Base station analysis method and device, computer equipment and readable storage medium

Abstract

The invention relates to a base station analysis method and device, computer equipment, a computer readable storage medium and a computer program product, and can be applied to the technical field of communication. The method comprises the following steps: constructing an energy-saving analysis model of the base station according to energy-saving characteristic data of the base station; determining an energy-saving performance analysis result, an energy-saving potential analysis result and an energy-saving benefit analysis result of the base station through the energy-saving analysis model; and generating energy-saving updating information of the base station according to the energy-saving performance analysis result, the energy-saving potential analysis result and the energy-saving benefit analysis result. By adopting the method, the energy-saving analysis efficiency of the base station can be improved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a base station analysis method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] With the development of communication technology, base stations have been widely deployed as important infrastructure of communication networks. Base stations consume a lot of power during operation, so base station energy-saving technology has gradually become a research hotspot in the field of communications. How to efficiently perform base station energy-saving analysis has become an important research direction.

[0003] Traditional technology usually performs base station energy-saving analysis by manually counting and analyzing the base station's operating data; however, performing base station energy-saving analysis in this way requires a lot of manual processing time, resulting in low efficiency of base station energy-saving analysis. Summary of the invention

[0004] Based on this, it is necessary to provide a base station analysis method, device, computer equipment, computer-readable storage medium and computer program product that can improve the efficiency of base station energy-saving analysis in response to the above technical problems.

[0005] In a first aspect, the present application provides a base station analysis method. The method comprises:

[0006] Constructing an energy-saving analysis model for the base station according to the energy-saving characteristic data of the base station;

[0007] Determining the energy-saving performance analysis result, energy-saving potential analysis result and energy-saving benefit analysis result of the base station through the energy-saving analysis model;

[0008] Energy-saving update information of the base station is generated according to the energy-saving performance analysis result, the energy-saving potential analysis result and the energy-saving benefit analysis result.

[0009] In one embodiment, determining the energy-saving performance analysis result, energy-saving potential analysis result and energy-saving benefit analysis result of the base station through the energy-saving analysis model includes:

[0010] Analyzing the energy-saving performance of the base station through the energy-saving analysis model to obtain an energy-saving performance analysis result of the base station;

[0011] Analyzing the energy-saving potential of the base station through the energy-saving analysis model to obtain an energy-saving potential analysis result of the base station;

[0012] The energy-saving benefit of the base station is analyzed through the energy-saving analysis model to obtain the energy-saving benefit analysis result of the base station.

[0013] In one embodiment, analyzing the energy-saving benefit of the base station by using the energy-saving analysis model to obtain the energy-saving benefit analysis result of the base station includes:

[0014] Acquire base station data of the base station through the energy-saving analysis model; the base station data at least includes energy-saving benefit data, operation cost data and equipment co-coverage impact data;

[0015] The energy-saving benefit of the base station is analyzed according to the base station data to obtain an energy-saving benefit analysis ecological map of the base station as the energy-saving benefit analysis result.

[0016] In one of the embodiments, analyzing the energy-saving potential of the base station by using the energy-saving analysis model to obtain the energy-saving potential analysis result of the base station includes:

[0017] Acquiring historical energy-saving data of the base station through the energy-saving analysis model;

[0018] According to the historical energy-saving data, an energy-saving potential analysis is performed on the geographical location information, user behavior trend information, user quantity information and traffic volume information of the base station to obtain a basic result of the energy-saving potential analysis of the base station;

[0019] The energy-saving potential analysis basic result is updated according to the hardware equipment information and implementation scenario information of the base station to obtain the energy-saving potential analysis result.

[0020] In one embodiment, analyzing the energy-saving performance of the base station by using the energy-saving analysis model to obtain the energy-saving performance analysis result of the base station includes:

[0021] Obtaining energy saving amount change data of the base station within a preset time period through the energy saving analysis model;

[0022] Determining the energy saving fluctuation type of the base station according to the energy saving amount change data;

[0023] The energy-saving performance of the base station is analyzed according to the energy-saving fluctuation type to obtain the energy-saving performance analysis result.

[0024] In one of the embodiments, constructing the energy-saving analysis model of the base station according to the energy-saving feature data of the base station includes:

[0025] Acquire target data of the base station; the target data at least includes energy-saving technical indicator data, revenue data, user behavior data and energy-saving scenario data;

[0026] An energy-saving footprint analysis model of the base station is constructed according to the target data as the energy-saving analysis model.

[0027] In one of the embodiments, after generating the energy-saving update information of the base station according to the energy-saving performance analysis result, the energy-saving potential analysis result and the energy-saving benefit analysis result, the method further includes:

[0028] Determining an energy-saving update type of the base station according to the energy-saving update information;

[0029] Perform energy-saving update processing on the base station according to the energy-saving update type.

[0030] In a second aspect, the present application also provides a base station analysis device. The device includes:

[0031] A model building module, used to build an energy-saving analysis model of the base station according to the energy-saving feature data of the base station;

[0032] A result determination module, used to determine the energy-saving performance analysis result, energy-saving potential analysis result and energy-saving benefit analysis result of the base station through the energy-saving analysis model;

[0033] The information generation module is used to generate energy-saving update information of the base station according to the energy-saving performance analysis result, the energy-saving potential analysis result and the energy-saving benefit analysis result.

[0034] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0035] Constructing an energy-saving analysis model for the base station according to the energy-saving characteristic data of the base station;

[0036] Determining the energy-saving performance analysis result, energy-saving potential analysis result and energy-saving benefit analysis result of the base station through the energy-saving analysis model;

[0037] Energy-saving update information of the base station is generated according to the energy-saving performance analysis result, the energy-saving potential analysis result and the energy-saving benefit analysis result.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0039] Constructing an energy-saving analysis model for the base station according to the energy-saving characteristic data of the base station;

[0040] Determining the energy-saving performance analysis result, energy-saving potential analysis result and energy-saving benefit analysis result of the base station through the energy-saving analysis model;

[0041] Energy-saving update information of the base station is generated according to the energy-saving performance analysis result, the energy-saving potential analysis result and the energy-saving benefit analysis result.

[0042] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0043] Constructing an energy-saving analysis model for the base station according to the energy-saving characteristic data of the base station;

[0044] Determining the energy-saving performance analysis result, energy-saving potential analysis result and energy-saving benefit analysis result of the base station through the energy-saving analysis model;

[0045] Energy-saving update information of the base station is generated according to the energy-saving performance analysis result, the energy-saving potential analysis result and the energy-saving benefit analysis result.

[0046] The above-mentioned base station analysis method, device, computer equipment, computer-readable storage medium and computer program product construct an energy-saving analysis model of the base station according to the energy-saving characteristic data of the base station; determine the energy-saving performance analysis results, energy-saving potential analysis results and energy-saving benefit analysis results of the base station through the energy-saving analysis model; generate energy-saving update information of the base station according to the energy-saving performance analysis results, the energy-saving potential analysis results and the energy-saving benefit analysis results. This solution is conducive to systematic modeling and analysis of the energy-saving status of the base station by constructing an energy-saving analysis model based on the energy-saving characteristic data of the base station; the model is used to simultaneously determine the analysis results of the three dimensions of energy-saving performance analysis results, energy-saving potential analysis results and energy-saving benefit analysis results, which is conducive to a comprehensive assessment of the energy-saving status of the base station; and the energy-saving update information is generated based on the analysis results of these three dimensions, which is conducive to the formulation of an accurate optimization plan; this multi-dimensional analysis and optimization technical solution is conducive to improving the efficiency and accuracy of base station energy-saving analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 is a flow chart of a base station analysis method in one embodiment;

[0049] Figure 2 A schematic diagram of a flow chart of a base station analysis method in another embodiment;

[0050] Figure 3 is another flowchart of a base station analysis method in another embodiment;

[0051] Figure 4 It is a schematic diagram of overall situation analysis of a base station in an energy-saving cycle in an embodiment;

[0052] Figure 5 A schematic diagram of a base station energy-saving footprint analysis system in one embodiment;

[0053] Figure 6 is a structural block diagram of a base station analysis device in one embodiment;

[0054] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0056] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0057] In an exemplary embodiment, Figure 1 As shown, a base station analysis method is provided. This embodiment uses the method applied to a base station analysis system (such as a terminal) as an example for illustration; it is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be but is not limited to various personal computers, laptops, smart phones, tablet computers, etc.; the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. In this embodiment, the method includes the following steps:

[0058] Step S101: construct an energy-saving analysis model for the base station according to the energy-saving characteristic data of the base station.

[0059] Step S102: determining the energy-saving performance analysis result, energy-saving potential analysis result and energy-saving benefit analysis result of the base station through the energy-saving analysis model.

[0060] Step S103: generating energy-saving update information of the base station according to the energy-saving performance analysis result, the energy-saving potential analysis result and the energy-saving benefit analysis result.

[0061] The base station may be a wireless access device in a mobile communication network, for example, the base station may be a communication base station that has been put into use and needs to be optimized for energy saving.

[0062] Among them, the energy-saving characteristic data can be various data indicators that reflect the energy-saving status and performance of the base station. For example, the energy-saving characteristic data can be energy-saving technical indicators, revenue data, user behavior data, energy-saving scenario data, etc.

[0063] Among them, the energy-saving analysis model can be a data analysis model used to evaluate the energy-saving performance of the base station. For example, the energy-saving analysis model can be an energy-saving footprint model that evaluates various technical indicators and business scenarios of the base station during the cycle from "non-energy-saving state-energy-saving turned on-energy-saving performance change-energy-saving strategy optimization".

[0064] Among them, the energy-saving performance analysis result can be an evaluation result of the energy-saving effect of the base station. For example, the energy-saving performance analysis result can be a quantitative evaluation result including energy-saving strategies, instructions, scene labels, user characteristics, environmental factors, etc.

[0065] Among them, the energy-saving potential analysis result can be a prediction result of the future energy-saving space of the base station. For example, the energy-saving potential analysis result can be an energy-saving potential score obtained by predicting the development of indicators such as the number of users and call volume based on the geographical location of the base station and user behavior trends.

[0066] Among them, the energy-saving benefit analysis result can be an evaluation result of the energy-saving benefits of the base station. For example, the energy-saving benefit analysis result can be an evaluation result of the profit and loss status obtained by integrating the energy-saving benefits, operating costs, and equipment coverage impact of the base station.

[0067] Among them, the energy-saving update information can be a specific solution suggestion for base station optimization. For example, the energy-saving update information can be an exclusive optimization solution such as a whitelist, policy switching, scene merging, and network exit.

[0068] Optionally, the base station analysis system first collects energy-saving characteristic data such as energy-saving technical indicators, revenue data, user behavior data and energy-saving scenario data of the base station, and builds an energy-saving footprint analysis model of the base station based on these energy-saving characteristic data; then, the base station analysis system analyzes the entire cycle of the base station from the non-energy-saving state to the energy-saving start and then to the energy-saving performance change through the energy-saving footprint analysis model, and determines the energy-saving performance analysis results, energy-saving potential analysis results and energy-saving benefit analysis results of the base station, wherein the energy-saving performance analysis results include quantitative evaluation data such as energy-saving strategies, instructions, scenario labels, user characteristics and environmental factors, the energy-saving potential analysis results predict the development of indicators such as the number of users and traffic volume based on the geographical location of the base station and the user behavior trend, and the energy-saving benefit analysis results integrate the energy-saving income, operating costs and equipment co-coverage impact of the base station data; finally, the base station analysis system generates exclusive energy-saving update information for the base station based on the above three types of analysis results (energy-saving performance analysis results, energy-saving potential analysis results and energy-saving benefit analysis results).

[0069] For example, when the base station analysis system detects that the energy-saving performance of the base station fluctuates, the base station analysis system first collects the energy-saving characteristic data of the base station, and builds an energy-saving footprint analysis model of the base station based on the energy-saving characteristic data; then, the base station analysis system calculates the energy-saving performance analysis results of the base station through the energy-saving footprint analysis model, and determines whether the base station belongs to the category of overspeed growth (the amount of energy saving increases by more than a certain percentage), stable growth, reasonable fluctuation or abnormal decline, and at the same time evaluates the energy-saving potential analysis results of the base station, including calculating indicators such as the green degree of the base station (traffic volume / base station energy consumption value), and evaluates the energy-saving benefit analysis results, including analyzing the relationship between the change in the amount of energy saving of the base station and the change in energy consumption cost; finally, the base station analysis system generates corresponding energy-saving update information for the base station based on the energy-saving performance analysis results, energy-saving potential analysis results and energy-saving benefit analysis results, such as adding the base station to the whitelist, switching policies, merging scenarios or suggesting network withdrawal and other optimization solutions.

[0070] In the above-mentioned base station analysis method, an energy-saving analysis model of the base station is constructed according to the energy-saving characteristic data of the base station; the energy-saving performance analysis results, energy-saving potential analysis results and energy-saving benefit analysis results of the base station are determined through the energy-saving analysis model; and the energy-saving update information of the base station is generated according to the energy-saving performance analysis results, energy-saving potential analysis results and energy-saving benefit analysis results. This solution constructs an energy-saving analysis model based on the energy-saving characteristic data of the base station, which is conducive to systematic modeling and analysis of the energy-saving status of the base station; the analysis results of the three dimensions of energy-saving performance analysis results, energy-saving potential analysis results and energy-saving benefit analysis results are determined by the model at the same time, which is conducive to a comprehensive assessment of the energy-saving status of the base station; and energy-saving update information is generated based on the analysis results of these three dimensions, which is conducive to the formulation of accurate optimization plans; this multi-dimensional analysis and optimization technical solution is conducive to improving the efficiency and accuracy of base station energy-saving analysis.

[0071] In an exemplary embodiment, the energy-saving performance analysis results, energy-saving potential analysis results and energy-saving benefit analysis results of the base station are determined through an energy-saving analysis model, which specifically include the following contents: through the energy-saving analysis model, the energy-saving performance of the base station is analyzed to obtain the energy-saving performance analysis results of the base station; through the energy-saving analysis model, the energy-saving potential of the base station is analyzed to obtain the energy-saving potential analysis results of the base station; through the energy-saving analysis model, the energy-saving benefits of the base station are analyzed to obtain the energy-saving benefit analysis results of the base station.

[0072] Among them, the energy-saving performance can be the various performance indicators and operating states exhibited by the base station during the energy-saving process. For example, the energy-saving performance can be the performance of various technical indicators and business scenarios of the base station during the period of "non-energy-saving state-energy-saving turned on-energy-saving performance change-energy-saving strategy optimization", including changes in energy-saving duration, T0 value (equipment energy consumption value when the cell is not in energy-saving state), call volume, number of users and other indicators.

[0073] Among them, the energy-saving potential can be the energy-saving space and optimization possibility that the base station can achieve in the future. For example, the energy-saving potential can be a predicted evaluation result of the future energy-saving performance of the base station based on factors such as the geographical location of the base station, user behavior trends, hardware equipment, and server resources.

[0074] Among them, the energy-saving benefit can be the comprehensive benefits brought about by the implementation of energy-saving measures by the base station. For example, the energy-saving benefit can be the evaluation result of the relationship between the change in energy saving caused by the base station under the action of energy-saving algorithms, etc. and the cost change caused by the simultaneous increase in base station energy consumption, including energy-saving benefits, operating costs, equipment coverage impact and other factors.

[0075] Optionally, the base station analysis system first analyzes the energy-saving performance of the base station through an energy-saving analysis model, specifically including analyzing the changes in indicators such as the energy-saving duration of the base station, T0 value (equipment energy consumption value when the cell is not in energy-saving state), traffic volume, and number of users, to obtain the energy-saving performance analysis results of the base station; then the energy-saving potential of the base station is analyzed through the energy-saving analysis model, specifically including predicting the development of indicators such as the number of users and traffic volume based on the geographical location of the base station and user behavior trends, calculating the green degree of the base station (traffic volume / base station energy consumption value), and obtaining the energy-saving potential analysis results of the base station; finally, the energy-saving benefits of the base station are analyzed through the energy-saving analysis model, specifically including integrating data such as the energy-saving income, operating costs, and equipment co-coverage impact of the base station, evaluating the relationship between the change in the energy-saving amount of the base station under the energy-saving algorithm and the cost change caused by the simultaneous increase in base station energy consumption, and obtaining the energy-saving benefit analysis results of the base station.

[0076] The technical solution provided in this embodiment is conducive to comprehensively evaluating the energy-saving status of the base station by systematically analyzing the three dimensions of energy-saving performance, energy-saving potential and energy-saving benefit of the base station, thereby facilitating improving the accuracy of the energy-saving analysis of the base station.

[0077] In an exemplary embodiment, the energy-saving benefits of the base station are analyzed through an energy-saving analysis model to obtain an energy-saving benefit analysis result of the base station, which specifically includes the following contents: the base station data of the base station is obtained through the energy-saving analysis model; the base station data at least includes energy-saving income data, operating cost data and equipment co-coverage impact data; based on the base station data, the energy-saving benefits of the base station are analyzed to obtain an energy-saving benefit analysis ecological map of the base station as the energy-saving benefit analysis result.

[0078] Among them, the base station data can be various data indicators that reflect the operating status and performance of the base station. For example, the base station data can be base station operation-related data including energy-saving benefit data, operating cost data, equipment co-coverage impact data, etc.

[0079] The energy-saving benefit data may be benefit data reflecting the base station's implementation of energy-saving measures. For example, the energy-saving benefit data may be data on energy-saving amount changes caused by the base station under the action of an energy-saving algorithm or the like.

[0080] Among them, the operating cost data can be various types of cost data generated during the operation of the base station. For example, the operating cost data can be cost change data caused by the synchronous increase in base station energy consumption, hardware equipment cost data, server resource cost data, etc.

[0081] Among them, the equipment co-coverage impact data can be data reflecting the degree of mutual influence when multiple base stations jointly cover a certain geographical area. For example, the equipment co-coverage impact data can be the data changes caused by users switching from a base station where the energy saving takes effect earlier to a base station where the energy saving takes effect later when the energy saving of two base stations with co-coverage does not take effect at the same time.

[0082] Among them, the energy-saving benefit analysis ecological map can be a visual chart that displays the results of the energy-saving benefit analysis of the base station. For example, the energy-saving benefit analysis ecological map can be a multi-dimensional analysis view that includes data such as the energy-saving benefits, operating costs, and equipment coverage impact of the base station, which is used to evaluate the profit and loss status of the base station.

[0083] Optionally, the base station analysis system first obtains the base station data of the base station through an energy-saving analysis model, including taking the energy-saving amount change data of the base station as energy-saving benefit data, the cost change data caused by the synchronous increase in base station energy consumption, the hardware equipment cost data and the server resource cost data as operating cost data, and when the energy saving of two base stations with common coverage does not take effect at the same time, the data change caused by the user switching from the base station where the energy saving takes effect earlier to the base station where the energy saving takes effect later is used as equipment co-coverage impact data; then, the base station analysis system constructs a base station area view based on these base station data, integrates the base station's energy-saving benefit, operating cost, equipment co-coverage impact and other data, and forms an energy-saving benefit analysis ecological map with one base station and one ecological map as the energy-saving benefit analysis result of the base station.

[0084] For example, when the base station analysis system performs energy-saving benefit analysis on a base station, the base station analysis system first obtains the base station data of the base station through the energy-saving analysis model, including the energy-saving improvement value after the base station adopts the new energy-saving strategy as the energy-saving benefit data, the hardware equipment costs and server resource costs generated by the deployment of the new energy-saving strategy of the base station as the operating cost data, and the energy-saving efficiency change data caused by user migration between the base station and the surrounding co-coverage base stations as the equipment co-coverage impact data; then, the base station analysis system evaluates whether the base station has a "seemingly profitable but actually losing" situation (although the energy-saving deployment of the base station has higher energy-saving efficiency, the actual energy-saving surplus is not as good as the old energy-saving strategy due to the high deployment cost) based on these base station data, and presents the evaluation results in a visual manner in the energy-saving benefit analysis ecosystem map as the energy-saving benefit analysis result of the base station, which is used to guide the optimization direction of the base station in the next cycle.

[0085] The technical solution provided in this embodiment is conducive to comprehensively evaluating the energy-saving benefits of base stations from multiple angles by obtaining multi-dimensional base station data including energy-saving benefit data, operating cost data and equipment co-coverage impact data; by integrating these multi-dimensional data to generate an energy-saving benefit analysis ecological map, it is conducive to intuitively displaying the energy-saving benefit analysis results of the base station, thereby facilitating accurate evaluation of the energy-saving benefits of the base station.

[0086] In an exemplary embodiment, the energy-saving potential of the base station is analyzed through an energy-saving analysis model to obtain an energy-saving potential analysis result of the base station, which specifically includes the following contents: obtaining historical energy-saving data of the base station through the energy-saving analysis model; based on the historical energy-saving data, performing energy-saving potential analysis on the geographical location information, user behavior trend information, user quantity information and traffic volume information of the base station to obtain the basic result of the energy-saving potential analysis of the base station; based on the hardware equipment information and implementation scenario information of the base station, updating the basic result of the energy-saving potential analysis to obtain the energy-saving potential analysis result.

[0087] The historical energy-saving data may be various energy-saving related data records of the base station in the past energy-saving cycles. For example, the historical energy-saving data may be data records of various technical indicators and business scenarios of the base station in the historical period.

[0088] The geographical location information may be information describing the geographical environment where the base station is located. For example, the geographical location information may be information such as the coverage of the area where the base station is located, the distribution of co-covered base stations, and the like.

[0089] The user behavior trend information may be information describing the usage habits and patterns of users in the base station coverage area. For example, the user behavior trend information may be information such as the service usage patterns of users in different time periods and user migration patterns.

[0090] The user quantity information may be information describing the scale of users in the base station coverage area. For example, the user quantity information may be information such as the actual number of access users and the number of potential users in the base station coverage area.

[0091] The traffic volume information may be information describing the service load of the base station. For example, the traffic volume information may be information such as the service volume and data flow of the base station in different time periods.

[0092] Among them, the basic results of the energy-saving potential analysis can be preliminary energy-saving potential assessment results based on historical data of the base station. For example, the basic results of the energy-saving potential analysis can be preliminary assessment results of the base station energy-saving potential obtained by analyzing the geographical location information, user behavior trend information, user quantity information and traffic volume information of the base station.

[0093] The hardware device information may be information describing the hardware configuration of the base station. For example, the hardware device information may be information such as the device model, performance parameters, and resource configuration of the base station.

[0094] Among them, the implementation scenario information can be information that describes the energy-saving implementation environment of the base station. For example, the implementation scenario information can be information such as the coverage scenario type of the base station, the applicability of the energy-saving algorithm, and the server resource status.

[0095] Optionally, the base station analysis system obtains data records of various technical indicators and business scenarios of the base station in the historical period as historical energy-saving data through the energy-saving analysis model; then the base station analysis system analyzes the geographical location information of the base station (including the coverage range of the base station, the distribution of co-covered base stations, etc.), user behavior trend information (including the service usage rules of users in different time periods, user migration rules, etc.), user quantity information (including the actual number of access users, the number of potential users, etc.) and traffic information (including the traffic volume and data traffic in different time periods) based on the historical energy-saving data, calculates the green degree of the base station (traffic volume / base station energy consumption value), evaluates the energy-saving stability of the base station, and obtains the basic results of the energy-saving potential analysis of the base station; finally, the base station analysis system updates the basic results of the energy-saving potential analysis of the base station based on the hardware equipment information of the base station (including equipment model, performance parameters, resource configuration, etc.) and implementation scenario information (including coverage scenario type, applicability of energy-saving algorithm, server resource status, etc.), and obtains the energy-saving potential analysis results of the base station.

[0096] The technical solution provided in this embodiment obtains the historical energy-saving data of the base station and analyzes multiple dimensions such as the geographical location information, user behavior trend information, user quantity information and traffic volume information of the base station based on the historical energy-saving data, which is conducive to comprehensively evaluating the energy-saving potential of the base station; by updating the basic results of the energy-saving potential analysis in combination with the hardware equipment information and implementation scenario information of the base station, it is conducive to making the energy-saving potential analysis results more in line with the actual application scenario, thereby facilitating the accurate evaluation of the actual energy-saving potential of the base station and providing reliable data support for the energy-saving optimization of the base station.

[0097] In an exemplary embodiment, the energy-saving performance of the base station is analyzed through an energy-saving analysis model to obtain an energy-saving performance analysis result of the base station, which specifically includes the following contents: obtaining energy-saving amount change data of the base station within a preset time period through the energy-saving analysis model; determining the energy-saving fluctuation type of the base station based on the energy-saving amount change data; analyzing the energy-saving performance of the base station based on the energy-saving fluctuation type to obtain an energy-saving performance analysis result.

[0098] The preset time period may be a specific time interval set for analyzing the energy-saving performance of the base station. For example, the preset time period may be a complete energy-saving cycle of the base station.

[0099] Among them, the energy saving amount change data can be data reflecting the change of energy saving effect of the base station within a preset time period. For example, the energy saving amount change data can be the difference change data between the T0 value (equipment energy consumption value when the cell is not in energy saving state) and the T1 value (equipment energy consumption value when the cell is in energy saving state) of the base station within the preset time period.

[0100] Among them, the energy-saving fluctuation type can be a type of classification of the energy-saving performance of the base station based on the energy-saving amount change data. For example, the energy-saving fluctuation type can be Class A (excessive growth type with energy-saving amount increasing by more than x%), Class B (steady growth type with energy-saving amount increasing by y%-x%), Class C (reasonable fluctuation type with energy-saving amount fluctuating within plus or minus y%), Class D (abnormal decrease type with energy-saving amount decreasing by more than y%), etc., where x can be the first preset value, y can be the second preset value, and x is greater than y.

[0101] Among them, the energy-saving performance analysis result can be an evaluation result obtained after analyzing the energy-saving fluctuation type of the base station. For example, the energy-saving performance analysis result can be a quantitative evaluation result including energy-saving strategies, instructions, scene labels, user characteristics, environmental factors, etc.

[0102] Optionally, the base station analysis system first obtains the energy saving amount change data of the base station within a preset time period through the energy saving analysis model, including the difference change data between the T0 value (equipment energy consumption value when the cell is not in energy saving state) and the T1 value (equipment energy consumption value when the cell is in energy saving state) of the base station; then the base station analysis system divides the energy saving fluctuation type of the base station into Class A (excessive growth type with energy saving amount increasing by more than x%), Class B (steady growth type with energy saving amount increasing by y%-x%), Class C (reasonable fluctuation type with energy saving amount fluctuating within plus or minus y%) and Class D (abnormal decrease type with energy saving amount decreasing by more than y%) according to the energy saving amount change data; finally, the base station analysis system analyzes the energy saving performance of the base station according to the energy saving fluctuation type of the base station, combined with the changes in indicators such as the base station energy saving duration, traffic volume, and number of users, and obtains energy saving performance analysis results including energy saving strategies, instructions, scenario labels, user characteristics, environmental factors, etc.

[0103] The technical solution provided in this embodiment, by acquiring the energy saving amount change data of the base station within a preset time period, and determining the energy saving fluctuation type of the base station based on the energy saving amount change data, is conducive to standardized classification evaluation of the energy saving performance of the base station; by analyzing the energy saving performance of the base station according to the energy saving fluctuation type of the base station, it is conducive to adopting corresponding analysis methods for different types of energy saving fluctuations; thereby facilitating accurate evaluation of the energy saving performance of the base station and providing a reliable analysis basis for the energy saving optimization of the base station.

[0104] In an exemplary embodiment, an energy-saving analysis model of the base station is constructed based on the energy-saving characteristic data of the base station, which specifically includes the following contents: obtaining target data of the base station; the target data at least includes energy-saving technical indicator data, revenue data, user behavior data and energy-saving scenario data; based on the target data, an energy-saving footprint analysis model of the base station is constructed as an energy-saving analysis model.

[0105] Among them, the target data can be a basic data set used to construct a base station energy-saving footprint analysis model. For example, the target data can be a multi-dimensional base station data set including energy-saving technical indicator data, revenue data, user behavior data and energy-saving scenario data.

[0106] Among them, the energy-saving technical indicator data can be data reflecting the energy-saving technical performance of the base station, for example, the energy-saving technical indicator data can be the energy-saving duration of the base station, T0 value (equipment energy consumption value when the cell is not in energy-saving state), T1 value (equipment energy consumption value when the cell is in energy-saving state) and other data.

[0107] The revenue data may be data reflecting the economic benefits brought about by energy saving of the base station. For example, the revenue data may be data such as the energy saving amount, operating cost savings, and market revenue of the base station.

[0108] The user behavior data may be data reflecting the usage characteristics of users in the base station coverage area. For example, the user behavior data may be data such as changes in the number of users, changes in call volume, and user migration trends.

[0109] Among them, the energy-saving scenario data can be data reflecting the energy-saving implementation environment of the base station. For example, the energy-saving scenario data can be data such as the coverage scenario type, energy-saving strategy environment, and co-coverage impact of the base station.

[0110] Among them, the energy-saving footprint analysis model can be an analysis model based on the entire energy-saving cycle of the base station. For example, the energy-saving footprint analysis model can be an analysis model that evaluates the impact of various technical indicators and business scenarios on energy saving in the base station during the cycle of "non-energy-saving state-energy-saving turned on-energy-saving performance changes-energy-saving strategy optimization".

[0111] Optionally, the base station analysis system first obtains the target data of the base station, including energy-saving technical indicator data (including the energy-saving duration of the base station, the equipment energy consumption value when the cell is not in energy-saving state, the equipment energy consumption value when the cell is in energy-saving state, etc.), revenue data (including the energy-saving revenue of the base station, operating costs, equipment co-coverage impact, etc.), user behavior data (including changes in call volume, changes in the number of users, user migration trends, etc.) and energy-saving scenario data (including the coverage scenario type of the base station, energy-saving strategy environment, co-coverage impact, etc.); then the base station analysis system constructs an energy-saving footprint analysis model based on the target data of the base station to evaluate the impact of various technical indicators and business scenarios on energy saving in the period of "non-energy-saving state-energy-saving turned on-energy-saving performance changes-energy-saving strategy optimization" as the energy-saving analysis model of the base station.

[0112] The technical solution provided in this embodiment is conducive to comprehensively grasping the energy-saving related characteristics of the base station by acquiring multi-dimensional target data including energy-saving technical indicator data, revenue data, user behavior data and energy-saving scenario data; by constructing an energy-saving footprint analysis model of the base station based on these multi-dimensional target data, it is conducive to systematically analyzing the energy-saving performance of the base station; thereby facilitating accurate evaluation of the energy-saving status of the base station within a complete energy-saving cycle, and providing an accurate analysis basis for the energy-saving optimization of the base station.

[0113] In an exemplary embodiment, after generating the energy-saving update information of the base station based on the energy-saving performance analysis results, the energy-saving potential analysis results and the energy-saving benefit analysis results, it also includes the following contents: determining the energy-saving update type of the base station based on the energy-saving update information; and performing energy-saving update processing on the base station based on the energy-saving update type.

[0114] Among them, the energy-saving update type can be a type of classification of base stations based on energy-saving update information. For example, the energy-saving update type can be a whitelist type (special agency base stations), a policy switching type (base stations that need to update energy-saving strategies), a scene merging type (base stations that need to merge energy-saving scenes), a network withdrawal type (inefficient energy-saving base stations that need to be shut down), and other types.

[0115] Among them, the energy-saving update processing can be the corresponding optimization measures taken according to the energy-saving update type of the base station. For example, the energy-saving update processing can be special protection for whitelist base stations, energy-saving algorithm updates for policy switching base stations, energy-saving scene integration for scene merging base stations, and shutdown processing for delisted base stations.

[0116] Optionally, the base station analysis system first generates energy-saving update information for the base station based on the energy-saving performance analysis results of the base station (including energy-saving strategies, instructions, scenario tags, user characteristics, environmental factors, etc.), energy-saving potential analysis results (including predictions of the base station's geographical location, user behavior trends, number of users, call volume, etc.) and energy-saving benefit analysis results (including the base station's energy-saving benefits, operating costs, and equipment co-coverage impact, etc.); then the base station analysis system divides the energy-saving update type of the base station into a whitelist category (special agency base stations), a policy switching category (base stations that need to update energy-saving strategies), a scenario merging category (base stations that need to merge energy-saving scenarios), and a network withdrawal category (inefficient energy-saving base stations that need to be shut down) based on the energy-saving update information of the base station; finally, the base station analysis system performs corresponding energy-saving update processing on the base station based on the energy-saving update type of the base station.

[0117] The technical solution provided in this embodiment generates energy-saving update information based on the energy-saving performance analysis results, energy-saving potential analysis results and energy-saving benefit analysis results of the base station, which is conducive to comprehensively evaluating the energy-saving status of the base station from multiple dimensions; by determining the energy-saving update type according to the energy-saving update information and performing corresponding update processing, it is conducive to taking targeted optimization measures for different types of base stations; thereby, it is conducive to achieving systematic and efficient energy-saving optimization of the base station and improving the effect of energy-saving optimization of the base station.

[0118] The following is an application example to illustrate the base station analysis method provided by the present application. This application example uses the method applied to a base station analysis system (such as a terminal) as an example.

[0119] First, let’s explain the following terms:

[0120] Co-coverage: Multiple base stations may co-cover a certain geographical area, and users in the area may access any of the covered base stations. If the energy saving of two co-covered base stations does not take effect at the same time, users may switch from the base station where the energy saving takes effect earlier to the base station where the energy saving takes effect later, causing the base station where the energy saving takes effect later to exit the energy saving state or reduce the energy saving due to the increase in data volume. In this case, the energy saving data of the base station where the energy saving takes effect earlier will be overestimated.

[0121] Base stations appear to be profitable but actually lose money: Energy-saving deployment of base stations requires specific prerequisites, such as hardware equipment, server resources, and manufacturer licenses. Although new energy-saving strategies have higher energy-saving efficiency and theoretically have higher energy-saving amounts, they are often accompanied by higher deployment costs. The actual energy-saving surplus may not be as good as the original energy-saving strategy, or even no surplus will be generated, resulting in misjudgment of energy-saving results.

[0122] With the rapid development of communication networks, the large-scale construction and use of base stations has led to a significant increase in base station power consumption, and the demand for base station energy saving has become increasingly urgent. At present, the coverage scenarios of base stations are becoming increasingly complex, and energy-saving strategies are sensitive to factors such as base station technical parameters, implementation environment, and user behavior. In addition, energy-saving strategies rely on the base station's profit and loss analysis and energy-saving potential estimation system to guide subsequent optimization directions. Existing base station energy-saving strategies have problems such as insufficient update and iteration basis, lack of post-evaluation system, and difficulty in judging whether base stations are suitable for energy saving.

[0123] This application example builds an energy-saving footprint model for base stations to evaluate the impact of various technical indicators and business scenarios on energy saving during the cycle of "non-energy-saving state - energy-saving on - energy-saving performance change - energy-saving strategy optimization". Combined with revenue data, the energy-saving performance and subsequent energy-saving potential of the base station are evaluated, providing a data basis for the optimization of the base station energy-saving strategy in the next cycle.

[0124] This application example will track the fluctuations in the energy-saving performance of the base station since the last cycle update and assign classification labels to the base station, associate the characteristic data of energy-saving fluctuations, and combine auxiliary indicator analysis to output root cause analysis results, such as energy-saving instruction category, data quality category, user behavior category, etc. Based on the root cause analysis, the stability, health, energy-saving potential, etc. of the base station are evaluated, and targeted optimization plans are formulated. For base stations with unstable energy-saving performance, this application example will conduct a profit and loss analysis of the base station by associating the base station revenue data, evaluate the relationship between the energy-saving changes caused by the new energy-saving algorithm and the cost changes caused by the simultaneous increase in base station energy consumption, avoid the situation of "seemingly profitable but actually losing", and provide data support for the subsequent adjustment of the energy-saving implementation plan of the base station.

[0125] This application example is mainly used to solve the following two problems:

[0126] 1. The energy-saving cycle of a base station is generally long, and the business scenarios, energy-saving algorithm performance, user trends and other states in the cycle are constantly changing. However, the performance and potential evaluation methods for the complete cycle of the base station are not yet complete, resulting in inaccurate profit and loss analysis of the base station, weak ability to combine business and operation data, and the phenomenon of "seemingly profitable but actually losing" occurs.

[0127] 2. The energy-saving potential of base stations is affected by factors such as energy-saving algorithms, hardware equipment, and implementation scenarios. If the energy-saving strategy cannot be adjusted in time according to the actual energy-saving environment, such as optimizing energy-saving algorithms, merging base station tags, and updating energy-saving scenarios, the energy-saving performance of base stations will gradually decline, and the energy-saving potential cannot be fully utilized, resulting in insufficient energy saving of base stations and waste of resources.

[0128] A flowchart reference for this application example Figure 2 When the energy-saving performance of a base station fluctuates, the system will perform a base station energy-saving footprint analysis. This analysis consists of the following four main parts:

[0129] The input data part includes: energy-saving technical indicators, revenue data, user behavior data, and energy-saving scenario data.

[0130] The analysis module part (analysis process) includes three parallel analysis modules:

[0131] 1. Base station energy-saving performance evaluation: Integrate the above data, combine the energy-saving time point backtracking function of footprint analysis, eliminate interference items, conduct independent analysis of each operation, and evaluate its contribution to energy-saving performance.

[0132] 2. Base station energy saving potential assessment: Based on the energy saving footprint and historical cycle data, the geographical location of the base station and the user behavior trend are analyzed, the development of indicators such as the number of users and traffic volume are predicted, and the energy saving potential of the base station is scored.

[0133] 3. Base station energy-saving profit and loss analysis: Build a base station regional view, integrate the base station's energy-saving benefits, operating costs, equipment coverage impact, etc., and evaluate the base station's profit and loss status.

[0134] Output result part (generating conclusion):

[0135] 1. Output classified evaluation results, including energy-saving strategies, instructions, scene labels, user characteristics, environmental factors, etc., to provide clear quantitative evaluation.

[0136] 2. Generate the energy-saving potential score of the base station and adjust the score based on the data.

[0137] 3. Output the profit and loss analysis ecological map of the base station to guide the optimization direction of the base station in the next cycle, continuously optimize and explore the base stations that generate profits from energy saving, and take adjustment measures for base stations that seem to be profitable but are actually losing money from energy saving.

[0138] The final step: optimization of base station energy-saving solutions.

[0139] Another flowchart reference for this application example Figure 3 . Figure 3 The comparison process between the existing method and this method is shown, which specifically includes the following contents:

[0140] Trigger conditions:

[0141] Both approaches start from the phenomenon that the energy-saving performance of base stations fluctuates during their energy-saving cycles.

[0142] Problems (existing methods):

[0143] The analysis methods are not well matched, and the analysis conclusions are one-sided or inaccurate;

[0144] The comprehensive perception of base station status is limited, and the effect of policy adjustment cannot be accurately evaluated;

[0145] There is no perfect follow-up improvement plan, and the ability to cope with the market and environment is weak, which is prone to energy saving and revenue loss;

[0146] The energy-saving update cycle of base stations is long, resulting in a large loss of energy-saving amount and energy-saving costs.

[0147] Solution (this method):

[0148] This method consists of four main steps:

[0149] Fluctuation root cause analysis: Based on the energy-saving footprint analysis of the base station, all important influencing factors are included;

[0150] Energy-saving strategy adjustment: Form a profile of the energy-saving status of the base station to accurately evaluate the impact of strategy adjustments, profit and loss analysis, and energy-saving potential;

[0151] Subsequent solution generation: The solution is highly responsive and explainable, taking into account both repair and potential development;

[0152] Solution application effect: Comprehensively analyze base station characteristics in a short period of time, efficiently generate optimization solutions and respond to them in a timely manner.

[0153] The main innovations of this application example are:

[0154] 1. Analysis of the root causes of fluctuations: Most existing methods analyze various factors independently, and the conclusions are rather one-sided. This application example takes the base station as the main perspective and conducts a comprehensive analysis of important factors such as strategies, scenarios, and user trends. The conclusions are based on the specific conditions of the base station.

[0155] 2. Energy-saving strategy adjustment: This application example builds a personalized analysis view of the base station, including impact assessment of the energy-saving strategy environment, profit and loss analysis, and energy-saving potential estimation, providing a complete data basis for subsequent adjustment plans.

[0156] 3. Subsequent solution generation and effects: Most existing methods are passive responses, only the base stations reported by specific institutions and users are included in the whitelist, and the remaining base stations remain in the energy-saving state. This leads to a large loss of energy saving and energy-saving costs. This application example generates exclusive solution suggestions for base stations based on base station feature data, which are divided into whitelist category (special protection category), policy switching category (energy-saving policy category that needs to be updated), scene merging category (energy-saving scene category that needs to be integrated), network withdrawal category (need to be shut down category), etc. It can timely tap the energy-saving potential of base stations, merge or shut down inefficient energy-saving base stations, improve energy-saving efficiency, and reduce the loss of energy-saving costs.

[0157] This application example provides an optimization solution for base stations that have been put into use in the communication network, including modules such as energy-saving performance evaluation, energy-saving potential analysis, and energy-saving profit and loss analysis of base stations. It realizes a personalized ecological map of base stations, can accurately obtain the periodic analysis results of each base station, and guide the continuous improvement of energy conservation based on this analysis result.

[0158] This application example can effectively improve the energy-saving efficiency of base stations through actual application, and the energy-saving efficiency is significantly improved.

[0159] For example, the overall situation analysis of a base station in an energy-saving cycle is referenced Figure 4 This figure shows a bar chart for analyzing the energy-saving fluctuations of base stations in various provinces. Figure 4 middle:

[0160] Vertical axis data range: energy-saving fluctuation cell quantity scale from 0 to 1200 (0, 200, 400, 600, 800, 1000, 1200).

[0161] Classification of statistical indicators: The figure contains two types of data statistics: small areas with energy saving fluctuations and small areas with the same proportion of fluctuations in related indicators.

[0162] Data display method: The horizontal axis represents provinces, and the vertical axis represents the number of communities.

[0163] Each province has two parallel bar graphs, corresponding to the above two types of statistical indicators. The height of the bar graph represents the number of communities of the corresponding type.

[0164] This statistical chart is used to visually display and compare the fluctuations in base station energy saving in each province during the energy saving cycle. By comparing the fluctuations in energy saving and related indicators, the stability and effectiveness of base station energy saving implementation in each province can be evaluated.

[0165] For example, the base station energy-saving footprint analysis system reference Figure 5 The analysis system first classifies and evaluates base stations, dividing them into four categories:

[0166] Class A cells: Over-speed growth (energy saving growth exceeds x%);

[0167] Class B community: stable growth (energy saving increases by y%-x%);

[0168] Class C community: reasonable fluctuation category (energy saving fluctuation within plus or minus y%);

[0169] Class D cell: abnormal decline (energy saving decreases by more than y%).

[0170] For different types of cells, the system performs the following analysis:

[0171] 1. For Class A cells, the system checks whether indicators such as cell energy-saving duration, T0 value, traffic volume, and number of users have increased by approximately x%; if so, it is classified as normal growth, and the reasons for the increase (strategy update, command success rate, increased sleep time, etc.) are analyzed for reference in the subsequent improvement of other base stations; if not, it is classified as abnormal growth, and data such as network management, strategy prediction, command issuance and effectiveness are checked to review the growth status and reasons.

[0172] 2. For Class B cells: Count the number of cells with steady growth according to the ranking of each province, compare with the previous cycle, and judge the stability of the province's overall energy-saving performance; classify them as normal growth cells; calculate the greenness of base stations, and evaluate the province's energy-saving stability and potential.

[0173] 3. For Class C cells: Count the number of cells with reasonable fluctuations according to the ranking of each province, compare with the previous cycle, and judge the stability of the province's overall energy-saving performance; classify them into the normal growth category; calculate the greenness of base stations, and evaluate the province's energy-saving stability and potential.

[0174] 4. For Class D cells, the system checks: whether the energy-saving duration of the cell is reduced; if so, whether there is an abnormal instruction; if not, whether the energy-saving efficiency is reduced.

[0175] If the judgment of whether there is an instruction abnormality is yes, it is confirmed to check the instruction implementation in the shutdown mode, which is divided into instruction category: early wake-up (subsequent statistics of abnormal data, occurrence frequency and other information to form a solution) and instruction category: delayed entry into energy saving (subsequent statistics of abnormal data, occurrence frequency and other information to form a solution).

[0176] If the judgment of whether there is an instruction abnormality is no, it is confirmed that due to the influence of indicators such as traffic volume, the time for the cell to enter energy saving is shortened; confirmed as load type: the energy saving time is shortened (subsequent evaluation is performed to see whether the root cause is objective reasons such as the increase in base station business, and the conclusion is output).

[0177] If the energy-saving efficiency is judged as decreased, it is confirmed that T0 is reduced, resulting in T0-T1 becoming smaller; it is confirmed as load type: the energy saving per unit time is reduced (subsequent evaluation is conducted to see whether the root cause is objective reasons such as the increase in base station business, and the conclusion is output).

[0178] If the answer to whether the energy-saving efficiency has decreased is no, then confirm whether the number of effective energy-saving days has decreased; if so, confirm it as data quality category: check whether the data is missing (subsequently count abnormal data, occurrence frequency and other information to form a solution); if not, confirm it as data quality category: check whether the energy consumption data is correct (count abnormal data, occurrence frequency and other information to form a solution).

[0179] Additional explanation: Green degree = business volume / base station energy consumption value. The lower the value, the greater the energy saving potential. T0 value: the energy consumption value of the equipment when the cell is not in energy saving state. T1 value: the energy consumption value of the equipment when the cell is in energy saving state.

[0180] The beneficial effects of this application example are as follows:

[0181] (1) Footprint analysis of the entire energy-saving cycle of base stations: This application example is the first to propose a footprint analysis method based on the entire energy-saving cycle of base stations in the field of base station energy saving, which evaluates the performance, potential, profit and loss of base stations in various energy-saving states. This application example supports customized independent analysis of the impact of a single feature on energy saving, and can accurately quantify the energy-saving contribution of the feature, providing a clear data foundation for subsequent optimization work.

[0182] (2) Generation of comprehensive optimization scheme for energy saving of base stations: This application example is based on the full-cycle footprint analysis model, integrates the important characteristic data of base stations, constructs a multi-dimensional ecological analysis diagram, and clearly presents the ecological data of each base station, such as energy saving performance, health analysis, potential assessment, profit and loss statistics, etc. This application example supports full analysis and lightweight custom query, and is equipped with prompt engineering and feature library data, and can be connected to large model analysis tools to realize a comprehensive optimization scheme generation system for base station energy saving with a wide analysis range and fast response speed.

[0183] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0184] Based on the same inventive concept, the embodiment of the present application also provides a base station analysis device for implementing the base station analysis method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more base station analysis device embodiments provided below can refer to the limitations on the base station analysis method above, and will not be repeated here.

[0185] In an exemplary embodiment, Figure 6 As shown, a base station analysis device is provided, and the base station analysis device 600 may include:

[0186] The model building module 601 is used to build an energy-saving analysis model of the base station according to the energy-saving characteristic data of the base station;

[0187] A result determination module 602 is used to determine the energy-saving performance analysis result, energy-saving potential analysis result and energy-saving benefit analysis result of the base station through the energy-saving analysis model;

[0188] The information generation module 603 is used to generate energy-saving update information of the base station according to the energy-saving performance analysis result, the energy-saving potential analysis result and the energy-saving benefit analysis result.

[0189] In an exemplary embodiment, the result determination module 602 is also used to analyze the energy-saving performance of the base station through an energy-saving analysis model to obtain the energy-saving performance analysis results of the base station; to analyze the energy-saving potential of the base station through the energy-saving analysis model to obtain the energy-saving potential analysis results of the base station; to analyze the energy-saving benefits of the base station through the energy-saving analysis model to obtain the energy-saving benefit analysis results of the base station.

[0190] In an exemplary embodiment, the result determination module 602 is also used to obtain the base station data of the base station through an energy-saving analysis model; the base station data includes at least energy-saving benefit data, operating cost data and equipment co-coverage impact data; based on the base station data, the energy-saving benefits of the base station are analyzed to obtain an energy-saving benefit analysis ecological map of the base station as the energy-saving benefit analysis result.

[0191] In an exemplary embodiment, the result determination module 602 is also used to obtain historical energy-saving data of the base station through an energy-saving analysis model; based on the historical energy-saving data, perform energy-saving potential analysis on the geographical location information, user behavior trend information, user quantity information and traffic volume information of the base station to obtain the basic results of the energy-saving potential analysis of the base station; based on the hardware equipment information and implementation scenario information of the base station, update the basic results of the energy-saving potential analysis to obtain the energy-saving potential analysis results.

[0192] In an exemplary embodiment, the result determination module 602 is also used to obtain the energy saving amount change data of the base station within a preset time period through an energy saving analysis model; determine the energy saving fluctuation type of the base station based on the energy saving amount change data; analyze the energy saving performance of the base station based on the energy saving fluctuation type to obtain the energy saving performance analysis result.

[0193] In an exemplary embodiment, the model building module 601 is also used to obtain target data of the base station; the target data includes at least energy-saving technical indicator data, revenue data, user behavior data and energy-saving scenario data; based on the target data, an energy-saving footprint analysis model of the base station is constructed as an energy-saving analysis model.

[0194] In an exemplary embodiment, the apparatus 600 further includes: a base station processing module, configured to determine an energy-saving update type of the base station according to the energy-saving update information; and perform energy-saving update processing on the base station according to the energy-saving update type.

[0195] Each module in the above-mentioned base station analysis device can be implemented in whole or in part by software, hardware or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0196] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a base station analysis method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0197] Those skilled in the art will understand that Figure 7The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0198] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0199] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0200] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0201] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0202] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0203] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A base station analysis method, characterized in that: The method comprises: Constructing an energy-saving analysis model for the base station according to the energy-saving characteristic data of the base station; Determining the energy-saving performance analysis result, energy-saving potential analysis result and energy-saving benefit analysis result of the base station through the energy-saving analysis model; Energy-saving update information of the base station is generated according to the energy-saving performance analysis result, the energy-saving potential analysis result and the energy-saving benefit analysis result.

2. The method according to claim 1, characterized in that The energy-saving performance analysis result, energy-saving potential analysis result and energy-saving benefit analysis result of the base station are determined by the energy-saving analysis model, including: Analyzing the energy-saving performance of the base station through the energy-saving analysis model to obtain an energy-saving performance analysis result of the base station; Analyzing the energy-saving potential of the base station through the energy-saving analysis model to obtain an energy-saving potential analysis result of the base station; The energy-saving benefit of the base station is analyzed through the energy-saving analysis model to obtain the energy-saving benefit analysis result of the base station.

3. The method according to claim 2, characterized in that The energy-saving benefit of the base station is analyzed by the energy-saving analysis model to obtain the energy-saving benefit analysis result of the base station, including: Acquire base station data of the base station through the energy-saving analysis model; the base station data at least includes energy-saving benefit data, operation cost data and equipment co-coverage impact data; The energy-saving benefit of the base station is analyzed according to the base station data to obtain an energy-saving benefit analysis ecological map of the base station as the energy-saving benefit analysis result.

4. The method according to claim 2, characterized in that: The energy-saving potential of the base station is analyzed by the energy-saving analysis model to obtain the energy-saving potential analysis result of the base station, including: Acquiring historical energy-saving data of the base station through the energy-saving analysis model; According to the historical energy-saving data, an energy-saving potential analysis is performed on the geographical location information, user behavior trend information, user quantity information and traffic volume information of the base station to obtain a basic result of the energy-saving potential analysis of the base station; The energy-saving potential analysis basic result is updated according to the hardware equipment information and implementation scenario information of the base station to obtain the energy-saving potential analysis result.

5. The method according to claim 2, characterized in that: The step of analyzing the energy-saving performance of the base station by using the energy-saving analysis model to obtain the energy-saving performance analysis result of the base station includes: Obtaining energy saving amount change data of the base station within a preset time period through the energy saving analysis model; Determining the energy saving fluctuation type of the base station according to the energy saving amount change data; The energy-saving performance of the base station is analyzed according to the energy-saving fluctuation type to obtain the energy-saving performance analysis result.

6. The method according to claim 1, characterized in that The step of constructing an energy-saving analysis model of the base station according to the energy-saving feature data of the base station includes: Acquire target data of the base station; the target data at least includes energy-saving technical indicator data, revenue data, user behavior data and energy-saving scenario data; An energy-saving footprint analysis model of the base station is constructed according to the target data as the energy-saving analysis model.

7. The method according to any one of claims 1 to 6, characterized in that After generating the energy-saving update information of the base station according to the energy-saving performance analysis result, the energy-saving potential analysis result and the energy-saving benefit analysis result, the method further includes: Determining an energy-saving update type of the base station according to the energy-saving update information; Perform energy-saving update processing on the base station according to the energy-saving update type.

8. A base station analysis device, characterized in that: The device comprises: A model building module, used to build an energy-saving analysis model of the base station according to the energy-saving feature data of the base station; A result determination module, used to determine the energy-saving performance analysis result, energy-saving potential analysis result and energy-saving benefit analysis result of the base station through the energy-saving analysis model; The information generation module is used to generate energy-saving update information of the base station according to the energy-saving performance analysis result, the energy-saving potential analysis result and the energy-saving benefit analysis result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images