Emergency broadcasting methods, user equipment, and base stations for core network outage scenarios

Abstract

This invention discloses an emergency broadcasting method and system for core network outage scenarios. The method is executed by a base station with edge computing capabilities. The base station autonomously determines that a link outage has occurred with the core network by monitoring the transmission link, neighbor cell information, and signaling interaction status. After the outage, active terminals within the coverage area are selected as information sources based on signal quality, service activity, and other conditions. Emergency information collection requests are initiated to these terminals through methods such as simulating core network signaling. Using the edge computing module deployed on the base station, the danger information crowdsourced from the terminals is fused, analyzed, and intelligently processed to determine the details of the emergency event. Finally, the base station autonomously generates and broadcasts a detailed public warning system alert. This invention overcomes the single-point failure defect of traditional centralized early warning systems during core network outages by introducing autonomous decision-making and information processing capabilities at the network edge, improving the reliability and timeliness of public safety early warnings in extreme situations.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to an emergency broadcasting method, user equipment, and base station for core network outage scenarios. Background Technology

[0002] With the large-scale deployment of 5G mobile communication technology, its enhanced multimedia broadcast multicast service (eMBMS) and related architecture provide a new technological foundation for public early warning systems. Among them, commercial mobile alert service (CMAS), as a key security function, aims to rapidly and widely disseminate emergency alert information, such as alerts for natural disasters and public safety threats, to user equipment (UEs) within a specific geographical area through cellular networks, which is of great significance in improving the ability to safeguard public safety.

[0003] A typical 5G CMAS implementation relies on a centralized, hierarchical core network architecture for message distribution. The general process is as follows: the alarm initiator sends the alarm information to the Cell Broadcast Center Function (CBCF). After processing the information, the CBCF passes it to the corresponding Access and Mobility Management Function (AMF) via the N50 interface. The AMF further distributes the alarm message to one or more base stations (gNBs) in the target area via the N2 interface, and finally, the base stations broadcast the message to all user equipment in the area via the radio interface. This process involves close coordination among multiple entities, including the CBCF, AMF, gNBs, and the transport network connecting them.

[0004] However, existing 5G CMAS solutions have the following major technical shortcomings in actual deployment and operation: 1. Single Point of Failure Risk in Centralized Architecture: Existing CMAS functionality heavily relies on the continuous availability of critical network elements in the core network and the stability of end-to-end transmission links. Successful broadcasting of alarm information requires traversing the complete chain from CBCF to AMF to gNB. If any critical node in this chain (such as a CBCF server failure or AMF malfunction) or a critical transmission link is interrupted, the entire alarm distribution path is severed, preventing alarms from reaching the target area. This centralized architecture inherently suffers from a single point of failure bottleneck. In situations where natural disasters (such as earthquakes and floods) can easily damage network infrastructure, the risk of system paralysis is highest precisely when alarms are most needed, potentially leading to severe information delays and public safety consequences.

[0005] 2. Lack of Precision and Context-Specificity in Alarm Messages: Current CMAS (China Mobile Communicator Assistant System) broadcasts mostly use pre-set, generic templates, resulting in concise but general information. For example, during natural disasters, users may only receive a generalized message like "Emergency situation, please respond with caution," lacking specific details about the disaster type (e.g., landslide, mudslide, or earthquake), the expected impact intensity, the precise affected area, and specific action guidelines for different locations and populations (e.g., mountain residents, coastal residents). This "one-size-fits-all" approach fails to provide effective decision support for users, reducing the practicality and response efficiency of the alarms. Users receiving vague information are unable to take the most appropriate protective measures, significantly diminishing the effectiveness of the early warning system.

[0006] 3. Broadcast interruption in scenarios where the core network and access network are disconnected: This is a particularly prominent technical challenge. During large-scale disasters or severe network failures, the backhaul link (N2 interface) between the base station (gNB) and the core network (AMF) may be interrupted. Under the existing architecture, once the base station loses connection with the AMF, it cannot receive new alarm commands or synchronize alarm status. Even if the base station's equipment and wireless coverage are intact, it cannot independently initiate or continue broadcasting emergency alarms. How to ensure that the base station still possesses a certain emergency broadcasting capability or status maintenance capability in the extreme case of disconnection from the core network, ensuring that early warning information is not "silent," is a technical problem that urgently needs to be solved and has not yet been properly overcome.

[0007] In summary, the CMAS functionality in existing 5G networks has significant shortcomings in terms of reliability, information intelligence, and network resilience. In particular, its heavy reliance on centralized core network control makes it vulnerable to failure in scenarios involving critical node failures or network disruptions, sharply contradicting its fundamental mission of ensuring public safety. Therefore, there is an urgent need to research a more resilient emergency broadcasting method that can adapt to abnormal network conditions and provide more accurate contextualized alerts. Summary of the Invention

[0008] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes an emergency broadcast method for core network outage scenarios, which overcomes the single-point failure defect of traditional centralized early warning systems during core network interruptions, and improves the reliability and timeliness of public safety early warnings in extreme situations.

[0009] An emergency broadcast method for a core network outage scenario according to an embodiment of the present invention, executed by a base station, includes the following steps: S1. Link Disconnection Determination: By real-time monitoring of the transmission link status, neighbor cell information and / or signaling interaction protocol status between the base station and the core network, it is determined that the base station has lost its link with the core network; S2. Terminal screening: After determining that the connection is lost, the target query terminal is selected from the terminals within the coverage area of ​​the base station according to the preset active terminal screening strategy. S3. Information Collection: Send an emergency information collection request to the target interrogation terminal and receive a reply from the target interrogation terminal containing information about the danger. S4. Edge processing: The received emergency response is processed and analyzed by the edge computing module deployed on the base station to determine the current emergency situation. S5. Autonomous Broadcasting: Based on the identified emergency situation, autonomously generate and broadcast public warning system messages containing emergency alert content.

[0010] In some embodiments of the present invention, the base station is a single-connection 5G base station; The emergency information collection request in step S3 is a high-priority message in the simulated core network downlink signaling format, which encodes the emergency situation type and its corresponding number; the received reply is a signaling or message sent by the terminal through the wireless air interface, containing the selected emergency situation type number.

[0011] In some embodiments of the present invention, the base station is a base station that supports dual 5G and WIFI connectivity; The method further includes: broadcasting WIFI access point information through a system information block before or after step S1; In step S3, a webpage containing an adaptive form is pushed to the accessed terminal via the established WIFI connection as an emergency information collection request. The webpage form contains interactive elements for reporting disaster type, severity, and / or geographical location. At the same time, the received response is the webpage form data submitted by the terminal through the WIFI connection.

[0012] In some embodiments of the present invention, step S5 is followed by: S6. Assisted Push: Push information containing detailed emergency alarm content and risk avoidance measures to connected terminals through the WIFI network.

[0013] In some embodiments of the present invention, the active terminal filtering strategy in step S2 includes: Set filtering conditions, including the reference signal received power being higher than a first threshold, the timing advance being lower than a second threshold, and there being service interaction within a preset historical time period; When the number of terminals that simultaneously meet the filtering conditions is greater than or equal to the preset number threshold N, the first M terminals are selected as the initial query targets, sorted from high to low according to the reference signal receiving power; if the response rate of the initial query targets is lower than the first response rate threshold, the next M terminals are selected. When the number of terminals that simultaneously meet the filtering conditions is less than N, a query is sent to all terminals that meet the filtering conditions. If the overall response rate is lower than the second response rate threshold within the preset inquiry timeout period, the inquiry scope will be expanded to standby terminals.

[0014] In some embodiments of the present invention, in step S4, the edge computing module fuses and processes at least two data sources from base station logs, terminal sensors, and pre-stored external data, and runs a prediction model to infer whether to initiate an offline emergency broadcast process based on the fused data.

[0015] In some embodiments of the present invention, in step S5, the public early warning system message adopts an enhanced cell broadcast format, which supports extended message length.

[0016] The present invention also discloses an emergency broadcasting system for core network outage scenarios, including a base station configured to execute the above method.

[0017] In some embodiments of the present invention, the base station includes: A connection monitoring unit is used to monitor the connection status with the core network and execute step S1. The terminal management unit is used to execute step S2; A communication interaction unit is used to perform step S3; An edge computing unit, equipped with the edge computing module, is used to execute step S4; A broadcast control unit is used to perform step S5.

[0018] In some embodiments of the present invention, a terminal is also included, the terminal being configured to: Receive an emergency information collection request from the base station; In response to the request, generate a reply containing hazard information based on user input or sensor data; The reply is sent to the base station; Receive and display the public early warning system messages broadcast by the base station.

[0019] In some embodiments of the present invention, when the base station is a base station supporting both 5G and WIFI connectivity, the terminal is further configured as follows: Receive and parse the WIFI access point information broadcast by the base station; Connect to the specified WIFI network based on the parsing results; After connecting to the Wi-Fi network, the system receives and displays a web form pushed by the base station, and reports the emergency information through this form. The present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0020] Compared with existing technologies, this invention, by constructing an emergency broadcast system autonomously driven by edge base stations, achieves a disruptive enhancement of public safety early warning capabilities in scenarios where the 5G network core network is interrupted. Its specific beneficial effects are as follows: First, traditional CMAS relies entirely on centralized control of the core network. Once the core network or transmission link is interrupted, the entire early warning system is paralyzed. The method of this invention decentralizes the decision-making and execution capabilities of early warning to each base station at the network edge, enabling it to continue working independently even after being completely disconnected from the core network. This completely solves the fatal weakness of the traditional architecture and builds a decentralized, highly resilient early warning network.

[0021] Second, by deploying edge computing modules and running lightweight prediction models on the base station side, the system can complete multi-source data fusion analysis and activation decision-making in a short time after the connection is lost, improving the emergency response from "minute level" that relies on remote communication to "millisecond level".

[0022] Third, by using multi-source data cross-validation of "base station data + terminal sensor + environmental information", the intelligent model can effectively distinguish between real disasters and local faults, significantly reduce false alarms and missed alarms, and make preliminary judgments on the type and scope of disasters, laying the foundation for accurate early warning.

[0023] Fourth, by initiating intelligent crowdsourced inquiries (coded responses or web forms) to on-site users, the system can obtain specific details of the hazard. The resulting alerts can clearly include the type of disaster, its severity, the specific affected areas, and customized action guidelines, completely changing the shortcomings of traditional CMAS systems, which are characterized by vague information and poor guidance.

[0024] Fifth, the system can periodically collect feedback, update analysis, and broadcast the latest instructions, realizing a leap from "one-time static notification" to "dynamic continuous guidance," which can better adapt to the ever-changing disaster scene.

[0025] Sixth, the tiered terminal screening strategy based on signal quality and activity can quickly obtain highly reliable feedback with minimal signaling overhead, avoiding broadcast storms. Intelligent decision-making also avoids unnecessary early warning issuance, saving air interface resources and terminal power.

[0026] Seventh, in the 5G+Wi-Fi dual-connection base station scenario, 5G is used to achieve wide-area reliable broadcasting, while Wi-Fi is used to achieve high-bandwidth, rich media information supplementation, interaction and push, forming a three-dimensional information transmission channel with complementary advantages, which greatly improves user experience and early warning effect.

[0027] Eighth, this invention constructs an autonomous closed loop consisting of intelligent edge base stations and collaborative sensing terminals. The base stations possess sensing, decision-making, and execution capabilities, while the terminals act as information sources and receivers. The two collaborate efficiently without centralized scheduling, forming an independently operable emergency microsystem. The core innovations of the system focus on base station-side software upgrades and edge capability enhancements, as well as the expansion of terminal application protocols, without requiring modifications to the existing core network architecture. This "enhanced edge, terminal collaboration" approach has low technological evolution costs, facilitates large-scale rapid deployment, and exhibits good compatibility with existing networks.

[0028] Ninth, traditional communication base stations are "passive" communication facilities. This invention empowers them to transform into "proactive" early warning and information hubs during major public safety incidents. Even before external assistance arrives, the network infrastructure itself can provide critical emergency communication services, significantly enhancing the inherent security resilience of cities and communities.

[0029] In summary, this invention not only technically addresses the functional deficiencies of traditional CMAS in network outage scenarios, but also conceptually reshapes the public safety early warning model. It endows the early warning system with core advantages such as resilience, real-time intelligence, precise dynamics, and efficient collaboration, upgrading the mobile communication network from a mere conduit for communication to an intelligent lifeline that proactively safeguards security, thus possessing significant social value and broad industrial application prospects. Attached Figure Description

[0030] Figure 1 This is a flowchart illustrating the emergency broadcasting method of the present invention for core network outage scenarios; Figure 2 This is a schematic diagram illustrating the application scenario of the present invention; Figure 3 This is a schematic diagram of the present invention when a single 5G base station is connected. Detailed Implementation

[0031] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0032] To facilitate understanding, before introducing the embodiments of this disclosure, several terms involved in the embodiments of this disclosure will be explained as follows: eMBMS: Evolved Multimedia Broadcast Multicast Services, which utilizes the broadcast channels of mobile communications to send data, especially high-bandwidth data such as video. CMAS: Commercial Mobile Alert Service; CBCF: Cell Broadcast Centre Function. As a key network function in the 5G core network, it is responsible for managing the entire lifecycle of Cell Broadcast Service (CBS) messages, including sequence number allocation, message content processing, broadcast range and time control, etc. AMF: Access and Mobility Management Function, provides a session management message transmission channel for UE and SMF, provides authentication and authorization functions for user access, and is the core network control plane access point for terminals and radio. UE: User Equipment.

[0033] The following is for reference. Figures 1-3 An emergency broadcast method for a core network outage scenario according to an embodiment of the present invention is described, executed by a base station, and includes the following steps: S1. Link Disconnection Determination: By real-time monitoring of the transmission link status, neighbor cell information and / or signaling interaction protocol status between the base station and the core network, it is determined that the base station has lost its link with the core network; S2. Terminal screening: After determining that the connection is lost, the target query terminal is selected from the terminals within the coverage area of ​​the base station according to the preset active terminal screening strategy. S3. Information Collection: Send an emergency information collection request to the target interrogation terminal and receive a reply from the target interrogation terminal containing information about the danger. S4. Edge processing: The received emergency response is processed and analyzed by the edge computing module deployed on the base station to determine the current emergency situation. S5. Autonomous Broadcasting: Based on the identified emergency situation, autonomously generate and broadcast public warning system messages containing emergency alert content.

[0034] For example, edge computing enhancements can be applied to base stations. The computing engine deployed on the base station can process information even when the network is down, including processing data received from multiple sensors to determine the scene, processing responses collected by the base station, and intelligently outputting evacuation commands for emergencies. MEC deployment solution: Hardware: x86 server (8 cores / 32GB memory); Lightweight container: Docker image <500MB; Processing latency: Sensitive services ≤15ms (percentile P99). Simultaneously, the base station can monitor the core network connection status in real time through the following indicators: 1) Transmission link status: Detecting whether the physical / logical link between the base station and the core network is interrupted; 2) Neighbor cell information: Monitoring whether the neighbor cell lists of surrounding base stations are lost or abnormal; 3) Protocol status: Detecting whether signaling interaction with the core network times out or fails. When the above indicators continuously exceed preset thresholds (e.g., three consecutive detection failures), it is determined as a link failure.

[0035] Understandably, after a sudden emergency causes a core network outage, once the base station senses the emergency anomaly (such as a power outage, link failure, or loss of all neighboring cells), it initiates inquiry requests to active UEs (indicating that the user is actively using the mobile phone and can immediately report the emergency) and receives responses containing emergency information from the target inquiry terminals; it processes and analyzes the received emergency responses to determine the current emergency situation; based on the determined emergency situation, it autonomously generates and broadcasts a public warning system message containing emergency alert content.

[0036] The emergency broadcasting method for core network outage scenarios according to embodiments of the present invention, by endowing base stations with autonomous outage perception, information collection, and broadcasting decision-making capabilities, effectively decentralizes and takes over the early warning function from the core side to the network edge (base station side) in the extreme scenario of core network connection interruption. This enables base stations located at the event site to independently and continuously provide emergency broadcasting services even when the entire core network or critical transmission links fail, fundamentally solving the vulnerability problem of traditional architectures and greatly improving the reliability and survivability of public safety early warning systems. Simultaneously, this method introduces a mode combining terminal crowdsourcing feedback and edge intelligent processing. Base stations actively inquire with on-site users to obtain first-hand, multi-dimensional information about the danger (such as type, severity, and specific location), and utilize edge computing capabilities for real-time fusion, analysis, and judgment. This allows the ultimately generated and broadcast alarm content to more accurately reflect the disaster type, impact range, and severity level based on real-time on-site data, and may even provide more instructive risk avoidance suggestions, thereby upgrading "generalized notification" to "precise guidance," enhancing the practical value of alarm information and the efficiency of public response. Moreover, the rapid detection and decision-making mechanism for base station link failures abandons the passive mode of waiting for core network instructions in traditional solutions. Once a link failure is detected and the prediction model or direct feedback indicates an emergency, the base station can immediately initiate local emergency procedures, significantly shortening the delay from the occurrence of the event to the issuance of the warning, which is more conducive to emergency rescue that races against time.

[0037] In some embodiments of the present invention, the base station is a single-connection 5G base station, and the emergency information collection request in step S3 is a high-priority message in the simulated core network downlink signaling format, wherein the emergency situation type and its corresponding number are encoded; the received reply is a signaling or message sent by the terminal through the wireless air interface, containing the selected emergency situation type number.

[0038] Understandably, for ordinary base stations with single connections, constrained by the protocol framework and limited to air interface transmission, a simulated SMS approach can be used. The base station can simulate a high-level message, which needs to include all types of emergencies and their corresponding numbers, such as: Type Number: 01-Earthquake, 02-Tsunami, 03-Fire; Level Indicator: 1-Minor, 5-Major. This simplifies the information, allowing end-users to quickly respond to the emergencies they discover. This message is sent to relatively active end-users selected by the base station, simulating core network messages. After receiving the emergency SMS, users reply with the danger they have discovered. Within 30 seconds, the base station uses an edge computing model to calculate the current danger from the responses received and encodes it into CMAS signaling. It then broadcasts the message according to the protocol flow, using PWS enhanced broadcast on the air interface, supporting a 512-byte extended message format to broadcast as many messages as possible.

[0039] In some embodiments of the present invention, the base station is a base station supporting dual 5G and WIFI connectivity, and the method further includes: broadcasting WIFI access point information through a system information block before or after step S1; in step S3, pushing a webpage containing an adaptive form to the accessed terminal through the established WIFI connection as the emergency information collection request, wherein the webpage form contains interactive elements for reporting disaster type, severity and / or geographical location; and the received response is the webpage form data submitted by the terminal through the WIFI connection.

[0040] Specifically, for 5G+WIFI dual-connectivity base stations, the added advantage of WIFI assistance effectively addresses many shortcomings of CMAS. The base station broadcasts the WIFI access point SSID (pre-shared key: CMAS_{PCI}_{timestamp}, completing 802.11ak fast association within 5ms) via SIB19. Accessing UEs are in dual-connectivity mode. The base station controls all WIFI-connected terminals to pop up web pages. UEs not connected to WIFI can connect via WIFI information provided by the base station system message and pop up web pages. The web page functions similarly to the SMS message above, using an adaptive form: 1. Multiple disaster type selection (landslide / fire, etc.); 2. Severity slider (levels 1-5); 3. Geofencing (drag map markers). This allows for detailed information on the location, nature of the event, and severity. After receiving feedback, the WIFI module simulates the AMF signaling to trigger the base station's CMAS process, broadcasting an alarm to all users in the cell. Simultaneously, WIFI assists by popping up a web page providing detailed information on the hazard and mitigation measures.

[0041] In some embodiments of the present invention, step S5 may be followed by: S6, assisted push: pushing information containing detailed emergency alarm content and risk avoidance measures to the accessed terminals through the WIFI network.

[0042] In some embodiments of the present invention, in step S4, the edge computing module fuses and processes at least two data sources from base station logs, terminal sensors, and pre-stored external data, and runs a prediction model to infer whether to initiate an offline emergency broadcast process based on the fused data.

[0043] Understandably, combining edge computing modules with multi-source data fusion and predictive models enables data fusion and intelligent inference to be completed at the base station level within milliseconds, achieving instantaneous autonomous decision-making independent of the core network and accelerating emergency response from "minutes" to "milliseconds." Through cross-validation of multi-source data from base stations, terminals, and the environment, it can intelligently distinguish between real disasters and localized faults, significantly reducing the risk of false alarms and missed alarms and improving the reliability of early warnings. Furthermore, it can preliminarily identify disaster types (such as earthquakes and fires) and assess the scope and extent of their impact, providing crucial evidence for subsequently issuing more precise and differentiated early warning information. Simultaneously, it can avoid initiating nationwide broadcasts for non-emergency situations and intelligently guide subsequent information collection and alarm generation processes, forming an efficient decision-making-execution closed loop.

[0044] In some embodiments of the present invention, in step S5, the public early warning system message adopts an enhanced cell broadcast format, which supports extended message length.

[0045] In some embodiments of the present invention, the active terminal filtering strategy in step S2 includes: Set filtering conditions, including the reference signal received power being higher than a first threshold, the timing advance being lower than a second threshold, and there being service interaction within a preset historical time period; When the number of terminals that simultaneously meet the filtering conditions is greater than or equal to the preset number threshold N, the first M terminals are selected as the initial query targets, sorted from high to low according to the reference signal receiving power; if the response rate of the initial query targets is lower than the first response rate threshold, the next M terminals are selected. When the number of terminals that simultaneously meet the filtering conditions is less than N, a query is sent to all terminals that meet the filtering conditions. If the overall response rate is lower than the second response rate threshold within the preset inquiry timeout period, the inquiry scope will be expanded to standby terminals.

[0046] For example, the screening rules could be as follows: Let the screening criteria be UEs with a signal strength (RSRP) > -85dBm, a TA value < 32, and who have had service interactions within the past 5 minutes. When the number of UEs is ≥ 50, sort them by RSRP from highest to lowest, and select the top 20 as the initial sample; if the initial sample response rate is < 80%, then supplement with the next 20. When the number of UEs is < 50, initiate queries for all active UEs. Timeout mechanism: If the response rate is < 30% within 30 seconds, then expand the query scope to all standby UEs. Example

[0047] Scenario: Emergency response for a single-connection base station (5G air interface only): a sudden geological disaster in a mountainous area (such as a mudslide), the core network is completely disconnected due to the interruption of the optical cable, the base station relies on backup power to maintain operation, and there are about 200 active users in the coverage area.

[0048] Processing flow: Step 1: The base station continuously monitors the following indicators: Ng interface signaling timeout (3 consecutive times > 5s); neighbor cell list loss rate > 80%; when the indicators are abnormal for 30 seconds, the offline emergency mode is triggered. Step 2: Base station user screening criteria: RSRP > -85dBm, data service records within the last 5 minutes. Send simulated emergency SMS messages to the 50 most active users selected, then start a 30-second countdown window to collect replies.

[0049] Step 3: Real-time analysis by the edge computing engine: 42 replies were received, 38 of which were "03". Combined with accelerometer data (detecting continuous low-frequency vibration) and barometer data showing a 12 hPa drop in a short period of time, it was determined to be a debris flow disaster with a confidence level of 92%.

[0050] Step 4: Generate standardized CMAS alarm signaling, broadcast frequency: repeat once every 2 minutes, continuing until the network recovers. Example

[0051] Scenario: Emergency response of dual-connection base station (5G+WIFI): A major fire breaks out in an urban area, the core network is interrupted due to power failure, the base station continues to operate through UPS, covering about 500 users in the area (300 of whom are connected to WIFI).

[0052] Processing flow: Step 1: After the base station detects the AMF connection interruption, it broadcasts the emergency WIFI access point using the SSID via SIB19, with WPA2-PSK encryption and a pre-shared key. It completes 802.11ak fast association within 5ms. Users who are not connected to WIFI can obtain access information through system messages. Step 2: The dual-connection user will automatically see an adaptive web form, which includes: multiple selection of disaster type (check "fire"), a severity slider (users generally choose level 4-5), geofence marking (users mark the location of the fire source on the map), and a text box to describe the scene (users can enter "thick smoke, explosion sound"). Step 3: Analyze and integrate multi-source data, and integrate with the edge computing engine: user form feedback (287 valid reports received), terminal sensor data (temperature sensor shows local high temperature), base station KPI log (abnormal signal attenuation mode), and meteorological API data (wind direction northwest, wind speed 5m / s). Step 4: Generate a detailed evacuation plan: {

Fire Emergency Alarm

[0053] Understandably, the strategy prioritizes terminals with good signal quality (RSRP > -85dBm), close proximity to the base station (low TA value), and recent activity. These terminals have the most stable connection to the base station, resulting in the highest success rate and speed of response, avoiding delays caused by repeated attempts to connect to terminals with weak signals. Secondly, by setting mechanisms such as "supplementing if the initial response rate is below the first response rate threshold (e.g., supplementing if the initial sample response rate is <80%)" and "expanding the query scope if the overall response rate within a preset time is below the second response rate threshold (expanding the scope if the total response rate within 30 seconds is <30%)", the strategy can adapt to the on-site terminal status, effectively overcoming the challenge of information collection failures in complex situations such as damaged terminals, power outages, or users being unable to respond, ensuring sufficient effective feedback samples for decision-making under any circumstances. In emergency situations, air interface and network resources may be strained or limited. This strategy, through tiered screening (active terminals first, then standby terminals), avoids signaling storms and resource congestion caused by simultaneously broadcasting queries to all terminals. This approach of testing first and then expanding... This approach uses a minimal number of query requests (initially targeting an optimal 20-40 terminals) to attempt to obtain the necessary information, expanding the scope only when necessary. This maximizes the conservation of valuable battery life and wireless channel resources while ensuring functionality. Furthermore, the filtering criteria directly impact the reliability of information feedback. Terminals with strong signals have lower error rates in their uplink data packets; nearby terminals report geographical location information that is more relevant to the base station's area; recently active terminals indicate they are being used, and their lit screens increase the likelihood that users will see and respond to queries promptly. This improves the signal-to-noise ratio of the collected hazard information from the source, enabling subsequent edge computing modules to make judgments based on more accurate and timely data, thereby generating more reliable early warning broadcasts.

[0054] Furthermore, this strategy does not rely on any user data or policy configuration from the core network side; it is entirely executed autonomously by the base station based on locally measured real-time wireless conditions. This makes the initiation and adjustment process of information collection completely decentralized. Even in extreme cases where there is a complete loss of connection with the core network and network management system, the screening mechanism can function independently and normally, making it a key component of the overall system resilience.

[0055] In summary, this screening strategy is not simply terminal selection, but a set of intelligent algorithms for resource scheduling and information source management optimized for emergency scenarios. It ensures that, in chaotic, resource-constrained, and disconnected environments, the system can quickly and reliably establish a "least resistant and most efficient information channel" from on-site users to early warning broadcasts. In other words, by adopting the above-mentioned hierarchical dynamic screening strategy, in emergency scenarios where the core network is disconnected, the most reliable on-site information can be obtained quickly with minimal communication overhead.

[0056] The present invention also discloses an emergency broadcasting system for core network outage scenarios, including a base station configured to execute the above method.

[0057] In some embodiments of the present invention, the base station includes: A connection monitoring unit is used to monitor the connection status with the core network and execute step S1. The terminal management unit is used to execute step S2; A communication interaction unit is used to perform step S3; An edge computing unit, equipped with the edge computing module, is used to execute step S4; A broadcast control unit is used to perform step S5.

[0058] Understandably, the base station can determine whether a link loss has occurred with the core network by monitoring the transmission link status, neighbor cell information, and / or signaling interaction protocol status. When a link loss with the core network is determined, it can select active terminals from its covered terminals according to a preset filtering strategy and send an emergency information collection request to the active terminals. Upon receiving a response containing emergency information from the active terminals, the edge computing module can process and analyze the response to determine the current emergency situation. Finally, based on the determined emergency situation, it autonomously generates and broadcasts a public warning system message containing emergency alert content.

[0059] In some embodiments of the present invention, a terminal is also included, the terminal being configured to: Receive an emergency information collection request from the base station; In response to the request, generate a reply containing hazard information based on user input or sensor data; The reply is sent to the base station; Receive and display the public early warning system messages broadcast by the base station.

[0060] In some embodiments of the present invention, when the base station is a base station supporting both 5G and WIFI connectivity, the terminal is further configured as follows: Receive and parse the WIFI access point information broadcast by the base station; Connect to the specified WIFI network based on the parsing results; After connecting to the Wi-Fi network, the system receives and displays a web form pushed by the base station, and reports the emergency information through this form. This invention's emergency broadcast system has different operating modes for both normal network and core network outage scenarios. The following focuses on describing the complete autonomous emergency operation process after a sudden emergency causes a link loss between the base station and the core network: Phase 1: Anomaly Detection and Autonomous Decision-Making The base station continuously monitors the status of the transmission link with the core network, neighboring base station information, and the status of key signaling interaction protocols. When multiple indicators mentioned above remain abnormal (such as transmission link interruption, loss of the entire neighboring cell list, or continuous signaling interaction timeouts) and exceed preset thresholds, the base station autonomously determines that the connection with the core network has been interrupted. The edge computing module deployed on the base station immediately starts.

[0061] This module integrates and processes the following real-time data: base station data such as KPI trends and fault logs before the connection loss; sensor information from access terminals such as the number and distribution of terminals exhibiting abnormal fluctuations in accelerometer and barometer readings; and pre-stored external data such as local geographic information and historical disaster data. Simultaneously, the edge computing module runs a lightweight predictive model to analyze the integrated multi-source data. If the model infers a high probability of an emergency public safety event (such as a natural disaster), it autonomously triggers a local offline emergency broadcast process.

[0062] Phase Two: On-site Information Crowdsourcing Collection Base stations employ different strategies based on their capabilities (single or dual connectivity) to collect firsthand on-site information from users within their coverage area.

[0063] A. Single-connectivity 5G base station scenario: The base station intelligently selects the terminals most likely to respond quickly and reliably based on signal strength (RSRP), terminal distance (TA value), and recent service activity. The base station simulates core network signaling and sends a special broadcast / paging message to the selected active terminals. This message contains a pre-defined list of emergency event type codes (e.g., 01: earthquake, 02: fire, etc.). Upon receiving the inquiry, the user terminal can select the appropriate code based on the situation and quickly respond via the uplink channel.

[0064] B. 5G+Wi-Fi dual-connectivity base station scenario: The base station immediately broadcasts a pre-configured emergency Wi-Fi access point (SSID and dynamically generated key) via system message. The terminal completes 802.11ak rapid association within 5ms and connects to the Wi-Fi network. The base station automatically displays an adaptive web form to the connected terminal via Wi-Fi connection. The form offers richer interactive features, such as: multiple selection of disaster types (landslide / fire / flood, etc.); a severity slider (level 1-5); and map selection or marking of affected locations. After the user fills out and submits the form, a structured, detailed hazard report is generated and sent back to the base station.

[0065] Phase 3: Edge Intelligent Processing and Early Warning Generation The edge computing module receives crowdsourced responses from terminals and aggregates, deduplicates, and spatially analyzes the response information. It then performs cross-validation and comprehensive assessment based on the multi-source data from the first phase. Ultimately, it determines the type, confidence level, core impact area, and severity level of the emergency.

[0066] Then, based on the analysis results, the system automatically generates specific alert text. The content may include a clear event type, a description of the affected area, and specific evacuation action recommendations, thereby overcoming the shortcomings of the general information in traditional CMAS.

[0067] Phase 4: Adaptive Emergency Broadcasting Based on network conditions and information content, the base station selects the optimal method for broadcasting.

[0068] The base station simulates the alarm triggering signaling of the core network and autonomously initiates cell broadcasting. Employing an enhanced PWS format and supporting extended message length, it broadcasts the generated detailed alarm content to all 5G terminals within its coverage area. Simultaneously with broadcasting over the 5G air interface, the base station can also push web pages containing more detailed text, images, escape route maps, and other rich media safety guidelines to connected terminals via an emergency Wi-Fi network, achieving high-bandwidth supplementary information distribution.

[0069] The system can periodically repeat the second to fourth stages, updating and refining the alarm content based on the new feedback information collected, thereby achieving dynamic emergency response.

[0070] Phase 5: Process Termination and Status Restoration When the edge computing module determines through continuous monitoring that the emergency has been resolved or the core network connection has been restored: it stops sending emergency query requests and broadcasts a final message of "alarm cleared" or "back to normal". Simultaneously, it uploads locally processed logs and event reports to the network management center after the core network connection is restored for post-event analysis and auditing.

[0071] In summary, after the core network connection was lost, the system fully realized a closed-loop workflow from "autonomous perception and decision-making" → "intelligent crowdsourced data collection" → "edge analysis and judgment" → "precise multi-channel broadcasting". It transformed base stations from simple signal coverage nodes into intelligent autonomous nodes capable of independently completing situational awareness, information processing, and emergency command in extreme situations, fundamentally strengthening the vulnerability of traditional public early warning systems.

[0072] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0073] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. An emergency broadcasting method for core network link failure scenarios, characterized in that, Performed by the base station, including the following steps: S1. Link Disconnection Determination: By real-time monitoring of the transmission link status, neighbor cell information and / or signaling interaction protocol status between the base station and the core network, it is determined that the base station has lost its link with the core network; S2. Terminal screening: After determining that the connection is lost, the target query terminal is selected from the terminals within the coverage area of ​​the base station according to the preset active terminal screening strategy. S3. Information Collection: Send an emergency information collection request to the target interrogation terminal and receive a reply from the target interrogation terminal containing information about the danger. S4. Edge processing: The received emergency response is processed and analyzed by the edge computing module deployed on the base station to determine the current emergency situation. S5. Autonomous Broadcasting: Based on the identified emergency situation, autonomously generate and broadcast public warning system messages containing emergency alert content.

2. The method according to claim 1, characterized in that, The base station is a single-connection 5G base station; The emergency information collection request in step S3 is a high-priority message in the simulated core network downlink signaling format, which encodes the emergency situation type and its corresponding number; the received reply is a signaling or message sent by the terminal through the wireless air interface, containing the selected emergency situation type number.

3. The method according to claim 1, characterized in that, The base station is a base station that supports both 5G and WIFI dual connectivity; The method further includes: broadcasting WIFI access point information through a system information block before or after step S1; In step S3, a webpage containing an adaptive form is pushed to the accessed terminal via the established WIFI connection as an emergency information collection request. The webpage form contains interactive elements for reporting disaster type, severity, and / or geographical location. At the same time, the received response is the webpage form data submitted by the terminal through the WIFI connection.

4. The method according to claim 3, characterized in that, Following step S5, the following is also included: S6. Assisted Push: Push information containing detailed emergency alarm content and risk avoidance measures to connected terminals through the WIFI network.

5. The method according to claim 1, characterized in that, The active terminal filtering strategy in step S2 includes: Set filtering conditions, including the reference signal received power being higher than a first threshold, the timing advance being lower than a second threshold, and there being service interaction within a preset historical time period; When the number of terminals that simultaneously meet the filtering conditions is greater than or equal to the preset number threshold N, the first M terminals are selected as the initial query targets, sorted from high to low according to the reference signal receiving power; if the response rate of the initial query targets is lower than the first response rate threshold, the next M terminals are selected. When the number of terminals that simultaneously meet the filtering conditions is less than N, a query is sent to all terminals that meet the filtering conditions. If the overall response rate is lower than the second response rate threshold within the preset inquiry timeout period, the inquiry scope will be expanded to standby terminals.

6. The method according to claim 1, characterized in that, In step S4, the edge computing module fuses and processes at least two data sources from base station logs, terminal sensors, and pre-stored external data, and runs a prediction model to infer whether to initiate the offline emergency broadcast process based on the fused data.

7. An emergency broadcasting system for core network outage scenarios, characterized in that, Includes a base station configured to perform the method as described in any one of claims 1 to 6.

8. The system according to claim 7, characterized in that, The base station includes: A connection monitoring unit is used to monitor the connection status with the core network and execute step S1. The terminal management unit is used to execute step S2; A communication interaction unit is used to perform step S3; An edge computing unit, equipped with the edge computing module, is used to execute step S4; A broadcast control unit is used to perform step S5.

9. The system according to claim 7, characterized in that, It also includes a terminal, which is configured as follows: Receive an emergency information collection request from the base station; In response to the request, generate a reply containing hazard information based on user input or sensor data; The reply is sent to the base station; Receive and display the public early warning system messages broadcast by the base station.

10. The system according to claim 10, characterized in that, When the base station is a base station that supports both 5G and WIFI connectivity, the terminal is further configured as follows: Receive and parse the WIFI access point information broadcast by the base station; Connect to the specified WIFI network based on the parsing results; After connecting to the WIFI network, the system receives and displays a web form pushed by the base station, and reports the emergency information through the form.

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