Optimization processing method and device of 5g private network interference, server and storage medium

Abstract

Embodiments of the present application provide a 5G private network interference optimization processing method and device, a server and a storage medium. The method comprises: receiving noise data of a 5G private network cell sent by an air interface resource monitoring probe; obtaining a 5G private network interference cell according to the noise data of the 5G private network cell; the 5G private network interference cell carries interference index data; if it is determined that an interference optimization process is triggered according to the interference index data, obtaining a 5G private network interference type through an interference type identification model; if the 5G private network interference type is inter-system interference, obtaining all terminal identifiers under the 5G private network interference cell; obtaining all performance-degraded terminal identifiers based on end-to-end perception data and wireless measurement data of each terminal; determining the interference source of all performance-degraded terminal identifiers through a tabu search algorithm; and optimizing the interference source according to a pre-constructed interference optimization knowledge graph. The time for locating the interference source is reduced, and the optimization efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to an optimized processing method, apparatus, server, and storage medium for 5G private network interference. Background Technology

[0002] 5G private network interference refers to phenomena that adversely affect normal communication signals during the operation of a 5G private network. This interference can degrade network performance and impact user experience. A 5G private network cell is a basic unit within a 5G private network, representing a wireless communication area divided according to communication needs.

[0003] Currently, existing technologies mainly rely on maintenance personnel to locate interference sources using frequency scanning tools. After identifying the interference source, maintenance personnel provide optimization suggestions, and then optimization is carried out.

[0004] However, manually locating and optimizing interference sources increases the time required to locate them and reduces optimization efficiency. Summary of the Invention

[0005] The 5G private network interference optimization processing method, device, server and storage medium provided in this application are used to reduce the time for locating interference sources and improve optimization efficiency.

[0006] In a first aspect, embodiments of this application provide a method for optimizing 5G private network interference, comprising: receiving noise data of a 5G private network cell sent by an air interface resource monitoring probe; obtaining a 5G private network interfering cell based on the noise data of the 5G private network cell; wherein the 5G private network interfering cell carries interference index data; determining whether an interference optimization process for the 5G private network interfering cell is triggered based on the interference index data; if the interference optimization process is triggered, inputting the noise data of the 5G private network cell into an interference type identification model to output the 5G private network interference type; if the 5G private network interference type is inter-system interference, obtaining all terminal identifiers under the 5G private network interfering cell; obtaining end-to-end sensing data and wireless measurement data of each terminal based on each terminal identifier; obtaining all performance-degraded terminal identifiers from all terminal identifiers based on the end-to-end sensing data and wireless measurement data of each terminal; determining the interference source of all performance-degraded terminal identifiers through a tabu search algorithm based on the wireless measurement data; and optimizing the interference source based on a pre-constructed interference optimization knowledge graph.

[0007] In one possible implementation, obtaining 5G private network interfering cells based on noise data of 5G private network cells includes: clustering the noise data of 5G private network cells in the time domain to obtain time-domain features and 5G private network interfering cells that are continuously interfered with; clustering the noise data of 5G private network cells in the frequency domain to obtain spectral features; clustering the 5G private network interfering cells that are continuously interfered with based on the spectral features to obtain 5G private network interfering cells that are interfered with by different interference sources; and from the 5G private network interfering cells that are interfered with by different interference sources, performing density clustering in the spatial dimension on 5G private network cells with the same time-domain features and spectral features to identify 5G private network interfering cell clusters, thereby obtaining 5G private network interfering cells; wherein the 5G private network interfering cell cluster includes at least one 5G private network interfering cell.

[0008] In one possible implementation, determining whether to trigger the interference optimization process for 5G private network interfering cells based on interference index data includes: obtaining the average received interference noise and interference duration from the interference index data; if the average received interference noise is greater than a preset threshold for the average received interference noise, the interference duration exceeds a preset time threshold, and the number of 5G private network interfering cells in the 5G private network interfering cell cluster to which the 5G private network interfering cell belongs is greater than a preset number threshold, then it is determined that the interference optimization process for 5G private network interfering cells has been triggered.

[0009] In one possible implementation, noise data of a 5G private network cell is input into an interference type identification model to output the 5G private network interference type, including: inputting noise data of a 5G private network cell into an interference type identification model, and the interference type identification model outputting the 5G private network interference type through a cross-validation method.

[0010] In one possible implementation, based on wireless measurement data, the interference sources for all degraded terminal identifiers are determined using a tabu search algorithm, including: constructing an initial solution space for the interference sources; calculating the evaluation score of each solution in the initial solution space based on the wireless measurement data and an evaluation function; obtaining the current solution in the initial solution space based on the evaluation score of each solution; iteratively performing the following steps on the current solution until the iteration termination condition is met; generating multiple neighborhood solutions based on the current solution; selecting the neighborhood solution with the best evaluation score from the multiple neighborhood solutions as the latest current solution based on the evaluation function; and determining the location information corresponding to the latest current solution as the interference source for all degraded terminal identifiers.

[0011] In one possible implementation, the evaluation score for each solution in the initial solution space is calculated based on wireless measurement data and an evaluation function, using the following formula:

[0012]

[0013] In the formula, Indicates the evaluation score. This indicates the number of interference indicators in the wireless measurement data. Indicates the first The weight of each interference indicator, Indicates the first One interference indicator.

[0014] In one possible implementation, before receiving noise data of the 5G private network cell sent by the air interface resource monitoring probe, the method further includes: performing cluster analysis on historical interference index data, historical end-to-end sensing data, and historical wireless measurement data to obtain historical interference types; and constructing an interference optimization knowledge graph based on the historical interference types and optimization processing methods for the historical interference types.

[0015] Secondly, embodiments of this application provide an optimization processing apparatus for 5G private network interference, comprising:

[0016] The receiving module is used to receive noise data of 5G private network cells sent by the air interface resource monitoring probe;

[0017] The first acquisition module is used to acquire 5G private network interfering cells based on the noise data of 5G private network cells; wherein the 5G private network interfering cells carry interference index data.

[0018] The judgment module is used to determine whether to trigger the interference optimization process of the 5G private network interference cell based on the interference index data.

[0019] The output module is used to input the noise data of the 5G private network cell into the interference type identification model if the interference optimization process is determined to be triggered, so as to output the interference type of the 5G private network.

[0020] The second acquisition module is used to acquire all terminal identifiers under the 5G private network interference cell if the 5G private network interference type is inter-system interference.

[0021] The third acquisition module is used to acquire end-to-end sensing data and wireless measurement data of each terminal based on the terminal identifier.

[0022] The fourth acquisition module is used to acquire all terminal identifiers with degraded performance from all terminal identifiers based on the end-to-end sensing data and wireless measurement data of each terminal.

[0023] The determination module is used to identify the interference sources of all degraded terminal identifiers based on wireless measurement data and a tabu search algorithm.

[0024] The optimization module is used to optimize interference sources based on a pre-built interference optimization knowledge graph.

[0025] Thirdly, embodiments of this application provide a server, including: a memory and a processor;

[0026] The memory stores computer-executed instructions;

[0027] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0028] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0029] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0030] The 5G private network interference optimization processing method, apparatus, server, and storage medium provided in this application embodiment obtain the interfering 5G private network cell and its interference indicators based on noise data of the 5G private network cell. If the interference indicator data determines that an interference optimization process has been triggered, the 5G private network interference type is obtained. If the 5G private network interference type is inter-system interference, all terminal identifiers under the interfering 5G private network cell are obtained. Based on end-to-end sensing data and wireless measurement data, all terminal identifiers with degraded performance are obtained. The interference source of all terminal identifiers with degraded performance is determined through a tabu search algorithm, and the interference source is optimized according to the interference optimization knowledge graph. No human intervention is required, which reduces the time for locating the interference source and improves the optimization efficiency. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0032] Figure 1 A schematic diagram illustrating a scenario for the optimized handling method of 5G private network interference provided in an embodiment of this application;

[0033] Figure 2 A flowchart illustrating the optimization method for handling 5G private network interference provided in this application embodiment;

[0034] Figure 3 A schematic diagram of the structure of the 5G private network interference optimization processing device provided in the embodiments of this application;

[0035] Figure 4 This is a schematic diagram of the server structure provided in an embodiment of this application.

[0036] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0037] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0038] Figure 1 A schematic diagram of a scenario for the optimization method for handling 5G private network interference provided in this application embodiment, as shown below. Figure 1 As shown, the server provided in this embodiment includes: a receiving device 101, a processor 102, and a display device 103.

[0039] It is understood that the structure illustrated in the embodiments of this application does not constitute a specific limitation on the optimization method for 5G private network interference. In other feasible embodiments of this application, the above architecture may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of both.

[0040] In the specific implementation process, the receiving device 101 can be an input / output interface or a communication interface, and can receive noise data of the 5G private network cell sent by the air interface resource monitoring probe.

[0041] The processor 102 can perform a series of processing on the noise data of the 5G private network cell to optimize the interference source.

[0042] The display device 103 can be used to display the optimization processing results.

[0043] It should be understood that the aforementioned processor can be implemented by reading instructions from memory and executing those instructions, or it can be implemented through chip circuitry.

[0044] Furthermore, the network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0045] 5G private network interference refers to phenomena that adversely affect normal communication signals during the operation of a 5G private network. This interference degrades network performance and impacts user experience. A 5G private network cell is a fundamental unit within a 5G private network, representing a wireless communication area divided according to communication needs. Currently, existing technology primarily relies on maintenance personnel using frequency scanning tools to locate interference sources. After identifying the source, maintenance personnel provide optimization suggestions, which are then implemented. However, manually locating and optimizing interference sources increases the time required for location and reduces optimization efficiency.

[0046] To address the aforementioned technical problems, this application proposes the following technical concept: Considering that manually locating and optimizing interference sources reduces the time required for location and lowers optimization efficiency, the inventors devised an automated method for locating and optimizing interference sources. Based on end-to-end sensing data and wireless measurement data, a tabu search algorithm is used to identify interference sources. Then, based on an interference optimization knowledge graph, the interference sources are optimized. This eliminates the need for human intervention, reducing the time required to locate interference sources and improving optimization efficiency. Based on noise data from 5G private network cells, interfering 5G private network cells and their associated interference indicators are obtained. If the interference indicator data indicates that an interference optimization process has been triggered, the 5G private network interference type is obtained. If the 5G private network interference type is inter-system interference, all terminal identifiers under the interfering 5G private network cell are obtained. Based on end-to-end sensing data and wireless measurement data, all degraded terminal identifiers are obtained. Using a tabu search algorithm, the interference sources for all degraded terminal identifiers are identified, and the interference sources are optimized based on the interference optimization knowledge graph. Without human intervention, it reduces the time required to locate interference sources and improves optimization efficiency.

[0047] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0048] Figure 2 A flowchart illustrating the optimization method for handling 5G private network interference provided in this application embodiment is shown below. Figure 2 As shown, the method includes:

[0049] S201: Receive noise data of 5G private network cells sent by the air interface resource monitoring probe.

[0050] Among them, the 5G private network cell is a basic unit in the 5G private network, which is a wireless communication area divided according to communication needs.

[0051] In this embodiment, the air interface resource monitoring probe is installed on both sides of the base station. Using its built-in sensors or interface with the base station equipment, it monitors the wireless signal environment of the 5G private network cell in real time and collects the noise data of the 5G private network cell.

[0052] Optionally, the noise data collected from the 5G private network cell can be the received interference power (RIP) data of the physical resource block (PRB) of the uplink of the 5G private network cell. In the 5G network, each cell is divided into multiple PRBs.

[0053] S202: Based on the noise data of the 5G private network cell, obtain the 5G private network interfering cell; the 5G private network interfering cell carries interference index data.

[0054] Each 5G private network cell is divided into multiple PRBs, with a total of 273 PRBs in the 5G private network cell. The RIP data of the 5G private network cell PRBs are comprehensively analyzed in the time domain, frequency domain, and spatial domain to remove outlier data and obtain reasonable and reliable standardized data.

[0055] Specifically, step S202 includes S2021~2024:

[0056] 2021: Cluster the noise data of 5G private network cells in the time domain to obtain time domain characteristics and 5G private network interference cells that are continuously interfered with.

[0057] In this embodiment, the air interface resource monitoring probe collects noise data from the 5G private network cell at regular time intervals, which can be selected between 1 minute and 15 minutes. Different granularities are chosen to meet the real-time and accuracy requirements of data in different scenarios. For example, in scenarios where interference changes rapidly, a 1-minute granularity might be chosen to obtain data more promptly; while in scenarios with relatively stable interference, a 15-minute granularity ensures the acquisition of effective data while reducing the burden on data processing.

[0058] In this embodiment, uplink noise causes changes in RIP data. By analyzing RIP data collected at different time points, the intensity of RIP data rise caused by uplink noise can be determined. For example, if the RIP value continues to rise continuously and the rise is significant over a certain period, it indicates that uplink noise is causing strong interference to the cell, resulting in a significant RIP rise. Specifically, through temporal clustering, the noise data of the 5G private network cell is clustered in the temporal domain to identify the 5G private network cells that are continuously interfered with.

[0059] 2022: Cluster the noise data of 5G private network cells in the frequency domain to obtain spectral characteristics.

[0060] Spectral characteristics refer to the distribution features of noise data at different frequencies. Different interference sources produce different patterns in the frequency domain. For example, some interference may be concentrated in a specific frequency range, forming a sharp spectral peak; while other interference may be evenly distributed over a wider frequency range. By analyzing the intensity distribution of noise data and extracting these unique patterns, the spectral characteristics of the interference can be identified.

[0061] 2023: Based on spectrum characteristics, cluster the 5G private network interference cells that are continuously interfered with to obtain the 5G private network interference cells affected by different interference sources.

[0062] In this embodiment, by monitoring each of the 273 PRBs (Pressure Blocks) of the continuously interfered 5G private network cells, the intensity value of the noise data on each PRB is obtained, thus revealing the distribution of noise data across the entire frequency band. The differences in this intensity distribution reflect the frequency domain characteristics of the interference. Once the spectral characteristics of each 5G interfering cell are identified, cells with similar spectral characteristics can be grouped together, as cells with similar spectral characteristics are likely affected by the same interference source. Through these steps, cells affected by the same interference source are ultimately grouped together to identify 5G private network interfering cells affected by different interference sources.

[0063] 2024: From 5G private network interference cells affected by different interference sources, density clustering is performed on 5G private network cells with the same time domain characteristics and spectrum characteristics in the spatial dimension to identify 5G private network interference cell clusters, so as to obtain 5G private network interference cells; wherein the 5G private network interference cell cluster includes at least one 5G private network interference cell.

[0064] In this embodiment, spatial dimension analysis is based on both time and frequency domain dimensions. The objects of spatial dimension analysis are 5G private network cells that exhibit consistency in time and spectral characteristics. This means that these cells have similar timing patterns of interference occurrence and similar spectral characteristics of the interference. Therefore, it can be inferred that they may be affected by the same interference source or that the interference propagation characteristics have some correlation, which provides a basis for analysis from the spatial dimension.

[0065] In this embodiment, density clustering analysis is a spatial data analysis method. In a 5G private network environment, each cell is treated as a point in space, and the spatial distribution density is calculated. In space, if cells are densely distributed within a certain area, and these cells have similar characteristics in both the time domain and spectrum, then this area is considered a high-density area. The algorithm automatically identifies these high-density areas and classifies them into a cluster. In this way, it is possible to find sets of cells that are spatially clustered and have similar interference characteristics; these sets are potential 5G private network interference cell clusters affected by the same interference source. After identifying the 5G private network interference cell clusters, an interference cell list is obtained. The interference cell list clearly lists the information of specific 5G private network interference cells belonging to each 5G private network interference cell cluster, including their numbers and locations.

[0066] S203: Based on the interference index data, determine whether to trigger the interference optimization process for 5G private network interference cells.

[0067] Optionally, the interference index data includes, but is not limited to: the average value of the interference noise detected on the 0th to the 272nd PRB, the maximum value of the interference noise detected on the 0th to the 272nd PRB, the average value of the received interference noise per PRB, the interference intensity, and the interference duration.

[0068] Specifically, the average received interference noise and interference duration are obtained from the interference index data. If the average received interference noise is greater than the preset average received interference noise threshold, the interference duration exceeds the preset time threshold, and the number of 5G private network interference cells in the 5G private network interference cell cluster is greater than the preset number threshold, then the interference optimization process for the 5G private network interference cell is determined to be triggered.

[0069] For example, if the average received interference noise of a 5G private network interfering cell is greater than -100, the interference duration exceeds 1 hour, and the number of 5G private network interfering cells in the 5G private network interfering cell cluster is greater than 3, then the interference optimization process for the 5G private network interfering cell is determined to be triggered.

[0070] S204: If the interference optimization process is triggered, the noise data of the 5G private network cell is input into the interference type identification model to output the 5G private network interference type.

[0071] Specifically, noise data from 5G private network cells is input into the interference type identification model, which then outputs the interference type of the 5G private network through cross-validation.

[0072] In this embodiment, 5G private network interference is categorized into intra-system interference and inter-system interference. Different types of interference sources exhibit significant differences in the waveforms of noise data across 273 PRBs in the frequency domain. The interference type identification model determines whether the interference is intra-system or inter-system based on the waveforms monitored across 0 to 272 PRBs. If it is intra-system interference, parameter optimization is performed; if it is inter-system interference, the next step of external interference assessment is initiated.

[0073] In this embodiment, based on the uplink service load and PRB base noise rise distribution of the 5G private network cell, the interference characteristic model of the uplink is modeled, the PRB frequency point distribution characteristics of the interference are analyzed, the interference type identification model is constructed, and the model performance evaluation results are achieved as expected through parameter optimization.

[0074] Optionally, the interference type identification model can also output whether it is a 5G private network interfering cell and the interference confidence level. Interference types also include external interference classification, inter-system interference, and inter-system external interference.

[0075] Optionally, the interference type identification model can be mirrored and deployed directly as a container.

[0076] Optionally, the interference type identification model has two modes: training mode and runtime mode. In the training mode, model training and periodic iterations are completed, including six functions: training data import, data preprocessing, feature engineering, data modeling and model optimization, model packaging, and model deployment. In the runtime mode, online model inference is performed, intelligently diagnosing uplink interference on the 5G private network and outputting whether uplink interference exists in the 5G cell and the corresponding interference type.

[0077] Optionally, the interference type identification model can be trained based on LightGBM: the RIP data of PRBs with interference characteristics are stored in the database and cleaned; based on the feature analysis and interference intensity analysis of the RIP data of uplink PRBs with known interference, the interference type identification model is constructed.

[0078] S205: If the 5G private network interference type is inter-system interference, then obtain the identifiers of all terminals under the 5G private network interference cell.

[0079] S206: Based on the identifier of each terminal, obtain the end-to-end sensing data and wireless measurement data of each terminal.

[0080] Optionally, end-to-end sensing data includes rate, latency, and stuttering.

[0081] Optionally, wireless measurement report metrics include Reference Signal Received Power (RSRP), Received Signal Strength Indicator (RSSI), and Signal to Interference Noise Ratio (SINR).

[0082] S207: Based on the end-to-end sensing data and wireless measurement data of each terminal, obtain the identifiers of all degraded terminals from all terminal identifiers.

[0083] In this embodiment, end-to-end sensing data can help understand the specific impact of interference on the use of 5G private networks from the perspective of actual user experience. For example, reduced speed will make users feel a significant slowdown when downloading files or watching videos; increased latency may cause delays and stuttering in real-time communication, seriously affecting communication quality; and stuttering will bring a very poor user experience in scenarios such as gaming and streaming media playback.

[0084] In this embodiment, by analyzing and comparing end-to-end sensing data and wireless measurement data, it is determined whether the sensing performance of each terminal has deteriorated. For example, if a terminal's download speed is significantly lower than normal, latency increases dramatically, and stuttering occurs frequently, and its wireless measurement report also shows poor performance in metrics such as RSRP, SINR, and RSRQ, then the terminal's sensing performance can be considered deteriorated. For instance, when interference increases, SINR decreases, leading to reduced data transmission efficiency and affecting the terminal's sensing performance.

[0085] S208: Based on wireless measurement data, use a tabu search algorithm to identify the interference sources for all degraded terminal identifiers.

[0086] Tabu search is a heuristic search algorithm designed to solve combinatorial optimization problems. In the scenario of locating wireless signal interference sources, it continuously explores the solution space, avoids repeated searches using a tabu list, and employs a certain strategy to escape local optima, thereby efficiently finding the location of the interference source.

[0087] In this embodiment, there are multiple private network scenarios in the customer's private network. The services carried by the terminals in each private network scenario are grouped and managed according to the customer's needs. Therefore, the interference source can be determined by the tabu algorithm.

[0088] Specifically, step S208 includes S2081 to S2087:

[0089] S2081: Construct the initial solution space for the interference source.

[0090] Specifically, the region where the interference source might exist is determined. All possible interference source locations within this region are combined to form an initial solution space. Optionally, each solution can be represented as a coordinate point or a set of related parameters, representing a possible hypothesis about the interference source location.

[0091] S2082: Based on wireless measurement data, calculate the evaluation score for each solution in the initial solution space according to the evaluation function.

[0092] The evaluation function is the key to measuring the quality of each solution.

[0093] Specifically, based on wireless measurement data, the evaluation score for each solution in the initial solution space is calculated using the evaluation function, and the formula is as follows:

[0094]

[0095] In the formula, Indicates the evaluation score. This indicates the number of interference indicators in the wireless measurement data. Indicates the first The weight of each interference indicator, Indicates the first One interference indicator.

[0096] S2083: Based on the evaluation score of each solution, obtain the current solution in the initial solution space.

[0097] In this embodiment, the smaller the SINR value, the greater the interference intensity. The current solution in the initial solution space can be obtained according to the user-preset evaluation criteria.

[0098] S2084: Perform the following steps iteratively on the current solution until the iteration termination condition is met.

[0099] Specifically, step S2084 includes Sa~Sb:

[0100] Sa: Generate multiple neighborhood solutions based on the current solution.

[0101] Alternatively, neighborhood solutions can be generated based on swap operations or move operations.

[0102] Sb: Based on the evaluation function, select the neighborhood solution with the best evaluation score from multiple neighborhood solutions as the latest current solution.

[0103] In this embodiment, the current solution is added to the tabu list to avoid repeated searches.

[0104] In this embodiment, solutions with slightly lower evaluation scores are accepted with a certain probability to escape local optima. During the search process, the algorithm may get stuck in local optima. A local optimum is a solution whose evaluation function value is optimal within a certain local region, but it is not the global optimum from the perspective of the entire solution space. For example, in a complex interference environment, the algorithm may get stuck in a local region where the interference situation is relatively good, but it is not the actual source of the interference. To escape local optima, the algorithm accepts solutions with slightly lower evaluation scores with a certain probability. Specifically, when selecting a new current solution, it does not only consider the solution with the best evaluation function value, but also accepts neighborhood solutions with evaluation scores slightly lower than the current optimal solution with a pre-set probability. This gives the algorithm more opportunities to explore different regions of the solution space; as iterations proceed, the probability value gradually decreases, and the algorithm gradually converges to the vicinity of the possible optimal solution.

[0105] S2087: Identify the location information corresponding to the latest current solution as the source of interference for all degraded terminal identifiers.

[0106] For example, using SINR as the measurement value of the evaluation function, the specific implementation steps are as follows:

[0107] Step 1: Select any interference scenario and set the initial "center" position, using the difference between the SINR value of the terminal in the private network scenario and the "center" value as the judgment standard.

[0108] Step 2: Set the taboo length to a difference greater than 0. During this period, scenes that have already been visited will be tabooed and will not be visited again. For each taboo period: if the SINR value of the current scene is lower than the SINR value of the "center" position, it indicates that the interference in the current scene may be more severe. In this case, update the "center" position to the current scene. Add the original center scene to the taboo list.

[0109] Repeat step 2 until the scenario with the lowest SINR for the terminal group is found. In the scenario with the highest interference, perform the same tabu algorithm on all terminals in the terminal group again. The final "center" position is the location of the interference source.

[0110] S209: Optimize interference sources based on a pre-built interference optimization knowledge graph.

[0111] In summary, based on noise data from 5G private network cells, interfering cells and their associated interference indicators are identified. If the interference indicator data triggers an interference optimization process, the 5G private network interference type is determined. If the interference type is inter-system interference, all terminal identifiers within the interfering cells are obtained. Based on end-to-end sensing data and wireless measurement data, all degraded terminal identifiers are acquired. A tabu search algorithm is used to identify the interference sources for all degraded terminal identifiers, and the interference sources are optimized using an interference optimization knowledge graph. This process requires no human intervention, reducing the time spent locating interference sources and improving optimization efficiency.

[0112] Based on the above embodiments, the process of constructing the interference optimization knowledge graph is described in detail below:

[0113] S301: Perform cluster analysis on historical interference index data, historical end-to-end sensing data, and historical wireless measurement data to obtain historical interference types;

[0114] In this embodiment, the optimized processing method and effective optimized processing results are saved to the data database, including the time domain, frequency domain, and spatial domain characteristics of the noise data, as well as the wireless measurement data and end-to-end sensing data of the end user.

[0115] S302: Construct an interference optimization knowledge graph based on historical interference types and optimization methods for those types.

[0116] In this embodiment, a set of labels for interference identification and optimization is designed for historical interference types and optimization processing methods. Clustering learning is performed through algorithms to record the interference type corresponding to each interference waveform and the optimization processing method, thereby constructing an interference optimization knowledge graph.

[0117] In summary, the interference optimization knowledge graph constructed based on the acquired historical interference types and optimization methods for those types can automatically recommend the best optimization methods for each interference source, thereby further improving optimization efficiency.

[0118] Based on the above embodiments, this embodiment also provides an optimization processing system for 5G private network interference, which includes: an automatic discovery unit, an automatic analysis unit, an automatic delimitation unit, an automatic optimization unit, and an automatic iteration unit.

[0119] The automatic discovery unit is used to identify interfering cells in the 5G private network based on noise data of the 5G private network cells sent by the air interface resource monitoring probe. Specifically, it acquires interference index data carried by the interfering cells.

[0120] The automatic analysis unit is used to obtain the identifiers of all degraded terminals in 5G private network interference cells based on interference index data.

[0121] The automatic delimitation unit is used to identify all degraded terminals and delimit interference sources by using tabu algorithms based on end-to-end sensing data and wireless measurement data from each terminal.

[0122] The automatic optimization unit is used to output optimization schemes for interference sources based on a pre-built interference optimization knowledge graph and to perform optimization processing.

[0123] Automatic Iteration Unit: If the output optimization scheme has deviations, it can further iterate and self-optimize based on the actual interference characteristics.

[0124] Optionally, the automatic discovery unit can also output the uplink interference time, and can also adjust the spatial range of the uplink impact and the degree of interference based on customer scenario requirements.

[0125] In summary, based on noise data from 5G private network cells, interfering cells and their associated interference indicators are identified. If the interference indicator data triggers an interference optimization process, the 5G private network interference type is determined. If the interference type is inter-system interference, all terminal identifiers within the interfering cells are obtained. Based on end-to-end sensing data and wireless measurement data, all degraded terminal identifiers are acquired. A tabu search algorithm is used to identify the interference sources for all degraded terminal identifiers, and the interference sources are optimized using an interference optimization knowledge graph. This process requires no human intervention, reducing the time spent locating interference sources and improving optimization efficiency.

[0126] Figure 3 This is a schematic diagram of the structure of the 5G private network interference optimization processing device provided in the embodiments of this application, as shown below. Figure 3 As shown, the 5G private network interference optimization processing device provided in this embodiment includes: a receiving module 301, a first acquisition module 302, a judgment module 303, an output module 304, a second acquisition module 305, a third acquisition module 306, a fourth acquisition module 307, a determination module 308, and an optimization module 309.

[0127] The receiving module 301 is used to receive noise data of the 5G private network cell sent by the air interface resource monitoring probe;

[0128] The first acquisition module 302 is used to acquire 5G private network interfering cells based on the noise data of the 5G private network cells; wherein the 5G private network interfering cells carry interference index data.

[0129] The judgment module 303 is used to determine whether the interference optimization process of the 5G private network interference cell is triggered based on the interference index data.

[0130] The output module 304 is used to input the noise data of the 5G private network cell into the interference type identification model if it is determined that the interference optimization process has been triggered, so as to output the 5G private network interference type.

[0131] The second acquisition module 305 is used to acquire all terminal identifiers under the 5G private network interference cell if the 5G private network interference type is inter-system interference.

[0132] The third acquisition module 306 is used to acquire end-to-end sensing data and wireless measurement data of each terminal based on the terminal identifier.

[0133] The fourth acquisition module 307 is used to acquire all terminal identifiers with degraded performance from all terminal identifiers based on the end-to-end sensing data and wireless measurement data of each terminal.

[0134] The determination module 308 is used to determine the interference sources of all degraded terminal identifiers based on wireless measurement data and through a tabu search algorithm;

[0135] The optimization module 309 is used to optimize the interference source based on the pre-built interference optimization knowledge graph.

[0136] In one possible implementation, the first acquisition module 302 is specifically configured to: cluster the noise data of the 5G private network cell in the time domain to acquire time domain features and 5G private network interfering cells that are continuously interfered with; cluster the noise data of the 5G private network cell in the frequency domain to acquire spectral features; based on the spectral features, cluster the 5G private network interfering cells that are continuously interfered with to acquire 5G private network interfering cells that are interfered with by different interference sources; from the 5G private network interfering cells that are interfered with by different interference sources, perform density clustering in the spatial dimension on 5G private network cells with the same time domain features and spectral features to identify 5G private network interfering cell clusters, thereby acquiring 5G private network interfering cells; wherein the 5G private network interfering cell cluster includes at least one 5G private network interfering cell.

[0137] In one possible implementation, the judgment module 303 is specifically used to: obtain the average value of received interference noise and the interference duration from the interference index data; if the average value of received interference noise is greater than a preset threshold for the average value of received interference noise, the interference duration exceeds a preset time threshold, and the number of 5G private network interference cells in the 5G private network interference cell cluster to which the 5G private network interference cell belongs is greater than a preset number threshold, then it is determined that the interference optimization process of the 5G private network interference cell has been triggered.

[0138] In one possible implementation, the output module 304 is specifically used to: input noise data of the 5G private network cell into the interference type identification model, and the interference type identification model outputs the 5G private network interference type through cross-validation.

[0139] In one possible implementation, the determining module 308 is specifically used for: constructing an initial solution space for interference sources; calculating the evaluation score of each solution in the initial solution space based on wireless measurement data and an evaluation function; obtaining the current solution of the initial solution space based on the evaluation score of each solution; iteratively performing the following steps on the current solution until the iteration termination condition is met; generating multiple neighborhood solutions based on the current solution; selecting the neighborhood solution with the best evaluation score from the multiple neighborhood solutions as the latest current solution based on the evaluation function; and determining the location information corresponding to the latest current solution as the interference source identified by all degraded terminals.

[0140] In one possible implementation, the evaluation score for each solution in the initial solution space is calculated based on wireless measurement data and an evaluation function, using the following formula:

[0141]

[0142] In the formula, Indicates the evaluation score. This indicates the number of interference indicators in the wireless measurement data. Indicates the first The weight of each interference indicator, Indicates the first One interference indicator.

[0143] In one possible implementation, the 5G private network interference optimization processing device further includes: a construction module, used to perform cluster analysis on historical interference index data, historical end-to-end sensing data and historical wireless measurement data to obtain historical interference types; and to construct an interference optimization knowledge graph based on the historical interference types and the optimization processing methods for the historical interference types.

[0144] The 5G private network interference optimization processing device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0145] Figure 4 This is a schematic diagram of the server structure provided in an embodiment of this application. Figure 4 As shown, the server provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the device 40 also includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0146] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0147] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0148] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0149] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0150] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0151] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0152] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0153] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0154] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0155] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0156] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0157] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0158] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0159] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0160] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and 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 the invention is limited only by the appended claims.

Claims

1. An optimized method for handling interference in 5G private networks, characterized in that, Applied to servers, including: Receive noise data of 5G private network cells sent by the air interface resource monitoring probe; Based on the noise data of the 5G private network cell, obtain the 5G private network interfering cell; wherein the 5G private network interfering cell carries interference index data; Based on the interference index data, determine whether to trigger the interference optimization process for the 5G private network interference cell; If the interference optimization process is triggered, the noise data of the 5G private network cell is input into the interference type identification model to output the 5G private network interference type. If the 5G private network interference type is inter-system interference, then obtain the identifiers of all terminals under the 5G private network interference cell; Based on the identifier of each terminal, obtain the end-to-end sensing data and wireless measurement data of each terminal; Based on the end-to-end sensing data and wireless measurement data of each terminal, obtain all terminal identifiers with degraded performance from all terminal identifiers; Based on the wireless measurement data, the interference sources of all the degraded terminal identifiers are determined by the tabu search algorithm; The interference sources are optimized based on a pre-constructed interference optimization knowledge graph.

2. The method according to claim 1, characterized in that, The step of obtaining 5G private network interference cells based on the noise data of the 5G private network cells includes: The noise data of the 5G private network cell is clustered in the time domain to obtain time domain characteristics and 5G private network interference cells that are continuously interfered with. The noise data of the 5G private network cell is clustered in the frequency domain to obtain spectral features; Based on the spectrum characteristics, the 5G private network interference cells that are continuously interfered with are clustered to obtain 5G private network interference cells that are interfered with by different interference sources. From the 5G private network interference cells affected by different interference sources, density clustering is performed on the spatial dimension for 5G private network cells with the same time-domain characteristics and spectrum characteristics to identify 5G private network interference cell clusters, thereby obtaining 5G private network interference cells; wherein the 5G private network interference cell cluster includes at least one 5G private network interference cell.

3. The method according to claim 2, characterized in that, The step of determining whether to trigger the interference optimization process for the 5G private network interference cell based on the interference index data includes: From the interference index data, obtain the average value of the received interference noise and the interference duration; If the average received interference noise is greater than a preset threshold for the average received interference noise, the duration of the interference exceeds a preset time threshold, and the number of 5G private network interference cells in the 5G private network interference cell cluster to which the 5G private network interference cell belongs is greater than a preset number threshold, then the interference optimization process for the 5G private network interference cell is determined to be triggered.

4. The method according to claim 1, characterized in that, The step of inputting the noise data of the 5G private network cell into the interference type identification model to output the 5G private network interference type includes: The noise data of the 5G private network cell is input into the interference type identification model, which outputs the interference type of the 5G private network through cross-validation.

5. The method according to claim 1, characterized in that, The step of determining the interference sources of all degraded terminal identifiers based on the wireless measurement data using a tabu search algorithm includes: Construct the initial solution space for the interference source; Based on the wireless measurement data, the evaluation score of each solution in the initial solution space is calculated according to the evaluation function; Based on the evaluation score of each solution, the current solution in the initial solution space is obtained; The following steps are performed iteratively on the current solution until the iteration termination condition is met; Based on the current solution, multiple neighborhood solutions are generated; Based on the evaluation function, the neighborhood solution with the best evaluation score is selected as the latest current solution from the plurality of neighborhood solutions; The location information corresponding to the latest current solution is identified as the interference source for all the terminal identifiers with degraded performance.

6. The method according to claim 5, characterized in that, The formula for calculating the evaluation score of each solution in the initial solution space based on the wireless measurement data and the evaluation function is as follows: In the formula, Indicates the evaluation score. This indicates the number of interference indicators in the wireless measurement data. Indicates the first The weight of each interference indicator, Indicates the first One interference indicator.

7. The method according to any one of claims 1-6, characterized in that, Before receiving the noise data of the 5G private network cell sent by the air interface resource monitoring probe, the method further includes: Cluster analysis was performed on historical interference index data, historical end-to-end sensing data, and historical wireless measurement data to obtain historical interference types; Based on historical interference types and optimization methods for those types, an interference optimization knowledge graph is constructed.

8. An optimization processing device for 5G private network interference, characterized in that, Applied to servers, including: The receiving module is used to receive noise data of 5G private network cells sent by the air interface resource monitoring probe; The first acquisition module is used to acquire 5G private network interfering cells based on the noise data of the 5G private network cell; wherein the 5G private network interfering cells carry interference index data. The judgment module is used to determine whether to trigger the interference optimization process of the 5G private network interference cell based on the interference index data. The output module is used to input the noise data of the 5G private network cell into the interference type identification model if it is determined that the interference optimization process has been triggered, so as to output the 5G private network interference type. The second acquisition module is used to acquire all terminal identifiers under the 5G private network interference cell if the 5G private network interference type is inter-system interference. The third acquisition module is used to acquire end-to-end sensing data and wireless measurement data of each terminal based on the terminal identifier. The fourth acquisition module is used to acquire all terminal identifiers with degraded performance from all terminal identifiers based on the end-to-end sensing data and wireless measurement data of each terminal. The determination module is used to determine the interference source of all the degraded terminal identifiers based on the wireless measurement data and through a tabu search algorithm; The optimization module is used to optimize the interference source based on a pre-built interference optimization knowledge graph.

9. A server, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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