CN115442846B - A data distribution method and device - Google Patents

Abstract

The invention provides a data distribution method and a data distribution device. The method comprises the following steps: based on the data splitting point of the target cell, determining a splitting threshold value at the next moment according to the splitting threshold value at the current moment, the splitting threshold predicted value at the next moment and the splitting threshold period predicted value at the next moment under the condition that the preset condition is met; data distribution is carried out according to the distribution threshold value at the next moment; wherein the preset condition includes any one of the following: the GBR terminal throughput of the target cell is larger than a first difference value between the current time throughput of the target cell and the current time shunting threshold value; the GBR terminal throughput is smaller than the first difference. The data distribution method and the device can realize the prediction of the distribution threshold value of the data distribution point and the dynamic adjustment, thereby effectively improving the perception of the user.

Description

A data distribution method and device

Technical Field

The present invention relates to the field of communication technology, and in particular to a data offloading method and device.

Background Art

5G (5th generation mobile networks) networks usually use the NSA networking mode (Non-Stand alone) as an important evolutionary technology for 5G commercial use. 3GPP (3rd Generation Partnership Project) provides a variety of NSA networking modes, including option 3 series, option 4 series, and option 7 series. In different NSA networking modes, the eNB (Evolved Node B) base station and the gNB (rext generation Node B) base station serve as data anchor points, that is, as the distribution and convergence points of NSA data, they undertake the task of NSA data diversion.

In the prior art, commonly used data diversion methods include MCG (Master Cell Group) diversion, SCG (Secondary Cell Group) diversion, and dynamic diversion based on RLC (Radio Link Control)/X2 status. MCG diversion or SCG diversion is to fix the data on one side of the MCG or SCG for diversion. This method cannot fully utilize the advantages of dual connections in NSA networking, which will cause waste of air interface resources on one side and congestion of resources on the other side. Dynamic diversion based on RLC/X2 status is based on the current RLC cache status of the base station and the X2 link status to adjust the diversion on the data anchor point side. This method does not take into account information such as the number of terminal users, the waiting time of unscheduled terminals, and user priority. Therefore, it cannot maximize the terminal demand throughput. At the same time, there is a certain lag, and it is impossible to predict the diversion threshold.

Summary of the invention

The present invention provides a data diversion method and device, which are used to solve the technical problem in the prior art that a data diversion point cannot predict a diversion threshold.

The present invention provides a data distribution method, comprising:

Based on the data diversion point of the target cell, when the preset conditions are met, the diversion threshold value at the next moment is determined according to the diversion threshold value at the current moment, the predicted value of the diversion threshold at the next moment, and the predicted value of the diversion threshold period at the next moment;

Perform data diversion according to the diversion threshold value at the next moment;

The preset condition includes any one of the following:

The GBR terminal throughput of the target cell is greater than a first difference between the current throughput of the target cell and the current offload threshold value;

The GBR terminal throughput is less than the first difference.

In one embodiment, when the preset conditions are met, determining the next moment shunt threshold value according to the current moment shunt threshold value, the next moment shunt threshold prediction value and the next moment shunt threshold cycle prediction value includes:

When the GBR terminal throughput is greater than the first difference, the next moment diversion threshold is determined based on the minimum value among the current moment diversion threshold, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value.

In one embodiment, when the preset conditions are met, determining the next moment shunt threshold value according to the current moment shunt threshold value, the next moment shunt threshold prediction value and the next moment shunt threshold cycle prediction value includes:

When the GBR terminal throughput is less than the first difference, determining an average waiting time of the first terminal and an average waiting time of the second terminal;

When a second difference between the average waiting time of the first terminal and the waiting time of the second terminal is greater than a preset difference, determining the next moment diversion threshold value based on the minimum value among the current moment diversion threshold value, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value;

In the case where the second difference is less than the preset difference, determining the next moment shunt threshold value based on the maximum value among the current moment shunt threshold value, the next moment shunt threshold prediction value and the next moment shunt threshold cycle prediction value;

Among them, the average waiting time of the first terminal is determined based on the number of unscheduled terminals on the first side corresponding to the data diversion point and the waiting time corresponding to each terminal, and the average waiting time of the second terminal is determined based on the number of unscheduled terminals on the second side corresponding to the data diversion point and the waiting time corresponding to each terminal.

In one embodiment, the next moment diversion threshold prediction value is determined by:

The predicted value of the diversion threshold at the next moment is determined according to the diversion threshold value at the current moment and the average value of the diversion threshold values from the preset starting moment to the previous moment.

In one embodiment, the predicted value of the next moment shunt threshold period is determined by:

The predicted value of the shunt threshold period at the next moment is determined according to the actual value of the shunt threshold period at the previous moment, the predicted value of the shunt threshold period at the previous moment and a preset average coefficient.

The present invention provides a data distribution device, comprising:

A determination module, for determining the next moment diversion threshold value based on the data diversion point of the target cell, when a preset condition is met, according to the current moment diversion threshold value, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value;

A traffic distribution module, used for performing data distribution according to the traffic distribution threshold value at the next moment;

The preset condition includes any one of the following:

The GBR terminal throughput of the target cell is greater than a first difference between the current throughput of the target cell and the current offload threshold value;

The GBR terminal throughput is less than the first difference.

In one embodiment, when the preset conditions are met, determining the next moment shunt threshold value according to the current moment shunt threshold value, the next moment shunt threshold prediction value and the next moment shunt threshold cycle prediction value includes:

When the GBR terminal throughput is greater than the first difference, the next moment diversion threshold is determined based on the minimum value among the current moment diversion threshold, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value.

In one embodiment, when the preset conditions are met, determining the next moment shunt threshold value according to the current moment shunt threshold value, the next moment shunt threshold prediction value and the next moment shunt threshold cycle prediction value includes:

When the GBR terminal throughput is less than the first difference, determining an average waiting time of the first terminal and an average waiting time of the second terminal;

When a second difference between the average waiting time of the first terminal and the waiting time of the second terminal is greater than a preset difference, determining the next moment diversion threshold value based on the minimum value among the current moment diversion threshold value, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value;

In the case where the second difference is less than the preset difference, determining the next moment shunt threshold value based on the maximum value among the current moment shunt threshold value, the next moment shunt threshold prediction value and the next moment shunt threshold cycle prediction value;

Among them, the average waiting time of the first terminal is determined based on the number of unscheduled terminals on the first side corresponding to the data diversion point and the waiting time corresponding to each terminal, and the average waiting time of the second terminal is determined based on the number of unscheduled terminals on the second side corresponding to the data diversion point and the waiting time corresponding to each terminal.

The present invention provides an electronic device, comprising a memory and a memory storing a computer program, wherein the processor implements the steps of the data diversion method when executing the program.

The present invention provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, and the computer program is used to enable the processor to execute the steps of the data diversion method.

The present invention provides a data diversion method and device, which fully considers the influence of time series on the diversion threshold by adopting the current moment diversion threshold value, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value to determine the next moment diversion threshold value; it also combines the relationship between the GBR terminal throughput of the target cell and the difference between the current moment throughput of the target cell and the current moment diversion threshold value to dynamically adjust the diversion threshold value, realizes the optimal prediction and selection of the next moment diversion threshold value, and thus effectively improves user perception.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG1 is a schematic diagram of a flow chart of a data distribution method provided by the present invention;

FIG2 is a second flow chart of the data distribution method provided by the present invention;

FIG3 is a schematic structural diagram of a data splitting device provided by the present invention;

FIG. 4 is a schematic diagram of the structure of an electronic device provided by the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

FIG1 is a flow chart of a data distribution method provided by the present invention. Referring to FIG1 , the data distribution method provided by the present invention includes:

Step 110: Based on the data diversion point of the target cell, when the preset conditions are met, the diversion threshold value at the next moment is determined according to the diversion threshold value at the current moment, the predicted diversion threshold value at the next moment, and the predicted diversion threshold period value at the next moment;

Step 120: perform data diversion according to the diversion threshold value at the next moment;

The preset condition includes any one of the following:

The GBR terminal throughput of the target cell is greater than a first difference between the current throughput of the target cell and the current offload threshold value;

The GBR terminal throughput is less than the first difference.

The execution subject of the data diversion method provided by the present invention can be an electronic device, a component in an electronic device, an integrated circuit, or a chip. The electronic device can be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc. The non-mobile electronic device can be a server, a network attached storage (NAS), a personal computer (PC), etc., which is not specifically limited by the present invention.

The following takes the data splitting method provided by the present invention executed by a computer as an example to describe the technical solution of the present invention in detail.

Optionally, the data diversion method provided by the present invention can be implemented using an NSA networking mode, such as option 3 mode, option 3X mode, option 7X mode, etc., or other networking modes that can achieve data diversion can be used.

Optionally, in step 110, the data diversion point in the NSA networking mode can be an eNB base station or a gNB base station. For example: in option 3X mode, the core network is EPC (Evolved Packet Core), and the deployment of the 5G NR (New Radio) wireless access network uses the eNB base station in the LTE (Long Term Evolution) system as the main base station, and the gNB base station as the slave base station. There are multiple paths for data plane transmission, so there are data diversion points. Data diversion in Option 3x mode is based on the packet level, and the data diversion point is the gNB base station. User plane data can divert part of the data to the eNB base station through the gNB base station, and the rest of the data continues to be carried on the gNB base station.

Optionally, in step 110, based on the target cell in the preset time period, according to the diversion threshold values of the data diversion points at multiple consecutive moments, the predicted diversion threshold value at the next moment is calculated; and according to the diversion threshold value at the same moment in the fixed period, the predicted diversion threshold period value at the next moment is calculated. Then, considering the user perception, the GBR terminal throughput of the target cell in the preset conditions and the throughput at the current moment, as well as the predicted diversion threshold value at the next moment and the predicted diversion threshold period value at the next moment are combined to determine the diversion threshold value at the next moment.

Optionally, the preset condition includes any one of the following: the GBR (Guaranteed Bit Rate) terminal throughput of the target cell is greater than a first difference between the current throughput of the target cell and the current offload threshold; the GBR terminal throughput is less than the first difference. The GBR terminal throughput is determined according to the number of GBR terminals and the minimum bit rate threshold corresponding to each terminal.

For example, assuming the target cell is an LTE cell, the current throughput is N _LTE , and the current offload threshold is The number of GBR terminals is m, and the minimum thresholds of the bit rate minimum thresholds corresponding to each terminal are x ₁ , x ₂ , … x _m , respectively. Then the minimum requirement for the GBR terminal throughput G _x is:

When the GBR terminal throughput is greater than the first difference between the current throughput and the current traffic diversion threshold, When the GBR terminal throughput is less than the first difference, it means that the minimum requirement of GBR terminal throughput G _x cannot be met and the GBR terminal bit rate cannot be guaranteed. , it means that the GBR terminal bit rate can be guaranteed at this time.

Optionally, in step 120, the diversion threshold of the data diversion point at the next moment is set to the diversion threshold value at the next moment, so that the diversion threshold at each moment is optimally predicted and selected.

The data diversion method provided by the present invention adopts the current diversion threshold value, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value to determine the next moment diversion threshold value, and fully considers the influence of the time series on the diversion threshold; it also combines the relationship between the GBR terminal throughput of the target cell and the difference between the current moment throughput of the target cell and the current moment diversion threshold value to dynamically adjust the diversion threshold value, thereby achieving the optimal prediction and selection of the next moment diversion threshold value, thereby effectively improving user perception.

In one embodiment, when the preset conditions are met, determining the next moment shunt threshold value according to the current moment shunt threshold value, the next moment shunt threshold prediction value and the next moment shunt threshold cycle prediction value includes:

When the GBR terminal throughput is greater than the first difference, the next moment diversion threshold is determined based on the minimum value among the current moment diversion threshold, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value.

Optionally, when the GBR terminal throughput is greater than the first difference between the current throughput and the current diversion threshold, it means that after the data diversion point diverts part of the bandwidth, the minimum GBR terminal throughput requirement cannot be met, that is, the GBR terminal bit rate cannot be guaranteed, and a fast decreasing strategy is needed to reduce the diversion threshold on the data diversion point side. Therefore, the diversion threshold at the next moment is determined based on the minimum value of the current diversion threshold, the predicted diversion threshold at the next moment, and the predicted diversion threshold period at the next moment.

Optionally, assume that the next moment diversion threshold is Current time traffic diversion threshold The next moment shunt threshold prediction value w ₁ , the next moment shunt threshold cycle prediction value w ₂ , then we have

The data diversion method provided by the present invention adopts a fast decreasing strategy to reduce the diversion threshold value at the next moment, thereby meeting the minimum requirement of the GBR terminal throughput to ensure the GBR terminal bit rate and effectively improve the user perception; and also realizes the dynamic adjustment of the diversion threshold by the data diversion point by judging the GBR terminal throughput and the first difference.

In one embodiment, when the preset conditions are met, determining the next moment shunt threshold value according to the current moment shunt threshold value, the next moment shunt threshold prediction value and the next moment shunt threshold cycle prediction value includes:

When the GBR terminal throughput is less than the first difference, determining an average waiting time of the first terminal and an average waiting time of the second terminal;

When a second difference between the average waiting time of the first terminal and the waiting time of the second terminal is greater than a preset difference, determining the next moment diversion threshold value based on the minimum value among the current moment diversion threshold value, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value;

In the case where the second difference is less than the preset difference, determining the next moment shunt threshold value based on the maximum value among the current moment shunt threshold value, the next moment shunt threshold prediction value and the next moment shunt threshold cycle prediction value;

Among them, the average waiting time of the first terminal is determined based on the number of unscheduled terminals on the first side corresponding to the data diversion point and the waiting time corresponding to each terminal, and the average waiting time of the second terminal is determined based on the number of unscheduled terminals on the second side corresponding to the data diversion point and the waiting time corresponding to each terminal.

Optionally, when the GBR terminal throughput is greater than the first difference, the average waiting time of the first terminal and the average waiting time of the second terminal are determined. The average waiting time of the terminal is determined according to the number of unscheduled terminals in the target cell and the waiting time corresponding to each terminal. The unscheduled terminals include unscheduled terminals on the first side and the second side of the data diversion point of the target cell. In the NSA networking mode, one side of the data diversion point can be the eNB side or the gNB side.

Optionally, assuming that option 3 mode is adopted, gNB is the data diversion point. If the target cell is an LTE cell, the first side unscheduled terminal is an eNB side terminal, and the second side unscheduled terminal is a gNB side terminal. Collect eNB terminal information and gNB terminal information in the cell, as well as the average waiting time of unscheduled terminals on the eNB side and gNB side.

For example, assuming that the number of terminals not currently scheduled on the eNB side is i, the waiting time corresponding to each terminal is t _i , and the number of such terminals is u ₁ , then the average waiting time of the terminals not currently scheduled on the eNB side is:

Assuming that the number of terminals not currently scheduled on the gNB side is j, the waiting time corresponding to each terminal is t _j , and the number of such terminals is u ₂ , then the average waiting time of the terminals not currently scheduled on the gNB side is:

When the GBR terminal throughput is less than the first difference, When T ₁ and T ₂ are determined, the average waiting time of the unscheduled terminals on the eNB side and the gNB side are compared.

Optionally, when the second difference between the average waiting time of the first terminal and the waiting time of the second terminal is greater than the preset difference, that is, the average waiting time of the first terminal is too long, and the next moment diversion threshold value needs to be appropriately lowered. Therefore, the next moment diversion threshold value is determined based on the minimum value among the current moment diversion threshold value, the next moment diversion threshold prediction value, and the next moment diversion threshold cycle prediction value.

For example: if T ₁ -T ₂ > Δ _t , that is, the average waiting time of the terminal on the eNB side is longer than the average waiting time of the terminal on the gNB side, the average waiting time of the terminal on the eNB side is too long, and the next moment diversion threshold value needs to be reduced. Then the next moment diversion threshold value is for:

Among them, _Δt is the preset difference, _v1 is the expected perceived rate of the terminal user on the eNB side, and _u1 is the number of unscheduled terminals on the eNB side (that is, the number of terminals waiting for scheduling).

Optionally, when the second difference is less than the preset difference, the average waiting time of the first terminal is less than the average waiting time of the second terminal, that is, the average waiting time of the second terminal is too long, and the next moment diversion threshold value needs to be appropriately increased. Therefore, the next moment diversion threshold value is determined based on the maximum value of the current moment diversion threshold value, the next moment diversion threshold prediction value, and the next moment diversion threshold cycle prediction value.

For example: if T ₁ -T ₂ ＜Δ _t , that is, the average waiting time of the terminal on the eNB side is less than the average waiting time of the terminal on the gNB side, the average waiting time of the terminal on the gNB side is too long, and the next moment diversion threshold value needs to be increased. Then there is the next moment diversion threshold value for:

Among them, _Δt is the preset difference, _v2 is the expected perceived rate of the terminal user on the gNB side, and _u2 is the number of unscheduled terminals on the eNB side (i.e., the number of terminals waiting for scheduling).

Optionally, when the absolute value of the second difference is less than the preset difference, the average waiting time of the first terminal and the average waiting time of the second terminal are slightly different, then a value is randomly selected from the diversion threshold value at the current moment, the diversion threshold prediction value at the next moment and the diversion threshold cycle prediction value at the next moment as the diversion threshold value at the next moment.

For example, if |T ₁ -T| ₂ ＜Δ _t , the average waiting time of the terminal on the eNB side is slightly different from the average waiting time of the terminal on the gNB side, then we randomly select As the next moment of diversion threshold value, the next moment of diversion threshold value is for:

The data diversion method provided by the present invention determines the diversion threshold value at the next moment by comparing the average waiting time of the first terminal and the average waiting time of the second terminal based on the GBR terminal throughput, thereby realizing dynamic adjustment of the diversion threshold by the data diversion point.

In one embodiment, the next moment diversion threshold prediction value is determined by:

The predicted value of the diversion threshold at the next moment is determined according to the diversion threshold value at the current moment and the average value of the diversion threshold values from the preset starting moment to the previous moment.

Optionally, according to the diversion threshold values of the data diversion point at multiple consecutive moments, the diversion threshold value at the current moment and the average value of the diversion threshold values from the preset starting moment to the previous moment can be obtained to determine the diversion threshold prediction value at the next moment.

For example, assuming that the average diversion threshold after n moments is W ₁ , that is, the predicted value of the diversion threshold at the next moment after the preset moment to the current moment, then:

in, The data diversion point is the current diversion threshold value; is the average value of the diversion threshold up to the last moment; the predicted value of the diversion threshold at the next moment _W1 reflects the longitudinal continuous change of the diversion threshold value at n moments within the preset period; the diversion threshold value w refers to the flow diverted from the data diversion point to the other side. Mainly refers to the bandwidth occupied by gNB-side terminals.

The data diversion method provided by the present invention fully utilizes the continuous changes of the diversion threshold values at different moments through the diversion threshold values at historical moments, determines the predicted diversion threshold value at the next moment, realizes the dynamic prediction of the diversion threshold value based on the time series, and improves the timeliness and accuracy of the diversion threshold value prediction.

In one embodiment, the predicted value of the next moment shunt threshold period is determined by:

The predicted value of the shunt threshold period at the next moment is determined according to the actual value of the shunt threshold period at the previous moment, the predicted value of the shunt threshold period at the previous moment and a preset average coefficient.

Optionally, based on the periodic change of the diversion threshold value of the data diversion point, the diversion threshold value at every fixed period can be calculated, and the diversion threshold period prediction value at the next moment can be determined based on the actual value of the diversion threshold period at the previous moment, the diversion threshold period prediction value at the previous moment and the preset average coefficient.

For example, assuming that the weighted average value of the shunt threshold value at every fixed period t is W ₂ , which is used as the prediction value of the shunt threshold period at the next moment, then:

Wherein, W ₂ is the actual setting value of the diversion threshold value in the previous time period t-1; is the cycle prediction value of the diversion threshold in the previous time period _W2 ; a is the sliding average coefficient, which can be set to a coefficient greater than 0.5; t is the cycle, which can be 1 hour, 24 hours, etc., and can be set according to actual conditions. _W2 can reflect the cycle prediction of the diversion value at the same time every day, and measure the periodic changes of the diversion threshold value within a certain time span.

The data diversion method provided by the present invention determines the predicted value of the diversion threshold period at the next moment by the change of the diversion threshold value at the same moment within a certain period, realizes the dynamic prediction of the diversion threshold value based on the time series, and improves the timeliness and accuracy of the diversion threshold value prediction.

FIG2 is a second flow chart of the data distribution method provided by the present invention. Referring to FIG2 , the data distribution method provided by the present invention includes:

Step 210, terminal information collection: collecting terminal information of the target cell, including GBR terminal throughput and average waiting time of unscheduled users in the target cell;

Step 220, longitudinal prediction of the diversion threshold value: determining the predicted diversion threshold value at the next moment according to the change of the diversion threshold value at each moment in the preset time period;

Step 230, predicting the diversion threshold value cycle: determining the predicted value of the diversion threshold cycle at the next moment according to the diversion threshold value at the same moment every fixed period;

Step 240, dynamically setting the data diversion point diversion threshold value: when the preset conditions are met, the data diversion point diversion threshold value at the next moment is determined according to the current moment diversion threshold value, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value.

The data splitting device provided by the present invention is described below. The data splitting device described below and the data splitting method described above can be referenced to each other.

FIG3 is a schematic diagram of the structure of a data splitting device provided by the present invention. Referring to FIG3 , the data splitting device provided by the present invention comprises:

The determination module 310 is used to determine the next moment diversion threshold value based on the data diversion point of the target cell, when the preset conditions are met, according to the current moment diversion threshold value, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value;

A traffic distribution module 320, configured to distribute data according to the traffic distribution threshold value at the next moment;

The preset condition includes any one of the following:

The GBR terminal throughput of the target cell is greater than a first difference between the current throughput of the target cell and the current offload threshold value;

The GBR terminal throughput is less than the first difference.

The data diversion method provided by the present invention adopts the current diversion threshold value, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value to determine the next moment diversion threshold value, and fully considers the influence of the time series on the diversion threshold; it also combines the relationship between the GBR terminal throughput of the target cell and the difference between the current moment throughput of the target cell and the current moment diversion threshold value to dynamically adjust the diversion threshold value, thereby achieving the optimal prediction and selection of the next moment diversion threshold value, thereby effectively improving user perception.

In one embodiment, the determining module 310 is further specifically configured to:

When the GBR terminal throughput is greater than the first difference, the next moment diversion threshold is determined based on the minimum value among the current moment diversion threshold, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value.

In one embodiment, the determining module 310 is further specifically configured to:

When the GBR terminal throughput is less than the first difference, determining an average waiting time of the first terminal and an average waiting time of the second terminal;

When a second difference between the average waiting time of the first terminal and the waiting time of the second terminal is greater than a preset difference, determining the next moment diversion threshold value based on the minimum value among the current moment diversion threshold value, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value;

In the case where the second difference is less than the preset difference, determining the next moment shunt threshold value based on the maximum value among the current moment shunt threshold value, the next moment shunt threshold prediction value and the next moment shunt threshold cycle prediction value;

Among them, the average waiting time of the first terminal is determined based on the number of unscheduled terminals on the first side corresponding to the data diversion point and the waiting time corresponding to each terminal, and the average waiting time of the second terminal is determined based on the number of unscheduled terminals on the second side corresponding to the data diversion point and the waiting time corresponding to each terminal.

In one embodiment, the next moment diversion threshold prediction value is determined by:

The predicted value of the diversion threshold at the next moment is determined according to the diversion threshold value at the current moment and the average value of the diversion threshold values from the preset starting moment to the previous moment.

In one embodiment, the predicted value of the next moment shunt threshold period is determined by:

The predicted value of the shunt threshold period at the next moment is determined according to the actual value of the shunt threshold period at the previous moment, the predicted value of the shunt threshold period at the previous moment and a preset average coefficient.

FIG4 illustrates a schematic diagram of the structure of an electronic device. As shown in FIG4 , the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call a computer program in the memory 430 to execute the steps of the data diversion method, for example, including:

Based on the data diversion point of the target cell, when the preset conditions are met, the diversion threshold value at the next moment is determined according to the diversion threshold value at the current moment, the predicted value of the diversion threshold at the next moment, and the predicted value of the diversion threshold period at the next moment;

Perform data diversion according to the diversion threshold value at the next moment;

The preset condition includes any one of the following:

The GBR terminal throughput of the target cell is greater than a first difference between the current throughput of the target cell and the current offload threshold value;

The GBR terminal throughput is less than the first difference.

In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

On the other hand, the present invention further provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, when the program instructions are executed by a computer, the computer can execute the data splitting method provided by the above methods, the method comprising:

Based on the data diversion point of the target cell, when the preset conditions are met, the diversion threshold value at the next moment is determined according to the diversion threshold value at the current moment, the predicted value of the diversion threshold at the next moment, and the predicted value of the diversion threshold period at the next moment;

Perform data diversion according to the diversion threshold value at the next moment;

The preset condition includes any one of the following:

The GBR terminal throughput of the target cell is greater than a first difference between the current throughput of the target cell and the current offload threshold value;

The GBR terminal throughput is less than the first difference.

On the other hand, an embodiment of the present application further provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, wherein the computer program is used to enable the processor to execute the data offloading method provided in the above embodiments, for example, including:

Based on the data diversion point of the target cell, when the preset conditions are met, the diversion threshold value at the next moment is determined according to the diversion threshold value at the current moment, the predicted value of the diversion threshold at the next moment, and the predicted value of the diversion threshold period at the next moment;

Perform data diversion according to the diversion threshold value at the next moment;

The preset condition includes any one of the following:

The GBR terminal throughput of the target cell is greater than a first difference between the current throughput of the target cell and the current offload threshold value;

The GBR terminal throughput is less than the first difference.

The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drive (SSD)), etc.

The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. A data offloading method, comprising: Based on the data diversion point of the target cell, when the preset conditions are met, the diversion threshold value at the next moment is determined according to the diversion threshold value at the current moment, the predicted value of the diversion threshold at the next moment, and the predicted value of the diversion threshold period at the next moment; Perform data diversion according to the diversion threshold value at the next moment; The preset conditions include: The GBR terminal throughput of the target cell is less than a first difference between the current throughput of the target cell and the current offload threshold value; The method of determining the shunt threshold value at the next moment according to the shunt threshold value at the current moment, the shunt threshold prediction value at the next moment, and the shunt threshold cycle prediction value at the next moment when the preset condition is met includes: When the GBR terminal throughput is less than the first difference, determining an average waiting time of the first terminal and an average waiting time of the second terminal; When a second difference between the average waiting time of the first terminal and the average waiting time of the second terminal is greater than a preset difference, determining the next moment diversion threshold value based on the minimum value among the current moment diversion threshold value, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value; In the case where the second difference is less than the preset difference, determining the next moment shunt threshold value based on the maximum value among the current moment shunt threshold value, the next moment shunt threshold prediction value and the next moment shunt threshold cycle prediction value; Among them, the average waiting time of the first terminal is determined based on the number of unscheduled terminals on the first side corresponding to the data diversion point and the waiting time corresponding to each terminal, and the average waiting time of the second terminal is determined based on the number of unscheduled terminals on the second side corresponding to the data diversion point and the waiting time corresponding to each terminal. 2. The data offloading method according to claim 1, characterized in that the preset condition further comprises that the GBR terminal throughput is greater than the first difference, and when the preset condition is met, determining the offloading threshold value at the next moment according to the offloading threshold value at the current moment, the offloading threshold prediction value at the next moment, and the offloading threshold cycle prediction value at the next moment, comprises: When the GBR terminal throughput is greater than the first difference, the next moment diversion threshold is determined based on the minimum value among the current moment diversion threshold, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value. 3. The data offloading method according to any one of claims 1 to 2, characterized in that the next moment offloading threshold prediction value is determined by: The predicted value of the diversion threshold at the next moment is determined according to the diversion threshold value at the current moment and the average value of the diversion threshold values from the preset starting moment to the previous moment. 4. The data offloading method according to any one of claims 1 to 2, characterized in that the prediction value of the offloading threshold period at the next moment is determined by: The predicted value of the shunt threshold period at the next moment is determined according to the actual value of the shunt threshold period at the previous moment, the predicted value of the shunt threshold period at the previous moment and a preset average coefficient. 5. A data distribution device, comprising: A determination module, for determining the next moment diversion threshold value based on the data diversion point of the target cell, when a preset condition is met, according to the current moment diversion threshold value, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value; A traffic distribution module, used for performing data distribution according to the traffic distribution threshold value at the next moment; The preset conditions include: The GBR terminal throughput of the target cell is less than a first difference between the current throughput of the target cell and the current offload threshold value; The determining module is further configured to determine, when the GBR terminal throughput is less than the first difference, an average waiting time of the first terminal and an average waiting time of the second terminal; When a second difference between the average waiting time of the first terminal and the average waiting time of the second terminal is greater than a preset difference, determining the next moment diversion threshold value based on the minimum value among the current moment diversion threshold value, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value; In the case where the second difference is less than the preset difference, determining the next moment shunt threshold value based on the maximum value among the current moment shunt threshold value, the next moment shunt threshold prediction value and the next moment shunt threshold cycle prediction value; Among them, the average waiting time of the first terminal is determined based on the number of unscheduled terminals on the first side corresponding to the data diversion point and the waiting time corresponding to each terminal, and the average waiting time of the second terminal is determined based on the number of unscheduled terminals on the second side corresponding to the data diversion point and the waiting time corresponding to each terminal. 6. The data offloading device according to claim 5, characterized in that the preset condition further comprises that the GBR terminal throughput is greater than the first difference, and when the preset condition is met, determining the offloading threshold value at the next moment according to the offloading threshold value at the current moment, the offloading threshold prediction value at the next moment, and the offloading threshold cycle prediction value at the next moment, comprises: When the GBR terminal throughput is greater than the first difference, the next moment diversion threshold is determined based on the minimum value among the current moment diversion threshold, the next moment diversion threshold prediction value and the next moment diversion threshold cycle prediction value. 7. An electronic device, comprising a processor and a memory storing a computer program, wherein the processor implements the steps of the data diversion method according to any one of claims 1 to 4 when executing the computer program. 8. A processor-readable storage medium, characterized in that the processor-readable storage medium stores a computer program, and the computer program is used to enable the processor to execute the steps of the data diversion method according to any one of claims 1 to 4.