5G base station operation and maintenance management method and system based on edge computing

Abstract

The invention relates to the technical field of operation and maintenance management, and discloses a 5G base station operation and maintenance management method and system based on edge computing, and the method comprises the following steps: S1, obtaining the number of base stations in a regional base station group, and classifying the base stations into a distribution base station, a standby base station, a high-score base station and a low-score base station according to the historical load state of each base station, each base station is provided with an edge computing node; s2, before the work cycle starts, generating a data buffer area according to the daily work log of each base station, and temporarily storing the received service demand data in the data buffer area; and S3, determining a matching algorithm matched with the corresponding base station according to a strategy library rule for undistributed service requirements in the data buffer area after the work period starts. Through a dynamic matching algorithm, a matching rule is adapted according to service characteristics, a base station load rate, a historical success rate, energy consumption cost and channel quality are comprehensively considered to calculate a matching degree, and the adaptability of computing power distribution is improved.

Description

Technical Field

[0001] The present invention relates to the field of operation and maintenance management technology, and specifically to a 5G base station operation and maintenance management method and system based on edge computing. Background Art

[0002] 5G base stations are the core equipment of 5G networks. They achieve wireless coverage through high-frequency bands and Massive MIMO technologies, connect wired communication networks and terminals, support high-speed, low-latency communications, and promote applications in scenarios such as smart cities and industrial control. They are key infrastructure for digital transformation. In existing operations and maintenance management, resource allocation is often based on fixed rules, such as equal allocation by region. These rules fail to take into account actual business needs, such as the rapid response requirements of urgent services and the need for redundant resources in high-load services. Dynamic adjustments lead to resource mismatches, with idle resources not prioritized for high-demand areas. This makes resource allocation prone to reactive expansion, often requiring urgent capacity expansion after business interruptions caused by insufficient resources. To this end, the present invention proposes a 5G base station operation and maintenance management method and system based on edge computing to address the shortcomings of the existing technology. Summary of the Invention

[0003] The purpose of the present invention is to provide a 5G base station operation and maintenance management method and system based on edge computing, which solves the problem that resource allocation in the prior art is mostly based on fixed rules and does not combine with the actual needs of the business, resulting in resource mismatch and resource allocation easily falling into a passive state. The purpose of the present invention can be achieved through the following technical solutions: A 5G base station operation and maintenance management method based on edge computing includes the following steps: S1. Obtain the number of base stations in the regional base station group and classify them into allocated base stations, backup base stations, high-scoring base stations, and low-scoring base stations based on their historical load status. Each base station is equipped with an edge computing node. S2. Before the start of the working cycle, a data buffer is generated according to the daily working log of each base station, and the received business demand data is temporarily stored in the data buffer; S3. Determine the matching algorithm for the corresponding base station based on the policy library rules based on the unallocated service demands in the data buffer after the start of the working cycle, calculate the matching degree between each base station and the unallocated service demands in the data buffer, and select the most suitable resource allocation method; S4. Allocate the computing power requirements to be allocated to each base station according to the selected resource allocation method; S5. Monitor the load changes and newly added business demands of each base station in real time, update the computing load of each base station, and allocate the newly added business demands to each base station; S6. Generate and store a corresponding base station work log according to the load change of the base station.

[0004] Preferably, the specific working steps of determining the matching algorithm for the corresponding base station according to the policy library rules based on the unallocated service requirements in the data buffer include: Obtain various demand parameters of unallocated business demands in the data buffer, including business type, computing power requirement, delay sensitivity, priority, and channel quality requirements; Call the policy library rules and match the appropriate matching algorithm based on the key parameters extracted from the unallocated business computing power requirements; The matching algorithms include a load margin priority algorithm, a comprehensive capability matching algorithm, and a load balancing algorithm.

[0005] Preferably, the matching rules of the matching algorithm are as follows: The load margin priority algorithm includes obtaining the current load rate of each base station and the current load margin of each base station, sorting them from high to low by margin, and giving priority to base stations with sufficient load margin and qualified channel quality and operating status for high-priority and low-latency services during matching; The comprehensive capability matching algorithm includes obtaining the current load rate of the base station, the historical success rate of processing similar services, the energy consumption per unit of computing power, the channel packet loss rate and fluctuation range, and obtaining the matching degree through weighted summation. When matching, for ordinary priority and high computing power demand services, the base station with the highest matching degree and whose load does not exceed the safety threshold is selected; The load balancing algorithm includes statistics on the current load value of each base station, the remaining computing capacity and the total load of the services to be allocated. By calculating the ratio of the total load of the base station current load plus the load to be allocated to the rated load, the allocation ratio is adjusted to minimize the load difference of each base station. When matching, for sudden intensive requests, the services are dispersed to base stations with lower loads.

[0006] Preferably, the method of screening the most suitable resource allocation method includes: Calculate the matching degree between each base station and service requirements based on the matching algorithm; Sort the matching scores of all base stations and preliminarily select the top three candidate base stations; Perform a secondary check on the candidate base stations: If the candidate base station is a high-scoring base station, it is necessary to verify whether its current load still has redundancy; if it is a backup base station, it is only enabled when the assigned base station and the low-scoring base station are not well matched; finally, the base station with the highest match and meeting the load safety threshold is selected and determined as the object of allocation of computing power demand.

[0007] Preferably, the assigned base stations, standby base stations, high-scoring base stations, and low-scoring base stations are classified in the following manner; Obtain the work logs of each base station in the base station group for the previous week, as well as the assigned base stations, backup base stations, high-scoring base stations, and low-scoring base stations for the previous day; The comprehensive score of each base station is calculated by normalizing the operating load data, operating energy consumption data, and channel quality data of each base station on the previous day and performing weighted summation. The base stations are then arranged from high to low according to their comprehensive scores to form a base station sequence. The first-ranked base station in the base station sequence is set as the assigned base station, the average score of the base station sequence is obtained, the two base stations with the smallest difference from the average score are set as standby base stations, the base station with a ranking higher than the standby base station and lower than the assigned base station is set as a high-scoring base station, and the base station with a ranking lower than the standby base station is set as a low-scoring base station.

[0008] Preferably, the data buffer is generated in the following manner: Based on the work logs of all base stations in the previous week, statistics are collected on the daily base station service fluctuation time period and the corresponding service demand load is calculated based on the data of each service demand; Statistics are collected on the edge computing resources of the currently assigned base station, high-scoring base stations, and backup base stations. The currently available maximum load is calculated and matched with the service demand load. When the currently available maximum load matches the service demand load, a data buffer is generated based on the assigned base station, high-scoring base stations, and backup base stations. When the allocation base station receives a business demand, it first temporarily stores the business demand in the data buffer and calculates the edge computing power demand load to be allocated in the data buffer.

[0009] Preferably, the specific working steps of step S5 include: Real-time collection of current load data of each base station and new service demand information; Conduct preliminary verification of newly added business requirements to determine whether they should be included in the queue to be allocated; Dynamically calculate and update the current computing load value of each base station based on real-time load changes and new business needs; If the load value exceeds the preset safety threshold, a load adjustment warning is triggered; Synchronize the updated load data to the management system.

[0010] The present invention also provides a 5G base station operation and maintenance management system based on edge computing, including the following modules: The data collection module is used to collect the number of base station groups in the area and obtain the work log of each base station; The data buffer module is used to obtain the business demand fluctuation frequency and business demand size based on the work log of each base station and build a data buffer; An algorithm matching module is used to match the appropriate matching algorithm to the unallocated service requirements in the data buffer and select the corresponding base station based on the matching algorithm; The resource monitoring module is used to allocate newly added business needs during the operation of the base station.

[0011] Beneficial effects of the present invention: 1. The present invention divides base stations into allocation base stations, standby base stations, high-load base stations and low-load base stations according to historical load data and comprehensive scores to achieve precise stratification of resources; by establishing a data buffer to temporarily store unallocated computing power requirements, it optimizes scheduling in combination with historical business load patterns to balance load fluctuations; through a dynamic matching algorithm, it adapts matching rules according to business characteristics, comprehensively considers the base station load rate, historical success rate, energy consumption cost and channel quality to calculate the matching degree, improves the adaptability of computing power allocation, realizes efficient utilization of base station resources, improves the timeliness and stability of business processing, reduces operating energy consumption, and provides reliable support for the intelligent operation and maintenance of 5G base stations.

[0012] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 This is a step flow chart of a 5G base station operation and maintenance management method and system based on edge computing of the present invention.

[0015] Figure 2 This is a module framework diagram of a 5G base station operation and maintenance management method and system based on edge computing in the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] See also Figure 1 - Figure 2As shown, the present invention is a 5G base station operation and maintenance management method and system based on edge computing, which includes a data acquisition module, a data buffer module, an algorithm matching module, and a resource monitoring module; wherein the data acquisition module is mainly used to collect the number of base station groups in the area, and obtain the available load data, operating energy consumption data, and channel quality data of each base station in the base station group. The available load data refers to the network load capacity that the base station can bear, and the operating energy consumption data refers to the energy consumption consumed by the base station during operation. If the load borne by the base station increases, the operating intensity of various electronic equipment in the base station, such as power supply equipment, transmitting equipment and other hardware equipment will also increase, and the heat generated will also be more. In order to maintain the normal operating temperature of the equipment, the cooling system such as air conditioning needs to increase its working intensity, which will also consume more electricity. Therefore, the energy consumption of the base station will also increase accordingly. With the development of science and technology, base stations that can withstand larger loads and have relatively lower energy consumption have gradually been developed in the existing technology. For example, Huawei's 5G AAU uses a self-developed highly integrated chip, reducing power consumption by over 15%. It also uses intelligent algorithms to optimally match service loads with operating modes. Channel quality refers to the quality of the transmission link between base stations and user equipment in wireless communications. Its core is to assess the impact of the channel on the reliability, rate, and stability of data transmission. Based on the above, the operating load data, operating energy consumption data, and channel quality data of each base station in the base station group are obtained, and each data is normalized. The weighted sum of each normalized data is performed to obtain the comprehensive score of each base station. All base stations in the base station group are arranged according to the comprehensive score, from high to low. The base station ranked first in the base station sequence is set as the assigned base station because it has the highest comprehensive score and the best processing capability, making it suitable for taking on the main computing power allocation task; At the same time, the average score of the base station sequence is calculated, and the two base stations with the smallest difference from the average score are set as backup base stations. The performance of these two base stations is close to the overall average level, and they can quickly replace the main base station when an abnormality occurs. The base stations ranked after the assigned base station and before the backup base station are set as high-load base stations. These base stations have a higher overall score but are slightly lower than the assigned base station. They still have certain processing capabilities but a relatively high load. The base stations ranked after the backup base station are set as low-load base stations. These base stations have a lower overall score, less load pressure, and more computing power redundancy, which can be used to share some of the additional computing power requirements. Through this classification, refined management of base station resources is achieved, laying the foundation for subsequent computing power allocation. The data collection module also includes collecting daily work logs for each base station. The work logs record the daily load fluctuations and the time nodes of the changes in real time. All base station work logs are aggregated to analyze the business demand at different time nodes. Based on the work logs of the past week, the business demand at similar time nodes can be compared to predict the business demand at nearby nodes, and the data buffer is further prepared based on the prediction results. The data buffer module is used to comprehensively collect the complete work logs of all base stations in the base station group in the previous week. These logs cover detailed information such as the service request initiation time, initiating entity, service type, data transmission volume, processing time, completion status, etc. in each time period of the day. Based on this, the total number of service requests per day is counted, and the statistics are split into smaller time granularities such as hours and minutes to clearly present the distribution pattern of service requests within a day, such as the specific time periods of the morning and evening peaks and the peak request volume.

[0018] Then, for each service request, the required data is analyzed. The required data includes the specific value of the data transmission volume, such as the video streaming data volume or text data packet size, the processing time, the data processing time, the service priority level (such as emergency communications involving public safety is the highest level, ordinary user browsing data is the medium level, and background system backup is the low level), as well as the sensitivity to network stability and latency. The data required by the service request is then converted into the corresponding service demand load value to obtain the corresponding service demand load; Then, based on the previous week's service request distribution and the statistical results of each service's demand load, we analyze the fluctuation characteristics of the overall service load, including the load difference between weekdays and weekends, the average and peak loads at different time periods, and the number and duration of sudden high-traffic requests. Based on this information, we determine the capacity of the data buffer. It is usually set to 1.2 times the total maximum unallocated computing power demand per day in the previous week, with a certain amount of redundancy reserved to cope with emergencies. At the same time, the allocation rules of the data buffer are set according to the storage order of different priority businesses. High-priority businesses occupy the front position in the buffer, and businesses with the same priority are sorted by initiation time. Real-time businesses and non-real-time businesses are partitioned and stored in the buffer to facilitate subsequent rapid positioning and retrieval.

[0019] The currently unallocated computing power demand is then categorized by service type into real-time communication services such as video calls and real-time monitoring of IoT devices, batch data transmission services such as cloud data synchronization and batch terminal software updates, and compute-intensive services such as data modeling for edge nodes. According to the degree of urgency, they are divided into urgent needs that affect core services, regular needs for general user experience, and low-priority needs that can be processed later. These classified computing power needs are then entered into the data buffer together with key information such as their initiation time, required computing power limit, and processing time limit to ensure that the detailed attributes of each need are fully recorded.

[0020] Finally, the real-time monitoring mechanism is activated. By continuously collecting indicators such as the amount of stored data in the buffer and the backlog duration of unprocessed demands, an early warning is immediately triggered when the storage volume reaches 80% of the total capacity or the backlog of high-priority demands exceeds the preset duration. The early warning signal will drive the matching algorithm for high-load scenarios in the system's priority scheduling strategy library, speed up the resource allocation process, and temporarily expand the dynamic capacity of the buffer to avoid business interruption due to demand overflow.

[0021] The algorithm matching module mainly matches the unallocated computing power requirements in the data buffer with the base station. First, it needs to analyze the computing power requirements to be allocated in the data buffer and extract the core parameters of each requirement one by one, including the service type, the specific value of the computing power requirement, and the priority level of the service. At the same time, it records the initiation time and expected processing time of each requirement to provide a comprehensive basis for subsequent matching. Then the system's built-in policy library is called, which contains a variety of scenario-based rules. For example, when the demand is high priority and delay-sensitive, the load margin priority algorithm is automatically matched, giving priority to whether the current remaining computing power of the base station can meet the immediate response; when the demand is high computing power and lasts for a long time, the comprehensive capability matching algorithm is matched to comprehensively evaluate the long-term processing stability and energy efficiency of the base station; when a large number of similar requests appear in a short period of time, the load balancing algorithm is matched to avoid overloading of a single base station due to centralized processing. These rules are used to accurately determine the adaptive matching algorithm.

[0022] Then, the matching degree calculation phase begins, collecting the real-time basic parameters of each base station, including the current load rate, which is the ratio of the actual load to the rated load; the historical success rate of similar services, which is the ratio of the number of successful completions in the past to the total number of processing times; the energy cost, which is the energy consumption required to process a unit of computing power; and the channel quality stability, which is the packet loss rate and fluctuation range of channel transmission in the past hour. The weights of various parameters are then dynamically adjusted according to the characteristics of the service. For example, the channel quality weight related to delay in real-time services is set to the highest, and the energy cost weight in energy-sensitive services is significantly increased. The matching degree value of each base station with the computing power demand is obtained through weighted summation. The higher the value, the stronger the adaptability, as shown in the following formula: The matching degree of the load margin priority algorithm is calculated by the following formula:

[0023] in, is the current load rate, is the state coefficient; By calculating the load margin matching degree of each base station, the available idle computing resources and operating status of the base station can be directly reflected. This allows for the rapid selection of suitable base stations for high-priority, low-latency services, ensuring that services can preferentially occupy sufficient idle resources and meet their stringent requirements for response speed. A higher matching degree indicates a better match between the base station and the service. The matching degree of the comprehensive ability matching algorithm is calculated by the following formula:

[0024] in, For matching, is the current load rate, The success rate of similar business in history. is the normalized energy consumption cost, is the normalized channel packet loss rate for nearly one hour, is the normalized channel fluctuation amplitude for nearly one hour, 、 、 、 is the weight coefficient; The matching degree of the load balancing algorithm is calculated by the following formula:

[0025] For the The current load rate of each base station, is the load factor increment to be allocated to the base station, For the The maximum load rate threshold of each base station, For the The current load rate of each base station, For the Maximum load rate threshold of each base station; Calculate the load rate of each base station after allocating the proposed new load: the current load rate plus the incremental load rate to be allocated divided by the difference between the maximum and minimum values ​​of the maximum load rate threshold. This quantifies the degree of load difference between base stations. The smaller the matching value, the more balanced the load of each base station. Based on this, adjust the load amount to be allocated to achieve balanced load distribution among base stations. After the matching is completed, the screening phase begins. First, all base stations are sorted by their matching degree from high to low. The top three base stations are initially selected as candidates. Then, a secondary verification is performed based on the previously determined base station classification results. If the candidate base station is a high-load base station, it is necessary to determine whether the current load of the high-load base station does not exceed the rated load of the base station to ensure stable operation after allocation. If the candidate base station is a backup base station, it is necessary to confirm that the matching degree of the assigned base station and the low-load base station is lower than that of the backup base station. It is only enabled when the adaptability of the primary base station is insufficient; Finally, the base station with the highest matching degree is selected from the candidate base stations that have passed the verification, and it is determined as the optimal allocation object for the computing power demand, completing the screening of resource allocation methods.

[0026] After screening the optimal computing power allocation targets, the optimal resource allocation plan is confirmed, the information of the candidate base stations with the highest matching degree is checked, and the allocation is graded according to business attributes. The computing power requirements in the data buffer are classified by priority and business type, and business requirements of different priorities and business types are allocated to different base stations. For each target base station, an allocation instruction containing specific computing power requirements is generated. The allocation instruction includes the data volume of the business to be processed, the processing time limit, and the resource occupation quota. The allocation instruction is sent to the corresponding base station, the data buffer status is updated synchronously, and the matching results are fed back in real time. Then, computing power allocation instructions are generated for each target base station. The instructions clearly include the specific data volume of the business to be processed, such as how many GB of data packets, the computing power quota required, such as the percentage of the total computing power of the base station, the specific frequency band of the channel allocation and the occupancy time, the start and end time nodes of the processing, and quality control indicators such as the maximum allowable packet loss rate and delay fluctuation range, to ensure that the base station can accurately allocate resources to perform tasks according to the instructions.

[0027] Next, the allocation process is executed, and the allocation instructions are sent to the corresponding base station through the dedicated communication link of the edge computing network. The data buffer is synchronously operated to remove the computing power requirements that have been allocated and update the remaining capacity of the buffer and the queue to be allocated. At the same time, the allocation results such as the target base station ID, service ID, allocation time, and instruction reception status are fed back to the central monitoring system in real time as the initial data basis for subsequent load updates.

[0028] The resource monitoring module is used to allocate newly added business demands during the operation of the base station. The allocation process includes verifying the load of the newly added business demands, checking whether the demand initiating terminal is within the effective coverage of the base station to confirm the legitimacy, checking the integrity of the business data packet such as whether there are missing fields or verification errors, and evaluating whether the urgency of the demand matches the declared priority. After verification, the demands that meet the conditions will be included in the queue to be allocated. If they do not meet the conditions, the reasons will be marked and fed back to the initiator.

[0029] Subsequently, the current computing load of each base station is updated based on the real-time collected base station load change data and the load value of the business demand newly included in the queue to be allocated. The model will assign different weights according to the different business types. For example, the real-time interactive business is highly sensitive to delay and has a higher weight, while the batch data transmission business has a high demand for bandwidth and the weight is adjusted accordingly to ensure that the updated load value can truly reflect the real-time pressure of the base station.

[0030] At the same time, multi-level load safety thresholds are set, and thresholds are set separately for different types of base stations, such as low-load base stations, high-load base stations. When the real-time load value of a base station exceeds the corresponding threshold, a load adjustment warning is immediately triggered. The warning information includes the base station ID, current load value, the proportion of exceeding the threshold and the type of business that may be affected, and the warning will be pushed to the operation and maintenance management interface.

[0031] Finally, the updated computing load data of all base stations are packaged in a unified format and synchronized to the database of the central management system through an encrypted transmission channel. The synchronization frequency is dynamically adjusted according to the business volume. The synchronization interval is shortened during peak business periods to ensure real-time performance, and appropriately extended during off-peak periods to reduce resource consumption.

[0032] Finally, the edge computing nodes of each base station are used to count the current load of each base station, the load change frequency, and the change amount each time the load changes. According to the load change frequency and change amount, records are made, and the corresponding base station work log is generated and uploaded to the cloud storage in real time for future query.

[0033] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A 5G base station operation and maintenance management method based on edge computing, characterized in that: The following steps are involved: S1. Obtain the number of base stations in the regional base station group and classify them into allocated base stations, backup base stations, high-scoring base stations, and low-scoring base stations based on their historical load status. Each base station is equipped with an edge computing node. S2. Before the start of the working cycle, a data buffer is generated according to the daily working log of each base station, and the received business demand data is temporarily stored in the data buffer; S3. Determine the matching algorithm for the corresponding base station based on the policy library rules based on the unallocated service demands in the data buffer after the start of the working cycle, calculate the matching degree between each base station and the unallocated service demands in the data buffer, and select the most suitable resource allocation method; S4. Allocate the computing power requirements to be allocated to each base station according to the selected resource allocation method; S5. Monitor the load changes and newly added business demands of each base station in real time, update the computing load of each base station, and allocate the newly added business demands to each base station; S6. Generate and store a corresponding base station work log according to the load change of the base station.

2. A 5G base station operation and maintenance management method based on edge computing according to claim 1, characterized in that: The specific working steps of determining the matching algorithm for the corresponding base station based on the policy library rules for the unallocated service requirements in the data buffer include: Obtain various demand parameters of unallocated business demands in the data buffer, including business type, computing power requirement, delay sensitivity, priority, and channel quality requirements; Call the policy library rules and match the appropriate matching algorithm based on the key parameters extracted from the unallocated business computing power requirements; The matching algorithms include a load margin priority algorithm, a comprehensive capability matching algorithm, and a load balancing algorithm.

3. A 5G base station operation and maintenance management method based on edge computing according to claim 2, characterized in that: The matching rules of the matching algorithm are as follows: The load margin priority algorithm includes obtaining the current load rate of each base station and the current load margin of each base station, sorting them from high to low by margin, and giving priority to base stations with sufficient load margin and qualified channel quality and operating status for high-priority and low-latency services during matching; The comprehensive capability matching algorithm includes obtaining the current load rate of the base station, the historical success rate of processing similar services, the energy consumption per unit of computing power, the channel packet loss rate and fluctuation range, and obtaining the matching degree through weighted summation. When matching, for ordinary priority and high computing power demand services, the base station with the highest matching degree and whose load does not exceed the safety threshold is selected; The load balancing algorithm includes statistics on the current load value of each base station, the remaining computing capacity and the total load of the services to be allocated. By calculating the ratio of the total load of the base station current load plus the load to be allocated to the rated load, the allocation ratio is adjusted to minimize the load difference of each base station. When matching, for sudden intensive requests, the services are dispersed to base stations with lower loads.

4. A 5G base station operation and maintenance management method based on edge computing according to claim 3, characterized in that: The most suitable resource allocation method for screening includes: Calculate the matching degree between each base station and service requirements based on the matching algorithm; Sort the matching scores of all base stations and preliminarily select the top three candidate base stations; Perform a secondary check on the candidate base stations: If the candidate base station is a high-scoring base station, it is necessary to verify whether its current load still has redundancy; if it is a backup base station, it is only enabled when the assigned base station and the low-scoring base station are not well matched; finally, the base station with the highest match and meeting the load safety threshold is selected and determined as the object of allocation of computing power demand.

5. A 5G base station operation and maintenance management method and system based on edge computing according to claim 4, characterized in that: The assigned base stations, standby base stations, high-scoring base stations, and low-scoring base stations are classified in the following manner; Obtain the work logs of each base station in the base station group for the previous week, as well as the assigned base stations, backup base stations, high-scoring base stations, and low-scoring base stations for the previous day; The comprehensive score of each base station is calculated by normalizing the operating load data, operating energy consumption data, and channel quality data of each base station on the previous day and performing weighted summation. The base stations are then arranged from high to low according to their comprehensive scores to form a base station sequence. The first-ranked base station in the base station sequence is set as the assigned base station, the average score of the base station sequence is obtained, the two base stations with the smallest difference from the average score are set as standby base stations, the base station with a ranking higher than the standby base station and lower than the assigned base station is set as a high-scoring base station, and the base station with a ranking lower than the standby base station is set as a low-scoring base station.

6. A 5G base station operation and maintenance management method and system based on edge computing according to claim 5, characterized in that: The data buffer is generated in the following way: Based on the work logs of all base stations in the previous week, statistics are collected on the daily base station service fluctuation time period and the corresponding service demand load is calculated based on the data of each service demand; Statistics are collected on the edge computing resources of the currently assigned base station, high-scoring base stations, and backup base stations. The currently available maximum load is calculated and matched with the service demand load. When the currently available maximum load matches the service demand load, a data buffer is generated based on the assigned base station, high-scoring base stations, and backup base stations. When the allocation base station receives a business demand, it first temporarily stores the business demand in the data buffer and calculates the edge computing power demand load to be allocated in the data buffer.

7. A 5G base station operation and maintenance management method and system based on edge computing according to claim 6, characterized in that: The specific working steps of step S5 include: Real-time collection of current load data of each base station and new service demand information; Conduct preliminary verification of newly added business requirements to determine whether they should be included in the queue to be allocated; Dynamically calculate and update the current computing load value of each base station based on real-time load changes and new business needs; If the load value exceeds the preset safety threshold, a load adjustment warning is triggered; Synchronize the updated load data to the management system.

8. A 5G base station operation and maintenance management system based on edge computing, according to a 5G base station operation and maintenance management method based on edge computing according to claim 7, characterized in that: Includes the following modules: The data collection module is used to collect the number of base station groups in the area and obtain the work log of each base station; The data buffer module is used to obtain the business demand fluctuation frequency and business demand size based on the work log of each base station and build a data buffer; An algorithm matching module is used to match the appropriate matching algorithm to the unallocated service requirements in the data buffer and select the corresponding base station based on the matching algorithm; The resource monitoring module is used to allocate newly added business needs during the operation of the base station.

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