A signal conditioning method and related apparatus

Abstract

This disclosure provides a signal conditioning method and related equipment, relating to the field of communication technology. The method includes: acquiring user dialogue data associated with a terminal device; determining the target base station connected to the terminal device in response to the data classification result being a signal quality complaint; analyzing the data based on a network optimization model to generate at least one set of candidate base station parameters in response to the detection that the target base station has not experienced a station failure alarm; sequentially configuring each set of candidate base station parameters; acquiring signal quality data of the target base station and the terminal device after configuring each set of candidate base station parameters; and stopping subsequent parameter configuration and generating signal quality conditioning information in response to all acquired signal quality data passing signal quality verification. This method achieves perception and response based on user feedback intent, improves the efficiency of base station parameter configuration and the response speed and processing accuracy of base station signal quality issues, and enhances the user experience.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a signal conditioning method, a signal conditioning device, an electronic device, a computer-readable storage medium, and a computer program product. Background Technology

[0002] With the development of mobile communication technology, users' demands for communication quality are constantly increasing. However, affected by factors such as environment, equipment, and network, base station signals suffer from instability, insufficient coverage, or severe interference, leading to a decline in user experience. In densely populated scenarios such as large gatherings, sporting events, and transportation hubs, a large number of users accessing the network simultaneously can easily overload base stations, causing network congestion. Furthermore, concurrent communication by multiple devices can lead to frequency conflicts, exacerbating signal interference and further deteriorating network quality. When network performance remains poor, especially in scenarios where users have high network demands, they are more likely to complain to operators about poor network conditions.

[0003] In related technologies, after receiving complaints, operators verify the situation and adjust the base station network signal manually or using preset parameters. However, manual adjustment requires professional personnel to conduct on-site surveys and operations, which is time-consuming, labor-intensive, has a delayed response, and limited adjustment accuracy, making it difficult to cope with complex dynamic scenarios. Preset parameter schemes lack real-time feedback and dynamic optimization mechanisms, and cannot adapt to environmental changes and fluctuations in user needs. Furthermore, user feedback, as the most direct and authentic source of communication quality, is often ignored in related technologies or used only for post-event analysis, failing to be effectively integrated into the real-time optimization process. Summary of the Invention

[0004] The purpose of this disclosure is to provide a signal conditioning method, a signal conditioning device, an electronic device, a computer-readable storage medium, and a computer program product, which at least partially solves the problems existing in the related technologies.

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

[0006] According to a first aspect of this disclosure, a signal conditioning method is provided, the method comprising: acquiring user dialogue data associated with a terminal device; determining a target base station connected to the terminal device in response to a classification result of the user dialogue data being a signal quality complaint; analyzing the user dialogue data based on a network optimization model in response to detecting that the target base station has not experienced a station failure alarm, generating at least one set of candidate base station parameters; sequentially selecting each set of candidate base station parameters from the at least one set of candidate base station parameters for parameter configuration, and after configuring each set of candidate base station parameters, acquiring first signal quality data of the target base station and second signal quality data of the terminal device; and stopping the parameter configuration of subsequent candidate base station parameters in response to both the first signal quality data and the second signal quality data passing signal quality verification, thereby generating signal quality conditioning information corresponding to the user dialogue data.

[0007] In some exemplary embodiments of this disclosure, the step of analyzing the user dialogue data based on a network optimization model to generate at least one set of candidate base station parameters includes: inputting the user dialogue data into the network optimization model; performing semantic understanding on the user dialogue data through a first prompt instruction to obtain a first output result; the first output result includes a user question description; based on the first output result, guiding the network optimization model to perform analysis and reasoning through a second prompt instruction to obtain a second output result; the second output result includes adjustable base station parameters; and based on the user question description, the adjustable base station parameters, the base station information of the target base station, and the base station information of the neighboring base stations of the target base station, guiding the network optimization model through a third prompt instruction to generate the at least one set of candidate base station parameters.

[0008] In some exemplary embodiments of this disclosure, the first output result also includes the type of problem and service type reported by the user; the second output result also includes the cause of the signal quality anomaly and the solution strategy.

[0009] In some exemplary embodiments of this disclosure, the adjustable base station parameters include one or more of the following options: Reference Signal Received Power (RSRP), Electronic Downtilt Angle, Inter-frequency Measurement A2 RSRP Trigger Threshold based on A4 or A5 Events, Inter-frequency Measurement A1 RSRP Trigger Threshold based on A4 or A5 Events, Inter-frequency Measurement A2 RSRP Trigger Threshold based on A3 Events, and Inter-frequency Measurement A1 RSRP Trigger Threshold based on A3 Events.

[0010] In some exemplary embodiments of this disclosure, the step of sequentially selecting each group of candidate base station parameters from the at least one group of candidate base station parameters for parameter configuration, and after configuring each group of candidate base station parameters, obtaining the first signal quality data of the target base station and the second signal quality data of the terminal device, includes: sorting the at least one group of candidate base station parameters according to a preset priority to obtain an ordered sequence of candidate base station parameters; wherein the preset priority satisfies one or more of the following principles: if the number of base stations involved in a candidate base station parameter is smaller, the sorting priority of the candidate base station parameter is higher; if the power consumption corresponding to a candidate base station parameter is lower, the sorting priority of the candidate base station parameter is higher; sequentially selecting the current candidate base station parameter in the candidate base station parameter sequence, configuring the corresponding base station according to the current candidate base station parameter; and obtaining the first signal quality data of the target base station after the current candidate base station parameter configuration and the second signal quality data of the terminal device after the current candidate base station parameter configuration.

[0011] In some exemplary embodiments of this disclosure, the first signal quality data includes one or more of the following indicators: wireless connection success rate, wireless call drop rate, handover success rate, total cell traffic, maximum number of online users, number of handover requests, number of successful handovers, number of random accesses with a timing advance (TA) value within a preset range, uplink weak coverage ratio, downlink average user plane delay, average channel quality indicator (CQI), and the percentage of CQI values ​​less than or equal to a preset value; the second signal quality data includes one or more of the following indicators: RSRP, reference signal reception quality (RSRQ), signal-to-noise ratio (SINR), channel state information (CSI) feedback, and signal strength.

[0012] In some exemplary embodiments of this disclosure, the method further includes: acquiring key performance indicator data from the first signal quality data; generating a current performance evaluation value of the target base station based on the first signal quality data; calculating a first performance gain based on the current performance evaluation value of the target base station and the original performance evaluation value of the target base station; and determining that the first signal quality data passes signal quality verification in response to the performance improvement value corresponding to the key performance indicator data being greater than a preset performance improvement threshold and the first performance gain being greater than a first preset threshold.

[0013] In some exemplary embodiments of this disclosure, the method further includes: generating a current performance evaluation value of the terminal device based on the second signal quality data; calculating a second performance gain based on the current performance evaluation value of the terminal device and the original performance evaluation value of the terminal device; and determining that the second signal quality data passes the signal quality verification in response to the second performance gain being greater than a second preset threshold.

[0014] In some exemplary embodiments of this disclosure, the method further includes: after configuring the parameters of each group of candidate base station parameters in the at least one group of candidate base station parameters, if the configuration result of any group of candidate base station parameters fails the signal quality verification, a manual operation and maintenance work order is generated.

[0015] In some exemplary embodiments of this disclosure, the step of obtaining user dialogue data associated with a terminal device includes: receiving dialogue text data collected by a voice platform; the dialogue text data being dialogue content related to the terminal device fed back by the user during interaction with the voice platform; and performing structured processing on the dialogue text data to obtain the user dialogue data.

[0016] In some exemplary embodiments of this disclosure, the method further includes: classifying the user dialogue data based on a multi-classification model to obtain the classification result of the user dialogue data.

[0017] In some exemplary embodiments of this disclosure, the method further includes: generating and sending information about the target base station losing its alarm in response to detecting that the target base station has lost its alarm.

[0018] According to a second aspect of this disclosure, a signal conditioning apparatus is provided, the apparatus comprising: a dialogue data acquisition module configured to acquire user dialogue data associated with a terminal device; a base station determination module configured to determine a target base station connected to the terminal device in response to a classification result of the user dialogue data being a signal quality complaint; a parameter generation module configured to analyze the user dialogue data based on a network optimization model and generate at least one set of candidate base station parameters in response to detecting that the target base station has not experienced a station failure alarm; a parameter configuration module configured to sequentially select each set of candidate base station parameters from the at least one set of candidate base station parameters for parameter configuration, and after configuring each set of candidate base station parameters, acquire first signal quality data of the target base station and second signal quality data of the terminal device; and a signal verification module configured to stop configuring subsequent candidate base station parameters and generate signal quality conditioning information corresponding to the user dialogue data in response to both the first signal quality data and the second signal quality data passing signal quality verification.

[0019] According to a third aspect of this disclosure, an electronic device is provided, including a processor and a memory, the memory being used to store executable instructions of the processor; wherein the processor is configured to perform the above-described signal conditioning method by executing the executable instructions.

[0020] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described signal modulation method.

[0021] According to a fifth aspect of this disclosure, a computer program product is provided, the computer program product storing instructions that, when executed by a computer, cause the computer to implement the above-described signal modulation method.

[0022] The signal conditioning method provided in this disclosure realizes perception and response based on user feedback intent. It automates the entire process from user feedback identification and base station parameter adjustment to optimization effect verification without manual intervention. This improves the efficiency of base station parameter configuration, the response speed and processing accuracy of base station signal quality issues, reduces the latency of user feedback processing, enhances the adaptive control capability against network congestion and signal interference in high-density, high-concurrency scenarios, and improves the user experience.

[0023] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0025] Figure 1 A flowchart of a signal conditioning method according to an embodiment of the present disclosure is shown.

[0026] Figure 2 The flowchart shown is illustrated in this embodiment of the present disclosure, which analyzes user dialogue data based on a network optimization model to generate at least one set of candidate base station parameters.

[0027] Figure 3 A flowchart illustrating the quality verification of the first signal quality data in an embodiment of this disclosure is shown.

[0028] Figure 4 A flowchart illustrating the quality verification of the second signal quality data in an embodiment of this disclosure is shown.

[0029] Figure 5 A flowchart of a signal conditioning method according to yet another embodiment of the present disclosure is shown.

[0030] Figure 6 A schematic diagram of a signal conditioning device according to an embodiment of the present disclosure is shown.

[0031] Figure 7 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0032] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0033] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0034] Figure 1 A flowchart of a signal conditioning method according to an embodiment of the present disclosure is shown. Figure 1 The provided signal conditioning method can be executed by equipment with network control and data processing capabilities, such as base station management equipment and network optimization platforms.

[0035] A base station management device can be understood as a network entity responsible for centralized monitoring, configuration, maintenance, and performance management of base stations. This device receives operational status information from base stations and sends control commands to adjust their operating parameters. It can also be called a radio network controller, base station controller, etc.

[0036] A network optimization platform can be understood as an integrated software system or server platform used to continuously monitor, analyze, and improve the overall performance and user experience of wireless networks. This platform integrates technologies such as big data analytics, artificial intelligence, and machine learning, enabling it to identify network problems from multiple data sources, including signaling data, drive test data, and user complaints, and automatically generate optimization strategies. The platform can proactively perform operations such as parameter adjustments, neighbor cell optimization, and load balancing. Network optimization platforms can also be called network performance management platforms or wireless network optimization systems.

[0037] Reference Figure 1 The signal conditioning method may include the following steps.

[0038] Step S110: Obtain user dialogue data associated with the terminal device.

[0039] Step S120: In response to the classification result of the user dialogue data being a signal quality complaint, determine the target base station to which the terminal device is connected.

[0040] Step S130: In response to the detection that no base station failure alarm has occurred at the target base station, the user dialogue data is analyzed based on the network optimization model to generate at least one set of candidate base station parameters.

[0041] Step S140: Select each set of candidate base station parameters from at least one set of candidate base station parameters for parameter configuration in sequence. After configuring each set of candidate base station parameters, obtain the first signal quality data of the target base station and the second signal quality data of the terminal device.

[0042] In step S150, in response to the fact that both the first signal quality data and the second signal quality data have passed the signal quality verification, the parameter configuration of subsequent candidate base station parameters is stopped, and signal quality adjustment information corresponding to the user dialogue data is generated.

[0043] In this embodiment, user dialogue data associated with the terminal device is first acquired. When the classification result of the user dialogue data is a signal quality complaint, the target base station connected to the terminal device is determined, thereby establishing a direct association between the user problem and the network entity. After confirming that the target base station has no downtime alarm, it indicates that the problem is not a hardware failure. Then, an analysis process based on the network optimization model is initiated to generate at least one set of candidate base station parameters. Subsequently, each set of candidate base station parameters is configured sequentially. After each parameter configuration, the first signal quality data of the target base station and the second signal quality data of the terminal device are collected. The optimization effect is verified by jointly verifying the data from both sides. Once both pass the signal quality verification, the subsequent configuration is terminated to avoid invalid adjustments.

[0044] Through the above steps, perception and response based on user feedback intent are realized. The entire process from user feedback identification and base station parameter adjustment to optimization effect verification is automated without human intervention. This improves the efficiency of base station parameter configuration, the response speed and processing accuracy of base station signal quality issues, reduces the latency of user feedback processing, enhances the adaptive control capability against network congestion and signal interference in high-density, high-concurrency scenarios, and improves the user experience.

[0045] The solutions provided in this disclosure will be further described below with reference to exemplary embodiments.

[0046] In step S110, user dialogue data associated with the terminal device is acquired.

[0047] In this embodiment, user dialogue data associated with a terminal device refers to semantic data that has a relationship with the terminal device. This relationship can be determined based on the object of the question raised by the user in the dialogue; it may point to the terminal device that initiated the interaction, or it may point to other terminal devices described by the user, such as the terminal device currently used by a family member. This user dialogue data serves as input for subsequent complaint intent identification and target base station location.

[0048] In some embodiments, acquiring user dialogue data associated with a terminal device includes: receiving dialogue text data collected by a voice platform; the dialogue text data being dialogue content related to the terminal device fed back by the user during interaction with the voice platform; and performing structured processing on the dialogue text data to obtain user dialogue data.

[0049] The voice platform can be an ASR (Automatic Speech Recognition) platform. The dialogue text data is collected during the user's interaction with the voice platform and may originate from transcripts, online customer service chat logs, or ticket descriptions. This dialogue text data can include the caller's dialogue content (caller_text), the called party's dialogue content (callee_text), and the complete dialogue content (full_text). The terminal device refers to the device provided by the user in the dialogue text data, such as the user's currently used terminal device or other terminal devices provided by the user.

[0050] After acquiring the dialogue text data, the caller's dialogue content (caller_text), the called party's dialogue content (callee_text), and the complete dialogue content (full_text) are structured based on timestamps or semantic order to reconstruct these dialogue contents into an ordered question-and-answer dialogue. Each dialogue is marked with a caller identifier (caller) or a called party identifier (callee), ultimately obtaining the structured user dialogue data (union_text). For example: Caller: My signal is extremely poor; I can't connect to the internet at all. Callee: Hello, where are you currently located? Caller: I'm in the waiting hall of the train station. Callee: We will check the network situation in this area as soon as possible.

[0051] By collecting user feedback related to terminal devices during real interactions, the directness and authenticity of the problem's source were ensured. The dialogue text data was structured to improve the data's analyzability and semantic integrity, thus providing a data foundation for accurately identifying user intent, associating specific terminal devices, and triggering automated parameter configuration, thereby enhancing the targeting and response efficiency of network optimization.

[0052] In step S120, in response to the classification result of the user dialogue data being a signal quality complaint, the target base station connected to the terminal device is determined.

[0053] In this embodiment, if the classification result of the user dialogue data is a signal quality complaint, the target base station connected to the terminal device at the time of the complaint can be determined based on the terminal device information, such as device identifier and location information, such as the base station ID or cell identifier of the serving cell. The target base station is a candidate for subsequent parameter optimization.

[0054] After classifying the user's dialogue data as a signal quality complaint, in addition to identifying the target base station connected to the terminal device, neighboring base stations with overlapping coverage or frequency interference with the target base station can be further identified. By analyzing the relationships between neighboring base stations, signal leakage, and load status, neighboring base stations that may interfere with the signal quality of the target base station are included in the optimization considerations, providing a basis for decision-making for subsequent inter-base station collaborative optimization, thereby improving the comprehensiveness and effectiveness of overall network optimization.

[0055] In some embodiments, the signal conditioning method further includes: classifying user dialogue data based on a multi-classification model to obtain classification results of the user dialogue data.

[0056] In this embodiment, the multi-classification model can be a BERT (Bidirectional Encoder Representations from Transformers) pre-trained model, a multi-task language model, or a fusion rule matching model, possessing the ability to recognize user feedback intent. The output of the multi-classification model includes: type judgment, such as determining whether the user's dialogue data belongs to the category of signal quality complaints; and the type of service affected, such as voice calls or video media services.

[0057] After obtaining the user dialogue data union_text, it is input into a multi-classification model to obtain the corresponding classification results. If the model outputs a classification result of signal quality complaint, it is determined that the user dialogue content belongs to a complaint about poor signal quality in the current terminal environment. Based on the location information or network access records of the terminal device, the target base station currently connected to the terminal device is determined, that is, the wireless access point that may affect the communication quality of the terminal device.

[0058] By introducing multi-classification models based on BERT pre-trained models, multi-task language models, or rule-matching models, accurate identification of user dialogue intent is achieved. This effectively distinguishes signal quality complaints from other service requests, improving the accuracy and robustness of classification. It can also identify the types of services affected, providing a basis for subsequent differentiated optimization.

[0059] In some embodiments, after determining that the user dialogue data classification result is a signal quality complaint, network signal quality indicators of the terminal device's location can also be obtained, including but not limited to: terminal-side reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise ratio (SINR), channel state information (CSI) feedback, and wireless network signal strength.

[0060] In this context, RSRP (Received Signal Reference Power) on the terminal side refers to the average power of the cell reference signal received by the terminal device from the serving cell or neighboring cells within a unit bandwidth, reflecting path loss and coverage capability from the base station to the terminal device. RSRQ (Received Signal Quotient) is the ratio of RSRP to total received power, used to measure signal quality and reflect channel interference and load levels. SINR (Signal Indicator Signal Ratio) is the ratio of useful signal power to interference plus noise power, used to evaluate link capacity and data rate. CSI (Current Channel Indicator) is comprehensive information about downlink channel characteristics fed back by the terminal device to the base station. Signal strength typically refers to the total RF signal strength received by the terminal, commonly including Received Signal Strength Indicator (RSSI).

[0061] For example, for signal quality indicators such as RSRP, RSRQ, SINR, CSI feedback, and signal strength, the indicator values ​​can be collected k times continuously and the average value can be taken as the original performance evaluation value of the current network status of the terminal device, where k is a positive integer, such as 5 times.

[0062] By collecting multi-dimensional signal quality indicators such as RSRP, RSRQ, SINR, CSI feedback, and wireless network signal strength, and calculating their average values, the actual network performance of the wireless environment in which the terminal device is located can be objectively reflected. At the same time, the recorded original performance evaluation values ​​can serve as a benchmark for comparison before and after subsequent parameter optimization, providing a quantitative basis for signal optimization.

[0063] In step S130, in response to the detection that no base station failure alarm has occurred at the target base station, the user dialogue data is analyzed based on the network optimization model to generate at least one set of candidate base station parameters.

[0064] In this embodiment, alarm log information from the target base station and other sources that can pinpoint the target base station's fault is obtained. Based on this alarm log information, it is determined whether the target base station has experienced a station outage alarm. If no station outage alarm has occurred, it indicates that the target base station is in normal operation, and the problem may stem from changes in the wireless environment or unreasonable parameter configuration. In this case, a network optimization model is triggered to perform deep analysis of user dialogue data, generating one or more sets of candidate base station parameters that may improve signal quality. The network optimization model is a prediction model or rule engine trained using machine learning.

[0065] In some embodiments, the signal conditioning method further includes: generating and sending information about the target base station losing its alarm in response to detecting that the target base station has lost its alarm.

[0066] In this embodiment, if a base station outage alarm is detected, it is determined that the base station is in an abnormal operating state. The root cause of the problem may be hardware failure, transmission interruption, or power abnormality. In this case, parameter optimization methods are not applicable. An alarm notification will be generated and pushed to the operation and maintenance management platform or relevant maintenance personnel, prompting them to conduct on-site troubleshooting and fault handling of the target base station. This enables differentiated responses to hardware failures and configuration problems, ensuring the accuracy and effectiveness of subsequent handling measures.

[0067] Figure 2 This illustration shows a flowchart of an embodiment of the present disclosure that analyzes user dialogue data based on a network optimization model to generate at least one set of candidate base station parameters. For example... Figure 2 As shown, it may include the following steps.

[0068] Step S210: Input user dialogue data into the network optimization model; perform semantic understanding on the user dialogue data through the first prompt instruction to obtain the first output result; the first output result includes the user's question description.

[0069] In some embodiments, the first output result may also include the type of problem and the type of service reported by the user.

[0070] In this embodiment, after determining that the target base station has not experienced a site outage alarm, the user dialogue data union_text is used as input to the network optimization model. A preset first prompt instruction (Prompt) guides the model to perform semantic parsing on the input user dialogue data union_text, extracting the core content of the user feedback and generating a first output result. This first output result may include: a user problem description, i.e., a summary text describing the signal quality problem described by the user, such as "The user reports weak signal in their area, making it impossible to access the internet normally"; a problem type, or classification identifier, such as weak signal quality, access failure, frequent handover, etc.; and a service type, i.e., identifying the specific service category affected, such as voice calls, video streaming, high-latency sensitive services, etc.

[0071] Step S220: Based on the first output result, the network optimization model is guided to perform analysis and reasoning through the second prompt instruction to obtain the second output result; the second output result includes adjustable base station parameters.

[0072] In some embodiments, the second output also includes the cause of the signal quality anomaly and the solution strategy.

[0073] In this embodiment, the first output result generated in step S210 is used as input, and combined with the preset second prompt instruction, the network optimization model is driven to perform causal reasoning and solution derivation, and output a second output result. The second output result may include: possible causes of abnormal signal quality, such as insufficient coverage, neighboring cell interference, improper handover parameter configuration, excessive load, etc.; recommended solutions, such as adjusting transmit power, optimizing handover threshold, adjusting beam direction, etc.; and a list of adjustable base station parameters, i.e., configurable parameters that can be used for optimization.

[0074] In some embodiments, the adjustable base station parameters include one or more of the following options: RSRP, electronic downtilt angle, inter-frequency measurement A2 RSRP trigger threshold based on A4 or A5 events, inter-frequency measurement A1 RSRP trigger threshold based on A4 or A5 events, inter-frequency measurement A2 RSRP trigger threshold based on A3 events, and inter-frequency measurement A1 RSRP trigger threshold based on A3 events.

[0075] In this embodiment, the RSRP on the base station side can be understood as the reference signal power on the base station transmitting side. Electronic downtilt refers to adjusting the phase of the antenna array elements to change the vertical pointing angle of the main beam, thereby controlling the signal coverage range in space.

[0076] Among them, event A1 indicates that the signal quality of the serving cell is higher than a set threshold, indicating that the current connection status is good, and the measurement of neighboring cells can be stopped or the existing connection can be maintained. Event A2 indicates that the signal quality of the serving cell is lower than a set threshold, triggering the terminal to start measuring neighboring cells of different frequencies or systems to prepare for possible handover. Event A3 indicates that the signal quality of the neighboring cell is better than that of the serving cell and the difference exceeds a preset offset, mainly used for triggering handover in the same frequency or different frequency. Event A4 indicates that the signal quality of the neighboring cell is higher than a set threshold, often used for handover decisions based on load balancing, or as a basis for selecting the target cell for handover in different frequencies or different systems. Event A5 indicates that the signal quality of the serving cell is lower than the first threshold while the signal quality of the neighboring cell is higher than the second threshold, used to trigger handover in different frequencies or different systems, ensuring timely migration to a cell with better quality when the serving cell deteriorates.

[0077] In this embodiment, the parameter "A2 RSRP trigger threshold" for inter-frequency measurement based on A4 or A5 events defines the RSRP threshold value for triggering the A2 event, used to control when the terminal device starts performing A4 or A5 event measurements on inter-frequency neighboring cells. The parameter "A1 RSRP trigger threshold" for inter-frequency measurement based on A4 or A5 events defines the RSRP threshold value for triggering the A1 event, used to control when the terminal stops measuring inter-frequency neighboring cells. The parameter "A2 RSRP trigger threshold" for inter-frequency measurement based on A3 events limits the triggering range of the A3 event, indicating that the terminal is only allowed to perform inter-frequency measurement and handover evaluation based on the A3 event when the RSRP of the serving cell is below this threshold. The parameter "A1 RSRP trigger threshold" for inter-frequency measurement based on A3 events indicates that the terminal is only allowed to perform inter-frequency measurement and handover evaluation based on the A3 event when the RSRP of the serving cell is above this threshold.

[0078] Step S230: Based on the user's problem description, adjustable base station parameters, base station information of the target base station, and base station information of neighboring base stations of the target base station, guide the network optimization model through a third prompt instruction to generate at least one set of candidate base station parameters.

[0079] In this embodiment, the user's problem description, a list of adjustable base station parameters, the base station information of the target base station, and the base station information of neighboring base stations are used as input content. These are encapsulated into a preset third prompt instruction (Prompt) to guide the network optimization model to perform collaborative optimization analysis and generate at least one set of candidate base station parameter configuration schemes. Specifically, the following information is encapsulated into the third prompt instruction as input content: a description of the problem reported by the user, such as weak signal, inability to access the internet, or video stuttering; identified adjustable base station parameters, such as electronic downtilt angle, A3 / A4 / A5 event-related measurement thresholds, etc.; base station information of the target base station, including but not limited to base station identifier, cell ID, geographical location, azimuth angle, mechanical tilt angle, and the actual values ​​of each adjustable parameter; and the corresponding configuration information of one or more neighboring base stations of the target base station, used to evaluate the relationship between inter-cell interference and coverage overlap.

[0080] The third prompt instruction allows for further specification of parameter adjustment strategies, including whether to optimize only the target base station independently or to perform joint optimization on the target base station and one or more neighboring base stations. Joint optimization primarily aims to avoid problems such as increased interference in neighboring cells or ping-pong handover caused by adjusting parameters of a single base station, thereby achieving coordinated optimization between cells.

[0081] Based on the above inputs, the network optimization model, combined with wireless propagation characteristics, neighbor cell relationships, and historical optimization experience, determines whether there is room for optimization in the current parameter configuration. If a feasible optimization path exists, at least one set of candidate base station parameters is generated. Each set of candidate base station parameters can contain n parameters to be adjusted and their suggested configuration values. For example, a set of candidate base station parameters might adjust the target base station's RSRP from 15dBm to 16dBm, the electronic downtilt angle from -6° to -8°, and the inter-frequency measurement A2 RSRP trigger threshold based on the A3 event from -105dBm to -102dBm. Each set of candidate parameters constitutes a complete and executable optimization strategy, which is used for subsequent verification and effect evaluation in a real network environment.

[0082] In some embodiments, a network optimization model is trained based on a large amount of historical operation and maintenance data, including user complaint dialogue records, base station operation logs, KPI performance indicators, parameter configuration snapshots, and manual optimization cases. First, addressing the semantic understanding requirement of step S210, the model takes user dialogue text as input and manually labeled "customer problem description," "problem type" (e.g., weak signal, dropped calls), and "service type" (e.g., voice, video) as tags, and is fine-tuned through supervised learning. An architecture based on BERT or a large language model is adopted, enabling the model to learn to accurately extract structured information from colloquial expressions such as "My internet speed is extremely slow" and "My calls keep dropping," forming standardized expressions of user-perceived problems.

[0083] Subsequently, to support the root cause analysis and adjustable parameter recommendation in step S220, the model uses the output of S210 as input and the "fault causes" (such as insufficient coverage, neighboring cell interference) and "actually adjusted parameter items" (such as electronic downtilt angle, A3 event threshold) recorded in historical work orders as training objectives to construct a multi-task learning framework. This trains the model to have the reasoning ability from problems to potential causes and operable parameters. In this process, a rule-guided label enhancement mechanism is introduced to ensure that the parameter list output by the model conforms to the adjustable range and logical constraints of the current network.

[0084] Finally, to achieve the candidate parameter generation in step S230, the model further introduces the configuration information of the target base station and its neighboring cells (such as base station location, current RSRP, downtilt angle, and adjacency relationship) as context input. Using the "parameters before adjustment → parameters after adjustment → optimization effect" triplet from historical optimization cases as training samples, the model is trained to generate one or more reasonable combinations of candidate parameters. This stage employs sequence generation or regression prediction methods, enabling the model to output specific numerical suggestions such as "adjusting the target base station's RSRP from 15dBm to 16dBm, and increasing the A3 threshold of neighboring base stations by 3dB," and supporting flexible selection of optimizing only a single station or collaboratively optimizing multiple stations based on prompts. After the entire model is trained on real data, the effectiveness of its generated schemes can be continuously evaluated through offline verification and online testing, and periodically retrained using feedback data to ensure that its output always aligns with the optimization patterns of the current network and the goal of improving user perception.

[0085] The method described above, which analyzes user dialogue data based on a network optimization model to generate at least one set of candidate base station parameters, achieves intelligent transformation from user dialogue data to executable network optimization strategies through a multi-stage prompt-guided step-by-step reasoning mechanism. First, a first prompt instruction extracts the user's problem description, problem type, and affected services. Second, a second prompt instruction drives the model to perform root cause analysis, identifying the causes of signal anomalies and outputting a list of adjustable parameters, establishing a mapping relationship between the problem and network parameters. Finally, combining the contextual information of the target base station and its neighboring cells, a third prompt instruction generates a candidate parameter configuration scheme for single-station or multi-station collaboration, balancing optimization effectiveness with overall network stability. This process significantly improves the semantic understanding of user complaints and the scientific nature of optimization decisions, effectively shortens the fault response cycle, and enhances network service quality and operational intelligence.

[0086] In step S140, each set of candidate base station parameters from at least one set of candidate base station parameters is selected sequentially for parameter configuration. After each set of candidate base station parameters is configured, the first signal quality data of the target base station and the second signal quality data of the terminal device are obtained.

[0087] In this embodiment, each set of candidate base station parameters is actually configured, and signal quality data on the network side and user side are collected after each configuration, namely the first signal quality data of the target base station and the second signal quality data of the terminal device, in order to verify the optimization effect of the set of parameters.

[0088] In some embodiments, each set of candidate base station parameters from at least one set of candidate base station parameters is sequentially selected for parameter configuration. After each set of candidate base station parameters is configured, first signal quality data of the target base station and second signal quality data of the terminal device are obtained. This includes: sorting at least one set of candidate base station parameters according to a preset priority to obtain an ordered sequence of candidate base station parameters; sequentially selecting the current candidate base station parameter from the candidate base station parameter sequence and configuring the corresponding base station according to the current candidate base station parameter; and obtaining the first signal quality data of the target base station after the current candidate base station parameter configuration and the second signal quality data of the terminal device after the current candidate base station parameter configuration. The preset priority satisfies one or more of the following principles: the fewer the number of base stations involved in the adjustment of a candidate base station parameter, the higher the sorting priority of the candidate base station parameter; the lower the power consumption corresponding to a candidate base station parameter, the higher the sorting priority of the candidate base station parameter.

[0089] In this embodiment, before performing parameter configuration, at least one set of candidate base station parameters can be sorted according to a preset priority to form an ordered sequence of candidate parameters. This sorting strategy aims to prioritize optimization schemes with smaller impact and higher resource efficiency, thereby improving optimization efficiency while ensuring network stability.

[0090] The preset priority rules are as follows: First, candidate schemes that only adjust parameters of a single base station (i.e., the target base station) are given priority. Since this only involves configuration changes to a single base station and does not involve coordination with neighboring base stations, its impact is controllable, the configuration logic is simple, and it can effectively avoid the impact of coverage abrupt changes, increased interference, or ping-pong handover that may be caused by multi-site coordinated adjustments. Therefore, among all candidate schemes, single-site optimization schemes are given higher priority and are deployed and verified first. Second, if all single-site adjustment schemes fail to achieve the expected signal quality improvement target after verification, the process moves to the multi-site coordinated optimization stage, attempting candidate schemes that involve joint adjustments to the target base station and one or more of its neighboring base stations. Within the same type of scheme (such as single-site or multi-site schemes), they are further sorted according to the adjusted network power consumption level, with priority given to parameter configurations with lower power consumption. For example, among multiple schemes that can improve signal quality, the scheme that enhances coverage by reducing the electronic downtilt angle or optimizing the handover threshold is prioritized, rather than by increasing RSRP, because the latter will directly increase the base station's transmit power and energy consumption. Based on this, after generating at least one set of candidate base station parameters, the estimated power consumption values ​​after adjusting each set of candidate base station parameters can be sorted in ascending order to ensure energy-saving operation while meeting performance requirements.

[0091] In this embodiment, the priority ranking of candidate base station parameters follows the principle of "single base station first, then multiple base stations, and in the case of the same number of base stations, power consumption from low to high". Only when the adjustment of a single base station cannot meet the performance target will the more complex multi-base station collaborative configuration be gradually introduced. This helps to reduce the potential impact of parameter adjustment on network stability, reduce coverage interference and ping-pong handover caused by multi-base station linkage configuration, and optimize base station energy consumption and improve network energy efficiency while ensuring user experience.

[0092] In some embodiments, the first signal quality data includes one or more of the following metrics: wireless call success rate, wireless call drop rate, handover success rate, total cell traffic, maximum number of online users, number of handover requests, number of successful handovers, number of random accesses with a timing advance (TA) value within a preset range, uplink weak coverage percentage, downlink average user plane delay, average channel quality indicator (CQI), and percentage of users with a CQI less than or equal to a preset value. The second signal quality data includes one or more of the following metrics: RSRP, RSRQ, SINR, channel state information (CSI) feedback, and signal strength.

[0093] The primary signal quality data originates from network-side statistics of the target base station, reflecting the overall operational performance of the cell. It mainly includes key performance indicators (KPIs), capacity indicators, coverage indicators, and perception indicators. Key KPIs may include, but are not limited to, wireless call success rate, wireless call drop rate, and handover success rate, used to measure basic network service availability and connection stability. Capacity indicators may include, but are not limited to, total cell traffic and maximum number of online users, used to assess network resource utilization and carrying capacity. Coverage indicators may include, but are not limited to, number of handover requests, number of successful handovers, number of random accesses within a preset time advance (TA) value, and the proportion of weak uplink coverage, used to identify changes in coverage. Perception indicators may include, but are not limited to, downlink average user plane latency, average CQI, and the proportion of CQI ≤ preset value (e.g., CQI ≤ 6), used to indirectly reflect user service experience. Each primary signal quality data indicator is continuously collected m times (e.g., once every 30 seconds, for a total of 5 times). The values ​​of each indicator are averaged, and the resulting statistical average is used as the final value for that indicator for subsequent evaluation.

[0094] The second signal quality data comes from reports or measurements taken by terminal devices and is a network environment indicator directly perceived by the user. It may include, but is not limited to, RSRP, RSRQ, CQI, CSI, and signal strength. Each second signal quality data indicator is continuously collected m times (e.g., once every 30 seconds, for a total of 5 times). The values ​​of each indicator are averaged, and the resulting statistical average is used as the final value of that indicator for subsequent evaluation.

[0095] In step S150, in response to the fact that both the first signal quality data and the second signal quality data have passed the signal quality verification, the parameter configuration of subsequent candidate base station parameters is stopped, and signal quality adjustment information corresponding to the user dialogue data is generated.

[0096] In this implementation, each group of candidate base station parameters is selected and configured sequentially. After each group of candidate base station parameters is configured, the first signal quality data of the target base station and the second signal quality data of the terminal device are collected. Then, it is determined whether the collected first and second signal quality data have passed the signal quality verification. If they have passed the verification, the attempt to select the remaining candidate base station parameters is stopped, and signal quality adjustment information is generated, including but not limited to a summary of the user feedback problem, the solution, the base station configuration parameters, the signal quality indicators, the parameter adjustment process, and the effect of the adjustment.

[0097] Figure 3 A flowchart illustrating the quality verification of the first signal quality data in an embodiment of this disclosure is shown. Figure 3 As shown, it may include the following steps.

[0098] Step S310: Obtain key performance index data from the first signal quality data.

[0099] Step S320: Generate the current performance evaluation value of the target base station based on the first signal quality data.

[0100] Step S330: Calculate the first performance gain based on the current performance evaluation value of the target base station and the original performance evaluation value of the target base station.

[0101] Step S340: In response to the performance improvement value corresponding to the key performance indicator data being greater than the preset performance improvement threshold, and the first performance gain being greater than the first preset threshold, it is determined that the first signal quality data has passed the signal quality verification.

[0102] After configuring the parameters of each candidate base station, the first signal quality data of the target base station is collected, which may include one or more of the following performance indicators: wireless connection rate, wireless drop rate, handover success rate, total cell traffic, maximum number of online users, number of handover requests, number of successful handovers, number of random accesses with a time advance (TA) value within a preset range, uplink weak coverage ratio, downlink average user plane latency, average channel quality indicator (CQI), and the percentage of CQI values ​​less than or equal to a preset value.

[0103] In this embodiment, key performance index data can be selected from the first signal quality data. By comparing the changes of various indicators before and after the base station parameter adjustment, it can be determined that the performance improvement value corresponding to the key performance index data is greater than the preset performance improvement threshold.

[0104] For example, in terms of connection performance, an improvement is considered if the wireless connection rate increases and the wireless drop rate decreases. For instance, an increase in connection rate from 90% to 95% (a 5 percentage point increase) exceeds the preset 2% improvement threshold, while a decrease in drop rate from 5% to 2% (a 3 percentage point decrease) meets the preset 1.5% improvement requirement. In terms of handover performance, an improvement in handover success rate exceeding the preset performance improvement threshold without an abnormal increase in the number of handover requests indicates optimized handover performance. For example, an increase in handover success rate from 85% to 92% (a 7 percentage point increase) exceeds the preset 5% threshold. Regarding coverage, a decrease in the proportion of weak uplink coverage is considered an improvement. The reduction in both downlink latency and average user plane latency must meet their respective preset performance improvement thresholds. For example, a decrease in the proportion of weak coverage from 15% to 8% (a 7 percentage point reduction) exceeds the preset 5% improvement target; a reduction in downlink latency from 30 milliseconds to 20 milliseconds (a 10 millisecond reduction) meets the preset 8 millisecond optimization threshold. Regarding user perception, the improvement in average CQI and the decrease in the proportion of CQI in the 0-6 range must both exceed their corresponding preset performance improvement thresholds. For example, an increase in average CQI from 7 to 8 (a 1-unit increase) meets the preset target; a decrease in the proportion of low CQI from 20% to 10% (a 10 percentage point reduction) exceeds the preset 8% improvement requirement. If the performance improvement values ​​of all the above key performance indicators are greater than their corresponding preset performance improvement thresholds, then the key performance indicators are determined to meet the overall optimization requirements.

[0105] In this embodiment, the current performance evaluation value of the target base station can also be calculated based on the following evaluation function formula.

[0106] F(x) = (1)

[0107] In formula (1), F(x) is the performance evaluation value. It is the first The weights of each performance metric parameter, It is the first The value of each performance metric is n, where n is the number of performance metrics.

[0108] After parameter adjustment, the signal quality data of the target base station after parameter adjustment is obtained, namely the first signal quality data. Based on the first signal quality data, the current performance evaluation value of the target base station is calculated according to formula (1). Also, before parameter adjustment, the signal quality data of the target base station before parameter adjustment can be obtained. Based on the signal quality data before parameter adjustment, the original performance evaluation value of the target base station is calculated according to formula (1). After obtaining the current performance evaluation value of the target base station after parameter adjustment of the current candidate base station and the original performance evaluation value before parameter adjustment, the percentage improvement in performance of the current performance evaluation value relative to the original performance evaluation value is calculated, and this percentage improvement is used as the first performance gain.

[0109] The system determines whether the first performance gain is greater than a first preset threshold. This first preset threshold can be set based on network operation goals, user perception requirements, or historical optimization experience; for example, it can be set to 5%, 10%, or dynamically adjusted according to the specific scenario. If the performance improvement value corresponding to the key performance indicator data is greater than the preset performance improvement threshold, and the first performance gain is greater than the first preset threshold, then the current candidate base station parameter adjustment is considered to have brought about the expected performance improvement on the network side, and the first signal quality data is determined to have passed signal quality verification.

[0110] Figure 4 A flowchart illustrating the quality verification of the second signal quality data in an embodiment of this disclosure is shown. Figure 4 As shown, it may include the following steps.

[0111] Step S410: Generate the current performance evaluation value of the terminal device based on the second signal quality data.

[0112] Step S420: Calculate the second performance gain based on the current performance evaluation value of the terminal device and the original performance evaluation value of the terminal device.

[0113] Step S430: In response to the second performance gain being greater than the second preset threshold, determine that the second signal quality data has passed the signal quality verification.

[0114] After configuring the parameters of the current candidate base station, the current performance evaluation value of the terminal device is generated based on the collected second signal quality data. The second signal quality data may include one or more of the following performance indicators: RSRP, RSRQ, SINR, CSI feedback, and signal strength. The current performance evaluation value of the terminal device is calculated based on the evaluation function formula (1) above. Furthermore, before parameter adjustment, the signal quality data of the terminal device before parameter adjustment can be obtained, and the original performance evaluation value of the terminal device is calculated according to formula (1) based on the signal quality data before parameter adjustment. After obtaining the current performance evaluation value of the terminal device after parameter adjustment of the current candidate base station and the original performance evaluation value before parameter adjustment, the percentage performance improvement of the current performance evaluation value relative to the original performance evaluation value is calculated, and this percentage performance improvement is used as the second performance gain.

[0115] The system determines whether the second performance gain is greater than a second preset threshold. This second preset threshold can be set based on network operation goals, user perception requirements, or historical optimization experience; for example, it can be set to 5%, 10%, or dynamically adjusted according to the specific scenario. If the second performance gain is greater than the second preset threshold, it is considered that the current adjustments to the candidate base station parameters have brought about the expected performance improvement on the user side, and the first signal quality data is determined to have passed signal quality verification.

[0116] In this embodiment, if both the first and second signal quality data pass the signal quality verification, the current candidate base station parameters are determined to be a valid optimization scheme. In this case, the configuration of this set of candidate base station parameters will be retained, attempts to obtain subsequent candidate base station parameters will be stopped, and relevant data from this optimization process will be recorded, including original parameters, adjusted parameters, changes in performance evaluation values, and gain results, for subsequent model feedback and knowledge accumulation. Conversely, if one or both of the first and second signal quality data fail the signal quality verification, the parameter adjustment is considered to have failed to fully meet the optimization objective, the current candidate base station parameter scheme is determined to be invalid, the original parameter configuration will be restored, or the next set of candidate base station parameters will be tried directly, and the configuration, acquisition, and evaluation process will continue until an optimization scheme that meets both signal quality requirements is found, or all candidate base station parameters have been traversed.

[0117] In some embodiments, the signal conditioning method further includes: after configuring the parameters of each group of candidate base station parameters in at least one group of candidate base station parameters, if the configuration result of any group of candidate base station parameters fails the signal quality verification, then generating a manual operation and maintenance work order.

[0118] In this embodiment, after each group of candidate base station parameters is configured, the corresponding first and second signal quality data are evaluated to see if they pass signal quality verification. If a group of parameters passes signal quality verification, the configuration is retained and subsequent attempts are terminated. If all candidate base station parameters have been tried but none have passed signal quality verification, the entire optimization process is recorded, including the user's problem description, the list of candidate parameters, the adjustment content of each group of parameters, the signal quality data collected before and after configuration, the performance gain calculation results, and the final reason for failing verification. This recorded information is pushed to the maintenance personnel along with the manual maintenance worksheet to help them quickly locate the problem. In addition, if all candidate base station parameters have been tried but none have passed signal quality verification, a recovery mechanism can be triggered to adjust all adjusted parameters of the target base station and related neighboring stations to the original configuration, ensuring network reversibility and stable operation.

[0119] Figure 5 A flowchart of a signal conditioning method according to yet another embodiment of this disclosure is shown. Figure 5 As shown, it may include the following steps.

[0120] Step S501: Receive dialogue text data collected by the voice platform, perform structured processing on the dialogue text data, and obtain user dialogue data.

[0121] The dialogue text data comprises the dialogue content related to the terminal device during the interaction between the user and the voice platform. This dialogue text data can include the caller's dialogue content (caller_text), the called party's dialogue content (callee_text), and the complete dialogue content (full_text). After obtaining the dialogue text data, the caller's dialogue content (caller_text), the called party's dialogue content (callee_text), and the complete dialogue content (full_text) are structured based on timestamps or semantic order to reconstruct these dialogue contents into an ordered question-and-answer dialogue. Each dialogue is then marked with a caller identifier (caller) or a called party identifier (callee), ultimately yielding the structured user dialogue data union_text.

[0122] Step S502: Classify the user dialogue data based on a multi-classification model to obtain the classification results of the user dialogue data.

[0123] After obtaining the user dialogue data (union_text), it is input into a multi-classification model to obtain the corresponding classification results. These results include: type determination, such as whether the user dialogue data belongs to the category of signal quality complaints; and the type of service affected, such as voice calls or video media services.

[0124] Step S503: In response to the classification result of the user dialogue data being a signal quality complaint, determine the target base station to which the terminal device is connected.

[0125] After determining that the user's dialogue data is classified as a signal quality complaint, in addition to identifying the target base station connected to the terminal device, it is also possible to further identify neighboring base stations that have coverage overlap or frequency interference with the target base station.

[0126] Furthermore, after determining that the user's dialogue data is classified as a signal quality complaint, the network signal quality indicators of the terminal device's location can also be obtained, including but not limited to: RSRP, RSRQ, SINR, CSI feedback, wireless network signal strength, etc.

[0127] Step S504: Detect whether the target base station has experienced a station outage alarm.

[0128] Obtain alarm log information of the target base station and other alarm log information that can locate the fault of the target base station, and determine whether the target base station has experienced a station outage alarm based on these alarm log information.

[0129] Step S505: If yes, generate a base station outage alarm message and report it to the maintenance personnel.

[0130] If a base station outage alarm is detected, it is determined that the base station is in an abnormal operating state. The root cause of the problem may be hardware failure, transmission interruption, or power abnormality. In this case, parameter optimization methods are not applicable. An alarm notification will be generated and pushed to the operation and maintenance management platform or relevant maintenance personnel, prompting them to conduct on-site troubleshooting and fault handling of the target base station.

[0131] Step S506: If not, proceed according to steps S210 to S230 above, analyze the user dialogue data based on the network optimization model, and generate at least one set of candidate base station parameters.

[0132] If no base station failure alarm occurs, it indicates that the target base station is operating normally. The problem may stem from changes in the wireless environment or unreasonable parameter configuration. In this case, the network optimization model is triggered to perform in-depth analysis of user dialogue data, generating one or more sets of candidate base station parameters that may improve signal quality. Steps S210 to S230 above have already described in detail the process of generating at least one set of candidate base station parameters, and will not be repeated here.

[0133] Step S507: Sort at least one set of candidate base station parameters according to a preset priority to obtain an ordered sequence of candidate base station parameters.

[0134] The preset priority satisfies one or more of the following principles: the fewer base stations a candidate base station parameter involves adjusting, the higher its ranking priority; the lower the power consumption of a candidate base station parameter, the higher its ranking priority. In other words, the priority ranking of candidate base station parameters follows the principle of "single base station first, then multiple base stations, and in the case of the same number of base stations, ranking by power consumption from low to high." Only when single-station adjustments cannot meet performance targets will more complex multi-station collaborative configurations be gradually introduced.

[0135] Step S508: Determine whether the candidate base station parameters in the candidate base station parameter sequence have been traversed. If yes, proceed to step S514; otherwise, proceed to step S509.

[0136] Step S509: Select the current candidate base station parameters from the candidate base station parameter sequence, and configure the corresponding base station according to the current candidate base station parameters.

[0137] Step S510: Obtain the first signal quality data of the target base station after the current candidate base station parameter configuration and the second signal quality data of the terminal device after the current candidate base station parameter configuration.

[0138] The first signal quality data includes one or more of the following indicators: wireless call success rate, wireless call drop rate, handover success rate, total cell traffic, maximum number of online users, number of handover requests, number of successful handovers, number of random accesses with a timing advance (TA) value within a preset range, uplink weak coverage percentage, downlink average user plane latency, average channel quality indicator (CQI), and percentage of users with a CQI value less than or equal to a preset value. The second signal quality data includes one or more of the following indicators: RSRP, RSRQ, SINR, channel state information (CSI) feedback, and signal strength.

[0139] Step S511: Perform signal quality verification on the first signal quality data according to steps S310 to S340 above, and perform signal quality verification on the first signal quality data according to steps S410 to S430 above.

[0140] Step S512: Determine whether the first and second signal quality data have both passed the signal quality verification. If yes, proceed to step S513; otherwise, proceed to step S508.

[0141] Step S513: Stop configuring the parameters of subsequent candidate base stations and generate signal quality adjustment information.

[0142] The signal quality condition information may include an overview of user-reported problems, solutions, base station configuration parameters, signal quality indicators, parameter adjustment process, and the effects of the adjustments.

[0143] Step S514: Generate a manual maintenance work order.

[0144] If all candidate base station parameters have been tried but none pass the signal quality verification, the entire optimization process is recorded, including the user's problem description, the list of candidate parameters, the adjustment content of each group of parameters, the signal quality data collected before and after configuration, the performance gain calculation results, and the reason for the final failure to pass the verification. This recorded information is pushed to the maintenance personnel along with the manual maintenance work order to help them quickly locate the problem.

[0145] Through the above steps, perception and response based on user feedback intent are realized. The entire process from user feedback identification and base station parameter adjustment to optimization effect verification is automated without human intervention. This improves the efficiency of base station parameter configuration, the response speed and processing accuracy of base station signal quality issues, reduces the latency of user feedback processing, enhances the adaptive control capability against network congestion and signal interference in high-density, high-concurrency scenarios, and improves the user experience.

[0146] Figure 6 A schematic diagram of a signal conditioning device according to an embodiment of the present disclosure is shown. Figure 6The signal conditioning device 600 shown may include a dialogue data acquisition module 610, a base station determination module 620, a parameter generation module 630, a parameter configuration module 640, and a signal verification module 650.

[0147] The dialogue data acquisition module 610 is configured to acquire user dialogue data associated with the terminal device. The base station determination module 620 is configured to determine the target base station connected to the terminal device in response to the classification result of the user dialogue data being a signal quality complaint. The parameter generation module 630 is configured to analyze the user dialogue data based on a network optimization model and generate at least one set of candidate base station parameters in response to the detection that no base station failure alarm has occurred at the target base station. The parameter configuration module 640 is configured to sequentially select each set of candidate base station parameters from the at least one set of candidate base station parameters for parameter configuration, and after configuring each set of candidate base station parameters, acquire the first signal quality data of the target base station and the second signal quality data of the terminal device. The signal verification module 650 is configured to stop the parameter configuration of subsequent candidate base station parameters and generate signal quality adjustment information corresponding to the user dialogue data in response to the first signal quality data and the second signal quality data both passing the signal quality verification.

[0148] In some exemplary embodiments of this disclosure, the parameter generation module 630 is further configured to: input user dialogue data into a network optimization model; perform semantic understanding on the user dialogue data through a first prompt instruction to obtain a first output result; the first output result includes a user question description; based on the first output result, guide the network optimization model to perform analysis and reasoning through a second prompt instruction to obtain a second output result; the second output result includes adjustable base station parameters; based on the user question description, adjustable base station parameters, base station information of the target base station, and base station information of neighboring base stations of the target base station, guide the network optimization model through a third prompt instruction to generate at least one set of candidate base station parameters.

[0149] In some exemplary embodiments of this disclosure, the first output result also includes the type of problem and service type reported by the user; the second output result also includes the cause of the signal quality anomaly and the solution strategy.

[0150] In some exemplary embodiments of this disclosure, the adjustable base station parameters include one or more of the following options: Reference Signal Received Power (RSRP), Electronic Downtilt Angle, Inter-frequency Measurement A2 RSRP Trigger Threshold based on A4 or A5 Events, Inter-frequency Measurement A1 RSRP Trigger Threshold based on A4 or A5 Events, Inter-frequency Measurement A2 RSRP Trigger Threshold based on A3 Events, and Inter-frequency Measurement A1 RSRP Trigger Threshold based on A3 Events.

[0151] In some exemplary embodiments of this disclosure, the parameter configuration module 640 is further configured to: sort at least one set of candidate base station parameters according to a preset priority to obtain an ordered sequence of candidate base station parameters; wherein the preset priority satisfies one or more of the following principles: the fewer the number of base stations involved in a candidate base station parameter, the higher the sorting priority of the candidate base station parameter; the lower the power consumption corresponding to a candidate base station parameter, the higher the sorting priority of the candidate base station parameter; sequentially select the current candidate base station parameter in the candidate base station parameter sequence, configure the corresponding base station according to the current candidate base station parameter; and obtain the first signal quality data of the target base station after the current candidate base station parameter configuration and the second signal quality data of the terminal device after the current candidate base station parameter configuration.

[0152] In some exemplary embodiments of this disclosure, the first signal quality data includes one or more of the following indicators: wireless call success rate, wireless call drop rate, handover success rate, total cell traffic, maximum number of online users, number of handover requests, number of successful handovers, number of random accesses with a timing advance (TA) value within a preset range, uplink weak coverage ratio, downlink average user plane delay, average channel quality indicator (CQI), and the percentage of CQI values ​​less than or equal to a preset value; the second signal quality data includes one or more of the following indicators: RSRP, reference signal reception quality (RSRQ), signal-to-noise ratio (SINR), channel state information (CSI) feedback, and signal strength.

[0153] In some exemplary embodiments of this disclosure, the signal verification module 650 is further configured to: acquire key performance indicator data in the first signal quality data; generate a current performance evaluation value of the target base station based on the first signal quality data; calculate a first performance gain based on the current performance evaluation value of the target base station and the original performance evaluation value of the target base station; and determine that the first signal quality data passes the signal quality verification in response to the performance improvement value corresponding to the key performance indicator data being greater than a preset performance improvement threshold and the first performance gain being greater than a first preset threshold.

[0154] In some exemplary embodiments of this disclosure, the signal verification module 650 is further configured to: generate a current performance evaluation value of the terminal device based on the second signal quality data; calculate a second performance gain based on the current performance evaluation value of the terminal device and the original performance evaluation value of the terminal device; and determine that the second signal quality data passes the signal quality verification in response to the second performance gain being greater than a second preset threshold.

[0155] In some exemplary embodiments of this disclosure, the device 600 further includes a manual operation and maintenance module 660, which is configured to generate a manual operation and maintenance work order if the configuration result of any set of candidate base station parameters fails the signal quality verification after configuring the parameters of each set of candidate base station parameters in at least one set of candidate base station parameters.

[0156] In some exemplary embodiments of this disclosure, the dialogue data acquisition module 610 is further configured to: receive dialogue text data collected by the voice platform; the dialogue text data is dialogue content related to the terminal device fed back by the user during the interaction between the user and the voice platform; and perform structured processing on the dialogue text data to obtain user dialogue data.

[0157] In some exemplary embodiments of this disclosure, the device 600 further includes a classification module 670 configured to classify user dialogue data based on a multi-classification model to obtain classification results of the user dialogue data.

[0158] In some exemplary embodiments of this disclosure, the device 600 further includes a base station failure alarm module 680, configured to generate and send information about a base station failure alarm in response to detecting a base station failure alarm at the target base station.

[0159] The principle of the signal conditioning device embodiment provided in this disclosure is similar to that of the above-described method embodiment. Therefore, the implementation of the signal conditioning device embodiment can be referred to the above-described method embodiment, and repeated details will not be repeated.

[0160] Figure 7 A structural block diagram of an electronic device according to an embodiment of this disclosure is shown. It should be noted that... Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0161] like Figure 7 As shown, the electronic device 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 702 or a program loaded from a storage section 708 into a random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device 700. The CPU 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0162] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0163] 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 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 section 709, and / or installed from removable medium 711. When the computer program is executed by central processing unit (CPU) 701, it performs the functions defined above in the system of this disclosure.

[0164] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, terminal device, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, 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 device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, terminal device, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit programs for use by or in connection with an instruction execution system, terminal device, or apparatus. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0165] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0166] The modules described in the embodiments of this disclosure can be implemented in software or hardware. The described modules can also be housed in a processor; for example, a processor may be described as including a dialogue data acquisition module, a base station determination module, a parameter generation module, a parameter configuration module, and a signal verification module. The names of these modules do not necessarily limit the module itself; for example, the dialogue data acquisition module may also be described as "a module for acquiring user dialogue data associated with a terminal device."

[0167] In another aspect, this disclosure also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps shown.

[0168] According to one aspect of this disclosure, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in various optional implementations of the above embodiments.

[0169] It should be understood that any number of elements in the accompanying drawings is for illustrative purposes only and not for limitation, and any naming is for distinction only and has no limiting meaning.

[0170] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0171] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A signal conditioning method, characterized in that, The method includes: Obtain user conversation data associated with the terminal device; In response to the classification result of the user dialogue data being a signal quality complaint, the target base station connected to the terminal device is determined; In response to the detection that no outage alarm has occurred at the target base station, the user dialogue data is analyzed based on the network optimization model to generate at least one set of candidate base station parameters; Each set of candidate base station parameters is selected sequentially and configured. After configuring each set of candidate base station parameters, the first signal quality data of the target base station and the second signal quality data of the terminal device are obtained. In response to the fact that both the first signal quality data and the second signal quality data have passed the signal quality verification, the parameter configuration of subsequent candidate base station parameters is stopped, and signal quality adjustment information corresponding to the user dialogue data is generated. The step of analyzing the user dialogue data based on a network optimization model to generate at least one set of candidate base station parameters includes: inputting the user dialogue data into the network optimization model; performing semantic understanding on the user dialogue data through a first prompt instruction to obtain a first output result; the first output result includes a user question description; based on the first output result, guiding the network optimization model to perform analysis and reasoning through a second prompt instruction to obtain a second output result; the second output result includes adjustable base station parameters; and based on the user question description, the adjustable base station parameters, the base station information of the target base station, and the base station information of the target base station's neighboring base stations, guiding the network optimization model through a third prompt instruction to generate the at least one set of candidate base station parameters.

2. The method according to claim 1, characterized in that, The first output also includes the type of problem and service type reported by the user; the second output also includes the cause of the signal quality anomaly and the solution strategy.

3. The method according to claim 1, characterized in that, The adjustable base station parameters include one or more of the following options: Reference Signal Received Power (RSRP), Electronic Downtilt Angle, Inter-frequency Measurement A2 RSRP Trigger Threshold based on A4 or A5 Events, Inter-frequency Measurement A1 RSRP Trigger Threshold based on A4 or A5 Events, Inter-frequency Measurement A2 RSRP Trigger Threshold based on A3 Events, and Inter-frequency Measurement A1 RSRP Trigger Threshold based on A3 Events.

4. The method according to claim 1, characterized in that, The method further includes: Before configuring the parameters, the at least one set of candidate base station parameters are sorted according to a preset priority. The preset priority is determined according to the following rules: the candidate base station parameter group with fewer base stations involved in the adjustment has a higher sorting priority; for multiple candidate base station parameter groups with the same number of base stations involved in the adjustment, they are sorted according to the principle of power consumption from low to high.

5. The method according to claim 4, characterized in that, The first signal quality data includes one or more of the following indicators: wireless connection rate, wireless drop rate, handover success rate, total cell traffic, maximum number of online users, number of handover requests, number of successful handovers, number of random accesses with a timing advance (TA) value within a preset range, uplink weak coverage ratio, downlink average user plane latency, average channel quality indicator (CQI), and the percentage of users with a CQI value less than or equal to a preset value. The second signal quality data includes one or more of the following metrics: RSRP, Reference Signal Received Quality (RSRQ), Signal-to-Noise Ratio (SINR), Channel State Information (CSI) Feedback, and Signal Strength.

6. The method according to claim 1, characterized in that, The method further includes: Obtain key performance index data from the first signal quality data; Based on the first signal quality data, generate the current performance evaluation value of the target base station; Calculate the first performance gain based on the current performance evaluation value of the target base station and the original performance evaluation value of the target base station; In response to the performance improvement value corresponding to the key performance indicator data being greater than a preset performance improvement threshold, and the first performance gain being greater than a first preset threshold, it is determined that the first signal quality data has passed the signal quality verification.

7. The method according to claim 1, characterized in that, The method further includes: Based on the second signal quality data, the current performance evaluation value of the terminal device is generated; Calculate the second performance gain based on the current performance evaluation value of the terminal device and the original performance evaluation value of the terminal device; In response to the second performance gain being greater than the second preset threshold, it is determined that the second signal quality data has passed the signal quality verification.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: After configuring the parameters for each of the at least one set of candidate base station parameters, if the configuration results of all sets of candidate base station parameters fail the signal quality verification, a manual maintenance work order is generated.

9. The method according to claim 1, characterized in that, The acquisition of user dialogue data associated with the terminal device includes: Receive dialogue text data collected by the voice platform; the dialogue text data is the dialogue content related to the terminal device that is fed back by the user during the interaction with the voice platform; The dialogue text data is structured to obtain the user dialogue data.

10. The method according to claim 1, characterized in that, The method further includes: The user dialogue data is classified based on a multi-classification model to obtain the classification results of the user dialogue data.

11. The method according to claim 1, characterized in that, The method further includes: In response to the detection of a base station outage alarm at the target base station, information about the base station outage alarm at the target base station is generated and sent.

12. A signal conditioning device, characterized in that, The device includes: The dialogue data acquisition module is configured to acquire user dialogue data associated with the terminal device; The base station determination module is configured to determine the target base station connected to the terminal device in response to the classification result of the user dialogue data being a signal quality complaint. The parameter generation module is configured to, in response to detecting that the target base station has not experienced a station failure alarm, analyze the user dialogue data based on a network optimization model and generate at least one set of candidate base station parameters. The parameter configuration module is configured to sequentially select each group of candidate base station parameters from the at least one group of candidate base station parameters for parameter configuration, and after configuring each group of candidate base station parameters, obtain the first signal quality data of the target base station and the second signal quality data of the terminal device; The signal verification module is configured to stop configuring the parameters of the subsequent candidate base station parameters and generate the signal quality adjustment information corresponding to the user dialogue data in response to both the first signal quality data and the second signal quality data passing the signal quality verification. The parameter generation module is further configured to: input the user dialogue data into the network optimization model; perform semantic understanding on the user dialogue data through a first prompt instruction to obtain a first output result; the first output result includes a user question description; based on the first output result, guide the network optimization model to perform analysis and reasoning through a second prompt instruction to obtain a second output result; the second output result includes adjustable base station parameters; based on the user question description, the adjustable base station parameters, the base station information of the target base station, and the base station information of the neighboring base stations of the target base station, guide the network optimization model through a third prompt instruction to generate the at least one set of candidate base station parameters.

13. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the method of any one of claims 1-11 by executing the executable instructions.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-11.

15. A computer program product, characterized in that, The computer program product stores instructions that, when executed by a computer, cause the computer to perform the method described in any one of claims 1-11.

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