Cell load balancing method, device, equipment and computer storage medium

Abstract

The present application discloses a cell load balancing method, apparatus, device, and computer storage medium. The method includes obtaining a first load balancing coefficient based on the index information of a target cell; determining a target optimization strategy corresponding to the first load balancing coefficient based on the correspondence between the preset first load balancing coefficient and the optimization strategy when the first load balancing coefficient is outside a preset balancing coefficient interval; and performing balanced optimization on the target cell according to the target optimization strategy. The embodiment of the present application utilizes the first load balancing coefficient to reflect the load condition of the target cell, and can select a suitable target optimization strategy for a variety of different scenarios to perform balanced optimization on the target cell, and the flexibility of balanced optimization is good.

Description

Technical Field

[0001] The present application relates to the wireless communication industry, and more particularly to a cell load balancing method, apparatus, device, and computer storage medium. Background Art

[0002] As we all know, with the growth of user scale and traffic, networks are gradually evolving into multi-layer networks. To ensure a reasonable distribution of users, cell balancing optimization has become a key focus of daily optimization work. Existing balancing methods and algorithms are relatively simple and often fail to cope with increasingly diverse business scenarios. For example, the same balancing strategy is used for both moderately and severely unbalanced cell loads, resulting in limited flexibility in balancing optimization. Summary of the Invention

[0003] The embodiments of the present application provide a method, apparatus, device, and computer storage medium for cell load balancing to solve the technical problem of poor flexibility in load balancing optimization.

[0004] In a first aspect, an embodiment of the present application provides a cell load balancing method, the method comprising:

[0005] When the indicator information of the target cell is obtained, obtaining a first load balancing coefficient according to the indicator information;

[0006] When the first load balancing coefficient is outside the preset balancing coefficient interval, determining a target optimization strategy corresponding to the first load balancing coefficient according to a preset correspondence between the first load balancing coefficient and the optimization strategy;

[0007] According to the target optimization strategy, the target cell is balanced optimized.

[0008] In one embodiment, when the first load balancing coefficient is outside a preset balancing coefficient interval, determining a target optimization strategy corresponding to the first load balancing coefficient according to a preset correspondence between the first load balancing coefficient and the optimization strategy includes:

[0009] When the first load balancing coefficient is within a first coefficient interval, determining the target optimization strategy as a first adjustment strategy, where the first adjustment strategy is used to adjust the handover hysteresis and / or the inter-frequency handover start-up threshold of the target cell;

[0010] When the first load balancing coefficient is within a second coefficient interval, determining the target optimization strategy as a second adjustment strategy, where the second adjustment strategy is used to adjust the power of the target cell;

[0011] The first coefficient interval and the second coefficient interval are both obtained based on a preset first eigenvalue and a preset second eigenvalue, wherein the equalization coefficient interval is a partial interval between the interval formed by the first eigenvalue and the second eigenvalue, the first coefficient interval is the interval formed by the first eigenvalue and the second eigenvalue excluding the equalization coefficient interval, and the second coefficient interval is the interval excluding the interval formed by the first eigenvalue and the second eigenvalue.

[0012] In one embodiment, the indicator information includes operating parameters and parameter thresholds corresponding to the operating parameters;

[0013] The step of determining a target optimization strategy corresponding to the first load balancing coefficient according to a preset correspondence between the first load balancing coefficient and the optimization strategy when the first load balancing coefficient satisfies the balanced optimization condition includes:

[0014] When the first load balancing coefficient is outside a preset balancing coefficient interval, performing parameter correction on the corresponding operating parameter based on the parameter threshold to obtain a corrected operating parameter;

[0015] Obtaining a second load balancing coefficient according to the corrected operating parameters;

[0016] In the case that the second load balancing coefficient is outside the preset balancing coefficient interval, a target optimization strategy corresponding to the second load balancing coefficient is determined according to the corresponding relationship between the preset second load balancing coefficient and the optimization strategy.

[0017] In one embodiment, when the target optimization strategy is the first adjustment strategy, performing balanced optimization on the target cell according to the target optimization strategy includes:

[0018] When the first load balancing coefficient is within the first coefficient interval, performing a first iterative operation, wherein, in an i-th first iteration cycle of the first iterative operation, obtaining a switching hysteresis output in an i-1-th first iteration cycle, and adjusting the switching hysteresis using a preset first step length to obtain a third load balancing coefficient;

[0019] When the third load balancing coefficient is within the balancing coefficient interval, the first iterative operation is terminated.

[0020] In one embodiment, performing balanced optimization on the target cell according to the target optimization strategy further includes:

[0021] If the number of iterations of the first iterative operation is greater than a preset first threshold, performing a second iterative operation, wherein in the j-th second iterative cycle of the second iterative operation, obtaining the inter-frequency handover detection threshold output in the j-1-th second iterative cycle, and adjusting the inter-frequency handover detection threshold using a preset second step size to obtain a fourth load balancing coefficient;

[0022] When the fourth load balancing coefficient is within the balancing coefficient interval, the second iterative operation is terminated.

[0023] In one embodiment, when the target optimization strategy is the second adjustment strategy, performing balanced optimization on the target cell according to the target optimization strategy includes:

[0024] When the first load balancing coefficient is within the second coefficient interval, performing a third iterative operation, wherein, in a kth third iteration cycle of the third iterative operation, obtaining a power output in a k-1th third iteration cycle, and adjusting the power using a preset third step size to obtain a fifth load balancing coefficient;

[0025] When the fifth load balancing coefficient is within the balancing coefficient interval, the third iterative operation is terminated.

[0026] In one embodiment, the second coefficient interval includes a first sub-coefficient interval, wherein any value in the first sub-coefficient interval is greater than any value in the equalization coefficient interval,

[0027] The performing balanced optimization on the target cell according to the target optimization strategy further includes:

[0028] When the first load balancing coefficient is within the first sub-coefficient interval, performing a third iterative operation, wherein, in a k-th third iteration cycle of the third iterative operation, obtaining a power output during a k-1-th third iteration cycle, and reducing the power using a preset third step size to obtain a fifth load balancing coefficient;

[0029] When the fifth load balancing coefficient is within the first coefficient interval, the first iterative operation is performed.

[0030] In one embodiment, the indicator information includes rated power, the second coefficient interval includes a second sub-coefficient interval, wherein any value in the second sub-coefficient interval is smaller than any value in the equalization coefficient interval,

[0031] The performing balanced optimization on the target cell according to the target optimization strategy further includes:

[0032] When the first load balancing coefficient is within the second sub-coefficient interval, performing a third iterative operation, wherein, in a kth third iteration cycle of the third iterative operation, obtaining a power output in a k-1th third iteration cycle, and increasing the power using a preset third step size to obtain a fifth load balancing coefficient;

[0033] When the increased power is greater than or equal to the rated power, or when the fifth load balancing coefficient is within the first coefficient range, the first iterative operation is performed.

[0034] In one embodiment, in the kth third iteration cycle of the third iteration operation, obtaining the power output in the k-1th third iteration cycle, adjusting the power using a preset third step size to obtain a fifth load balancing coefficient, further includes:

[0035] Obtaining a real-time user number change range of the target cell in the kth third iteration cycle;

[0036] When the change range of the number of users is greater than a preset change range constant, adjusting the first eigenvalue and the second eigenvalue according to a preset fourth step size;

[0037] An updated first coefficient interval and an updated second coefficient interval are obtained according to the adjusted first eigenvalue and second eigenvalue.

[0038] In one embodiment, when the indicator information of the target cell is obtained, after obtaining the first load balancing coefficient according to the indicator information, the method further includes:

[0039] In the case of obtaining the first input, determining a third adjustment strategy matching the first input;

[0040] Performing balance optimization on the target cell according to the third adjustment strategy that matches the first input.

[0041] In one embodiment, the indicator information includes the target sector to which the target cell belongs, and the perceived rate and low channel quality ratio of each cell in the target sector.

[0042] The obtaining of a first load balancing coefficient according to the indicator information includes:

[0043] Acquire a first steady-state coefficient based on the perceived rate of the target cell, wherein the first steady-state coefficient indicates a fluctuation margin of the perceived rate of the target cell within a preset first time period;

[0044] Acquire a second steady-state coefficient based on the low channel quality ratio of the target cell and the low channel quality ratio of the cell with the worst perceived rate in the target sector, wherein the second steady-state coefficient indicates a discretization of the perceived rate in the target sector;

[0045] Obtaining a perception weight according to the first steady-state coefficient and the second steady-state coefficient;

[0046] A first load balancing coefficient is obtained according to the perception weight.

[0047] In one embodiment, the indicator information further includes the number of users in the target cell and the average number of users in the cells within the target sector.

[0048] The acquiring a first load balancing coefficient according to the perception weight includes:

[0049] When the perception weight is less than or equal to a preset second threshold, obtaining a first load balancing coefficient based on the number of users in the target cell and the average number of users in cells within the target sector;

[0050] When the perception weight is greater than the second threshold, a first load balancing coefficient is obtained based on the number of users and the perception weight of the target cell, and the average number of users and the average perception weight of cells in the target sector.

[0051] In one embodiment, the indicator information includes the target sector to which the target cell belongs, the number of users in the target cell, and the average number of users in cells within the target sector.

[0052] The obtaining of a first load balancing coefficient according to the indicator information includes:

[0053] Obtaining a predicted number of users of the target cell in a preset third time period according to the number of users of the target cell in a preset second time period;

[0054] Obtaining an average predicted number of users of the cell within the target sector in a preset third time period according to the average number of users of the cell within the target sector in a preset second time period;

[0055] A first load balancing coefficient is obtained based on the predicted number of users and the average predicted number of users.

[0056] In a second aspect, an embodiment of the present application provides a cell load balancing device, the device comprising:

[0057] an acquisition module, configured to obtain a first load balancing coefficient according to the indicator information of the target cell when the indicator information of the target cell is acquired;

[0058] a determination module configured to determine, when the first load balancing coefficient is outside a preset balancing coefficient interval, a target optimization strategy corresponding to the first load balancing coefficient based on a preset correspondence between the first load balancing coefficient and the optimization strategy;

[0059] A balancing module is used to perform balanced optimization on the target cell according to the target optimization strategy.

[0060] In a third aspect, an embodiment of the present application provides an electronic device, the device comprising:

[0061] A processor and a memory storing computer program instructions, wherein the processor implements the above method when executing the computer program instructions.

[0062] In a fourth aspect, an embodiment of the present application provides a computer storage medium having computer program instructions stored thereon, and the computer program instructions implement the above method when executed by a processor.

[0063] The cell load balancing method, apparatus, device, and computer storage medium provided in the embodiments of the present application can obtain a first load balancing coefficient based on indicator information. When the first load balancing coefficient is outside a preset balancing coefficient interval, the target optimization strategy corresponding to the first load balancing coefficient is determined based on the preset correspondence between the first load balancing coefficient and the optimization strategy, thereby performing balanced optimization on the target cell. The embodiments of the present application utilize the first load balancing coefficient to reflect the load condition of the target cell, and can select an appropriate target optimization strategy for a variety of different scenarios to perform balanced optimization on the target cell, thereby providing good flexibility in balanced optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0065] Figure 1 This is a flow chart of a cell load balancing method provided by an embodiment of the present application;

[0066] Figure 2 This is a flow chart of a cell load balancing identification provided by an embodiment of the present application;

[0067] Figure 3 This is a flowchart of another cell load balancing identification provided by an embodiment of the present application;

[0068] Figure 4 This is a schematic diagram of hierarchical balancing optimization provided by an embodiment of the present application;

[0069] Figure 5 is a schematic diagram of an equalization coefficient interval provided by an embodiment of the present application;

[0070] Figure 6 This is an overall flow chart of the balanced optimization provided by one embodiment of the present application;

[0071] Figure 7 This is a flow chart of a first adjustment strategy provided by an embodiment of the present application;

[0072] Figure 8 This is a flow chart of a second adjustment strategy provided by an embodiment of the present application;

[0073] Figure 9 is another flow chart of a second adjustment strategy provided by one embodiment of the present application;

[0074] Figure 10 is another flow chart of a second adjustment strategy provided by an embodiment of the present application;

[0075] Figure 11 This is an overall flow chart of a cell load balancing method provided by an embodiment of the present application;

[0076] Figure 12 is a structural diagram of a cell load balancing device provided by another embodiment of the present application;

[0077] Figure 13 This is a structural diagram of an electronic device provided in yet another embodiment of the present application. DETAILED DESCRIPTION

[0078] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0079] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0080] In order to solve the problems of the prior art, the embodiments of the present application provide a cell load balancing method, apparatus, device and computer storage medium.

[0081] Figure 1 FIG. 1 shows a flow chart of a cell load balancing method provided by an embodiment of the present application. Figure 1 As shown, the cell load balancing method includes:

[0082] Step S101: when the indicator information of the target cell is obtained, a first load balancing coefficient is obtained according to the indicator information;

[0083] Step S102: when the first load balancing coefficient is outside the preset balancing coefficient interval, determining a target optimization strategy corresponding to the first load balancing coefficient according to a preset correspondence between the first load balancing coefficient and the optimization strategy;

[0084] Step S103: performing balanced optimization on the target cell according to the target optimization strategy.

[0085] In this embodiment, the target cell may be a cell to be balanced and optimized, which may be one cell or multiple cells. The indicator information may include basic network information and operating parameters of the target cell, such as manufacturer information, latitude and longitude, frequency band, Common Gateway Interface (CGI) and sector number information, as well as handover hysteresis, inter-frequency handover start-up threshold, power, etc. The basic network information and operating parameters may be obtained by importing, or automatically obtained by sharing resources and exchanging information with other balanced optimization platform interfaces.

[0086] Obtaining the indicator information of the target cell may also include real-time import of the number of users in the target cell and other cells in the same sector as the target cell, the proportion of low channel quality (CQI) and perceived rate, etc., where the perceived rate is the most intuitive experience brought to users by the LTE network. For example, the throughput rate can be introduced as an indicator for evaluating the user's perceived rate, or other methods can be used as indicators of the perceived rate, which are not specifically limited here.

[0087] In step S101, a first load balancing coefficient is obtained based on the indicator information. For example, based on the network basic information and working parameters of the target cell, indicators such as the number of users, the proportion of low CQI and the perception rate can be obtained in real time, and then the first load balancing coefficient is obtained after judgment and calculation based on the indicator.

[0088] In step S102, the first load balancing coefficient is outside the preset balancing coefficient interval. Specifically, the balancing coefficient interval can be a preset range value. For example, a balancing threshold S can be preset, and the interval [1-S, 1+S] is used as the balancing coefficient interval. For the sake of simplicity, the following description takes the balancing coefficient interval as the interval [1-S, 1+S] as an example.

[0089] Among them, the value of the balancing threshold S can be selected according to actual conditions. For example, the empirical value 0.2 can be taken. At this time, the balancing coefficient interval can be [0.8, 1.2]. When the first load balancing coefficient is within [0.8, 1.2], the load state of the target cell is determined to be balanced, and there is no need to perform balancing optimization on the target cell.

[0090] On the contrary, if the first load balancing coefficient is outside the preset balancing coefficient range, it means that the load state of the target cell at this time is determined to be unbalanced, and the target cell needs to be balanced and optimized.

[0091] According to the correspondence between the preset first load balancing coefficient and the optimization strategy, the target optimization strategy corresponding to the first load balancing coefficient is determined. Specifically, the correspondence between the preset first load balancing coefficient and the optimization strategy can be based on different coefficient intervals in which the first load balancing coefficient is located, corresponding to different optimization strategies.

[0092] For example, when the first load balancing coefficient is in the severe imbalance coefficient interval, that is, the value of the first load balancing coefficient is relatively far away from the balance coefficient interval, at this time, an optimization strategy of adjusting power can be adopted to balance the target cell; and when the first load balancing coefficient is in the general imbalance coefficient interval, that is, the value of the first load balancing coefficient is relatively close to the balance coefficient interval, at this time, an optimization strategy of adjusting the switching hysteresis and / or the start-up threshold of the heterofrequency switching can be adopted to balance the target cell.

[0093] In addition, for example, an optimization strategy such as correcting the parameter threshold of the target cell's operating parameters can be adopted, or when other optimization strategies cannot automatically adjust the target cell to a balanced state, a manual adjustment optimization strategy can be adopted to perform balanced optimization on the target cell, etc.

[0094] In step S103, the target cell is balanced and optimized according to the target optimization strategy. Specifically, after determining the target optimization strategy corresponding to the first load balancing coefficient, the target cell can be balanced and optimized according to the target optimization strategy until the load state of the target cell is determined to be balanced, and the balanced optimization is stopped.

[0095] The cell load balancing method provided in the embodiment of the present application can obtain a first load balancing coefficient based on the indicator information. When the first load balancing coefficient is outside the preset balancing coefficient interval, the target optimization strategy corresponding to the first load balancing coefficient is determined based on the correspondence between the preset first load balancing coefficient and the optimization strategy, thereby performing balanced optimization on the target cell. The present application uses the first load balancing coefficient to reflect the load condition of the target cell, and can select a suitable target optimization strategy for a variety of different scenarios to perform balanced optimization on the target cell. The flexibility of balanced optimization is good, which helps to select a suitable optimization strategy for balanced optimization under different degrees of cell imbalance, effectively improving the efficiency of balanced optimization.

[0096] Optionally, in one embodiment, the indicator information includes the target sector to which the target cell belongs, and the perceived rate and low channel quality ratio of each cell in the target sector.

[0097] Step S101, obtaining a first load balancing coefficient according to indicator information, may include:

[0098] Based on the perceived rate of the target cell, obtaining a first steady-state coefficient, wherein the first steady-state coefficient indicates a fluctuation margin of the perceived rate of the target cell within a preset first time period;

[0099] Obtaining a second steady-state coefficient based on the low channel quality ratio of the target cell and the low channel quality ratio of the cell with the worst perceived rate in the target sector, wherein the second steady-state coefficient indicates a discretization of the perceived rate in the target sector;

[0100] Obtaining a perception weight according to the first steady-state coefficient and the second steady-state coefficient;

[0101] A first load balancing coefficient is obtained according to the perception weight.

[0102] In this embodiment, the indicator information includes the target sector to which the target cell belongs, as well as the perceived rate and low CQI ratio of each cell in the target sector. Based on the perceived rate of each cell, the cell with the worst perceived rate in the target sector and the low CQI ratio corresponding to the cell can be obtained.

[0103] Based on the perceived rate of the target cell, a first steady-state coefficient is obtained, wherein the first steady-state coefficient δ1 can be the fluctuation margin of the perceived rate of the target cell within a preset first time period, and is mainly used to eliminate the sharp fluctuations in the perceived rate caused by user behavior. Specifically, this embodiment can be based on minute-level data analysis and processing. Taking the first steady-state coefficient δ1 as the fluctuation margin of the perceived rate of the target cell within the first 5 minutes as an example, the calculation formula of the first steady-state coefficient δ1 can be:

[0104]

[0105] In this formula, a negative value of t means that G n_t is the perceived rate of the target cell at time t, where the time range in the formula can be selected according to actual conditions. The first steady-state coefficient δ1 of other cells in the sector can be obtained using the same calculation formula.

[0106] Based on the low channel quality ratio of the target cell and the low channel quality ratio of the cell with the worst perceived rate in the target sector, a second steady-state coefficient is obtained, wherein the second steady-state coefficient δ2 can be the ratio of the low CQI ratio of the target cell to the cell with the worst perceived rate in the sector, and is mainly used to eliminate the problem that the discretization of the perceived rate in the sector causes the equalization identification result to deviate from the expectation. Specifically, the calculation formula of the second steady-state coefficient δ2 can be:

[0107]

[0108] In this formula, J n_cqi is the proportion of low CQI in the target cell, J min_cqi is the low CQI ratio of the cell with the worst perceived rate in the sector. The second steady-state coefficient δ2 of other cells in the sector can be obtained by adopting the same calculation formula.

[0109] According to the first steady-state coefficient and the second steady-state coefficient, the perception weight is obtained. Specifically, the perception weight K of the target cell is n The calculation formula can be:

[0110]

[0111] In this formula, G n is the target cell sensing rate (can be the downlink sensing rate), G minis the perceived rate of the cell with the worst perception in the sector. The perception weights K of other cells in the sector can be obtained using the same calculation formula.

[0112] According to the perception weight, a first load balancing coefficient is obtained. Specifically, different balancing decision modes can be selected according to the value range of the perception weight of the target cell, that is, different calculation formulas can be selected to obtain the first load balancing coefficient.

[0113] For example, according to the perception weight of the target cell, it is possible to choose to calculate the first load balancing coefficient based on the number of users, or to calculate the first load balancing coefficient based on the number of users and the perception weight.

[0114] This embodiment stabilizes the perceived rate, eliminating volatility and discretization of the perceived rate using the first and second stabilization coefficients δ1 and δ2. Based on the perceived rate stabilization, a stable perceived weight is obtained, and the first load balancing coefficient is derived based on the stable perceived weight. This ensures the accuracy of the balance determination basis and enables more accurate identification of whether the target cell's load is in a balanced state. Based on the balance identification result, the balance of the number of users in the target cell is adjusted, allowing cells with good perceived rates to attract more users and cells with poor perceived rates to attract fewer users.

[0115] Optionally, in one embodiment, the indicator information further includes the number of users in the target cell and the average number of users in the cells within the target sector.

[0116] Obtaining a first load balancing coefficient according to the perception weight may include:

[0117] When the perception weight is less than or equal to a preset second threshold, obtaining a first load balancing coefficient based on the number of users in the target cell and the average number of users in cells within the target sector;

[0118] When the perception weight is greater than the second threshold, a first load balancing coefficient is obtained based on the number of users and the perception weight of the target cell and the average number of users and the average perception weight of cells in the target sector.

[0119] In one embodiment, the indicator information further includes the number of users in the target cell and the average number of users in the cells within the target sector.

[0120] Based on the relationship between the perception weight and the preset second threshold, different methods are selected to obtain the first load balancing coefficient. Specifically, the second threshold can be selected according to actual conditions. To facilitate understanding of the solution of this embodiment, the following description is based on an example in which the second threshold is 1.

[0121] like Figure 2As shown, when the perception weight is less than or equal to the preset second threshold, the first load balancing coefficient is obtained based on the number of users in the target cell and the average number of users in the cells within the target sector. n When ≤1, the load balancing decision based on the number of users is performed. The first load balancing coefficient B is only related to the number of users in other cells in the sector. The calculation formula of the first load balancing coefficient B can be:

[0122]

[0123] In this formula, is the average number of users in the cell within the sector, Y n The maximum number of users in the target cell.

[0124] like Figure 2 As shown in FIG, when the perception weight is greater than the second threshold, the first load balancing coefficient is obtained based on the number of users and the perception weight of the target cell, and the average number of users and the average perception weight of the cells in the target sector. Specifically, when K n When >1, the perception-based balancing decision is executed, and the calculation formula of the first load balancing coefficient B can be:

[0125]

[0126] In this formula, is the average number of users in the cell within the sector, Y n is the maximum number of users in the target cell, K avg is the average perception weight of the cells in the sector, K n is the sensing weight of the target cell.

[0127] In this embodiment, the value of the perception weight can be used to determine whether to make a balancing decision based on the number of users or the perception rate, so as to cope with increasingly diverse business scenarios and more accurately identify whether the target cell is in a balanced state, thereby improving the efficiency of cell load balancing optimization.

[0128] In one example, when the average number of users in a cell within a sector is When the average number of users in the cell is When , it is assumed that the target cell does not require equalization optimization.

[0129] Optionally, in one embodiment, the indicator information includes the target sector to which the target cell belongs, the number of users in the target cell, and the average number of users in the cells within the target sector.

[0130] Step S101, obtaining a first load balancing coefficient according to indicator information, may include:

[0131] Obtaining a predicted number of users of the target cell in a preset third time period based on the number of users of the target cell in the preset second time period;

[0132] Obtaining an average predicted number of users in the cell within the target sector in a preset third time period based on the average number of users in the cell within the target sector in a preset second time period;

[0133] A first load balancing coefficient is obtained based on the predicted number of users and the average predicted number of users.

[0134] In this embodiment, the indicator information includes the target sector to which the target cell belongs, the number of users in the target cell, and the average number of users in the target sector. Based on the user number data in the preset second time period, the user change trend in the preset third time period can be predicted using a mathematical model.

[0135] Specifically, this embodiment can be based on minute-level data analysis and processing. For example, the existing 5-minute user data can be used to predict the user change trend in the next 1 minute using the Autoregressive Integrated Moving Average Model (ARIMA).

[0136] The ARIMA model is constructed by fitting the trend calculations of the autoregressive (AR) model and the random calculations of the moving average (MA) model. The time-varying sequence of the forecast object is treated as a random series. The non-stationary series is then converted into a stationary series through differencing. The MA and AR models are then used together to simulate and analyze the changing values ​​and trends of this stationary series. By processing and understanding the sequence trends, methods such as parameter fitting can be used to predict future values ​​from historical data in the time series.

[0137] like Figure 3 As shown in the figure, before the ARIMA(p, d, q) model is used to calculate the observed variables, they are first stabilized, processed by order d, and then calculated by AP(p) and MA(q).

[0138] The AR(p) model is an autoregressive model that processes the time series and calculates the trend. The target variable is the nth-order lag variable of the analysis variable. The definition of the AR(p) model is as follows:

[0139] y t =θ0+θ1y t-1 +θ2y t-2 +…+f t

[0140] Among them, y t is the autoregressive target variable, {θ1, θ2, …, θ p} is called the autoregressive coefficient, θ0 is white noise, {y t-1 ,y t-2 ,…,y t-n} is the observed variable, f t is a constant.

[0141] The MA(q) model, also known as the Moving Average MA model, is constructed by averaging the historical forecast errors of a time series. The corresponding forecast errors are added to the previous set of forecast data to derive the current forecast data and forecast set, calculating the randomness. The MA(q) model is defined as follows:

[0142] e t =θ0-(θ1e t-1 +θ2e t-2 +…+θ q e t-q )

[0143] Among them, e t is the moving average target variable, {θ1, θ2, …, θ q} is called the moving average coefficient, θ0 is white noise, {e t-1 , e t-1 ,…,e t-q} is the observed variable.

[0144] According to the above definition, the MA model is substituted into the RA model through θ0, and the general form of the ARIMA (p, d, q) model is obtained as follows:

[0145] y t -θ1y t-1 -θ2y t-2 -…θ p y t-p =e t +θ1e t-1 +θ2e t-2 +…+θ q e t-q

[0146] The above formula shows that the left side of the equation represents the autoregressive component of the model, while the right side represents the moving average component. This model incorporates both trend and randomness. Here, p and q are the autoregressive term and the moving average order, respectively, and d is the order of differencing required during the stabilization process.

[0147] In this embodiment, based on the existing 5-minute user number data of the target cell and the existing 5-minute average user number data of the cells within the sector, the user change trend in the next 1 minute is predicted using the ARIMA (p, d, q) model, and the predicted number of users of the target cell in the next 1 minute and the average predicted number of users of the cells within the sector are obtained, thereby calculating the first load balancing coefficient of the target cell in the next 1 minute.

[0148] This embodiment uses the ARIMA model and minute-granularity indicators to predict the future trend of the number of users in advance, thereby preventing the possibility of high sudden risks in the scenario in advance, implementing balanced optimization, and effectively improving the efficiency of cell load balancing optimization.

[0149] Optionally, in one embodiment, the indicator information includes operating parameters and parameter thresholds corresponding to the operating parameters;

[0150] Step S102, when the first load balancing coefficient satisfies the balancing optimization condition, determines a target optimization strategy corresponding to the first load balancing coefficient according to a preset correspondence between the first load balancing coefficient and the optimization strategy, which may include:

[0151] When the first load balancing coefficient is outside the preset balancing coefficient interval, performing parameter correction on the corresponding working parameter based on the parameter threshold to obtain the corrected working parameter;

[0152] Obtaining a second load balancing coefficient according to the corrected working parameters;

[0153] When the second load balancing coefficient is outside the preset balancing coefficient interval, a target optimization strategy corresponding to the second load balancing coefficient is determined according to a preset correspondence between the second load balancing coefficient and the optimization strategy.

[0154] In this embodiment, the indicator information includes working parameters and parameter thresholds corresponding to the working parameters. For details, please refer to the following working parameter table:

[0155] Working parameter category Adjusting goals Parameter threshold Different frequency load balancing switch Service area Open Load balancing trigger mode Service area User number mode Load balancing trigger user number threshold Service area ≤45 (take the average of the maximum number of activated users in the sector) Load balancing user number bias Service area ≤5 Maximum number of users switched out by load balancing Service area ≥20 Load balancing user selection PRB threshold Service area ≥40 User number difference threshold Service area ≥5 Overlapping mark Neighboring cell yes Heterogeneous load balancing evaluation period Service area ≤10 Reference signal power Cells within a sector D≥E≥F(FDD1800) Switch Event Cells within a sector A3 Inter-frequency switching hysteresis Cells within a sector ≥4 Inter-frequency detection threshold Cells within a sector ≥-82

[0156] When the first load balancing coefficient is outside the preset balancing coefficient range, the corresponding operating parameter is corrected based on the parameter threshold to obtain the corrected operating parameter. Specifically, for a target cell with an unbalanced cell load, the parameter correction can be preferentially performed by combining the target cell's operating parameter with the corresponding parameter threshold.

[0157] As shown in the working parameter table, when the threshold value of a certain working parameter is within the parameter threshold range, the working parameter does not need to be adjusted; when the threshold value of a certain working parameter is outside the parameter threshold range, the working parameter can be adjusted according to the corresponding parameter threshold.

[0158] For example, if the inter-frequency load evaluation period threshold is ≤10, when the target cell threshold is 5, no adjustment is required; and when the target cell threshold is 30, the threshold can be adjusted to 10.

[0159] A second load balancing coefficient is obtained based on the corrected operating parameters. Specifically, after correcting the operating parameters of the target cell, indicator information corresponding to the target cell can be re-acquired. For example, the perceived rate, low CQI ratio, number of users, and average number of users of cells within the target sector can be re-acquired. Based on the re-acquired indicator information, the second load balancing coefficient can be calculated using the relevant calculation formula shown above. That is, the second load balancing coefficient can be used to reflect the load balancing status of the target cell after the parameter correction.

[0160] In an example, when the second load balancing coefficient is in the interval [1-S, 1+S], it can be indicated that the load of the target cell is already in a balanced state after parameter correction, and further balancing optimization of the target cell is not required.

[0161] When the second load balancing coefficient is outside the preset balancing coefficient interval, the target optimization strategy corresponding to the second load balancing coefficient is determined according to the corresponding relationship between the preset second load balancing coefficient and the optimization strategy. Figure 4 As shown, when it is determined that the load of the target cell is in an unbalanced state, parameter filtering (i.e., parameter correction) is first performed according to the parameter threshold standard to preliminarily achieve balanced optimization of the target cell. After parameter correction, the appropriate target optimization strategy is selected based on the correspondence between the second load balancing coefficient and the optimization strategy.

[0162] For example, a power adjustment optimization strategy can be employed to balance the target cell. Alternatively, a handover hysteresis and / or inter-frequency handover detection threshold can be adjusted to balance the target cell. Alternatively, a power adjustment optimization strategy can be employed first, followed by a handover hysteresis and / or inter-frequency handover detection threshold. The specific balanced optimization strategy and process are selected based on the actual load of the target cell.

[0163] This embodiment can first perform parameter correction optimization during the equalization optimization process, thereby realizing a method for hierarchical equalization optimization of the target cell. This method is highly flexible and adaptable, can cope with increasingly diverse business scenarios, and effectively improves the efficiency of equalization optimization.

[0164] Optionally, in one embodiment, in step S102, when the first load balancing coefficient is outside a preset balancing coefficient range, determining a target optimization strategy corresponding to the first load balancing coefficient based on a preset correspondence between the first load balancing coefficient and the optimization strategy may include:

[0165] When the first load balancing coefficient is within the first coefficient interval, the target optimization strategy is determined as a first adjustment strategy, where the first adjustment strategy is used to adjust the handover hysteresis and / or the inter-frequency handover start-up threshold of the target cell;

[0166] When the first load balancing coefficient is within the second coefficient interval, determining the target optimization strategy as a second adjustment strategy, the second adjustment strategy being used to adjust the power of the target cell;

[0167] The first coefficient interval and the second coefficient interval are both obtained based on the preset first eigenvalue and the preset second eigenvalue, wherein the equalization coefficient interval is the partial interval between the interval formed by the first eigenvalue and the second eigenvalue, the first coefficient interval is the interval formed by the first eigenvalue and the second eigenvalue excluding the equalization coefficient interval, and the second coefficient interval is the interval excluding the interval formed by the first eigenvalue and the second eigenvalue.

[0168] In this embodiment, the interval outside the preset equalization coefficient interval includes a first coefficient interval and a second coefficient interval. The first coefficient interval and the second coefficient interval are both obtained based on a preset first characteristic value and a preset second characteristic value. For example, Figure 5 As shown, an allocation threshold M can be preset, 1-SM can be used as the first characteristic value, and 1+S+M can be used as the second characteristic value.

[0169] The equalization coefficient interval can be a partial interval between the interval formed by the first eigenvalue and the second eigenvalue, that is, the interval [1-S, 1+S]; the first coefficient interval can be the interval formed by the first eigenvalue and the second eigenvalue excluding the equalization coefficient interval, that is, the interval [1-SM, 1-S) or the interval (1+S, 1+S+M]; the second coefficient interval can be the interval excluding the interval formed by the first eigenvalue and the second eigenvalue, that is, an interval less than (1-SM) or greater than (1+S+M).

[0170] To simplify the explanation, the following description takes the first coefficient interval as the interval [1-SM, 1-S) or the interval (1+S, 1+S+M], and the second coefficient interval as the interval less than (1-SM) or greater than (1+S+M) as an example.

[0171] Among them, the value of the allocation threshold M can be selected according to actual conditions, such as the empirical value 0.1. In this case, the first coefficient interval can be the interval [0.7, 0.8) or the interval (1.2, 1.3), and the second coefficient interval can be an interval less than 0.7 or greater than 1.3.

[0172] When the first load balancing coefficient is in the first coefficient interval, the target optimization strategy is determined as the first adjustment strategy, and the first adjustment strategy is used to adjust the switching hysteresis of the target cell and / or the start-up threshold of the heterofrequency switching. Specifically, when the first load balancing coefficient is in the interval [1-SM, 1-S) or the interval (1+S, 1+S+M], the first adjustment strategy can be used to perform balanced optimization on the target cell. Among them, the first adjustment strategy achieves balanced optimization of the target cell by iteratively optimizing the switching hysteresis (CellIndivOffset, CIO) and / or the start-up threshold of the heterofrequency switching between the target cells.

[0173] When the first load balancing coefficient is within the second coefficient range, the target optimization strategy is determined to be the second adjustment strategy, which is used to adjust the power of the target cell. Specifically, when the first load balancing coefficient is within the range less than (1-SM) or greater than (1+S+M), the second adjustment strategy can be used to balance and optimize the target cell. The second adjustment strategy iteratively adjusts the power of the target cell to achieve balanced optimization of the target cell.

[0174] This embodiment can reflect whether the target cell load is in a balanced state and the degree of load imbalance of the target cell according to the first load balancing coefficient, thereby selecting a suitable optimization strategy to perform balanced optimization on the target cell, which is more flexible and effectively improves the efficiency of balanced optimization.

[0175] In one example, if Figure 6 As shown, the target cell is started to be balanced and optimized. The parameters can be corrected first to obtain the second load balancing coefficient after the parameter correction. The second load balancing coefficient is used to determine whether the target cell after the parameter correction is balanced. If it is balanced, the balance optimization can be stopped. If it is still unbalanced, further optimization strategy can be selected according to the interval of the second load balancing coefficient.

[0176] Specifically, when the second load balancing coefficient is in an interval less than (1-SM) or greater than (1+S+M), the power of the target cell can be iteratively adjusted for balancing optimization. When the second load balancing coefficient is in the interval [1-SM, 1-S) or the interval (1+S, 1+S+M], the CIO and / or the start-up threshold of the heterogeneous frequency switching between the target cells can be iteratively adjusted for balancing optimization.

[0177] Optionally, in one embodiment, in step S101, after obtaining the indicator information of the target cell and obtaining the first load balancing coefficient according to the indicator information, the cell load balancing method may further include:

[0178] When the first input is obtained, determining a third adjustment strategy that matches the first input;

[0179] The target cell is balanced and optimized according to a third adjustment strategy that matches the first input.

[0180] In this embodiment, the first input may be information about operations performed by a relevant manager during the balancing optimization process for the target cell. Upon obtaining the first input, a third adjustment strategy matching the first input is determined. Specifically, an optimization strategy matching the operations performed by the relevant manager may be selected. That is, the third adjustment strategy is an optimization strategy derived from human operations.

[0181] The target cell is balanced and optimized according to a third adjustment strategy that matches the first input. Specifically, if the parameter correction, the first adjustment strategy, and the second adjustment strategy fail to achieve balanced optimization, the target cell may be faulty or the above optimization strategies may not be applicable to balanced optimization of the target cell. In this case, the third adjustment strategy, i.e., manual adjustment, may be used to balance optimize the target cell.

[0182] The specific adjustment process of the third adjustment strategy can be implemented using existing manual monitoring and optimization methods. For example, during the balancing optimization process, network element performance data, alarm data, and industrial parameter data are manually analyzed, and balancing optimization adjustments are made based on the analysis results and experience. This will not be further described here.

[0183] Optionally, in one embodiment, when the target optimization strategy is the first adjustment strategy, step S103, performing balanced optimization on the target cell according to the target optimization strategy, may include:

[0184] When the first load balancing coefficient is within the first coefficient interval, performing a first iterative operation, wherein, in an i-th first iteration cycle of the first iterative operation, obtaining a switching hysteresis outputted in an i-1-th first iteration cycle, and adjusting the switching hysteresis using a preset first step length to obtain a third load balancing coefficient;

[0185] When the third load balancing coefficient is within the balancing coefficient interval, the first iterative operation ends.

[0186] In this embodiment, the target optimization strategy may be a first adjustment strategy. Specifically, when the first load balancing coefficient is in the interval [1-SM, 1-S) or the interval (1+S, 1+S+M), a first iterative operation is performed to iteratively adjust the target cell CIO.

[0187] When the first load balancing coefficient is in the interval [1-SM, 1-S), it is a low-load interval; when the first load balancing coefficient is in the interval (1+S, 1+S+M], it is a high-load interval. Set a positive CIO for high-load objects to low-load objects, and set a negative CIO for low-load objects to high-load objects. For example, the first load balancing coefficient B of the high-load cell a is in the interval (1+S, 1+S+M), and the first load balancing coefficient B of the low-load cell b is in the interval [1-SM, 1-S). In this case, the initial CIO from a to b neighboring cells is set to 1, and the reverse CIO from b to a neighboring cell is set to -1.

[0188] The CIO between target cells is adjusted using a preset first step length. The first step length can be selected based on actual conditions, for example, 1 dB. In the i-th first iteration cycle, the CIO output in the (i-1)-th first iteration cycle is obtained. After adjusting the CIO using a step length of 1 dB, the target cell indicator information is re-obtained, where i is an integer greater than 1.

[0189] For example, the perceived rate, low CQI ratio, number of users, and average number of users of cells within the target sector can be re-obtained. Based on this re-obtained indicator information, the third load balancing coefficient can be calculated using the aforementioned calculation formula. This third load balancing coefficient can be used to reflect the load balancing status of the target cell after the CIO is adjusted.

[0190] If the third load balancing coefficient is within the balancing coefficient interval, the first iterative operation ends. Specifically, if the third load balancing coefficient is within the interval [1-S, 1+S], it indicates that the load of the target cell is already balanced after the CIO adjustment, and further balancing optimization of the target cell is not required. At this point, the first iterative operation ends.

[0191] Optionally, in one embodiment, step S103, performing balanced optimization on the target cell according to the target optimization strategy, may further include:

[0192] If the number of iterations of the first iterative operation is greater than a preset first threshold, a second iterative operation is performed, wherein in the j-th second iterative cycle of the second iterative operation, the inter-frequency handover detection threshold output in the j-1-th second iterative cycle is obtained, and the inter-frequency handover detection threshold is adjusted using a preset second step size to obtain a fourth load balancing coefficient;

[0193] When the fourth load balancing coefficient is within the balancing coefficient interval, the second iterative operation ends.

[0194] In this embodiment, when the number of iterations of the first iterative operation is greater than a preset first threshold, the second iterative operation is performed. Figure 7As shown, there is a limit on the number of times the CIO between target cells can be iteratively adjusted. The first threshold can be selected based on actual conditions, for example, 10 times. That is, if the number of iterations of the first iterative operation exceeds 10, the CIO adjustment can be stopped and a second iterative operation can be performed to iteratively adjust the detection threshold for inter-frequency handover of the target cell.

[0195] The target cell inter-frequency handover detection threshold is adjusted using a preset second step size, where the second step size can be selected based on actual conditions, for example, 1 dB. In the jth second iteration cycle, the inter-frequency handover detection threshold output in the j-1th second iteration cycle is obtained. After adjusting the inter-frequency handover detection threshold by a step size of 1 dB, the target cell indicator information is re-obtained, where j is an integer greater than 1.

[0196] For example, the perceived rate, low CQI ratio, number of users, and average number of users in the target sector for each cell within the target sector can be re-obtained. Based on this re-obtained indicator information, the fourth load balancing coefficient can be calculated using the relevant calculation formula shown above. This fourth load balancing coefficient can be used to reflect the load balancing status of the target cell after adjusting the inter-frequency handover initiation threshold.

[0197] If the fourth load balancing coefficient is within the balancing coefficient interval, the second iterative operation ends. Specifically, if the fourth load balancing coefficient is within the interval [1-S, 1+S], it indicates that the target cell's load is already balanced after adjusting the inter-frequency handover detection threshold, and further balancing optimization of the target cell is not required. At this point, the second iterative operation ends.

[0198] In this embodiment, when the number of iterations reaches the maximum number of adjustments, the adjustment object is changed in a timely manner, which can further improve the efficiency of the balanced optimization.

[0199] In one example, when the number of iterations of the first iterative operation is greater than a preset first threshold, a second iterative operation is performed, wherein the second iterative operation mainly iteratively adjusts the start-up threshold of the inter-frequency switching for the high-load object a, wherein the high-load object a can be one cell or multiple cells, and the start-up threshold of the inter-frequency switching can be raised in steps of 1dB.

[0200] If the inter-frequency handover detection threshold reaches the threshold, for example, if the inter-frequency handover detection threshold is ≥ -65, and the fourth load balancing coefficient is still outside the balancing coefficient interval, the second iteration is terminated. This indicates that the target cell load is still unbalanced, which may indicate a target cell failure or that the above optimization strategy is not suitable for balancing optimization of the target cell. In this case, the third adjustment strategy, namely manual adjustment, can be used to continue balancing optimization of the target cell.

[0201] Optionally, in one embodiment, when the target optimization strategy is the second adjustment strategy, step S103, performing balanced optimization on the target cell according to the target optimization strategy, may include:

[0202] When the first load balancing coefficient is within the second coefficient interval, performing a third iterative operation, wherein, in a kth third iteration cycle of the third iterative operation, obtaining the power output in the k-1th third iteration cycle, and adjusting the power using a preset third step size to obtain a fifth load balancing coefficient;

[0203] When the fifth load balancing coefficient is within the balancing coefficient interval, the third iterative operation is terminated.

[0204] In this embodiment, the target optimization strategy may be the second adjustment strategy. Specifically, when the first load balancing coefficient is less than (1-SM) or greater than (1+S+M), a third iterative operation is performed to iteratively adjust the power between the target cells.

[0205] like Figure 8 As shown, when the first load balancing coefficient is in the interval greater than (1+S+M), the number of users in the target cell is significantly higher than that of other cells in the same sector. At this time, the power can be lowered to reduce the coverage range; when the first load balancing coefficient is in the interval less than (1-SM), the number of users in the target cell is significantly lower than that of other cells in the same sector. At this time, the power can be increased to increase the coverage range.

[0206] The power between the target cells is adjusted using a preset third step size, where the value of the third step size can be selected based on actual conditions, for example, 1 dB. In the kth third iteration period, the power output in the k-1th third iteration period is obtained, the power is adjusted according to the step size of 1 dB, and then the indicator information of the target cell is obtained again, where k is an integer greater than 1.

[0207] For example, the perceived rate, low CQI ratio, number of users, and average number of users of cells within the target sector can be re-obtained. Based on this re-obtained indicator information, the fifth load balancing coefficient can be calculated using the aforementioned calculation formula. This fifth load balancing coefficient can be used to reflect the load balancing status of the target cell after power adjustment.

[0208] If the fifth load balancing coefficient is within the balancing coefficient interval, the third iterative operation ends. Specifically, if the fifth load balancing coefficient is within the interval [1-S, 1+S], it indicates that the load of the target cell is already balanced after power adjustment, and further balancing optimization of the target cell is not required. At this point, the third iterative operation ends.

[0209] Optionally, in one embodiment, the second coefficient interval includes a first sub-coefficient interval, wherein any value in the first sub-coefficient interval is greater than any value in the equalization coefficient interval,

[0210] Step S103, performing balanced optimization on the target cell according to the target optimization strategy, may also include:

[0211] When the first load balancing coefficient is within the first sub-coefficient interval, performing a third iterative operation, wherein, in a k-th third iteration cycle of the third iterative operation, obtaining the power output in the k-1-th third iteration cycle, and reducing the power using a preset third step size to obtain a fifth load balancing coefficient;

[0212] When the fifth load balancing coefficient is within the first coefficient interval, a first iterative operation is performed.

[0213] In this embodiment, the second coefficient interval includes a first sub-coefficient interval, wherein any value in the first sub-coefficient interval is greater than any value in the balancing coefficient interval. Specifically, the first sub-coefficient interval may be an interval greater than (1+S+M). When the first load balancing coefficient is greater than (1+S+M), the power between the target cells is iteratively adjusted downward using a preset third step size.

[0214] A fifth load balancing coefficient after power reduction is obtained, and when the fifth load balancing coefficient is within the first coefficient interval, a first iterative operation is performed. Specifically, when the fifth load balancing coefficient is within the interval (1+S, 1+S+M]), the fifth load balancing coefficient satisfies the corresponding relationship with the first adjustment strategy, and the first adjustment strategy can be used to continue balancing optimization of the target cell, i.e., the first iterative operation is performed.

[0215] In one example, when the number of iterative power reduction operations reaches a preset iteration threshold, the iterative power reduction operation is terminated. For example, the iteration threshold may be 6. If the number of iterative power reduction operations reaches 6 and the fifth load balancing coefficient is still outside the balancing coefficient interval, the third iteration operation is terminated.

[0216] In this case, it can be explained that the load of the target cell is still in an unbalanced state. There may be a problem with the target cell failure, or the above optimization strategy is not suitable for balanced optimization of the target cell. At this time, the third adjustment strategy, that is, manual adjustment, can be adopted to continue balanced optimization of the target cell.

[0217] Optionally, in one embodiment, the indicator information includes rated power, the second coefficient interval includes a second sub-coefficient interval, wherein any value in the second sub-coefficient interval is smaller than any value in the equalization coefficient interval,

[0218] Step S103, performing balanced optimization on the target cell according to the target optimization strategy, may also include:

[0219] When the first load balancing coefficient is within the second sub-coefficient interval, performing a third iterative operation, wherein, in a k-th third iteration cycle of the third iterative operation, obtaining the power output during the k-1-th third iteration cycle, and increasing the power using a preset third step size to obtain a fifth load balancing coefficient;

[0220] When the increased power is greater than or equal to the rated power, or the fifth load balancing coefficient is within the first coefficient interval, the first iterative operation is performed.

[0221] In this embodiment, the second coefficient interval includes a second sub-coefficient interval, wherein any value in the second sub-coefficient interval is smaller than any value in the balancing coefficient interval. Specifically, the first sub-coefficient interval may be an interval smaller than (1-SM). When the first load balancing coefficient is within an interval smaller than (1-SM), the power between the target cells is iteratively increased using a preset third step size.

[0222] A fifth load balancing coefficient after the power is increased is obtained, and when the fifth load balancing coefficient is within the first coefficient interval, a first iterative operation is performed. Specifically, when the fifth load balancing coefficient is within the interval (1+S, 1+S+M]), the fifth load balancing coefficient satisfies the corresponding relationship with the first adjustment strategy, and the first adjustment strategy can be used to continue balancing optimization of the target cell, that is, the first iterative operation is performed.

[0223] Alternatively, when the increased power is greater than or equal to the rated power, the first iterative operation is performed. Specifically, the rated power can be determined based on the indicator information of the target cell, or can be set based on actual conditions, and is not specifically limited here. In the kth third iteration cycle of the third iterative operation, the power is increased using a preset third step size. When the power setting value reaches the rated power, the first adjustment strategy can be used to continue to perform balanced optimization on the target cell, that is, the first iterative operation is performed.

[0224] Optionally, in one embodiment, in the kth third iteration cycle of the third iteration operation, obtaining the power output in the k-1th third iteration cycle, adjusting the power using a preset third step size to obtain a fifth load balancing coefficient may also include:

[0225] Obtaining a real-time user number change range of the target cell in the kth third iteration cycle;

[0226] When the change range of the number of users is greater than the preset change range constant, adjusting the first eigenvalue and the second eigenvalue according to a preset fourth step size;

[0227] An updated first coefficient interval and an updated second coefficient interval are obtained according to the adjusted first eigenvalue and second eigenvalue.

[0228] In this embodiment, if Figure 9 、 Figure 10 As shown, the corresponding target optimization strategy is the second adjustment strategy, which can compare the user number change range fp before and after power adjustment with the preset change range constant f.

[0229] The magnitude of the change in the real-time number of users in the target cell is obtained in the k-th third iteration cycle. Specifically, in the k-th third iteration cycle of the third iterative operation, the power output in the k-1-th third iteration cycle can be obtained, the power can be adjusted using a preset third step size, and the number of users in the target cell after the power adjustment can be re-obtained. The number of users is compared with the number of users in the k-1-th third iteration cycle, thereby obtaining the magnitude of the change in the number of users.

[0230] When the change in the number of users is greater than a preset change constant, the first eigenvalue and the second eigenvalue are adjusted according to a preset fourth step size. The specific preset change constant f can be selected based on actual conditions, for example, f can be 20%. The fourth step size can also be selected based on actual conditions, for example, the fourth step size can be 0.1 dB.

[0231] To facilitate understanding of the solution in this embodiment, an example is given in which the first eigenvalue is 1-SM and the second eigenvalue is 1+S+M.

[0232] When fp>f, the number of users changes significantly. In this case, increasing the M threshold reduces the possibility of adjusting power and increases the probability of adjusting CIO. The calculation formula for the M threshold can be:

[0233] M=M0+n*d

[0234] In this formula, n is the number of times fp>f after power adjustment, f is a constant of the change amplitude, which can be 20%, d is the fourth step length, which can be 0.1dB, and M0 is the initial allocation threshold, which can be 0.1.

[0235] When fp≤f, the current M threshold is maintained.

[0236] This embodiment takes advantage of the quick effect of power adjustment. At the same time, by dynamically adjusting the allocation threshold after power adjustment and updating the first coefficient interval and the second coefficient interval, the number of power adjustments is effectively controlled to avoid drastic changes in the network structure caused by over-adjustment, which may cause network instability.

[0237] like Figure 11As shown, in one embodiment, by correcting the user population balance based on the steady-state perceived rate, cells with good perceived rates can accommodate more users, while cells with poor perceived rates can accommodate fewer users. The perceived rate stabilization process eliminates perceived rate volatility and discretization using the first steady-state coefficient δ1 and the second steady-state coefficient δ2, resulting in a stable perceived weight K (i.e., the perceived weight is obtained based on the first steady-state coefficient and the second steady-state coefficient).

[0238] When Kn>1, the perception-based balancing identification is triggered (i.e., when the perception weight is greater than the second threshold, the first load balancing coefficient is obtained based on the number of users and the perception weight of the target cell, and the average number of users and the average perception weight of the cells in the target sector); when Kn≤1, the user number-based balancing identification is triggered (i.e., when the perception weight is less than or equal to the preset second threshold, the first load balancing coefficient is obtained based on the number of users in the target cell and the average number of users in the cells in the target sector).

[0239] Different from the existing technology that mainly identifies whether the balance is based on the number of users and throughput, which is easy to deviate from the actual user perception, this embodiment introduces a perception rate indicator to trigger the balance decision, adapt to complex network environments, and achieve optimal user perception.

[0240] By setting up a three-level adjustment mechanism, the first level is parameter correction, which quickly traverses and adjusts parameters through parameter filtering (that is, based on the parameter threshold, the corresponding working parameters are corrected); the second level is coarse adjustment, which optimizes the power parameters and significantly adjusts the user distribution (that is, when the first load balancing coefficient is in the second coefficient interval, the target optimization strategy is determined as the second adjustment strategy, and the second adjustment strategy is used to adjust the power of the target cell); the third level is fine adjustment, which optimizes the switching parameters, fine-tunes the user distribution, and achieves improved user perception (that is, when the first load balancing coefficient is in the first coefficient interval, the target optimization strategy is determined as the first adjustment strategy, and the first adjustment strategy is used to adjust the switching delay and / or the start-up threshold of the frequency switching of the target cell).

[0241] Dynamic "power / handover" allocation thresholds are set between levels two and three, enabling rapid and precise cyclical adjustments based on power and handover. Based on the initial threshold, the "power / handover" threshold dynamically adjusts the allocation threshold based on the fluctuation in the number of users (i.e., if the change in the number of users exceeds a preset constant, the first and second eigenvalues ​​are adjusted according to the preset fourth step size). This improves automatic optimization efficiency, avoids over-adjustment, and enhances automatic optimization adaptability.

[0242] By collecting minute-level indicators and then using background programs for automatic analysis and modification, the process from discovering an imbalance problem to completing the modification can be completed in as little as 70 seconds.

[0243] The number of users in each scenario fluctuates rapidly. In some areas with high sudden user numbers, the efficiency of the automatic optimization process is still difficult to meet. Therefore, an ARIMA model is introduced on the basis of the self-optimization process. By predicting the trend of user number changes in advance (that is, based on the number of users in the target cell in the preset second time period, the predicted number of users in the target cell in the preset third time period is obtained; based on the average number of users in the cells in the target sector in the preset second time period, the average predicted number of users in the cells in the target sector in the preset third time period is obtained), and then combined with the automatic optimization algorithm, adjustments are made in advance to prevent high sudden risks in the scenario in advance.

[0244] For example, by using the user number change area in the previous 5 minutes, the user changes in the next 1 minute can be predicted. The fastest time from discovering a user emergency problem to completing the modification is 10 seconds. This embodiment can achieve efficient resolution of perception and user emergency problems.

[0245] Figure 12 A structural diagram of a cell load balancing device provided in another embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0246] Reference Figure 12 , the cell load balancing device includes:

[0247] The acquisition module 1201 may be configured to obtain a first load balancing coefficient according to the indicator information when the indicator information of the target cell is acquired;

[0248] The determination module 1202 may be configured to determine a target optimization strategy corresponding to the first load balancing coefficient based on a preset correspondence between the first load balancing coefficient and the optimization strategy when the first load balancing coefficient is outside a preset balancing coefficient interval;

[0249] The balancing module 1203 may be configured to perform balancing optimization on the target cell according to the target optimization strategy.

[0250] Optionally, the determination module 1202 may be specifically configured to:

[0251] When the first load balancing coefficient is within the first coefficient interval, the target optimization strategy is determined as a first adjustment strategy, where the first adjustment strategy is used to adjust the handover hysteresis and / or the inter-frequency handover start-up threshold of the target cell;

[0252] When the first load balancing coefficient is within the second coefficient interval, determining the target optimization strategy as a second adjustment strategy, the second adjustment strategy being used to adjust the power of the target cell;

[0253] The first coefficient interval and the second coefficient interval are both obtained based on the preset first eigenvalue and the preset second eigenvalue, wherein the equalization coefficient interval is the partial interval between the interval formed by the first eigenvalue and the second eigenvalue, the first coefficient interval is the interval formed by the first eigenvalue and the second eigenvalue excluding the equalization coefficient interval, and the second coefficient interval is the interval excluding the interval formed by the first eigenvalue and the second eigenvalue.

[0254] Optionally, the indicator information includes operating parameters and parameter thresholds corresponding to the operating parameters. The determining module 1202 may include:

[0255] The correction unit may be configured to perform parameter correction on the corresponding operating parameter based on the parameter threshold when the first load balancing coefficient is outside the preset balancing coefficient interval to obtain the corrected operating parameter;

[0256] The first acquisition unit may be configured to obtain a second load balancing coefficient according to the corrected working parameters;

[0257] The first determining unit may be configured to determine a target optimization strategy corresponding to the second load balancing coefficient according to a preset correspondence between the second load balancing coefficient and the optimization strategy when the second load balancing coefficient is outside a preset balancing coefficient interval.

[0258] Optionally, when the target optimization strategy is the first adjustment strategy, the balancing module 1203 may include:

[0259] The first iteration unit may be configured to perform a first iterative operation when the first load balancing coefficient is within a first coefficient interval, wherein, in an i-th first iteration cycle of the first iterative operation, a switching hysteresis outputted in an i-1-th first iteration cycle is obtained, and the switching hysteresis is adjusted using a preset first step length to obtain a third load balancing coefficient;

[0260] The first balancing unit may be configured to terminate the first iterative operation when the third load balancing coefficient is within the balancing coefficient interval.

[0261] Optionally, the balancing module 1203 may further include:

[0262] The second iterative unit may be configured to perform a second iterative operation if the number of iterations of the first iterative operation is greater than a preset first threshold, wherein, in a j-th second iterative cycle of the second iterative operation, the second iterative unit obtains an inter-frequency handover detection threshold outputted in a j-1-th second iterative cycle, and adjusts the inter-frequency handover detection threshold using a preset second step size to obtain a fourth load balancing coefficient;

[0263] The first balancing unit may be configured to terminate the second iterative operation when the fourth load balancing coefficient is within the balancing coefficient interval.

[0264] Optionally, when the target optimization strategy is the second adjustment strategy, the balancing module 1203 may include:

[0265] The third iterative unit may be configured to perform a third iterative operation when the first load balancing coefficient is within the second coefficient interval, wherein, in a kth third iteration cycle of the third iterative operation, the power output in the k-1th third iteration cycle is obtained, and the power is adjusted using a preset third step size to obtain a fifth load balancing coefficient;

[0266] The third balancing unit may be configured to terminate the third iterative operation when the fifth load balancing coefficient is within the balancing coefficient interval.

[0267] Optionally, the second coefficient interval includes a first sub-coefficient interval, wherein any value in the first sub-coefficient interval is greater than any value in the equalization coefficient interval. The equalization module 1203 may further include:

[0268] The fourth iterative unit may be configured to perform a third iterative operation when the first load balancing coefficient is within the first sub-coefficient interval, wherein, in a k-th third iteration cycle of the third iterative operation, the power output in the k-1-th third iteration cycle is obtained, and the power is reduced using a preset third step size to obtain a fifth load balancing coefficient;

[0269] The fourth balancing unit may be configured to perform a first iterative operation when the fifth load balancing coefficient is within the first coefficient interval.

[0270] Optionally, the indicator information includes rated power, the second coefficient interval includes a second sub-coefficient interval, wherein any value in the second sub-coefficient interval is smaller than any value in the balancing coefficient interval, and the balancing module 1203 may further include:

[0271] The fifth balancing unit may be configured to perform a third iterative operation when the first load balancing coefficient is within the second sub-coefficient interval, wherein, in a k-th third iteration cycle of the third iterative operation, the power output during the k-1-th third iteration cycle is obtained, and the power is increased using a preset third step size to obtain a fifth load balancing coefficient.

[0272] The fifth balancing unit may be configured to perform a first iterative operation when the increased power is greater than or equal to the rated power, or when the fifth load balancing coefficient is within the first coefficient interval.

[0273] Optionally, the third iteration unit may further include:

[0274] The second acquisition unit may be configured to acquire a real-time user number change range of the target cell in the kth third iteration cycle;

[0275] The adjusting unit may be configured to adjust the first eigenvalue and the second eigenvalue according to a preset fourth step size when the change range of the number of users is greater than a preset change range constant;

[0276] The updating unit may be configured to obtain an updated first coefficient interval and an updated second coefficient interval based on the adjusted first eigenvalue and the adjusted second eigenvalue.

[0277] Optionally, the device may further include:

[0278] A third acquisition unit may be configured to determine a third adjustment strategy matching the first input when the first input is acquired;

[0279] The second determining unit may be configured to perform balanced optimization on the target cell according to a third adjustment strategy that matches the first input.

[0280] Optionally, the indicator information includes a target sector to which the target cell belongs, and a sensed rate and a proportion of low channel quality of each cell in the target sector. The acquisition module 1201 may include:

[0281] The fourth acquisition unit may be configured to acquire a first steady-state coefficient based on the perceived rate of the target cell, wherein the first steady-state coefficient indicates a fluctuation margin of the perceived rate of the target cell within a preset first time period;

[0282] A fifth acquisition unit may be configured to acquire a second steady-state coefficient based on the low channel quality ratio of the target cell and the low channel quality ratio of the cell with the worst perceived rate in the target sector, wherein the second steady-state coefficient indicates a discretization of the perceived rate in the target sector;

[0283] a sixth obtaining unit, configured to obtain a perception weight according to the first steady-state coefficient and the second steady-state coefficient;

[0284] The seventh acquisition unit can be used to acquire the first load balancing coefficient according to the perception weight.

[0285] Optionally, the indicator information further includes the number of users in the target cell and the average number of users in the cell within the target sector. The seventh acquisition unit may be specifically configured to:

[0286] When the perception weight is less than or equal to a preset second threshold, obtaining a first load balancing coefficient based on the number of users in the target cell and the average number of users in cells within the target sector;

[0287] When the perception weight is greater than the second threshold, a first load balancing coefficient is obtained based on the number of users and the perception weight of the target cell and the average number of users and the average perception weight of cells in the target sector.

[0288] Optionally, the indicator information includes the target sector to which the target cell belongs, the number of users in the target cell, and the average number of users in the cell within the target sector. Optionally, the acquisition module 1201 may include:

[0289] An eighth acquiring unit may be configured to acquire a predicted number of users of the target cell in a preset third time period based on the number of users of the target cell in a preset second time period;

[0290] The ninth obtaining unit may be configured to obtain an average predicted number of users of the cells in the target sector in a preset third time period based on an average number of users of the cells in the target sector in a preset second time period;

[0291] The tenth obtaining unit may be configured to obtain a first load balancing coefficient based on the predicted number of users and the average predicted number of users.

[0292] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application, and are devices corresponding to the above-mentioned cell load balancing method. All implementation methods in the above-mentioned method embodiment are applicable to the embodiment of the device. Its specific functions and the technical effects brought about can be found in the method embodiment part, which will not be repeated here.

[0293] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0294] Figure 13 A schematic diagram of the hardware structure of an electronic device provided in yet another embodiment of the present application is shown.

[0295] The device may include a processor 1301 and a memory 1302 storing computer program instructions.

[0296] When the processor 1301 executes the computer program, the steps in any of the above method embodiments are implemented.

[0297] For example, the computer program may be divided into one or more modules / units, one or more of which are stored in the memory 1302 and executed by the processor 1301 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the device.

[0298] Specifically, the processor 1301 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0299] Memory 1302 may include a large capacity memory for data or instructions. By way of example and not limitation, memory 1302 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1302 may include removable or non-removable (or fixed) media. Where appropriate, memory 1302 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, memory 1302 is a non-volatile solid-state memory.

[0300] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0301] The processor 1301 implements any one of the methods in the above embodiments by reading and executing computer program instructions stored in the memory 1302 .

[0302] In one example, the electronic device may further include a communication interface 1303 and a bus 1310. The processor 1301, the memory 1302, and the communication interface 1303 are connected via the bus 1310 and communicate with each other.

[0303] The communication interface 1303 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0304] Bus 1310 includes hardware, software or both, and the components of online data flow metering equipment are coupled to each other. For example, and not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 1310 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0305] In addition, in combination with the methods in the above embodiments, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the methods in the above embodiments is implemented.

[0306] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0307] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), suitable firmware, a plug-in unit, a function card or the like. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, or the like. The code segment can be downloaded via a computer grid such as the Internet, an intranet, or the like.

[0308] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0309] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0310] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A cell load balancing method, characterized in that: include: When the indicator information of the target cell is obtained, obtaining a first load balancing coefficient according to the indicator information; When the first load balancing coefficient is outside the preset balancing coefficient interval, determining a target optimization strategy corresponding to the first load balancing coefficient according to a preset correspondence between the first load balancing coefficient and the optimization strategy; performing balanced optimization on the target cell according to the target optimization strategy; When the first load balancing coefficient is outside a preset balancing coefficient interval, determining a target optimization strategy corresponding to the first load balancing coefficient according to a preset correspondence between the first load balancing coefficient and the optimization strategy includes: When the first load balancing coefficient is within a first coefficient interval, determining the target optimization strategy as a first adjustment strategy, where the first adjustment strategy is used to adjust the handover hysteresis and / or the inter-frequency handover start-up threshold of the target cell; When the first load balancing coefficient is within a second coefficient interval, determining the target optimization strategy as a second adjustment strategy, where the second adjustment strategy is used to adjust the power of the target cell; The first coefficient interval and the second coefficient interval are both obtained based on a preset first eigenvalue and a preset second eigenvalue, wherein the equalization coefficient interval is a partial interval between an interval formed by the first eigenvalue and the second eigenvalue, the first coefficient interval is an interval formed by the first eigenvalue and the second eigenvalue excluding the equalization coefficient interval, and the second coefficient interval is an interval excluding an interval formed by the first eigenvalue and the second eigenvalue; The indicator information includes a target sector to which the target cell belongs, and a perceived rate and a proportion of low channel quality of each cell in the target sector. Obtaining a first load balancing coefficient according to the indicator information includes: Acquire a first steady-state coefficient based on the perceived rate of the target cell, wherein the first steady-state coefficient indicates a fluctuation margin of the perceived rate of the target cell within a preset first time period; Acquire a second steady-state coefficient based on the low channel quality ratio of the target cell and the low channel quality ratio of the cell with the worst perceived rate in the target sector, wherein the second steady-state coefficient indicates a discretization of the perceived rate in the target sector; Obtaining a perception weight according to the first steady-state coefficient and the second steady-state coefficient; A first load balancing coefficient is obtained according to the perception weight.

2. The method according to claim 1, characterized in that The indicator information includes operating parameters and parameter thresholds corresponding to the operating parameters. The step of determining a target optimization strategy corresponding to the first load balancing coefficient according to a preset correspondence between the first load balancing coefficient and the optimization strategy when the first load balancing coefficient satisfies the balanced optimization condition includes: When the first load balancing coefficient is outside a preset balancing coefficient interval, performing parameter correction on the corresponding operating parameter based on the parameter threshold to obtain a corrected operating parameter; Obtaining a second load balancing coefficient according to the corrected operating parameters; In the case that the second load balancing coefficient is outside the preset balancing coefficient interval, a target optimization strategy corresponding to the second load balancing coefficient is determined according to the corresponding relationship between the preset second load balancing coefficient and the optimization strategy.

3. The method according to claim 1, characterized in that When the target optimization strategy is the first adjustment strategy, performing balanced optimization on the target cell according to the target optimization strategy includes: When the first load balancing coefficient is within the first coefficient interval, performing a first iterative operation, wherein, in an i-th first iteration cycle of the first iterative operation, obtaining a switching hysteresis output in an i-1-th first iteration cycle, and adjusting the switching hysteresis using a preset first step length to obtain a third load balancing coefficient; When the third load balancing coefficient is within the balancing coefficient interval, the first iterative operation is terminated.

4. The method according to claim 3, characterized in that The performing balanced optimization on the target cell according to the target optimization strategy further includes: If the number of iterations of the first iterative operation is greater than a preset first threshold, performing a second iterative operation, wherein in the j-th second iterative cycle of the second iterative operation, obtaining the inter-frequency handover detection threshold output in the j-1-th second iterative cycle, and adjusting the inter-frequency handover detection threshold using a preset second step size to obtain a fourth load balancing coefficient; When the fourth load balancing coefficient is within the balancing coefficient interval, the second iterative operation is terminated.

5. The method according to claim 4, characterized in that When the target optimization strategy is the second adjustment strategy, performing balanced optimization on the target cell according to the target optimization strategy includes: When the first load balancing coefficient is within the second coefficient interval, performing a third iterative operation, wherein, in a kth third iteration cycle of the third iterative operation, obtaining a power output in a k-1th third iteration cycle, and adjusting the power using a preset third step size to obtain a fifth load balancing coefficient; When the fifth load balancing coefficient is within the balancing coefficient interval, the third iterative operation is terminated.

6. The method according to claim 5, characterized in that The second coefficient interval includes a first sub-coefficient interval, wherein any value in the first sub-coefficient interval is greater than any value in the equalization coefficient interval, The performing balanced optimization on the target cell according to the target optimization strategy further includes: When the first load balancing coefficient is within the first sub-coefficient interval, performing a third iterative operation, wherein, in a k-th third iteration cycle of the third iterative operation, obtaining a power output during a k-1-th third iteration cycle, and reducing the power using a preset third step size to obtain a fifth load balancing coefficient; When the fifth load balancing coefficient is within the first coefficient interval, the first iterative operation is performed.

7. The method according to claim 5, characterized in that The indicator information includes rated power, the second coefficient interval includes a second sub-coefficient interval, wherein any value in the second sub-coefficient interval is smaller than any value in the equalization coefficient interval, The performing balanced optimization on the target cell according to the target optimization strategy further includes: When the first load balancing coefficient is within the second sub-coefficient interval, performing a third iterative operation, wherein, in a kth third iteration cycle of the third iterative operation, obtaining a power output in a k-1th third iteration cycle, and increasing the power using a preset third step size to obtain a fifth load balancing coefficient; When the increased power is greater than or equal to the rated power, or when the fifth load balancing coefficient is within the first coefficient range, the first iterative operation is performed.

8. The method according to any one of claims 5 to 7, characterized in that The method further includes: obtaining, in the kth third iteration cycle of the third iteration operation, the power output in the k-1th third iteration cycle, and adjusting the power using a preset third step size to obtain a fifth load balancing coefficient. Obtaining a real-time user number change range of the target cell in the kth third iteration cycle; When the change range of the number of users is greater than a preset change range constant, adjusting the first eigenvalue and the second eigenvalue according to a preset fourth step size; An updated first coefficient interval and an updated second coefficient interval are obtained according to the adjusted first eigenvalue and second eigenvalue.

9. The method according to claim 1, characterized in that In the case where the indicator information of the target cell is acquired, after obtaining the first load balancing coefficient according to the indicator information, the method further includes: In the case of obtaining the first input, determining a third adjustment strategy matching the first input; Performing balance optimization on the target cell according to the third adjustment strategy that matches the first input.

10. The method according to claim 1, characterized in that The indicator information also includes the number of users in the target cell and the average number of users in the cells within the target sector. The acquiring a first load balancing coefficient according to the perception weight includes: When the perception weight is less than or equal to a preset second threshold, obtaining a first load balancing coefficient based on the number of users in the target cell and the average number of users in cells within the target sector; When the perception weight is greater than the second threshold, a first load balancing coefficient is obtained based on the number of users and the perception weight of the target cell, and the average number of users and the average perception weight of cells in the target sector.

11. A cell load balancing method, characterized in that: include: When the indicator information of the target cell is obtained, obtaining a first load balancing coefficient according to the indicator information; When the first load balancing coefficient is outside the preset balancing coefficient interval, determining a target optimization strategy corresponding to the first load balancing coefficient according to a preset correspondence between the first load balancing coefficient and the optimization strategy; performing balanced optimization on the target cell according to the target optimization strategy; When the first load balancing coefficient is outside a preset balancing coefficient interval, determining a target optimization strategy corresponding to the first load balancing coefficient according to a preset correspondence between the first load balancing coefficient and the optimization strategy includes: When the first load balancing coefficient is within a first coefficient interval, determining the target optimization strategy as a first adjustment strategy, where the first adjustment strategy is used to adjust the handover hysteresis and / or the inter-frequency handover start-up threshold of the target cell; When the first load balancing coefficient is within a second coefficient interval, determining the target optimization strategy as a second adjustment strategy, where the second adjustment strategy is used to adjust the power of the target cell; The first coefficient interval and the second coefficient interval are both obtained based on a preset first eigenvalue and a preset second eigenvalue, wherein the equalization coefficient interval is a partial interval between an interval formed by the first eigenvalue and the second eigenvalue, the first coefficient interval is an interval formed by the first eigenvalue and the second eigenvalue excluding the equalization coefficient interval, and the second coefficient interval is an interval excluding an interval formed by the first eigenvalue and the second eigenvalue; The indicator information includes a target sector to which the target cell belongs, the number of users in the target cell, and an average number of users in cells within the target sector. Obtaining a first load balancing coefficient based on the indicator information includes: Obtaining a predicted number of users of the target cell in a preset third time period according to the number of users of the target cell in a preset second time period; Obtaining an average predicted number of users of the cell within the target sector in a preset third time period according to the average number of users of the cell within the target sector in a preset second time period; A first load balancing coefficient is obtained based on the predicted number of users and the average predicted number of users.

12. The method according to claim 11, characterized in that The indicator information includes operating parameters and parameter thresholds corresponding to the operating parameters. The step of determining a target optimization strategy corresponding to the first load balancing coefficient according to a preset correspondence between the first load balancing coefficient and the optimization strategy when the first load balancing coefficient satisfies the balanced optimization condition includes: When the first load balancing coefficient is outside a preset balancing coefficient interval, performing parameter correction on the corresponding operating parameter based on the parameter threshold to obtain a corrected operating parameter; Obtaining a second load balancing coefficient according to the corrected operating parameters; In the case that the second load balancing coefficient is outside the preset balancing coefficient interval, a target optimization strategy corresponding to the second load balancing coefficient is determined according to the corresponding relationship between the preset second load balancing coefficient and the optimization strategy.

13. The method according to claim 11, characterized in that When the target optimization strategy is the first adjustment strategy, performing balanced optimization on the target cell according to the target optimization strategy includes: When the first load balancing coefficient is within the first coefficient interval, performing a first iterative operation, wherein, in an i-th first iteration cycle of the first iterative operation, obtaining a switching hysteresis output in an i-1-th first iteration cycle, and adjusting the switching hysteresis using a preset first step length to obtain a third load balancing coefficient; When the third load balancing coefficient is within the balancing coefficient interval, the first iterative operation is terminated.

14. The method according to claim 13, characterized in that The performing balanced optimization on the target cell according to the target optimization strategy further includes: If the number of iterations of the first iterative operation is greater than a preset first threshold, performing a second iterative operation, wherein in the j-th second iterative cycle of the second iterative operation, obtaining the inter-frequency handover detection threshold output in the j-1-th second iterative cycle, and adjusting the inter-frequency handover detection threshold using a preset second step size to obtain a fourth load balancing coefficient; When the fourth load balancing coefficient is within the balancing coefficient interval, the second iterative operation is terminated.

15. The method according to claim 14, characterized in that When the target optimization strategy is the second adjustment strategy, performing balanced optimization on the target cell according to the target optimization strategy includes: When the first load balancing coefficient is within the second coefficient interval, performing a third iterative operation, wherein, in a kth third iteration cycle of the third iterative operation, obtaining a power output in a k-1th third iteration cycle, and adjusting the power using a preset third step size to obtain a fifth load balancing coefficient; When the fifth load balancing coefficient is within the balancing coefficient interval, the third iterative operation is terminated.

16. The method according to claim 15, characterized in that The second coefficient interval includes a first sub-coefficient interval, wherein any value in the first sub-coefficient interval is greater than any value in the equalization coefficient interval, The performing balanced optimization on the target cell according to the target optimization strategy further includes: When the first load balancing coefficient is within the first sub-coefficient interval, performing a third iterative operation, wherein, in a k-th third iteration cycle of the third iterative operation, obtaining a power output during a k-1-th third iteration cycle, and reducing the power using a preset third step size to obtain a fifth load balancing coefficient; When the fifth load balancing coefficient is within the first coefficient interval, the first iterative operation is performed.

17. The method according to claim 15, characterized in that The indicator information includes rated power, the second coefficient interval includes a second sub-coefficient interval, wherein any value in the second sub-coefficient interval is smaller than any value in the equalization coefficient interval, The performing balanced optimization on the target cell according to the target optimization strategy further includes: When the first load balancing coefficient is within the second sub-coefficient interval, performing a third iterative operation, wherein, in a kth third iteration cycle of the third iterative operation, obtaining a power output in a k-1th third iteration cycle, and increasing the power using a preset third step size to obtain a fifth load balancing coefficient; When the increased power is greater than or equal to the rated power, or when the fifth load balancing coefficient is within the first coefficient range, the first iterative operation is performed.

18. The method according to any one of claims 15 to 17, characterized in that The method further includes: obtaining, in the kth third iteration cycle of the third iteration operation, the power output in the k-1th third iteration cycle, and adjusting the power using a preset third step size to obtain a fifth load balancing coefficient. Obtaining a real-time user number change range of the target cell in the kth third iteration cycle; When the change range of the number of users is greater than a preset change range constant, adjusting the first eigenvalue and the second eigenvalue according to a preset fourth step size; An updated first coefficient interval and an updated second coefficient interval are obtained according to the adjusted first eigenvalue and second eigenvalue.

19. The method according to claim 11, characterized in that In the case where the indicator information of the target cell is acquired, after obtaining the first load balancing coefficient according to the indicator information, the method further includes: In the case of obtaining the first input, determining a third adjustment strategy matching the first input; Performing balance optimization on the target cell according to the third adjustment strategy that matches the first input.

20. A cell load balancing device, characterized in that: The device comprises: an acquisition module, configured to obtain a first load balancing coefficient according to the indicator information of the target cell when the indicator information of the target cell is acquired; a determination module configured to determine, when the first load balancing coefficient is outside a preset balancing coefficient interval, a target optimization strategy corresponding to the first load balancing coefficient based on a preset correspondence between the first load balancing coefficient and the optimization strategy; A balancing module, configured to perform balancing optimization on the target cell according to the target optimization strategy; The determining module is further configured to: When the first load balancing coefficient is within a first coefficient interval, determining the target optimization strategy as a first adjustment strategy, where the first adjustment strategy is used to adjust the handover hysteresis and / or the inter-frequency handover start-up threshold of the target cell; When the first load balancing coefficient is within a second coefficient interval, determining the target optimization strategy as a second adjustment strategy, where the second adjustment strategy is used to adjust the power of the target cell; The first coefficient interval and the second coefficient interval are both obtained based on a preset first eigenvalue and a preset second eigenvalue, wherein the equalization coefficient interval is a partial interval between an interval formed by the first eigenvalue and the second eigenvalue, the first coefficient interval is an interval formed by the first eigenvalue and the second eigenvalue excluding the equalization coefficient interval, and the second coefficient interval is an interval excluding an interval formed by the first eigenvalue and the second eigenvalue; The indicator information includes a target sector to which the target cell belongs, and a sensed rate and a proportion of low channel quality of each cell in the target sector. The acquisition module is further configured to: Acquire a first steady-state coefficient based on the perceived rate of the target cell, wherein the first steady-state coefficient indicates a fluctuation margin of the perceived rate of the target cell within a preset first time period; Acquire a second steady-state coefficient based on the low channel quality ratio of the target cell and the low channel quality ratio of the cell with the worst perceived rate in the target sector, wherein the second steady-state coefficient indicates a discretization of the perceived rate in the target sector; Obtaining a perception weight according to the first steady-state coefficient and the second steady-state coefficient; A first load balancing coefficient is obtained according to the perception weight.

21. A cell load balancing device, characterized in that: The device comprises: an acquisition module, configured to obtain a first load balancing coefficient according to the indicator information of the target cell when the indicator information of the target cell is acquired; a determination module configured to determine, when the first load balancing coefficient is outside a preset balancing coefficient interval, a target optimization strategy corresponding to the first load balancing coefficient based on a preset correspondence between the first load balancing coefficient and the optimization strategy; A balancing module, configured to perform balancing optimization on the target cell according to the target optimization strategy; The determining module is further configured to: When the first load balancing coefficient is within a first coefficient interval, determining the target optimization strategy as a first adjustment strategy, where the first adjustment strategy is used to adjust the handover hysteresis and / or the inter-frequency handover start-up threshold of the target cell; When the first load balancing coefficient is within a second coefficient interval, determining the target optimization strategy as a second adjustment strategy, where the second adjustment strategy is used to adjust the power of the target cell; The first coefficient interval and the second coefficient interval are both obtained based on a preset first eigenvalue and a preset second eigenvalue, wherein the equalization coefficient interval is a partial interval between an interval formed by the first eigenvalue and the second eigenvalue, the first coefficient interval is an interval formed by the first eigenvalue and the second eigenvalue excluding the equalization coefficient interval, and the second coefficient interval is an interval excluding an interval formed by the first eigenvalue and the second eigenvalue; The indicator information includes a target sector to which the target cell belongs, the number of users in the target cell, and an average number of users in cells within the target sector. The acquisition module is further configured to: Obtaining a predicted number of users of the target cell in a preset third time period according to the number of users of the target cell in a preset second time period; Obtaining an average predicted number of users of the cell within the target sector in a preset third time period according to the average number of users of the cell within the target sector in a preset second time period; A first load balancing coefficient is obtained based on the predicted number of users and the average predicted number of users.

22. An electronic device, characterized in that: The device comprises: a processor and a memory storing computer program instructions, and when the processor executes the computer program instructions, it implements the method according to any one of claims 1 to 10, or any one of claims 11 to 19.

23. A computer storage medium, characterized in that The computer storage medium stores computer program instructions, which, when executed by a processor, implement the method according to any one of claims 1 to 10 or any one of claims 11 to 19.

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