Communication base station evaluation method and system, electronic device, and storage medium

Abstract

This disclosure relates to the field of mobile communication technology, specifically to a communication base station evaluation method, a communication base station evaluation system, an electronic device, and a storage medium. The method includes: acquiring communication data corresponding to resident users under a base station to be evaluated; calculating evaluation scores for the base station to be evaluated in preset evaluation dimensions based on the communication data; performing cluster analysis on the evaluation scores for each of the evaluation dimensions corresponding to the base station to be evaluated to obtain cluster evaluation results for each of the evaluation dimensions; and combining the cluster evaluation results for each of the evaluation dimensions and the evaluation scores to determine a comprehensive evaluation result for the base station to be evaluated. This method enables accurate value assessment of communication base stations.

Description

Technical Field

[0001] This disclosure relates to the field of mobile communication technology, specifically to a communication base station evaluation method, a communication base station evaluation system, an electronic device, and a storage medium. Background Technology

[0002] With the development of network technology, base stations are becoming increasingly important. Currently, the number of base stations is increasing, making operation and maintenance more challenging. Given limited operational resources, it is crucial to allocate these resources to high-value areas. Existing technologies typically utilize user consumption data from operators to evaluate high-value base station cells. However, this evaluation method relies on a single statistical dimension, and when user consumption data is missing or inaccurate, it can easily lead to inaccurate evaluation results.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this disclosure is to provide a communication base station evaluation method, a communication base station evaluation system, an electronic device, and a storage medium; to achieve accurate value evaluation of communication base stations.

[0005] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0006] According to a first aspect of this disclosure, a method for evaluating a communication base station is provided, the method comprising:

[0007] Obtain communication data corresponding to resident users under the base station to be evaluated;

[0008] The evaluation score of the base station to be evaluated in the preset evaluation dimension is calculated based on the communication data.

[0009] Cluster analysis is performed on the evaluation scores of each evaluation dimension corresponding to the base station to be evaluated to obtain the cluster evaluation results corresponding to each evaluation dimension;

[0010] By combining the clustering evaluation results corresponding to each of the evaluation dimensions and the evaluation scores, the comprehensive evaluation result corresponding to the base station to be evaluated is determined.

[0011] In one exemplary embodiment of this disclosure, the method further includes:

[0012] The permanent users corresponding to the base station to be evaluated are selected based on the service records of terminal devices residing in the cell within a preset statistical period.

[0013] In one exemplary embodiment of this disclosure, the preset evaluation dimensions include at least one of the following: business evaluation dimension, user evaluation dimension, and scenario evaluation dimension.

[0014] In one exemplary embodiment of this disclosure, calculating the evaluation score of the base station to be evaluated in the service evaluation dimension based on the communication data includes:

[0015] The evaluation score for the business evaluation dimension is calculated by combining the traffic data and the number of regular users corresponding to the station to be evaluated.

[0016] In one exemplary embodiment of this disclosure, calculating the evaluation score of the base station to be evaluated in the user evaluation dimension based on the communication data includes:

[0017] The evaluation score for the user evaluation dimension is calculated by combining user consumption data and user level data.

[0018] In one exemplary embodiment of this disclosure, calculating the evaluation score of the base station to be evaluated in the scene evaluation dimension based on the communication data includes:

[0019] The evaluation score corresponding to the scene evaluation dimension is calculated by combining the preset coverage scene parameters and the inter-station spacing parameters corresponding to the base station to be evaluated.

[0020] In one exemplary embodiment of this disclosure, the step of performing cluster analysis on the evaluation scores of each evaluation dimension corresponding to the base station to be evaluated to obtain the cluster evaluation results corresponding to each evaluation dimension includes:

[0021] Cluster the evaluation scores of each evaluation dimension corresponding to the base station to be evaluated, and configure the distance between each evaluation dimension and the preset center point as the clustering weight value corresponding to the evaluation dimension;

[0022] The weight coefficients corresponding to each evaluation dimension are determined based on the clustering weight values ​​corresponding to each evaluation dimension, and the weight coefficients are configured as the clustering evaluation results.

[0023] According to a second aspect of this disclosure, a communication base station evaluation system is provided, comprising:

[0024] The resident user data acquisition module is used to acquire the communication data corresponding to the resident users under the base station to be evaluated;

[0025] The evaluation score calculation module is used to calculate the evaluation score of the base station to be evaluated in a preset evaluation dimension based on the communication data.

[0026] The clustering evaluation module is used to perform clustering analysis on the evaluation scores of each evaluation dimension corresponding to the base station to be evaluated, so as to obtain the clustering evaluation results corresponding to each evaluation dimension.

[0027] The comprehensive evaluation result calculation module is used to combine the cluster evaluation results corresponding to each of the evaluation dimensions and the evaluation scores to determine the comprehensive evaluation result corresponding to the base station to be evaluated.

[0028] According to a third aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform a communication base station evaluation method as described in any of the above embodiments by executing the executable instructions.

[0029] According to a fourth aspect of this disclosure, a storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the communication base station evaluation method as described in any of the above embodiments.

[0030] In one embodiment of this disclosure, a communication base station evaluation method is provided. By acquiring communication data corresponding to resident users under the base station to be evaluated, the communication data can be used to calculate score data under different evaluation dimensions. Cluster analysis is performed on the score data under different evaluation dimensions to obtain corresponding cluster evaluation results. The comprehensive evaluation result of the base station to be evaluated can be calculated using the cluster evaluation results and evaluation scores corresponding to each preset evaluation dimension. This enables the digital evaluation of the base station's value from different dimensions using different types of communication data from resident users of the base station, and improves the comprehensiveness and accuracy of the evaluation.

[0031] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0032] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0033] Figure 1 The illustration shows a schematic diagram of a communication base station evaluation method according to an exemplary embodiment of the present disclosure;

[0034] Figure 2 This illustration schematically shows a method for obtaining clustering evaluation results corresponding to evaluation dimensions in an exemplary embodiment of this disclosure;

[0035] Figure 3 This schematic diagram illustrates a communication base station evaluation apparatus according to an exemplary embodiment of the present disclosure;

[0036] Figure 4 A schematic diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown.

[0037] Figure 5 The schematic diagram illustrates the composition of a storage medium in an exemplary embodiment of the present disclosure. Detailed Implementation

[0038] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0039] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0040] In existing technologies, base station value assessment typically uses operator user consumption data, setting thresholds for consumption levels to filter high-value base station cells. However, this assessment method has several problems, including: incomplete or inaccurate statistics on resident users within a base station cell lead to errors in user consumption data, resulting in insufficient accuracy in calculating the base station cell's value; inconsistent statistical dimensions for base station cell value result in missing value dimensions, leading to insufficient accuracy in calculating the total value; or, failure to consider the different coverage scenarios of each base station leads to incorrect evaluation results. For example, without considering the substantial differences in the coverage scenarios of base station cells, some base station cells that are intended for wide coverage may be misvalued based on factors such as user numbers and revenue, resulting in their exclusion from the high-value base station cell category, insufficient maintenance investment, and ultimately, impacting user experience.

[0041] In this exemplary embodiment, to address the technical deficiencies in the prior art, a method for evaluating communication base stations is first provided. (Reference) Figure 1 The communication base station evaluation method shown in the figure may specifically include:

[0042] Step S11: Obtain the communication data corresponding to the resident users under the base station to be evaluated;

[0043] Step S12: Calculate the evaluation score of the base station to be evaluated in the preset evaluation dimension based on the communication data;

[0044] Step S13: Perform cluster analysis on the evaluation scores of each evaluation dimension corresponding to the base station to be evaluated to obtain the cluster evaluation results corresponding to each evaluation dimension.

[0045] Step S14: Combine the clustering evaluation results corresponding to each evaluation dimension with the evaluation score to determine the comprehensive evaluation result corresponding to the base station to be evaluated.

[0046] The communication base station evaluation method provided in this example implementation method, on the one hand, acquires the communication data corresponding to the resident users under the base station to be evaluated, and uses this communication data as the data foundation, thereby realizing the evaluation using different types of communication data of the resident users of the base station, avoiding the problem of incomplete evaluation caused by using a single type of data; on the other hand, it uses this communication data to calculate the score data under different evaluation dimensions; it performs cluster analysis on the score data under multiple different evaluation dimensions to obtain the corresponding cluster evaluation results, thereby using the cluster evaluation results and evaluation scores corresponding to each preset evaluation dimension to calculate the comprehensive evaluation result corresponding to the base station to be evaluated; thus realizing the digital evaluation of the value of the base station from different dimensions, improving the comprehensiveness and accuracy of the evaluation.

[0047] The following will describe in more detail each step of the communication base station evaluation method in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0048] In step S11, the communication data corresponding to the resident users under the base station to be evaluated is obtained.

[0049] In this example implementation, the method described above can be implemented on the server side or through collaboration between the user-side terminal and the server side. Specifically, a corresponding base station evaluation task can be created for the base station to be evaluated on the user-side terminal. When executing the base station evaluation task, communication data corresponding to the base station to be evaluated can be collected first. This communication data can include any one or a combination of user consumption data, user level data, user periodic measurement reports, and user browsing records. User periodic measurement reports can be obtained through MR (Measurement Report) data; user browsing records can be obtained through DPI (Deep Packet Inspection) technology. For example, communication data corresponding to the base station to be evaluated can be obtained from the operator through a big data platform.

[0050] Specifically, the resident users corresponding to the base station to be evaluated can be screened based on the service records of terminal devices residing in the cell within a preset statistical period.

[0051] For example, the statistical period can be configured to 7 days. If a terminal device has service records for at least 4 out of 7 consecutive days in the same cell, and spends more than 5 hours in the same cell on one of those days, with at least one service record per hour, then the user is considered a resident user of that cell. The communication data of these resident users is used as the data basis for subsequent base station evaluation. By using the above method to count resident users, errors in user consumption data statistics caused by missing or inaccurate resident user statistics are effectively avoided, leading to insufficient accuracy in base station cell value calculation. For example, one month or three months of communication data from resident users can be collected as the data basis. By using multiple types of data as the data basis for base station value evaluation, the lack of value dimensions due to inconsistent data statistical dimensions is avoided, preventing insufficient accuracy in total value calculation.

[0052] The method for identifying resident users takes into full account the time length (4 days out of 7 days) and business continuity (5 consecutive hours), ensuring the accuracy of identifying resident users in the designated cells and avoiding the shortcomings of existing technologies that have too few resident cells.

[0053] In step S12, the evaluation score of the base station to be evaluated in the preset evaluation dimension is calculated based on the communication data.

[0054] In this example implementation, the aforementioned preset evaluation dimensions include at least one of the following: service evaluation dimension, user evaluation dimension, and scenario evaluation dimension. Specifically, the service evaluation dimension can be used to analyze the average / peak number of resident users / traffic of each base station cell, i.e., the service distribution of that cell; the service evaluation dimension can be used to analyze the average total consumption of resident users of each base station cell over a period of time, as well as the user star rating, i.e., user distribution; and the scenario evaluation dimension can be used to analyze the inter-site spacing and coverage scenario of each base station cell. By pre-configuring multiple data analysis dimensions, the problems of inaccurate evaluation and low precision caused by a single data dimension can be avoided.

[0055] In this example implementation, calculating the evaluation score of the base station to be evaluated in the service evaluation dimension based on the communication data includes:

[0056] The evaluation score for the business evaluation dimension is calculated by combining the traffic data and the number of regular users corresponding to the station to be evaluated.

[0057] Specifically, the formulas for calculating the evaluation scores corresponding to the business evaluation dimensions may include:

[0058] Business Value = Y1 * Traffic Ranking / Total Number of Cells + Y2 * User Ranking / Total Number of Cells

[0059] Wherein, Y1 and Y2 are coefficients, which are constants; the traffic ranking is the ranking of the overall traffic of the cell corresponding to the base station to be evaluated within the preset statistical data collection period among all cells; for example, the data collection period is the aforementioned 1 month or 3 months; or, the traffic ranking can also be the ranking result based on the monthly average traffic of 3 months; the aforementioned user number ranking can be the ranking of the total data of all resident users of the base station to be evaluated within the preset data collection period among all cells.

[0060] In this example implementation, calculating the evaluation score of the base station to be evaluated in the user evaluation dimension based on the communication data includes:

[0061] The evaluation score for the user evaluation dimension is calculated by combining user consumption data and user level data.

[0062] Specifically, the formula for calculating the evaluation score corresponding to the user evaluation dimension may include:

[0063] User Value = U1 * User Consumption + U2 * User Star Rating

[0064] Wherein, U1 and U2 are coefficients, which are constants; user consumption can be the total consumption of a user within a preset statistical data collection period; user star rating can be the user level corresponding to that user.

[0065] In this example implementation, the evaluation score of the base station to be evaluated in the scene evaluation dimension is calculated based on the communication data, including:

[0066] The evaluation score corresponding to the scene evaluation dimension is calculated by combining the preset coverage scene parameters and the inter-station spacing parameters corresponding to the base station to be evaluated.

[0067] Specifically, the formula for calculating the evaluation score corresponding to the scenario evaluation dimension may include:

[0068] Scene value = C1 * Coverage scene coefficient + C2 * Station spacing / 1000

[0069] Where C1 and C2 are coefficients, which are constants; the coverage scenario coefficient is the preset coefficient corresponding to the coverage scenario of the base station to be evaluated; the inter-station distance is the distance between the base station to be evaluated and the nearest other base station. Specifically, the scenario division and the corresponding coefficient values ​​for each scenario are shown in Table 1.

[0070]

[0071]

[0072] Table 1

[0073] By setting the above three dimensions, base station cells, due to their different geographical locations and coverage populations, have different tendencies in business value, user value, and scenario value. By fully utilizing the scenario-based information of base station cells and digitizing it as weights, the accuracy of value assessment is greatly increased.

[0074] In step S13, cluster analysis is performed on the evaluation scores of each evaluation dimension corresponding to the base station to be evaluated to obtain the cluster evaluation results corresponding to each evaluation dimension.

[0075] In this example implementation, refer to Figure 2 As shown, step S13 above may include:

[0076] Step S131: Cluster the evaluation scores of each evaluation dimension corresponding to the base station to be evaluated, and configure the distance between each evaluation dimension and the preset center point as the clustering weight value corresponding to the evaluation dimension.

[0077] Step S132: Determine the weight coefficients corresponding to each evaluation dimension based on the clustering weight values ​​corresponding to each evaluation dimension, and configure the weight coefficients as the clustering evaluation results.

[0078] Specifically, after obtaining the evaluation scores for the business evaluation dimension, user evaluation dimension, and scenario evaluation dimension, cluster analysis can be performed on the evaluation scores of these three dimensions. For example, the K-Means clustering algorithm can be used to calculate the distance from the base station to be evaluated to the cluster center in each of the three dimensions, which serves as the cluster weight value for each evaluation dimension, denoted as D1 for the user evaluation dimension, D2 for the business evaluation dimension, and D3 for the scenario evaluation dimension. For example, the rating results of all cells in the above three dimensions can be calculated, and the data of these rating results can be used to perform clustering and grouping in each of the three dimensions until convergence, thereby calculating the distance from the current base station to be evaluated to the preset cluster center.

[0079] Based on the clustering weight values ​​mentioned above, the weight coefficients corresponding to each evaluation dimension are calculated. Specifically, the calculation formula may include:

[0080] J1=(D2+D3) / (2*(D1+D2+D3))

[0081] J2=(D1+D3) / (2*(D1+D2+D3))

[0082] J3=(D1+D2) / (2*(D1+D2+D3))

[0083] Where J1 is the weight coefficient corresponding to the user evaluation dimension, J2 is the weight coefficient corresponding to the business evaluation dimension, and J3 is the weight coefficient corresponding to the scenario evaluation dimension.

[0084] In step S14, the comprehensive evaluation result corresponding to the base station to be evaluated is determined by combining the cluster evaluation results corresponding to each evaluation dimension and the evaluation score.

[0085] In this example implementation, after determining the evaluation scores and weighting coefficients corresponding to each evaluation dimension, the comprehensive evaluation result for the base station to be evaluated can be calculated. The calculation formula for the comprehensive evaluation result may include:

[0086] Total Value = J1 * User Value + J2 * Business Value + J3 * Scenario Value

[0087] In some exemplary embodiments, when calculating the comprehensive evaluation result of the base station to be evaluated, only any two of the above three evaluation dimensions may be used for calculation. For example, only the evaluation scores and weight coefficients of the service evaluation dimension and the scenario evaluation dimension may be used for calculation; or, only the evaluation scores and weight coefficients of the service evaluation dimension and the user evaluation dimension may be used for calculation; or, only the evaluation scores and weight coefficients of the scenario evaluation dimension and the user evaluation dimension may be used for calculation.

[0088] In some exemplary implementations, after calculating the comprehensive evaluation results for each base station, the base stations can be ranked according to the scoring results, thereby selecting high-value base stations based on the ranking results. When a base station needs maintenance, maintenance personnel can be assigned to the base stations ranked higher in the ranking based on the ranking results.

[0089] In some exemplary embodiments, the comprehensive evaluation results and ranking results of the base station to be evaluated can be displayed on the user-side terminal device. Additionally, basic data such as resident users and their corresponding communication data can be updated periodically to update the comprehensive evaluation results of the base station to be evaluated.

[0090] The communication base station evaluation method provided in this disclosure can utilize operator user data and base station data to obtain communication data of resident users under each base station cell as a data foundation. It then calculates value scores corresponding to service evaluation dimensions, user evaluation dimensions, and scenario evaluation dimensions for each resident user, and uses clustering to contextualize the value tendency of the base station cell, thereby obtaining a scenario-based weight coefficient for each base station cell. Finally, it uses the value scores and weight coefficients corresponding to each evaluation dimension to calculate the total value score of the communication base station to be evaluated, achieving a comprehensive evaluation from multiple dimensions. This method can improve user satisfaction by prioritizing network optimization issues based on the comprehensive evaluation of base station cell value. Simultaneously, it can guide existing network optimization personnel to optimize work efficiency, prioritize high-value issues, and maximize the utilization of network optimization resources in production processes. It achieves digital evaluation of base station cell value, improves the comprehensiveness of the evaluation, and enhances the accuracy of the evaluation results.

[0091] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0092] Further reference Figure 3 As shown, this example embodiment also provides a communication base station evaluation system 30, which includes: a resident user data acquisition module 301, an evaluation score calculation module 302, a clustering evaluation module 303, and a comprehensive evaluation result calculation module 304; wherein,

[0093] The resident user data acquisition module 301 can be used to acquire the communication data corresponding to the resident users under the base station to be evaluated.

[0094] The evaluation score calculation module 302 can be used to calculate the evaluation score of the base station to be evaluated in a preset evaluation dimension based on the communication data.

[0095] The clustering evaluation module 303 can be used to perform clustering analysis on the evaluation scores of each evaluation dimension corresponding to the base station to be evaluated, so as to obtain the clustering evaluation results corresponding to each evaluation dimension.

[0096] The comprehensive evaluation result calculation module 304 can be used to combine the clustering evaluation results corresponding to each of the evaluation dimensions and the evaluation score to determine the comprehensive evaluation result corresponding to the base station to be evaluated.

[0097] In some exemplary embodiments, the system further includes a resident user filtering module.

[0098] The resident user screening module can be used to screen the resident users corresponding to the base station to be evaluated based on the service records of terminal devices residing in the cell within a preset statistical period.

[0099] In some exemplary embodiments, the preset evaluation dimensions include at least one of the following: business evaluation dimension, user evaluation dimension, and scenario evaluation dimension.

[0100] In some exemplary embodiments, the evaluation score calculation module 302 may include: a business evaluation dimension calculation module.

[0101] The business evaluation dimension calculation module can be used to calculate the evaluation score of the business evaluation dimension by combining the traffic data and the number of resident users corresponding to the station to be evaluated.

[0102] In some exemplary embodiments, the evaluation score calculation module 302 may include a user evaluation dimension calculation module.

[0103] The user evaluation dimension calculation module can be used to calculate the evaluation score of the user evaluation dimension by combining user consumption data and user level data.

[0104] In some exemplary embodiments, the evaluation score calculation module 302 may include: a scene evaluation dimension calculation module.

[0105] The scenario evaluation dimension calculation module can be used to calculate the evaluation score corresponding to the scenario evaluation dimension by combining the preset coverage scenario parameters and the inter-station spacing parameters corresponding to the base station to be evaluated.

[0106] In some exemplary embodiments, the clustering evaluation module 303 may include: a clustering processing module and a weight system calculation module; wherein,

[0107] The clustering processing module can be used to cluster the evaluation scores of each evaluation dimension corresponding to the base station to be evaluated, and configure the distance between each evaluation dimension and the preset center point as the clustering weight value corresponding to the evaluation dimension.

[0108] The weight system calculation module can be used to determine the weight coefficients corresponding to each evaluation dimension based on the clustering weight values ​​corresponding to each evaluation dimension, and configure the weight coefficients as the clustering evaluation results.

[0109] The specific details of each module in the aforementioned communication base station evaluation system have been described in detail in the corresponding communication base station evaluation methods, so they will not be repeated here.

[0110] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0111] Furthermore, this example embodiment provides an electronic device 400 capable of implementing the above method. Figure 4 The electronic device 400 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0112] like Figure 4 As shown, the components of the electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one storage unit 420, and a bus 430 connecting different system components (including storage unit 420 and processing unit 410).

[0113] The storage unit stores program code that can be executed by the processing unit 410, causing the processing unit 410 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 410 can perform actions such as... Figure 1 The steps are shown in the figure.

[0114] Storage unit 420 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 4201 and / or cache memory 4202, and may further include a read-only memory (ROM) 4203.

[0115] Storage unit 420 may also include a program / utility 4204 having a set (at least one) program module 4205, such program module 4205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0116] Bus 430 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0117] Computer system 400 can also communicate with one or more external devices 50 (e.g., keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with computer system 400, and / or any device that enables computer system 400 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 450. Furthermore, computer system 400 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 460. As shown, network adapter 460 communicates with other modules of computer system 400 via bus 430. Processing unit 410 is connected to display unit 440 via bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with computer system 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0118] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0119] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0120] refer to Figure 5As shown, a program product 500 for implementing the above-described method according to an embodiment of the present invention is described. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0121] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0122] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0123] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0124] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0125] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0126] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0127] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for evaluating communication base stations, characterized in that, The method includes: Obtain communication data corresponding to resident users under the base station to be evaluated; wherein, the communication data includes at least one of user consumption data, user level data, user periodic measurement reports, and user browsing history; The evaluation score of the base station to be evaluated is calculated based on the communication data according to the preset evaluation dimensions. The preset evaluation dimensions include at least one of a service evaluation dimension, a user evaluation dimension, and a scenario evaluation dimension. The service evaluation dimension is used to analyze the average number of resident users, peak number of users, and traffic of each base station cell. The user evaluation dimension is used to analyze the average total consumption and user star rating of resident users of each base station cell over a period of time. The scenario evaluation dimension is used to analyze the base station spacing and coverage scenario of each base station cell. Cluster analysis is performed on the evaluation scores of each evaluation dimension corresponding to the base station to be evaluated to obtain the cluster evaluation results corresponding to each evaluation dimension. This includes: using the K-Means algorithm to cluster the evaluation scores of each evaluation dimension corresponding to the base station to be evaluated, configuring the distance between each evaluation dimension and a preset center point as the cluster weight value corresponding to the evaluation dimension; determining the weight coefficient corresponding to each evaluation dimension based on the cluster weight value corresponding to each evaluation dimension, and configuring the weight coefficient as the cluster evaluation result. By combining the clustering evaluation results corresponding to each evaluation dimension and the evaluation score, the comprehensive evaluation result corresponding to the base station to be evaluated is determined, and the base stations to be evaluated are ranked according to the comprehensive evaluation result. Based on the ranking result of the base stations, maintenance personnel are assigned to the base stations ranked higher.

2. The communication base station evaluation method according to claim 1, characterized in that, The method further includes: The permanent users corresponding to the base station to be evaluated are selected based on the service records of terminal devices residing in the cell within a preset statistical period.

3. The communication base station evaluation method according to claim 1, characterized in that, The evaluation score of the base station to be evaluated in the service evaluation dimension is calculated based on the communication data, including: The evaluation score for the service evaluation dimension is calculated by combining the traffic data and the number of resident users corresponding to the base station to be evaluated.

4. The communication base station evaluation method according to claim 1, characterized in that, The evaluation score of the base station to be evaluated in the user evaluation dimension is calculated based on the communication data, including: The evaluation score for the user evaluation dimension is calculated by combining user consumption data and user level data.

5. The communication base station evaluation method according to claim 1, characterized in that, The evaluation score of the base station to be evaluated in the scene evaluation dimension is calculated based on the communication data, including: The evaluation score corresponding to the scene evaluation dimension is calculated by combining the preset coverage scene parameters and the inter-station spacing parameters corresponding to the base station to be evaluated.

6. A communication base station evaluation system, characterized in that, The system includes: The resident user data acquisition module is used to acquire communication data corresponding to resident users under the base station to be evaluated; wherein, the communication data includes at least one of user consumption data, user level data, user periodic measurement reports, and user browsing history; The evaluation score calculation module is used to calculate the evaluation score of the base station to be evaluated in a preset evaluation dimension based on the communication data; wherein, the preset evaluation dimension includes at least one of a service evaluation dimension, a user evaluation dimension, and a scenario evaluation dimension; the service evaluation dimension is used to analyze the average number of resident users, the peak number of users, and the traffic of each base station cell; the user evaluation dimension is used to analyze the average total consumption and user star rating of resident users of each base station cell over a period of time; the scenario evaluation dimension is used to analyze the base station spacing and coverage scenario of each base station cell. The clustering evaluation module is used to perform clustering analysis on the evaluation scores of each evaluation dimension corresponding to the base station to be evaluated, so as to obtain the clustering evaluation results corresponding to each evaluation dimension. This includes: using the K-Means algorithm to cluster the evaluation scores of each evaluation dimension corresponding to the base station to be evaluated, configuring the distance between each evaluation dimension and a preset centroid as the clustering weight value corresponding to the evaluation dimension; determining the weight coefficient corresponding to each evaluation dimension based on the clustering weight value corresponding to each evaluation dimension, and configuring the weight coefficient as the clustering evaluation result. The comprehensive evaluation result calculation module is used to combine the cluster evaluation results corresponding to each evaluation dimension and the evaluation score to determine the comprehensive evaluation result corresponding to the base station to be evaluated, and to sort the base stations to be evaluated according to the comprehensive evaluation result, so as to prioritize the allocation of operation and maintenance personnel to the base stations ranked higher based on the base station ranking results.

7. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the communication base station evaluation method of any one of claims 1 to 5 by executing the executable instructions.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the communication base station evaluation method as described in any one of claims 1 to 5.

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