Base station evaluation methods, apparatus, non-volatile storage media and electronic equipment

Abstract

This invention discloses a base station evaluation method, apparatus, non-volatile storage medium, and electronic device. The method includes: determining relevant data of a target base station, wherein the relevant data includes at least one type of sub-relevant data; determining a data evaluation result set of the relevant data, wherein the data evaluation result set includes the data evaluation result for each type of data in the relevant data; and determining the base station evaluation result of the target base station based on the data evaluation result set. This invention solves the technical problem that related technologies can only evaluate the value of a base station using a single type of data, resulting in inaccurate evaluation of the base station's value.

Description

Technical Field

[0001] This invention relates to the field of base station value assessment, and more specifically, to a base station evaluation method, apparatus, non-volatile storage medium, and electronic device. Background Technology

[0002] With the large-scale deployment of 5G, telecommunications wireless networks now have three networks (3G / 4G / 5G) simultaneously providing communication services to users. Under the premise of reducing costs and increasing efficiency, it is essential to accurately evaluate the value of base stations. However, current related technologies suffer from outdated verification methods, a lack of verification approaches, incomplete verification standards, and the fact that departments such as finance, planning, construction, network optimization, and maintenance evaluate base station value according to their own statistical standards. Due to technological gaps, mutual supervision is impossible, resulting in ever-increasing investment and operating costs, decreasing base station efficiency, and stagnant user satisfaction.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a base station evaluation method, apparatus, non-volatile storage medium, and electronic device to at least solve the technical problem that the value of a base station cannot be accurately evaluated due to the fact that related technologies can only evaluate the value of a base station through a single type of data.

[0005] According to one aspect of the present invention, a base station evaluation method is provided, comprising: determining relevant data of a target base station, wherein the relevant data includes at least one type of sub-relevant data; determining a data evaluation result set of the relevant data, wherein the data evaluation result set includes data evaluation results for each type of data in the relevant data; and determining a base station evaluation result of the target base station based on the data evaluation result set.

[0006] Optionally, determining the base station evaluation result of the target base station based on the data evaluation result set includes: determining a preset base station evaluation result set, wherein the preset base station evaluation result set includes sub-evaluation indicators corresponding to various sub-related data in the relevant data; and determining the base station evaluation result corresponding to the data evaluation result from the preset base station evaluation result set based on the data evaluation result set.

[0007] Optionally, the set of data evaluation results for determining the relevant data includes: determining the frequency characteristic information of the sub-related data; and determining the evaluation result corresponding to the sub-related data based on the frequency characteristic information, wherein the evaluation result includes sub-related data that is higher than the standard value and sub-related data that is lower than the standard value.

[0008] Optionally, after determining the set of data evaluation results for relevant data, the base station evaluation method further includes responding to the selection instruction of the target object, determining the target sub-related data from various types of sub-related data, and displaying the evaluation results corresponding to the target sub-related data to the target object through a visual interface.

[0009] Optionally, the sub-related data includes financial data, equipment data, and user data. The financial data includes at least one of the following: the total rental fee of the target base station during the preset time period, the total electricity cost of the target base station during the preset time period, and the total supporting cost data of the target base station during the preset time period. The equipment data includes at least one of the following: the average current value of the target base station during the preset time period and the number of shares of the target base station during the preset time period. The user data includes at least one of the following: the total data traffic of the target base station during the preset time period and the total call volume of the target base station during the preset time period.

[0010] Optionally, determining the total supporting cost parameters includes: determining the characteristic information of the target equipment room in the target base station, the maintenance cost of the target base station within a preset time period, and the supporting service cost of the target base station within a preset time period, wherein the target equipment room is the equipment room in the target base station whose equipment room cost can be reduced; and determining the total supporting cost parameters based on the characteristic information, maintenance cost, and supporting service cost.

[0011] Optionally, after determining the base station evaluation result of the target base station based on the data evaluation result set, the base station evaluation method further includes: obtaining a preset base station optimization scheme set, wherein the base station optimization scheme set includes at least one base station optimization scheme, and each base station optimization scheme in the at least one base station optimization method corresponds to a base station evaluation result; and determining the base station optimization scheme corresponding to the target base station from the base station optimization scheme set based on the base station evaluation result of the target base station.

[0012] According to another aspect of the present invention, a base station evaluation apparatus is also provided, comprising: an acquisition module, configured to determine relevant data of a target base station, wherein the relevant data includes at least one type of sub-relevant data; a first processing module, configured to determine a data evaluation result set of the relevant data, wherein the data evaluation result set includes a data evaluation result for each type of data in the relevant data; and a second processing module, configured to determine a base station evaluation result of the target base station based on the data evaluation result set.

[0013] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute a base station evaluation method.

[0014] According to another aspect of the present invention, an electronic device is also provided, the electronic device including a processor for running a program, wherein the program executes a base station evaluation method during runtime.

[0015] In this embodiment of the invention, the method involves determining relevant data of a target base station, wherein the relevant data includes at least one type of sub-relevant data; determining a set of data evaluation results for the relevant data, wherein the set of data evaluation results includes the data evaluation results for each type of data in the relevant data; and determining the base station evaluation result of the target base station based on the set of data evaluation results. By determining the evaluation result of each data item among multiple data associated with the base station, the method achieves the purpose of comprehensively evaluating the base station using multiple different types of data, thereby realizing the technical effect of accurately evaluating the value of the base station. This solves the technical problem that the value of the base station cannot be accurately evaluated because related technologies can only evaluate the value of the base station using a single type of data. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 This is a flowchart illustrating a base station evaluation method according to an embodiment of the present invention;

[0018] Figure 2 This is a flowchart illustrating a base station evaluation process according to an embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram of an evaluation result provided by an embodiment of the present invention;

[0020] Figure 4 This is a schematic diagram of another evaluation result provided by an embodiment of the present invention;

[0021] Figure 5 This is a schematic diagram of base station data visualization provided according to an embodiment of the present invention;

[0022] Figure 6 This is a schematic diagram of a frequency histogram provided according to an embodiment of the present invention;

[0023] Figure 7 This is a schematic diagram of the structure of a base station evaluation device provided according to an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] Example 1

[0027] According to an embodiment of the present invention, a method embodiment for base station evaluation is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] Figure 1 This is a base station evaluation method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0029] Step S102: Determine the relevant data of the target base station, wherein the relevant data includes at least one type of sub-relevant data;

[0030] In some embodiments of this application, the sub-related data includes financial data, equipment data, and user data. The financial data includes at least one of the following: the total rental fee of the target base station within a preset time period, the total electricity cost of the target base station within the preset time period, and the total supporting costs of the target base station within the preset time period. The equipment data includes at least one of the following: the average current value of the target base station within the preset time period, and the number of shared operators of the target base station within the preset time period, wherein the number of shared operators refers to the number of base stations sharing the same operator. The user data includes at least one of the following: the total data traffic of the target base station within the preset time period, and the total call volume of the target base station within the preset time period.

[0031] In some embodiments of this application, the method for determining the total supporting cost parameter includes: determining the characteristic information of the target equipment room in the target base station, the maintenance cost of the target base station within the preset time period, and the supporting service cost of the target base station within the preset time period, wherein the target equipment room is an equipment room in the target base station whose equipment room cost can be reduced; and determining the total supporting cost parameter based on the characteristic information, the maintenance cost, and the supporting service cost.

[0032] Step S104: Determine the data evaluation result set of the relevant data, wherein the data evaluation result set includes the data evaluation result of each category of data in the relevant data;

[0033] Step S106: Determine the base station evaluation result of the target base station based on the data evaluation result set.

[0034] In some embodiments of this application, determining the base station evaluation result of the target base station based on the data evaluation result set includes: determining a preset base station evaluation result set, wherein the preset base station evaluation result set includes sub-evaluation indicators corresponding to various sub-related data in the related data; and determining the base station evaluation result corresponding to the data evaluation result from the preset base station evaluation result set based on the data evaluation result set.

[0035] In some embodiments of this application, determining the data evaluation result set of the relevant data includes: determining the frequency characteristic information of the sub-related data; and determining the evaluation result corresponding to the sub-related data based on the frequency characteristic information, wherein the evaluation result includes the sub-related data being higher than a standard value and the sub-related data being lower than a standard value. Specifically, Figure 3 and Figure 4 This is a schematic diagram of a partial set of evaluation results provided in some embodiments of this application. As can be seen, different sets of data evaluation results will correspond to different base station evaluation results.

[0036] In some embodiments of this application, the frequency characteristic information mentioned above includes, for example: Figure 6 The frequency histogram is shown.

[0037] In some embodiments of this application, after determining the set of data evaluation results for the relevant data, the base station evaluation method further includes: responding to a selection instruction for a target object, determining target sub-related data from the various types of sub-related data; and displaying the evaluation results corresponding to the target sub-related data to the target object through a visual interface.

[0038] Specifically, the data evaluation results are displayed to the target audience through a visual interface, such as... Figure 5 As shown.

[0039] In some embodiments of this application, as shown in the table below, after determining the base station evaluation result of the target base station based on the data evaluation result set, the base station evaluation method further includes: obtaining a preset base station optimization scheme set, wherein the base station optimization scheme set includes at least one base station optimization scheme, and each of the at least one base station optimization scheme corresponds to a base station evaluation result; and determining the base station optimization scheme corresponding to the target base station from the base station optimization scheme set based on the base station evaluation result of the target base station.

[0040]

[0041] The "leakage" in the table above refers to checking all equipment at the site for potential electricity theft or leakage, and repairing it promptly.

[0042] For ease of understanding Figure 1 The solution provided below, in conjunction with Figure 2 The base station evaluation process shown in the figure further explains the above method.

[0043] The first step is to collect historical data on various financial rents and electricity charges, collect field equipment data from the base station network management system, collect configuration and billing parameters from the tower CRM system, and collect traffic data from the base station network optimization management system, in accordance with relevant rules.

[0044] The second step is to categorize and summarize the acquired traffic and call data: 2G call volume and 3G / 4G / 5G traffic by site.

[0045] The third step is to use the obtained field equipment information to calculate the total field current and power consumption based on the standard equipment power consumption meter formed by combining big data analysis and field testing (based on various factors such as equipment type, high and low call volume, high and low traffic volume, busy and idle time).

[0046] Specifically, standard current and power consumption of various devices can be obtained from manufacturers to obtain an initial device power consumption meter. Then, the actual operating current of different devices can be obtained through on-site sampling tests. Power consumption data at different times can be obtained by sampling and installing smart meters. The average current value of various devices can be obtained through the environmental monitoring system. The initial device power consumption meter is then corrected to finally form a standard device power consumption meter that includes multiple factors such as device type, high and low call volume, high and low traffic volume, and busy and idle times.

[0047] The fourth step is to summarize the monthly rent and electricity bills received annually to increase the sample size and avoid errors in monthly payments, thereby generating annual rent and electricity bill information by site.

[0048] The fifth step is to classify the rental billing parameters according to the characteristics of cost reduction and efficiency improvement, and summarize the three parameters that can be reduced, namely, data center, supporting facilities and maintenance fees, and combine them into the key parameters of annual supporting facilities. This helps to highlight the proportion of variable factors and reduce the impact of invariant factors.

[0049] The sixth step is to summarize the seven keywords (annual rent, annual electricity cost, total equipment current, number of shared facilities, annual supporting facilities, monthly traffic, monthly call volume, etc.) by site location to form a basic database for analysis by site location.

[0050] Step 7: Use Python to implement cluster analysis in the discretization of continuous data in data processing, and establish a group dictionary library to discretize the seven keywords.

[0051] Specifically, since the seven keywords belong to different dimensions and units, they cannot be merged for evaluation. Therefore, all continuous data must be discretized. This method employs cluster analysis (i.e., 1. Clustering the values ​​of continuous attributes using a clustering algorithm, setting the number of groups to a uniform 6; 2. Processing the clusters obtained, merging the continuous attribute values ​​of one cluster, and labeling them uniformly). This results in grouped data for the seven keywords (annual rent, annual electricity cost, total equipment current, shared resources, annual infrastructure, monthly traffic, monthly call volume, etc.) ranging from 0 to 5. It is understood that the 6-group number mentioned above is merely an illustrative explanation and does not equate to the requirement that the number of groups in the data discretization process of the solution provided in this application must be 6. In fact, it is sufficient to ensure that the number of groups for different types of data is the same; the specific number of groups can be set by the target object according to its own needs.

[0052] During discretization, after setting the number of groups, the target object can designate cluster centers in each type of continuous data, equal to the number of groups, as needed. Then, a clustering algorithm is used to cluster the continuous data, thus dividing it into different data groups. To enable unified calculations for different data types, each data group can be assigned a fixed value, such as an integer from 0 to 5. This not only achieves discretization of continuous data but also normalizes different data types, allowing for unified analysis and calculation of different data types.

[0053] The eighth step involves using statistical methods to determine the central tendency of the seven keywords to perform grouped dictionary predictions and adjustments, ensuring that the prediction graph basically conforms to a bell-shaped distribution, meaning that most of the data is centrally trending, with fewer problematic data at both ends.

[0054] Specifically, to select the TOPN problem points, the central tendency of the grouped data, measured using statistical frequency analysis, must conform to a bell-shaped distribution. This allows for a focused approach to problem-solving. Therefore, the classification dictionary must be dynamically adjusted in a timely manner. The central tendency of the grouped data frequency for each keyword (except for sharing rate) must ensure the minimum output of the most critical problem, minimizing on-site workload and maximizing effectiveness while reducing costs. Thus, to achieve the minimum output of the most critical problem, after obtaining various historical data from the signal base station, it is necessary to predict and adjust the grouped dictionary for the six keywords of continuous characteristics (annual rent, annual electricity cost, total equipment current, annual infrastructure, monthly traffic, and monthly call volume). This ensures that the predicted histogram outline (i.e., the height of the histogram) basically conforms to a bell-shaped distribution, meaning that most data has a central tendency, with fewer problem data at both ends.

[0055] The ninth step is to establish a model combining seven keywords and output a detailed list of evaluation issues for nine types of questions, including grouped data, raw data, traffic data, and on-site data, for the next step of cost reduction and efficiency improvement.

[0056] Specifically, by grouping the seven keywords into different types of combinations based on their importance, different problems can be generated, and different solutions can be developed, ultimately achieving different cost reduction and efficiency improvement effects.

[0057] In some embodiments of this application, a method for evaluating the value of mobile base stations based on cost reduction and efficiency improvement is also provided. This method is based on accurate financial accounting data of tower rental and electricity fees, the group network optimization platform, the group tower leasing system and the provincial network optimization platform, and the innovative establishment of key technical fields (annual rent, annual electricity fee, total equipment current, number of shared facilities, annual supporting facilities, monthly traffic, etc.) for performance evaluation in various stages such as coverage construction, network optimization and maintenance. This method guides, analyzes and processes the cost reduction and efficiency improvement work of wireless network tower rental and electricity fees in the construction, network optimization and maintenance stages, and creates a comprehensive and accurate mobile base station performance value evaluation system.

[0058] The specific process of this method includes the following steps:

[0059] The first step is data acquisition. The data source consists of five sub-modules: tower rental expense reimbursement data, electricity expense reimbursement data, group tower leasing data, group network optimization system data, and provincial network optimization system data. The tower rental and electricity expense reimbursement data are directly summarized into annual rent and annual electricity cost data using SQL scripts and output as EXECL tables. The group tower leasing system, group network optimization system data, and provincial network optimization platform data are exported as EXECL tables.

[0060] The second step is to import the data. Using Python, import (single or batch) data into the MySQL database to create the original data sample library.

[0061] The third step is to merge site call volume and traffic data. All cell-level 2G / 3G / 4G / 5G raw data are parsed using the AiNet Optimization site association table, and the data is merged into site-level 2G / 3G / 4G / 5G data.

[0062] The fourth step is to create theoretical current and energy consumption meters for field equipment. This involves obtaining standard current and power consumption data for various devices from manufacturers, acquiring actual operating currents for different devices through on-site sampling tests, obtaining power consumption data for different time periods through sampling and installation of smart meters, and obtaining average current values ​​for various devices through an environmental monitoring system. The final result is a standard equipment power consumption meter (based on various factors such as device type, call volume, data flow, and peak / off-peak hours).

[0063] The fifth step is to calculate the total current and total energy consumption of the site. Based on the data of the AiNet Optimization equipment, including CDMA base stations, CDMA cells, LDMA base stations, LDMA cells, 5G base stations, 5G cells, 2.1G cells, 800M cells, 2.6G cells, and 1.8G cells, and using the formula to calculate the number of S200 transmission equipment and IPRAN, the total current of the equipment is finally obtained, and then the energy consumption is converted.

[0064] The sixth step is to calculate the variable annual ancillary costs for site selection to reduce costs and increase efficiency. Based on relevant data from the group's tower leasing system, the three parameters that can be reduced—computer room, ancillary, and maintenance costs—are combined into key annual ancillary costs parameters to reduce the impact of fixed costs and thus improve accuracy.

[0065] The seventh step involves discretizing continuous data using a clustering analysis model. Since the data analyzed comes from three different parts with different dimensions and units, it cannot be merged and analyzed at the site level. Therefore, this patent addresses the problem from a statistical modeling perspective, using the Q-type clustering statistic and the Minkowski distance measure. The algorithm categorizes all data, grouping data with similar properties into the same category and observations with significantly different properties into different categories. After determining the algorithm, all original data are grouped.

[0066] Step 8: Adjust the grouped data to a bell-shaped distribution using the classification dictionary: The determination of the central tendency of the frequency in statistics must conform to a bell-shaped distribution in order to focus on solving key problems. Therefore, the classification dictionary must be dynamically adjusted in a timely manner. The central tendency of the grouped data frequency of each keyword (except for the sharing rate) should ensure that the output of the most critical problem is minimized, thereby reducing the workload on site and maximizing the effect of cost reduction and efficiency improvement.

[0067] The ninth step involves organically combining seven keywords to form nine types of questions, including a detailed list of evaluation questions for grouped data, raw data, traffic data, and on-site data. This process provides a multi-dimensional analysis of the characteristics of mobile base station value and outputs TOPN question points according to the principle of bell-shaped distribution, thereby constructing a complete base station value evaluation method based on cost reduction and efficiency improvement.

[0068] Through the above steps, it is possible to organically combine the financial data of mobile base stations, such as rental and electricity costs, user behavior, call volume and traffic, as well as the power consumption and reducible configuration parameters of on-site equipment, based on cost reduction and efficiency improvement efforts, thereby forming the original data combination of base station site level and sub-scenario.

[0069] Furthermore, this application establishes a scientific correlation between the number of on-site mobile base station devices, power consumption, and actual electricity bill payments through theoretical analysis of equipment power consumption. This solves the long-standing problem of unverifiable base station electricity bills and provides a reliable reference for electricity bill verification based on cost reduction and efficiency improvement. Moreover, this application employs continuous data clustering analysis to discretize the original data across different dimensions, resolving the issue of the inability to merge and evaluate data due to the seven keywords belonging to different dimensions and units. It also utilizes a dynamically adjusted classification dictionary to achieve long-term dynamic analysis of changes in the original network data and employs data visualization tools such as radar charts to achieve visual comparisons by region and problem type.

[0070] Finally, this application uses seven keywords to form nine types of questions, including a detailed list of evaluation questions for grouped data, raw data, traffic data, and field data. It conducts a multi-dimensional analysis of the characteristics of mobile base station value and outputs TOPN question points according to the principle of bell-shaped distribution. This constructs a complete base station value evaluation method based on cost reduction and efficiency improvement, which can evaluate different base stations under the same standard.

[0071] In summary, this application proposes a method for evaluating the value of mobile base stations based on cost reduction and efficiency improvement. Firstly, it establishes a theoretical energy consumption database for standard equipment through various means, including manufacturer provision, network management monitoring, and on-site testing, to address the issue of unverifiable electricity costs. Secondly, it establishes key technical fields for performance evaluation (annual rent, annual electricity cost, total equipment current, number of shared devices, annual infrastructure costs, monthly traffic, etc.) for each stage of coverage construction, network optimization, and maintenance, based on the needs of cost reduction and efficiency improvement. Thirdly, it utilizes cluster analysis to address the problem of not being able to combine keywords from different dimensions and units for the same site for analysis. Fourthly, it uses statistical frequency central tendency determination to perform bell-shaped distribution processing on the keywords, solving the TOPN screening problem. Finally, it organically combines the seven major keywords to analyze specific issues and provide corresponding guidance and suggestions, creating a comprehensive and precise method for evaluating the performance value of mobile base stations for cost reduction and efficiency improvement.

[0072] Example 2

[0073] According to an embodiment of this application, an embodiment of a base station evaluation device is provided. Figure 7 This is a base station evaluation device provided according to an embodiment of this application. For example... Figure 7 As shown, the device includes: an acquisition module 70, used to determine relevant data of a target base station, wherein the relevant data includes at least one type of sub-relevant data; a first processing module 72, used to determine a set of data evaluation results for the relevant data, wherein the set of data evaluation results includes the data evaluation results for each type of data in the relevant data; and a second processing module 74, used to determine the base station evaluation result of the target base station based on the set of data evaluation results.

[0074] It should be noted that the apparatus provided in this embodiment can be used to execute the method provided in Embodiment 1. Therefore, the relevant explanations and descriptions of the base station evaluation method in Embodiment 1 also apply to the embodiments of this application, and will not be repeated here.

[0075] Example 3

[0076] According to an embodiment of this application, a non-volatile storage medium is also provided. The non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device where the storage medium is located to execute the following base station evaluation method: determining relevant data of a target base station, wherein the relevant data includes at least one type of sub-relevant data; determining a set of data evaluation results for the relevant data, wherein the set of data evaluation results includes the data evaluation results for each type of data in the relevant data; and determining the base station evaluation result of the target base station based on the set of data evaluation results.

[0077] According to an embodiment of this application, an electronic device is also provided. The electronic device includes a processor for running a program, wherein the program executes the following base station evaluation method: determining relevant data of a target base station, wherein the relevant data includes at least one type of sub-relevant data; determining a data evaluation result set of the relevant data, wherein the data evaluation result set includes the data evaluation result of each type of data in the relevant data; and determining the base station evaluation result of the target base station based on the data evaluation result set.

[0078] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0079] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0081] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0082] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0083] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0084] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A base station evaluation method characterized by comprising: The method comprises: determining relevant data of a target base station, wherein the relevant data comprises at least one type of sub-relevant data, the sub-relevant data comprises financial data, equipment data and user data, and the at least one type of sub-relevant data corresponds to different dimensions and units respectively; determining a data evaluation result set of the relevant data, wherein the data evaluation result set comprises a data evaluation result of each type of data in the relevant data; determining a base station evaluation result of the target base station according to the data evaluation result set; after determining the relevant data of the target base station, further comprising: using a clustering algorithm to cluster continuous data of a first type of sub-relevant data to divide the continuous data into a plurality of data groups, wherein the first type of sub-relevant data is any one of the at least one type of sub-relevant data, the number of data groups corresponding to all sub-relevant data is the same, and the number of data groups is the same as the number of clustering centers; determining a target value corresponding to the data group corresponding to the continuous data, wherein the target value comprises any one integer from 0 to 5; the method further comprises: adjusting the grouping data corresponding to the data group through a classification dictionary library to a bell-shaped distribution, wherein each grouping data corresponds to a keyword, the classification dictionary library is used to make the frequency concentration trend of the grouping data of the keyword present a bell-shaped distribution, the bell-shaped distribution is that a first proportion of data corresponding to each keyword is located in the center region of a histogram, a second proportion of data is located in the two end regions of the histogram, and the first proportion is greater than the second proportion; organically combining a plurality of keywords to obtain a plurality of problems, wherein the problems are composed of grouping data, original data, traffic data and field data of the target base station; outputting at least one problem point from the plurality of problems according to the principle of the bell-shaped distribution to obtain a base station evaluation result.

2. The base station evaluation method of claim 1, characterized by determining a base station evaluation result of the target base station according to the data evaluation result set comprises: determining a preset base station evaluation result set, wherein the preset base station evaluation result set comprises a sub-evaluation index corresponding to each type of sub-relevant data in the relevant data; determining the base station evaluation result corresponding to the data evaluation result from the preset base station evaluation result set according to the data evaluation result set.

3. The base station evaluation method of claim 2, characterized by determining a data evaluation result set of the relevant data comprises: determining frequency characteristic information of the sub-relevant data; determining an evaluation result corresponding to the sub-relevant data according to the frequency characteristic information, wherein the evaluation result comprises that the sub-relevant data is higher than a standard value and that the sub-relevant data is lower than the standard value.

4. The base station evaluation method of claim 2, characterized by after determining the data evaluation result set of the relevant data, the base station evaluation method further comprises: in response to a selection instruction of a target object, determining a target sub-relevant data from the each type of sub-relevant data; displaying the evaluation result corresponding to the target sub-relevant data to the target object through a visual interface.

5. The base station evaluation method of claim 2, characterized by The sub-related data includes financial data, equipment data and user data, the financial data includes at least one of the following: total rent of the target base station in a preset time period, total electricity fee of the target base station in the preset time period, total supporting cost data of the target base station in the preset time period; the equipment data includes at least one of the following: average current value of the target base station in the preset time period, sharing number of the target base station in the preset time period; the user data includes at least one of the following: total data flow of the target base station in the preset time period, total traffic volume of the target base station in the preset time period.

6. The base station evaluation method of claim 5, characterized by The total supporting cost data is determined by: determining feature information of a target machine room in the target base station, maintenance cost of the target base station in the preset time period, and supporting service cost of the target base station in the preset time period, wherein the target machine room is a machine room with reducible cost in the target base station; determining the total supporting cost data according to the feature information, the maintenance cost and the supporting service cost.

7. The base station evaluation method of claim 1, characterized by, After determining the base station evaluation result of the target base station according to the data evaluation result set, the base station evaluation method further includes: obtaining a preset base station optimization scheme set, wherein the base station optimization scheme set includes at least one base station optimization scheme, and each base station optimization scheme in the at least one base station optimization scheme corresponds to a base station evaluation result; determining a base station optimization scheme corresponding to the target base station from the base station optimization scheme set according to the base station evaluation result of the target base station.

8. A base station evaluation device, characterized by comprising: includes: an acquisition module configured to determine relevant data of a target base station, wherein the relevant data includes at least one type of sub-related data, the sub-related data includes financial data, equipment data and user data, and the at least one type of sub-related data corresponds to different dimensions and units; after determining the relevant data of the target base station, the method further includes: clustering continuous data of a first type of sub-related data using a clustering algorithm to divide the continuous data into a plurality of data groups, wherein the first type of sub-related data is any one of the at least one type of sub-related data, the number of data groups corresponding to all sub-related data is the same, and the number of data groups is the same as the number of clustering centers; determining a target value corresponding to a data group corresponding to the continuous data, wherein the target value includes any one of integers from 0 to 5; a first processing module configured to determine a data evaluation result set of the relevant data, wherein the data evaluation result set includes a data evaluation result of each type of data in the relevant data; The second processing module is configured to determine a base station evaluation result of the target base station according to the data evaluation result set, and to adjust the grouped data corresponding to the data packets to a bell-shaped distribution through a classification dictionary library, wherein each of the grouped data corresponds to a keyword, the classification dictionary library is configured to make the frequency centralized trend of the grouped data of the keyword present a bell-shaped distribution, the bell-shaped distribution is that a first proportion of data corresponding to each of the keywords is located in a central region of a histogram, and a second proportion of data is located in two end regions of the histogram, the first proportion is greater than the second proportion, a plurality of the keywords are organically combined to obtain a plurality of questions, the questions are composed of the grouped data, original data, traffic data and field data of the target base station, at least one problem point is output from the plurality of questions according to the principle of the bell-shaped distribution, and a base station evaluation result is obtained.

9. A non-volatile storage medium, comprising: The non-volatile storage medium comprises a stored program, wherein the program controls the device in which the storage medium is located to perform the base station evaluation method in any one of claims 1 to 7 when the program is running.

10. An electronic device comprising a processor, characterized in that The processor is configured to run a program, wherein the program performs the base station evaluation method in any one of claims 1 to 7 when the program is running.

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