Method and apparatus for evaluating three-carrier aggregation groups, electronic device, medium and product

Abstract

Embodiments of the present disclosure disclose a three-carrier aggregation group evaluation method and device, electronic equipment, medium and product. The method comprises: obtaining base station engineering parameters of a target area, determining a three-carrier aggregation group from the target area based on the base station engineering parameters and a three-carrier aggregation check condition; collecting measured performance index values of three target cells in a preset time period under the condition that the three-carrier aggregation group is started in a three-carrier aggregation mode; determining a performance evaluation result according to the measured performance index values corresponding to the three target cells and an expected performance index value of the three-carrier aggregation group; and optimizing the three-carrier aggregation group using multi-level optimization logic in the case that the performance evaluation result is substandard. The technical solution realizes accurate selection of the three-carrier aggregation group in the target area, quantitative evaluation of performance after starting, and multi-level intelligent optimization when substandard, and forms a full-process closed loop of three-carrier aggregation deployment, evaluation and optimization.

Description

Technical Field

[0001] This disclosure relates to the field of wireless communication technology, and in particular to a method, apparatus, electronic device, medium, and product for evaluating three-carrier aggregation groups. Background Technology

[0002] As 5G-Advanced (5G-A) technology enters the stage of large-scale commercial use, the network's requirements for speed, capacity, latency and reliability continue to increase. Three-component carrier aggregation (3CC), as a core enhancement technology of 5G-A, can significantly improve system bandwidth, user peak rate and network carrying capacity by aggregating and using multiple carrier resources. It has become a key means to improve the user experience in high-density scenarios, hotspot areas and high-value user experiences.

[0003] In related technologies, the deployment of 3CC technology typically relies on manual planning, static configuration, and post-event evaluation. However, this approach has significant limitations in deployment and parameter optimization: at the deployment level, a strategy of uniform activation across the entire region is usually adopted, resulting in low deployment flexibility and a tendency for resource over- or under-allocation, making it difficult to guarantee actual gains; at the parameter optimization level, it depends on manual experience, leading to long optimization cycles, slow response times, and certain limitations in application scenarios, making it difficult to match the needs of rapidly changing networks. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, medium, and product for evaluating three-carrier aggregation groups, enabling precise selection of three-carrier aggregation groups within a target area, quantitative evaluation of their performance after activation, and multi-level intelligent optimization when performance fails to meet standards, thus forming a closed-loop process for the deployment, evaluation, and optimization of three-carrier aggregation.

[0005] According to one aspect of this disclosure, a method for evaluating three-carrier aggregation groups is provided, the method comprising: The base station engineering parameters corresponding to the target area are obtained, and based on the base station engineering parameters and the three-carrier aggregation verification conditions, a three-carrier aggregation group is determined from multiple cells in the target area; wherein, the three-carrier aggregation group is a set of cells for which the three-carrier aggregation mode is to be enabled; the three-carrier aggregation group includes three target cells; the target cells belong to multiple cells; the three-carrier aggregation group is determined based on the evaluation scores of the cells under multiple performance evaluation dimensions; When the three-carrier aggregation group is in three-carrier aggregation mode, the measured performance index values ​​of the three target cells included in the three-carrier aggregation group are collected within a preset time period. Based on the measured performance index values ​​corresponding to the three target cells respectively and the predetermined expected performance index values ​​corresponding to the three-carrier aggregation group, the performance evaluation result corresponding to the three-carrier aggregation group is determined; If the performance evaluation result is not up to standard, a multi-level optimization logic is used to optimize the three-carrier aggregation group to obtain the optimized three-carrier aggregation group.

[0006] According to another aspect of this disclosure, a three-carrier aggregation group evaluation apparatus is provided, the apparatus comprising: The three-carrier aggregation group determination module is used to acquire base station engineering parameters corresponding to the target area, and determine a three-carrier aggregation group from multiple cells in the target area based on the base station engineering parameters and three-carrier aggregation verification conditions; wherein, the three-carrier aggregation group is a set of cells for which three-carrier aggregation mode is to be enabled; the three-carrier aggregation group includes three target cells; the target cells belong to multiple cells; the three-carrier aggregation group is determined based on the evaluation scores of the cells under multiple performance evaluation dimensions; The measured data acquisition module is used to collect the measured performance index values ​​of the three target cells included in the three-carrier aggregation group within a preset time period when the three-carrier aggregation group is in three-carrier aggregation mode. The evaluation result determination module is used to determine the performance evaluation result corresponding to the three-carrier aggregation group based on the measured performance index values ​​corresponding to the three target cells respectively and the pre-determined expected performance index values ​​corresponding to the three-carrier aggregation group. The three-carrier aggregation group optimization module is used to optimize the three-carrier aggregation group using multi-level optimization logic when the performance evaluation result is not up to standard, so as to obtain the optimized three-carrier aggregation group.

[0007] According to another aspect of this disclosure, an electronic device is provided, the electronic device comprising: One or more processors; Storage device for storing one or more programs. When one or more programs are executed by one or more processors, the one or more processors implement a three-carrier aggregation group evaluation method as described in any of the embodiments of this disclosure.

[0008] According to another aspect of this disclosure, a computer-readable storage medium is provided that stores computer instructions for causing a processor to execute and implement any of the three-carrier aggregation group evaluation methods in the embodiments of this disclosure.

[0009] According to another aspect of the present disclosure, a computer program product is provided, which, when executed by a processor, implements a three-carrier aggregation group evaluation method as described in any of the embodiments of the present disclosure.

[0010] The technical solution of this disclosure, based on base station engineering parameters and three-carrier aggregation verification conditions, selects a three-carrier aggregation group consisting of three target cells. This achieves precise selection of cell combinations for the three-carrier aggregation mode to be activated, ensuring that the combination meets the basic technical requirements for three-carrier aggregation deployment. Furthermore, the combination is determined through multi-dimensional performance evaluation scores, improving the rationality and adaptability of the three-carrier aggregation deployment. Further, after the three-carrier aggregation group activates the three-carrier aggregation mode, measured performance index values ​​of the three target cells are collected over a preset time period to obtain real performance data after the actual implementation of the three-carrier aggregation mode, providing objective and accurate measured data for subsequent effect evaluation. Further, the measured performance index values ​​of the three target cells are compared and analyzed with preset expected performance index values ​​to quantitatively determine the actual operating effect of the three-carrier aggregation group, forming an intuitive and quantifiable performance evaluation result, clarifying whether the performance has reached the preset target after the three-carrier aggregation mode is activated. Furthermore, for three-carrier aggregation groups that fail to meet performance evaluation standards, multi-level optimization logic is employed to precisely locate and resolve performance issues during operation. This achieves performance tuning of the three-carrier aggregation groups, ensuring that they meet preset performance targets after activation and fully leveraging the performance enhancement value of three-carrier aggregation technology. The technical solution of this disclosure addresses the problems of poor deployment flexibility, potential resource over- or under-allocation, and limitations in application scenarios inherent in related technologies. It achieves precise selection of three-carrier aggregation groups within the target area, quantitative performance evaluation after activation, and multi-level intelligent optimization when performance fails to meet standards. This forms a closed-loop process for three-carrier aggregation deployment, evaluation, and optimization, ensuring that the performance of the three-carrier aggregation mode meets standards after implementation and fully leveraging the speed and capacity enhancement value of the technology.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1A flowchart illustrating a three-carrier aggregation group evaluation method provided in this embodiment of the disclosure; Figure 2 This is a flowchart illustrating a multi-level optimization method for three-carrier aggregation groups provided in an embodiment of this disclosure. Figure 3 A flowchart illustrating another three-carrier aggregation group evaluation method provided in this embodiment of the disclosure; Figure 4 A flowchart illustrating another three-carrier aggregation group evaluation method provided in this embodiment of the disclosure; Figure 5 This is a schematic diagram of the structure of a three-carrier aggregation group evaluation device provided in an embodiment of the present disclosure; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present disclosure, the technical solutions of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present disclosure, and not all embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present disclosure.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure 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 this disclosure 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 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.

[0016] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0017] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0018] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0019] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0020] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0021] Before introducing this technical solution, an exemplary application scenario can be provided. The technical solution provided in this disclosure can be applied to any scenario requiring intelligent management and control of 5G network three-carrier aggregation groups (3CC).

[0022] Currently, in the field of mobile communication network operation and optimization, with the continuous growth of 5G user scale and service traffic, high-value scenarios such as hotspot areas, densely populated urban areas, and transportation hubs are placing higher demands on network speed and capacity. Three-carrier aggregation, as a key technology for improving system bandwidth and user experience, has been widely applied in network performance enhancement deployments in these scenarios. In the actual deployment of three-carrier aggregation groups, methods typically involve manually selecting carrier combinations based on experience, setting performance targets in a coarse manner, monitoring indicators at a single granularity, and passively adjusting the data manually. However, these methods have the following technical problems: the selection of three-carrier aggregation groups mostly relies on manual experience, making it difficult to quantitatively select the best option by comprehensively considering multiple dimensions such as interference, resource utilization, coverage distance, scene attributes, and neighbor cell cooperation. This can easily lead to unreasonable combinations and low aggregation gains. There is a lack of scientific performance prediction mechanisms, making it impossible to accurately predict the rate and traffic improvement effects after 3CC is enabled, resulting in unclear deployment goals and non-objective evaluation benchmarks. The monitoring granularity of 3CC operation is relatively coarse, making it difficult to achieve real-time collection of multi-dimensional and multi-granular indicators and anomaly identification, resulting in insufficient accuracy of evaluation results. When the performance of the three-carrier aggregation group is substandard, there is a lack of systematic and hierarchical closed-loop optimization strategies. Often, manual adjustments or direct cancellation of aggregation are the only options, leading to low operation and maintenance efficiency and difficulty in ensuring network stability.

[0023] To address the aforementioned issues, this disclosure provides a three-carrier aggregation implementation scheme based on multi-dimensional evaluation and multi-level optimization. It automatically selects the best three-carrier aggregation group through quantitative scoring, achieves accurate performance evaluation based on indicator comparison, and performs closed-loop adjustment using multi-level optimization logic when the target is not met, thereby improving the rationality of 3CC deployment and the stability of network operation.

[0024] Figure 1 This is a flowchart illustrating a three-carrier aggregation group evaluation method provided in this embodiment. This embodiment is applicable to any situation requiring intelligent management and control of 5G network three-carrier aggregation groups (3CCs). The method can be executed by a three-carrier aggregation group evaluation device, which can be implemented in hardware and / or software and can be configured in electronic devices such as computers or servers. Figure 1 As shown, the method in this embodiment includes: S110. Obtain the base station engineering parameters corresponding to the target area, and determine the three-carrier aggregation group from multiple cells in the target area based on the base station engineering parameters and the three-carrier aggregation verification conditions.

[0025] The target area can be understood as a specific geographical area requiring the deployment of three-carrier aggregation mode, such as densely populated urban areas, popular business districts, transportation hubs, and industrial parks—any 5G network coverage area requiring improved network speed and capacity. This is the selection scope of the three-carrier aggregation group and the application scope of the technical solution in this disclosure embodiment. Base station engineering parameters refer to a set of various technical parameters used to characterize the physical deployment, wireless environment, resource allocation, and operational characteristics of base stations and their cells within the target area during the construction, planning, and optimization of mobile communication networks. Simply put, base station engineering parameters can be understood as the basic technical parameters related to the deployment of base stations and cells within the target area. Base station engineering parameters can serve as the fundamental data source for 5G network cell selection, three-carrier aggregation group evaluation, performance prediction, and effect analysis, directly determining the rationality and operational stability of the three-carrier aggregation group (3CC) selection. Optionally, base station engineering parameters include, but are not limited to, base station location information (including base station latitude and longitude coordinates, base station antenna height, antenna azimuth and downtilt angles, and relative distances to surrounding cells), cellular coverage area (including cell coverage radius and coverage boundaries), frequency band configuration information (including the frequency bands used by the cell, carrier bandwidth configuration, aggregation capabilities supported by the cell, frequency points, and synchronization information), transmit power, antenna parameters, cell identifiers, and other parameters used to characterize the hardware deployment and wireless coverage characteristics of the base station and cell. A cellular cell can be understood as a wireless communication service unit formed by the coverage of base station sectors, and is the basic coverage and service carrying unit of the 5G network. Each cellular cell corresponds to specific frequency band resources and coverage area, and can independently provide wireless communication services to 5G terminals within the target area. Three-carrier aggregation verification conditions can be understood as preset conditions used to screen cellular cells that meet the requirements for three-carrier aggregation deployment. It is the core judgment criterion for selecting candidate cellular cells and determining three-carrier aggregation groups from multiple cellular cells within the target area. That is, three-carrier aggregation verification conditions can be used to verify whether a cellular cell has three-carrier aggregation capabilities, or to verify whether a cellular cell is suitable for enabling three-carrier aggregation mode. Three-carrier aggregation verification conditions can be used to eliminate cells that do not meet the basic requirements for 3CC cooperative operation, ensuring that the selected candidate cells can achieve the goals of bandwidth aggregation and rate improvement through carrier aggregation, while guaranteeing the stability and reliability of the network operation after aggregation. Optionally, three-carrier aggregation verification conditions include, but are not limited to, cell level threshold conditions, physical resource block (PRB) utilization range conditions, frequency band complementarity requirements, coverage coordination requirements, and hardware and capability adaptation conditions.

[0026] It is understandable that the cell level threshold condition can be used to limit the upper limit of the received interference level of a cell, ensuring that the interference level between cells is within a controllable range, and avoiding signal transmission instability and rate reduction during three-carrier aggregation due to excessive interference. For example, setting the interference level to not exceed a preset level threshold. The PRB utilization range condition can clarify the PRB utilization range of a cell, avoiding the selection of cells with excessively high PRB utilization (resources are saturated and cannot be further increased in capacity through aggregation) and excluding cells with excessively low PRB utilization (insufficient regional service demand, no need to deploy 3CC), ensuring the rational use of aggregation resources. The frequency band complementarity requirement can be used to ensure that the frequency band configuration of cells is complementary, avoiding resource waste and increased interference caused by high frequency band overlap, ensuring that the carriers of the three cells can be effectively aggregated to achieve the superposition of system bandwidth. For example, selecting cells with different frequency bands or different frequency points of the same frequency band for combination. The coverage range coordination requirement can be used to verify whether the coverage range of the cells can form a synergistic complementarity, ensuring that the coverage area of ​​the aggregation group is continuous and without obvious blind spots, while avoiding resource redundancy caused by excessive coverage overlap, ensuring that the terminal can stably access the three carriers within the aggregation area. Hardware and capability compatibility conditions can be used to verify whether the base station hardware and software versions of the cell support the three-carrier aggregation function, ensuring that the cell has the basic capabilities of carrier collaborative scheduling and joint resource allocation, and avoiding the inability to start the 3CC mode normally due to hardware incompatibility.

[0027] In this context, a three-carrier aggregation group can be understood as a set of cells selected from multiple cells within a target area, with three target cells included in this set. Within the three-carrier aggregation group, the three target cells work collaboratively, achieving bandwidth aggregation through carrier aggregation to improve network speed and capacity. The three-carrier aggregation mode can be understood as aggregating and binding the carrier resources corresponding to the three target cells within the three-carrier aggregation group, enabling a working mode of three-carrier aggregation scheduling, joint resource allocation, and parallel data transmission. When the three-carrier aggregation mode is enabled, terminals supporting three-carrier aggregation can simultaneously access and use three carriers for uplink and downlink service transmission, thereby achieving a 5G network enhancement mode that expands system bandwidth, improves user experience speed, and enhances network capacity. Target cells are cells selected from multiple cells within the target area, verified under three-carrier aggregation conditions, and chosen based on multi-dimensional performance evaluation scores. These target cells are the specific components of the three-carrier aggregation group, undertaking service transmission and collaborative work tasks under the three-carrier aggregation mode. Performance evaluation dimensions can be understood as a range of performance indicators used to assess whether a cell is suitable for inclusion in a three-carrier aggregation group. These dimensions are the core evaluation criteria for determining whether a three-carrier aggregation group is suitable. Optional performance evaluation dimensions include interference level, PRB utilization, distance to the center point of the target area, cell scenario attributes (such as ordinary scenarios, scenic spots, companies, etc.), and neighbor cell coordination coefficients.

[0028] It should be noted that the number of three-carrier aggregation groups identified from multiple cells within the target area can be one or more.

[0029] In one implementation, when a target area for deploying the three-carrier aggregation mode is determined, base station engineering parameters corresponding to the target area can be retrieved based on the area identifier of the target area. Furthermore, using the center of the target area as the center and a preset distance as the radius, a screening sub-area is divided from the target area, identifying multiple cells within each screening sub-area, and these cells are designated as cells to be eliminated. Further, based on the retrieved base station engineering parameters and the three-carrier aggregation verification conditions, cells meeting the three-carrier aggregation verification conditions are selected from the multiple cells to be eliminated, and these selected cells are designated as candidate cells. Further, the evaluation scores of each candidate cell are determined across multiple performance evaluation dimensions. Based on the evaluation scores of the multiple candidate cells across multiple evaluation dimensions, a three-carrier aggregation group is determined, and the candidate cells within the determined three-carrier aggregation group are designated as target cells.

[0030] S120. When the three-carrier aggregation group is in three-carrier aggregation mode, collect the measured performance index values ​​of the three target cells included in the three-carrier aggregation group within a preset time period.

[0031] The preset time period can be understood as a pre-defined time interval for collecting measured performance indicators after the three-carrier aggregation group is enabled. Optionally, the preset time period can include 1 day, 3 days, or 1 week, etc. The measured performance indicator values ​​can be understood as the actual operating performance data of each of the three target cells collected in real time within the preset time period after the three-carrier aggregation group is enabled. It is the core data for evaluating the operating effect of the three-carrier aggregation group. The measured performance indicator values ​​may include, but are not limited to, key performance indicators such as downlink experience rate, service traffic, handover success rate, interference level, and PRB utilization.

[0032] In one implementation, after identifying a three-carrier aggregation group within a target area to be activated, the communication base station corresponding to the target area sends a three-carrier aggregation activation command to the central control center and the target cells through the radio resource control layer. This enables carrier binding and coordinated scheduling configuration of the three target cells within the three-carrier aggregation group, activates a multi-carrier parallel transmission mechanism (either inter-frequency or intra-frequency), and establishes a three-carrier joint resource allocation and data transmission channel. This allows the terminal to simultaneously access three carriers for service transmission, thus completing the activation of the three-carrier aggregation mode and entering actual operation. Furthermore, with the three-carrier aggregation group activated, for each target cell within the three-carrier aggregation group, a low-latency transmission protocol can be used to collect performance index data of the target cell according to a preset collection step size to obtain the measured performance index values ​​of the target cell within a preset time period.

[0033] S130. Based on the measured performance index values ​​corresponding to the three target cells and the predetermined expected performance index values ​​corresponding to the three-carrier aggregation group, determine the performance evaluation results corresponding to the three-carrier aggregation group.

[0034] The expected performance index value can be understood as a predetermined performance target value that the three-carrier aggregation group is expected to achieve. It serves as a benchmark for measuring whether the operation of the three-carrier aggregation group meets the target, providing a clear basis for comparison in subsequent performance evaluations. By collecting and comparing the measured performance index values ​​of the three-carrier aggregation group with this expected performance index value, it can be determined whether the operation of the three-carrier aggregation group meets expectations and achieves the preset target. Simply put, the expected performance index value indicates the expected effect of the three-carrier aggregation group after enabling three-carrier aggregation mode. Generally, the expected performance index value can be determined by comprehensively considering factors such as the service requirements of the target area, network load, terminal usage scenarios, base station hardware configuration, and carrier aggregation capabilities. The expected performance index value may include, but is not limited to, key indicators such as the expected downlink experience rate target value, the expected service traffic increase target value, and the expected handover success rate target value. It should be noted that the expected performance index value is the overall target for the entire three-carrier aggregation group, not the performance target of a single target cell. That is, one expected performance index value corresponds to one three-carrier aggregation group, used to characterize the overall performance target expected to be achieved by the three-carrier aggregation group. The performance evaluation result can be understood as a judgment of the operational effect of the three-carrier aggregation group by comparing the measured performance index values ​​of the three target cells with the expected performance index values ​​of the three-carrier aggregation group. There are two situations: "meets the standard" and "does not meet the standard". If the measured performance index values ​​as a whole meet the expected performance index values, it is considered to meet the standard; if at least one of the measured performance index values ​​does not meet the expected performance index value, it is considered to fail to meet the standard.

[0035] In this embodiment, before activating the three-carrier aggregation mode, the expected performance index value corresponding to the three-carrier aggregation group can be determined first. Optionally, determining the expected performance index value corresponding to the three-carrier aggregation group includes at least one of the following: predicting the expected performance index value corresponding to the three-carrier aggregation group based on the historical performance index values ​​of the control reference group corresponding to the three-carrier aggregation group within a preset historical time period; or predicting the expected performance index value corresponding to the three-carrier aggregation group based on a pre-trained performance data prediction model and the historical performance index values ​​of the three-carrier aggregation group within a preset historical time period.

[0036] In one implementation, before activating the three-carrier aggregation mode, the expected performance index value corresponding to the three-carrier aggregation group can be predicted based on the historical performance index values ​​of the control reference group corresponding to the three-carrier aggregation group within a preset historical time period. Furthermore, after collecting the measured performance index values ​​of the three target cells within the three-carrier aggregation group within the preset time period, the measured index value corresponding to the three-carrier aggregation group can be determined based on the measured performance index values ​​corresponding to the three target cells respectively. The measured index value is then compared with the predetermined expected performance index value to determine the performance evaluation result corresponding to the three-carrier aggregation group.

[0037] S140. If the performance evaluation result is not up to standard, multi-level optimization logic is used to optimize the three-carrier aggregation group to obtain the optimized three-carrier aggregation group.

[0038] The multi-level optimization logic can be understood as a hierarchical, progressive strategy used to systematically optimize the three-carrier aggregation group when the performance evaluation result is unsatisfactory. It is the core logic for achieving closed-loop optimization of three-carrier aggregation. Optionally, the multi-level optimization logic may include three optimization logics with decreasing priority, in descending order of priority: reassessment of combination rationality (replacing unsatisfactory target cells within the aggregation group), intelligent tuning of cell parameters (adjusting parameters such as carrier coordination and interference control), and aggregation mode rollback (if the performance is still unsatisfactory after optimization using the first two levels of optimization logic, the three-carrier aggregation mode of the three-carrier aggregation group is turned off).

[0039] In one implementation, when the performance evaluation result of a three-carrier aggregation group is determined to be substandard, a multi-level optimization logic can be immediately initiated to execute the optimization process. First, the cause of the substandard performance is precisely located. Based on the differences between the measured and expected performance indicators of the three target cells, it is determined whether the root cause is an unreasonable aggregation group combination, improper cell parameter configuration, or hardware compatibility issues. Then, following a layered optimization strategy, the rationality of the aggregation group combination is re-evaluated first. Target cells with severely substandard measured performance and poor coordination within the three-carrier aggregation group are replaced, and three-carrier aggregation groups that meet the three-carrier aggregation verification conditions and have high comprehensive scores are re-selected for inclusion in the three-carrier aggregation group. If the performance still fails to meet the standards after optimization, core parameters such as carrier coordination scheduling, interference control, and transmit power of the three target cells are intelligently adjusted to optimize resource allocation efficiency. If the expected performance indicators still cannot be achieved after parameter adjustment, an aggregation mode rollback operation is performed, temporarily disabling the three-carrier aggregation mode of the three-carrier aggregation group. It is then reopened after further optimization and adjustments, ultimately resulting in an optimized three-carrier aggregation group that meets the performance standards and can operate stably.

[0040] For example, Figure 2 This is a flowchart illustrating a multi-level optimization method for three-carrier aggregation groups provided in an embodiment of this disclosure. Figure 2 As shown, firstly, the measured performance index values ​​of the three target cells included in the three-carrier aggregation group are collected within a preset time period. Based on the measured performance index values ​​corresponding to the three target cells and the pre-determined expected performance index values ​​corresponding to the three-carrier aggregation group, the performance of the three-carrier aggregation group is evaluated. If the performance evaluation result is satisfactory, the current configuration of the three-carrier aggregation group is maintained. If the performance evaluation result is unsatisfactory, a multi-level optimization strategy is triggered. The first level: cell combination re-evaluation. Based on real-time collected interference level, PRB utilization, and neighbor cell coordination coefficient data, the fitness of the three-carrier aggregation group is recalculated. If the fitness < 0.7 (out of 1.0), the three-carrier aggregation group is deemed unreasonable, and a combination adjustment mechanism is triggered: a real-time iterative module based on a genetic algorithm replaces 1-2 target cells (prioritizing the top 20% of current fitness backup cells) on the original three-carrier aggregation group, regenerates the optimal three-carrier aggregation group, and distributes the configuration. If the fitness ≥ 0.7, the three-carrier aggregation group is considered reasonable, and a second-level optimization strategy is triggered for further optimization. The second level involves functional parameter tuning. A decision tree-based rule base for parameter adjustment is established, linking "indicator anomaly type - parameter adjustment direction - adjustment magnitude." For example, low downlink rate (indicator anomaly type) - A2 threshold (linked parameter) - decrease (adjustment direction) - 1~3dBm (adjustment magnitude); high inter-band interference (indicator anomaly type) - frequency priority (linked parameter) - adjust dominant frequency band (adjustment direction) - increase priority of low-interference frequency band (adjustment magnitude). Afterwards, the adjusted parameters are distributed using a canary release method (initially effective in 30% of target cells), continuously monitored for 15 minutes (minute-level evaluation) or 1 hour (hour-level evaluation). If the indicators meet the standards, the entire set is released; otherwise, a second adjustment is performed based on the rule base, with a maximum of 3 iterations. If the performance evaluation results still fail to meet the standards after the first two levels of optimization, the 3CC mode fallback mechanism is triggered, and an alarm message is sent to the network management platform. Simultaneously, the current scenario parameters and optimization logs are recorded to provide data support for subsequent algorithm iterations.

[0041] The technical solution of this disclosure, based on base station engineering parameters and three-carrier aggregation verification conditions, selects a three-carrier aggregation group consisting of three target cells. This achieves precise selection of cell combinations for the three-carrier aggregation mode to be activated, ensuring that the combination meets the basic technical requirements for three-carrier aggregation deployment. Furthermore, the combination is determined through multi-dimensional performance evaluation scores, improving the rationality and adaptability of the three-carrier aggregation deployment. Further, after the three-carrier aggregation group activates the three-carrier aggregation mode, measured performance index values ​​of the three target cells are collected over a preset time period to obtain real performance data after the actual implementation of the three-carrier aggregation mode, providing objective and accurate measured data for subsequent effect evaluation. Further, the measured performance index values ​​of the three target cells are compared and analyzed with preset expected performance index values ​​to quantitatively determine the actual operating effect of the three-carrier aggregation group, forming an intuitive and quantifiable performance evaluation result, clarifying whether the performance has reached the preset target after the three-carrier aggregation mode is activated. Furthermore, for three-carrier aggregation groups that fail to meet performance evaluation standards, multi-level optimization logic is employed to precisely locate and resolve performance issues during operation. This achieves performance tuning of the three-carrier aggregation groups, ensuring that they meet preset performance targets after activation and fully leveraging the performance enhancement value of three-carrier aggregation technology. The technical solution of this disclosure addresses the problems of poor deployment flexibility, potential resource over- or under-allocation, and limitations in application scenarios inherent in related technologies. It achieves precise selection of three-carrier aggregation groups within the target area, quantitative performance evaluation after activation, and multi-level intelligent optimization when performance fails to meet standards. This forms a closed-loop process for three-carrier aggregation deployment, evaluation, and optimization, ensuring that the performance of the three-carrier aggregation mode meets standards after implementation and fully leveraging the speed and capacity enhancement value of the technology.

[0042] Figure 3 This is a flowchart illustrating another three-carrier aggregation group evaluation method provided in this embodiment. The technical solution of this embodiment can be combined with other embodiments; for the same or related parts, they can be described in conjunction with the descriptions of other embodiments, and will not be repeated here. Figure 3 As shown, the method in this embodiment may specifically include: S210. Obtain the base station engineering parameters corresponding to the target area, and determine multiple candidate cells from multiple cells in the target area based on the base station engineering parameters and the three-carrier aggregation verification conditions.

[0043] Candidate cells can be understood as cells that meet the three-carrier aggregation verification conditions and have the potential to be included in the three-carrier aggregation group.

[0044] Optionally, based on base station engineering parameters and three-carrier aggregation verification conditions, multiple candidate cells are determined from multiple cells in the target area, including: filtering multiple cells in the target area according to a preset screening radius to obtain multiple cells to be eliminated; determining the radio configuration parameters corresponding to each cell to be eliminated based on the base station engineering parameters; determining whether the cells to be eliminated meet the three-carrier aggregation verification conditions based on the radio configuration parameters corresponding to the cells to be eliminated; and determining the cells to be eliminated that meet the three-carrier aggregation verification conditions as candidate cells to obtain multiple candidate cells.

[0045] The preset screening radius can be a pre-defined geographical distance threshold used to initially screen all cells within the target area, identifying cells within this radius that are at a preset location (e.g., the area center) within the target area, thus forming the initial range of cells to be eliminated. Optionally, the preset screening radius can be 300 meters, 500 meters, or 1000 meters. Cells to be eliminated can be those that have passed the initial screening by the preset radius and are now entering the subsequent verification stage; they are cells that need further verification through three-carrier aggregation to determine their eligibility as candidates. Wireless configuration parameters can be understood as parameters that characterize the wireless operation of each cell to be eliminated, determined individually based on base station engineering parameters. These are the core basis for determining whether a cell to be eliminated meets the three-carrier aggregation verification conditions. Optionally, wireless configuration parameters include interference levels, PRB utilization, frequency band configuration, coverage area, hardware support capabilities, and other parameters directly related to three-carrier aggregation verification.

[0046] In one implementation, a screening sub-region can be divided from the target area using the center of the target area as the center and a preset screening radius as the radius. Multiple cellular cells within each screening sub-region are identified and designated as cells to be eliminated. Further, based on the base station engineering parameters corresponding to each cell to be eliminated within the target area, the radio configuration parameters of each cell to be eliminated are extracted and quantified. Further, for each cell to be eliminated, the radio configuration parameters corresponding to the cell to be eliminated are compared item by item with preset three-carrier aggregation verification conditions to determine whether the cell to be eliminated meets core verification requirements such as interference threshold, frequency band complementarity, and hardware compatibility. Finally, all cells to be eliminated that meet the three-carrier aggregation verification conditions are selected and designated as candidate cellular cells, thus obtaining multiple qualified candidate cellular cells.

[0047] S220. Determine the evaluation score of each candidate cell under multiple performance evaluation dimensions, and determine the three-carrier aggregation group based on the evaluation scores of multiple candidate cells under multiple performance evaluation dimensions.

[0048] In this embodiment, for each candidate cell, the radio configuration parameters corresponding to the candidate cell are input into the normalized evaluation formula corresponding to each performance evaluation dimension to obtain the evaluation score of the candidate cell under multiple performance evaluation dimensions.

[0049] For example, the evaluation score corresponding to the interference level dimension can be determined using the following formula: in, This represents the evaluation score of the candidate cell in the interference level dimension; This represents the measured interference level of the candidate cell (unit: dBm); in 5G networks, the interference level is typically in the range of -120dBm (optimal, no interference) to -150dBm (worst, strong interference). hour, ;when hour, .

[0050] The evaluation score corresponding to the PRB utilization dimension can be determined using the following formula: in, This represents the evaluation score of the candidate cell in the PRB utilization dimension; Indicates the measured PRB utilization rate of the candidate cell; when hour, ;when hour, .

[0051] The evaluation score corresponding to the regional distance dimension can be determined using the following formula: in, This represents the evaluation score of the candidate cell in the regional distance dimension; Indicates the distance from the candidate cell to the center point of the target area (unit: kilometers); when When the candidate cell is at the center of the target area, ;when When the candidate cell is 1 kilometer away from the center of the target area, .

[0052] The evaluation score corresponding to the cell scene attribute dimension can be determined according to the following rules: the evaluation score of the candidate cell under the cell scene attribute dimension is determined according to the priority of the scene attribute. For example, the highest priority (such as important government agencies) corresponds to 1, and the lowest priority (such as ordinary scene) corresponds to 0.25.

[0053] The evaluation score corresponding to the neighboring area synergy coefficient dimension can be determined using the following formula: in, This represents the evaluation score of the candidate cell in terms of the neighbor cell coordination coefficient dimension; This represents the neighbor cell cooperation coefficient of a candidate cell, characterizing the cooperative adaptation ability of the candidate cell with its surrounding neighboring cells (the higher the value, the better the cooperation); when hour, ;when hour, .

[0054] In this embodiment, a three-carrier aggregation group is determined based on the evaluation scores of multiple candidate cells across multiple evaluation dimensions, including at least one of the following: using a genetic algorithm to process the evaluation scores of multiple candidate cells across multiple evaluation dimensions to determine the three-carrier aggregation group; using an enumeration method to traverse all compliant combinations of multiple candidate cells and determining the three-carrier aggregation group based on the comprehensive evaluation score of each combination across multiple evaluation dimensions; or using a support vector machine algorithm to train a model based on the evaluation score of the historical best three-carrier aggregation group, classifying and filtering combinations of multiple candidate cells to determine the three-carrier aggregation group. One of these determination methods is described in detail below.

[0055] Optionally, a three-carrier aggregation group is determined based on the evaluation scores of multiple candidate cells under multiple evaluation dimensions, including: processing the evaluation scores of multiple candidate cells under multiple performance evaluation dimensions using a weighted summation operation to obtain the cell evaluation score corresponding to each candidate cell; taking the multiple candidate cells as the current population and obtaining individuals in the current population; each individual represents the cell evaluation score corresponding to the three candidate cells respectively; for each obtained individual, determining the fitness value corresponding to the individual based on the cell evaluation score corresponding to the three candidate cells respectively represented by the individual; generating a new population based on the fitness values ​​corresponding to each individual, and returning to the operation of obtaining individuals in the current population after taking the new population as the current population, until a preset iteration stopping condition is met; determining the target individuals whose fitness values ​​meet the preset conditions in each iteration, and determining the three-carrier aggregation group based on the target individuals.

[0056] The cell evaluation score can be understood as the comprehensive performance quantification value obtained by weighted summation of candidate cells. The cell evaluation score is a core indicator characterizing the ability of a candidate cell to adapt to three-carrier aggregation deployment; a higher score indicates that the candidate cell is more suitable for inclusion in the three-carrier aggregation group. The current population can be the set of candidate cell combinations used in a genetic algorithm's iteration process for operations such as iteration, crossover, and mutation; it is the object of the algorithm's iteration. The current population consists of multiple individuals, and the population is continuously updated with algorithm iterations. Initially, the current population is the initial combination set composed of all candidate cells; subsequent populations are new populations generated in the previous iteration. An individual can be understood as the basic unit of the population in the genetic algorithm. An individual can uniquely represent a 3CC candidate combination composed of three candidate cells, and this individual is directly associated with the cell evaluation scores of the three candidate cells it contains. That is, the comprehensive performance score of each cell in the 3CC candidate combination can be directly obtained through the individual, making it the basic unit for evaluating the merits of the 3CC combination in the genetic algorithm. The fitness value can be understood as a quantitative score characterizing the quality of a single individual (3CC candidate combination) in a genetic algorithm. It is calculated from the cell evaluation scores of the three candidate cells corresponding to that individual through a preset fitness function (which can be a weighted sum of the three cell evaluation scores or modified in conjunction with 3CC collaborative constraints). It is the core basis for the selection, crossover, and mutation operations of the genetic algorithm. The higher the fitness value, the better the overall performance of the 3CC candidate combination, and the more suitable it is as the final three-carrier aggregation group. The new population can be the set of next-generation candidate cell combinations generated by the genetic algorithm after performing selection, crossover, mutation, and combination validity verification operations on the current population. It can be understood that the selection operation retains high-quality individuals with high fitness values ​​in the current population; the crossover operation swaps the cells of two high-quality individuals' 3CC combinations to generate new individuals; the mutation operation randomly replaces some cells in the individuals and then removes invalid individuals with overlapping frequency bands and unsatisfactory collaboration, finally forming a new population and realizing the iterative evolution of the population. The iteration termination condition refers to the criteria set for terminating the iteration of a genetic algorithm. It serves as the basis for ending the algorithm and can be flexibly set according to the 3CC selection requirements. Common conditions include reaching a preset threshold for the number of iterations (e.g., 50 iterations), the fitness value of the best individual in the population stabilizing (e.g., the rate of change of the best fitness value <1% over 10 consecutive iterations), and the emergence of an individual in the population whose fitness value reaches a preset maximum threshold. The algorithm stops iterating when any of these conditions are met. The target individual can be a high-quality individual whose fitness value meets the preset conditions throughout all iterations of the genetic algorithm. The preset conditions are typically "the maximum fitness value among all iterated individuals" or "fitness value ≥ preset high-quality threshold." The three candidate cell combinations corresponding to the target individual are the optimal 3CC candidate combinations selected by the algorithm iterations and are the direct basis for determining the final three-carrier aggregation group.

[0057] In one implementation, for the selected candidate cells, a weighted summation operation is first performed on the evaluation scores of each candidate cell across multiple performance evaluation dimensions, including interference level, PRB utilization, distance, scene attributes, and neighbor cell cooperation coefficient, to obtain the comprehensive cell evaluation score for each candidate cell. Further, all candidate cells are used as the initial current population for a genetic algorithm. Individuals composed of three candidate cells are extracted from this current population, each individual representing the cell evaluation scores of its three candidate cells. Further, for each individual in the current population, based on the cell evaluation scores of its three candidate cells, a preset fitness function is used to calculate the fitness value corresponding to that individual, thereby quantifying the merits of each 3CC candidate combination. Furthermore, based on the fitness values ​​of all individuals in the current population, selection, crossover, mutation, and combination validity checks are performed. High-quality individuals are retained, new effective individuals are generated, and a new population is formed and used as the new current population. The operations of extracting individuals and calculating fitness values ​​are performed again, and this process is repeated iteratively until the calculation process meets the preset iteration stopping condition. Finally, from all the individuals generated in the iteration process, target individuals whose fitness values ​​meet the preset high-quality conditions are selected, and the three candidate cells represented by the target individual are determined as the final three-carrier aggregation group.

[0058] S230. When the three-carrier aggregation group is in three-carrier aggregation mode, collect the measured performance index values ​​of the three target cells included in the three-carrier aggregation group within a preset time period.

[0059] S240. Based on the measured performance index values ​​corresponding to the three target cells and the predetermined expected performance index values ​​corresponding to the three-carrier aggregation group, determine the performance evaluation results corresponding to the three-carrier aggregation group.

[0060] S250. If the performance evaluation result is not up to standard, multi-level optimization logic is used to optimize the three-carrier aggregation group to obtain the optimized three-carrier aggregation group.

[0061] The technical solution of this disclosure identifies multiple candidate cells from multiple cells in a target area based on base station engineering parameters and three-carrier aggregation verification conditions. Furthermore, it determines the evaluation score of each candidate cell under multiple performance evaluation dimensions, and determines a three-carrier aggregation group based on the evaluation scores of the multiple candidate cells under multiple performance evaluation dimensions. This achieves accurate and scientific selection of cell combinations for the three-carrier aggregation mode to be activated, ensuring that the selected three-carrier aggregation group meets the collaborative adaptation requirements for the deployment of three-carrier aggregation technology, laying the foundation for the efficient implementation of the subsequent three-carrier aggregation mode.

[0062] Figure 4 This is a flowchart illustrating another three-carrier aggregation group evaluation method provided in this embodiment. The technical solution of this embodiment can be combined with other embodiments; for the same or related parts, they can be described in conjunction with the descriptions of other embodiments, and will not be repeated here. Figure 4 As shown, the method in this embodiment may specifically include: S310. Obtain the base station engineering parameters corresponding to the target area, and determine the three-carrier aggregation group from multiple cells in the target area based on the base station engineering parameters and the three-carrier aggregation verification conditions.

[0063] S320. Based on the three-carrier association characteristics corresponding to the three-carrier aggregation group, predict the expected performance index value corresponding to the three-carrier aggregation group.

[0064] Among them, the three-carrier correlation characteristics can be understood as quantifiable core characteristic variables that are strongly correlated with the 3CC deployment effect of the three-carrier aggregation group. The three-carrier correlation characteristics can include the network attributes of the three-carrier aggregation group itself, as well as external characteristics such as the scene, services, and terminals in the target area. Specifically, they are divided into two categories: the core characteristics of the aggregation group itself and the regional correlation characteristics. The core characteristics of the aggregation group itself can characterize the network attributes and cooperative characteristics of the three-carrier aggregation group itself. They are the intrinsic key factors that determine the 3CC deployment effect. These include the frequency band configuration type of the three target cells in the three-carrier aggregation group, the combination value of the neighbor cell cooperative coefficient, the comprehensive score of the scene attributes, the average interference level, the average PRB utilization rate, and the average distance to the center point of the target area. These characteristics are directly extracted from the evaluation data and base station engineering parameters in the early cell selection stage and reflect the basic adaptability and cooperative compatibility of the three-carrier aggregation group. Regional correlation characteristics can characterize the network environment, user characteristics, geographical attributes, and other external conditions of the coverage area of ​​the three-carrier aggregation group. They are important external factors affecting the actual performance of 3CC. Specifically, they include regional population density, average 4G download speed, 5G terminal penetration rate, average daily traffic per person, and regional building density. These characteristics are collected through network management systems and big data statistics platforms, reflecting the region's network demand potential, terminal usage base, geographical limitations, etc., and are key to accurately predicting the speed and traffic increase after 3CC is enabled.

[0065] In this embodiment, to accurately quantify the performance improvement effect of the three-carrier aggregation group after enabling the three-carrier aggregation mode, and to scientifically set its performance target after activation, this solution can also use a reference group matching prediction method to determine the expected performance index value. Specifically, this can be achieved in the following way: based on the three-carrier association characteristics corresponding to the three-carrier aggregation group, a control reference group corresponding to the three-carrier aggregation group is determined from multiple cells other than the three-carrier aggregation group; based on the historical performance index values ​​of the control reference group within a preset historical time period, the expected performance index value corresponding to the three-carrier aggregation group is predicted.

[0066] The control reference group can be understood as a set of virtual cells determined based on high similarity matching of three-carrier association features from all cells except the three-carrier aggregation group. Its core features highly match those of the three-carrier aggregation group, and the three-carrier aggregation mode is not enabled. It serves as a benchmark for simulating the performance trend of the three-carrier aggregation group when the three-carrier aggregation mode is not enabled. The preset historical time period refers to a pre-defined time range (e.g., the last 30 days, the last 90 days) for predicting the expected performance of the three-carrier aggregation group. Network status and user behavior within this time period are stable and referential, serving as the time basis for extracting historical performance data from the control reference group, ensuring that historical data effectively reflects the network performance patterns under normal conditions. Historical performance index values ​​can be understood as the quantitative network performance data actually collected by the control reference group within the preset historical time period. These are the core data foundation for predicting the expected performance of the three-carrier aggregation group, including indicators strongly correlated with the three-carrier aggregation effect such as downlink experience rate, service traffic, handover success rate, interference level, and PRB utilization rate. These are real performance data without the three-carrier aggregation mode enabled.

[0067] In this embodiment, to identify a control reference group that highly matches the features of a three-carrier aggregation group based on three-carrier correlation features, and to provide a reliable reference benchmark for accurate prediction of subsequent expected performance indicators, this scheme determines the control reference group through feature vector extraction, candidate group screening, and weighted synthesis. Specifically, it includes: extracting features from the three-carrier correlation features corresponding to the three-carrier aggregation group to obtain a target feature vector; dividing multiple cells other than the three-carrier aggregation group into at least one candidate reference group, and determining the candidate feature vector corresponding to each candidate reference group; each candidate reference group includes three cells; using a similarity determination algorithm to determine the similarity between the target feature vector and at least one candidate feature vector to obtain at least one similarity; selecting at least one candidate reference group whose similarity satisfies a second preset condition; using a preset regression algorithm to process the candidate feature vectors corresponding to the selected at least one candidate reference group to obtain a synthetic feature vector and a synthetic reference group corresponding to the synthetic feature vector; the synthetic reference group consists of at least one candidate reference group; and determining the synthetic reference group corresponding to the synthetic feature vector as the control reference group corresponding to the three-carrier aggregation group when the deviation rate between the synthetic feature vector and the target feature vector is not greater than a preset deviation threshold.

[0068] The target feature vector can be understood as the feature vector obtained after structured extraction and quantization integration of the three-carrier correlation features of the three-carrier aggregation group. It serves as the benchmark vector for subsequent similarity comparison with candidate feature vectors. Its dimensions and feature types are completely consistent with the candidate feature vectors, and it can be directly used for quantization calculation. The candidate reference group refers to the reference units to be matched, formed by dividing all cells (excluding the three-carrier aggregation group) into groups of three cells. Its combination dimensions are completely consistent with the three-carrier aggregation group, and none of them have three-carrier aggregation mode enabled. It forms the physical basis for the control reference group. The candidate feature vector can be understood as the feature vector obtained after extracting and quantizing the three-carrier correlation features of the candidate reference group according to the same dimensions and rules as the target feature vector. It serves as the comparison object for similarity calculation with the target feature vector, ensuring the comparability of their quantization calculations. The similarity determination algorithm refers to the algorithm used to quantify the degree of fit between the target feature vector and the candidate feature vector (such as the improved cosine similarity algorithm). The higher the similarity value calculated by the algorithm, the better the three-carrier correlation features of the corresponding candidate reference group and the three-carrier aggregation group match. Similarity can refer to the quantitative value calculated by the similarity determination algorithm, which characterizes the degree of feature fit between the target feature vector and a single candidate feature vector. It is the core criterion for screening high-matching candidate reference groups. The second preset condition can be a similarity judgment standard pre-set for screening high-matching candidate reference groups (such as similarity ≥ 0.85). Only candidate reference groups whose similarity meets this standard will be included in the subsequent synthesis processing stage. The preset regression algorithm can refer to the algorithm used to perform weighted synthesis processing on the screened high-similarity candidate feature vectors (such as the ridge regression algorithm with a regularization parameter of 0.01). The core of this algorithm is to satisfy the dual constraints of "the sum of the weights of each candidate reference group is 1 and the sum of the squared errors between the synthesized feature vector and the target feature vector is minimized". It is the core algorithm for generating synthesized feature vectors and synthesized reference groups. The synthesized feature vector can be understood as a comprehensive feature vector obtained by using the preset regression algorithm to perform a dimensional weighted summation on at least one screened candidate feature vector. It is the feature quantification expression of the synthesized reference group, and its dimension is completely consistent with the target feature vector and candidate feature vectors. It is used to check the deviation rate with the target feature vector. A synthetic reference group can be understood as a virtual cell combination formed by weighted fusion of at least one selected candidate reference group after being assigned specific weights by a preset regression algorithm. It has no physically independent cell carriers, and its overall characteristics are uniquely represented by the synthetic feature vector. Essentially, it is a feature fusion formed by multiple highly similar candidate reference groups according to their weighted contributions, and it is also the core object for subsequently determining the control reference group corresponding to the three-carrier aggregation group. The deviation rate can be understood as the degree of quantitative deviation between the synthetic feature vector and the target feature vector. It is obtained by calculating the deviation of the feature values ​​of each dimension of the two vectors and performing comprehensive quantization. It is the core indicator for determining whether the synthetic reference group highly matches the characteristics of the three-carrier aggregation group.The preset deviation threshold can be a pre-set upper limit standard for the deviation rate (such as 5%) for judging the feature matching degree. If the deviation rate between the synthesized feature vector and the target feature vector does not exceed the standard, it means that the features of the synthesized reference group and the three-carrier aggregation group are highly matched.

[0069] In one implementation, the three-carrier association features corresponding to the three-carrier aggregation group are uniformly extracted and quantized to form a target feature vector for feature matching. Further, the remaining cells in the target area, excluding the three-carrier aggregation group, are divided into at least one candidate reference group, with each group containing three cells. Candidate feature vectors are generated for each candidate reference group using the same feature dimensions and calculation method as the target feature vector. Further, a similarity determination algorithm is used to calculate the similarity between the target feature vector and each candidate feature vector. Based on the calculated similarity values, high-matching candidate reference groups that meet the second preset condition are selected. Furthermore, a preset regression algorithm is used to perform weighted fusion processing on the candidate feature vectors corresponding to at least one candidate reference group obtained through similarity screening. The algorithm assigns corresponding weights to each candidate reference group and completes the dimensional weighting calculation of the feature vectors to obtain a comprehensive synthetic feature vector. At the same time, a synthetic reference group corresponding to the synthetic feature vector is formed by fusing the above at least one candidate reference group according to the weights. Then, the deviation rate between the synthetic feature vector and the target feature vector of the three-carrier aggregation group is calculated. If the deviation rate does not exceed the preset deviation threshold, the synthetic reference group is officially determined as the control reference group that matches the three-carrier aggregation group.

[0070] Optionally, based on the historical performance index values ​​of at least one candidate reference group within the first historical time period, the expected performance index value corresponding to the three-carrier aggregation group is predicted, including: obtaining the historical performance index value of each candidate reference group within the first historical time period; performing weighted fusion based on the weights corresponding to each candidate reference group and the historical performance index value to obtain the baseline performance index value corresponding to the control reference group; and correcting the baseline performance index value based on the time decay parameter and the network load growth rate of the target area within the second historical time period to obtain the expected performance index value corresponding to the three-carrier aggregation group.

[0071] The weights corresponding to each candidate reference group can be understood as unique weight values ​​assigned to each candidate reference group through a preset regression algorithm. These weights satisfy the dual constraints of the sum of all weights being 1 and the sum of squared errors between the synthesized feature vector and the target feature vector being minimized. These weights directly determine the contribution of each candidate reference group's historical performance index value to the baseline performance index value. Weighted fusion refers to the process of calculating the weighted sum of the historical performance index values ​​of each candidate reference group according to its corresponding weight, one by one along the index dimension (e.g., weighting the downlink experience rate dimension separately, and weighting the traffic dimension separately). This is the core calculation method for integrating the performance data of multiple candidate reference groups into unified performance data for the control reference group. The baseline performance index value can be understood as the comprehensive performance index value of the control reference group obtained after weighted fusion of the historical performance index values ​​of each candidate reference group. This value represents the normal baseline performance level of the network area matching the characteristics of the three-carrier aggregation group in the absence of three-carrier aggregation mode, and serves as the basis for subsequent calculations of expected performance index values. The time decay parameter is a time decay factor set to correct for error accumulation in long-term predictions. It can be dynamically adjusted based on the timing of the three-carrier aggregation mode activation, making the prediction results more closely match the actual network state at the time of activation. An example is the formula for calculating the time decay parameter. ,in, This represents the number of days since the three-carrier aggregation group activated its three-carrier aggregation mode. The second historical time period can refer to a pre-defined short-term monitoring time range (such as the last 24 hours, last 48 hours, etc.) for statistically analyzing network load changes in the target area. Shorter than the first historical time period, it accurately reflects the recent dynamic trends of network load changes in the target area and serves as the time basis for obtaining the network load growth rate. The network load growth rate can be understood as the month-on-month increase in network load in the target area within the second historical time period (such as the percentage increase in service traffic or PRB utilization). It is a core indicator reflecting the recent network congestion level in the target area and is used to adapt to the impact of dynamic network changes on the expected performance of the three-carrier aggregation mode.

[0072] In one implementation, for all candidate reference groups constituting the control reference group, historical performance index values ​​of each candidate reference group without enabling three-carrier aggregation mode are collected during the first historical time period. Then, the corresponding weight values ​​assigned to each candidate reference group when the control reference group was determined are retrieved, and the historical performance index values ​​of each candidate reference group in the same dimension are weighted and summed according to the weight values. The baseline performance index value that can characterize the normal performance level of the control reference group is obtained through this weighted fusion method. Subsequently, a pre-set time decay parameter is obtained, and the network load growth rate of the target area during the second historical time period is statistically analyzed. Combining the time decay parameter and the network load growth rate, the obtained baseline performance index value is subjected to dual dynamic correction processing. Finally, the expected performance index value corresponding to the three-carrier aggregation group after enabling three-carrier aggregation mode is obtained through correction calculation.

[0073] S330. When the three-carrier aggregation group is in three-carrier aggregation mode, collect the measured performance index values ​​of the three target cells included in the three-carrier aggregation group within a preset time period.

[0074] S340. Based on the measured performance index values ​​corresponding to the three target cells and the predetermined expected performance index values ​​corresponding to the three-carrier aggregation group, determine the performance evaluation results corresponding to the three-carrier aggregation group.

[0075] S350. If the performance evaluation result is not up to standard, multi-level optimization logic is used to optimize the three-carrier aggregation group to obtain the optimized three-carrier aggregation group.

[0076] The technical solution of this disclosure predicts the expected performance index value corresponding to the three-carrier aggregation group based on the three-carrier association characteristics, thereby achieving scientific and accurate quantification of the performance target after the three-carrier aggregation mode is enabled. This provides an objective and matching reference benchmark for the subsequent performance effect evaluation after the three-carrier aggregation group is enabled, and improves the accuracy and rationality of the performance evaluation.

[0077] Figure 5 This is a schematic diagram of a three-carrier aggregation group evaluation device provided in an embodiment of this disclosure. Figure 5As shown, the three-carrier aggregation group evaluation device includes: a three-carrier aggregation group determination module 410, a measured data acquisition module 420, an evaluation result determination module 430, and a three-carrier aggregation group optimization module 440. The three-carrier aggregation group determination module 410 is used to acquire base station engineering parameters corresponding to the target area, and based on the base station engineering parameters and three-carrier aggregation verification conditions, determine a three-carrier aggregation group from multiple cells within the target area; wherein, the three-carrier aggregation group is a set of cells for which three-carrier aggregation mode is to be enabled; the three-carrier aggregation group includes three target cells; the target cells belong to multiple cells; the three-carrier aggregation group is determined based on the evaluation scores of the cells under multiple performance evaluation dimensions; the measured data acquisition module 420 is used to optimize the three-carrier aggregation group after it is enabled. In the three-carrier aggregation mode, the measured performance index values ​​of the three target cells included in the three-carrier aggregation group are collected within a preset time period; the evaluation result determination module 430 is used to determine the performance evaluation result corresponding to the three-carrier aggregation group based on the measured performance index values ​​corresponding to the three target cells and the pre-determined expected performance index values ​​corresponding to the three-carrier aggregation group; the three-carrier aggregation group optimization module 440 is used to optimize the three-carrier aggregation group using multi-level optimization logic when the performance evaluation result is not up to standard, so as to obtain the optimized three-carrier aggregation group.

[0078] The technical solution of this disclosure, based on base station engineering parameters and three-carrier aggregation verification conditions, selects a three-carrier aggregation group consisting of three target cells. This achieves precise selection of cell combinations for the three-carrier aggregation mode to be activated, ensuring that the combination meets the basic technical requirements for three-carrier aggregation deployment. Furthermore, the combination is determined through multi-dimensional performance evaluation scores, improving the rationality and adaptability of the three-carrier aggregation deployment. Further, after the three-carrier aggregation group activates the three-carrier aggregation mode, measured performance index values ​​of the three target cells are collected over a preset time period to obtain real performance data after the actual implementation of the three-carrier aggregation mode, providing objective and accurate measured data for subsequent effect evaluation. Further, the measured performance index values ​​of the three target cells are compared and analyzed with preset expected performance index values ​​to quantitatively determine the actual operating effect of the three-carrier aggregation group, forming an intuitive and quantifiable performance evaluation result, clarifying whether the performance has reached the preset target after the three-carrier aggregation mode is activated. Furthermore, for three-carrier aggregation groups that fail to meet performance evaluation standards, multi-level optimization logic is employed to precisely locate and resolve performance issues during operation. This achieves performance tuning of the three-carrier aggregation groups, ensuring that they meet preset performance targets after activation and fully leveraging the performance enhancement value of three-carrier aggregation technology. The technical solution of this disclosure addresses the problems of poor deployment flexibility, potential resource over- or under-allocation, and limitations in application scenarios inherent in related technologies. It achieves precise selection of three-carrier aggregation groups within the target area, quantitative performance evaluation after activation, and multi-level intelligent optimization when performance fails to meet standards. This forms a closed-loop process for three-carrier aggregation deployment, evaluation, and optimization, ensuring that the performance of the three-carrier aggregation mode meets standards after implementation and fully leveraging the speed and capacity enhancement value of the technology.

[0079] In some embodiments of this disclosure, optionally, the three-carrier aggregation group determination module 410 includes: a candidate cell determination unit and a three-carrier aggregation group determination unit. The candidate cell determination unit is used to determine multiple candidate cells from multiple cells within the target area based on the base station engineering parameters and three-carrier aggregation verification conditions. The three-carrier aggregation group determination unit is used to determine the evaluation score of each candidate cell under multiple performance evaluation dimensions, and determine a three-carrier aggregation group based on the evaluation scores of the multiple candidate cells under multiple performance evaluation dimensions.

[0080] In some embodiments of this disclosure, optionally, the candidate cell determination unit is specifically configured to: filter multiple cells in the target area according to a preset screening radius to obtain multiple cells to be eliminated; determine radio configuration parameters corresponding to each cell to be eliminated based on the base station engineering parameters; determine whether the cells to be eliminated meet the three-carrier aggregation verification condition based on the radio configuration parameters corresponding to the cells to be eliminated; and determine the cells to be eliminated that meet the three-carrier aggregation verification condition as candidate cells to obtain multiple candidate cells.

[0081] In some embodiments of this disclosure, optionally, the three-carrier aggregation group determination unit is specifically configured to: process the evaluation scores of the candidate cells under multiple performance evaluation dimensions using a weighted summation operation for a plurality of candidate cells to obtain cell evaluation scores corresponding to the candidate cells; take the plurality of candidate cells as the current population and obtain individuals in the current population; each individual represents the cell evaluation scores corresponding to the three candidate cells respectively; for each obtained individual, determine the fitness value corresponding to the individual based on the cell evaluation scores corresponding to the three candidate cells respectively represented by the individual; generate a new population based on the fitness values ​​corresponding to each individual, and return to the operation of obtaining individuals in the current population after taking the new population as the current population, until a preset iteration stop condition is met; determine the target individuals whose fitness values ​​satisfy the first preset condition in each iteration, and determine the three-carrier aggregation group based on the target individuals.

[0082] In some embodiments of this disclosure, optionally, the apparatus further includes: an expected performance index prediction module, configured to predict an expected performance index value corresponding to the three-carrier aggregation group based on the three-carrier aggregation correlation features corresponding to the three-carrier aggregation group after determining the three-carrier aggregation group from multiple cells in the target area based on the base station engineering parameters and the three-carrier aggregation verification conditions; wherein the expected performance index value is used to indicate the expected effect achieved by the three-carrier aggregation group after enabling the three-carrier aggregation mode.

[0083] In some embodiments of this disclosure, optionally, the expected performance index prediction module includes: a control reference group determination unit and an expected performance index prediction unit. The control reference group determination unit is used to determine a control reference group corresponding to the three-carrier aggregation group from multiple cells other than the three-carrier aggregation group based on the three-carrier association characteristics corresponding to the three-carrier aggregation group; wherein the control reference group consists of at least one candidate reference group; the candidate reference group includes three cells; the expected performance index prediction unit is used to predict the expected performance index value corresponding to the three-carrier aggregation group based on the historical performance index values ​​of at least one candidate reference group within a first historical time period.

[0084] In some embodiments of this disclosure, optionally, the control reference group determination unit is specifically used to extract features from the three-carrier association features corresponding to the three-carrier aggregation group to obtain a target feature vector; divide the multiple cells other than the three-carrier aggregation group into at least one candidate reference group, and determine the candidate feature vector corresponding to each candidate reference group; the candidate reference group includes three cells; use a similarity determination algorithm to determine the similarity between the target feature vector and at least one candidate feature vector to obtain at least one similarity; select at least one candidate reference group whose similarity satisfies a second preset condition; use a preset regression algorithm to process the candidate feature vectors corresponding to the selected at least one candidate reference group to obtain a synthetic feature vector and a synthetic reference group corresponding to the synthetic feature vector; the synthetic reference group is composed of at least one candidate reference group; if the deviation rate between the synthetic feature vector and the target feature vector is not greater than a preset deviation threshold, determine the synthetic reference group corresponding to the synthetic feature vector as the control reference group corresponding to the three-carrier aggregation group.

[0085] In some embodiments of this disclosure, optionally, the expected performance indicator prediction unit is specifically used to obtain the historical performance indicator values ​​of each of the candidate reference groups in a first historical time period; perform weighted fusion based on the weights corresponding to each candidate reference group and the historical performance indicator values ​​to obtain the baseline performance indicator value corresponding to the control reference group; and correct the baseline performance indicator value based on the time decay parameter and the network load growth rate corresponding to the target area in a second historical time period to obtain the expected performance indicator value corresponding to the three-carrier aggregation group.

[0086] The three-carrier aggregation group evaluation device provided in this disclosure can execute the three-carrier aggregation group evaluation method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of the method execution.

[0087] It is worth noting that the various units and modules included in the above-mentioned three-carrier aggregation group evaluation device are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this disclosure.

[0088] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0089] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0090] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0091] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the three-carrier aggregation group evaluation method.

[0092] In some embodiments, the three-carrier aggregation group evaluation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via read-only memory (ROM) 12 and / or communication unit 19. When the computer program is loaded into random access memory (RAM) 13 and executed by processor 11, one or more steps of the three-carrier aggregation group evaluation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the three-carrier aggregation group evaluation method by any other suitable means (e.g., by means of firmware).

[0093] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0094] Computer programs used to implement the three-carrier aggregation group evaluation method of this disclosure can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0095] This disclosure provides a computer-readable storage medium storing computer instructions for causing a processor to execute a three-carrier aggregation group evaluation method, comprising: acquiring base station engineering parameters corresponding to a target area, and determining a three-carrier aggregation group from multiple cells within the target area based on the base station engineering parameters and three-carrier aggregation verification conditions; wherein the three-carrier aggregation group is a set of cells for which three-carrier aggregation mode is to be enabled; the three-carrier aggregation group includes three target cells; the target cells belong to multiple cells; the three-carrier aggregation group is determined based on the evaluation scores of the cells under multiple performance evaluation dimensions; when the three-carrier aggregation group is enabled, collecting measured performance index values ​​of the three target cells included in the three-carrier aggregation group within a preset time period; determining the performance evaluation result corresponding to the three-carrier aggregation group based on the measured performance index values ​​corresponding to the three target cells and the predetermined expected performance index values ​​corresponding to the three-carrier aggregation group; and when the performance evaluation result is unsatisfactory, optimizing the three-carrier aggregation group using multi-level optimization logic to obtain the optimized three-carrier aggregation group.

[0096] In the context of this disclosure, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0097] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0098] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0099] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0100] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of embodiments of this disclosure.

[0101] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements a three-carrier aggregation group evaluation method according to any embodiment of this disclosure.

[0102] In implementing a computer program product, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0103] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.

[0104] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for evaluating three-carrier aggregation groups, characterized in that, include: The base station engineering parameters corresponding to the target area are obtained, and based on the base station engineering parameters and the three-carrier aggregation verification conditions, a three-carrier aggregation group is determined from multiple cells in the target area; wherein, the three-carrier aggregation group is a set of cells for which the three-carrier aggregation mode is to be enabled; the three-carrier aggregation group includes three target cells; the target cells belong to multiple cells; the three-carrier aggregation group is determined based on the evaluation scores of the cells under multiple performance evaluation dimensions; When the three-carrier aggregation group is in three-carrier aggregation mode, the measured performance index values ​​of the three target cells included in the three-carrier aggregation group are collected within a preset time period. Based on the measured performance index values ​​corresponding to the three target cells respectively and the predetermined expected performance index values ​​corresponding to the three-carrier aggregation group, the performance evaluation result corresponding to the three-carrier aggregation group is determined; If the performance evaluation result is not up to standard, a multi-level optimization logic is used to optimize the three-carrier aggregation group to obtain the optimized three-carrier aggregation group.

2. The three-carrier aggregation group evaluation method according to claim 1, characterized in that, The step of determining a three-carrier aggregation group from multiple cells within the target area based on the base station engineering parameters and three-carrier aggregation verification conditions includes: Based on the base station engineering parameters and three-carrier aggregation verification conditions, multiple candidate cells are determined from multiple cells within the target area; The evaluation scores of each candidate cell under multiple performance evaluation dimensions are determined, and a three-carrier aggregation group is determined based on the evaluation scores of the multiple candidate cells under multiple performance evaluation dimensions.

3. The three-carrier aggregation group evaluation method according to claim 2, characterized in that, Based on the base station engineering parameters and three-carrier aggregation verification conditions, multiple candidate cells are determined from multiple cells within the target area, including: Multiple cells within the target area are filtered according to a preset filtering radius to obtain multiple cells to be removed; Based on the base station engineering parameters, the radio configuration parameters corresponding to each of the cells to be eliminated are determined respectively; For multiple cells to be removed, it is determined whether the cells to be removed meet the three-carrier aggregation verification condition based on the radio configuration parameters corresponding to the cells to be removed. Cells that meet the three-carrier aggregation verification conditions are identified as candidate cells to obtain a plurality of candidate cells.

4. The three-carrier aggregation group evaluation method according to claim 2, characterized in that, The step of determining a three-carrier aggregation group based on the evaluation scores of multiple candidate cells across multiple performance evaluation dimensions includes: For multiple candidate cells, a weighted summation operation is used to process the evaluation scores of the candidate cells under multiple performance evaluation dimensions to obtain the cell evaluation score corresponding to the candidate cells; The candidate cells are used as the current population, and individuals in the current population are obtained; wherein, each individual represents the cell evaluation score corresponding to the three candidate cells respectively; For each of the acquired individuals, a fitness value corresponding to the individual is determined based on the cell evaluation scores corresponding to the three candidate cells represented by the individual. A new population is generated based on the fitness values ​​of each individual, and the new population is used as the current population. The process of obtaining individuals from the current population is repeated until the preset iteration stop condition is met. Identify the target individuals whose fitness values ​​satisfy the first preset condition during each iteration, and determine the three-carrier aggregation group based on the target individuals.

5. The three-carrier aggregation group evaluation method according to claim 1, characterized in that, After determining the three-carrier aggregation group from multiple cells within the target area based on the base station engineering parameters and the three-carrier aggregation verification conditions, the method further includes: Based on the three-carrier association features corresponding to the three-carrier aggregation group, the expected performance index value corresponding to the three-carrier aggregation group is predicted; wherein, the expected performance index value is used to indicate the expected effect achieved by the three-carrier aggregation group after the three-carrier aggregation mode is enabled.

6. The three-carrier aggregation group evaluation method according to claim 5, characterized in that, The step of predicting the expected performance index value corresponding to the three-carrier aggregation group based on the three-carrier association features includes: Based on the three-carrier association features corresponding to the three-carrier aggregation group, a control reference group corresponding to the three-carrier aggregation group is determined from multiple cells other than the three-carrier aggregation group; wherein, the control reference group consists of at least one candidate reference group; the candidate reference group includes three cells; Based on the historical performance index values ​​of at least one of the candidate reference groups within the first historical time period, the expected performance index value corresponding to the three-carrier aggregation group is predicted.

7. The three-carrier aggregation group evaluation method according to claim 6, characterized in that, The step of determining a control reference group corresponding to the three-carrier aggregation group from multiple cells other than the three-carrier aggregation group based on the three-carrier association characteristics corresponding to the three-carrier aggregation group includes: Feature extraction is performed on the three-carrier association features corresponding to the three-carrier aggregation group to obtain the target feature vector; The multiple cells other than the three-carrier aggregation group are divided into at least one candidate reference group, and a candidate feature vector corresponding to each candidate reference group is determined; the candidate reference group includes three cells. A similarity determination algorithm is used to determine the similarity between the target feature vector and at least one candidate feature vector, thereby obtaining at least one similarity. Select at least one candidate reference group whose similarity meets the second preset condition; A preset regression algorithm is used to process the candidate feature vectors corresponding to at least one of the selected candidate reference groups to obtain a synthetic feature vector and a synthetic reference group corresponding to the synthetic feature vector; the synthetic reference group is composed of at least one of the candidate reference groups. If the deviation rate between the synthesized feature vector and the target feature vector is not greater than a preset deviation threshold, the synthesized reference group corresponding to the synthesized feature vector is determined as the control reference group corresponding to the three-carrier aggregation group.

8. The three-carrier aggregation group evaluation method according to claim 6, characterized in that, The step of predicting the expected performance index value corresponding to the three-carrier aggregation group based on the historical performance index values ​​of at least one of the candidate reference groups within a first historical time period includes: Obtain the historical performance index values ​​of each candidate reference group within the first historical time period; The baseline performance index value corresponding to the control reference group is obtained by weighted fusion based on the weights corresponding to each candidate reference group and the historical performance index value. The baseline performance index value is corrected based on the time decay parameter and the network load growth rate of the target area in the second historical time period to obtain the expected performance index value corresponding to the three-carrier aggregation group.

9. A three-carrier aggregation group evaluation device, characterized in that, include: The three-carrier aggregation group determination module is used to acquire base station engineering parameters corresponding to the target area, and determine a three-carrier aggregation group from multiple cells in the target area based on the base station engineering parameters and three-carrier aggregation verification conditions; wherein, the three-carrier aggregation group is a set of cells for which three-carrier aggregation mode is to be enabled; the three-carrier aggregation group includes three target cells; the target cells belong to multiple cells; the three-carrier aggregation group is determined based on the evaluation scores of the cells under multiple performance evaluation dimensions; The measured data acquisition module is used to collect the measured performance index values ​​of the three target cells included in the three-carrier aggregation group within a preset time period when the three-carrier aggregation group is in three-carrier aggregation mode. The evaluation result determination module is used to determine the performance evaluation result corresponding to the three-carrier aggregation group based on the measured performance index values ​​corresponding to the three target cells respectively and the pre-determined expected performance index values ​​corresponding to the three-carrier aggregation group. The three-carrier aggregation group optimization module is used to optimize the three-carrier aggregation group using multi-level optimization logic when the performance evaluation result is not up to standard, so as to obtain the optimized three-carrier aggregation group.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the three-carrier aggregation group evaluation method according to any one of claims 1-8.

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