Property point-level user internet surfing rate evaluation method and device
By determining the user experience rate at the property management site level based on the business baseline rate and the community business model, combined with configuration data, the problem of accuracy and efficiency in assessing the internet speed of users at the property management site level has been solved, and accurate assessment without on-site testing has been achieved.
Patent Information
- Application Number
- CN202411753572.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-02
- Publication Date
- 2025-12-16
AI Technical Summary
Existing technologies cannot accurately characterize the internet speed of users at the property management point level, and the evaluation efficiency is low. They cannot eliminate the one-to-one correspondence between property management points and covered communities, as well as the differences in internet speed among users. On-site testing is time-consuming and labor-intensive.
The uplink and downlink demand rates at the cell level are determined based on the baseline rates of each service and the cell service model. Combined with the uplink and downlink demand rates of the main service cell and the number of sampling points within the property management area, the user experience rate at the property management level is determined using configuration data, call statistics data, OTT crowdsourcing data, and engineering parameter data. A linear normalization method is then used for perception evaluation.
It enables accurate perception and assessment of internet speed for users at the property management level, improves assessment efficiency, eliminates the need for on-site testing, and enhances the accuracy and efficiency of the assessment.
Smart Images
Figure CN121151963A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of wireless communication, in particular to a method and device for evaluating the internet access rate of users at a property point level, equipment, storage medium and product. BACKGROUND
[0002] Currently, the evaluation of the internet access rate of users at a property point level mainly adopts the following two methods: (1) Rate average method: first, the signal coverage cells within a 100m range of the property point boundary are circled through a geographic information system software; second, the average rate of the cells and the average internet access rate of users under the cells are calculated based on the cell-level rate of the cells extracted from the network management platform; and finally, the average rate of the cells and the average internet access rate of users under the cells are calculated.
[0003] (2) Test evaluation method: through a test software, a CQT (Critical Quality Test) of simulated user behavior is performed on site. First, business configuration, parameter configuration and the like are performed on the test terminal; second, single-point multi-internet access rate tests are performed for different business types; and finally, the user behavior path and behavior characteristics within the property point are simulated, and on-site point traversal tests are performed, and the test rates of all points and all businesses are fused to obtain the internet access rate of users at a property point level.
[0004] However, the above evaluation methods have the following problems: (1) the property point and the coverage cell are not in a one-to-one correspondence, and there is a case where multiple property points are covered by the same cell, and the average value of the user rate under the coverage cell cannot accurately represent the user rate under a specific property point; (2) the method of dividing the cell rate by the number of users to obtain the internet access rate of a single user cannot obtain the differentiated internet access rate of users; and (3) personnel need to go to the site to perform CQT tests of simulated user behavior paths and behavior characteristics, which is time-consuming and labor-intensive, and has low efficiency.
[0005] Therefore, how to improve the accuracy and efficiency of the evaluation of the internet access rate of users at a property point level is a problem to be solved at present. SUMMARY
[0006] The present application provides a method and device for evaluating the internet access rate of users at a property point level, equipment, storage medium and product, to solve the defects in the prior art that the evaluation method cannot accurately represent the internet access rate of users at a property point level and has low evaluation efficiency, and to realize accurate perception evaluation of the internet access rate of users at a property point level and improve the evaluation efficiency.
[0007] The present application provides a method for evaluating the internet access rate of users at a property point level, comprising: determining the uplink and downlink demand rate at a cell level based on the baseline rate of each business and the cell business model; Determine a property point level user demand rate based on uplink and downlink demand rates of a main service cell in a property point area and sample point numbers; Determine a property point level user experience rate based on configuration data, voice data, over-the-top (OTT) crowd testing data and engineering parameter data of the main service cell; Conduct perception evaluation on a property point level user online rate based on the property point level user experience rate and the property point level user demand rate.
[0008] According to the property point level user online rate evaluation method provided by the application, the cell level uplink and downlink demand rates are determined based on each service baseline rate and a cell service model, which comprises: Determine each service baseline rate by analyzing target service characteristics, user behavior characteristics and network performance index test data; Determine a cell service model based on each service traffic proportion of the main service cell in the property point area; Calculate cell level uplink and downlink demand rates based on each service baseline rate, the cell service model and an average concurrent service coefficient.
[0009] According to the property point level user online rate evaluation method provided by the application, the main service cell in the property point area is determined by the following ways: Arrange each cell in the same property point area in a preset statistical period based on OTT crowd testing sample point numbers of the cells to obtain a cell arrangement order; Determine a corresponding cell as the main service cell in the property point area by sequentially accumulating OTT crowd testing sample point numbers of the cells based on the cell arrangement order until the accumulated OTT crowd testing sample point numbers reach a preset number threshold.
[0010] According to the property point level user online rate evaluation method provided by the application, the property point level user experience rate is determined based on configuration data, voice data, over-the-top (OTT) crowd testing data and engineering parameter data of the main service cell, which comprises: Divide the main service cell in the property point area into a plurality of grid areas to obtain boundary information of the plurality of grids; Determine OTT sample point numbers under each grid by grid processing the OTT crowd testing data based on the boundary information of the plurality of grids and sample point position information of the OTT crowd testing data; Determine a grid level user experience rate for each grid based on configuration data, voice data, OTT crowd testing data and engineering parameter data of the main service cell in the grid; Determine a property point level user experience rate based on each grid level user experience rate in each grid and OTT sample point numbers under each grid.
[0011] According to the present invention, a method for evaluating the internet access speed of users at the property level, wherein determining the grid-level user experience speed based on the configuration data, call statistics data, OTT crowdsourcing data, and engineering parameter data of the main serving cell within the property area includes: Calculate the number of uplink and downlink scheduling operations per second based on the number of time slots per second and the ratio of uplink to downlink symbols; The total number of REs is calculated based on the number of RBs, the number of subcarriers per RB, and the number of symbols per time slot; The number of available REs is calculated based on the ratio of cell SSB overhead to cell system message SIB1 and the total number of REs. Calculate spectral efficiency based on coding rate and modulation order; The grid-level uplink and downlink user experience rate is calculated based on the number of uplink and downlink scheduling per second, the number of available REs, the spectrum efficiency, the number of uplink and downlink rank layers in the cell, the bit error rate, and the number of users transmitting data simultaneously in uplink and downlink.
[0012] According to the present invention, a method for evaluating the internet speed of property-level users includes, in which the evaluation of the internet speed of property-level users is performed based on the user experience speed and the user demand speed, comprising: Calculate the difference between the property-level user experience rate and the property-level user demand rate; The difference is quantified using a linear normalization method to obtain a quantitative score for the user rate at the property level. Based on the quantitative score of the property-level user speed, the perceived internet speed of property-level users is evaluated.
[0013] The present invention also provides a property-level user internet speed assessment device, comprising: The first rate determination module is used to determine the cell-level uplink and downlink demand rates based on the baseline rates of each service and the cell service model. The second rate determination module is used to determine the user demand rate at the property point level based on the uplink and downlink demand rates of the main service cell within the property point area and the number of sampling points. The third rate determination module is used to determine the property-level user experience rate based on the configuration data, call statistics data, over-the-top OTT crowdsourcing test data, and engineering parameter data of the main serving cell. Based on the property-level user experience rate and the property-level user demand rate, a perception assessment of the internet speed of property-level users is conducted.
[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 property-level user internet speed evaluation 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 property-level user internet speed evaluation 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 property-level user internet speed evaluation method as described above.
[0017] The present invention provides a method, apparatus, device, storage medium, and product for evaluating the internet speed of property-level users. It determines the uplink and downlink demand rates at the cell level based on baseline rates of various services and cell service models; determines the demand rate of property-level users based on the uplink and downlink demand rates and the number of sampling points of the main serving cell within the property area; determines the user experience rate at the property level based on configuration data, call statistics data, OTT crowdsourcing data, and engineering parameter data of the main serving cell; and performs a perceptual evaluation of the internet speed of property-level users based on the user experience rate and the demand rate. This allows for accurate perceptual evaluation of the internet speed of property-level users using data from the main serving cell within the property area, without requiring on-site simulation testing, thus improving the accuracy and efficiency of the evaluation. 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 a flowchart illustrating the method for evaluating the internet speed of users at the property management level provided in this embodiment of the invention.
[0020] Figure 2 This is a schematic diagram of the structure of the property-level user internet speed evaluation device provided in an embodiment of the present invention.
[0021] Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation
[0022] 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.
[0023] 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.
[0024] Figure 1 This is a flowchart illustrating the method for evaluating internet speed for users at the property management level, as provided in an embodiment of the present invention. (Refer to...) Figure 1 This invention provides a method for evaluating the internet speed of users at the property management level, which may specifically include the following steps: Step 101: Determine the cell-level uplink and downlink demand rates based on the baseline rates of each service and the cell service model.
[0025] It should be noted that the executing entity of the property-level user internet speed assessment method 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.
[0026] Different users may have different business usage behaviors. By analyzing mainstream business characteristics and user behavior characteristics and conducting on-site testing, this invention can eliminate the differences in business usage behaviors among different users, thereby determining the baseline rate of each business.
[0027] The baseline rates for different types of services can vary. A single service's baseline rate can include uplink and downlink demand baseline rates under three perception levels: good perception, average perception, and poor perception. As an example, the baseline rates for each service can be shown in Table 1. Table 1
[0028] In this embodiment of the invention, the main service cell within each property area can be identified, the service type and corresponding service traffic of the main service cell can be extracted, different service models can be established based on different service traffic proportions, and then the cell-level uplink and downlink demand rates can be calculated based on different service baseline rates and cell service models.
[0029] Step 102: Determine the user demand rate at the property point level based on the uplink and downlink demand rates of the main service communities within the property point area and the number of sampling points.
[0030] The demand rate of property point-level users can include the uplink demand rate and the downlink demand rate of property point-level users.
[0031] In this embodiment of the invention, the uplink demand rate of a property-level user can be calculated by weighting the uplink demand rate of the primary serving cell and the number of over-the-top (OTT) sampling points; the downlink demand rate of a property-level user can also be calculated by weighting the downlink demand rate of the primary serving cell and the number of OTT sampling points. The primary serving cell may include multiple cells.
[0032] Specifically, the formula for calculating the uplink demand rate of property-level users can be: Uplink demand rate of property-level users = (Uplink demand rate of community A × Number of OTT sampling points in community A + ... + Uplink demand rate of community N × Number of OTT sampling points in community N) / (Total number of OTT sampling points from community A to community N).
[0033] Specifically, the formula for calculating the downlink demand rate of property point-level users can be: Downlink demand rate of property point-level users = (Downlink demand rate of community A × Number of OTT sampling points of community A + ... + Downlink demand rate of community N × Number of OTT sampling points of community N) / (Total number of OTT sampling points from community A to community N).
[0034] Step 103: Determine the user experience rate at the property management point level based on configuration data, call statistics data, YueDing OTT crowdsourcing data, and engineering parameter data.
[0035] Configuration data can refer to the configuration parameters of a network or device. Traffic statistics data can refer to statistical data related to communication network performance and traffic (calls, data transmission). OTT crowdsourcing data can refer to network performance test data conducted through OTT applications (such as video streaming, social media, etc.). Engineering parameter data can refer to engineering parameter data in a wireless communication system, used to describe the technical parameters, location, and functional configuration of various devices in the network.
[0036] In this embodiment of the invention, basic data from four dimensions, including configuration data, call statistics data, OTT crowdsourcing data, and engineering parameter data, can be obtained from the Operation and Maintenance Center (PMC) platform, the network optimization big data platform, the virtual drive test platform, and the planning platform. The actual user experience rate of uplink and downlink at the grid level can be calculated, and then the actual user experience rate at the property point level can be calculated.
[0037] Step 104: Based on the property-level user experience rate and the property-level user demand rate, perform a perception assessment of the internet speed of the property-level user.
[0038] Specifically, based on the user experience rate and user demand rate at each property location, the difference between the user experience rate and user demand rate at each property location can be calculated and quantified to achieve a perception assessment of the internet speed of users at the property location level.
[0039] This invention determines the cell-level uplink and downlink demand rates based on the baseline rates of various services and the cell service model; it determines the property-point-level user demand rates based on the uplink and downlink demand rates and the number of sampling points of the main serving cell within the property-point area; it determines the property-point-level user experience rates based on the configuration data, call statistics data, YueDing OTT crowdsourcing data, and engineering parameter data of the main serving cell; and it performs a perception assessment of the property-point-level user internet speed based on the property-point-level user experience rate and the property-point-level user demand rate. This allows for accurate perception assessment of the property-point-level user internet speed using data from the main serving cell within the property-point area, without requiring on-site simulation testing, thus improving the accuracy and efficiency of the property-point-level user internet speed assessment.
[0040] In one optional embodiment, determining the cell-level uplink and downlink demand rates based on each service baseline rate and cell service model may specifically include: Step S11: Analyze the test data of target service characteristics, user behavior characteristics, and network performance indicators to determine the baseline rate of each service.
[0041] Specifically, a big data platform can be used to extract cell-level service data within a preset time period, analyze the characteristics of mainstream services and user behavior, conduct network terminal service perception tests based on multiple dimensions of network performance indicators, and integrate call statistics data and test data for fitting analysis to determine the baseline rate of each service.
[0042] Network performance metrics can include latency, buffering, resolution, bitrate, etc.
[0043] The preset time period can be one month or one week, and this invention does not impose any restrictions on it.
[0044] Step S12: Determine the community business model based on the traffic share of each business in the main service community within the property management area.
[0045] Specifically, the service types and corresponding traffic of the main service cell in a typical regional scenario within a preset time period can be statistically analyzed to determine the cell's service model. The service types and corresponding traffic will vary between cells due to the influence of mainstream service characteristics and user behavior characteristics. Typical regional scenarios include universities and transportation hubs.
[0046] The formula for calculating the traffic proportion of different services within the same cell can be as follows: ; in, The traffic of service n under cell i is represented by N, and N represents the N types of services under cell i (n=1, 2, ..., N). This represents the sum of service traffic for all service types within community i.
[0047] Step S13: Calculate the cell-level uplink and downlink demand rates based on the baseline rates of each service, the cell service model, and the average concurrent service coefficient.
[0048] In this embodiment of the invention, the uplink and downlink demand rates of users at the cell level can be calculated based on the product of the baseline rates of each service, the cell service model, and the average concurrent service coefficient K.
[0049] Specifically, the formula for calculating the uplink and downlink demand rates for cell-level users can be as follows: Cell-level user uplink demand rate = ∑ ( ×service n uplink baseline rate ×K). Cell-level user downlink demand rate = ∑ ( ×downlink baseline rate of service n × K). Where n = 1, 2, ..., N; K represents the average concurrent business coefficient.
[0050] Specifically, the formula for calculating the average concurrent business coefficient K can be as follows: ; Where P is the average number of users using the service per day, L is the average user operation time per day, and T is the duration of user use of the service per day.
[0051] In one optional embodiment, the primary service area within the property location is determined through the following steps: Step S21: Based on the number of OTT crowdsourcing sampling points in each community within the same property area within a preset statistical period, arrange the communities to obtain the community arrangement order; Step S22: Based on the arrangement order of the communities, the number of OTT crowdsourcing sampling points in each community is accumulated sequentially until the accumulated number of OTT crowdsourcing sampling points reaches a preset threshold, and the corresponding community is determined as the main service community in the property point area.
[0052] In this embodiment of the invention, property point boundary information, OTT crowdsourcing data and engineering parameter data can be obtained to identify the main service area of the property point.
[0053] Specifically, the property area of each property can be determined based on the property boundary information. For each property, the number of OTT crowdsourcing sampling points for each community under the same property ID (Identity Document) within a preset period (e.g., one month) can be counted, and the communities can be sorted in descending order according to the number of sampling points, thus obtaining the community sorting order.
[0054] After sorting, the number of OTT crowdsourcing sampling points in the community can be accumulated sequentially. When the accumulated number of sampling points exceeds a preset threshold, the community corresponding to the accumulated sampling points will be designated as the primary service community within the property management area. The preset threshold can be set according to a preset proportion of the total number of samples within the property management area, for example, 90% of the total number of samples within the property management area.
[0055] The formula for calculating the cumulative number of OTT crowdsourcing sampling points is as follows: ; Among them, M i The number of OTT crowdsourcing sampling points in the i-th community is represented by i=1,2,...,n, and N represents the sum of the number of OTT crowdsourcing sampling points in the property area.
[0056] In one optional embodiment, determining the property-level user experience rate based on the configuration data, call statistics data, over-the-top OTT crowdsourcing data, and engineering parameter data of the primary serving cell may specifically include: Step S31: Divide the main service area within the property point area into multiple grid areas to obtain the boundary information of multiple grids; Step S32: Based on the boundary information of the multiple grids and the sampling point location information of the OTT crowdsourcing data, perform rasterization processing on the OTT crowdsourcing data to determine the number of OTT sampling points under each grid. Step S33: For each grid, determine the grid-level user experience rate based on the configuration data, call statistics data, OTT crowdsourcing data, and engineering parameter data of the primary serving cell within the grid; Step S34: Determine the property point-level user experience rate based on the user experience rate of each grid level within the grid and the number of OTT sampling points under each grid.
[0057] In some embodiments, internet map data can be acquired, which may include property name, latitude and longitude, and boundary information, thereby determining the property area. In this embodiment of the invention, the property area can be determined based on map data, and a grid area of a preset size can be divided within the property area. The latitude and longitude information in the OTT crowdsourcing data is matched with the grid boundary information to complete the rasterization processing of the OTT data, thereby determining the number of OTT sampling points under each grid.
[0058] Specifically, a 20m×20m grid area can be divided within the property area, including the latitude and longitude of the upper left and lower right corners of the grid. The location information of the grid can be obtained from this information, and the OTT crowdsourcing data can be matched with the grid boundary information to achieve the grid-based processing of the OTT crowdsourcing data.
[0059] In this embodiment of the invention, for each grid, the grid-level user experience rate can be determined based on various factors such as the number of uplink / downlink scheduling times per second, the number of available REs (Resource Elements) per second for uplink / downlink, the Spectrum Efficiency (SE), the number of uplink / downlink Rank layers, the bit error rate, and the number of users transmitting data simultaneously. Then, the user experience rates of different grid levels within the property point are weighted according to the proportion of OTT crowdsourcing sampling points on the grid to obtain the property point-level user experience rate.
[0060] Specifically, the formula for calculating the uplink and downlink user experience rate at the property management point level can be as follows: Property-level uplink user experience rate = (uplink user experience rate of grid A × number of OTT sampling points of grid A + ... + uplink user experience rate of grid N × number of OTT sampling points of grid N) / (total number of OTT sampling points from grid A to grid N); Downlink user experience rate at property level = (Downlink user experience rate of grid A × number of OTT sampling points of grid A + ... + Downlink user experience rate of grid N × number of OTT sampling points of grid N) / (Total number of OTT sampling points from grid A to grid N).
[0061] In one optional embodiment, determining the grid-level user experience rate based on the configuration data, call statistics data, OTT crowdsourcing data, and engineering parameter data of the main service cell within the property area may specifically include: Step S341: Calculate the number of uplink and downlink scheduling times per second based on the number of time slots per second and the ratio of uplink to downlink symbols; Step S342: Calculate the total number of REs based on the number of RBs, the number of subcarriers per RB, and the number of symbols per time slot; Step S343: Calculate the number of available REs based on the ratio of cell SSB overhead to cell system message SIB1 and the total number of REs; Step S344: Calculate the spectral efficiency based on the coding rate and modulation order; Step S345: Calculate the grid-level uplink and downlink user experience rate based on the number of uplink and downlink scheduling times per second, the number of available REs, the spectrum efficiency, the number of uplink and downlink Rank layers in the cell, the bit error rate, and the number of users transmitting data simultaneously in uplink and downlink.
[0062] Configuration data may include network type, bandwidth, subcarrier spacing, uplink / downlink subframe ratio, number of RBs (Resource Blocks), MCS (Modulation and Coding Scheme) value, coding rate, modulation order, etc.
[0063] The call statistics data may include cell SSB (Synchronization Signal Block) overhead, cell system message SIB1 (System Information Block #1) overhead, cell uplink / downlink Rank layers, BLER (Block Error Rate), number of users transmitting data simultaneously, etc.
[0064] OTT crowdsourcing data can include sampling point longitude, latitude, level value, SINR (Signal-to-Interference-plus-Noise Ratio), etc.
[0065] Engineering parameter data can include city, scene, community, latitude and longitude, station type, and other engineering parameter data.
[0066] Specifically, the formula for calculating the uplink and downlink user experience rate at the grid level can be: Uplink Throughput = UL Grant / s × Available REs × Number of SEs × RANK × (1-BLER) / Number of users transmitting simultaneously uplink; Downlink Throughput = DL Grant / s × Available RE × SE × RANK × (1-BLER) / Number of users simultaneously transmitting data downlink; Wherein, UL Grant / DL Grant represent the number of uplink / downlink scheduling times per second, available RE represents the number of available PUSCH (Physical Uplink Shared Channel) REs per second for uplink / downlink in the cell, SE represents spectral efficiency, RANK represents the number of uplink / downlink RANK layers in the cell, and BLER represents the bit error rate.
[0067] Specifically, the formula for calculating the number of uplink and downlink scheduling operations per second can be: Cellular UL Grant / s = Number of time slots per second × Uplink symbol percentage; Cell DL Grant / s = Number of slots per second × Downlink symbol percentage.
[0068] Specifically, the formula for calculating the number of available REs can be: Available REs = Total REs × (1 - SSB / SIB1 overhead ratio); Total REs = Number of RBs × 12 (Number of subcarriers per RB) × 14 (Number of symbols per slot).
[0069] Specifically, the formula for calculating SE (spectral efficiency) can be: ; in, Represents the coding rate. This represents the modulation order.
[0070] In one optional embodiment, the assessment of the internet speed of property-level users based on the property-level user experience rate and the property-level user demand rate may specifically include: Step S41: Calculate the difference between the property-level user experience rate and the property-level user demand rate; Step S42: The difference is quantified using a linear normalization method to obtain the property point-level user rate quantification score. Step S43: Based on the quantitative score of the property-level user speed, perform a perception assessment of the internet speed of the property-level users.
[0071] In this embodiment of the invention, the difference between the user experience rate and the demand rate at each property point can be calculated based on the user demand rate and the actual user experience rate at each property point. A linear normalization method is then used to quantify the difference between the user demand rate and the actual user experience rate at each property point, thereby completing the property point-level user rate perception assessment.
[0072] Specifically, the linear normalization formula can be: ; Where x represents the difference between the experience rate and the demand rate, and min(x) and max(x) represent the minimum and maximum values of the interval to which x belongs, respectively.
[0073] The quantitative scores for user rate perception at the property management level are shown in Table 2: Table 2
[0074] The overall score is calculated as follows: (Upward score × 30%) + (Downward score × 70%).
[0075] The method for calculating the actual experience rate at the grid user level provided in this embodiment of the invention can solve the problem of insufficient evaluation of user experience rate at the fine geographic level in related technologies, and can accurately evaluate the user experience rate of the property point by aggregating the sampling points of the main service cell to the property point through the proportion relationship of the sampling points.
[0076] The method provided in this invention, which obtains the actual experience rate of a single user at the grid level based on the configuration data, call statistics data, OTT crowdsourcing data, engineering parameter data, and map data of the main service community of the property management point, can accurately evaluate the actual experience rate of grid users. Compared with the previous method, it is more refined to evaluate the actual experience rate of community users, thus improving the accuracy of the geographical user experience rate evaluation.
[0077] The present invention provides a method for quantifying the difference between the user demand rate at each property location and the actual experienced rate, thereby obtaining an assessment of the internet speed at each property location. This method quantifies the degree of speed demand at each property location, reflects the urgency of network demand at each property location, and can serve as a primary basis for hierarchical operation and maintenance. It also helps network optimization personnel to conduct differentiated network optimization and resource investment.
[0078] The following describes the property-level user internet speed evaluation device provided by the present invention. The property-level user internet speed evaluation device described below can be referred to in correspondence with the property-level user internet speed evaluation method described above.
[0079] Figure 2 This is a schematic diagram of the structure of the property-level user internet speed assessment device provided in an embodiment of the present invention. (Refer to...) Figure 2This invention provides a property-level user internet speed assessment device, which may specifically include the following modules: The first rate determination module 210 is used to determine the cell-level uplink and downlink demand rates based on the baseline rates of each service and the cell service model. The second rate determination module 220 is used to determine the user demand rate at the property point level based on the uplink and downlink demand rates of the main service cell within the property point area and the number of sampling points. The third rate determination module 230 is used to determine the property-level user experience rate based on configuration data, call statistics data, YueDing OTT crowdtesting data, and engineering parameter data. The perception evaluation module 240 is used to determine the property-level user experience rate based on the configuration data, call statistics data, over-the-top OTT crowdsourcing data, and engineering parameter data of the main serving cell.
[0080] In one optional embodiment, the first rate determination module includes: The service baseline rate determination submodule is used to analyze target service characteristics, user behavior characteristics, and network performance indicator test data to determine the baseline rate of each service. The Community Business Model Determination Submodule is used to determine the community business model based on the traffic share of each business in the main service community within the property management area. The demand rate calculation submodule is used to calculate the cell-level uplink and downlink demand rates based on the baseline rates of each service, the cell service model, and the average concurrent service coefficient.
[0081] In one optional embodiment, the primary service area within the property location is determined in the following way: The community sorting submodule is used to sort the communities based on the number of OTT crowdsourcing sampling points of each community in the same property area within a preset statistical period, and obtain the community sorting order. The main serving cell determination submodule is used to sequentially accumulate the number of OTT crowdsourcing sampling points in the cells based on the cell arrangement order, until the accumulated number of OTT crowdsourcing sampling points reaches a preset threshold, and then determine the corresponding cell as the main serving cell in the property point area.
[0082] In one optional embodiment, the third rate determination module includes: The grid division submodule is used to divide the main service area within the property point area into multiple grid areas to obtain the boundary information of multiple grids; The rasterization processing submodule is used to perform rasterization processing on the OTT crowdsourcing data based on the boundary information of the multiple rasteres and the sampling point location information of the OTT crowdsourcing data, and to determine the number of OTT sampling points under each raster. The grid-level rate determination submodule is used to determine the grid-level user experience rate for each grid based on the configuration data, call statistics data, OTT crowdsourcing data and engineering parameter data of the primary serving cell within the grid. The property-level rate determination submodule is used to determine the property-level user experience rate based on the user experience rate of each grid within the grid and the number of OTT sampling points under each grid.
[0083] In one optional embodiment, the grid-level rate determination submodule includes: The scheduling count calculation unit is used to calculate the number of uplink and downlink scheduling times per second based on the number of time slots per second and the ratio of uplink and downlink symbols. The total RE count calculation unit is used to calculate the total RE count based on the number of RBs, the number of subcarriers per RB, and the number of symbols per time slot; The available RE count calculation unit is used to calculate the available RE count based on the ratio of cell SSB overhead to cell system message SIB1 and the total RE count; The spectral efficiency calculation unit is used to calculate the spectral efficiency based on the coding rate and modulation order. The user experience rate calculation unit is used to calculate the grid-level uplink and downlink user experience rate based on the number of uplink and downlink scheduling per second, the number of available REs, the spectrum efficiency, the number of uplink and downlink Rank layers in the cell, the bit error rate, and the number of users transmitting data simultaneously in uplink and downlink.
[0084] In one optional embodiment, the perception evaluation module includes: The rate difference calculation submodule is used to calculate the difference between the property-level user experience rate and the property-level user demand rate. The quantization submodule is used to quantify the difference using a linear normalization method to obtain a quantified score of the user rate at the property point level. The Internet speed perception and evaluation submodule is used to perform perception and evaluation of the Internet speed of property-level users based on the quantitative score of the property-level user speed.
[0085] This invention determines the cell-level uplink and downlink demand rates based on the baseline rates of various services and the cell service model; it determines the property-point-level user demand rates based on the uplink and downlink demand rates and the number of sampling points of the main serving cell within the property-point area; it determines the property-point-level user experience rates based on the configuration data, call statistics data, YueDing OTT crowdsourcing data, and engineering parameter data of the main serving cell; and it performs a perception assessment of the property-point-level user internet speed based on the property-point-level user experience rate and the property-point-level user demand rate. This allows for accurate perception assessment of the property-point-level user internet speed using data from the main serving cell within the property-point area, without requiring on-site simulation testing, thus improving the accuracy and efficiency of the property-point-level user internet speed assessment.
[0086] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include a processor 310, a communications interface 320, a memory 330, and a communication bus 340. The processor 310, communications interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute a property-level user internet speed assessment method. This method includes: determining the cell-level uplink and downlink demand rates based on the baseline rates of various services and the cell service model; determining the property-level user demand rate based on the uplink and downlink demand rates and the number of sampling points of the main serving cell within the property area; determining the property-level user experience rate based on the configuration data, call statistics data, OTT crowdsourcing data, and engineering parameter data of the main serving cell; and performing a perceptual assessment of the property-level user internet speed based on the property-level user experience rate and the property-level user demand rate.
[0087] Furthermore, the logical instructions in the aforementioned memory 330 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, essentially, 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.
[0088] 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 property-level user internet speed evaluation method provided by the above methods. The method includes: determining the cell-level uplink and downlink demand rates based on the baseline rates of each service and the cell service model; determining the property-level user demand rate based on the uplink and downlink demand rates and the number of sampling points of the main serving cell within the property area; determining the property-level user experience rate based on the configuration data, call statistics data, over-the-top OTT crowdsourcing data, and engineering parameter data of the main serving cell; and performing a perception evaluation of the property-level user internet speed based on the property-level user experience rate and the property-level user demand rate.
[0089] 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 implements the property-level user internet speed evaluation method provided by the above methods. The method includes: determining the cell-level uplink and downlink demand rates based on the baseline rates of each service and the cell service model; determining the property-level user demand rate based on the uplink and downlink demand rates and the number of sampling points of the main serving cell within the property area; determining the property-level user experience rate based on the configuration data, call statistics data, over-the-top OTT crowdsourcing data, and engineering parameter data of the main serving cell; and performing a perceptual evaluation of the property-level user internet speed based on the property-level user experience rate and the property-level user demand rate.
[0090] 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.
[0091] 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.
[0092] 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 evaluating the internet speed of users at a property management point level, characterized in that, include: Based on the baseline rates of each service and the cell service model, determine the cell-level uplink and downlink demand rates; Based on the uplink and downlink demand rates of the main service communities within the property management area and the number of sampling points, the demand rate of users at the property management level is determined. Based on the configuration data, call statistics data, YueDing OTT crowdsourcing data, and engineering parameter data of the main service cell, the user experience rate at the property point level is determined; Based on the property-level user experience rate and the property-level user demand rate, a perception assessment of the internet speed of property-level users is conducted.
2. The method for evaluating internet speed for property-level users according to claim 1, characterized in that, The process of determining cell-level uplink and downlink demand rates based on baseline rates for each service and cell service models includes: Analyze the test data of target service characteristics, user behavior characteristics, and network performance indicators to determine the baseline rate of each service; The business model for each community is determined based on the traffic share of each service community within the property management area. Based on the baseline rates of each service, the cell service model, and the average concurrent service coefficient, the cell-level uplink and downlink demand rates are calculated.
3. The method for evaluating internet speed for property-level users according to claim 2, characterized in that, The primary residential communities served within the property management area are determined in the following ways: Based on the number of OTT crowdsourcing sampling points in each community within the same property area within a preset statistical period, the communities are arranged to obtain the community arrangement order. Based on the order of the communities, the number of OTT crowdsourcing sampling points in each community is accumulated sequentially until the accumulated number of OTT crowdsourcing sampling points reaches a preset threshold, and the corresponding community is determined as the main service community within the property management area.
4. The method for evaluating internet speed for property-level users according to claim 1, characterized in that, The determination of the property-level user experience rate based on the configuration data, call statistics data, over-the-top OTT crowdsourcing data, and engineering parameter data of the primary serving cell includes: The main service area within the property location is divided into multiple grid areas to obtain the boundary information of multiple grids; Based on the boundary information of the multiple grids and the sampling point location information of the OTT crowdsourcing data, the OTT crowdsourcing data is rasterized to determine the number of OTT sampling points under each grid. For each grid, the grid-level user experience rate is determined based on the configuration data, call statistics data, OTT crowdsourcing data, and engineering parameter data of the primary serving cell within the grid. The property-level user experience rate is determined based on the user experience rate of each grid level within the grid and the number of OTT sampling points under each grid.
5. The method for evaluating internet speed for property-level users according to claim 4, characterized in that, The determination of grid-level user experience rate based on configuration data, call statistics data, OTT crowdsourcing data, and engineering parameter data of the main service cell within the property area includes: Calculate the number of uplink and downlink scheduling operations per second based on the number of time slots per second and the ratio of uplink to downlink symbols; The total number of REs is calculated based on the number of RBs, the number of subcarriers per RB, and the number of symbols per time slot; The number of available REs is calculated based on the ratio of cell SSB overhead to cell system message SIB1 and the total number of REs. Calculate spectral efficiency based on coding rate and modulation order; The grid-level uplink and downlink user experience rate is calculated based on the number of uplink and downlink scheduling per second, the number of available REs, the spectrum efficiency, the number of uplink and downlink rank layers in the cell, the bit error rate, and the number of users transmitting data simultaneously in uplink and downlink.
6. The method for evaluating internet speed for property-level users according to claim 1, characterized in that, The assessment of internet speed for property-level users based on the user experience rate and user demand rate at the property level includes: Calculate the difference between the property-level user experience rate and the property-level user demand rate; The difference is quantified using a linear normalization method to obtain a quantitative score for the user rate at the property level. Based on the quantitative score of the property-level user speed, the perceived internet speed of property-level users is evaluated.
7. A property-level user internet speed assessment device, characterized in that, include: The first rate determination module is used to determine the cell-level uplink and downlink demand rates based on the baseline rates of each service and the cell service model. The second rate determination module is used to determine the user demand rate at the property point level based on the uplink and downlink demand rates of the main service cell within the property point area and the number of sampling points. The third rate determination module is used to determine the property-level user experience rate based on the configuration data, call statistics data, over-the-top OTT crowdsourcing test data, and engineering parameter data of the main serving cell. The perception assessment module is used to perform perception assessment of the internet speed of property-level users based on the property-level user experience rate and the property-level user demand rate.
8. 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 property-level user internet speed evaluation method as described in any one of claims 1 to 6.
9. 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 property-level user internet speed evaluation method as described in any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the property-level user internet speed evaluation method as described in any one of claims 1 to 6.