Method and system for dynamically turning off services of 5g emergency base station

Abstract

The application provides a 5G emergency base station service dynamic shutdown method and system, acquires service data of a user terminal accessing a 5G emergency base station, identifies a service attribute label of the service data, monitors an emergency level, if the emergency level changes, updates a shutdown strategy parameter according to the emergency level, the shutdown strategy parameter includes a service priority mapping table and a shutdown trigger threshold, calculates a comprehensive load index of the 5G emergency base station, if the comprehensive load index exceeds the shutdown trigger threshold in the updated shutdown strategy parameter, calculates a shutdown priority of each service data according to the service attribute label of all service data and the service priority mapping table in the updated shutdown strategy parameter, and performs a shutdown operation on a service corresponding to the service data based on the shutdown priority, which can accurately relieve congestion and maximize the retention of non-critical services, thereby improving the reliability of emergency communication.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a method and system for dynamically shutting down services in 5G emergency base stations. Background Technology

[0002] With the development of communication technology, more and more areas have deployed base stations, and there are an increasing number of users such as mobile phones and internet devices. If a natural disaster (earthquake, flood, typhoon, etc.) or some large-scale emergency occurs, causing the originally deployed base stations to lose connection, and an emergency base station suddenly receives a large number of users, the emergency base station is prone to excessive resource consumption due to high-traffic services. This results in high-priority services such as voice calls, emergency text messages, and rescue command being unable to be effectively guaranteed, seriously affecting the core functions of emergency communication and posing a severe challenge to emergency communication support. Existing technologies lack the ability to incorporate the "dynamic change of emergency level" factor unique to emergency scenarios into the shutdown decision mechanism, failing to consider the special needs of emergency scenarios. Shutdown decisions rely solely on load comparison, lacking refined judgment of service type and emergency level; moreover, the shutdown granularity is based on the base station, which cannot achieve precise shutdown control for individual users or individual service flows, resulting in insufficient flexibility. In emergency communication, the emergency level changes in real time as the disaster develops, rescue efforts progress, and emergencies occur, while existing solutions are all static configuration strategies, unable to automatically switch shutdown strategies according to the dynamic adjustment of the emergency level. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for dynamic shutdown of services of 5G emergency base stations, which can effectively alleviate congestion of emergency base stations and improve the reliability of emergency communication.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for dynamically shutting down services in a 5G emergency base station includes: Acquire service data from user terminals connected to 5G emergency base stations; Identify the business attribute tags of the business data; Monitor the emergency level; if the emergency level changes, update the shutdown policy parameters according to the emergency level. The shutdown policy parameters include a service priority mapping table and a shutdown trigger threshold. Calculate the comprehensive load index of the 5G emergency base station; If the comprehensive load index exceeds the shutdown trigger threshold in the updated shutdown policy parameters, then the shutdown priority of each business data is calculated based on the business attribute tags of all the business data and the business priority mapping table in the updated shutdown policy parameters. The services corresponding to the service data are shut down based on the shutdown priority.

[0005] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: A service dynamic shutdown system for a 5G emergency base station includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the aforementioned service dynamic shutdown method for a 5G emergency base station.

[0006] The beneficial effects of this invention are as follows: It acquires service data from user terminals accessing 5G emergency base stations, identifies service attribute tags of the service data, monitors the emergency level, and updates shutdown policy parameters according to the emergency level if the emergency level changes. The shutdown policy parameters include a service priority mapping table and a shutdown trigger threshold. It calculates the comprehensive load index of the 5G emergency base station. If the comprehensive load index exceeds the shutdown trigger threshold in the updated shutdown policy parameters, it calculates the shutdown priority of each service data based on the service attribute tags of all service data and the service priority mapping table in the updated shutdown policy parameters. Based on the shutdown priority, it performs shutdown operations on the services corresponding to the service data. By dynamically monitoring the emergency level and updating the shutdown policy parameters in real time, the shutdown decision can automatically adapt to sudden changes such as disaster development and rescue progress, avoiding policy failures caused by static configuration. Combining service attribute tags with refined shutdown priority calculation ensures that critical services receive priority resources, rather than relying solely on the total load for coarse shutdown. Furthermore, the shutdown operation targets individual service data rather than the entire base station, providing finer granularity, which can accurately alleviate congestion while maximizing the preservation of non-critical services, thus improving the reliability of emergency communications. Attached Figure Description

[0007] Figure 1 This is a flowchart of a method for dynamically shutting down services in a 5G emergency base station according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the service type label determination process in a 5G emergency base station service dynamic shutdown method according to an embodiment of the present invention. Figure 3 This is a schematic diagram of differentiated shutdown in a service dynamic shutdown method for a 5G emergency base station according to an embodiment of the present invention; Figure 4 This is a flowchart illustrating the rollback and recovery judgment process in a 5G emergency base station service dynamic shutdown method according to an embodiment of the present invention. Figure 5 This is a schematic diagram of a 5G emergency base station service dynamic shutdown system according to an embodiment of the present invention. Detailed Implementation

[0008] Definitions:

[0009] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0010] Existing technologies lack the ability to incorporate the dynamic changes in emergency levels unique to emergency scenarios into shutdown decision-making mechanisms. They fail to consider the specific needs of emergency scenarios, relying solely on load comparison for shutdown decisions, lacking refined judgment of service types and emergency levels. Furthermore, the shutdown granularity is based on base stations, making it impossible to achieve precise shutdown control for individual users or service flows, resulting in insufficient flexibility. In emergency communications, the development of disasters, the progress of rescue efforts, and the sudden occurrence of emergencies all lead to real-time changes in emergency levels, while existing solutions are all static configuration strategies, unable to automatically switch shutdown strategies based on the dynamic adjustment of emergency levels.

[0011] To address at least the aforementioned issues, this approach acquires service data from user terminals connected to the 5G emergency base station, identifies service attribute tags for this data, monitors the emergency level, and updates shutdown policy parameters based on changes in the emergency level. These shutdown policy parameters include a service priority mapping table and a shutdown trigger threshold. The comprehensive load index of the 5G emergency base station is calculated. If the comprehensive load index exceeds the shutdown trigger threshold in the updated shutdown policy parameters, the shutdown priority for each service data point is calculated based on its service attribute tags and the service priority mapping table in the updated shutdown policy parameters. Shutdown operations are then performed on the services corresponding to the service data based on these priorities. This method, by dynamically monitoring the emergency level and updating shutdown policy parameters in real time, allows shutdown decisions to automatically adapt to sudden changes such as disaster development and rescue progress, avoiding policy failures caused by static configuration. Combining service attribute tags with refined shutdown priority calculation ensures that critical services receive priority access to resources, rather than relying solely on total load for coarse shutdown. Furthermore, the shutdown operation targets individual service data points rather than the entire base station, providing finer granularity. This not only accurately alleviates congestion but also maximizes the preservation of non-critical services, improving the reliability of emergency communications.

[0012] The following details a method for dynamically shutting down services in a 5G emergency base station according to the present invention. Please refer to [link / reference]. Figure 1 The method 100 includes steps 101 to 106: Step 101: Obtain service data from user terminals connected to the 5G emergency base station.

[0013] In one optional implementation, the service data includes information such as the source IP address, destination IP address, port number, and transmission protocol type of the service data.

[0014] Step 102: Identify the business attribute tags of the business data.

[0015] Step 103: Monitor the emergency level. If the emergency level changes, update the shutdown policy parameters according to the emergency level. The shutdown policy parameters include a service priority mapping table and a shutdown trigger threshold.

[0016] The emergency response level is obtained from a third party, issued by the network management system, or manually configured. Each emergency response level corresponds to a shutdown policy parameter. The service priority mapping table includes different service attribute tags and their corresponding shutdown priorities.

[0017] Step 104: Calculate the comprehensive load index of the 5G emergency base station.

[0018] Step 105: If the comprehensive load index exceeds the shutdown trigger threshold in the updated shutdown policy parameters, then calculate the shutdown priority of each business data according to the business attribute tags of all the business data and the business priority mapping table in the updated shutdown policy parameters.

[0019] Among them, the higher the processing priority of business data, the lower its shutdown priority.

[0020] Step 106: Perform a shutdown operation on the service corresponding to the service data based on the shutdown priority.

[0021] Please refer to Figure 2 In one embodiment of the present invention, step 102 includes steps 1021 to 1026: Step 1021: Use deep packet inspection technology to perform packet inspection on the business data to obtain application layer features.

[0022] The application layer features include port number, HTTP header information, DNS query information, TLS handshake information, and application protocol signature codes.

[0023] Step 1022: Extract traffic features from the business data to obtain traffic features.

[0024] The traffic characteristics include instantaneous rate, average rate, data packet size distribution, data packet arrival interval, connection duration, and uplink / downlink traffic ratio.

[0025] Step 1023: Determine the weights of the application layer features and the traffic features under different preset business type labels.

[0026] The preset service type tags include voice calls (highest processing priority), emergency text messages (highest processing priority), rescue command data (highest processing priority), video conferencing (high processing priority), ordinary video streams (medium processing priority), file downloads (medium processing priority), web browsing (medium to low processing priority), background application updates (low processing priority), and P2P file sharing (lowest processing priority).

[0027] Step 1024: Calculate the weighted score under different preset service type labels based on the weight, the application layer features, and the traffic features.

[0028] Step 1025: Determine the preset business type label corresponding to the largest weighted score as the business type label of the business data.

[0029] Step 1026: Generate business attribute tags for the business data based on the business type tags and the traffic characteristics.

[0030] In an optional implementation, the method may further include: obtaining a service flow identifier, a user terminal identifier, cumulative traffic, and resource utilization rate, and adding the service flow identifier, the user terminal identifier, the cumulative traffic, and the resource utilization rate to the service attribute tag. This ensures that the data can be directly extracted when needed later.

[0031] As described above, by combining application layer features extracted by deep packet inspection (DPI) with traffic features through weighted fusion, the business type can be identified more comprehensively and accurately, avoiding misjudgments caused by a single feature.

[0032] When emergency base stations encounter a large number of traffic flows using unknown or vendor-defined protocols, they cannot correctly label them using DPI (Digital Protocol Identification). To accurately identify these traffic flows, a dedicated protocol table can be maintained to prevent misclassification as low-priority traffic or direct discarding due to unparseable protocols, thus ensuring uninterrupted critical rescue communications. Each record in the dedicated protocol table includes the protocol name, feature code (such as a specific port number, fixed header byte sequence, specific TLV format), service type label (such as rescue command data, drone video transmission, Steam download service), and service priority. The dedicated protocol table is pre-loaded with a library of proprietary protocol features for common emergency communication devices (such as PDT (Dedicated Digital Trunking), B-TrunC (Broadband Trunking Communication System based on LTE technology), and certain tactical data link protocols).

[0033] If unknown service data exists that cannot be matched with any known protocol, its flow characteristics (including 5-tuple, packet length distribution, and arrival interval) are cached. A specific type of unknown service data can be manually selected via web or network management, tagged with a service type and priority, and added to the proprietary protocol table. When the proprietary protocol table is updated, newly discovered proprietary protocol entries can be exchanged through the inter-base station interface (Xn) to synchronize the proprietary protocol tables between base stations. The configuration can also be exported for import into devices without an Xn interface or used as the default configuration for subsequent versions.

[0034] In one embodiment of the present invention, step 104 includes steps 1041 to 1042: Step 1041: Calculate the resource block utilization rate, time slot resource utilization rate, and power resource utilization rate of the 5G emergency base station, and determine the weighting coefficients of the resource block utilization rate, the time slot resource utilization rate, and the power resource utilization rate.

[0035] To avoid the problem that static weights cannot adapt to changes in business scenarios, a dynamic weight adjustment strategy based on real-time business awareness is introduced. In one optional implementation, the 5G emergency base station initially uses the default initial weight coefficients of the resource block utilization rate, the time slot resource utilization rate, and the power resource utilization rate, and statistically analyzes the distribution of currently identified service data types at a preset period; it calculates the proportion of time slot sensitive services (such as high-priority services like voice, VoIP, and real-time conferencing) and the proportion of bandwidth sensitive services (such as high-priority services like high-definition video and file distribution); if the proportion of time slot sensitive services or the proportion of bandwidth sensitive services reaches a preset proportion, the corresponding weight coefficient is increased. For example, if the proportion of time-slot sensitive services is greater than 40%, it indicates that a large number of small-packet, low-latency, high-priority services need to be frequently scheduled. In this case, the weight coefficient w2 of time-slot resource utilization is automatically increased. If it is greater than 50%, the weight coefficient w2 of time-slot resource utilization is further increased. Similarly, if the proportion of bandwidth sensitive services is greater than 50%, it indicates that high-volume, high-priority services have a high demand for continuous RB resources. In this case, the weight coefficient w1 of resource block utilization is automatically increased. If high-priority services (such as rescue command data) are found to frequently experience scheduling failures or retransmissions, the weight coefficient w3 of power resource utilization is automatically and temporarily increased to increase the sensitivity to interference and coverage-limited scenarios.

[0036] Step 1042: Calculate the comprehensive load index of the 5G emergency base station based on the resource block utilization rate, the time slot resource utilization rate, the power resource utilization rate, and the weighting coefficient. Specifically: Load index =w1×(B used / B total )+w2×(S used / S total)+w3×(P used / P total ); In the formula, Load index B represents the comprehensive load index of 5G emergency base stations. used / B total S represents the resource block utilization rate, w1 represents the weighting coefficient of the resource block utilization rate, and S represents the resource block utilization rate. used / S total P represents the time slot resource utilization rate, w2 represents the weighting coefficient of the time slot resource utilization rate, and P represents the time slot resource utilization rate. used / P total W represents the power resource utilization rate, w3 represents the weighting coefficient of the power resource utilization rate, and B represents the power resource utilization rate. used B represents the number of resource blocks used. total S represents the total number of available resource blocks. used S represents the number of time slots used. total P represents the total number of available time slots. used P represents the amount of power resources already used. total This indicates the total amount of available power resources.

[0037] As described above, by simultaneously considering three dimensions—resource block utilization, time slot resource utilization, and power resource utilization—and assigning each a weight coefficient, the actual load status of emergency base stations can be reflected more comprehensively and accurately. This avoids misjudgments caused by a single resource indicator. Through weighted comprehensive calculation, congestion risks can be identified more accurately, providing a reliable basis for shutdown triggering decisions. This avoids premature or late shutdowns and improves the rationality of resource scheduling and the reliability of communication assurance in emergency scenarios.

[0038] In one embodiment of the present invention, calculating the shutdown priority of each service data based on the service attribute tags of all the service data and the service priority mapping table in the updated shutdown policy parameters includes: The shutdown priority of each business data is determined from the business priority mapping table in the updated shutdown policy parameters based on the business attribute labels of all the aforementioned business data.

[0039] As described above, the shutdown priority can be obtained by directly looking up the table through business attribute tags, which transforms complex business types, traffic characteristics and other diverse information into a unified priority value. This method has high computational efficiency and low latency, making it suitable for real-time decision-making needs in high-concurrency scenarios of emergency base stations.

[0040] In one embodiment of the present invention, step 106 includes steps 1061 to 1064: Step 1061: Generate a multi-level candidate shutdown queue.

[0041] Step 1062: Add the service data to the multi-level candidate shutdown queue in descending order of shutdown priority.

[0042] Step 1063: Perform a shutdown operation on the service data corresponding to the highest-level candidate shutdown queue in the multi-level candidate shutdown queue.

[0043] Step 1064: After the shutdown operation is completed, the comprehensive load index of the 5G emergency base station is recalculated, and it is determined whether the recalculated comprehensive load index exceeds the shutdown trigger threshold. If so, the service corresponding to the service data in the next level candidate shutdown queue is shut down, and the process returns to the step of recalculating the comprehensive load index of the 5G emergency base station after the shutdown operation is completed, until there is no next level candidate shutdown queue. If not, the shutdown operation is stopped.

[0044] For example, a first-level candidate shutdown queue, a second-level candidate shutdown queue, and a third-level candidate shutdown queue are generated. Service data is added to these three candidate shutdown queues in descending order of shutdown priority. First, the services corresponding to the service data in the first-level candidate shutdown queue are shut down. After the shutdown operation is completed, the comprehensive load index of the 5G emergency base station is recalculated. If the recalculated comprehensive load index exceeds the shutdown trigger threshold, the services corresponding to the service data in the second-level candidate shutdown queue are shut down. The comprehensive load index is then recalculated. If the recalculated comprehensive load index still exceeds the shutdown trigger threshold, the services corresponding to the service data in the third-level candidate shutdown queue are shut down. If the recalculated comprehensive load index does not exceed the shutdown trigger threshold, the shutdown operation is stopped.

[0045] As described above, the adoption of a multi-level candidate queue and a tiered shutdown mechanism enables refined, hierarchical traffic control. This avoids resource waste or a sharp drop in user experience caused by shutting down too many services at once. After each shutdown, the load is recalculated and the threshold is dynamically determined to ensure that the shutdown operation precisely meets the needs of congestion relief, achieving precise control. Services in the highest priority shutdown queue are prioritized for shutdown, maximizing resource utilization efficiency and user experience in emergency scenarios. Furthermore, the tiered processing approach possesses good scalability and fault tolerance, adapting to dynamic load changes and enhancing system stability.

[0046] like Figure 3As shown, the shutdown operation includes rate-limiting shutdown, soft shutdown, and hard shutdown. Rate-limiting shutdown reduces the bandwidth allocation rate of the shut-down service to a preset lower limit, maintaining the service connection but strictly limiting the transmission rate. This is suitable for services with a certain tolerance, such as web browsing and non-urgent data transmission. Hard shutdown immediately disconnects the shut-down service, releasing all related resources. This is suitable for services that can be interrupted at any time, such as P2P sharing and background updates. Soft shutdown lowers the service scheduling priority to the lowest level, keeping the service in the queue and temporarily caching the service data until system resources are available for scheduling. If the cache queue size is exceeded, the data will be discarded. This is suitable for non-real-time file synchronization, backup, and other services that can be delayed. Furthermore, to prevent services from "starving" due to prolonged lack of scheduling, a small amount of scheduling will occur after a certain period.

[0047] In one embodiment of the present invention, step 1063 includes steps 10631 to 10637: Step 10631: Perform a rate-limiting shutdown operation on the service corresponding to the first service data in the highest-level candidate shutdown queue of the multi-level candidate shutdown queue.

[0048] Step 10632: After the rate limiting shutdown operation is completed, the comprehensive load index of the 5G emergency base station is recalculated, and it is determined whether the recalculated comprehensive load index exceeds the shutdown trigger threshold. If so, the rate limiting shutdown operation is performed on the service corresponding to the next service data, and the process returns to the step of recalculating the comprehensive load index of the 5G emergency base station after the rate limiting shutdown operation is completed, until there is no next service data. If not, the rate limiting shutdown operation is stopped.

[0049] In one optional implementation, after the rate-limiting shutdown operation is completed, recalculating the comprehensive load index of the 5G emergency base station includes: After the rate limiting shutdown operation is completed, the load prediction model is used to predict the service situation, and the comprehensive load index of the 5G emergency base station is recalculated based on the service situation.

[0050] In one optional implementation, the load prediction model is an LSTM (Long Short-Term Memory) model, which continuously updates the input historical resource blocks, time slots, and power sequences, and outputs predicted resource blocks, time slots, and power sequences.

[0051] Step 10633: If the service corresponding to each service data in the highest-level candidate shutdown queue has completed the rate-limiting shutdown operation, and the recalculated comprehensive load index still exceeds the shutdown trigger threshold, then a soft shutdown operation is performed on the first service data in the highest-level candidate shutdown queue.

[0052] Step 10634: After the soft shutdown operation is completed, the comprehensive load index of the 5G emergency base station is recalculated, and it is determined whether the recalculated comprehensive load index exceeds the shutdown trigger threshold. If so, a soft shutdown operation is performed on the service corresponding to the next service data, and the process returns to the step of recalculating the comprehensive load index of the 5G emergency base station after the soft shutdown operation is completed, until there is no next service data. If not, the soft shutdown operation is stopped.

[0053] Step 10635: If the service corresponding to each service data in the highest-level candidate shutdown queue has completed the soft shutdown operation, and the recalculated comprehensive load index still exceeds the shutdown trigger threshold, then the service corresponding to the first service data in the highest-level candidate shutdown queue is subjected to a hard shutdown operation.

[0054] Step 10636: After the hard shutdown operation is completed, the comprehensive load index of the 5G emergency base station is recalculated, and it is determined whether the recalculated comprehensive load index exceeds the shutdown trigger threshold. If so, a hard shutdown operation is performed on the service corresponding to the next service data, and the process returns to the step of recalculating the comprehensive load index of the 5G emergency base station after the hard shutdown operation is completed, until there is no next service data. If not, the hard shutdown operation is stopped.

[0055] Step 10637: If the service corresponding to each service data in the highest-level candidate shutdown queue has completed the hard shutdown operation, then the shutdown operation is completed.

[0056] For example, service A in the highest-level candidate shutdown queue is first rate-limited shut down, and then the comprehensive load index is recalculated. If the comprehensive load index exceeds the shutdown trigger threshold, then service B in the queue is rate-limited shut down, and the comprehensive load index is recalculated. When all services in the queue are rate-limited shut down and the comprehensive load index still exceeds the shutdown trigger threshold, then service A is downgraded and shut down, that is, service A is soft-shut down. This operation is repeated until the comprehensive load index does not exceed the shutdown trigger threshold or all services are hard-shut down.

[0057] As described above, when shutting down business data in a candidate shutdown queue at a certain level, a three-tiered progressive shutdown mechanism is adopted: rate limiting → soft shutdown → hard shutdown. For the same business data, mild rate limiting measures are applied first, and soft shutdown or hard shutdown is only upgraded when rate limiting is ineffective. This minimizes the impact on user experience and avoids a one-size-fits-all, crude shutdown. After each operation, the load is recalculated and the standard is dynamically determined to achieve precise on-demand shutdown. This ensures both congestion relief and avoids excessive shutdown that wastes resources. Furthermore, the tiered progressive strategy takes into account the differentiated protection needs of different businesses in emergency scenarios, improving the flexibility of resource scheduling and system reliability.

[0058] Please refer to Figure 4 In one embodiment of the present invention, after step 106, the method further includes: Step 107: Calculate the comprehensive load index of the 5G emergency base station.

[0059] Step 108: If the calculated comprehensive load index exceeds the shutdown trigger threshold, then return to the step of calculating the shutdown priority of each business data based on the business attribute labels of all the business data and the business priority mapping table in the updated shutdown policy parameters.

[0060] Step 109: If the calculated comprehensive load index does not exceed the shutdown trigger threshold, then monitor the key business indicators.

[0061] The key business metrics include voice call connection rate, emergency SMS delivery success rate, and delay of rescue command data.

[0062] Step 110: If the key business performance indicator fails to reach the preset indicator, determine whether it is caused by the shutdown operation. If so, perform a rollback and recovery operation on the business data corresponding to the key business performance indicator.

[0063] As described above, by continuously monitoring the load after shutdown, if the threshold is still exceeded, shutdown iteration is triggered again, forming a closed-loop dynamic adjustment to ensure that congestion is completely alleviated. At the same time, key business indicators are monitored to proactively identify whether the shutdown operation has inadvertently damaged key businesses, and targeted rollback and recovery are carried out to avoid damage to core functions due to shutdown. This balances the dual goals of congestion control and service quality assurance, improves the self-healing capability and service reliability of the emergency communication system, and avoids irreversible negative impacts caused by excessive shutdown.

[0064] In one embodiment of the present invention, it further includes: If an emergency deactivation command is received, the default policy configuration parameters in normal mode will be restored, and the disconnected service connections will be gradually restored.

[0065] Specifically, upon receiving an emergency deactivation command obtained from a third party, issued by the network management system, or manually configured, the system restores the default policy configuration parameters in normal mode and gradually restores the disconnected service connections. This means restoring them in descending order of service processing priority, prioritizing the restoration of connections for critical services such as voice, SMS, and rescue command, while continuously monitoring network load.

[0066] As described above, once the emergency is over, promptly restoring the default policy configuration and gradually reconnecting the shut-down services can quickly and smoothly switch the system from emergency protection status back to normal service status, avoiding the long-term effect of emergency policies that could restrict normal business operations.

[0067] Please refer to Figure 5 The present invention also provides a service dynamic shutdown system 200 for a 5G emergency base station, including a memory 201, a processor 202, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the service dynamic shutdown method for a 5G emergency base station as described above.

[0068] In summary, the above-described 5G emergency base station service dynamic shutdown method and system of the present invention acquires service data from user terminals accessing the 5G emergency base station, identifies service attribute tags of the service data, monitors the emergency level, and updates shutdown policy parameters according to the emergency level if the emergency level changes. The shutdown policy parameters include a service priority mapping table and a shutdown trigger threshold. The comprehensive load index of the 5G emergency base station is calculated. If the comprehensive load index exceeds the shutdown trigger threshold in the updated shutdown policy parameters, the shutdown priority of each service data is calculated based on the service attribute tags of all service data and the service priority mapping table in the updated shutdown policy parameters. Based on the shutdown priority, the corresponding service is shut down. This allows for dynamic monitoring of the emergency level and real-time updating of the shutdown policy parameters, enabling shutdown decisions to adapt to the development of the disaster and the progress of rescue efforts. Automatic adaptation to sudden changes in processes and other factors avoids policy failures caused by static configurations. Combined with business attribute tags and fine-grained shutdown priority calculation, it ensures that critical services receive resources first, rather than relying solely on total load for coarse shutdown. Furthermore, shutdown operations are targeted at individual service data rather than the entire base station, providing finer granularity. This precisely alleviates congestion while maximizing the preservation of non-critical services, improving the reliability of emergency communications. In addition, the use of multi-level candidate queues and a tiered shutdown mechanism enables refined hierarchical traffic control, avoiding resource waste or a sharp drop in user experience caused by shutting down too many services at once. After each shutdown, the load is recalculated and thresholds are dynamically assessed to ensure that the shutdown operation precisely meets the congestion relief requirements, achieving precise control and prioritizing the shutdown of services in the highest shutdown priority queue, maximizing resource utilization efficiency and user experience in emergency scenarios. Simultaneously, the tiered processing approach has good scalability and fault tolerance, adapting to dynamic load changes and enhancing system stability.

[0069] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for dynamically shutting down services in a 5G emergency base station, characterized in that, include: Acquire service data from user terminals connected to the 5G emergency base station; Identify the business attribute tags of the business data; Monitor the emergency level; if the emergency level changes, update the shutdown policy parameters according to the emergency level. The shutdown policy parameters include a service priority mapping table and a shutdown trigger threshold. Calculate the comprehensive load index of the 5G emergency base station; If the comprehensive load index exceeds the shutdown trigger threshold in the updated shutdown policy parameters, then the shutdown priority of each business data is calculated based on the business attribute tags of all the business data and the business priority mapping table in the updated shutdown policy parameters. The services corresponding to the service data are shut down based on the shutdown priority.

2. The method according to claim 1, characterized in that, The business attribute tags that identify the business data include: Deep packet inspection technology is used to perform packet inspection on the business data to obtain application layer features; Traffic features are extracted from the business data to obtain traffic features; The weights of the application layer features and the traffic features under different preset business type tags are determined respectively; Calculate the weighted score under different preset service type labels based on the weights, application layer features, and traffic features; The preset business type label corresponding to the highest weighted score is determined as the business type label of the business data; The business attribute tags of the business data are generated based on the business type tags and the traffic characteristics.

3. The method according to claim 1, characterized in that, The calculation of the comprehensive load index of the 5G emergency base station includes: Calculate the resource block utilization rate, time slot resource utilization rate, and power resource utilization rate of the 5G emergency base station, and determine the weighting coefficients of the resource block utilization rate, the time slot resource utilization rate, and the power resource utilization rate; The comprehensive load index of the 5G emergency base station is calculated based on the resource block utilization rate, the time slot resource utilization rate, the power resource utilization rate, and the weighting coefficient.

4. The method according to claim 3, characterized in that, The comprehensive load index of the 5G emergency base station is calculated based on the resource block utilization rate, the time slot resource utilization rate, the power resource utilization rate, and the weighting coefficient, specifically as follows: Load index =w1×(B used / B total )+w2×(S used / S total )+w3×(P used / P total ); In the formula, Load index B represents the comprehensive load index of 5G emergency base stations. used / B total S represents the resource block utilization rate, w1 represents the weighting coefficient of the resource block utilization rate, and S represents the resource block utilization rate. used / S total P represents the time slot resource utilization rate, w2 represents the weighting coefficient of the time slot resource utilization rate, and P represents the time slot resource utilization rate. used / P total W represents the power resource utilization rate, w3 represents the weighting coefficient of the power resource utilization rate, and B represents the power resource utilization rate. used B represents the number of resource blocks used. total S represents the total number of available resource blocks. used S represents the number of time slots used. total P represents the total number of available time slots. used P represents the amount of power resources already used. total This indicates the total amount of available power resources.

5. The method according to claim 1, characterized in that, The service priority mapping table includes different service attribute tags and their corresponding shutdown priorities; The shutdown priority of each piece of business data is calculated based on the business attribute tags of all the aforementioned business data and the business priority mapping table in the updated shutdown policy parameters, including: The shutdown priority of each business data is determined from the business priority mapping table in the updated shutdown policy parameters based on the business attribute labels of all the aforementioned business data.

6. The method according to claim 1, characterized in that, Shutting down the services corresponding to the service data based on the shutdown priority includes: Generate a multi-level candidate shutdown queue; The service data is added to the multi-level candidate shutdown queue in descending order of shutdown priority. The service corresponding to the service data in the highest-level candidate shutdown queue in the multi-level candidate shutdown queue is shut down. After the shutdown operation is completed, the comprehensive load index of the 5G emergency base station is recalculated, and it is determined whether the recalculated comprehensive load index exceeds the shutdown trigger threshold. If so, the service corresponding to the service data in the next level candidate shutdown queue is shut down, and the process returns to the step of recalculating the comprehensive load index of the 5G emergency base station after the shutdown operation is completed, until there is no next level candidate shutdown queue. If not, the shutdown operation is stopped.

7. The method according to claim 6, characterized in that, The shutdown operation for the service data corresponding to the highest-level candidate shutdown queue in the multi-level candidate shutdown queue includes: A rate-limiting shutdown operation is performed on the service corresponding to the first service data in the highest-level candidate shutdown queue of the multi-level candidate shutdown queue. After the rate limiting shutdown operation is completed, the comprehensive load index of the 5G emergency base station is recalculated, and it is determined whether the recalculated comprehensive load index exceeds the shutdown trigger threshold. If so, the rate limiting shutdown operation is performed on the service corresponding to the next service data, and the process returns to the step of recalculating the comprehensive load index of the 5G emergency base station after the rate limiting shutdown operation is completed, until there is no next service data. If not, the rate limiting shutdown operation is stopped. If the service corresponding to each service data in the highest-level candidate shutdown queue has completed the rate-limiting shutdown operation, and the recalculated comprehensive load index still exceeds the shutdown trigger threshold, then a soft shutdown operation is performed on the first service data in the highest-level candidate shutdown queue. After the soft shutdown operation is completed, the comprehensive load index of the 5G emergency base station is recalculated, and it is determined whether the recalculated comprehensive load index exceeds the shutdown trigger threshold. If so, a soft shutdown operation is performed on the service corresponding to the next service data, and the process returns to the step of recalculating the comprehensive load index of the 5G emergency base station after the soft shutdown operation is completed, until there is no next service data. If not, the soft shutdown operation is stopped. If the service corresponding to each service data in the highest-level candidate shutdown queue has completed the soft shutdown operation, and the recalculated comprehensive load index still exceeds the shutdown trigger threshold, then the service corresponding to the first service data in the highest-level candidate shutdown queue will be subjected to a hard shutdown operation. After the hard shutdown operation is completed, the comprehensive load index of the 5G emergency base station is recalculated, and it is determined whether the recalculated comprehensive load index exceeds the shutdown trigger threshold. If so, a hard shutdown operation is performed on the service corresponding to the next service data, and the process returns to the step of recalculating the comprehensive load index of the 5G emergency base station after the hard shutdown operation is completed, until there is no next service data. If not, the hard shutdown operation is stopped. If the service corresponding to each service data in the highest-level candidate shutdown queue has completed the hard shutdown operation, then the shutdown operation is complete.

8. The method according to claim 1, characterized in that, After shutting down the service corresponding to the service data based on the shutdown priority, the process further includes: Calculate the comprehensive load index of the 5G emergency base station; If the calculated comprehensive load index exceeds the shutdown trigger threshold, then return to the step of calculating the shutdown priority of each business data based on the business attribute tags of all the business data and the business priority mapping table in the updated shutdown policy parameters; If the calculated comprehensive load index does not exceed the shutdown trigger threshold, then key business indicators are monitored. If the key business performance indicator fails to reach the preset target, it is determined whether the shutdown operation is the cause. If so, the business data corresponding to the key business performance indicator is rolled back and restored.

9. The method according to claim 1, characterized in that, Also includes: If an emergency deactivation command is received, the default policy configuration parameters in normal mode will be restored, and the disconnected service connections will be gradually restored.

10. A service dynamic shutdown system for a 5G emergency base station, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements each step of the service dynamic shutdown method for a 5G emergency base station as described in any one of claims 1 to 9.

Images