predict the performance trend in the next 5-10 seconds and outputs the predicted values ​​of delay and packet loss rate.

[0117] The Transformer model effectively captures long-term and short-term dependencies through the self-attention mechanism, while avoiding the gradient vanishing problem of the traditional RNN model.

[0118] The evaluation and decision module is used to reinforce the learning agent, including state, action, and reward function attributes. The state attributes include: current predicted value, historical evaluation value, and link priority; the action attributes include: switching priority, adjusting link weight, and triggering optimization strategy. The reward function attributes are dynamically generated based on the objective function.

[0119] Specifically, this module is the core of the reinforcement learning agent and includes state, action, and reward function attributes. State attributes include the current predicted value, historical evaluation value, and link priority; action attributes include switching priority, adjusting link weights, and triggering optimization strategies; and reward function attributes are dynamically generated based on the objective function.

[0120] The output layer is used to generate evaluation values ​​and optimization instructions. Specifically, the output layer is used to generate evaluation values ​​and optimization instructions to guide the allocation and optimization operations of actual network resources.

[0121] The objective function needs to balance performance stability and resource utilization, and is divided into two parts: prediction and optimization.

[0122] Among them, the time series prediction target loss function is:

[0123] L pred =α·MSE(y delay )+β·MAE(y loss )+γ·Huber(y signal ),

[0124]

[0125] Among them, the weighted mean square error MSE(y delay ) represents the delay y delay Indicator, mean absolute error MAE(y loss ) represents the packet loss rate y loss Indicator, Huber(y signal ) represents the loss smoothing signal strength y signal Outliers, α, β, and γ represent the adjustment factors of the corresponding indicators, L and N are the ordinal and total numbers of the sampled data, respectively;

[0126] The optimization decision objective function is:

[0127]

[0128] in, It represents the ratio of the current link quality to the threshold, SwitchCost represents the switching cost, Fairness represents the fairness item to prevent low-priority links from being idle for a long time, and w1, w2, and w3 represent the corresponding weights respectively.

[0129] Specifically, the objective function must balance performance stability and resource utilization, and is divided into two parts: prediction and optimization. The objective loss function for time series prediction includes weighted mean squared error (for latency) and mean absolute error (for packet loss rate), while also taking into account loss smoothing outliers in signal strength. This design ensures a good balance between prediction accuracy and stability.

[0130] This model's design fully combines the advantages of CNN and LSTM, leveraging the powerful spatial feature extraction capabilities of CNN and the time series modeling capabilities of LSTM. It also incorporates the Transformer model to further enhance its ability to capture long-term and short-term dependencies. Furthermore, the introduction of a reinforcement learning module enables the model to dynamically adjust its strategy to optimize network performance and improve resource utilization.

[0131] The AI ​​big model includes a data processing module, a model training module, an inference module, and an optimization control module. These modules work together to achieve real-time monitoring, prediction, and optimization of network performance.

[0132] The data processing module is used to collect and pre-process network performance parameters and input them into the AI ​​model;

[0133] The model training module uses historical data to perform model training to learn the relationship between network performance and parameters and generate optimization suggestions;

[0134] The inference module predicts the current network status based on the trained model and outputs an optimization strategy;

[0135] The optimization control module dynamically adjusts network parameters according to the inference results, including priority allocation and link switching, to improve network performance.

[0136] The AI ​​big model achieves efficient prediction and optimization of network management performance parameters through multi-module fusion. Its design is highly innovative and practical both in theory and practice.

[0137] Example 2

[0138] like Figure 4 As shown, the present application provides an emergency information service method based on the integration of satellite communication and terrestrial mobile communication, which is applied to an emergency information service system based on the integration of satellite communication and terrestrial mobile communication as described in Example 1, including:

[0139] Disaster monitoring steps: The operator's monitoring system detects an out-of-service alarm for a village-level conventional base station;

[0140] System activation steps: Automatically or manually activate the village-level broadband satellite emergency communication station;

[0141] Network establishment steps: High-throughput satellite fixed terminals establish satellite links, and pico base stations access the core network through the satellite network;

[0142] Signal coverage steps: Pico base stations provide 4G network signal coverage to the disaster-stricken areas;

[0143] Communication service steps: The mobile phones of the affected people are automatically connected to the emergency network to communicate with the outside world;

[0144] Management and maintenance steps: Remotely monitor and manage equipment through the network management system, and use AI large models to comprehensively evaluate network performance and adaptively adjust and optimize the network.

[0145] It can be understood that in this embodiment, establishing a satellite communication link includes automatically aligning with the Asia-Pacific 6D high-throughput satellite, establishing a 50Gbps communication bandwidth, and providing regional 4G coverage through pico base stations, including using external antennas to achieve 70-100 meters of precise coverage.

[0146] The present application provides an emergency information service system and method based on the integration of satellite communication and terrestrial mobile communication. The system includes: a high-throughput satellite fixed station terminal, using a Ku-band 0.6-meter VSAT terminal, integrating ACU and MODEM, with a three-axis structure and a carbon fiber antenna; a terrestrial mobile communication subsystem, including a 4G pico base station, a security gateway, and a signaling gateway; multi-WAN port routing equipment to achieve dynamic load balancing between satellite and terrestrial broadband; an emergency power supply system using a generator and a power bank to form a three-level power supply; and a network management monitoring system that supports remote configuration and AI optimization. The method includes disaster monitoring, system activation, network establishment, signal coverage, communication services, and management and maintenance steps. The present invention integrates satellite and terrestrial communications, combined with AI-driven network optimization, to quickly establish a reliable communication network in emergency scenarios, with the characteristics of wind load resistance, low latency, and intelligent scheduling, significantly improving the emergency communication guarantee capability.

[0147] Figure 5 This is an electronic device provided by an embodiment of the present application. Figure 5 As shown, the electronic device includes at least the following parts: a processor 101 and a memory 100 , a communication interface 103 , and a bus 102 .

[0148] In the embodiment of the present application, the memory 100 is used to store instructions executable by the processor 101. The processor 101 is configured to execute the instructions to implement the following Figure 5 The device module shown is an emergency information service based on the integration of satellite communication and terrestrial mobile communication.

[0149] In an embodiment of the present application, a computer-readable storage medium includes instructions, and the instructions instruct a device to execute the method of the first aspect. For example, the instructions instruct the device to execute Figure 4 The method is shown in the process steps.

[0150] The program running in the electronic device involved in one embodiment of the present application can be a program that controls a central processing unit (CPU) and the like to realize the functions of the above-mentioned embodiment involved in one embodiment of the present invention (a program that enables a computer to function). Then, the information processed by these devices is temporarily stored in a random access memory (RAM) during its processing, and then stored in various ROMs such as read-only memory (Flash ROM) and hard disk drive (HDD), and is read, modified, and written by the CPU as needed.

[0151] It should be noted that a portion of the electronic device of the above embodiment may also be implemented by a computer. In this case, a program for implementing the control function may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read into a computer and executed.

[0152] It should be noted that the "computer" mentioned here refers to a computer built into an electronic device, employing hardware including an operating system (OS) and peripheral devices. Furthermore, "computer-readable recording medium" refers to removable media such as floppy disks, magneto-optical disks, ROMs, and CD-ROMs, as well as storage devices such as hard disks built into computers.

[0153] Furthermore, "computer-readable recording media" may include: media that dynamically store programs for a short period of time, such as communication lines when transmitting programs via networks such as the Internet or communication lines such as telephone lines; and media that store programs for a fixed period of time, such as volatile memory within computers acting as servers or clients in this context. Furthermore, the aforementioned program may be a program for implementing a portion of the aforementioned functions, or a program that can achieve the aforementioned functions by combining with a program already stored in a computer.

[0154] Furthermore, the electronic device in the above-described embodiments can also be implemented as a collection (device group) consisting of multiple devices. Each device constituting the device group may have a portion or all of the functions or functional blocks of the electronic device in the above-described embodiments. A device group only needs to have all the functions or functional blocks of the electronic device.

[0155] Those skilled in the art should recognize that the above embodiments are merely intended to illustrate the present application and are not intended to limit the present application. As long as they are within the spirit of the present application, appropriate changes and modifications to the above embodiments are within the scope of protection claimed in the present application.

Claims

1. An emergency information service system based on the integration of satellite communication and terrestrial mobile communication, characterized in that: The system comprises: High-throughput satellite fixed station terminal: This terminal uses a Ku-band 0.6-meter terrestrial automatic VSAT terminal, integrates an antenna control unit (ACU) and a modem, and features a three-axis structure and a high-strength carbon fiber composite antenna surface to achieve satellite broadband access. Ground mobile communication subsystem: includes 4G pico base stations, pico base station gateways, and network management systems. The pico base station gateways include security gateways and signaling gateways, providing LTE terminal access services. Multi-WAN port routing device: configured to access satellite broadband and terrestrial broadband simultaneously and achieve dynamic load balancing; Emergency power supply system: including small generators and high-power power banks, forming a three-level power supply guarantee structure to ensure power supply to equipment in the event of a power outage; Network management and monitoring system: This includes satellite fixed station network management and pico base station network management, supports remote parameter configuration and status monitoring, and uses AI large models to comprehensively evaluate network performance and adaptively adjust and optimize the network.

2. The emergency information service system based on the integration of satellite communication and terrestrial mobile communication according to claim 1, characterized in that: The high-throughput satellite fixed station terminal includes: The three-axis stabilization structure consists of a crossed roller bearing on the azimuth axis, a harmonic reducer on the pitch axis, and a stepper motor on the polarization axis. The pointing error is ≤0.3° under wind load conditions of level 6. The antenna surface is made of a 2.5mm thick carbon fiber composite material with a tensile strength of ≥800MPa, and the total weight is less than 15kg. The ACU and MODEM are integrated into the antenna feed through a co-aperture design, with signal insertion loss less than 0.5dB. It supports adaptive coding modulation from QPSK to 64A PSK, and has a built-in TCP acceleration engine optimized for the SCP STP protocol, ensuring end-to-end transmission latency of less than 800ms in GEO satellite scenarios. The 4G pico base station in the ground mobile communication subsystem adopts a software-defined radio (SDR) architecture, supports dynamic spectrum sharing (DSS) technology, can simultaneously access LTE Band 3 / 5 / 8 frequency bands within a 10MHz bandwidth, has an adjustable transmission power of 0.1W-5W, and a coverage radius of 70-100 meters; the security gateway adopts a hardware encryption module, supports IP Se c protocol and national secret SM 4 algorithm, and has a throughput ≥200M bps; the signaling gateway dynamically selects MM E nodes through the S1-flex interface, and the signaling processing capacity ≥200E PS / s.

3. The emergency information service system based on the integration of satellite communication and terrestrial mobile communication according to claim 2, characterized in that: The emergency power supply system includes: The small generator has a rated power of 2kVA, uses variable frequency technology, has a fuel consumption of less than 0.3L / kWh, and supports 48 hours of continuous operation. The high-power power bank uses a lithium iron phosphate battery pack with a capacity of 20kWh and an operating temperature range of -20°C to 60°C. In the event of a mains power outage, the UPS switches to the power supply in less than 10ms, and the generator starts within 30 seconds. The multi-WAN port routing device: Built-in multi-path transport protocol MP TCP dynamically allocates traffic weights based on satellite link latency and terrestrial broadband packet loss rate; assigns DSCP 46 priority to voice services through the DiffServ QoS mechanism, ensuring a MOS value of ≥3.5; and integrates an 8GB SSD local cache for storing emergency command information. The network management monitoring system adopts dual-channel monitoring: satellite network management and base station network management; The satellite network management system monitors EIR P and G / T value radio frequency parameters in real time and supports remote adjustment of symbol rates. The base station network management system visualizes user distribution and traffic heat maps and supports one-click triggering of the SON self-organizing network function. Alarm linkage automatically triggers base station power reduction and pushes a work order to the operation and maintenance app when the satellite link packet loss rate exceeds 5% for 5 minutes.

4. The emergency information service system based on the integration of satellite communication and terrestrial mobile communication according to claim 3, characterized in that: The AI ​​big model is used to comprehensively evaluate network performance and adaptively adjust and optimize the network, including: S1, set different priorities for satellite network management and base station network management; S2, the network management with high priority is used as the first communication link, and the network management with low priority is used as the second communication link; S3, monitoring parameter data of the network management performance of the first communication link, the parameter data including: signal strength, transmission delay, and packet loss rate; S 4, performing time series performance prediction on the parameter data using the AI ​​big model; S5, comparing the time series performance prediction with each corresponding preset reference value, and comprehensively evaluating each comparison result to obtain a first evaluation value; S6: When the evaluation value is greater than or equal to the preset threshold, return to S3; S7, otherwise, switch the network management priority, return to steps S2-S5, and use the obtained result as the second evaluation value; S8, comparing the first evaluation value and the second evaluation value, and when the first evaluation value is smaller than the second evaluation value, optimizing the communication link of the network management; S9: When the first evaluation value is greater than or equal to the second evaluation value, switch the network management priority and optimize the network management communication link.

5. The emergency information service system based on the integration of satellite communication and terrestrial mobile communication according to claim 4, characterized in that: The AI ​​large model adopts a hybrid neural network architecture with multi-module fusion, combining time series prediction and reinforcement learning optimization capabilities. The network architecture includes: input layer, feature extraction module, performance prediction module, evaluation and decision module, and output layer; The input layer is used to receive network management performance parameters monitored in real time, and the data format is the sliding window data of the past 60 seconds; The feature extraction module is a hybrid layer of CNN and LSTM combined with an attention mechanism. The CNN branch is used to extract the spatial local features of the parameters, including the mutation pattern of the signal strength; the LSTM branch is used to capture the temporal dependencies, including the periodic fluctuations of the packet loss rate; and the attention mechanism is used to dynamically weight the importance of different parameters. The performance prediction module includes a time series prediction sub-network, which is used to use Transformer to predict the performance trend in the next 5-10 seconds and output the predicted values ​​of delay and packet loss rate; The evaluation and decision module is used for reinforcement learning agents, including state, action, and reward function attributes. The state attributes include: current predicted value, historical evaluation value, and link priority; the action attributes include: switching priority, adjusting link weight, and triggering optimization strategy; and the reward function attributes are dynamically generated based on the objective function. The output layer is used to generate evaluation values ​​and optimization instructions; The objective function needs to balance performance stability and resource utilization, and is divided into two parts: prediction and optimization: Among them, the time series prediction target loss function is: L pred =α·MSE(y delay )+β·MAE(y loss )+γ·Huber(y signal ), Among them, the weighted mean square error MSE(y delay ) represents the delay y delay Indicator, mean absolute error MAE(y loss ) represents the packet loss rate y loss Indicator, Huber(y signal ) represents the loss smoothing signal strength y signal Outliers, α, β, and γ represent the adjustment factors of the corresponding indicators, L and N are the ordinal and total numbers of the sampled data, respectively; The optimization decision objective function is: in, It represents the ratio of the current link quality to the threshold, SwitchCost represents the switching cost, Fairness represents the fairness item to prevent low-priority links from being idle for a long time, and w1, w2, and w3 represent the corresponding weights respectively.

6. The emergency information service system based on the integration of satellite communication and terrestrial mobile communication according to claim 5, characterized in that: The AI ​​big model includes a data processing module, a model training module, an inference module, and an optimization control module. These modules work together to achieve real-time monitoring, prediction, and optimization of network performance. The data processing module is used to collect and pre-process network performance parameters and input them into the AI ​​model; The model training module uses historical data to perform model training to learn the relationship between network performance and parameters and generate optimization suggestions; The inference module predicts the current network status based on the trained model and outputs an optimization strategy; The optimization control module dynamically adjusts network parameters according to the inference results, including priority allocation and link switching, to improve network performance.

7. An emergency information service method based on the integration of satellite communication and terrestrial mobile communication, applied to an emergency information service system based on the integration of satellite communication and terrestrial mobile communication as claimed in any one of claims 1 to 6, characterized in that: include: Disaster monitoring steps: The operator's monitoring system detects an out-of-service alarm for a village-level conventional base station; System activation steps: Automatically or manually activate the village-level broadband satellite emergency communication station; Network establishment steps: High-throughput satellite fixed terminals establish satellite links, and pico base stations access the core network through the satellite network; Signal coverage steps: Pico base stations provide 4G network signal coverage to the disaster-stricken areas; Communication service steps: The mobile phones of the affected people are automatically connected to the emergency network to communicate with the outside world; Management and maintenance steps: Remotely monitor and manage equipment through the network management system, and use AI large models to comprehensively evaluate network performance and adaptively adjust and optimize the network.

8. The emergency information service method based on the integration of satellite communication and terrestrial mobile communication according to claim 7, characterized in that: Establishing a satellite communication link includes automatically aligning with the Asia-Pacific 6D high-throughput satellite, establishing a 50G bps communication bandwidth, and providing regional 4G coverage through pico base stations, including the use of external antennas to achieve 70-100 meters of precise coverage.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement an emergency information service method based on the integration of satellite communication and terrestrial mobile communication as described in any one of claims 7-8 when executing the instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, and the program instructs the device to execute the emergency information service method based on the integration of satellite communication and terrestrial mobile communication as described in any one of claims 7-8.

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