Frequency band migration method and storage medium

Abstract

The embodiment of the invention discloses a frequency band migration method and a storage medium. The method comprises the following steps: for a target service running in a target terminal, obtaining a first identification result representing whether the target service is a sensitive service sensitive to a network rate; in response to the first identification result representing that the target service is a sensitive service, obtaining a second identification result representing that the sensitive service is an uplink sensitive service sensitive to an uplink rate in the network rates or a downlink sensitive service sensitive to a downlink rate in the network rates; and acquiring a frequency band migration event corresponding to the second identification result, and issuing a frequency band migration strategy corresponding to the frequency band migration event to the target terminal under the condition that the sensitive service triggers the frequency band migration event, so that the target terminal performs frequency band migration based on the received frequency band migration strategy. According to the technical scheme provided by the embodiment of the invention, the use experience of a user on sensitive services can still be well ensured in a scene of large uplink and downlink path loss difference.

Description

Technical Field

[0001] The present invention relates to the field of wireless technology, and in particular to a frequency band migration method and a storage medium. Background Technology

[0002] In the deployment of fifth-generation mobile communication (5G) networks, a multi-band networking strategy is usually adopted to balance coverage and capacity, which involves the frequency band migration process.

[0003] Currently, frequency band switching (i.e., migration) is mainly achieved through a coverage switching scheme based on downlink reference signal received power (RSRP). However, for services that require network connectivity, this scheme may affect the user experience in scenarios with significant differences in uplink and downlink path losses, and urgently needs to be addressed. Summary of the Invention

[0004] This invention provides a frequency band migration method and storage medium, which solves the problem of poor user experience in scenarios with large differences in uplink and downlink path loss.

[0005] According to one aspect of the present invention, a frequency band migration method is provided, which may include:

[0006] For the target service running on the target terminal, a first identification result is obtained, wherein the first identification result indicates whether the target service is a sensitive service that is sensitive to network speed.

[0007] In response to the first identification result indicating that the target service is a sensitive service, a second identification result is obtained, wherein the second identification result indicates that the sensitive service is either an uplink sensitive service that is sensitive to the uplink rate in the network rate or a downlink sensitive service that is sensitive to the downlink rate in the network rate.

[0008] The frequency band migration event corresponding to the second identification result is obtained, and when the frequency band migration event is triggered by a sensitive service, the frequency band migration policy corresponding to the frequency band migration event is sent to the target terminal so that the target terminal can perform frequency band migration based on the received frequency band migration policy.

[0009] According to another aspect of the present invention, a frequency band shifting device is provided, which may include:

[0010] The first identification result obtaining module is used to obtain a first identification result for the target service running in the target terminal, wherein the first identification result indicates whether the target service is a sensitive service that is sensitive to network speed.

[0011] The second identification result obtaining module is used to obtain a second identification result in response to the first identification result indicating that the target service is a sensitive service. The second identification result indicates that the sensitive service is either an uplink sensitive service that is sensitive to the uplink rate in the network rate or a downlink sensitive service that is sensitive to the downlink rate in the network rate.

[0012] The frequency band migration module is used to obtain the frequency band migration event corresponding to the second identification result, and when the frequency band migration event is triggered by a sensitive service, it sends the frequency band migration policy corresponding to the frequency band migration event to the target terminal so that the target terminal can perform frequency band migration based on the received frequency band migration policy.

[0013] According to another aspect of the present invention, a target base station is provided, which may include:

[0014] At least one processor; and

[0015] A memory that is communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor to implement the band migration method provided in any embodiment of the present invention when executed by at least one processor.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided having computer instructions stored thereon for causing a processor to execute and implement the frequency band migration method provided in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is provided, on which a computer program is stored, which, when executed by a processor, implements the frequency band migration method provided in any embodiment of the present invention.

[0019] The technical solution of this invention, for a target service running on a target terminal, obtains a first identification result characterizing whether the target service is a sensitive service sensitive to network speed. If the target service is determined to be a sensitive service based on the first identification result, it can be further identified to obtain a second identification result characterizing whether the sensitive service is an uplink sensitive service sensitive to uplink speed or a downlink sensitive service sensitive to downlink speed. Then, the frequency band migration event corresponding to the second identification result can be obtained, and when the sensitive service triggers the frequency band migration event, the frequency band migration policy corresponding to the frequency band migration event is sent to the target terminal, so that the target terminal performs frequency band migration based on the received frequency band migration policy. Compared to related solutions that only rely on downlink RSRP to trigger frequency band migration to a frequency band with better downlink speed, the above technical solution identifies whether the sensitive service is an uplink sensitive service or a downlink sensitive service, and then triggers frequency band migration based on the corresponding frequency band migration event. The frequency band migrated to is a frequency band that matches the sensitive service, that is, the frequency band can provide a speed that meets the speed requirements of the sensitive service. Therefore, even in scenarios with large differences in uplink and downlink path loss, the user experience for the sensitive service can still be better guaranteed.

[0020] It should be understood that the description in this section is not intended to identify key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of an optional example of various frequency band migration methods provided according to embodiments of the present invention;

[0023] Figure 2 This is a flowchart of a frequency band migration method provided according to an embodiment of the present invention;

[0024] Figure 3 This is a flowchart of another frequency band migration method provided by an embodiment of the present invention;

[0025] Figure 4 This is a flowchart of another frequency band migration method provided by an embodiment of the present invention;

[0026] Figure 5 This is a flowchart of an optional example of another frequency band migration method provided according to an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of an optional example of another frequency band migration method provided according to an embodiment of the present invention;

[0028] Figure 7 This is a structural block diagram of a frequency band migration device provided according to an embodiment of the present invention;

[0029] Figure 8 This is a schematic diagram of the structure of the target base station for implementing the frequency band migration method of this invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The same applies to "target," "original," etc., and will not be repeated here. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] It should be noted that the collection, gathering, updating, analysis, processing, use, transmission, and storage of user personal information involved in the technical solution of this invention all comply with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken to prevent unauthorized access to user personal information data and to maintain the security of user personal information and network security.

[0033] Before describing the embodiments of the present invention, an exemplary application scenario of the embodiments of the present invention will be provided. For example, in 5G networks, Time-Sensitive Biased Index (NR) and Spectrum-Sensitive Biased Index (NSBRI) New Radio (NR) technologies coexist, each with different characteristics. NR, through flexible uplink and downlink time slot configuration, can better adapt to the needs of asymmetric services, but may face the problem of uneven uplink and downlink resource allocation in some scenarios. NSB, on the other hand, has advantages in providing stable symmetric services due to its symmetrical uplink and downlink spectrum characteristics, but its spectrum resources are relatively limited. It should be noted that the Time-Sensitive Biased Index described above corresponds to the Time Division Duplex (TDD) frequency band, while the Spectrum-Sensitive Biased Index corresponds to the Frequency Division Duplex (FDD) frequency band.

[0034] Currently, with the rapid development of mobile internet, users' demand for data services is increasing, especially for sensitive services (such as high-definition video streaming, large file downloads, and real-time cloud gaming). These sensitive services place higher demands on network speeds (such as uplink and / or downlink speeds). However, related sensitive service migration mechanisms are often based solely on simple signal strength. For example, the downlink RSRP coverage switching scheme described above mainly relies on downlink RSRP to trigger frequency band migration, but it only optimizes the user experience from the perspective of downlink coverage, ignoring uplink quality. This can potentially affect the user experience in scenarios with large differences in uplink and downlink path loss.

[0035] To address the aforementioned technical issues, this invention proposes an experience-based sensitive migration architecture and a corresponding full-domain dynamic migration process. At the architectural level, it implements a closed-loop design from sensitive service identification and decision analysis to migration implementation. The aim is to achieve accurate identification and frequency-band-level dynamic migration of uplink and downlink sensitive services through an intelligent service sensitivity determination mechanism and a dual-band dynamic migration strategy, combined with a hierarchical decision model and a differentiated threshold system. This improves the user's network experience and the efficiency of network resource utilization. For example, see... Figure 1The system utilizes network data analysis on a performance big data platform to identify mainstream services. Then, it analyzes the perception sensitivity requirements of these mainstream services, specifically identifying whether they have high-sensitivity requirements and whether they are indeed sensitive services. If a mainstream service is sensitive, it analyzes the type of high-sensitivity requirement (uplink / downlink), determining whether it is an uplink-sensitive or downlink-sensitive service. Further analysis identifies the minimum threshold for high-sensitivity requirements, and based on this, it determines the service migration initiation threshold (uplink / downlink), i.e., the uplink migration threshold and downlink migration threshold. This process is repeated periodically to implement frequency band migration.

[0036] This will be explained in detail below.

[0037] Figure 2 This is a flowchart of a frequency band migration method provided in an embodiment of the present invention. This embodiment is applicable to frequency band migration, especially experience-based frequency band migration. The method can be executed by the frequency band migration apparatus provided in this embodiment of the present invention, which can be implemented in software and / or hardware and can be integrated into the target base station.

[0038] See Figure 2 The method of this invention specifically includes the following steps:

[0039] S110. For the target service running in the target terminal, obtain a first identification result, wherein the first identification result indicates whether the target service is a sensitive service that is sensitive to network speed.

[0040] The target terminal can be understood as a terminal that communicates with the target base station, and more specifically, as a terminal that can receive the frequency band migration policy issued by the target base station and perform frequency band migration based on that policy. The target service can be understood as any task or mainstream service running on the target terminal, which can be configured according to actual needs and is not specifically limited here.

[0041] Based on this, optionally, whether a certain service is a mainstream service can be determined by combining the traffic share threshold α%. Specifically, when the traffic share of a certain service is ≥ α%, it is marked as a mainstream service to be analyzed. In this embodiment of the invention, the same α% can be set for different services; alternatively, different α% can be set for different services, for example, an adaptive protection threshold can be introduced. To prevent accidental triggering by low-demand business processes, γ here... ω (t) is equivalent to α%. Specifically, ,in, The basic protection threshold can be configured differently depending on the specific business requirements. λ is the business urgency coefficient; λ is the learning rate, which controls the speed of threshold adjustment. This refers to the threshold adjustment range. Furthermore, dynamic protection and rollback mechanisms can be used for adaptive threshold shrinkage, dynamically reducing the protection threshold through an exponential decay function in scenarios of sudden traffic surges. Where η is the attenuation coefficient, controlling the threshold shrinkage rate. If N consecutive cycles are determined to be the mainstream business, but the actual resource utilization rate U thresh If this happens, a threshold rollback will be triggered to avoid oversensitivity.

[0042] The first identification result is obtained by identifying whether the target service is a network rate-sensitive service. Optionally, in this embodiment of the invention, this identification process can be based on a multi-dimensional feature vector, which can be obtained as follows: During the intelligent modeling of service characteristics, historical network data of mainstream 5G services (such as ultra-high-definition video, cloud gaming, and industrial control) is first collected based on a performance big data platform. This historical network data can cover traffic characteristic dimensions (such as service traffic, uplink and downlink rates, and packet interval jitter). pkt The preprocessing stage optimizes data quality through two steps: data cleaning: outliers such as rate spikes are removed using the 3σ principle, and zero-traffic samples are filtered out; feature engineering is then performed on this basis: a time-domain feature set is constructed (mean traffic μ within a sliding window). UL / DL and variance σ 2 UL / DL ) and frequency domain feature set (spectral energy E extracted through fast Fourier transform) freq ), forming a multidimensional feature vector F=[μ,σ 2 E freq Based on this, optionally, the modeling stage employs a Long Short-Term Memory (LSTM) network-autoencoder deep learning framework, whose loss function can be defined as: Where x(t) is the input flow sequence, To reconstruct the traffic sequence, T is the sequence length, and λ is the L2 regularization coefficient. Here, through unsupervised training, the model can automatically capture the temporal dependencies of business traffic patterns (such as the periodic pulse characteristics of industrial control operations and the burst characteristics of video streams), ultimately generating a business feature fingerprint database. This database is then used to extract multidimensional feature vectors F=[μ,σ] 2 E freq ].

[0043] ​S120. In response to the first identification result indicating that the target service is a sensitive service, a second identification result is obtained, wherein the second identification result indicates that the sensitive service is an uplink sensitive service that is sensitive to the uplink rate in the network rate or a downlink sensitive service that is sensitive to the downlink rate in the network rate.

[0044] In cases where the target service is a sensitive service, further identification can be made based on the uplink and downlink rates in the network speed. This identification process can distinguish between uplink-sensitive services that are sensitive to uplink rates and downlink-sensitive services that are sensitive to downlink rates. This is key to solving the technical problems described above. Optionally, in this embodiment of the invention, this identification process can be implemented based on uplink sensitivity thresholds related to uplink rates and downlink sensitivity thresholds related to downlink rates. Of course, other methods can also be used, which can be set according to actual needs and are not specifically limited here.

[0045] S130. Obtain the frequency band migration event corresponding to the second identification result, and when the frequency band migration event is triggered by a sensitive service, send the frequency band migration strategy corresponding to the frequency band migration event to the target terminal so that the target terminal can perform frequency band migration based on the received frequency band migration strategy.

[0046] As explained above, the second identification result can characterize whether the sensitive service is an uplink sensitive service or a downlink sensitive service. Corresponding frequency band migration events are pre-set for uplink sensitive services and downlink sensitive services, so the frequency band migration event corresponding to the second identification result can be obtained. This frequency band migration event can characterize under what circumstances frequency band migration is carried out.

[0047] When a sensitive service triggers a frequency band migration event, it indicates that a frequency band migration is required for the target terminal running the sensitive service to operate on a compatible frequency band. The frequency band migration policy corresponding to the migration event can then be sent to the target terminal, specifying the desired frequency band. The target terminal can then perform the frequency band migration based on the received policy, for example, migrating from a TDD band to an FDD band or vice versa. This depends on the specific circumstances and is not specifically limited here.

[0048] The technical solution of this invention, for a target service running on a target terminal, obtains a first identification result characterizing whether the target service is a sensitive service sensitive to network speed. If the target service is determined to be a sensitive service based on the first identification result, it can be further identified to obtain a second identification result characterizing whether the sensitive service is an uplink sensitive service sensitive to uplink speed or a downlink sensitive service sensitive to downlink speed. Then, the frequency band migration event corresponding to the second identification result can be obtained, and when the sensitive service triggers the frequency band migration event, the frequency band migration policy corresponding to the frequency band migration event is sent to the target terminal, so that the target terminal performs frequency band migration based on the received frequency band migration policy. Compared to related solutions that only rely on downlink RSRP to trigger frequency band migration to a frequency band with better downlink speed, the above technical solution identifies whether the sensitive service is an uplink sensitive service or a downlink sensitive service, and then triggers frequency band migration based on the corresponding frequency band migration event. The frequency band migrated to is a frequency band that matches the sensitive service, that is, the frequency band can provide a speed that meets the speed requirements of the sensitive service. Therefore, even in scenarios with large differences in uplink and downlink path loss, the user experience for the sensitive service can still be better guaranteed.

[0049] In one optional technical solution, the second identification result indicates that the sensitive service is an uplink sensitive service, and the corresponding frequency band migration event is determined in the following way:

[0050] Acquire multiple time-based uplink data under the time-division duplex frequency band and multiple spectrum uplink data under the frequency-division duplex frequency band. The time-based uplink data includes the received power of the time-based uplink reference signal and the time-based uplink rate, and the spectrum uplink data includes the received power of the spectrum uplink reference signal and the spectrum uplink rate.

[0051] Using the time-dependent uplink rate as the dependent variable and the received power of the time-dependent uplink reference signal as the independent variable, the time-dependent uplink curve is obtained based on the received power of each time-dependent uplink reference signal and each time-dependent uplink rate; and using the spectrum uplink rate as the dependent variable and the received power of the spectrum uplink reference signal as the independent variable, the spectrum uplink curve is obtained based on the received power of each spectrum uplink reference signal and each spectrum uplink rate.

[0052] Based on the time-dependent uplink curve and the spectrum uplink curve, a first uplink reference signal received power range where the time-dependent uplink rate is better than the spectrum uplink rate, and a second uplink reference signal received power range where the spectrum uplink rate is better than the time-dependent uplink rate are determined. An uplink migration threshold is then determined based on the first uplink reference signal received power range and the second uplink reference signal received power range, so as to construct a frequency band migration event based on the uplink migration threshold.

[0053] In the case of sensitive services being uplink sensitive services, multiple time-sensitive uplink data in the TDD band and multiple spectrum uplink data in the FDD band are acquired. The time-sensitive uplink data includes the time-sensitive uplink RSRP and the time-sensitive uplink rate, and the spectrum uplink data includes the spectrum uplink RSRP and the spectrum uplink rate. Then, curve fitting can be performed based on these time-sensitive uplink data to obtain the time-sensitive uplink curve and curve fitting can be performed based on these spectrum uplink data to obtain the spectrum uplink curve.

[0054] For example, this section uses timely uplink data as an example. A mapping table of timely uplink RSRP and timely uplink rate can be obtained, where the timely uplink RSRP is rounded to two decimal places. The average rate of the timely uplink rate corresponding to the same timely uplink RSRP is calculated. Then, curve fitting can be performed based on the corresponding timely uplink RSRP and average rate to obtain the timely uplink curve UL. TDD . Spectrum uplink curve UL FDD The process of obtaining it is similar and will not be repeated here.

[0055] ;

[0056] Among them, the coefficients a1, b1, c1, a2, b2, d2 can be determined by regression analysis of the time-sensitive uplink data and spectrum uplink data described above. 700M corresponds to the FDD band, and 2.6G corresponds to the TDD band.

[0057] Furthermore, based on the time-dependent uplink curve and the spectrum-dependent uplink curve, a first uplink RSRP range ΩUL1 is determined where the time-dependent uplink rate is superior to the spectrum-dependent uplink rate (i.e., AVG_ULnk(R) > AVG_ULnz(R)), and a second uplink RSRP range ΩUL2 is determined where the spectrum-dependent uplink rate is superior to the time-dependent uplink rate (i.e., AVG_ULnz(R) > AVG_ULnk(R)). For example, ΩUL1 = {R | AVG_ULnk(R) > AVG_ULnz(R)}, which is the first uplink RSRP range where the TDD band is dominant; ΩUL2 = {R | AVG_ULnz(R) > AVG_ULnk(R)}, which is the second uplink RSRP range where the FDD band is dominant.

[0058] Then, the uplink migration threshold used when migrating from the TDD band to the FDD band can be obtained based on the two uplink RSRP ranges, so as to construct the corresponding band migration event (i.e., uplink handover event) based on the uplink migration threshold. On this basis, optionally, assuming that the event type of the band migration event is A5 event, the uplink migration threshold may include the uplink A5-1 threshold and the uplink A5-2 threshold. In this case, the uplink migration threshold is determined as follows: obtain the pre-configured uplink hysteresis coefficient; determine the initial uplink A5-1 threshold based on the second uplink reference signal received power range; determine the uplink A5-1 threshold based on the initial uplink A5-1 threshold and the uplink hysteresis coefficient; determine the initial uplink A5-2 threshold based on the first uplink reference signal received power range; determine the uplink A5-2 threshold based on the initial uplink A5-2 threshold and the uplink hysteresis coefficient.

[0059] For example, {[ROUNDDOWN(MIN(ΩUL2),0)],[MAX(ROUNDUP(MIN(ΩUL1),0),α]}, where [ROUNDDOWN(MIN(ΩUL2),0)] is the initial uplink A5-1 threshold, representing the floor function of the minimum value in ΩUL2, and the uplink A5-1 threshold can be expressed as the initial uplink A5-1 threshold minus the uplink hysteresis coefficient (UL_Handover_Threshold); [MAX(ROUNDUP(MIN(ΩUL1),0),α]] is the initial uplink A5-2 threshold, representing the floor function of the minimum value in ΩUL1, and the uplink A5-2 threshold can be expressed as the initial uplink A5-2 threshold plus the uplink hysteresis coefficient. The uplink hysteresis coefficient > ηUL (ηUL recommended value 1.2, plus 2dB hysteresis) to avoid meaningless band migration processes caused by slight network fluctuations. In addition, α is the threshold for switching 4G based on the current network spectrum sensitivity bias index (700MHz).

[0060] When an uplink-sensitive service triggers an uplink handover event, i.e., when the real-time RSRP of the serving cell is less than the uplink A5-1 threshold and the real-time RSRP of the adjacent cell is greater than the uplink A5-2 threshold, the target terminal can be migrated from the current carrier (e.g., 2.6GHz carrier) to the target carrier (e.g., 700MHz carrier).

[0061] It should be noted that while the relevant uplink quality switching solutions consider user experience, they only passively trigger frequency band migration logic when user experience issues arise, essentially a "better late than never" strategy. In other words, they suffer from lagging user experience optimization and insufficient support for high-speed scenarios such as high-definition video streaming and cloud gaming.

[0062] Correspondingly, the above-mentioned technical solutions enable uplink-sensitive services to migrate from a frequency band where the uplink experience is about to deteriorate to a frequency band with a better uplink experience with almost no interruption. This allows for early optimization of the user experience and ensures the smoothness of uplink-sensitive services, thus effectively supporting high-speed scenarios.

[0063] Another optional technical solution is that the second identification result indicates that the sensitive service is a downlink sensitive service, and the corresponding frequency band migration event is determined in the following way:

[0064] Acquire multiple time-based downlink data in the time-division duplex frequency band and multiple spectrum downlink data in the frequency-division duplex frequency band. The time-based downlink data includes the received power of the time-based downlink reference signal and the time-based downlink rate, while the spectrum downlink data includes the received power of the spectrum downlink reference signal and the spectrum downlink rate.

[0065] Using the downlink rate as the dependent variable and the downlink reference signal received power as the independent variable, the downlink curve is obtained based on the received power of each downlink reference signal and the downlink rate. Similarly, using the downlink rate as the dependent variable and the downlink reference signal received power as the independent variable, the downlink curve is obtained based on the received power of each downlink reference signal and the downlink rate.

[0066] Based on the time-dependent downlink curve and the spectrum downlink curve, a first downlink reference signal received power range where the spectrum downlink rate is better than the time-dependent downlink rate is determined, and a second downlink reference signal received power range where the time-dependent downlink rate is better than the spectrum downlink rate is determined. A downlink migration threshold is then determined based on the first downlink reference signal received power range and the second downlink reference signal received power range, so as to construct a band migration event based on the downlink migration threshold.

[0067] Based on this, optionally, the frequency band migration event is the A5 event, and the downlink migration threshold may include the downlink A5-1 threshold and the downlink A5-2 threshold. The downlink migration threshold can be determined as follows: obtain the pre-configured downlink hysteresis coefficient; determine the initial downlink A5-1 threshold based on the first downlink reference signal received power range; determine the downlink A5-1 threshold based on the initial downlink A5-1 threshold and the downlink hysteresis coefficient; determine the initial downlink A5-2 threshold based on the second downlink reference signal received power range; determine the downlink A5-2 threshold based on the initial downlink A5-2 threshold and the downlink hysteresis coefficient.

[0068] It should be noted that the process for determining the downlink migration threshold is similar to that for the uplink migration threshold, and will not be repeated here.

[0069] For example, the time-delayed downward curve DL is obtained by fitting. 2.6G and the downlink spectrum curve DL 700M :

[0070] ;

[0071] Among them, the coefficients a3, b3, c3, a4, b4, c4 can be determined by regression analysis of the downlink data and spectrum data described above. 700M corresponds to the FDD band and 2.6G corresponds to the TDD band.

[0072] Furthermore, based on the time-dependent downlink curve and the spectrum downlink curve, the first downlink RSRP range ΩDL1 where the spectrum downlink rate is better than the time-dependent downlink rate (i.e., AVG_DLnz(R)>AVG_DLnk(R)) and the second downlink RSRP range ΩDL2 where the time-dependent downlink rate is better than the spectrum downlink rate (i.e., AVG_DLnk(R)>AVG_DLnz(R)) can be determined. Then, the downlink migration threshold used when migrating from the FDD band to the TDD band can be obtained based on these two downlink RSRP ranges, so as to construct the corresponding band migration event (i.e., downlink handover event) based on the downlink migration threshold.

[0073] ΩDL1={R|AVG_DLnz(R)>AVG_DLnk(R)}, which is the range where the FDD band is dominant;

[0074] ΩDL2={R∣AVG_DLnk(R)>AVG_DLnz(R)}, which is the range where the TDD band is dominant.

[0075] {[ROUNDDOWN(MIN(ΩUL1),0)],[MAX(ROUNDUP(MIN(ΩUL2),0),β]},UL_Handover_Threshold>ηUL (ηUL recommended value 1.2, plus 2dB hysteresis). The interpretation of these formulas is similar to the example above, and will not be repeated here. In addition, β can be the threshold for switching 4G based on the current network spectrum sensitivity bias index (2.6MHz).

[0076] When a downlink-sensitive service triggers a downlink handover event, i.e., the real-time RSRP of the serving cell is less than the downlink A5-1 threshold and the real-time RSRP of the adjacent cell is greater than the downlink A5-2 threshold, the target terminal can be migrated from the current carrier (e.g., 700MHz carrier) to the target carrier (e.g., 2.6GHz carrier).

[0077] The above technical solution enables downlink-sensitive services to migrate from a frequency band where the downlink experience is about to deteriorate to a frequency band with a better downlink experience with almost no interruption, thereby ensuring the user experience of downlink-sensitive services.

[0078] In summary, the above technical solutions analyze the historical RSRP and rate mapping relationship through a performance big data platform, fit uplink and downlink dual-band curves, define frequency band migration events based on the A5 threshold, and optimize the migration triggering logic by superimposing a hysteresis coefficient, thereby achieving seamless service migration between time-sensitive offset index and spectrum-sensitive offset index carriers.

[0079] Figure 3 This is a flowchart of another frequency band migration method provided by an embodiment of the present invention. This embodiment is based on and optimized from the above-described technical solutions. In this embodiment, optionally, obtaining the first identification result may include: acquiring the real-time uplink rate and real-time downlink rate of the target service, and acquiring the uplink sensitivity threshold and downlink sensitivity threshold corresponding to the service type to which the target service belongs; obtaining an uplink sensitivity score based on the real-time uplink rate and uplink sensitivity threshold, and obtaining a downlink sensitivity score based on the real-time downlink rate and downlink sensitivity threshold; obtaining a comprehensive sensitivity score based on the uplink sensitivity score and downlink sensitivity score, and obtaining the first identification result based on the comprehensive sensitivity score. The explanations of terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0080] See Figure 3 The method in this embodiment may specifically include the following steps:

[0081] S210. For the target service running in the target terminal, obtain the real-time uplink rate and real-time downlink rate of the target service, and for the service type to which the target service belongs, obtain the uplink sensitivity threshold and downlink sensitivity threshold corresponding to the service type.

[0082] Specifically, for the business type to which the target business belongs, its corresponding sensitivity thresholds (i.e., uplink sensitivity thresholds and downlink sensitivity thresholds) can be determined. Then, the sensitivity thresholds and the real-time rate of the target business (i.e., the real-time uplink rate corresponding to the uplink sensitivity threshold and the downlink real-time rate corresponding to the downlink sensitivity threshold) can be used to identify whether the target business is a sensitive business.

[0083] In this embodiment of the invention, optionally, the uplink sensitivity threshold can be determined as follows: multiple historical uplink rates of the target service and a first correction coefficient corresponding to the service type are obtained, and an uplink reference rate is obtained based on the multiple historical uplink rates and the first correction coefficient; an uplink protection threshold pre-configured for the service type is obtained, and an uplink sensitivity threshold is determined based on the uplink reference rate and the uplink protection threshold. Similarly, the downlink sensitivity threshold can be determined as follows: multiple historical downlink rates of the target service and a first correction coefficient corresponding to the service type are obtained, and a downlink reference rate is obtained based on the multiple historical downlink rates and the first correction coefficient; a downlink protection threshold pre-configured for the service type is obtained, and a downlink sensitivity threshold is determined based on the downlink reference rate and the downlink protection threshold.

[0084] For example, generating uplink / downlink reference rates: , where μ UL / DL σ represents the average of multiple historical upward / downward rates over a historical period. UL / DL δ represents the standard deviation of multiple historical uplink / downlink rates. ω The first correction factor for business type ω (e.g., δ for industrial control business) ToB =1.5, video service δ ToC =0.8), enabling service-aware adaptive adjustment of the reference rate. For example, the generated uplink / downlink reference rate can be expressed as:

[0085] .

[0086] Furthermore, obtain the minimum protection threshold (i.e., the uplink protection threshold γ) pre-configured for the service type. UL and downlink protection threshold γ DL This means configuring the minimum protection threshold differently based on the business type ω, such as γ for industrial control business. UL It is 5Mbps and γ DL It is 3 Mbps, while the gamma of ultra-high definition video is 3 Mbps. UL It is 2 Mbps and γ DL It is 8Mbps; then, the sensitivity threshold (i.e., the uplink sensitivity threshold UL) is determined based on the reference rate and the minimum protection threshold. Large and downlink sensitivity threshold DL Large ).

[0087] For example, to prevent low-demand services from triggering false triggers, an uplink / downlink sensitivity threshold (UL) can be set. Large and DL Large :

[0088] ;

[0089] in, For dynamic adjustment coefficient (i.e. It is an upward adjustment factor and (This is the downlink adjustment factor), which can be adjusted based on the network load L. net Real-time updates: δ is the adjustment amplitude coefficient, L thresh The tanh function is used to achieve a smooth transition in threshold adjustment, which is the load threshold.

[0090] The above technical solution obtains a baseline rate through historical rates and a first correction coefficient. Based on this, and combined with the sensitivity threshold determined by the minimum protection threshold, it can accurately identify whether the target service is sensitive to network rate.

[0091] Optionally, after obtaining the uplink reference rate based on multiple historical uplink rates and correction coefficients, a third identification result can be obtained, wherein the third identification result indicates whether the target business is a business oriented to enterprises (ToB) or a business oriented to consumers (ToC); then, a second correction coefficient corresponding to the third identification result is obtained, and the uplink reference rate is corrected according to the second correction coefficient, and the uplink reference rate is updated based on the obtained correction result.

[0092] For example, to address the difference in rate sensitivity between ToB and ToC services, independent weights ω are configured for ToB / ToC services to adjust the baseline rate sensitivity: ,in, This represents the second correction factor, ranging from -0.2 to 0.3, which limits the adjustment range and prevents distortion of the reference rate. For example, in industrial control applications (…). =0.3), uplink command transmission requires low latency, the base rate is increased by 30% to prioritize resource allocation, video services ( =-0.1): Downlink traffic is high but some congestion is tolerated; the base rate is reduced by 10% to avoid excessive resource consumption. This solves the misjudgment problem of related solutions in mixed service scenarios. For example, in scenarios where industrial control and video services coexist, a high uplink sensitivity threshold (γ) for industrial control services can be achieved through differentiated minimum protection thresholds. UL =5Mbps), high downlink sensitivity threshold for video services (γ) DL =8Mbps).

[0093] S220. Obtain an uplink sensitivity score based on the real-time uplink rate and the uplink sensitivity threshold, and obtain a downlink sensitivity score based on the real-time downlink rate and the downlink sensitivity threshold.

[0094] S230. A comprehensive sensitivity score is obtained based on the uplink sensitivity score and the downlink sensitivity score. A first identification result is obtained based on the comprehensive sensitivity score, wherein the first identification result indicates whether the target service is a sensitive service that is sensitive to network speed.

[0095] Specifically, by comprehensively analyzing the target service's sensitivity to uplink and downlink speeds, the target service's sensitivity to network speed is identified, thus obtaining the corresponding first identification result.

[0096] Optionally, based on this, the service priority weighting coefficient and / or spectrum energy concentration corresponding to the service type can also be obtained; then, a comprehensive sensitivity score is obtained based on the uplink sensitivity score and downlink sensitivity score, as well as the service priority weighting coefficient and / or spectrum energy concentration. Here, the service priority weighting coefficient is introduced. and / or spectral energy concentration Cfreq This allows for a more accurate calculation of the overall sensitivity score, and thus a more accurate identification of whether a target business is a sensitive business.

[0097] For example, the overall sensitivity score S ω The calculation is performed as follows:

[0098] ;

[0099] Among them, R UL For real-time uplink speed; R DL This refers to the real-time downlink rate; λ1, λ2, and λ3 are the spectral energy concentration of the business type ω; λ1, λ2, and λ3 are weighting coefficients, which are dynamically adjusted according to the importance of business characteristics using the entropy weighting method. It can be set according to the Quality of Service (QoS) requirements, such as industrial control. prio =1.2, video service ω prio =1.0.

[0100] Among them, C reflects the degree of concentration of service traffic in the frequency domain. freq The flow spectrum features can be extracted using wavelet packet transform to obtain: E k Let be the energy of the k-th sub-band.

[0101] Optionally, a time-domain reference, such as the average uplink and downlink rates μ, can also be calculated based on historical traffic sequences. UL / DL Peak rate P UL / DL and suddenness B UL / DL (defined as the ratio of peak value to mean value); and / or combined with base station load L cell and user distribution D user Constructing spatial benchmarks, such as the spatial correlation coefficient ρ space : Here, α and β are weighting coefficients reflecting the impact of spatial factors on business sensitivity. Then, a comprehensive sensitivity score is calculated by combining temporal and / or spatial benchmarks. Of course, this can be set according to actual needs and is not specifically limited here.

[0102] S240. In response to the first identification result indicating that the target service is a sensitive service, a second identification result is obtained, wherein the second identification result indicates that the sensitive service is an uplink sensitive service that is sensitive to the uplink rate in the network rate or a downlink sensitive service that is sensitive to the downlink rate in the network rate.

[0103] S250. Obtain the frequency band migration event corresponding to the second identification result, and when the frequency band migration event is triggered by a sensitive service, send the frequency band migration policy corresponding to the frequency band migration event to the target terminal so that the target terminal can perform frequency band migration based on the received frequency band migration policy.

[0104] The technical solution of this invention achieves adaptive and accurate identification of sensitive services by constructing a multi-dimensional business sensitivity assessment framework and combining it with a dynamic threshold model (i.e., uplink and downlink sensitivity thresholds).

[0105] Figure 4 This is a flowchart of another frequency band migration method provided by an embodiment of the present invention. This embodiment is based on the above-described technical solutions and optimized. In this embodiment, optionally, obtaining the second identification result includes: for the service type to which the target service belongs, obtaining the uplink sensitivity threshold and downlink sensitivity threshold corresponding to the service type; sliding a time window across multiple real-time uplink rates and calculating the average uplink rate of multiple real-time uplink rates within the time window, and sliding a time window across multiple real-time downlink rates and calculating the average downlink rate of multiple real-time downlink rates within the time window; obtaining the second identification result based on the average uplink rate and the uplink sensitivity threshold, and the average downlink rate and the downlink sensitivity threshold. The explanations of terms that are the same as or corresponding to those in the above embodiments are not repeated here.

[0106] See Figure 4 The method in this embodiment may specifically include the following steps:

[0107] S310. For the target service running in the target terminal, obtain a first identification result, wherein the first identification result indicates whether the target service is a sensitive service that is sensitive to network speed.

[0108] S320. In response to the first identification result indicating that the target service is a sensitive service, obtain the uplink sensitivity threshold and downlink sensitivity threshold corresponding to the service type to which the target service belongs.

[0109] S330. Slide a time window across multiple real-time uplink rates and calculate the average uplink rate of the multiple real-time uplink rates within the time window; and slide a time window across multiple real-time downlink rates and calculate the average downlink rate of the multiple real-time downlink rates within the time window.

[0110] This involves sliding a time window across multiple real-time uplink rates, and calculating the average (i.e., the average uplink rate) of these multiple real-time uplink rates within a single time window. ).

[0111] Average downlink rate The process of obtaining it is similar and will not be repeated here.

[0112] In this embodiment of the invention, optionally, the target window length of the time window can be determined as follows: obtain the service traffic change rate of the target service and a preset base window length; obtain the target window length based on the service traffic change rate and the base window length. For example, it can be determined based on the service traffic change rate... and base window length T base Adaptively adjust the target window length: Where α is the adjustment coefficient. This optional scheme allows the time window to change with the rate of change of business traffic, thereby ensuring the matching between the average rate and the business traffic.

[0113] S340. Based on the average uplink rate and the uplink sensitivity threshold, and the average downlink rate and the downlink sensitivity threshold, a second identification result is obtained, wherein the second identification result indicates that the sensitive service is either an uplink sensitive service that is sensitive to the uplink rate in the network rate or a downlink sensitive service that is sensitive to the downlink rate in the network rate.

[0114] In this step, the sensitivity type of sensitive services is identified by combining the average rate and the sensitivity threshold.

[0115] For example, scenario-based uplink and downlink business sensitivity classification decision-making:

[0116] Scenario 1 (Uplink Sensitive): If and If so, it is determined to be an uplink sensitive service;

[0117] Scenario 2 (Downlink Sensitive): If and If so, it is determined to be a downlink sensitive business;

[0118] Scenario 3 (Mixed Sensitivity): If and If so, then historical decision analysis will be used.

[0119] For scenario 3 (the average uplink rate is greater than the uplink sensitivity threshold, i.e.) And the average downlink rate is greater than the downlink sensitivity threshold (i.e.) Here is an example:

[0120] Obtain the number of times C was made for sensitive business transactions within the historical period. UL (The number of times it was identified as an uplink sensitive transaction) and the number of downlink identifications C DL and the frequency difference threshold N corresponding to the business type ω ;further,

[0121] The response is that the absolute value of the difference between the number of uplink decisions and the number of downlink decisions is greater than the frequency difference threshold (i.e., |C) UL -C DL |>N ω Using a high-frequency direction, the second recognition result is obtained based on the number of uplink and downlink judgments, for example, C. UL >C DL This is considered an uplink-sensitive service, and vice versa. Based on this, optionally, the high-frequency direction corresponds to a frequency-dominant mode, and a hysteresis coefficient θ can be introduced. hyst To prevent frequent switching, a second identification result can be obtained based on the number of uplink and downlink judgments, as well as preset hysteresis coefficients, differences, and frequency difference thresholds. For example, in frequency-dominated mode (i.e., ), using high-frequency direction:

[0122] .

[0123] The response is given when the absolute value is less than or equal to the frequency difference threshold (i.e., |C) UL -C DL |≤N ω Using the high-flow direction, a second identification result is obtained based on the average uplink rate and the average downlink rate, for example... > This indicates an uplink-sensitive service, and vice versa. Based on this, optionally, the high-traffic direction corresponds to the traffic-dominated mode. A second identification result can be obtained by comparing the real-time average rate with the sensitivity threshold. Specifically, the second identification result is obtained based on the average uplink rate and the uplink sensitivity threshold, as well as pre-set traffic bias coefficients, downlink average rate, and downlink sensitivity thresholds. For example, in the traffic-dominated mode (i.e.,...) Under these conditions, use the high-flow direction:

[0124] ;

[0125] Where, θ flow This is the flow bias coefficient.

[0126] The above example demonstrates how optimizing priority rules using hysteresis and flow bias coefficients can ensure accurate identification of uplink and downlink sensitive services, thereby ensuring the accuracy and stability of migration decisions and improving the response performance of millisecond-level highly sensitive services such as industrial control.

[0127] Based on this, optionally, the second identification result can also be obtained by combining the following methods:

[0128] A three-dimensional collaborative decision-making framework of "real-time-historical-business attributes" is constructed, which enables refined decision-making for sensitive business identification through dynamic weight allocation and historical behavior modeling.

[0129] Specifically, a refined decision-making process is triggered when the business simultaneously meets the following multi-dimensional constraints:

[0130] Real-time traffic constraints: and ( (slack coefficient for business type awareness).

[0131] Historical frequency: and ( Dynamic configuration based on business type);

[0132] Service attribute constraint: Service QoS level QoSreq ≥ Q thresh (e.g., industrial control business Q) thresh =3).

[0133] Based on this, a comprehensive decision factor D is constructed that integrates real-time traffic, historical frequency, and business attributes. total And based on the rate mean and sensitivity threshold, combined with D total The second recognition result is obtained:

[0134] ;

[0135] Among them, the contribution of real-time traffic is ;

[0136] Historical behavioral contribution (using exponentially weighted moving average) is ,in, The number of judgments in the most recent N periods, where α is the decay factor;

[0137] Business attribute contribution , where δ SLA Q is the service level impact factor. base This is the baseline QoS level.

[0138] Based on this, optionally, the various decision weights in the above formula can be dynamically adjusted according to the network state:

[0139] ;

[0140] Among them, L net For the current network load, L max This is the maximum load capacity. This mechanism prioritizes real-time traffic during low loads and enhances historical behavior references during high loads.

[0141] The above example introduces real-time traffic constraints, historical frequency enhancement, and business attribute constraints into the joint judgment process, and constructs a comprehensive decision factor calculation model, thereby ensuring the accurate identification of uplink sensitive business and downlink sensitive business.

[0142] S350. Obtain the frequency band migration event corresponding to the second identification result, and when the frequency band migration event is triggered by a sensitive service, send the frequency band migration policy corresponding to the frequency band migration event to the target terminal so that the target terminal can perform frequency band migration based on the received frequency band migration policy.

[0143] The technical solution of this invention introduces a hierarchical decision-making and collaboration mechanism, analyzes real-time traffic characteristics (i.e., real-time uplink rate and real-time downlink rate) based on a time window sliding analysis, and combines this with uplink sensitivity thresholds and downlink sensitivity thresholds, thereby achieving accurate identification of whether a sensitive service is an uplink sensitive service or a downlink sensitive service.

[0144] Based on this, in order to better understand the various technical solutions described above as a whole, the following examples illustrate them. For examples, see [link to example]. Figure 5 The specific implementation process is as follows:

[0145] 1) Analyze the corresponding data containing frequency bands, RSRP, and uplink / downlink rates through the performance big data platform;

[0146] 2) Divide the data into downlink and uplink, and further divide the data into time-sensitive bias index and spectrum-sensitive bias index. Take the second decimal place of the RSRP of the time-sensitive bias index and spectrum-sensitive bias index respectively [ROUND(RSRP,2)] to obtain RSRP1, RSRP2, ... RSRPn. Then, average the uplink rate / downlink rate corresponding to each RSRP to obtain the corresponding AV.G DL and AV.G UL for each RSRP. Among them, the spectrum-sensitive bias index ROUND(RSRP,2) corresponds to the downlink rate AV.G DLnx, the time-sensitive bias index ROUND(RSRP,2) corresponds to the downlink rate AV.G DLyx, the spectrum-sensitive bias index ROUND(RSRP,2) corresponds to the uplink rate AV.G ULnz, and the time-sensitive bias index ROUND(RSRP,2) corresponds to the uplink rate AV.G ULnk.

[0147] 3) Compare the relationship between the spectrum-sensitive bias index ROUND(RSRP,2) and the time-sensitive bias index ROUND(RSRP,2) and the downlink rate to obtain the range of values ​​for ROUND(RSRP,2) when the time-sensitive bias index DL rate x > the spectrum-sensitive bias index DL rate y.

[0148] 4) Fit the relationship between the spectrum-sensitive bias index ROUND(RSRP,2) and the time-sensitive bias index ROUND(RSRP,2) and the uplink rate, and calculate the range of ROUND(RSRP,2) when the time-sensitive bias index UL rate k > the spectrum-sensitive bias index UL rate z according to the formula.

[0149] 5) Obtain the RSRP threshold (i.e., the uplink migration threshold) for uplink sensitive migration through steps 3) and 4).

[0150] 6) Analyze the entire network's services using performance platform data to identify mainstream services;

[0151] 7) Conduct tests and theoretical value analysis on the rate requirements of mainstream businesses to obtain the rate requirements of related businesses (differentiating between the rate sensitivity of related businesses based on the differences in requirements of ToB, ToC, etc.).

[0152] 8) When the rate is lower than the γ value, the mainstream business is considered a non-sensitive business and is removed from the list.

[0153] 9) For sensitive services with a rate not lower than γ, calculate the rate requirement: uplink sensitivity = MIN ROUND(UL rate, downlink sensitivity = MIN ROUND(DL rate, 2), and obtain the corresponding threshold.

[0154] 10) End.

[0155] Based on this, the results are as follows Figure 6 As shown, in scenario 1, the uplink experience is poor in weak field conditions of Time-division New Radio (TNR). When users experience a deterioration in the uplink large packet experience in the weak field of 2.6G, they are migrated to the 700M cell to improve the uplink speed. In scenario 2, the downlink experience is poor in weak field conditions of Frequency-division New Radio (FNR). When users experience a deterioration in the downlink large packet experience in the 700M cell, they are migrated to the 2.6G cell to improve the downlink speed.

[0156] The above example has at least the following advantages:

[0157] 1. Through intelligent traffic modeling and multi-dimensional benchmark generation mechanisms, a deep learning framework is used to automatically extract the temporal fluctuations, frequency energy distribution, and spatial correlation features of services, constructing a dynamic fingerprint database. Combined with service type weighted correction and adaptive threshold adjustment techniques, accurate identification of resource-sensitive services is achieved. The system can automatically shrink or roll back the threshold determination based on real-time network conditions, stably distinguishing high-sensitivity service demands in scenarios with differences in uplink and downlink path loss, effectively preventing false triggering of low-demand services.

[0158] 2. A traffic characteristic analysis mechanism based on a sliding time window can dynamically adjust the decision cycle to adapt to the rate of service change. By integrating a three-dimensional weighted model that combines real-time traffic intensity, historical judgment frequency, and service attributes, automated classification and determination of uplink-sensitive, downlink-sensitive, and mixed-sensitive services can be achieved. Combined with dual-band curve fitting and threshold hysteresis optimization techniques, carrier migration decisions can be matched with the existing network environment in real time, improving the efficiency of policy execution coordination.

[0159] 3. Establish an independent threshold configuration system for services such as industrial control and high-definition video. Strengthen traffic baseline constraints, historical behavior verification, and QoS priority judgment in the joint decision-making process. Suppress frequent switching through hysteresis coefficients, thereby ensuring millisecond-level response stability for highly sensitive services and maintaining decision robustness in high-load mixed service scenarios.

[0160] Figure 7 This is a structural block diagram of a frequency band migration apparatus provided in an embodiment of the present invention. This apparatus is used to execute the frequency band migration method provided in any of the above embodiments. This apparatus and the frequency band migration methods of the above embodiments belong to the same inventive concept. Details not described in detail in the embodiments of the frequency band migration apparatus can be found in the embodiments of the above frequency band migration methods. See also... Figure 7 The device may specifically include: a first identification result obtaining module 410, a second identification result obtaining module 420, and a frequency band shifting module 430.

[0161] The first identification result obtaining module 410 is used to obtain a first identification result for the target service running in the target terminal, wherein the first identification result indicates whether the target service is a sensitive service that is sensitive to network speed.

[0162] The second identification result obtaining module 420 is used to obtain the second identification result in response to the first identification result indicating that the target service is a sensitive service. The second identification result indicates that the sensitive service is an uplink sensitive service that is sensitive to the uplink rate in the network rate or a downlink sensitive service that is sensitive to the downlink rate in the network rate.

[0163] The frequency band migration module 430 is used to acquire the frequency band migration event corresponding to the second identification result, and when the frequency band migration event is triggered by a sensitive service, it sends the frequency band migration strategy corresponding to the frequency band migration event to the target terminal so that the target terminal can perform frequency band migration based on the received frequency band migration strategy.

[0164] Optionally, the first recognition result obtaining module 410 may include:

[0165] The downlink sensitive threshold acquisition unit is used to acquire the real-time uplink rate and real-time downlink rate of the target service, and to acquire the uplink sensitive threshold and downlink sensitive threshold corresponding to the service type to which the target service belongs.

[0166] The downlink sensitivity scoring unit is used to obtain an uplink sensitivity score based on the real-time uplink rate and the uplink sensitivity threshold, and to obtain a downlink sensitivity score based on the real-time downlink rate and the downlink sensitivity threshold.

[0167] The first identification result obtaining unit is used to obtain a comprehensive sensitivity score based on the uplink sensitivity score and the downlink sensitivity score, and to obtain the first identification result based on the comprehensive sensitivity score.

[0168] Based on this, an optional unit for obtaining the first recognition result may include:

[0169] The spectrum energy concentration acquisition subunit is used to acquire the service priority weighting coefficient and / or spectrum energy concentration corresponding to the service type.

[0170] The comprehensive sensitivity score yields a sub-unit, which is used to obtain the comprehensive sensitivity score based on the uplink sensitivity score, downlink sensitivity score, business priority weighting coefficient, and / or spectrum energy concentration.

[0171] Alternatively, the uplink sensitivity threshold is determined by the following module:

[0172] The uplink reference rate acquisition module is used to obtain multiple historical uplink rates of the target service and a first correction coefficient corresponding to the service type, and to obtain the uplink reference rate based on the multiple historical uplink rates and the first correction coefficient.

[0173] The uplink sensitivity threshold determination module is used to obtain the uplink protection threshold pre-configured for the service type, and determine the uplink sensitivity threshold based on the uplink base rate and the uplink protection threshold.

[0174] In addition, the aforementioned frequency band shifting device may optionally include:

[0175] The third identification result module is used to obtain the third identification result after obtaining the uplink reference rate based on multiple historical uplink rates and correction coefficients. The third identification result indicates whether the target business is a business oriented towards enterprises or a business oriented towards consumers.

[0176] The uplink reference rate update module is used to obtain the second correction coefficient corresponding to the third identification result, correct the uplink reference rate according to the second correction coefficient, and update the uplink reference rate based on the obtained correction result.

[0177] Optionally, the second recognition result obtaining module 420 may include:

[0178] The downlink sensitivity threshold acquisition submodule is used to acquire the uplink sensitivity threshold and downlink sensitivity threshold corresponding to the business type of the target business.

[0179] The downlink rate average calculation submodule is used to slide a time window over multiple real-time uplink rates and calculate the average uplink rate of multiple real-time uplink rates within the time window, and to slide a time window over multiple real-time downlink rates and calculate the average downlink rate of multiple real-time downlink rates within the time window.

[0180] The second identification result acquisition submodule is used to obtain the second identification result based on the average uplink rate and the uplink sensitivity threshold, and the average downlink rate and the downlink sensitivity threshold.

[0181] Based on this, optionally, the second recognition result submodule may include:

[0182] The frequency difference threshold acquisition unit is used to acquire the number of uplink determinations and downlink determinations for sensitive services, as well as the frequency difference threshold corresponding to the service type, in response to the average uplink rate being greater than the uplink sensitivity threshold and the average downlink rate being greater than the downlink sensitivity threshold.

[0183] The second identification result first obtaining unit is used to obtain the second identification result based on the number of uplink decisions and the number of downlink decisions in response to the absolute value of the difference between the number of uplink decisions and the number of downlink decisions being greater than the frequency difference threshold.

[0184] The second identification result second obtaining unit is used to obtain the second identification result based on the average uplink rate and the average downlink rate in response to the absolute value being less than or equal to the frequency difference threshold.

[0185] Based on this, optionally, the unit for obtaining the second recognition result first may include:

[0186] The second identification result first sub-unit is used to obtain the second identification result based on the number of uplink judgments and downlink judgments, as well as the pre-set hysteresis coefficient, difference and frequency difference threshold;

[0187] And / or,

[0188] The second identification result, the second obtaining unit, may include:

[0189] The second identification result second sub-unit is used to obtain the second identification result based on the average uplink rate and the uplink sensitivity threshold, as well as the pre-set traffic bias coefficient, the average downlink rate and the downlink sensitivity threshold.

[0190] Optionally, the second identification result indicates that the sensitive service is an uplink sensitive service, and the corresponding frequency band migration event is determined by the following module:

[0191] The spectrum uplink data acquisition module is used to acquire multiple time-based uplink data in the time-division duplex frequency band and multiple spectrum uplink data in the frequency-division duplex frequency band. The time-based uplink data includes the received power of the time-based uplink reference signal and the time-based uplink rate, and the spectrum uplink data includes the received power of the spectrum uplink reference signal and the spectrum uplink rate.

[0192] The uplink spectrum curve acquisition module is used to obtain the uplink spectrum curve based on the uplink spectrum rate as the dependent variable and the uplink spectrum reference signal received power as the independent variable, and based on the received power of each uplink reference signal and each uplink spectrum rate.

[0193] The first construction module for the frequency band migration event is used to determine, based on the time-based uplink curve and the spectrum uplink curve, a first uplink reference signal received power range where the time-based uplink rate is better than the spectrum uplink rate, and a second uplink reference signal received power range where the spectrum uplink rate is better than the time-based uplink rate. It then determines the uplink migration threshold based on the first and second uplink reference signal received power ranges, and constructs the frequency band migration event based on the uplink migration threshold.

[0194] Based on this, optionally, the band migration event is an A5 event, the uplink migration threshold includes the uplink A5-1 threshold and the uplink A5-2 threshold, and the first construction module of the band migration event may include:

[0195] Uplink hysteresis coefficient acquisition unit, used to acquire the pre-configured uplink hysteresis coefficient;

[0196] The uplink A5-1 threshold determination unit is used to determine the initial uplink A5-1 threshold based on the second uplink reference signal received power range, and to determine the uplink A5-1 threshold based on the initial uplink A5-1 threshold and the uplink hysteresis coefficient.

[0197] The uplink A5-2 threshold determination unit is used to determine the initial uplink A5-2 threshold based on the first uplink reference signal received power range, and to determine the uplink A5-2 threshold based on the initial uplink A5-2 threshold and the uplink hysteresis coefficient.

[0198] Optionally, the second identification result indicates that the sensitive service is a downlink sensitive service, and the corresponding frequency band migration event is determined by the following module:

[0199] The spectrum downlink data acquisition module is used to acquire multiple time-based downlink data in the time-division duplex frequency band and multiple spectrum downlink data in the frequency-division duplex frequency band. The time-based downlink data includes the received power of the time-based downlink reference signal and the time-based downlink rate, and the spectrum downlink data includes the received power of the spectrum downlink reference signal and the spectrum downlink rate.

[0200] The spectrum downlink curve acquisition module is used to obtain the time-dependent downlink curve based on the received power of the time-dependent downlink reference signal and the received power of the time-dependent downlink reference signal, and the spectrum downlink curve based on the received power of the corresponding time-dependent downlink reference signal and the received power of the spectrum downlink reference signal, and the spectrum downlink curve based on the spectrum downlink rate and the received power of the spectrum downlink reference signal, and the received power of the spectrum downlink reference signal.

[0201] The second construction module for the frequency band migration event is used to determine, based on the time-dependent downlink curve and the spectrum downlink curve, a first downlink reference signal received power range where the spectrum downlink rate is better than the time-dependent downlink rate, and a second downlink reference signal received power range where the time-dependent downlink rate is better than the spectrum downlink rate. It then determines the downlink migration threshold based on the first downlink reference signal received power range and the second downlink reference signal received power range, and constructs the frequency band migration event based on the downlink migration threshold.

[0202] Based on this, optionally, if the band migration event is an A5 event and the downlink migration threshold includes the downlink A5-1 threshold and the downlink A5-2 threshold, then the second building module of the band migration event can include:

[0203] Downlink hysteresis coefficient acquisition unit, used to acquire the pre-configured downlink hysteresis coefficient;

[0204] The downlink A5-1 threshold determination unit is used to determine the initial downlink A5-1 threshold based on the first downlink reference signal received power range, and to determine the downlink A5-1 threshold based on the initial downlink A5-1 threshold and the downlink hysteresis coefficient.

[0205] The downlink A5-2 threshold determination unit is used to determine the initial downlink A5-2 threshold based on the received power range of the second downlink reference signal, and to determine the downlink A5-2 threshold based on the initial downlink A5-2 threshold and the downlink hysteresis coefficient.

[0206] The frequency band migration apparatus provided in this embodiment of the invention obtains a first identification result module for a target service running in a target terminal, indicating whether the target service is a sensitive service that is sensitive to network speed. Then, through a second identification result module, if the target service is determined to be a sensitive service based on the first identification result, a second identification result can be obtained indicating whether the sensitive service is an uplink sensitive service that is sensitive to uplink speed or a downlink sensitive service that is sensitive to downlink speed. Furthermore, through a frequency band migration module, a frequency band migration event corresponding to the second identification result can be obtained. When a sensitive service triggers a frequency band migration event, a frequency band migration strategy corresponding to the frequency band migration event is sent to the target terminal, enabling the target terminal to perform frequency band migration based on the received frequency band migration strategy. Compared to solutions that trigger band migration solely based on downlink RSRP to migrate to a band with better downlink speeds, the aforementioned device identifies whether a sensitive service is an uplink-sensitive service or a downlink-sensitive service, and then triggers band migration based on the corresponding band migration event. The migrated band is a band that matches the sensitive service, meaning that the band can provide the speed required by the sensitive service. Thus, even in scenarios with large differences in uplink and downlink path loss, the user experience for sensitive services can still be well guaranteed.

[0207] The frequency band migration device provided in the embodiments of the present invention can execute the frequency band migration method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0208] It is worth noting that in the embodiments of the frequency band migration device described above, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0209] Figure 8 A schematic diagram of a target base station 10, which can be used to implement embodiments of the present invention, is shown. The target base station is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The target base station can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0210] like Figure 8As shown, the target base station 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the target base station 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0211] Multiple components in the target base station 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the target base station 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0212] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as frequency band shifting methods.

[0213] In some embodiments, the band migration method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the target base station 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the band migration method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the band migration method by any other suitable means (e.g., by means of firmware).

[0214] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chips or system-on-a-chips (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0215] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0216] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0217] To provide interaction with a user, the systems and techniques described herein can be implemented on a target base station, which includes: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the target base station. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0218] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0219] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0220] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0221] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0222] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A frequency band migration method, characterized in that, include: For a target service running on a target terminal, a first identification result is obtained, wherein the first identification result indicates whether the target service is a sensitive service that is sensitive to network speed. In response to the first identification result indicating that the target service is the sensitive service, a second identification result is obtained, wherein the second identification result indicates that the sensitive service is an uplink sensitive service that is sensitive to the uplink rate in the network rate or a downlink sensitive service that is sensitive to the downlink rate in the network rate. Obtain the frequency band migration event corresponding to the second identification result, and when the sensitive service triggers the frequency band migration event, send the frequency band migration policy corresponding to the frequency band migration event to the target terminal so that the target terminal performs frequency band migration based on the received frequency band migration policy.

2. The method according to claim 1, characterized in that, The process of obtaining the first identification result includes: Obtain the real-time uplink rate and real-time downlink rate of the target service, and for the service type to which the target service belongs, obtain the uplink sensitivity threshold and downlink sensitivity threshold corresponding to the service type; An uplink sensitivity score is obtained based on the real-time uplink rate and the uplink sensitivity threshold, and a downlink sensitivity score is obtained based on the real-time downlink rate and the downlink sensitivity threshold; A comprehensive sensitivity score is obtained based on the uplink sensitivity score and the downlink sensitivity score, and a first identification result is obtained based on the comprehensive sensitivity score.

3. The method according to claim 2, characterized in that, The process of obtaining a comprehensive sensitivity score based on the uplink sensitivity score and the downlink sensitivity score includes: Obtain the service priority weighting coefficient and / or spectrum energy concentration corresponding to the service type; A comprehensive sensitivity score is obtained based on the uplink sensitivity score, the downlink sensitivity score, the service priority weighting coefficient, and / or the spectrum energy concentration.

4. The method according to claim 2, characterized in that, The uplink sensitivity threshold is determined in the following way: Obtain multiple historical uplink rates of the target service and a first correction coefficient corresponding to the service type, and obtain the uplink reference rate based on the multiple historical uplink rates and the first correction coefficient; Obtain the uplink protection threshold pre-configured for the service type, and determine the uplink sensitivity threshold based on the uplink reference rate and the uplink protection threshold.

5. The method according to claim 4, characterized in that, After obtaining the uplink reference rate based on the plurality of historical uplink rates and the correction coefficient, the method further includes: A third identification result is obtained, wherein the third identification result indicates whether the target business is a business oriented towards enterprises or a business oriented towards consumers; The second correction coefficient corresponding to the third identification result is obtained, and the uplink reference rate is corrected according to the second correction coefficient. The uplink reference rate is then updated based on the obtained correction result.

6. The method according to claim 1, characterized in that, The process of obtaining the second identification result includes: For the service type to which the target service belongs, obtain the uplink sensitivity threshold and downlink sensitivity threshold corresponding to the service type; The time window is used to slide across multiple real-time uplink rates and the average uplink rate of the multiple real-time uplink rates within the time window is calculated; the time window is also used to slide across multiple real-time downlink rates and the average downlink rate of the multiple real-time downlink rates within the time window is calculated. The second identification result is obtained based on the average uplink rate and the uplink sensitivity threshold, and the average downlink rate and the downlink sensitivity threshold.

7. The method according to claim 6, characterized in that, The step of obtaining the second identification result based on the average uplink rate and the uplink sensitivity threshold, and the average downlink rate and the downlink sensitivity threshold, includes: In response to the average uplink rate being greater than the uplink sensitivity threshold and the average downlink rate being greater than the downlink sensitivity threshold, the number of uplink determinations and the number of downlink determinations for the sensitive service, as well as the frequency difference threshold corresponding to the service type, are obtained. In response to the absolute value of the difference between the number of uplink determinations and the number of downlink determinations being greater than the frequency difference threshold, a second identification result is obtained based on the number of uplink determinations and the number of downlink determinations; In response to the absolute value being less than or equal to the frequency difference threshold, the second identification result is obtained based on the average uplink rate and the average downlink rate.

8. The method according to claim 7, characterized in that, The step of obtaining the second identification result based on the number of uplink determinations and the number of downlink determinations includes: The second identification result is obtained based on the number of uplink determinations and the number of downlink determinations, as well as the preset hysteresis coefficient, the difference, and the frequency difference threshold. And / or, The step of obtaining the second identification result based on the average uplink rate and the average downlink rate includes: The second identification result is obtained based on the average uplink rate and the uplink sensitivity threshold, as well as the preset traffic bias coefficient, the average downlink rate and the downlink sensitivity threshold.

9. The method according to claim 1, characterized in that, The second identification result indicates that the sensitive service is the uplink sensitive service, and the corresponding frequency band migration event is determined in the following way: The system acquires multiple time-based uplink data in the time-division duplex frequency band and multiple spectrum uplink data in the frequency-division duplex frequency band. The time-based uplink data includes the received power of the time-based uplink reference signal and the time-based uplink rate, and the spectrum uplink data includes the received power of the spectrum uplink reference signal and the spectrum uplink rate. Using the time-dependent uplink rate as the dependent variable and the time-dependent uplink reference signal received power as the independent variable, a time-dependent uplink curve is obtained based on the corresponding time-dependent uplink reference signal received power and the time-dependent uplink rate; and using the spectrum uplink rate as the dependent variable and the spectrum uplink reference signal received power as the independent variable, a spectrum uplink curve is obtained based on the corresponding spectrum uplink reference signal received power and the spectrum uplink rate. Based on the time-dependent uplink curve and the spectrum uplink curve, a first uplink reference signal received power range in which the time-dependent uplink rate is better than the spectrum uplink rate, and a second uplink reference signal received power range in which the spectrum uplink rate is better than the time-dependent uplink rate are determined. An uplink migration threshold is then determined based on the first uplink reference signal received power range and the second uplink reference signal received power range, so as to construct the frequency band migration event based on the uplink migration threshold.

10. The method according to claim 9, characterized in that, The frequency band migration event is an A5 event, and the uplink migration threshold includes an uplink A5-1 threshold and an uplink A5-2 threshold. Therefore, determining the uplink migration threshold based on the first uplink reference signal received power range and the second uplink reference signal received power range includes: Obtain the pre-configured uplink hysteresis coefficient; The initial uplink A5-1 threshold is determined based on the second uplink reference signal received power range, and the uplink A5-1 threshold is determined based on the initial uplink A5-1 threshold and the uplink hysteresis coefficient. The initial uplink A5-2 threshold is determined based on the first uplink reference signal received power range, and the uplink A5-2 threshold is determined based on the initial uplink A5-2 threshold and the uplink hysteresis coefficient.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the frequency band migration method as described in any one of claims 1-10.

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