Method and device for determining coverage effect of wireless communication network

Abstract

The present application discloses a method and device for determining the coverage effect of a wireless communication network. The method includes: obtaining the location information of a target sampling point, wherein the target sampling point is used to sample the terminal device of the target object; calibrating the location information according to a target calibration algorithm to determine the target location information, wherein the target calibration algorithm at least includes the 5G high frequency band level attenuation rate; determining the location information of the location of the target sampling point according to the target location information; determining the coverage effect of the wireless communication network according to the target location information and the location information of the location of the target sampling point. The present application solves the technical problems of low matching rate between the sampling point and the user's location and inaccurate coverage geography due to low accuracy of the latitude and longitude of the sampling point in the wireless network coverage evaluation system, failure to consider the movement speed of the sampling point, and failure to identify the location of the sampling point.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method and device for determining the coverage effect of a wireless communication network. Background Art

[0002] The current wireless network coverage problem assessment system has three sections: "extracting data sources + obtaining user locations + geographically presenting coverage effects". First, extract the minimized drive test data and measurement report data from the network management or data storage and processing platform, and extract the user signaling information from the user information collection platform; second, filter out the latitude and longitude and coverage information of the sampling points in the minimized drive test data and measurement report data in combination with the user signaling; finally, scatter the latitude and longitude and coverage information of the sampling points in the minimized drive test data and measurement report data on the electronic map to present the road and indoor coverage map, analyze the coverage problem points based on the coverage map and output the coverage assessment report.

[0003] The shortcomings of the current evaluation system: 1. In the current evaluation system, the accuracy of the longitude and latitude of the sampling points in the minimized road test data and the measurement report data is low. The longitude and latitude of the indoor sampling points in the minimized road test data have large deviations due to building obstruction. Since the accuracy of the time advance reported in the measurement report data is 1TA (about 78 meters), the longitude and latitude calculated by the current evaluation system are randomly distributed within a radius of 78 meters, resulting in errors in the longitude and latitude of the sampling points in the measurement report data. 2. The current evaluation system does not take into account the user's movement speed and does not identify the user's location, resulting in 7% of road users on the coverage evaluation map being distributed in outdoor areas such as rivers and green belts where users do not frequently move, and 13% of indoor users are distributed in the edge areas outside the building, resulting in a low matching rate between the sampling points and the locations in the minimized road test data and the measurement report data, and inaccurate coverage geography.

[0004] In addition, the current wireless network coverage problem assessment system is mainly built for 4G networks and can be used for 5G networks, but it does not fully match 5G networks.

[0005] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0006] The embodiments of the present application provide a method and device for determining the coverage effect of a wireless communication network, so as to at least solve the technical problems of low matching rate between the sampling points and the user's location and inaccurate coverage geography due to low accuracy of the longitude and latitude of the sampling points in the wireless network coverage evaluation system, failure to consider the movement speed of the sampling points, and failure to identify the locations of the sampling points.

[0007] According to one aspect of an embodiment of the present application, a method for determining the coverage effect of a wireless communication network is provided, including: obtaining location information of a target sampling point, wherein the target sampling point is used to sample a terminal device of a target object; calibrating the location information according to a target calibration algorithm to determine the target location information, wherein the target calibration algorithm at least includes a 5G high frequency band level attenuation rate; determining location information of a location where the target sampling point is located according to the target location information; determining the coverage effect of the wireless communication network according to the target location information and the location information of the location where the target sampling point is located.

[0008] Optionally, the position information is calibrated according to a target calibration algorithm to determine the target position information, including: determining a matching value between a grid in an electronic map where the target sampling point is located and the target sampling point according to the target calibration algorithm; when the matching value is a target matching value, determining the grid corresponding to the target matching value as an attribution grid of the target sampling point; and determining the target position information according to the electronic map and the attribution grid.

[0009] Optionally, the 5G high frequency band level attenuation rate is determined by at least one of the following parameters: 5G network frequency band wavelength, 5G network channel noise, and the distance between the target sampling point and the base station serving the target sampling point.

[0010] Optionally, determining the location information of the location of the target sampling point based on the target position information includes: obtaining the distance between the target sampling point and the target sampling point after a preset time interval; determining the movement rate of the target sampling point based on the preset time interval and the distance; determining the probability that the target sampling point is located indoors and the probability that the target sampling point is located outside of the indoors based on parameters including at least the distance and the movement rate; determining the location information of the location of the target sampling point based on the probability that the target sampling point is located indoors and the probability that the target sampling point is located outside of the indoors.

[0011] Optionally, before determining the location information of the target sampling point according to the target location information, the method also includes: determining a first distance and a second distance, wherein the first distance is the coverage radius of the base station serving the sampling point, and the second distance is the distance between the sampling point and the base station; when the second distance is greater than the first distance, determining that the sampling point is an abnormal sampling point, and deleting the abnormal sampling point from the sampling points.

[0012] Optionally, the location information includes at least longitude and latitude information, and obtaining the location information of the target sampling point includes: obtaining the location information of the target sampling point according to at least one of the following data: measurement report data, minimization of drive test data, and signaling data.

[0013] Optionally, the coverage effect of the wireless communication network is determined based on the target position information and the location information of the target sampling point, including: fitting the target sampling point with the target layer of the electronic map to determine the wireless communication network coverage effect map, wherein the target layer includes at least: a road segmentation layer and a building grid layer; and determining the coverage effect of the wireless communication network based on the wireless communication network coverage effect map.

[0014] According to another aspect of an embodiment of the present application, a device for determining the coverage effect of a wireless communication network is also provided, including: an acquisition module, used to acquire location information of a target sampling point, wherein the target sampling point is used to sample a terminal device of a target object; a calibration module, used to calibrate the location information according to a target calibration algorithm to determine the target location information, wherein the target calibration algorithm at least includes a 5G high frequency band level attenuation rate; a determination module, used to determine location information of a location where the target sampling point is located according to the target location information; and an evaluation module, used to determine the coverage effect of the wireless communication network according to the target location information and the location information of the location where the target sampling point is located.

[0015] According to another aspect of an embodiment of the present application, a non-volatile storage medium is further provided, the storage medium including a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the above method for determining the coverage effect of a wireless communication network.

[0016] According to yet another aspect of an embodiment of the present application, a processor is further provided, and the processor is used to run a program stored in a memory, wherein the above method for determining the coverage effect of a wireless communication network is executed when the program is running.

[0017] In an embodiment of the present application, the location information of a target sampling point is obtained, wherein the target sampling point is used to sample a terminal device of a target object; the location information is calibrated according to a target calibration algorithm to determine the target location information, wherein the target calibration algorithm at least includes a 5G high frequency band level attenuation rate; according to the target location information, the location information of the location where the target sampling point is located is determined; according to the target location information and the location information of the location where the target sampling point is located, the coverage effect of the wireless communication network is determined. By calibrating the location information of the sampling point, the location information of the location where the sampling point is located is determined, thereby achieving the purpose of improving the matching rate between the sampling point and the location where the sampling point is located, thereby achieving the technical effect of more accurately determining the coverage effect of the wireless communication network, and further solving the technical problems of low matching rate between the sampling point and the location where the user is located and inaccurate coverage geography caused by low accuracy of latitude and longitude of the sampling point in the wireless network coverage evaluation system, failure to consider the movement speed of the sampling point, and failure to identify the location where the sampling point is located. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 is a flow chart of a method for determining the coverage effect of a wireless communication network according to an embodiment of the present application;

[0020] Figure 2 is a flowchart of another method for determining the coverage effect of a wireless communication network according to an embodiment of the present application;

[0021] Figure 3 It is a structural diagram of a device for determining the coverage effect of a wireless communication network according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] According to an embodiment of the present application, an embodiment of a method for determining the coverage effect of a wireless communication network is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] Figure 1 is a flow chart of a method for determining the coverage effect of a wireless communication network according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0026] Step S102: acquiring location information of a target sampling point, wherein the target sampling point is used to sample a terminal device of a target object.

[0027] According to an optional embodiment of the present application, minimized drive test data and measurement report data are extracted from a network management system or a data storage and processing platform, and user signaling information is extracted from a user information collection platform. The location information of the sampling point can be obtained from the minimized drive test data, measurement report data, and user signaling information. Among them, the network management system refers to planning, supervising, and controlling the use of network resources and various network activities to optimize the performance of the network. Network management has five major functions: failure management, configuration management, performance management, security management, and billing management.

[0028] Step S104, calibrating the location information according to the target calibration algorithm to determine the target location information, wherein the target calibration algorithm at least includes the 5G high frequency band level attenuation rate.

[0029] According to another optional embodiment of the present application, after extracting the user sampling point location information, the present application proposes a new minimization of drive test and measurement report sampling point location calibration model. This model integrates the grid location, neighboring area relationship, coverage level, and 5G high frequency band level attenuation rate dimensional information to construct a new grid MDT and MR sampling point feature matching rate scoring algorithm. After iterative calculation, the grid with the highest score is used as the belonging grid of the user sampling point. The details are as follows:

[0030] 1. Grid location dimension. The grid location dimension is characterized by the cell matching weight, which is the probability of the sampling point appearing in the grid:

[0031] k n =Nk n / (N 1 k 1 +N 2 k 2 +…+N n k n )

[0032] Among them, k n is the cell matching weight, and N is the number of cells.

[0033] 2. Neighborhood relationship dimension. MatchConstant Used to characterize the relationship between neighboring areas and level matching:

[0034] C MatchConstant =P 0 *P 1 *P2 *P 3

[0035] Among them, P 0 , P 1 , P 2 , P 3 They are the matching rates of the primary service cell, the first neighboring cell, the second neighboring cell, and the third neighboring cell respectively.

[0036] 3. Coverage level dimension. The difference X between the level of cell n in the measurement report and the level of cell n in the grid represents the coverage level dimension. The smaller X is, the higher the probability that the sampling point belongs to the grid:

[0037] X=Rx n -rx n

[0038] Among them, Rx n is the level of cell n in the measurement report, rx n is the level of cell n in the grid.

[0039] 4.5G high frequency band level attenuation dimension. 5G high frequency band generally refers to the frequency band above 6GHz. Its continuous large bandwidth can meet the extremely high user experience rate and system capacity requirements in hot spots. Factors such as air density, rain, and distance cause significant level attenuation in the high frequency band of the 5G network. According to the level attenuation rate P n Characterize the 5G high frequency band level attenuation rate dimension:

[0040]

[0041] Among them, λ is the wavelength of the 5G network frequency band, n LOS 5G network channel noise, σ LOS is the atmospheric noise, and d is the distance between the target sampling point and the base station serving the target sampling point.

[0042] Combining the above grid location dimension, neighboring area relationship dimension, coverage level dimension and 5G high frequency band level attenuation rate dimension, the grid sampling point feature matching rate scoring algorithm formula is as follows:

[0043]

[0044] The grid sampling point feature matching rate scoring algorithm formula is the core formula of the new grid minimization road test and measurement report sampling point feature matching rate scoring algorithm. Among them, M is the matching value between the sampling point and the grid. After iterative calculation of the grid sampling point feature matching rate scoring algorithm formula, each grid calculates an M value. The larger the M value, the higher the matching degree between the grid and the sampling point. The grid with the largest M value is the sampling point grid. The sampling point determines the grid to which the sampling point belongs based on the size of the M value.

[0045] According to the above steps, the new minimization of drive test and measurement report sampling point location calibration model has obvious advantages. On the one hand, the current evaluation system directly extracts longitude and latitude and has no longitude and latitude calibration model; on the other hand, this model adopts a new grid minimization of drive test and measurement report sampling point feature matching rate scoring algorithm. The algorithm integrates four dimensions such as 5G high frequency band level attenuation rate, taking into account the comprehensiveness of longitude and latitude calibration. The grid with the highest iterative calculation score is the grid to which the sampling point belongs, taking into account the accuracy of longitude and latitude calibration.

[0046] Step S106: Determine the location information of the target sampling point according to the target location information.

[0047] In some optional embodiments of the present application, first, the movement speed of the sampling point is calculated, and the sampling points of the valid longitude and latitude of each sampling point are numbered in time order; starting from the second sampling point, the distance difference and time difference between each sampling point and the previous sampling point are calculated, and the movement speed is calculated as the instantaneous speed of the current sampling point. Movement speed calculation formula:

[0048]

[0049] Among them, Dist i-1_i is the distance from sampling point i-1 to sampling point i, T i -T i-1 is the time difference from sampling point i-1 to sampling point i.

[0050] Then, the location of the user at the sampling point is determined based on the new user location matching algorithm based on the user's motion state, thereby improving the matching rate between the user at the sampling point and the location. The algorithm is divided into five steps: determining the characteristic attributes of the user's location; obtaining training sample data; calculating the probability of each category in the training sample; calculating the probability of each characteristic attribute division under each condition; and using a classifier to identify whether the user's location at the sampling point is road or indoor.

[0051] 1. Determine the characteristic attributes of the sampling points and classify each characteristic attribute. Based on the behavioral characteristics of road users, three characteristic attributes are selected to achieve: a 1 : User movement rate (unit time); a 2 : User relative distance (unit time); a 3 : User location information matches the road contour, setting C = 0 indicates that the user is on the road, and C = 1 indicates that the user is indoors. 1 The classification criteria are: α 1 =0.5m / s,α 2 =1m / s; definition category: definition category 1: a 1 <0.5m / s, definition category 2: 0.5m / s <a1 <1m / s, definition category 3: a 1 >1m / s. Relative distance a 2 The classification criteria are: β 1 =100m,β 2 =300m; definition category: definition category 1: a 2 <100m, definition category 2: 100m 2 <300m, definition category 3: a 2 >300m. User location information and road profile matching a 3 , classification standard C in road user attributes = 0, road user category 1: a 3 =0; Classification standard C in indoor user attributes = 1, indoor user category 1: a 3 =1.

[0052] 2. Obtain training sample data. 1,000 users who have been manually tested by on-site personnel are used as training samples, and the Bayesian formula is used to obtain:

[0053]

[0054] Let the training data set be X, |X| represents the number of samples. Suppose there are K classes C K ,k=1,2,3…k,|C K | belongs to class C K The number of samples is:

[0055] ∑ k |C K |=|X|

[0056] Assume that feature C has n different values ​​{c 1 ,c 2 …c n}, divide X into N parts according to the value of feature C 1 ,X 2 …X n ,|X i | for X i The number of samples is:

[0057] ∑ i |X i |=|X i |

[0058] Note subset X i Belongs to class C K The set of samples is X ik ,|X ik | for X ik The number of samples.

[0059] ​3. Calculate the probability of each category in the training sample. Calculate the ratio of the number of road users and indoor users in the training sample, and get: the probability of road user samples is P(c=0), and the probability of indoor user samples is P(c=1).

[0060] 4. Calculate the probability of each feature attribute partition under each condition.

[0061] According to Bayes' theorem, the eigenvalues ​​are defined to be relatively independent, and the formula is derived as follows:

[0062]

[0063] Among them, P(c) is the class prior probability; P(x|c) is the likelihood probability of sample x relative to class label c; P(x) is the evidence factor used for normalization. For a given sample x, the evidence factor P(x) is independent of the class label, so the problem of estimating P(c|x) is transformed into how to estimate the prior probability P(c) and the likelihood probability P)x|c) based on the training data D. Since the denominator is a constant for all categories, it is sufficient to maximize the numerator. And because each feature attribute is conditionally independent, so:

[0064]

[0065] It can be seen that the conditional probability of division P(a|c) is the key step of Bayesian classification, which directly affects the discrimination characteristics of subsequent data classification. Therefore, the algorithm uses the conditional probability of feature attribute division under the estimated category and Laplace calibration to calibrate the conditional probability.

[0066] 5. Use the classifier to identify the user's location. For given data, use the classifier to identify and assign the data corresponding user attributes: For example, if a sample data is obtained, define α 1 =0.5m / s,α 2 =1m / s; β 1 =100m,β 2 =300m, the user's unit time movement speed is 0.8m / s, and the relative running distance is 389m, which belongs to road attribute a 3 =0. Then,

[0067] The probability that the user is in a road location is:

[0068] P(c=0)P(x|c=0)

[0069] =P(c=0)P(0.5 <a1<1|c=0)P(a2> 300|c=0)P(a3=0|c=0)

[0070] =P(c=0)P a1-c02 P a2-c03 Pa3-c01

[0071] Among them, P a1-c02 :a 1 is the motion rate attribute, c 0 For road users, 2 means the movement speed is in the category of 0.5m / s 1 <1m / s; P a2-c03 :a 2 is the user's relative distance attribute, c 0 For road users, 3 refers to users whose relative distance is in category a 2 >300m;P a3-c01 :a 3 is the user's relative distance attribute, c 0 is a road user, and 1 means that the road user is in category 1.

[0072] The probability that the user is in an indoor place is:

[0073] P(c=1)P(x|c=1)

[0074] =P(c=1)P(0.5 <a1<1|c=1)P(a2> 300|c=1)P(a3=0|c=1)

[0075] =P(c=1)P a2-c13 P a1-c12 P a3-c11

[0076] Among them, P a1-c12 :a 1 is the motion rate attribute, c 1 For indoor users, 2 fingers moving at a speed of 0.5 m / s 1 <1m / s; P a2-c13 :a 2 is the user's relative distance attribute, c 1 For indoor users, 3 refers to users whose relative distance is in category a 2 >300m;P a3-c11 :a 3 Road user attributes, c 1 Indoor user, 1 means the indoor user is in category 1.

[0077] The probability of the user being in a road place and the probability of the user being in an indoor place are used to determine whether the user is in a road place or an indoor place.

[0078] ​​Through the above steps, the new user location preprocessing model has obvious advantages: on the one hand, the current evaluation system does not have a model that minimizes the matching of users at the sampling points of road tests and measurement reports with the locations where the users are located (roads, indoors); on the other hand, this model eliminates sampling points with abnormal longitude and latitude, determines the user's motion status, and uses a new user location matching algorithm based on the user's motion status. This ensures the matching rate between the users at the sampling points and the locations where they are located, and can accurately identify indoor users and road users.

[0079] Step S108, determining the coverage effect of the wireless communication network according to the target location information and the location information of the location where the target sampling point is located.

[0080] As an optional embodiment of the present application, the road user sampling points are fitted with the road segmentation layer of the electronic map to present a road coverage evaluation map; the indoor user sampling points are fitted with the building grid layer of the electronic map to present an indoor coverage evaluation map.

[0081] According to the above steps, by calibrating the location information of the sampling point and determining the location information of the sampling point, the purpose of improving the matching rate between the sampling point and the location is achieved, thereby achieving the technical effect of more accurately determining the coverage effect of the wireless communication network, and further solving the technical problems of low matching rate between the sampling point and the user's location and inaccurate coverage geography caused by low accuracy of the longitude and latitude of the sampling point in the wireless network coverage evaluation system, failure to consider the movement speed of the sampling point, and failure to identify the location of the sampling point.

[0082] According to an optional embodiment of the present application, the position information is calibrated according to a target calibration algorithm to determine the target position information, including the following steps: determining a matching value between a grid in an electronic map where a target sampling point is located and the target sampling point according to the target calibration algorithm; when the matching value is a target matching value, determining a grid corresponding to the target matching value as an attribution grid of the target sampling point; and determining the target position information according to the electronic map and the attribution grid.

[0083] According to another optional embodiment of the present application, the target calibration algorithm is:

[0084]

[0085] Among them, K n is the cell matching weight, C MatchConstant To adjust the relationship between neighboring cells and level matching, Rx n is the level of cell n in the measurement report, rx n is the level of cell n in the grid, P n is the attenuation rate of the 5G network frequency band.

[0086] In some optional embodiments of the present application, the 5G high frequency band level attenuation rate is determined by at least one of the following parameters: 5G network frequency band wavelength, 5G network channel noise, and the distance between the target sampling point and the base station serving the target sampling point.

[0087] As an optional embodiment of the present application, the formula for the 5G high frequency band level attenuation rate is:

[0088]

[0089] Among them, λ is the wavelength of the 5G network frequency band, n LOS 5G network channel noise, σ LOS is the atmospheric noise, and d is the distance between the target sampling point and the base station serving the target sampling point.

[0090] In some optional embodiments of the present application, determining the location information of the location of the target sampling point based on the target position information can be achieved by the following method: obtaining the distance between the target sampling point and the target sampling point after a preset time interval; determining the movement rate of the target sampling point based on the preset time interval and the distance; determining the probability that the target sampling point is located indoors and the probability that the target sampling point is located non-indoors based on parameters including at least the distance and the movement rate; determining the location information of the location of the target sampling point based on the probability that the target sampling point is located indoors and the probability that the target sampling point is located non-indoors.

[0091] As another optional embodiment of the present application, the sampling points of the valid longitude and latitude of each sampling point are numbered in time order; starting from the second sampling point, the distance difference and time difference between each sampling point and the previous sampling point are calculated, and the movement speed is calculated as the instantaneous speed of the current sampling point. The movement speed calculation formula is:

[0092]

[0093] Among them, Dist i-1_i is the distance from sampling point i-1 to sampling point i, T i -T i-1 is the time difference from sampling point i-1 to sampling point i.

[0094] The new user location matching algorithm based on the user's motion state determines the location of the user at the sampling point. The algorithm is divided into five steps: determine the characteristic attributes of the user's location; obtain training sample data; calculate the probability of each category in the training sample; calculate the probability of each characteristic attribute division under each condition; use the classifier to identify the location of the user at the sampling point as road or indoor. Through the above steps, the probability of the user being in a road location and the probability of the user being in an indoor location can be obtained. When the probability of the user being in a road location is greater than the probability of the user being in an indoor location, the user is determined to be in a road location; when the probability of the user being in an indoor location is greater than the probability of the user being in a road location, the user is determined to be in an indoor location.

[0095] According to an optional embodiment of the present application, before determining the location information of the target sampling point according to the target location information, the method also includes: determining a first distance and a second distance, wherein the first distance is the coverage radius of the base station serving the sampling point, and the second distance is the distance between the sampling point and the base station; when the second distance is greater than the first distance, determining that the sampling point is an abnormal sampling point, and deleting the abnormal sampling point from the sampling points.

[0096] According to another optional embodiment of the present application, sampling points with obvious abnormalities in longitude and latitude are eliminated, and the abnormalities in longitude and latitude are determined based on the cell coverage range and the maximum time advance in the measurement report. The maximum time advance refers to the difference between the actual time when the mobile station signal arrives at the base station and the time when the mobile station signal arrives at the base station when the distance between the mobile station and the base station is 0. Eliminate unreasonable longitude and latitude according to the cell coverage range. The fused data is now combined with the fields in the cell identification number in combination with the basic engineering parameters to determine the cell coverage range, and unreasonable longitude and latitude whose locations are obviously beyond the cell coverage range are eliminated. Sampling points with abnormal longitude and latitude are eliminated based on the maximum time advance in the measurement report. Due to the influence of the geographical environment on the propagation path, the base station cell signal received by the terminal is usually not a straight line propagation. The distance between the terminal and the base station should be less than the path distance for the signal to reach the base station. The formula is expressed as:

[0097]

[0098] Among them, c is the speed of light, and TADV is the MR.LteScTadv field. Through the above algorithm, the longitude and latitude sampling points with inaccurate positioning can be eliminated to further improve the positioning accuracy.

[0099] In some optional embodiments of the present application, the location information includes at least longitude and latitude information, and obtaining the location information of the target sampling point includes: obtaining the location information of the target sampling point according to at least one of the following data: measurement report data, minimization of drive test data, and signaling data.

[0100] As an optional embodiment of the present application, minimizing drive testing is a technology for the communication system to automatically collect and analyze user equipment measurement reports containing location information, which is used to minimize the workload of manual drive testing. However, the minimization of drive testing technology cannot completely replace drive testing, so it is called "minimized" drive testing. User equipment refers to a device in a wireless network that enables users to communicate through a wireless communication network. The measurement report is the original network data measured by the user summary segment. The measurement report carries relevant information about the uplink and downlink wireless links, including the receiving level, interference signal code power, block error rate and transmission power. In-depth analysis based on measurement reports is one of the effective means of network performance evaluation and optimization such as network problem location, network coverage analysis and neighboring area optimization. The transmission of any actual application information between communication devices is always accompanied by the transmission of some control information. They work according to the established communication protocol and transmit the application information to the destination safely, reliably and efficiently. This information is called protocol control information in computer networks and signaling in telecommunications networks. Signaling data refers to control instructions in the communication system, also known as "signaling". It can guide the coordinated operation of terminals, switching systems and transmission systems, establish temporary communication channels between designated terminals, and maintain the normal operation of the network itself. The signaling system is an important part of the communication network and is the nervous system of the communication network. In addition to user information during communication (including voice information and non-voice service information), the signal that controls the operation of the switch is signaling data.

[0101] In some optional embodiments of the present application, the coverage effect of the wireless communication network is determined based on the target position information and the location information of the target sampling point, and this is achieved by the following method: fitting the target sampling point with the target layer of the electronic map to determine the wireless communication network coverage effect map, wherein the target layer includes at least: a road segmentation layer and a building grid layer; and determining the coverage effect of the wireless communication network based on the wireless communication network coverage effect map.

[0102] As another optional embodiment of the present application, the road user sampling points are fitted with the road segmentation layer of the electronic map to present a road coverage assessment map; the indoor user sampling points are fitted with the building grid layer of the electronic map to present an indoor coverage assessment map. Based on the coverage assessment map, wireless network coverage issues such as weak coverage and signal to interference plus noise ratio are analyzed, and a coverage assessment report is output.

[0103] Figure 2 is a flowchart of another method for determining the coverage effect of a wireless communication network according to an embodiment of the present application, such as Figure 2As shown, the method includes the following steps: extracting the longitude and latitude of the global positioning system and coverage information reported by the terminal in the minimized drive test sampling point, extracting the relative displacement from the current position of the measurement report sampling point to the cell, and matching the cell engineering parameters to deduce the longitude and latitude of the measurement report sampling point. Minimized drive test and measurement report data that do not contain longitude and latitude are associated with user signaling to obtain longitude and latitude and coverage information. Engineering parameters refer to basic engineering parameters, including: azimuth, downtilt, pitch angle, etc. of the cell; coverage distance, frequency, etc. of the base station. The position of the sampling point is calibrated using a new sampling point position calibration model, and the user's motion state is identified through the calibrated position information. The user is judged to be an outdoor user or an indoor user based on the new user location matching algorithm of the user's motion state. When the user is an outdoor user, a road coverage effect diagram is presented; when the user is an indoor user, an indoor coverage effect diagram is presented. A coverage evaluation report is obtained based on the road coverage effect diagram and the indoor coverage effect diagram.

[0104] According to the above steps, relative to the current evaluation system, this application proposes a new minimized road test and measurement report sampling point location calibration model, which mainly adopts a new minimized road test and measurement report grid sampling point feature matching rate scoring algorithm, which improves the latitude and longitude accuracy of the sampling points by more than 8pp; the present invention proposes a new user location preprocessing model, which mainly adopts a new user location matching algorithm based on the user's motion state, which improves the matching degree between outdoor users and their roads by more than 12pp, and improves the matching degree between indoor users and their buildings by more than 11pp.

[0105] As an optional embodiment of the present application, in the core urban area of ​​a certain city, after the evaluation system of the present invention is implemented, compared with the current evaluation system, the accuracy of longitude and latitude is improved from 85% to 94%, the matching rate of outdoor user sampling points and the roads where they are located is improved from 79% to 91%, and the matching rate of indoor user sampling points and the buildings where they are located is improved from 74% to 86%.

[0106]

[0107]

[0108]

[0109] Figure 3 is a structural diagram of a device for determining the coverage effect of a wireless communication network according to an embodiment of the present application, such as Figure 3 As shown, the device comprises:

[0110] An acquisition module 30, used to acquire location information of a target sampling point, wherein the target sampling point is used to sample a terminal device of a target object;

[0111] A calibration module 32, configured to calibrate the position information according to a target calibration algorithm to determine the target position information, wherein the target calibration algorithm at least includes a 5G high frequency band level attenuation rate;

[0112] A determination module 34, used to determine the location information of the location of the target sampling point according to the target location information;

[0113] The evaluation module 36 is used to determine the coverage effect of the wireless communication network according to the target location information and the location information of the location where the target sampling point is located.

[0114] It should be noted that Figure 3 The preferred implementation of the illustrated embodiment can be seen in Figure 1 The relevant description of the illustrated embodiment will not be repeated here.

[0115] An embodiment of the present application further provides a non-volatile storage medium, which includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the above method for determining the coverage effect of the wireless communication network.

[0116] A program for a non-volatile storage medium to perform the following functions: obtaining location information of a target sampling point, wherein the target sampling point is used to sample a terminal device of a target object; calibrating the location information according to a target calibration algorithm to determine the target location information, wherein the target calibration algorithm at least includes a 5G high frequency band level attenuation rate; determining location information of a location where the target sampling point is located according to the target location information; determining the coverage effect of a wireless communication network according to the target location information and the location information of the location where the target sampling point is located.

[0117] An embodiment of the present application further provides a processor, which is used to run a program stored in a memory, wherein the above method for determining the coverage effect of a wireless communication network is executed when the program is run.

[0118] The processor is used to run a program that performs the following functions: obtaining location information of a target sampling point, wherein the target sampling point is used to sample a terminal device of a target object; calibrating the location information according to a target calibration algorithm to determine the target location information, wherein the target calibration algorithm at least includes a 5G high frequency band level attenuation rate; determining location information of a location where the target sampling point is located according to the target location information; determining a coverage effect of a wireless communication network according to the target location information and the location information of the location where the target sampling point is located.

[0119] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0120] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0122] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0123] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0124] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0125] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining the coverage effect of a wireless communication network, characterized in that: include: Acquire location information of a target sampling point, wherein the target sampling point is used to sample a terminal device of a target object; The position information is calibrated according to a target calibration algorithm to determine the target position information, wherein the target calibration algorithm is: Wherein, M is the matching value, which is used to calibrate the position information, and K n is the cell matching weight, which is used to indicate the probability of cell n appearing as the primary serving cell, N is the number of cells, C MatchConstant is the relationship between the neighboring area relationship and the level matching degree, Rx n is the level of cell n in the measurement report, rx n is the level of cell n in the grid, P n is the level attenuation rate of 5G high frequency band; Among them, C MatchConstant Determined according to the following formula: C MatchConstant =P0*P1*P2*P3 Among them, P0, P1, P2, and P3 are the matching rate of the primary service cell, the matching rate of the first neighboring cell, the matching rate of the second neighboring cell, and the matching rate of the third neighboring cell respectively; The target location information is calibrated according to a target calibration algorithm to determine the target location information, including: determining a matching value between a grid in an electronic map where the target sampling point is located and the target sampling point according to the target calibration algorithm; when the matching value is a target matching value, determining a grid corresponding to the target matching value as an attribution grid of the target sampling point; determining the target location information according to the electronic map and the attribution grid, wherein the target matching value is the largest matching value among the matching values; and determining location information of the location where the target sampling point is located according to the target location information; The coverage effect of the wireless communication network is determined according to the target location information and the location information of the location where the target sampling point is located.

2. The method according to claim 1, characterized in that The 5G high frequency band level attenuation rate is determined by at least one of the following parameters: 5G network frequency band wavelength, 5G network channel noise, and a distance between the target sampling point and a base station serving the target sampling point.

3. The method according to claim 1, characterized in that Determining the location information of the target sampling point according to the target location information includes: Acquire the distance between the target sampling point and the target sampling point after a preset time interval; Determine the movement rate of the target sampling point according to the preset time interval and the distance; Determine, according to parameters including at least the distance and the movement rate, a probability that the target sampling point is located indoors and a probability that the target sampling point is located outside of the indoors; According to the probability that the target sampling point is located indoors and the probability that the target sampling point is located outside of indoors, the location information of the location where the target sampling point is located is determined.

4. The method according to claim 1, characterized in that: Before determining the location information of the location of the target sampling point according to the target location information, the method further includes: Determine a first distance and a second distance, wherein the first distance is a coverage radius of a base station serving a sampling point, and the second distance is a distance between the sampling point and the base station; When the second distance is greater than the first distance, the sampling point is determined to be an abnormal sampling point, and the abnormal sampling point is deleted from the sampling points.

5. The method according to claim 1, characterized in that: The location information at least includes longitude and latitude information, and obtaining the location information of the target sampling point includes: The location information of the target sampling point is obtained according to at least one of the following data: measurement report data, minimization of drive tests data, and signaling data.

6. The method according to claim 1, characterized in that Determining the coverage effect of the wireless communication network according to the target location information and the location information of the target sampling point includes: Fitting the target sampling point with the target layer of the electronic map to determine the wireless communication network coverage effect map, wherein the target layer at least includes: a road segmentation layer and a building grid layer; The coverage effect of the wireless communication network is determined according to the wireless communication network coverage effect diagram.

7. A device for determining coverage effect of a wireless communication network, characterized in that: include: An acquisition module, used to acquire location information of a target sampling point, wherein the target sampling point is used to sample a terminal device of a target object; A calibration module is used to calibrate the position information according to a target calibration algorithm to determine the target position information, wherein the target calibration algorithm is: Wherein, M is the matching value, which is used to calibrate the position information, and K n is the cell matching weight, which is used to indicate the probability of cell n appearing as the primary serving cell, N is the number of cells, C MatchConstant is the relationship between the neighboring area relationship and the level matching degree, Rx n is the level of cell n in the measurement report, rx n is the level of cell n in the grid, P n is the level attenuation rate of 5G high frequency band; Among them, C MatchConstant Determined according to the following formula: C MatchConstant =P0*P1*P2*P3 Among them, P0, P1, P2, and P3 are the matching rate of the primary service cell, the matching rate of the first neighboring cell, the matching rate of the second neighboring cell, and the matching rate of the third neighboring cell respectively; The target location information is calibrated according to a target calibration algorithm to determine the target location information, including: determining a matching value between a grid in an electronic map where the target sampling point is located and the target sampling point according to the target calibration algorithm; when the matching value is a target matching value, determining a grid corresponding to the target matching value as an attribution grid of the target sampling point; determining the target location information according to the electronic map and the attribution grid, wherein the target matching value is the largest matching value among the matching values; A determination module, used to determine the location information of the location of the target sampling point according to the target location information; An evaluation module is used to determine the coverage effect of the wireless communication network according to the target location information and the location information of the location where the target sampling point is located.

8. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the method for determining the coverage effect of a wireless communication network as described in any one of claims 1 to 6.

9. A processor, characterized in that: The processor is used to run a program stored in the memory, wherein the program, when running, executes the method for determining the coverage effect of a wireless communication network as described in any one of claims 1 to 6.

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