Congested area network load optimization method, device, equipment, medium and product

Abstract

The invention provides a congestion area network load optimization method and device, equipment, a medium and a product, and the method comprises the steps: when detecting that a space area is congested, carrying out the grid position optimization of a plurality of grids based on the position of a congestion area in the space area and the grid positions of a plurality of grids obtained through the rasterization of the space area; matching the congestion area with the plurality of grids, and determining a congestion grid corresponding to the congestion area; and if the network cells in the congestion grid have the high-load risk, executing a high-load pre-embedded solution corresponding to the congestion grid. According to the invention, the rapid and accurate positioning of the high load position of the network can be realized, and a corresponding load optimization solution can be timely made for the high load risk network, so that the triggering of the high load of the network can be timely and effectively avoided when the congestion occurs, and the method has the advantages of high response efficiency, accurate positioning of the network cell position and high optimization solution efficiency.

Description

Technical Field

[0001] This invention relates to the field of wireless technology, and in particular to a method, apparatus, device, medium, and product for optimizing network load in congested areas. Background Technology

[0002] During holidays, highway traffic volume typically increases dramatically. This increase in traffic volume introduces unpredictable road factors, easily leading to traffic congestion. Traffic congestion increases network load, and because the locations of congested sections are not fixed and are random, the locations of high network loads are also random, making it difficult for optimization personnel to take effective emergency response and handling measures for the network in a timely manner.

[0003] Currently, the ability to detect network overload is mainly through manual monitoring. However, by the time high load is detected manually, it has usually already been triggered, making it impossible to "prevent problems before they occur." At the same time, the analysis of high-load cells, the formulation of solutions, and the implementation of measures all heavily rely on the technical skills of the optimization personnel. The level of technical skills introduces considerable uncertainty into the ability to analyze, locate, and resolve problems, resulting in low efficiency and low accuracy.

[0004] Therefore, how to take timely and effective emergency measures for the network before the network cell triggers high load during congestion, and avoid triggering high load, is an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a method, apparatus, device, medium, and product for optimizing network load in congested areas. It addresses the shortcomings of existing technologies where the analysis, solution formulation, and implementation of high-load cells heavily rely on the technical skills of optimization personnel. The level of technical expertise significantly impacts the ability to analyze, locate, and resolve problems, resulting in low efficiency and accuracy. This invention enables rapid and accurate location of high-load network locations, allowing for timely and appropriate load optimization solutions for high-load risk networks. This effectively prevents triggering of high network loads during congestion, offering advantages such as high response efficiency, accurate network cell location, and high optimization efficiency.

[0006] This invention provides a method for optimizing network load in congested areas, comprising: When congestion is detected in a spatial region, the congested region is matched with the multiple grids obtained by rasterizing the spatial region based on the location of the congested region in the spatial region and the grid positions of multiple grids obtained by rasterizing the spatial region to determine the congested grid corresponding to the congested region. If a network cell within the congestion grid has a high load risk, then the high load pre-embedded solution corresponding to the congestion grid is executed.

[0007] According to a method for optimizing network load in congested areas provided by the present invention, the high-load pre-embedded solution is pre-established in the following manner: The spatial area covered by the network is rasterized to obtain multiple grids; For each grid cell, a corresponding grid cell identifier (CGI) library is established; the grid CGI library is a database used to store and manage the CGI and network quality data of multiple network cells within the grid cell. Based on the network quality data of multiple network cells within the grid, the multiple network cells are divided using the spatial slicing method to obtain spatial slice cells; Load pre-assessment is performed based on the network quality data of the spatial slice cells, and a corresponding high-load pre-embedded solution is established based on the load pre-assessment results.

[0008] According to the present invention, a method for optimizing network load in congested areas includes network quality data such as reference signal received power, measurement report (MR) sampling points, and the number of users. The method further involves dividing the network quality data of multiple network cells within the grid using a spatial slicing method to obtain spatially sliced ​​cells, including: Based on the reference signal received power, measurement report MR sampling points, and number of users of each network cell, the entropy weight method is used to evaluate the value of each network cell and determine the effective cells. Based on the reference signal received power, measurement report MR sampling points, and number of users of each effective cell, the entropy weight method is used to evaluate the value of each effective cell and determine a preset number of capacity-optimized cells. Based on the value assessment scores of the preset number of capacity-optimized cells, the preset number of capacity-optimized cells are divided using the spatial slicing method to obtain the preset number of spatial slice cells.

[0009] According to a method for optimizing network load in congested areas provided by the present invention, the method includes performing a load pre-assessment based on network quality data of the spatial slice cells, and establishing a corresponding high-load pre-buried solution based on the load pre-assessment results, comprising: Determine the baseline value for the maximum number of users in each grid based on the historical maximum number of users in each grid. For each grid, the number of users to be admitted in each spatial slice cell is determined based on the high load threshold of the number of users in each spatial slice cell within the grid; Based on the number of cells and the number of users admitted in the spatial slice, multiple load intervals are determined; Based on the load range to which the maximum number of users of the grid belongs, determine the multi-carrier expansion scheme corresponding to the grid.

[0010] According to a method for optimizing network load in congested areas provided by the present invention, the method includes performing a load pre-assessment based on network quality data of the spatial slice cells, and establishing a corresponding high-load pre-buried solution based on the load pre-assessment results, comprising: For each grid cell, the number of grid users in a single acquisition cycle is determined based on the sum of the number of users in each spatial slice cell within the grid cell in a single acquisition cycle. The maximum number of users per grid is determined based on the maximum number of grid users in multiple acquisition cycles. The load ratio of each spatial slice cell is determined based on the ratio of each spatial slice cell within the grid to the maximum number of users in a single grid. The load ratios of each spatial slice cell are calculated pairwise to obtain the load balancing ratios of multiple cell groups. If the load balancing ratio of any group of cells is greater than the preset load balancing ratio threshold, then a pre-embedded solution for load balancing of the group of cells is determined.

[0011] According to a method for optimizing network load in congested areas provided by the present invention, the spatial slice cell includes a coverage cell and at least one capacity cell, wherein the capacity cell is a cell that shares the load with the coverage cell when the coverage cell experiences high load; the method of performing load pre-assessment based on network quality data of the spatial slice cell and establishing a corresponding high-load pre-embedded solution based on the load pre-assessment results includes: The reference signal received power difference between the coverage cell and each capacity cell is determined based on the reference signal received power of the coverage cell and each capacity cell within the grid occupied by the maximum number of users in a single grid. If the difference in reference signal received power between the coverage cell and the capacity cell is within a first preset difference range, then based on the sign of the difference in reference signal received power, a power-optimized cell is determined from the coverage cell and the capacity cell, and a pre-embedded solution for power optimization for the power-optimized cell is determined.

[0012] According to a method for optimizing network load in congested areas provided by the present invention, the method includes performing a load pre-assessment based on network quality data of the spatial slice cells, and establishing a corresponding high-load pre-buried solution based on the load pre-assessment results, comprising: The reference signal received power difference between the coverage cell and each capacity cell is determined based on the reference signal received power of the coverage cell and each capacity cell within the grid occupied by the maximum number of users in a single grid. If the difference in reference signal received power between the coverage cell and the capacity cell is within a second preset difference range, then based on the sign of the difference in reference signal received power, an antenna feeder adjustment optimization cell is determined from the coverage cell and the capacity cell, and a pre-embedded solution for antenna feeder adjustment optimization for the antenna feeder adjustment optimization cell is determined.

[0013] The present invention also provides a network load optimization device for congested areas, comprising: The matching module is used to, when congestion is detected in a spatial area, match the congested area with the multiple grids obtained by rasterizing the spatial area, based on the location of the congested area in the spatial area and the grid positions of multiple grids obtained by rasterizing the spatial area, and determine the congested grid corresponding to the congested area. The execution module is used to execute the high-load pre-buried solution corresponding to the congestion grid if there is a high load risk in the network cell within the congestion grid.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the congested area network load optimization method as described above.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the congested area network load optimization method as described above.

[0016] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the congested area network load optimization method as described above.

[0017] The network load optimization method, apparatus, equipment, medium, and product for congested areas provided by this invention, when congestion is detected in a spatial area, matches the congested area with multiple grids obtained by rasterizing the spatial area to determine the corresponding congested grid. It then performs load assessment on the network cells within the congested grid, enabling rapid and accurate location of high-load network locations. This facilitates timely load optimization solutions for networks at high risk of congestion. This invention can effectively prevent triggering high network loads when congestion occurs, thus solving the problems of delayed human detection and the high dependence of problem analysis and solutions on the technical skills of optimization personnel. It has the advantages of high response efficiency, accurate network cell location, and high optimization efficiency. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is one of the flowcharts illustrating the network load optimization method for congested areas provided in this embodiment of the invention.

[0020] Figure 2 This is a schematic diagram of a congested section of a highway provided in an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of the matching and fusion of highway congestion sections and grids provided in an embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram of the process for establishing a high-load pre-embedded solution provided in an embodiment of the present invention.

[0023] Figure 5 This is a schematic diagram of spatial region rasterization processing provided in an embodiment of the present invention.

[0024] Figure 6 This is the second flowchart of the network load optimization method for congested areas provided in this embodiment of the invention.

[0025] Figure 7 This is a schematic diagram of the network load optimization device for congested areas provided in an embodiment of the present invention.

[0026] Figure 8 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0028] In the description of embodiments of the present invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Those skilled in the art will understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0029] Figure 1 This is one of the flowcharts illustrating the network load optimization method for congested areas provided in this embodiment of the invention. (Refer to...) Figure 1 This invention provides a method for optimizing network load in congested areas, which may specifically include the following steps: Step 101: When congestion is detected in a spatial area, the congested area is matched with the multiple grids obtained by rasterizing the spatial area based on the location of the congested area in the spatial area and the grid positions of the multiple grids obtained by rasterizing the spatial area to determine the congested grid corresponding to the congested area.

[0030] It should be noted that the execution subject of the network load optimization method for congested areas provided in this embodiment of the invention can be an electronic device, a component in the electronic device, an integrated circuit, or a chip. The electronic device can be a mobile electronic device or a non-mobile electronic device. For example, a mobile electronic device can be a mobile phone, tablet computer, laptop computer, PDA, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc., while a non-mobile electronic device can be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This embodiment of the invention does not specifically limit the specific implementation of these methods.

[0031] In this embodiment of the invention, the spatial area can refer to an area with large network user flow and large fluctuations in network load demand, such as highways and tourist scenic spots.

[0032] Figure 2 This is a schematic diagram of a congested section of a highway provided in an embodiment of the present invention. (Refer to...) Figure 2 As an example, when highway congestion is detected, web crawling technology can be used to obtain data on congested sections of the highway within a certain time period.

[0033] Figure 3 This is a schematic diagram illustrating the matching and fusion of congested highway sections and grids according to an embodiment of the present invention. (Refer to...) Figure 3 As an example, based on the location of the congested section of the highway and the grid positions of multiple grids obtained by rasterizing the highway, the congested section and the grids can be geographically fused and matched to obtain the multiple congested grids corresponding to the congested section.

[0034] This invention addresses the randomness of congested areas in a spatial region by continuously acquiring their locations (in real-time, meaning performing related operations at an hourly granularity). It then matches these congested areas with multiple pre-divided grids to determine the corresponding congested grid. This facilitates rapid discovery of congested locations, enabling quick identification of network cells occupied by these congested areas, and rapid load assessment of the network cells within the grids. Ultimately, this allows for rapid and accurate location of high-load network areas.

[0035] Step 102: If there is a high load risk in the network cells within the congestion grid, then execute the high load pre-embedded solution corresponding to the congestion grid.

[0036] When congestion occurs in a spatial area, it does not necessarily trigger a high network load. Therefore, embodiments of the present invention can determine whether there is a high load risk by monitoring the real-time load of network cells within the congestion grid corresponding to the congestion area.

[0037] Specifically, after matching congested areas with multiple grids to determine the congested grids corresponding to the congested areas, the grid IDs (Identity Documents) of the multiple congested grids corresponding to the congested areas can be extracted. Based on the grid IDs of the multiple congested grids, the corresponding grid CGI (Cell Global Identity) set database can be obtained. Based on the network quality data of the network cells in the grid CGI database, real-time load analysis is performed on the network cells occupied by the congested grids to determine whether there is a high load risk in the network cells within the congested grids.

[0038] For example, congested area = congested grid ID [1, 3, n, ...], Congestion grid corresponding to congested areas n A CGI library can be shown as follows:

[0039] The data in the grid CGI library may include CGI and network quality data of at least one network cell within the grid.

[0040] Network quality data includes RSRP (Reference Signal Receiving Power), Measurement Report (tMR) sampling points, and the number of users, among other things.

[0041] Specifically, when the ratio of the real-time number of users in a network cell within a congested grid to the maximum number of users in the network cell (the number of users triggered in the high-load definition) exceeds the network cell load warning threshold, it can be determined that the network cell within the congested grid is at high risk and is about to reach high load. The high-load pre-planned solution corresponding to the congested grid is then immediately implemented to prevent cells in the grid's CGI library from triggering high load. The high-load pre-planned solution for each grid is predetermined, allowing for timely and rapid deployment of corresponding emergency response plans to optimize network load in congested areas and prevent high load triggering.

[0042] Specifically, the network cell load warning threshold can be:

[0043] When congestion is detected in a spatial region, this invention matches the congested area with multiple grids obtained by rasterizing the spatial region, determining the corresponding congested grid. Load assessment is then performed on the network cells within the congested grid, enabling rapid and accurate location of high-load network locations. This facilitates timely load optimization solutions for networks at high risk of congestion. This invention can effectively prevent triggering high network loads when congestion occurs, thus addressing the issues of delayed human detection and the high dependence of problem analysis and solutions on the technical skills of optimization personnel. It offers advantages such as high response efficiency, accurate network cell location, and high optimization efficiency.

[0044] Figure 4 This is a schematic diagram of the process for establishing a high-load pre-embedded solution according to an embodiment of the present invention. (Refer to...) Figure 4 In an optional embodiment, the high-load pre-embedded solution can be pre-established through the following steps: Step 401: Rasterize the spatial area covered by the network to obtain multiple grids.

[0045] Figure 5 This is a schematic diagram of spatial region rasterization processing provided in an embodiment of the present invention. (Refer to...) Figure 5 Specifically, the spatial area covered by the network can be rasterized, and the network data of the spatial area can be overlaid on the grid to obtain multiple grids corresponding to the spatial area.

[0046] In some embodiments, the resulting multiple grids can be numbered, with grid ID = [1, 2, 3, 4, ..., n, ...].

[0047] Step 402: For each grid cell, establish a corresponding grid cell identifier (CGI) library; the grid CGI library is a database used to store and manage the CGI and network quality data of multiple network cells within the grid cell.

[0048] Specifically, after the spatial area network data is rasterized, the raster data can be parsed to obtain the cell CGI of all network cells occupied in the raster over multiple periods and the corresponding network quality data, thereby establishing a raster CGI database corresponding to all raster cells in the spatial area.

[0049] The grid corresponding to the spatial region n A CGI library can be shown as follows:

[0050] In some embodiments, the raster CGI library data can define an update data cycle.

[0051] Step 403: Based on the network quality data of multiple network cells within the grid, the multiple network cells are divided using the spatial slicing method to obtain spatial slice cells.

[0052] In this embodiment of the invention, it can be based on the grid corresponding to the spatial region. n The network quality data of network cells in each grid of the CGI library are used to divide multiple network cells in the same grid using the CGI spatial slicing method, thereby clarifying the CGI usage function in the grid and obtaining multiple spatial slice cells.

[0053] Specifically, spatial slice cells can include coverage cells and capacity cells. Coverage cells can be used to provide continuous network coverage and capacity for a spatial area; capacity cells can be used as auxiliary cells to share the load of coverage cells when they are under high load, thereby avoiding high load on network cells within the grid.

[0054] Step 404: Perform load pre-assessment based on the network quality data of the spatial slice cell, and establish a corresponding high-load pre-embedded solution based on the load pre-assessment results.

[0055] For load optimization in scenarios with high load and uncertain location in congested spatial networks, sound load forecasting and contingency plans are essential. In this embodiment of the invention, a grid CGI library can be established using multi-period data analysis, and its load can be assessed. Based on the load pre-assessment scheme of the grid CGI library, the load pre-assessment results of each dimension are directed to the corresponding smart modules. A comprehensive analysis is then performed based on MR data, KPI indicators, and cell configuration tables. The solution is then output and stored, completing the pre-embedding of the solution corresponding to the grid CGI library.

[0056] Within the same spatial area, the maximum number of user terminals that a congested grid can handle is the same as the maximum number of user terminals that other congested grids can handle. Specifically, multi-cycle, multi-segment grids can be acquired. n The maximum number of users that the CGI library can accommodate is used to estimate the load on all other grids, and corresponding solutions are pre-installed. When congestion occurs in a spatial area or the network experiences high load, the pre-installed solutions can be directly used to handle the high network load in the congested area.

[0057] This invention establishes a grid CGI library by employing multi-period data analysis and pre-embeds load pre-assessment and high-load optimization solutions. When congestion occurs in a spatial area or the network experiences high load, the pre-embedded solutions can be directly used to handle the high load in the congested area without relying on relevant technical personnel to analyze the problem and determine the solution. This allows for the rapid and accurate handling of high network load in congested areas.

[0058] In one optional embodiment, the network quality data includes reference signal received power, measurement report (MR) sampling points, and the number of users; the method of dividing the multiple network cells within the grid using spatial slicing to obtain spatially sliced ​​cells, based on the network quality data of multiple network cells within the grid, may specifically include: Step S11: Based on the reference signal received power, measurement report MR sampling points, and number of users of each network cell, the entropy weight method is used to evaluate the value of each network cell and determine the effective cells.

[0059] In this embodiment of the invention, the value score of each network cell can be evaluated using the entropy weight method based on the reference signal received power, MR sampling points, and number of users of each network cell in the grid CGI library corresponding to each grid. For example, the grid n corresponds to the grid... n The value score evaluation results for each network cell in the CGI library can be shown below:

[0060] In this embodiment of the invention, network cells can be ranked by value based on their value score evaluation results. For example, the value ranking result for each network cell can be a grid. n CGI = CGI Score (First, Second, Third, ...), where CGI First It can refer to the neighborhood with the highest value assessment score, CGI. Second It could refer to the neighborhood with the second-highest value assessment score, CGI. Third It could refer to the neighborhood with the third highest value assessment score.

[0061] In this embodiment of the invention, based on the value score evaluation results of each network cell, network cells with low value evaluation scores (CGI) and cells with high value evaluation scores but significant discrepancies with the best cell in various indicators are identified as invalid cells.

[0062] Specifically, if the ratio of the number of MR sampling points in a cell to the total number of MR sampling points in the grid is greater than a preset ratio threshold, and the difference in RSRP between the cell with the highest value assessment score and a certain cell is less than a preset difference threshold, then the cell can be determined as a valid cell. For example, network cells that meet the following conditions can be determined as valid cells: CGI 有效 = (CGI MR sampling points) / (grid) n The total number of MR sampling points is ≥20%, and CGI First·RSRP -CGI 有效·RSRP ≤10dB.

[0063] Step S12: Based on the reference signal received power, measurement report MR sampling points, and number of users of each effective cell, the entropy weight method is used to evaluate the value of each effective cell and determine a preset number of capacity-optimized cells.

[0064] In this embodiment of the invention, the value score of each effective cell can be evaluated using the entropy weight method based on the reference signal received power, MR sampling points and number of users of each effective cell in the grid CGI library corresponding to each grid, and a preset number of effective cells with the highest value evaluation scores can be selected as capacity optimization cells.

[0065] It should be noted that in practical applications, different preset numbers of capacity-optimized cells can be determined based on the characteristics of different spatial regions. For example, considering the special characteristic that there are few areas with high overlap coverage for stations along highways, the three effective cells with the highest value assessment scores can be selected as capacity-optimized cells.

[0066] For example, a capacity-optimized cell can be: a gridn CGI 有效 =TOP Score [First, Second, Third].

[0067] Step S13: Based on the value assessment scores of the preset number of capacity-optimized cells, the preset number of capacity-optimized cells are divided using the spatial slicing method to obtain the preset number of spatial slice cells.

[0068] In this embodiment of the invention, based on the value assessment scores of a preset number of capacity-optimized cells (CGIs), the CGIs within the same grid are divided into coverage and capacity functions using a spatial slicing method to obtain spatially sliced ​​cells. This ensures that all grids in a spatial region have corresponding first, second, and third spatial slices, and that there are relatively stable grids. n CGI collection library.

[0069] Specifically, the CGI with the highest value assessment score can be selected. First As a coverage cell (i.e., a first spatial slice cell), the coverage cell can provide high-quality continuous network coverage for highways while also meeting the capacity requirements of the underlying network. Based on the value assessment scores of other capacity-optimized cells, it can be divided into second and third spatial slices for use as a capacity cell, assisting CGI. First Complete load sharing.

[0070] This invention employs a spatial slicing method to functionally divide a preset number of capacity-optimized cells. A three-level spatial hierarchy is used: the first spatial slice cell is responsible for continuous coverage and basic capacity requirements in the spatial area, while other spatial slice cells assist in load sharing for the first spatial slice cell. By clearly distinguishing between primary and secondary optimizations, the load on the grid CGI library can be more accurately assessed, thereby providing precise pre-embedded optimization solutions. This maximizes the utilization of existing resources and avoids blindly implementing optimizations, which would result in a double waste of network construction and optimization resources.

[0071] In one optional embodiment, the load pre-assessment based on the network quality data of the spatial slice cell, and the establishment of a corresponding high-load pre-embedded solution based on the load pre-assessment results, may specifically include: Step S21: Determine the baseline value for the maximum number of users in each grid based on the historical maximum number of users in each grid.

[0072] The maximum number of users in the raster CGI database serves as a reference for assessing the maximum number of users that can be accommodated in all raster loads within a spatial area. All raster data are standardized, and the maximum number of users that can be accommodated can be taken from historical multi-period, multi-segment, and multi-raster CGI databases, making it variable.

[0073] Specifically, the maximum number of users in each grid can be determined based on the historical maximum number of users in each grid, and a grid user reservation scheme can be determined based on the maximum number of users in each grid to obtain a benchmark value for the maximum number of users in each grid, which can then be uniformly used as the benchmark value for the maximum number of users in all grids.

[0074] For example, the maximum number of users for the raster CGI library is MAX(raster1CGI, raster2CGI, ..., raster2CGI). n CGI), the baseline value for the maximum number of users in the raster CGI library = the maximum number of users in the raster CGI library + the maximum number of users in the raster CGI library × 20%.

[0075] It should be noted that, through multi-cycle training and evaluation, 20% is the optimal maximum user number reserve scheme for the grid CGI library. In practical applications, other ratios can be selected, and this invention does not impose any restrictions on them.

[0076] Step S22: For each grid, determine the number of users to be admitted in each spatial slice cell based on the high load threshold value of the number of users in each spatial slice cell within the grid.

[0077] For each grid cell, the number of users to be accommodated and the grid cell size can be determined before load pre-assessment. n The number of cells in the CGI database is used to assist in the pre-assessment of inter-area load.

[0078] When the number of users in a spatial slice cell's CGI exceeds the number of users it can accept, the threshold for triggering a high-load warning for the cell's CGI is about to be reached. In this embodiment of the invention, for each grid, the number of users it can accept for each spatial slice cell can be determined based on the high-load threshold value for the number of users in each spatial slice cell within the grid.

[0079] For example, the number of users admitted by the cell's CGI is equal to 80% of the maximum number of users high load threshold. Here, 80% of the maximum number of users high load threshold can be aligned with the cell's high load trigger threshold.

[0080] Step S23: Based on the number of cells and the number of users admitted in the spatial slice, determine multiple load intervals.

[0081] For example, if the grid n The CGI library contains 3 spatial slice cells, so the raster... n The maximum number of cells in the CGI library can be 3, i.e., 3 grid cells. n CGI library num =CGI num ≤3.

[0082] In this embodiment of the invention, the interval coverage evaluation method involves all grid cells... nThe number of users that the CGI library can accommodate is evaluated based on the maximum number of users per grid, because each grid n The number of CGI libraries varies, resulting in different estimates of the maximum number of users they can accommodate. Consequently, the pre-installed solutions for handling high loads also differ, and it's possible to achieve this with a single grid. n The CGI library corresponds to one pre-embedded solution. Variable grid. n The maximum number of users for the CGI library will always fall within one of four load intervals, corresponding to the CGI number requirement in that interval, and is related to the grid. n By comparing the number of cells in the CGI database, the capacity requirements and corresponding load handling pre-plans are evaluated in turn.

[0083] Specifically, the interval load assessment can be performed as follows:

[0084] In this case, only one CGI is required for interval 1, namely the first spatial slice cell CGI. First No new CGI cells need to be added; Interval 2 requires 2 CGIs, namely the first spatial slice cell CGI. First Second spatial slice cell CGI Second If there is no CGI Second Then we need to fill in the gaps, that is, CGI requires num=2; Interval 3 requires 3 CGIs, including CGIFirst for the first spatial slice and CGI for the second spatial slice cell. Second and third-space slice cell CGI third No padding is needed; however, padding is required if there is no CGI, meaning that CGI requires num=3. Interval 4 requires more than 3 CGIs, exceeding the grid. n The number of cells in the CGI library can be solved by adding sites, i.e., CGI demand num > grid nCGI library num.

[0085] Step S24: Based on the load range to which the maximum number of users of the grid belongs, determine the multi-carrier expansion scheme corresponding to the grid.

[0086] Specifically, when the grid n When a cell in the CGI database cannot meet the demand based on the maximum number of users in the grid CGI database, the CGI demand can be determined by comparing the maximum number of users with the current grid's CGI demand within the load interval to which the maximum number of users falls. n Compare the number of cells in the CGI library. If the expansion conditions are met, obtain the number of CGI requirements for the expansion cells and automatically update the corresponding grid. nThe cell pointer in the CGI library outputs a multi-carrier capacity expansion scheme to the multi-carrier capacity expansion module, completing the pre-installation of the multi-carrier capacity expansion solution; if the expansion conditions are not met, it indicates that the current grid... n The number of cells in the CGI library already meets the demand, and no expansion is needed.

[0087] Among them, the expansion conditions can be: grid n CGI library cell expansion demand = Cell CGI demand corresponding to the load interval of the grid's maximum user count baseline - Grid n The number of cells in the CGI library is greater than 0.

[0088] In some embodiments, when the grid n When the number of cells in the CGI library is still insufficient to meet the demand of the maximum number of users in the grid CGI library, that is, when the demand for interval 4 exceeds 3 CGIs, exceeding the grid limit... n The number of cells that the CGI library can accommodate (the number of cells with CGI demand corresponding to the load interval of the grid's maximum number of users benchmark > the number of grid cells) n When dealing with the number of cells in the CGI library, this can be resolved by adding more sites, automatically matching the corresponding grid. n The CGI library's cell-to-site planning module outputs planning schemes to complete the pre-embedding of supplementary site planning schemes.

[0089] This invention, through pre-evaluation of the number of CGI cell carriers in the grid database, can output and store corresponding expansion schemes when the cells in the grid database cannot meet the requirements of the maximum number of users in the grid CGI database. This completes the pre-embedding of multi-carrier expansion schemes and supplementary site schemes, which is beneficial for timely and rapid issuance of corresponding emergency response plans when a high load risk is detected in a network cell, in order to optimize the network load in congested areas. This helps to prevent the high load from being triggered in the network cell before congestion occurs.

[0090] In one optional embodiment, the load pre-assessment based on the network quality data of the spatial slice cell, and the establishment of a corresponding high-load pre-embedded solution based on the load pre-assessment results, may specifically include: Step S31: For each grid cell, determine the number of grid users in a single acquisition cycle based on the sum of the number of users in each spatial slice cell within the grid cell in a single acquisition cycle. Step S32: Determine the maximum number of users per grid cell based on the maximum number of grid users in multiple acquisition cycles; Step S33: Determine the load ratio of each spatial slice cell based on the ratio of each spatial slice cell within the grid to the maximum number of users in a single grid. Step S34: Perform pairwise difference calculations on the load ratios of each spatial slice cell to obtain the load balancing ratios of multiple cell groups; Step S35: If the load balancing ratio of any group of cells is greater than the preset load balancing ratio threshold, then determine the pre-embedded solution for load balancing of the group of cells.

[0091] Specifically, the number of grid users in each grid during a single acquisition period can be obtained by summing the number of access users in each spatial slice cell within each grid during a single acquisition period (this value is variable); the maximum number of grid users in a single grid can be determined based on the number of grid users in each grid during all acquisition periods; and the maximum number of users in a single grid can be determined based on the maximum number of grid users in a single grid across multiple acquisition periods.

[0092] For example, the number of users in a single raster CGI library (i.e., the maximum number of users in a single raster) = MAX_Number of users in a single raster (period 1, period 2, ..., period n), where the number of users in a single raster is equal to the number of CGI users. First CGI Second CGI Third The sum of the number of users.

[0093] Specifically, the load ratio of each spatial slice cell within the grid can be calculated to the maximum number of users in a single grid, thus obtaining the load ratio of each spatial slice cell. That is, CGI (First, Second, Third) load ratio = number of CGI (First, Second, Third) users / maximum number of users in a single grid.

[0094] Specifically, the grid can be n The load ratios of each spatial slice cell in the CGI library are calculated pairwise to obtain the load balancing ratios of multiple cell groups. If the load balancing ratio of any cell group satisfies the following load balancing condition, it indicates that the grid... n The cell load in the CGI library is unbalanced, and the grid... n The cells in the CGI library point to the relevant modules to output parameter adjustment schemes, thus completing the pre-installation of solutions for load balancing; conversely, the load between cells is balanced.

[0095] Load balancing conditions can be:

[0096] The 20% threshold is defined in the context of load imbalance. If the load ratio difference between any two groups of cells exceeds 20%, it indicates that there is an imbalance in the load between the two cells.

[0097] This invention, through pre-evaluation of the load balancing of CGI cells in the grid database, can output parameter adjustment schemes for cells in the grid CGI database to relevant modules when there is an uneven load between two cells. This completes the pre-embedding of solutions for load balancing, which is beneficial for timely and rapid issuance of corresponding emergency response plans to optimize the network load in congested areas when a high load risk is detected in a network cell. This helps to prevent the high load from being triggered in network cells before congestion occurs.

[0098] In one optional embodiment, the spatial slice cell includes a coverage cell and at least one capacity cell, wherein the capacity cell is a cell that shares the load with the coverage cell when the coverage cell experiences high load; the load pre-assessment based on the network quality data of the spatial slice cell, and the establishment of a corresponding high-load pre-embedded solution based on the load pre-assessment results, may specifically include: Step S41: Based on the reference signal received power of the coverage cell and each capacity cell within the grid occupied by the maximum number of users in a single grid, determine the reference signal received power difference between the coverage cell and each capacity cell. Step S42: If the difference in reference signal received power between the coverage cell and the capacity cell is within a first preset difference range, then based on the sign of the difference in reference signal received power, a power-optimized cell is determined from the coverage cell and the capacity cell, and a pre-embedded solution for power optimization for the power-optimized cell is determined.

[0099] In this embodiment of the invention, it can be based on a single grid. n The grid occupied by the maximum number of users in the CGI library, and the first spatial slice cell CGI within the corresponding grid CGI library. First The RSRP of the cell is compared with the CGI cells of other spatial slices to obtain the RSRP power difference. If the following two power optimization judgment conditions are met, the CGI of the spatial slice cell with the smaller value is pointed to the power optimization module to output the power optimization scheme, thus completing the pre-embedding of the solution for cell power optimization; otherwise, the status quo is maintained.

[0100] The power optimization judgment condition can be: if RSRP power difference = CGI First -CGI Second ≥±(3-6) dB, if the power difference is positive, then it is CGI. Second Increasing power, a negative power difference indicates CGI. First Increase power; if RSRP power difference = CGI First -CGI Third ≥±(3-6) dB, if the power difference is positive, then it is CGI. ThirdIncreasing power, a negative power difference indicates CGI. First Increase power.

[0101] This invention embodiment pre-evaluates the transmit power of CGI cells within the grid library. If the CGI of the first spatial slice cell within the grid library... First If the RSRP difference with other spatial slice cells is between 3-6dB, the CGI of the spatial slice cell with the smaller value will be directed to the power optimization module to output the power optimization scheme. This completes the pre-embedding of the solution for cell power, which is beneficial to promptly and quickly issue the corresponding emergency response plan when a high load risk is detected in a network cell, so as to optimize the network load in the congested area and avoid triggering high load in time before the network cell triggers high load when congestion occurs.

[0102] In one optional embodiment, the step of performing load pre-assessment based on the network quality data of the spatial slice cell, and establishing a corresponding high-load pre-buried solution based on the load pre-assessment results, includes: Step S51: Based on the reference signal received power of the coverage cell and each capacity cell within the grid occupied by the maximum number of users in a single grid, determine the reference signal received power difference between the coverage cell and each capacity cell. Step S52: If the difference in reference signal received power between the coverage cell and the capacity cell is within a second preset difference range, then based on the sign of the difference in reference signal received power, determine the antenna feeder adjustment optimization cell from the coverage cell and the capacity cell, and determine the pre-embedded solution for antenna feeder adjustment optimization for the antenna feeder adjustment optimization cell.

[0103] In this embodiment of the invention, it can be based on a single grid. n The grid occupied by the maximum number of users in the CGI library, and the first spatial slice cell CGI within the corresponding grid CGI library. First The RSRP of the cell is compared with the CGI cells of other spatial slices to obtain the RSRP power difference. If the following two antenna feeder adjustment and optimization judgment conditions are met, the CGI of the spatial slice cell with the smaller value is pointed to the RF (Radio Frequency) antenna feeder adjustment and optimization module to output the antenna feeder adjustment and optimization scheme, thus completing the pre-embedded solution for the antenna feeder adjustment and optimization of the cell; otherwise, the status quo is maintained.

[0104] The judgment condition for antenna adjustment and optimization can be: if RSRP power difference = CGI First -CGI Second ≥±(6-10) dB, if the power difference is positive, then it is a CGI. Second For RF optimization, a negative power difference indicates CGI. FirstPerform RF optimization; if RSRP power difference = CGI First -CGI Third ≥±(6-10) dB, if the power difference is positive, then it is a CGI. Third For RF optimization, a negative power difference indicates CGI. First Perform RF optimization.

[0105] This invention embodiment pre-evaluates the RF adjustment of CGI cells within the grid database. If the CGI of the first spatial slice cell within the grid database... First If the RSRP difference with other spatial slice cells is between 6-10dB, the CGI of the spatial slice cell with the smaller value will be directed to the antenna adjustment and optimization module to output the antenna adjustment and optimization scheme. This completes the pre-embedding of the solution for the cell antenna adjustment, which is beneficial to promptly and quickly issue the corresponding emergency response plan when a high load risk is detected in a network cell, so as to optimize the network load in the congested area and avoid triggering high load in the network cell before congestion occurs.

[0106] Figure 6 This is the second flowchart illustrating the network load optimization method for congested areas provided in this embodiment of the invention. (Refer to...) Figure 6 Taking highways as an example in spatial terms, highway network data can be rasterized to create a grid. n The CGI (Cellular Geographic Information System) database utilizes multi-period data analysis to evaluate the value of cells within the database, eliminating low-value, invalid cells. It then employs spatial slicing to functionally categorize effective cells within the grid CGI into coverage cells and capacity cells, clearly defining cell roles. Multi-dimensional, multi-period load pre-assessment is conducted on cells within the grid CGI database, and high-load pre-planning solutions are developed based on the assessment results. Simultaneously, web crawling technology is used to acquire real-time congestion data from network highways, matching and fusing congestion locations with locations in the grid CGI database. Real-time load analysis of cells within the grid CGI database is performed based on network data to determine if congested sections pose a high-load risk. If so, the corresponding high-load pre-planning solution in the grid CGI database is immediately implemented to avoid triggering high loads and improve user experience.

[0107] This invention can quickly identify network cells occupied by congested road sections through congestion data before network cells trigger high load during highway congestion, quickly conduct load assessment and analysis of network cells, and immediately implement high load pre-planning solutions if there is a risk of triggering high load. It has the ability to quickly identify, respond to and handle network high load in advance, and achieves pre-analysis and pre-planning of solutions, avoiding the drawback of existing technologies that are "too late to avoid high load" after the fact.

[0108] The congested area network load optimization device provided by the present invention is described below. The congested area network load optimization device described below and the congested area network load optimization method described above can be referred to in correspondence.

[0109] Figure 7 This is a schematic diagram of the network load optimization device for congested areas provided in an embodiment of the present invention. (Refer to...) Figure 7 This invention provides a network load optimization device for congested areas, which may specifically include the following modules: The matching module 710 is used to, when congestion is detected in a spatial area, match the congested area with the multiple grids based on the location of the congested area in the spatial area and the grid positions of multiple grids obtained by rasterizing the spatial area, and determine the congested grid corresponding to the congested area. The execution module 720 is used to execute the high-load pre-buried solution corresponding to the congestion grid if there is a high load risk in the network cell within the congestion grid.

[0110] When congestion is detected in a spatial region, this invention matches the congested area with multiple grids obtained by rasterizing the spatial region, determining the corresponding congested grid. Load assessment is then performed on the network cells within the congested grid, enabling rapid and accurate location of high-load network locations. This facilitates timely load optimization solutions for networks at high risk of congestion. This invention can effectively prevent triggering high network loads when congestion occurs, thus addressing the issues of delayed human detection and the high dependence of problem analysis and solutions on the technical skills of optimization personnel. It offers advantages such as high response efficiency, accurate network cell location, and high optimization efficiency.

[0111] Figure 8 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 8As shown, the electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communications interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a congested area network load optimization method. The method includes: when congestion is detected in a spatial area, matching the congested area with the multiple grids obtained by rasterizing the spatial area based on the location of the congested area in the spatial area, and determining the congested grid corresponding to the congested area; if the network cell within the congested grid has a high load risk, then executing a high load pre-embedded solution corresponding to the congested grid.

[0112] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0113] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the network load optimization method for congested areas provided by the above methods. The method includes: when congestion is detected in a spatial area, matching the congested area with the multiple grids obtained by rasterizing the spatial area based on the location of the congested area in the spatial area and the grid positions of multiple grids obtained by rasterizing the spatial area to determine the congested grid corresponding to the congested area; if the network cell in the congested grid has a high load risk, then executing the high load pre-embedded solution corresponding to the congested grid.

[0114] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program is implemented to perform the network load optimization method for congested areas provided by the above methods. The method includes: when congestion is detected in a spatial area, matching the congested area with the multiple grids based on the location of the congested area in the spatial area and the grid positions of multiple grids obtained by rasterizing the spatial area, and determining the congested grid corresponding to the congested area; if the network cell within the congested grid has a high load risk, then executing the high load pre-embedded solution corresponding to the congested grid.

[0115] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0116] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing network load in congested areas, characterized in that, include: When congestion is detected in a spatial region, the congested region is matched with the multiple grids obtained by rasterizing the spatial region based on the location of the congested region in the spatial region and the grid positions of multiple grids obtained by rasterizing the spatial region, and the congested grid corresponding to the congested region is determined. If a network cell within the congestion grid has a high load risk, then the high load pre-embedded solution corresponding to the congestion grid is executed.

2. The method for optimizing network load in congested areas according to claim 1, characterized in that, The high-load pre-embedded solution is pre-established in the following ways: The spatial area covered by the network is rasterized to obtain multiple grids; For each grid cell, a corresponding grid cell identifier (CGI) library is established; the grid CGI library is a database used to store and manage the CGI and network quality data of multiple network cells within the grid cell. Based on the network quality data of multiple network cells within the grid, the multiple network cells are divided using the spatial slicing method to obtain spatial slice cells; Load pre-assessment is performed based on the network quality data of the spatial slice cells, and a corresponding high-load pre-embedded solution is established based on the load pre-assessment results.

3. The method for optimizing network load in congested areas according to claim 2, characterized in that, The network quality data includes reference signal received power, measurement report MR sampling points, and the number of users; based on the network quality data of multiple network cells within the grid, the multiple network cells are divided using a spatial slicing method to obtain spatial slice cells, including: Based on the reference signal received power, measurement report MR sampling points, and number of users of each network cell, the entropy weight method is used to evaluate the value of each network cell and determine the effective cells. Based on the reference signal received power, measurement report MR sampling points, and number of users of each effective cell, the entropy weight method is used to evaluate the value of each effective cell and determine a preset number of capacity-optimized cells. Based on the value assessment scores of the preset number of capacity-optimized cells, the preset number of capacity-optimized cells are divided using the spatial slicing method to obtain the preset number of spatial slice cells.

4. The method for optimizing network load in congested areas according to claim 3, characterized in that, The process of performing load pre-assessment based on network quality data from the spatial slice cells, and establishing corresponding high-load pre-buried solutions based on the load pre-assessment results, includes: Determine the baseline value for the maximum number of users in each grid based on the historical maximum number of users in each grid. For each grid, the number of users to be admitted in each spatial slice cell is determined based on the high load threshold of the number of users in each spatial slice cell within the grid; Based on the number of cells and the number of users admitted in the spatial slice, multiple load intervals are determined; Based on the load range to which the maximum number of users of the grid belongs, determine the multi-carrier expansion scheme corresponding to the grid.

5. The method for optimizing network load in congested areas according to claim 3, characterized in that, The process of performing load pre-assessment based on network quality data from the spatial slice cells, and establishing corresponding high-load pre-buried solutions based on the load pre-assessment results, includes: For each grid cell, the number of grid users in a single acquisition cycle is determined based on the sum of the number of users in each spatial slice cell within the grid cell in a single acquisition cycle. The maximum number of users per grid is determined based on the maximum number of grid users in multiple acquisition cycles. The load ratio of each spatial slice cell is determined based on the ratio of each spatial slice cell within the grid to the maximum number of users in a single grid. The load ratios of each spatial slice cell are calculated pairwise to obtain the load balancing ratios of multiple cell groups. If the load balancing ratio of any group of cells is greater than the preset load balancing ratio threshold, then a pre-embedded solution for load balancing of the group of cells is determined.

6. The method for optimizing network load in congested areas according to claim 3, characterized in that, The spatial slice cell includes a coverage cell and at least one capacity cell, wherein the capacity cell is a cell that shares the load with the coverage cell when the coverage cell experiences high load; the load pre-assessment based on the network quality data of the spatial slice cell, and the establishment of a corresponding high-load pre-embedded solution based on the load pre-assessment results, includes: The reference signal received power difference between the coverage cell and each capacity cell is determined based on the reference signal received power of the coverage cell and each capacity cell within the grid occupied by the maximum number of users in a single grid. If the difference in reference signal received power between the coverage cell and the capacity cell is within a first preset difference range, then based on the sign of the difference in reference signal received power, a power-optimized cell is determined from the coverage cell and the capacity cell, and a pre-embedded solution for power optimization for the power-optimized cell is determined.

7. The method for optimizing network load in congested areas according to claim 3, characterized in that, The process of performing load pre-assessment based on network quality data from the spatial slice cells, and establishing corresponding high-load pre-buried solutions based on the load pre-assessment results, includes: The reference signal received power difference between the coverage cell and each capacity cell is determined based on the reference signal received power of the coverage cell and each capacity cell within the grid occupied by the maximum number of users in a single grid. If the difference in reference signal received power between the coverage cell and the capacity cell is within a second preset difference range, then based on the sign of the difference in reference signal received power, an antenna feeder adjustment optimization cell is determined from the coverage cell and the capacity cell, and a pre-embedded solution for antenna feeder adjustment optimization for the antenna feeder adjustment optimization cell is determined.

8. A network load optimization device for congested areas, characterized in that, include: The matching module is used to, when congestion is detected in a spatial area, match the congested area with the multiple grids obtained by rasterizing the spatial area, based on the location of the congested area in the spatial area and the grid positions of multiple grids obtained by rasterizing the spatial area, and determine the congested grid corresponding to the congested area. The execution module is used to execute the high-load pre-buried solution corresponding to the congestion grid if there is a high load risk in the network cell within the congestion grid.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the network load optimization method for congested areas as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the network load optimization method for congested areas as described in any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the network load optimization method for congested areas as described in any one of claims 1 to 7.

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