Base station control method and device, storage medium and electronic device

Abstract

The invention discloses a base station control method and device, a storage medium and an electronic device, and the method comprises the steps: forming a parameter sequence reflecting the current operation condition of a target base station through real-time continuous collection of the operation parameters of the target base station when the current operation mode of the target base station is detected to be an energy-saving mode, the method comprises the following steps: acquiring a running parameter sequence of a target base station, predicting a load surge event of the target base station according to the acquired running parameter sequence, and immediately switching the target base station from an energy-saving mode to a performance mode when the load surge event causing that the load growth rate of the target base station is greater than a preset rate is predicted. And the base station is low in control efficiency in the abnormal scene.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a base station control method and device, a storage medium, and an electronic device. Background Art

[0002] With the large-scale deployment of 5G (5th Generation Wireless System) networks, pico base stations, key nodes for coverage gap filling and capacity expansion, pose a core challenge for operators in reducing costs and increasing efficiency. Energy consumption at pico base stations is primarily concentrated in the base station, transmission, and power supply components, with base stations accounting for the largest share and directly impacting operational and maintenance costs.

[0003] Therefore, in related technologies, energy optimization of mobile networks is mainly achieved through base station energy-saving optimization. Common methods include: a control mechanism based on fixed thresholds, such as performing symbol shutdown, carrier dormancy and other operations through preset load, temperature and other indicator thresholds, or relying on hardware integration improvements and heat dissipation technology to reduce basic energy consumption. However, the above solutions have significant defects: the control mechanism based on fixed thresholds lacks dynamic adaptive capabilities and is difficult to adapt to the needs of diverse scenarios. For example, when user traffic fluctuates or the environment is abnormal, this method often leads to network congestion due to delayed response, or deteriorates signal quality due to excessive degradation (such as forced carrier shutdown), ultimately leading to a decline in communication quality, and unable to achieve a balanced control between user experience and energy-saving optimization.

[0004] In relation to the related technologies, effective solutions have not yet been proposed for problems such as low control efficiency of base stations in abnormal scenarios. Summary of the Invention

[0005] The embodiments of the present application provide a base station control method and device, a storage medium, and an electronic device to at least solve the problem of low control efficiency of the base station in abnormal scenarios in the related art.

[0006] According to one embodiment of the present application, a base station control method is provided, including:

[0007] Detecting the current operating mode of the target base station;

[0008] When detecting that the operating mode is the energy-saving mode, continuously collecting operating parameters of the target base station to obtain an operating parameter sequence, wherein the operating parameters are used to indicate a current operating status of the target base station, and the target base station in the energy-saving mode reduces base station energy consumption by reducing base station performance;

[0009] Predicting a load surge event of the target base station based on the operating parameter sequence, wherein the load surge event is an event that causes a load growth rate of the target base station to be greater than a preset rate, and the load growth rate is used to indicate an amount of change in a load parameter of the target base station per unit time;

[0010] When the load surge event is predicted, the target base station is immediately switched from the energy-saving mode to the performance mode, wherein the target base station in the performance mode reduces the response time of the communication service by improving the base station performance.

[0011] Optionally, the continuously collecting the operating parameters of the target base station to obtain an operating parameter sequence includes:

[0012] The operating parameters of the target base station are collected multiple times according to a preset detection period to obtain multiple sets of corresponding collection time points and the operating parameters;

[0013] The plurality of operating parameters in the plurality of groups of corresponding acquisition time points and operating parameters are sorted according to the time sequence of the acquisition time points to obtain the operating parameter sequence.

[0014] Optionally, the collecting the operating parameters of the target base station multiple times according to a preset detection period includes:

[0015] The service load parameters, component operation parameters, and software operation parameters of the target base station are collected multiple times according to a preset detection cycle to obtain multiple groups of corresponding collection time points, the service load parameters, the component operation parameters, and the software operation parameters, wherein the service load parameters are used to indicate the load condition of the target base station in processing a service request at the corresponding collection time point, the service request is used to request processing of the communication service, the component operation parameters are used to indicate the component resource usage of the resource component in the target base station at the corresponding collection time point, the resource component is used to provide resources for the target base station to process the service request, and the software operation parameters are used to indicate the operation condition of the software system that processes the communication service at the corresponding collection time point, and the operation parameters include the service load parameters, the component operation parameters, and the software operation parameters;

[0016] The step of sorting the plurality of operating parameters in the plurality of groups of corresponding collection time points and operating parameters according to the chronological order of the collection time points includes:

[0017] Multiple business load parameters, multiple component operation parameters, and multiple software operation parameters in multiple groups of corresponding acquisition time points, the business load parameters, the component operation parameters, and the software operation parameters are sorted in chronological order of the acquisition time points to obtain a business load parameter sequence, a component operation parameter sequence, and a software operation parameter sequence, wherein the operation parameter sequence includes the business load parameter sequence, the component operation parameter sequence, and the software operation parameter sequence.

[0018] Optionally, the collecting the service load parameters, component operation parameters, and software operation parameters of the target base station multiple times according to a preset detection period includes:

[0019] collecting the resource block utilization, transmission rate, number of users, and service flow of the target base station according to the preset detection period, and generating the service load parameter based on the resource block utilization, the transmission rate, the number of users, and the service flow, wherein the resource block utilization is used to indicate the utilization of the time-frequency resources of the resource block by the target base station in the process of processing the service request, the transmission rate is used to indicate the amount of data transmitted per unit time by the target base station in the process of processing the service request, the number of users is the number of user terminals that send the service request to the target base station, and the service flow is used to indicate the total amount of data transmitted by the target base station in the process of processing the service request;

[0020] Collecting component temperature information, processor usage, and storage usage of the target base station according to the preset detection period, wherein the component temperature information is used to indicate the temperature of one or more of the resource components, and generating the component operating parameters based on the component temperature information, the processor usage, and the storage usage, the processor usage is used to indicate the target base station's usage of processor computing resources, and the storage usage is used to indicate the target base station's usage of storage resources of a storage device, and the resource components include the processor and the storage device;

[0021] The log generation information and file upload information of the target base station are collected according to the preset detection period, and the software operation parameters are generated based on the log generation information and the file upload information, wherein the log generation information is used to indicate the number of logs generated by the software system per unit time, and the file upload information is used to indicate the number of files uploaded by the software system per unit time.

[0022] Optionally, the operating parameter sequence includes N operating parameters collected at N collection time points, where N is an integer greater than 1, and the time interval between the i-1th sampling time point and the i-th sampling time point satisfies a preset detection period, where i is an integer greater than 1. Predicting the load surge event of the target base station based on the operating parameter sequence includes:

[0023] Extracting M reference operating parameters collected from the N-M+1th sampling time point to the Nth sampling time point from the N operating parameters included in the operating parameter sequence, and obtaining a current target load characteristic of the target base station based on the M reference operating parameters, where M is an integer greater than 1 and less than or equal to N, and the target load characteristic is used to indicate a change in the reference operating parameters of the target base station from the N-M+1th sampling time point to the Nth sampling time point;

[0024] detecting a target matching parameter of the target load feature and the abnormal load feature, wherein the abnormal load feature is a load feature of the load surge event, and the target matching parameter is used to indicate a degree of matching between the target load feature and the abnormal load feature; a larger target matching parameter indicates a higher degree of matching between the target load feature and the abnormal load feature;

[0025] When the target matching parameter is greater than a preset parameter threshold, it is determined that the load surge event is predicted.

[0026] Optionally, obtaining a current target load characteristic of the target base station according to the M reference operating parameters includes:

[0027] Extracting the largest first operating parameter and the smallest second operating parameter from the M reference operating parameters;

[0028] performing a subtraction operation on the first operating parameter and the second operating parameter to obtain a first difference, and performing an addition operation on the first operating parameter and the second operating parameter to obtain a first sum;

[0029] A division operation is performed on the first difference value and the first sum value to obtain the target load characteristic.

[0030] Optionally, after detecting the current operating mode of the target base station, the method further includes:

[0031] When it is detected that the operating mode is the performance mode, continuously collecting candidate operating parameters of the target base station to obtain a candidate operating parameter sequence;

[0032] Predicting a load drop event of the target base station according to the candidate operating parameter sequence, wherein the load drop event is an event that causes the load parameter of the target base station to be less than a preset load threshold;

[0033] When the load shedding event is predicted, the target base station is immediately switched from the performance mode to the energy-saving mode.

[0034] According to another embodiment of the present application, a control device of a base station is further provided, including:

[0035] A detection module, used to detect the current operating mode of the target base station;

[0036] a first acquisition module, configured to, when detecting that the operating mode is the energy-saving mode, continuously acquire operating parameters of the target base station to obtain an operating parameter sequence, wherein the operating parameters are used to indicate a current operating status of the target base station, and the target base station in the energy-saving mode reduces base station energy consumption by reducing base station performance;

[0037] a first prediction module, configured to predict a load surge event of the target base station based on the operating parameter sequence, wherein the load surge event is an event that causes a load growth rate of the target base station to be greater than a preset rate, and the load growth rate is used to indicate an amount of change in a load parameter of the target base station per unit time;

[0038] The first switching module is used to immediately switch the target base station from the energy-saving mode to the performance mode when the load surge event is predicted, wherein the target base station in the performance mode reduces the response time of the communication service by improving the base station performance.

[0039] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is configured to execute the above-mentioned base station control method when running.

[0040] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the base station control method through the computer program.

[0041] In an embodiment of the present application, when it is detected that the current operating mode of the target base station is the energy-saving mode, a parameter sequence reflecting the current operating status of the target base station is formed by continuously collecting the operating parameters of the target base station in real time, and a load surge event of the target base station is predicted based on the collected operating parameter sequence. When a load surge event is predicted that causes the load growth rate of the target base station to be greater than the preset rate, the target base station is immediately switched from the energy-saving mode to the performance mode. That is, through the closed-loop control mechanism of operating mode detection, operating parameter sequence collection, load surge event prediction and dynamic mode switching, early perception and active response to sudden changes in the base station load in the energy-saving mode are achieved, ensuring that the base station can actively switch to the performance mode in abnormal scenarios, effectively improving the base station's response speed to sudden loads and communication quality assurance capabilities, and avoiding the network congestion and poor user experience caused by the lack of flexibility of the control mechanism and the inability to adjust the energy-saving mode of the base station in time in the related technology. The above technical solution solves the problems of low control efficiency of the base station in abnormal scenarios in the related technology, and achieves the technical effect of improving the control efficiency of the base station in abnormal scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0044] Figure 1 1 is a schematic diagram of a hardware environment of a base station control method according to an embodiment of the present application;

[0045] Figure 2 is a flow chart of a base station control method according to an embodiment of the present application;

[0046] Figure 3 is a schematic diagram of a connection relationship between a base station and a network management system according to an embodiment of the present application;

[0047] Figure 4 is a flow chart of a base station control method based on load evaluation according to an embodiment of the present application;

[0048] Figure 5 This is a structural block diagram of a base station control device according to an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0050] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0051] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal, a device terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 Schematic diagram of the hardware environment of a base station control method according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU (Microcontroller Unit) or a programmable logic device FPGA (Field Programmable Gate Array) and a processing device) and a memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. For example, the computer terminal may also include Figure 1 More or fewer components than shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 Shown are different configurations with more functionality.

[0052] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for sending message push in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above-mentioned method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0053] Transmission device 106 is used to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by a computer terminal's communications provider. In one embodiment, transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0054] In this embodiment, a base station control method is provided, which is applied to the above-mentioned computer terminal. Figure 2 is a flow chart of a base station control method according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0055] Step S202: detecting the current operation mode of the target base station;

[0056] Step S204: When it is detected that the operating mode is the energy-saving mode, continuously collect operating parameters of the target base station to obtain an operating parameter sequence, wherein the operating parameters are used to indicate the current operating status of the target base station. The target base station in the energy-saving mode reduces base station energy consumption by reducing base station performance;

[0057] Step S206: predicting a load surge event of the target base station based on the operating parameter sequence, wherein the load surge event is an event that causes the load growth rate of the target base station to be greater than a preset rate, and the load growth rate is used to indicate an amount of change in the load parameter of the target base station per unit time;

[0058] Step S208: When the load surge event is predicted, the target base station is immediately switched from the energy-saving mode to the performance mode, wherein the target base station in the performance mode reduces the response time of the communication service by improving the base station performance.

[0059] Through the closed-loop control mechanism of operating mode detection, operating parameter sequence collection, load surge event prediction and dynamic mode switching in the above steps, early perception and active response to sudden changes in base station load in energy-saving mode are achieved, ensuring that the base station can actively switch to performance mode in abnormal scenarios, effectively improving the base station's response speed to sudden loads and communication quality assurance capabilities, and avoiding the network congestion and poor user experience caused by the lack of flexibility of the control mechanism and the inability to adjust the energy-saving mode of the base station in time in related technologies. The above technical solution solves the problems of low control efficiency of base stations in abnormal scenarios in related technologies, and achieves the technical effect of improving the control efficiency of base stations in abnormal scenarios.

[0060] Optionally, in this embodiment, the base station may be controlled by, but is not limited to, an Operation and Maintenance Center (OMC). Figure 3 is a schematic diagram of a connection relationship between a base station and a network management system according to an embodiment of the present application, such as Figure 3 As shown in the figure, base stations are responsible for signal reception and transmission, data transmission, and other functions in the communication network. The operation and maintenance system monitors the operating status of all base stations in the logical area (such as base station 1 to base station N) in real time and adjusts parameters to achieve operation, maintenance, and management of the base stations, ensuring stable and efficient operation of the communication network.

[0061] In the technical solution provided in step S202 above, the target base station may be, but is not limited to, Figure 3 Any one or more base stations among the N base stations (including base station 1 to base station N) that need to be energy-efficiently optimized.

[0062] Optionally, in this embodiment, the operating mode of the target base station can be, but is not limited to, a working mode set by the base station to achieve different functional goals, including: energy-saving mode, which reduces energy consumption by reducing base station performance, and is suitable for scenarios such as low-peak business periods; performance mode, which reduces the response time of communication services by improving base station performance, and ensures service quality during peak business periods; balanced mode, which seeks a dynamic balance between energy consumption and communication quality, and flexibly adjusts hardware resource allocation according to load; emergency mode, which gives priority to ensuring smooth communication in special scenarios, and concentrates resources to improve coverage and capacity; maintenance mode, which temporarily reduces the service level during equipment maintenance, etc., to ensure the safe implementation of maintenance operations; sleep mode, which deeply sleeps some hardware modules during extremely low load periods to minimize energy consumption, and other modes.

[0063] In the technical solution provided in step S204 above, the operating parameters may be, but are not limited to, indicator data indicating the current operating status of the base station. The operating parameter sequence may be, but is not limited to, a time series data set formed by continuously collecting operating parameters, such as time series data obtained by continuously sampling the current operating status of the target base station at fixed time intervals (e.g., 1 second, 5 seconds).

[0064] In an exemplary embodiment, obtaining the operating parameter sequence may include, but is not limited to: collecting the operating parameters of the target base station multiple times according to a preset detection period to obtain multiple sets of corresponding collection time points and the operating parameters; and sorting multiple operating parameters in the multiple sets of corresponding collection time points and the operating parameters according to the chronological order of the collection time points to obtain the operating parameter sequence.

[0065] Optionally, in this embodiment, the operating mode of the target base station can be detected by the OMC, but is not limited to it, and the operating parameter collection strategy of the target base station can be automatically configured after detecting that the operating mode is the energy-saving mode, including: first, automatically setting the base station URL (Uniform Resource Locator) collection period (such as 15 minutes), and turning on the performance data (equivalent to the operating parameters) collection function; then, controlling the target base station to collect its own operating parameters according to the collection period, and generate a performance file, and then automatically reporting it to the OMC according to a preset period; obtaining the performance file through the URL, and using the performance file reception time point as the collection time point, and binding it with the operating parameters in the performance file to form multiple sets of parameter data with timestamps (equivalent to corresponding collection time points and operating parameters); finally, the OMC constructs the operating parameters in the multiple sets of parameter data into an operating parameter sequence arranged in order along the time axis according to the order of the collection time points.

[0066] Optionally, in this embodiment, before the OMC arranges the operating parameters in the multiple sets of parameter data in chronological order of acquisition time points, it also performs preprocessing on the operating parameters in the performance files. This includes: the OMC automatically parses the performance files and extracts the operating parameters. First, the OMC automatically standardizes the timestamps and converts all acquisition time points to the UTC (Coordinated Universal Time) format to eliminate time zone differences. The OMC then performs data cleaning and alignment, identifying and eliminating abnormal data (such as jump values ​​that clearly exceed the normal range) based on preset thresholds. For missing data points caused by transmission interruptions or equipment failures, the OMC automatically fills in the missing data points using linear interpolation (taking the average of the two adjacent valid data points) to ensure data continuity and integrity. Through these preprocessing steps, the original acquired operating parameters are converted into a structured data set with a unified time dimension, continuous values, and reliable quality.

[0067] Optionally, in this embodiment, when collecting the operating parameters of the target base station, the operating parameters are orderly integrated based on the time dimension to obtain an operating parameter sequence that can reflect the changes in the operating status of the base station over time, thereby providing a structured data basis for subsequent analysis of load trends and prediction of load surge events.

[0068] In an exemplary embodiment, the operating parameters of the target base station can be collected multiple times according to a preset detection period in the following manner, but is not limited to: the service load parameters, component operating parameters and software operating parameters of the target base station are collected multiple times according to the preset detection period to obtain multiple groups of corresponding collection time points, the service load parameters, the component operating parameters and the software operating parameters, wherein the service load parameters are used to indicate the load status of the target base station in processing the service request at the corresponding collection time point, the service request is used to request processing of the communication service, the component operating parameters are used to indicate the component resource usage of the resource component in the target base station at the corresponding collection time point, the resource component is used to provide resources for the target base station to process the service request, and the software operating parameters are used to indicate the operation status of the software system that processes the communication service at the corresponding collection time point, and the operating parameters include the service load parameters, the component operating parameters and the software operating parameters.

[0069] Optionally, in this embodiment, multiple operating parameters in multiple groups of corresponding acquisition time points and operating parameters can be sorted according to the chronological order of the acquisition time points, but are not limited to, in the following manner, including: sorting multiple business load parameters, multiple component operating parameters, and multiple software operating parameters in multiple groups of corresponding acquisition time points, business load parameters, component operating parameters, and software operating parameters respectively according to the chronological order of the acquisition time points to obtain a business load parameter sequence, a component operating parameter sequence, and a software operating parameter sequence, wherein the operating parameter sequence includes the business load parameter sequence, the component operating parameter sequence, and the software operating parameter sequence.

[0070] Optionally, in this embodiment, the operating parameters of the target base station may include, but are not limited to, business load parameters, component operating parameters, and software operating parameters. After the OMC automatically sets the base station URL collection period and turns on the performance data collection function, it may also include: controlling the target base station to collect its own business load parameters, component operating parameters, and software operating parameters in accordance with the collection period, and generate a performance file, and then automatically report it to the OMC according to the preset period; obtaining the performance file through the URL, and using the performance file receiving time point as the collection time point, and binding it with the business load parameters, component operating parameters, and software operating parameters in the performance file to form multiple sets of parameter data with timestamps (equivalent to corresponding collection time points and operating parameters), such as "10:00 :00" corresponds to business load parameter A, component operation parameter A, and software operation parameter A; finally, OMC arranges the business load parameters, component operation parameters, and software operation parameters in multiple groups of data in the order of collection time points. For example, parameter group A collected at "10:00:00" (including business load parameter A, component operation parameter A, and software operation parameter A) is arranged first, and then parameter group B collected at "10:00:05" (including business load parameter B, component operation parameter B, and software operation parameter B) is arranged, and so on, to construct a parameter group sequence arranged in order along the time axis (equivalent to an operation parameter sequence).

[0071] In an exemplary embodiment, the service load parameters, component operation parameters, and software operation parameters of the target base station may be collected multiple times according to a preset detection period in the following manner, but is not limited to: collecting the resource block utilization, transmission rate, number of users, and service traffic of the target base station according to the preset detection period, and generating the service load parameter based on the resource block utilization, the transmission rate, the number of users, and the service traffic, wherein the resource block utilization is used to indicate the utilization of the time-frequency resources of the resource block by the target base station in the process of processing the service request, the transmission rate is used to indicate the amount of data transmitted per unit time by the target base station in the process of processing the service request, the number of users is the number of user terminals that send the service request to the target base station, and the service traffic is used to indicate the total amount of data transmitted by the target base station in the process of processing the service request;

[0072] Collecting component temperature information, processor usage, and storage usage of the target base station according to the preset detection period, wherein the component temperature information is used to indicate the temperature of one or more of the resource components, and generating the component operating parameters based on the component temperature information, the processor usage, and the storage usage, the processor usage is used to indicate the target base station's usage of processor computing resources, and the storage usage is used to indicate the target base station's usage of storage resources of a storage device, and the resource components include the processor and the storage device;

[0073] The log generation information and file upload information of the target base station are collected according to the preset detection period, and the software operation parameters are generated based on the log generation information and the file upload information, wherein the log generation information is used to indicate the number of logs generated by the software system per unit time, and the file upload information is used to indicate the number of files uploaded by the software system per unit time.

[0074] Optionally, in this embodiment, the method for collecting traffic load parameters of the target base station may include, but is not limited to: collecting PRB (Physical Resource Block) utilization (equivalent to resource block utilization) at a preset detection period to assess whether the target base station's time-frequency resources (including time domain resources and frequency domain resources) are fully utilized; collecting throughput (equivalent to transmission rate) at a preset detection period to assess the amount of data transmitted per unit time by the base station, indicating the base station's data transmission capacity when processing service requests; collecting the number of connected users at a preset detection period and calculating the ratio with the maximum number of supported users (which can be customized or calculated by calculating the maximum number of users in the base station's historical operation) to obtain a user load ratio (equivalent to the number of users) to assess the current operating pressure of the target base station; a higher ratio indicates greater pressure on the target base station to process service requests; collecting the total amount of transmitted data at a preset detection period and calculating the traffic pressure (equivalent to service traffic) based on the maximum occupied bandwidth to assess the congestion risk of the target base station's service traffic. Finally, the collected data, such as resource block utilization, transmission rate, number of users, and service traffic, are integrated to generate a traffic load parameter to quantify the load on the target base station when processing service requests.

[0075] Optionally, in this embodiment, the method for collecting component operating parameters of the target base station may include, but is not limited to: collecting component temperature according to a preset detection period, calculating a ratio with a reference temperature (which can be obtained by calculating the average component temperature information during the base station's historical operation) to obtain a temperature coefficient (equivalent to component temperature information), which can be used to evaluate the temperature status of the target base station's resource components. A higher temperature coefficient indicates a greater risk of resource component failure caused by overtemperature in the target base station; collecting CPU (Central Processing Unit) usage (equivalent to processor usage) according to a preset detection period to evaluate whether the target base station is currently operating in an overloaded state. A higher CPU usage indicates a greater risk of delays in processing service requests or even system crashes in the target base station; and collecting memory utilization (equivalent to storage utilization) according to a preset detection period to evaluate the target base station's usage of storage device resources. A higher memory utilization indicates a more limited storage resource. Finally, the collected component temperature information, processor utilization, and storage utilization data are integrated to generate component operating parameters to quantify the operating status of resource components such as processors and storage devices in the target base station.

[0076] Optionally, in this embodiment, the method for collecting software operating parameters of the target base station may include, but is not limited to: collecting the number of logs generated per unit time by the software system processing communication services according to a preset detection period to obtain a log generation rate (equivalent to log generation information), which can be used to evaluate the impact of log generation on system performance. A higher log generation rate indicates a greater log performance consumption by the software system; collecting the number of files uploaded per unit time by the software system processing communication services according to a preset detection period to obtain a file upload frequency (equivalent to file upload information), which can be used to evaluate the impact of file uploads on system performance. A higher file upload frequency indicates a greater file upload performance consumption by the software system, and therefore, more network bandwidth and system resources are consumed by the target base station in processing service requests. Finally, the collected log generation information and file upload information are integrated to generate software operating parameters, which quantify the operating status of the software system processing communication services at the sampling time point.

[0077] In the technical solution provided in the above step S206, the load parameters of the target base station can be obtained through, but not limited to, service load parameters, component operation parameters, and software operation parameters.

[0078] Optionally, in this embodiment, the load growth rate may be, but is not limited to, a ratio obtained by calculating the difference between a current load parameter and a historical load parameter and the difference between a sampling time point corresponding to the current load parameter and a sampling time point corresponding to the historical load parameter, and is used to determine the fluctuation range of load indicators such as the number of users and service traffic within a unit time. A load surge event may be, but is not limited to, an event that causes the target base station load growth rate to exceed a preset rate. For example, when a parameter such as the service traffic or number of users of the target base station within a unit time suddenly and significantly increases, and the growth rate exceeds the tolerance range of the target base station, it is determined to be a load surge event, which may cause base station performance degradation or failure.

[0079] In an exemplary embodiment, the operating parameter sequence includes N operating parameters collected at N collection time points, where N is an integer greater than 1, and the time interval between the i-1th sampling time point and the i-th sampling time point satisfies a preset detection period, where i is an integer greater than 1. The load surge event of the target base station can be predicted based on the operating parameter sequence in the following manner, but is not limited to:

[0080] Extracting M reference operating parameters collected from the N-M+1th sampling time point to the Nth sampling time point from the N operating parameters included in the operating parameter sequence, and obtaining a current target load characteristic of the target base station based on the M reference operating parameters, where M is an integer greater than 1 and less than or equal to N, and the target load characteristic is used to indicate a change in the reference operating parameters of the target base station from the N-M+1th sampling time point to the Nth sampling time point;

[0081] detecting a target matching parameter of the target load feature and the abnormal load feature, wherein the abnormal load feature is a load feature of the load surge event, and the target matching parameter is used to indicate a degree of matching between the target load feature and the abnormal load feature; a larger target matching parameter indicates a higher degree of matching between the target load feature and the abnormal load feature;

[0082] When the target matching parameter is greater than a preset parameter threshold, it is determined that the load surge event is predicted.

[0083] Optionally, in this embodiment, it is assumed that the preset detection period is set to 5 minutes, that is, the interval between adjacent sampling time points is 5 minutes, and as of the current moment, operating parameters have been collected at N collection time points (e.g., 120, covering 10 hours), that is, the operating parameter sequence records the operating status of the target base station over the past 10 hours. Among them, operating parameters cover multiple dimensions: service load parameters include PRB utilization, throughput, user load factor, and traffic pressure; component operating parameters include temperature coefficient, CPU utilization, and memory occupancy; software operating parameters include log generation rate and file upload rate.

[0084] Optionally, in this embodiment, the step of predicting a load surge event of the target base station based on the operating parameter sequence may be, but is not limited to, the following:

[0085] Step S401: extracting the operating parameters of the latest M sampling time points (i.e., the N-M+1th sampling time point to the Nth sampling time point) from the operating parameter sequence to obtain M reference operating parameters;

[0086] Optionally, in this embodiment, assuming that M is set to 24, the operating parameters from the 97th sampling time point to the 120th sampling time point (i.e., 24 sampling time points) can be extracted. Combined with the interval between adjacent sampling time points being 5 minutes, the 24 reference operating parameters extracted can record the operating status of the target base station in the past 2 hours.

[0087] Step S402: obtaining a current target load characteristic of the target base station based on M reference operating parameters;

[0088] Optionally, in this embodiment, the target load characteristics can be obtained in the following ways, but are not limited to: calculating the linear regression slope of the reference operating parameter based on the ratio of the difference between the current parameter value and the historical parameter value and the difference between the sampling time point corresponding to the current parameter value and the sampling time point corresponding to the historical parameter value; calculating the parameter volatility based on the maximum parameter value and the minimum parameter value of the parameter in the historical time period; calculating the similarity of the fluctuation amplitudes between different parameters and calculating the parameter correlation coefficient; generating the target load characteristics based on the linear regression slope, parameter volatility and parameter correlation coefficient.

[0089] Optionally, in this embodiment, the method for calculating the linear regression slope of the reference operating parameters may be, but is not limited to, as follows: assuming that at the 97th collection time point T97, the collected operating parameter group is: PRB utilization = 70%, throughput = 600 Mbps, user load rate = 80%, traffic pressure = 550 Mbps, temperature coefficient = 70%, CPU utilization = 70%, memory occupancy = 80%, log generation rate = 200 items / minute, and file upload frequency = 10 times / minute; at the 120th collection time point T120, the collected operating parameter group is: PRB utilization = 85%, throughput = 800 Mbps, user load rate = 90%, traffic pressure = 780 Mbps, temperature coefficient = 80%, CPU utilization = 85%, memory occupancy = 90%, log generation rate = 300 items / minute, and file upload frequency = 15 times / minute. Obtain the linear regression slope of each parameter. For example, the PRB utilization change rate is 7.5% / hour; the throughput change rate is 100 Mbps / hour; the user load change rate is 5% / hour; the traffic pressure change rate is 115 Mbps / hour; the temperature coefficient change rate is 5% / hour; the CPU utilization change rate is 7.5% / hour; the memory usage change rate is 5% / hour; the log generation rate change rate is 50 entries / hour; and the file upload frequency change rate is 2.5 times / hour.

[0090] Step S403, detecting target matching parameters of target load characteristics and abnormal load characteristics;

[0091] Optionally, in this embodiment, the abnormal load signature is a typical feature summarized based on historical load surge events (e.g., the common features of 10 load surge events within the previous three months). This feature is derived from a statistical analysis of operating parameters from these historical events. For example, in past load surge events, when the throughput growth rate exceeded 80 Mbps / hour and the CPU utilization rate increased by more than 6% / hour, the probability of base station performance degradation reached 90%, thus classifying this as an abnormal feature. In addition, the abnormal load signature may also include a combination of abnormal features corresponding to other types of abnormal load events.

[0092] Optionally, in this embodiment, the step of detecting target matching parameters of the target load feature and the abnormal load feature may be, but is not limited to: comparing the target load feature with the abnormal load feature to obtain a matching score; and then calculating the target matching parameter based on the matching score.

[0093] Optionally, in this embodiment, the step of comparing the target load feature with the abnormal load feature to obtain a matching score may include, but is not limited to, detecting whether the parameter features in the target load feature satisfy the abnormal features included in the abnormal features; if so, marking the matching score of the corresponding abnormal feature as "1", and if not, marking the matching score of the corresponding abnormal feature as "0". For example, for the two parameter features "throughput growth rate > 80 Mbps / hour" and "CPU growth rate > 6% / hour" in abnormal feature 1, if the throughput growth rate in the target load feature is 100 Mbps / hour and the CPU growth rate is 7.5% / hour, then both abnormal features of the abnormal feature combination are determined to be satisfied, and the matching scores of both abnormal features are marked as "1".

[0094] Optionally, in this embodiment, the step of calculating a target matching parameter based on the matching score may include, but is not limited to, calculating the target matching parameter for each abnormal feature combination based on the category (e.g., service load parameter, component operation parameter, and software operation parameter) of each abnormal feature in the abnormal feature combination, the matching score of each abnormal feature, and the operating parameter weights (e.g., service load weight of 0.4, component operation weight of 0.3, and software operation weight of 0.3). For example, for abnormal feature 1, where "throughput growth rate" is a service load parameter and "CPU growth rate" is a component operation parameter, the service parameter contribution value for this abnormal feature combination is calculated to be 1.0 × 0.4 = 0.4, and the component parameter contribution value is calculated to be 1.0 × 0.3 = 0.3, resulting in a target matching parameter of 0.4 + 0.3 = 0.7. Similarly, the contribution values ​​of the abnormal features in all abnormal feature combinations are calculated and accumulated to ultimately obtain a target matching parameter that comprehensively reflects the degree of match between the target load feature and the abnormal load feature.

[0095] Optionally, in this embodiment, the operating parameter weights can be preset or derived based on target load characteristics. For example, when a base station enters a maintenance health check cycle, priority is given to ensuring long-term stable operation of the equipment, prioritizing equipment health. Because equipment health is closely related to component wear and tear indicators, the lower the hardware wear and tear indicator (for example, chronically high CPU temperatures accelerate aging, while low temperatures minimize wear and tear), the healthier the equipment. At this point, the system adjusts the weighting priorities of component operating parameters. Specifically, component operating parameters are given the highest weight, prioritizing monitoring metrics such as CPU utilization, component temperature, and memory usage to assess hardware stress and wear and tear. Service load parameters are given the next highest weight, assessing service demand based on metrics such as throughput, PRB utilization, and user load. Software operating parameters are given the lowest weight, focusing on system overhead such as log generation rate and file upload frequency. This weighting allows the system to focus on core equipment health indicators during maintenance, promptly identifying and addressing potential hardware risks, and preventing service fluctuations or software load from interfering with equipment health assessments, thereby ensuring long-term stable operation of the equipment. The sum of the component operating weights, service load weights, and software operating parameter weights is 1.

[0096] Step S404 : comparing the target matching parameter with a preset parameter threshold to determine whether a load surge event is predicted.

[0097] Optionally, in this embodiment, if there is an abnormal feature combination whose corresponding target matching parameter is greater than a preset parameter threshold, it means that a load surge event corresponding to the abnormal feature combination is predicted.

[0098] Optionally, in this embodiment, predicting a load surge event of the target base station based on the operating parameter sequence may include, but is not limited to, detecting the growth rate of the operating parameters included in the operating parameter sequence. If it is detected that the growth rate of the operating parameters included in the operating parameter sequence within a specific time period is greater than a preset growth rate, it indicates that a load surge event is about to occur after the specific time period. The growth rate is equal to the magnitude of the increase in the operating parameters per unit time. The growth rate may be, but is not limited to, obtained by plotting a function curve between the operating parameters included in the operating parameter sequence and time, and taking the derivative of the function curve at a certain time point, with the derivative value being the growth rate. Similarly, for the load drop event described below, the decrease rate of the operating parameters included in the operating parameter sequence may be, but is not limited to, detected. If it is detected that the decrease rate of the operating parameters included in the operating parameter sequence within a specific time period is greater than a preset decrease rate, it indicates that a load drop event is about to occur after the specific time period. The decrease rate is equal to the magnitude of the decrease in the operating parameters per unit time. The decrease rate may be, but is not limited to, obtained by plotting a function curve between the operating parameters included in the operating parameter sequence and time, and taking the derivative of the function curve at a certain time point, with the derivative value being the decrease rate.

[0099] In an exemplary embodiment, the current target load characteristics of the target base station can be obtained based on the M reference operating parameters in the following manner, but not limited to: extracting the largest first operating parameter and the smallest second operating parameter from the M reference operating parameters; performing a subtraction operation on the first operating parameter and the second operating parameter to obtain a first difference, and performing an addition operation on the first operating parameter and the second operating parameter to obtain a first sum; performing a division operation on the first difference and the first sum to obtain the target load characteristics.

[0100] Optionally, in this embodiment, the method for calculating the parameter fluctuation rate may be, but is not limited to, extracting the largest first operating parameter (max_load) and the smallest second operating parameter (min_load) from M reference operating parameters; performing a subtraction operation on the first operating parameter and the second operating parameter to obtain a first difference (max_load-min_load), and performing an addition operation on the first operating parameter and the second operating parameter to obtain a first sum (max_load+min_load); performing a division operation on the first difference and the first sum to obtain the target load characteristic.

[0101] In the technical solution provided in the above step S208, the method of switching the target base station from the energy-saving mode to the performance mode may include, but is not limited to: obtaining the business load status, component operation status and software operation status respectively according to the values ​​of the business load parameters, component operation parameters and software operation parameters at the current sampling time point (the Nth sampling time point); obtaining the business load weight, component operation weight and software operation weight, and switching to the corresponding performance mode according to the business load weight, component operation weight, software operation weight, business load status, component operation status and software operation status.

[0102] Optionally, in this embodiment, the service load status of the target base station can also be evaluated according to the value of the service load parameter at the current sampling time point (the Nth sampling time point), including detecting the PRB utilization rate. When the PRB utilization rate is lower than 30%, the PRB utilization rate status is marked as 1, indicating that the time-frequency resources of the base station have control space, otherwise it is marked as 0; detecting the throughput. When it is lower than 1 Mbps, it means that the target base station has a small amount of transmitted data. The throughput load status is marked as 1, otherwise it is marked as 0; detecting the user number load rate. When it is higher than 85%, it means that the current operating pressure of the target base station is overloaded. The current user number load status is automatically recorded as 1, indicating that it is controllable. If it is lower than 30%, it means that the current operating pressure of the target base station is lightly loaded. Otherwise, it is marked as 0; detecting the traffic pressure. If it is higher than the 90% threshold, it means that the congestion risk of the service traffic of the target base station is high. The current traffic pressure load status is automatically recorded as 1, indicating that it is controllable. Otherwise, it is marked as 0; detecting the average channel quality index. If it is lower than 0 dB, it means that the signal quality of the target base station is very poor. The current average channel quality load status is automatically recorded as 1, indicating that it is controllable. Otherwise, it is marked as 0.

[0103] Optionally, in this embodiment, the component operating status of the target base station can also be evaluated based on the numerical value of the component operating parameter at the current sampling time point (the Nth sampling time point), including: detecting the temperature coefficient, if it is higher than 85%, it is determined that the target base station is overheated, and the current temperature coefficient status is automatically recorded as 1, indicating that it is adjustable, otherwise it is marked as 0; detecting the CPU utilization, if it is higher than 90%, it means that the target base station is currently in an overloaded operating state, and the current CPU utilization load status is automatically recorded as 1, indicating that it is adjustable, otherwise it is marked as 0; detecting the memory occupancy, if it is higher than 90%, it means that the current hard disk memory of the target base station is about to be exhausted, and the current memory occupancy load status is automatically recorded as 1, indicating that it is adjustable, otherwise it is marked as 0.

[0104] Optionally, in this embodiment, the software running status of the software system can also be evaluated based on the numerical values ​​of the software running parameters at the current sampling time point (the Nth sampling time point), including: detecting the log generation rate, if it is higher than 85%, it means that the log performance consumption of the current software system is large, and the current log generation rate load status is automatically recorded as 1, indicating that it is adjustable, otherwise it is marked as 0; detecting the file upload frequency, if it is higher than 85%, it means that the file upload performance consumption of the current software system is large, and the current file upload frequency load status is automatically recorded as 1, indicating that it is adjustable, otherwise it is marked as 0.

[0105] Optionally, in this embodiment, the business load weight, component operation weight and software operation weight are obtained, and the corresponding performance mode is switched according to the business load weight, component operation weight, software operation weight, business load status, component operation status and software operation status, including: obtaining the business load weight, component operation weight and software operation weight, and when it is detected that the business load weight is the largest, it is determined that the current period is a business priority period, and switching to a performance mode that prioritizes user experience; when it is detected that the component operation weight is the largest, it is determined that the current period is a device health priority period, and switching to a performance mode that prioritizes device health.

[0106] Optionally, in this embodiment, the method of switching to a performance mode that prioritizes user experience may include: detecting the service load status, component operation status, and software operation status; if the user load status is 1, automatically controlling user switching, counting the difference between the load rate of the adjacent base station cell and the current base station, and if the pre-allocated minimum value is met, switching the UE (User Equipment) to the adjacent lightly loaded cell. If the throughput load status is 1, automatically controlling dynamic bandwidth allocation to achieve load balancing, summing the data traffic requirements of all access user terminal devices to obtain the total data traffic of the entire system, and calculating the bandwidth required by each user based on the traffic used by each access user and the total bandwidth. For example, if the total bandwidth is 10M, and there are 3 users requesting traffic of 2Mbps, 3Mbps, and 1Mbps respectively, with a total traffic of 6Mbps, then the bandwidth allocated to user A is 10Mbps. (2 / 6) = 3.33Mbps, the bandwidth allocated to user B is 10 (3 / 6)=5Mbps, the bandwidth allocated to user C is 10 (1 / 6) = 1.67. This approach allows for flexible allocation of limited bandwidth resources based on each user's specific needs, improving overall network efficiency and service quality. If transmit power is low, increase it. When the average channel quality load reaches 1, an intelligent algorithm can be used to rationally allocate power resources to balance user quality of experience.

[0107] Optionally, in this embodiment, the method of switching to a performance mode that prioritizes device health may include: detecting the service load status, component operation status, and software operation status; if the CPU utilization load status and the temperature coefficient status are both 1, automatically starting a cooling device such as a fan; when the temperature coefficient monitoring status improves, automatically shutting down the cooling device, and reducing the CPU utilization load by shutting down low-priority tasks. If the performance task is already turned on at this time and the file upload frequency load status is 1, the default upload cycle of 15 minutes / time is dynamically extended to 1 hour / time. If the log generation frequency load and the memory occupancy load status are both 1, the system automatically deletes historical logs to free up memory. The above mechanism ensures the stable operation of the base station in the device health priority mode.

[0108] In an exemplary embodiment, after the current operating mode of the target base station is detected, it may include, but is not limited to: when it is detected that the operating mode is the performance mode, continuously collecting candidate operating parameters of the target base station to obtain a candidate operating parameter sequence; predicting a load drop event of the target base station based on the candidate operating parameter sequence, wherein the load drop event is an event that causes the load parameter of the target base station to be less than a preset load threshold; when the load drop event is predicted, immediately switching the target base station from the performance mode to the energy-saving mode.

[0109] Optionally, in this embodiment, the operating mode of the target base station can be detected by the OMC, but is not limited to, and the operating parameter collection strategy of the target base station is automatically configured after detecting that the operating mode is the performance mode, including: first, automatically setting the base station URL collection period (such as 5 minutes) to enable the performance data collection function; then, controlling the target base station to collect service load parameters such as the collection throughput, number of connected users, PRB utilization, component operating parameters such as CPU utilization, memory occupancy, component temperature, and software operating parameters such as log generation rate during its own operation according to the collection period, and generate a performance file, which is then automatically reported to the OMC according to a preset period; obtaining the performance file through the URL, and using the performance file reception time point as the collection time point, and binding it with the candidate operating parameters in the performance file to form multiple sets of parameter data with timestamps (equivalent to corresponding collection time points and candidate operating parameters); finally, the OMC constructs the operating parameters in the multiple sets of parameter data into a candidate operating parameter sequence arranged in order along the time axis according to the order of the collection time points.

[0110] Optionally, in this embodiment, the load drop event of the target base station may be predicted according to the candidate operating parameter sequence in the following manner, but is not limited to:

[0111] Step S501: extracting the operating parameters of the most recent Q acquisition time points from the operating parameter sequence to obtain Q reference candidate operating parameters;

[0112] Step S502, obtaining a current candidate load characteristic of the target base station based on Q reference candidate operating parameters;

[0113] Optionally, in this embodiment, the target load characteristics can be obtained in the following ways, but are not limited to: calculating the linear regression slope of the reference operating parameter based on the ratio of the difference between the current parameter value and the historical parameter value and the difference between the sampling time point corresponding to the current parameter value and the sampling time point corresponding to the historical parameter value; calculating the parameter volatility based on the maximum parameter value and the minimum parameter value of the parameter in the historical time period; calculating the similarity of the fluctuation amplitudes between different parameters and calculating the parameter correlation coefficient; generating candidate load characteristics based on the linear regression slope, parameter volatility and parameter correlation coefficient.

[0114] Step S503, detecting candidate matching parameters of the candidate load characteristics and the abnormal fallback load characteristics;

[0115] Optionally, in this embodiment, the abnormal load drop feature is a typical feature summarized based on historical load drop events (such as the common features of 10 load drop events in the previous three months), and is derived from a statistical analysis of operating parameters in historical events. For example, in past load drop events, when the following conditions are simultaneously met: the throughput drops by more than 200Mbps within 30 minutes and the current value is less than 500Mbps; the CPU utilization drops for five consecutive sampling points and is less than 60%; the number of users decreases to less than 1,500, and the probability of the base station entering a low-load state reaches 85%, it is classified as an abnormal load drop feature. In addition, the abnormal load drop feature can also include feature combinations corresponding to other types of load drop events, such as abnormal load drop feature combinations such as memory usage less than 40% and the log generation rate dropping to less than 100 records / minute.

[0116] Optionally, in this embodiment, the step of detecting candidate matching parameters of candidate load features and abnormal fallback load features may be, but is not limited to: comparing the candidate load features with the abnormal fallback load features to obtain a candidate matching score; and then calculating the candidate matching parameters based on the candidate matching score.

[0117] Optionally, in this embodiment, the step of comparing the target candidate load feature with the abnormal fallback load feature to obtain the candidate matching score is similar to the step of comparing the target load feature with the abnormal load feature to obtain the matching score, and will not be repeated here.

[0118] Step S504 : comparing the candidate matching parameter with the candidate parameter threshold to determine whether a load shedding event is predicted.

[0119] Optionally, in this embodiment, if it is determined that a load drop event is predicted, the target base station is immediately switched from performance mode to energy-saving mode. Switching from performance mode to energy-saving mode may include: when the traffic pressure load state is 0, it indicates that some carriers are shut down during low traffic periods, and only necessary resources are reserved for active users. When the temperature coefficient load state is 0, it indicates that the current device is in a low-temperature state, and the speed of the internal fan is intelligently and dynamically adjusted. When the user number load state is 0, when no user activity is detected, it automatically enters deep sleep mode and suspends non-critical functions.

[0120] Optionally, in this embodiment, after switching from performance mode to energy-saving mode, the OMC continuously monitors the operating parameters of the target base station. If a load surge event is predicted, it switches back to performance mode to achieve dynamic adaptation of the base station operating mode and load conditions, effectively reducing energy consumption and improving resource utilization efficiency while ensuring stable business operation.

[0121] In order to better understand the process of the control method of the above-mentioned base station, the process of the control method of the above-mentioned base station is described below in combination with an optional embodiment, but it is not used to limit the technical solution of the embodiment of the present application.

[0122] In this embodiment, a base station control method is provided. Figure 4 is a flow chart of a base station control method based on load evaluation according to an embodiment of the present application, such as Figure 4 As shown, it mainly includes the following steps:

[0123] Step S601: Performance task and parameter collection: The network management system initiates a performance task to the target base station. After receiving the performance task request, the target base station sets a collection cycle and collects operating parameters.

[0124] Step S602: Data processing and load evaluation: The target base station reports performance data, and the network management system aggregates the operating parameters, and then performs service load evaluation, component operation load evaluation, and software operation load evaluation to autonomously evaluate the base station operation status.

[0125] Step S603: Credibility evaluation: The credibility of the current load state is evaluated by measuring the degree of deviation between the actual value and the set value. If the evaluation result is negative, multiple cycle data are reselected; if positive, proceed to the next step.

[0126] Step S604: Priority evaluation: perform multi-objective priority adaptation, including evaluation of energy saving priority, user experience priority, device health priority, etc., to complete the priority evaluation.

[0127] Step S605: Policy control and implementation: perform policy control adaptation, then implement the policy and dynamically adjust the base station configuration.

[0128] This embodiment achieves intelligent switching of base station operating modes through intelligent analysis and dynamic regulation of target base station operating parameter sequences. Specifically, base stations proactively report service load parameters, component operating parameters, and software operating parameters at preset intervals. The OMC automatically collects and aggregates these operating parameters, analyzing the current load status and changing trends by calculating the load growth rate, extracting target load characteristics, and detecting matching parameters with abnormal load characteristics. When a load drop event is predicted (i.e., load parameters fall below a preset load threshold), the base station automatically switches from performance mode to energy-saving mode, reducing resource consumption by shutting down some carriers, adjusting heat dissipation devices, and suspending non-critical functions. If a load surge event is predicted, the base station switches back to performance mode to ensure service stability. This intelligent dynamic regulation mechanism not only effectively improves base station energy efficiency and significantly reduces operation and maintenance costs, but also enhances the reliability of remote base station control through automated analysis and decision-making, optimizing the convenience and efficiency of maintenance operations.

[0129] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the existing technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0130] Figure 5 is a structural block diagram of a base station control device according to an embodiment of the present application; Figure 5 Shown, including:

[0131] A detection module 502 is configured to detect the current operation mode of the target base station;

[0132] a first acquisition module 504 configured to, when detecting that the operating mode is the energy-saving mode, continuously acquire operating parameters of the target base station to obtain an operating parameter sequence, wherein the operating parameters are used to indicate a current operating status of the target base station, and the target base station in the energy-saving mode reduces base station energy consumption by reducing base station performance;

[0133] a first prediction module 506, configured to predict a load surge event of the target base station based on the operating parameter sequence, wherein the load surge event is an event that causes a load growth rate of the target base station to be greater than a preset rate, and the load growth rate indicates a change in a load parameter of the target base station per unit time;

[0134] The first switching module 508 is used to immediately switch the target base station from the energy-saving mode to the performance mode when the load surge event is predicted, wherein the target base station in the performance mode reduces the response time of the communication service by improving the base station performance.

[0135] Through the closed-loop control mechanism of operation mode detection, operation parameter sequence acquisition, load surge event prediction and dynamic mode switching in the above embodiment, early perception and active response to sudden changes in base station load are achieved in energy-saving mode, ensuring that the base station can actively switch to performance mode in abnormal scenarios, effectively improving the base station's response speed to sudden loads and communication quality assurance capabilities, and avoiding the network congestion and poor user experience caused by insufficient flexibility of the control mechanism and the inability to adjust the energy-saving mode of the base station in time in related technologies. The above technical solution solves the problems of low control efficiency of base stations in abnormal scenarios in related technologies, and achieves the technical effect of improving the control efficiency of base stations in abnormal scenarios. In an exemplary embodiment, the first acquisition module includes:

[0136] A collection unit, configured to collect the operating parameters of the target base station multiple times according to a preset detection period, to obtain multiple sets of corresponding collection time points and the operating parameters;

[0137] The sorting unit is used to sort the plurality of operating parameters in the plurality of groups of corresponding collection time points and operating parameters according to the time sequence of the collection time points to obtain the operating parameter sequence.

[0138] In an exemplary embodiment, the collection unit is further used to: collect the service load parameters, component operation parameters and software operation parameters of the target base station multiple times according to a preset detection cycle, and obtain multiple groups of corresponding collection time points, the service load parameters, the component operation parameters and the software operation parameters, wherein the service load parameters are used to indicate the load status of the target base station in processing the service request at the corresponding collection time point, the service request is used to request processing of the communication service, the component operation parameters are used to indicate the component resource usage of the resource component in the target base station at the corresponding collection time point, the resource component is used to provide resources for the target base station to process the service request, and the software operation parameters are used to indicate the operation status of the software system that processes the communication service at the corresponding collection time point, and the operation parameters include the service load parameters, the component operation parameters and the software operation parameters.

[0139] In an exemplary embodiment, the sorting unit is further used to: sort multiple business load parameters, multiple component operating parameters and multiple software operating parameters in multiple groups of corresponding acquisition time points, the business load parameters, the component operating parameters and the software operating parameters according to the chronological order of the acquisition time points, to obtain a business load parameter sequence, a component operating parameter sequence and a software operating parameter sequence, wherein the operating parameter sequence includes the business load parameter sequence, the component operating parameter sequence and the software operating parameter sequence.

[0140] In an exemplary embodiment, the acquisition unit is further used to: collect the resource block utilization, transmission rate, number of users and service traffic of the target base station according to the preset detection period, and generate the service load parameter based on the resource block utilization, the transmission rate, the number of users and the service traffic, wherein the resource block utilization is used to indicate the utilization of the time-frequency resources of the resource block by the target base station in the process of processing the service request, the transmission rate is used to indicate the amount of data transmitted per unit time by the target base station in the process of processing the service request, the number of users is the number of user terminals that send the service request to the target base station, and the service traffic is used to indicate the total amount of data transmitted by the target base station in the process of processing the service request; collect the component temperature information and processor usage of the target base station according to the preset detection period and storage usage rate, wherein the component temperature information is used to indicate the temperature of one or more of the resource components, and the component operating parameters are generated based on the component temperature information, the processor usage rate and the storage usage rate, the processor usage rate is used to indicate the target base station's usage of the processor's computing resources, and the storage usage rate is used to indicate the target base station's usage of the storage resources of the storage device, and the resource components include the processor and the storage device; according to the preset detection period, the log generation information and file upload information of the target base station are collected, and the software operating parameters are generated based on the log generation information and the file upload information, wherein the log generation information is used to indicate the number of logs generated by the software system per unit time, and the file upload information is used to indicate the number of files uploaded by the software system per unit time.

[0141] In an exemplary embodiment, the operating parameter sequence includes N operating parameters collected at N collection time points, where N is an integer greater than 1, and the time interval between the i-1th sampling time point and the i-th sampling time point satisfies a preset detection period, where i is an integer greater than 1. The first prediction module includes:

[0142] an extraction unit, configured to extract, from the N operating parameters included in the operating parameter sequence, M reference operating parameters collected from the N-M+1th sampling time point to the Nth sampling time point, and obtain a current target load characteristic of the target base station based on the M reference operating parameters, where M is an integer greater than 1 and less than or equal to N, and the target load characteristic is used to indicate a change in the reference operating parameters of the target base station from the N-M+1th sampling time point to the Nth sampling time point;

[0143] a detection unit, configured to detect a target matching parameter between the target load feature and the abnormal load feature, wherein the abnormal load feature is a load feature of the load surge event, and the target matching parameter is configured to indicate a degree of matching between the target load feature and the abnormal load feature; a larger target matching parameter indicates a higher degree of matching between the target load feature and the abnormal load feature;

[0144] The determination unit is configured to determine that the load surge event is predicted when the target matching parameter is greater than a preset parameter threshold.

[0145] In an exemplary embodiment, the extraction unit is further used to: extract the largest first operating parameter and the smallest second operating parameter from the M reference operating parameters; perform a subtraction operation on the first operating parameter and the second operating parameter to obtain a first difference, and perform an addition operation on the first operating parameter and the second operating parameter to obtain a first sum; perform a division operation on the first difference and the first sum to obtain the target load characteristic.

[0146] In an exemplary embodiment, the apparatus further comprises:

[0147] a second collection module configured to, after detecting the current operating mode of the target base station, continuously collect candidate operating parameters of the target base station when detecting that the operating mode is the performance mode, to obtain a candidate operating parameter sequence;

[0148] a second prediction module, configured to predict a load drop event of the target base station based on the candidate operating parameter sequence, wherein the load drop event is an event that causes the load parameter of the target base station to be less than a preset load threshold;

[0149] The second switching module is configured to immediately switch the target base station from the performance mode to the energy-saving mode when the load drop event is predicted.

[0150] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0151] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0152] S1, detecting the current operating mode of the target base station;

[0153] S2, when detecting that the operating mode is the energy-saving mode, continuously collecting operating parameters of the target base station to obtain an operating parameter sequence, wherein the operating parameters are used to indicate a current operating status of the target base station, and the target base station in the energy-saving mode reduces base station energy consumption by reducing base station performance;

[0154] S3, predicting a load surge event of the target base station based on the operating parameter sequence, wherein the load surge event is an event that causes a load growth rate of the target base station to be greater than a preset rate, and the load growth rate is used to indicate an amount of change in a load parameter of the target base station per unit time;

[0155] S4. When the load surge event is predicted, the target base station is immediately switched from the energy-saving mode to the performance mode, wherein the target base station in the performance mode reduces the response time of the communication service by improving the base station performance.

[0156] An embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0157] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0158] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0159] S1, detecting the current operating mode of the target base station;

[0160] S2, when detecting that the operating mode is the energy-saving mode, continuously collecting operating parameters of the target base station to obtain an operating parameter sequence, wherein the operating parameters are used to indicate a current operating status of the target base station, and the target base station in the energy-saving mode reduces base station energy consumption by reducing base station performance;

[0161] S3, predicting a load surge event of the target base station based on the operating parameter sequence, wherein the load surge event is an event that causes a load growth rate of the target base station to be greater than a preset rate, and the load growth rate is used to indicate an amount of change in a load parameter of the target base station per unit time;

[0162] S4. When the load surge event is predicted, the target base station is immediately switched from the energy-saving mode to the performance mode, wherein the target base station in the performance mode reduces the response time of the communication service by improving the base station performance.

[0163] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0164] Optionally, specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0165] Obviously, those skilled in the art should understand that the modules or steps of the present application described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices. Alternatively, they can be implemented using program code executable by the computing device, so that they can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described can be performed in a different order than herein, or they can be made into separate integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0166] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A base station control method, characterized in that: include: Detecting the current operating mode of the target base station; When detecting that the operating mode is the energy-saving mode, continuously collecting operating parameters of the target base station to obtain an operating parameter sequence, wherein the operating parameters are used to indicate a current operating status of the target base station, and the target base station in the energy-saving mode reduces base station energy consumption by reducing base station performance; Predicting a load surge event of the target base station based on the operating parameter sequence, wherein the load surge event is an event that causes a load growth rate of the target base station to be greater than a preset rate, and the load growth rate is used to indicate an amount of change in a load parameter of the target base station per unit time; When the load surge event is predicted, the target base station is immediately switched from the energy-saving mode to the performance mode, wherein the target base station in the performance mode reduces the response time of the communication service by improving the base station performance.

2. The method according to claim 1, characterized in that The continuously collecting the operating parameters of the target base station to obtain an operating parameter sequence includes: The operating parameters of the target base station are collected multiple times according to a preset detection period to obtain multiple sets of corresponding collection time points and the operating parameters; The plurality of operating parameters in the plurality of groups of corresponding acquisition time points and operating parameters are sorted according to the time sequence of the acquisition time points to obtain the operating parameter sequence.

3. The method according to claim 2, characterized in that The collecting the operating parameters of the target base station multiple times according to a preset detection period includes: The service load parameters, component operation parameters, and software operation parameters of the target base station are collected multiple times according to a preset detection cycle to obtain multiple groups of corresponding collection time points, the service load parameters, the component operation parameters, and the software operation parameters, wherein the service load parameters are used to indicate the load condition of the target base station in processing a service request at the corresponding collection time point, the service request is used to request processing of the communication service, the component operation parameters are used to indicate the component resource usage of the resource component in the target base station at the corresponding collection time point, the resource component is used to provide resources for the target base station to process the service request, and the software operation parameters are used to indicate the operation condition of the software system that processes the communication service at the corresponding collection time point, and the operation parameters include the service load parameters, the component operation parameters, and the software operation parameters; The step of sorting the plurality of operating parameters in the plurality of groups of corresponding collection time points and operating parameters according to the chronological order of the collection time points includes: Multiple business load parameters, multiple component operation parameters, and multiple software operation parameters in multiple groups of corresponding acquisition time points, the business load parameters, the component operation parameters, and the software operation parameters are sorted in chronological order of the acquisition time points to obtain a business load parameter sequence, a component operation parameter sequence, and a software operation parameter sequence, wherein the operation parameter sequence includes the business load parameter sequence, the component operation parameter sequence, and the software operation parameter sequence.

4. The method according to claim 3, characterized in that The collecting of the service load parameters, component operation parameters, and software operation parameters of the target base station multiple times according to a preset detection period includes: collecting the resource block utilization, transmission rate, number of users, and service flow of the target base station according to the preset detection period, and generating the service load parameter based on the resource block utilization, the transmission rate, the number of users, and the service flow, wherein the resource block utilization is used to indicate the utilization of the time-frequency resources of the resource block by the target base station in the process of processing the service request, the transmission rate is used to indicate the amount of data transmitted per unit time by the target base station in the process of processing the service request, the number of users is the number of user terminals that send the service request to the target base station, and the service flow is used to indicate the total amount of data transmitted by the target base station in the process of processing the service request; Collecting component temperature information, processor usage, and storage usage of the target base station according to the preset detection period, wherein the component temperature information is used to indicate the temperature of one or more of the resource components, and generating the component operating parameters based on the component temperature information, the processor usage, and the storage usage, the processor usage is used to indicate the target base station's usage of processor computing resources, and the storage usage is used to indicate the target base station's usage of storage resources of a storage device, and the resource components include the processor and the storage device; The log generation information and file upload information of the target base station are collected according to the preset detection period, and the software operation parameters are generated based on the log generation information and the file upload information, wherein the log generation information is used to indicate the number of logs generated by the software system per unit time, and the file upload information is used to indicate the number of files uploaded by the software system per unit time.

5. The method according to claim 1, wherein The operating parameter sequence includes N operating parameters collected at N collection time points, where N is an integer greater than 1, and a time interval between an i-1th sampling time point and an i-th sampling time point satisfies a preset detection period, where i is an integer greater than 1. The predicting the load surge event of the target base station based on the operating parameter sequence includes: Extracting M reference operating parameters collected from the N-M+1th sampling time point to the Nth sampling time point from the N operating parameters included in the operating parameter sequence, and obtaining a current target load characteristic of the target base station based on the M reference operating parameters, where M is an integer greater than 1 and less than or equal to N, and the target load characteristic is used to indicate a change in the reference operating parameters of the target base station from the N-M+1th sampling time point to the Nth sampling time point; detecting a target matching parameter of the target load feature and the abnormal load feature, wherein the abnormal load feature is a load feature of the load surge event, and the target matching parameter is used to indicate a degree of matching between the target load feature and the abnormal load feature; a larger target matching parameter indicates a higher degree of matching between the target load feature and the abnormal load feature; When the target matching parameter is greater than a preset parameter threshold, it is determined that the load surge event is predicted.

6. The method according to claim 5, characterized in that Obtaining the current target load characteristic of the target base station according to the M reference operating parameters includes: Extracting the largest first operating parameter and the smallest second operating parameter from the M reference operating parameters; performing a subtraction operation on the first operating parameter and the second operating parameter to obtain a first difference, and performing an addition operation on the first operating parameter and the second operating parameter to obtain a first sum; A division operation is performed on the first difference value and the first sum value to obtain the target load characteristic.

7. The method according to claim 1, characterized in that After detecting the current operating mode of the target base station, the method further includes: When it is detected that the operating mode is the performance mode, continuously collecting candidate operating parameters of the target base station to obtain a candidate operating parameter sequence; Predicting a load drop event of the target base station according to the candidate operating parameter sequence, wherein the load drop event is an event that causes the load parameter of the target base station to be less than a preset load threshold; When the load shedding event is predicted, the target base station is immediately switched from the performance mode to the energy-saving mode.

8. A control device for a base station, characterized in that: include: A detection module, used to detect the current operating mode of the target base station; a first acquisition module, configured to, when detecting that the operating mode is the energy-saving mode, continuously acquire operating parameters of the target base station to obtain an operating parameter sequence, wherein the operating parameters are used to indicate a current operating status of the target base station, and the target base station in the energy-saving mode reduces base station energy consumption by reducing base station performance; a first prediction module, configured to predict a load surge event of the target base station based on the operating parameter sequence, wherein the load surge event is an event that causes a load growth rate of the target base station to be greater than a preset rate, and the load growth rate is used to indicate an amount of change in a load parameter of the target base station per unit time; The first switching module is used to immediately switch the target base station from the energy-saving mode to the performance mode when the load surge event is predicted, wherein the target base station in the performance mode reduces the response time of the communication service by improving the base station performance.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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