Patent Description:
This application relates to the field of wireless signals, and in particular, to a wireless signal propagation prediction methods and corresponding apparatuses, chip systems, computer readable storage mediums and computer program products.

Wireless signal propagation prediction is a core capability for wireless communications network planning, construction, and optimization, and is a part of competitiveness of wireless network solutions. There were a plurality of lawsuits related to the precision of wireless signal propagation prediction in the past. For a long time, research methods of wireless signal propagation prediction mainly include propagation models, ray tracing, and the like. However, based on current research results, current research methods cannot achieve a balance between precision and operation efficiency of wireless signal propagation prediction. In particular, this problem is aggravated with accelerated <NUM> deployment around the world.

With rapid development of digitalization, the Internet of Things becomes a primary focus of <NUM>. Internet of Awareness and Internet of Everything gradually become a reality. A large quantity of fixed connections provide new input for wireless signal propagation prediction and lay a foundation for new wireless signal propagation prediction solutions. Conventional wireless signal propagation prediction solutions mainly include a drive test mode and prediction based on a wireless propagation model.

The drive test mode specifically includes: After a network is stable, a drive test path is planned. In a planning process, spatial unbiased sampling of sampling points needs to be focused on, and a wireless propagation feature needs to be fully considered. The following key points need to be met simultaneously: covering far and near areas of a base station; covering a direct-radiated area and a non-direct-radiated area; and covering various spatial geographical features in a to-be-tested area, for example, different heights and different spatial functional areas. If manpower and material resources are sufficient, all coverage areas of a to-be-tested cell need to be traversed as much as possible. After the planned path is determined, drive test personnel usually use test devices to collect signals point by point based on the planned path, and then perform signal prediction based on the collected signals. Costs of the drive test mode are high. Every year, a lot of manpower and material resources are consumed for drive tests, and a length may reach tens of millions of kilometers or more. However, coverage areas are mainly roads, and non-road areas and even indoor space are still blind spots. Usually, a service is generated in non-road space. Therefore, a result obtained by using the drive test method may have an estimation deviation.

Propagation model-based prediction is performing an abstract process for a wireless channel based on a mathematical language after fully understanding an environment that affects wireless propagation and a propagation feature of wireless propagation. Propagation models are mainly classified into statistical models and deterministic models. However, both the statistical model and the deterministic model are wireless channel propagation models obtained based on an electromagnetic wave propagation theory, and some simplification processing needs to be performed in a process of model and method establishment. Especially, in the statistical model, a large difference from an actual measured signal usually exists. In comparison, precision of the deterministic model is slightly improved, but a complex calculation process in the deterministic model and a strict requirement on calculation input (for example, precise construction and restoration of three-dimensional building information) restrict wide use of this method.

Embodiments of this application provide a wireless signal propagation prediction methods and corresponding apparatuses, chip systems, computer readable storage mediums and computer program products. According to embodiments of this application, precision of a wireless signal propagation model is improved, and accuracy of obtaining wireless signal received strength of a terminal at any location in prediction space based on the wireless signal propagation model is improved.

According to a first aspect, an embodiment of this application provides a wireless signal propagation prediction method, and the method includes:
obtaining location information of each of S first sampling points in prediction space and wireless signal received strength of a terminal at a location indicated by the location information, where S is an integer greater than <NUM>; obtaining a first parameter of the prediction space through calculation based on location information of the S first sampling points and corresponding wireless signal received strength, where the first parameter is used to indicate a degree of a global spatial autocorrelation characteristic of the prediction space; obtaining a target algorithm based on the first parameter, and generating a wireless signal propagation model of the prediction space based on the target algorithm, the location information of the S first sampling points, and the corresponding wireless signal received strength; and obtaining wireless signal received strength of the terminal at any location in the prediction space based on the wireless signal propagation model of the prediction space.

A larger value of the first parameter indicates a higher degree of the global spatial autocorrelation characteristic of the prediction space.

Optionally, the location information may be coordinates, or may be grid information indicating a location.

A suitable algorithm is selected according to a specific policy, and a wireless signal propagation model is generated based on the algorithm and a plurality of sampling points in the prediction space. This improves precision of the wireless signal propagation model, and improves accuracy of obtaining wireless signal received strength of the terminal at any location in the prediction space based on the wireless signal propagation model.

In a feasible embodiment, the obtaining a target algorithm based on the first parameter of the prediction space includes:
obtaining a first algorithm and determining the first algorithm as the target algorithm when determining, based on the first parameter, that the prediction space has the global spatial autocorrelation characteristic; or obtaining a second algorithm and determining the second algorithm as the target algorithm when determining, based on the first parameter, that the prediction space has the global spatial autocorrelation characteristic.

In an example, in a process of obtaining the target algorithm based on the first parameter of the prediction space, whether the prediction space has the global spatial autocorrelation characteristic is determined through the following steps:
if the first parameter of the prediction space is greater than a first preset threshold, the prediction space has the global spatial autocorrelation characteristic; or if the first parameter of the prediction space is not greater than the first preset threshold, the prediction space does not have the global spatial autocorrelation characteristic.

In a feasible embodiment, the first algorithm includes any method in a Kriging method cluster or a BHM algorithm, and the obtaining a first algorithm includes:
determining whether the wireless signal received strength of the S first sampling points meets preset probability distribution; and determining the BHM algorithm as the first algorithm if the wireless signal received strength of the S first sampling points meets the preset probability distribution; or determining any method in the Kriging method cluster as the first algorithm if the wireless signal received strength of the S first sampling points does not meet the preset probability distribution.

Optionally, the preset probability distribution may be normal distribution, Poisson distribution, binomial distribution, Gaussian distribution, or other probability distribution.

Further determining is performed according to the above method, and a more suitable algorithm can be selected for the prediction space. This improves precision of a wireless signal propagation model generated based on the algorithm, and further improves accuracy of obtaining wireless signal received strength of the terminal at any location in the prediction space based on the wireless signal propagation model.

In a feasible embodiment, the second algorithm includes a machine learning algorithm, any method in a B-shade method cluster, any method in an MSN method cluster, or a stratified Kriging algorithm, and the obtaining a second algorithm includes:
performing area division on the prediction space to obtain a plurality of target areas; and determining the stratified Kriging algorithm as the second algorithm when each of the plurality of target areas meets a preset condition; or obtaining a third algorithm and determining the third algorithm as the second algorithm when any one of the plurality of target areas does not meet the preset condition, where the third algorithm includes a machine learning algorithm, any method in the B-shade method cluster, or any method in the MSN method cluster.

In a feasible embodiment, the preset condition includes a first preset condition, a second preset condition, and a third preset condition, and the determining the stratified Kriging algorithm as the second algorithm when each of the plurality of target areas meets a preset condition includes:.

In a feasible embodiment, the first preset condition includes that the target area has a global spatial autocorrelation characteristic; the second preset condition includes that the target area includes a first sampling point; and the third preset condition includes that a quantity of first sampling points in the target area is greater than a preset quantity, or the quantity of first sampling points in the target area is greater than the preset quantity and wireless signal received strength of the first sampling point in the target area meets spatial second-order stationarity.

In a feasible embodiment, the performing area division on the prediction space to obtain a plurality of target areas includes:.

The T dimension indicators include a visible area and an invisible area, building information (for example, a building type, a geometric size, and a construction age), a terrain classification, a city function, and the like.

According to a second aspect, an embodiment of this application provides another wireless signal propagation prediction method, and the method includes:
obtaining one or more second areas when precision of a first wireless signal propagation model is lower than a prediction precision expectation, where an absolute value of a difference between a predicted value of wireless signal received strength of a terminal in the second area and a check value is greater than a second preset threshold, the predicted value of the wireless signal received strength is obtained through prediction by using the first wireless signal propagation model, and the first wireless signal propagation model is generated based on location information of S first sampling points and corresponding wireless signal received strength, wherein S is an integer greater than <NUM>; obtaining N second sampling points, and generating a target wireless signal propagation model based on location information of the S first sampling points and the N second sampling points and corresponding wireless signal received strength, where location information of any one of the N second sampling points is different from location information of each of the S first sampling points, each of the N second sampling points is located in the second area, and N is an integer greater than <NUM>; and obtaining wireless signal received strength of the terminal at any location in prediction space based on the target wireless signal propagation model.

Optionally, the check data may be drive test data, data reported by the terminal, or data in another form.

When the precision of the wireless signal propagation model is lower than the prediction precision expectation, a plurality of sampling points are added, and then a new wireless signal propagation model is regenerated based on the plurality of newly added sampling points and the sampling points used for generating the wireless signal propagation model. This improves precision of the wireless signal propagation model, and further improves prediction precision for wireless signal received strength.

In a feasible embodiment, the obtaining N second sampling points, and generating a target wireless signal propagation model based on location information of the S first sampling points and the N second sampling points and corresponding wireless signal received strength includes:.

In a feasible embodiment, the obtaining Ni initial sampling points includes:.

Optionally, the preset algorithm is an algorithm used when a reference wireless signal propagation model corresponding to the smallest error value is generated.

It should be noted herein that, for a specific implementation process of generating the plurality of reference wireless signal propagation models based on the location information of the sampling point in each of the plurality of second reference sampling point sets and the corresponding wireless signal received strength, refer to related descriptions of the method in the first aspect.

In a feasible embodiment, that the first wireless signal propagation model is generated based on location information of S first sampling points and corresponding wireless signal received strength specifically includes:
calculating a first parameter of the prediction space based on the location information of the S first sampling points and the corresponding wireless signal received strength, and obtaining an algorithm C based on the first parameter of the prediction space, where the first parameter is used to indicate a degree of a global spatial autocorrelation characteristic of the prediction space; and generating the first wireless signal receiving model W based on the algorithm C, the location information of the S first sampling points, and the corresponding wireless signal received strength.

In a feasible embodiment, the obtaining an algorithm C based on the first parameter of the prediction space includes:
obtaining a first algorithm and determining the first algorithm as the algorithm C when determining, based on the first parameter, that the prediction space has the global spatial autocorrelation characteristic; or obtaining a second algorithm and determining the second algorithm as the algorithm C when determining, based on the first parameter, that the prediction space does not have the global spatial autocorrelation characteristic.

In a feasible embodiment, the first algorithm includes a Bayesian hierarchical model BHM algorithm or any method in a Kriging method cluster, and the obtaining a first algorithm includes:
determining the BHM algorithm as the first algorithm when the wireless signal received strength of the S first sampling points meets preset probability distribution; or determining any method in the Kriging method cluster as the first algorithm when the wireless signal received strength of the S first sampling points does not meet the preset probability distribution.

In a feasible embodiment, the second algorithm includes a machine learning algorithm, any method in a B-shade method cluster, any method in an MSN method cluster, or a stratified Kriging algorithm, and the obtaining a second algorithm includes:
performing area division on the prediction space to obtain a plurality of target areas; determining whether each of the plurality of target areas meets a preset condition; and determining the stratified Kriging algorithm as the second algorithm when each of the plurality of target areas meets the preset condition; or obtaining a third algorithm and determining the third algorithm as the second algorithm when any one of the plurality of target areas does not meet the preset condition, where the third algorithm includes a machine learning algorithm, any method in the B-shade method cluster, or any method in the MSN method cluster.

In a feasible embodiment, the preset condition includes a first preset condition, a second preset condition, and a third preset condition, and the determining the stratified Kriging algorithm as the second algorithm when each of the plurality of target areas meets the preset condition includes:.

In a feasible embodiment, the first preset condition includes that the target area has a global spatial autocorrelation characteristic;
the second preset condition includes that the target area includes a first sampling point; and the third preset condition includes that a quantity of first sampling points in the target area is greater than a preset quantity, or the quantity of first sampling points in the target area is greater than the preset quantity and wireless signal received strength of the first sampling point in the target area meets spatial second-order stationarity.

According to a third aspect, an embodiment of this application provides a wireless signal propagation prediction apparatus, including:.

When executing the instructions, the processor performs the following method:
obtaining location information of each of S first sampling points in prediction space and wireless signal received strength of a terminal at a location indicated by the location information, where S is an integer greater than <NUM>; obtaining a first parameter of the prediction space through calculation based on location information of the S first sampling points and corresponding wireless signal received strength, where the first parameter is used to indicate a degree of a global spatial autocorrelation characteristic of the prediction space; obtaining a target algorithm based on the first parameter, and generating a wireless signal propagation model of the prediction space based on the target algorithm, the location information of the S first sampling points, and the corresponding wireless signal received strength; and obtaining wireless signal received strength of the terminal at any location in the prediction space based on the wireless signal propagation model of the prediction space.

In a feasible embodiment, in an aspect of obtaining a target algorithm based on the first parameter of the prediction space, the processor is specifically configured to:
obtain a first algorithm and determine the first algorithm as the target algorithm when determining, based on the first parameter, that the prediction space has the global spatial autocorrelation characteristic; or obtain a second algorithm and determine the second algorithm as the target algorithm when determining, based on the first parameter, that the prediction space has the global spatial autocorrelation characteristic.

In a feasible embodiment, the first algorithm includes any method in a Kriging method cluster or a BHM algorithm, and in an aspect of obtaining a first algorithm, the processor is specifically configured to:
determine whether the wireless signal received strength of the S first sampling points meets preset probability distribution; and determine the BHM algorithm as the first algorithm if the wireless signal received strength of the S first sampling points meets the preset probability distribution; or determine any method in the Kriging method cluster as the first algorithm if the wireless signal received strength of the S first sampling points does not meet the preset probability distribution.

In a feasible embodiment, the second algorithm includes a machine learning algorithm, any method in a B-shade method cluster, any method in an MSN method cluster, or a stratified Kriging algorithm, and in an aspect of obtaining a second algorithm, the processor is specifically configured to:
perform area division on the prediction space to obtain a plurality of target areas; and determine the stratified Kriging algorithm as the second algorithm when each of the plurality of target areas meets a preset condition; or obtain a third algorithm and determine the third algorithm as the second algorithm when any one of the plurality of target areas does not meet the preset condition, where the third algorithm includes a machine learning algorithm, any method in the B-shade method cluster, or any method in the MSN method cluster.

In a feasible embodiment, the preset condition includes a first preset condition, a second preset condition, and a third preset condition, and in an aspect of determining the stratified Kriging algorithm as the second algorithm when each of the plurality of target areas meets a preset condition, the processor is specifically configured to:.

In a feasible embodiment, in an aspect of performing area division on the prediction space to obtain a plurality of target areas, the processor is specifically configured to perform the following steps:.

According to a fourth aspect, an embodiment of this application provides another wireless signal propagation prediction apparatus, including:.

When executing the instructions, the processor performs the following method:
obtaining one or more second areas when precision of a first wireless signal propagation model is lower than a prediction precision expectation, where an absolute value of a difference between a predicted value of wireless signal received strength of a terminal in the second area and a check value is greater than a second preset threshold, the predicted value of the wireless signal received strength is obtained through prediction by using the first wireless signal propagation model, and the first wireless signal propagation model is generated based on location information of S first sampling points and corresponding wireless signal received strength; obtaining N second sampling points, and generating a target wireless signal propagation model based on location information of the S first sampling points and the N second sampling points and corresponding wireless signal received strength, where location information of any one of the N second sampling points is different from location information of each of the S first sampling points, each of the N second sampling points is located in the second area, and N is an integer greater than <NUM>; and obtaining wireless signal received strength of the terminal at any location in prediction space based on the target wireless signal propagation model.

In a feasible embodiment, in an aspect of obtaining N second sampling points, and generating a target wireless signal propagation model based on location information of the S first sampling points and the N second sampling points and corresponding wireless signal received strength, the processor is specifically configured to perform the following steps:.

The second convergence condition is different from the first convergence condition, and when i = <NUM>, the sampling point set Ai-<NUM> is an empty set.

In a feasible embodiment, in an aspect of obtaining Ni initial sampling points, the processor is specifically configured to:.

In a feasible embodiment, when the first wireless signal propagation model is generated based on the location information of the S first sampling points and the corresponding wireless signal received strength, the processor is specifically configured to:
calculate a first parameter of the prediction space based on location information of a plurality of sampling points and corresponding wireless signal received strength; obtain an algorithm C based on the first parameter of the prediction space, where the first parameter indicates a degree of a global spatial autocorrelation characteristic of the prediction space; and generate a wireless signal receiving model W based on the algorithm C, location information of a sampling point in a sampling point set, and corresponding wireless signal received strength, where when the plurality of sampling points are the S first sampling points, the wireless signal propagation model W is a first wireless signal propagation model, or when the plurality of sampling points are sampling points in the jth second reference sampling point set in the plurality of second reference sampling point sets, the wireless signal propagation model W is a jth reference wireless signal propagation model in the plurality of reference wireless signal propagation models.

In a feasible embodiment, in an aspect of obtaining an algorithm C based on the first parameter of the prediction space, the processor is specifically configured to:
obtain a first algorithm and determine the first algorithm as the algorithm C when determining, based on the first parameter, that the prediction space has the global spatial autocorrelation characteristic; or obtain a second algorithm and determine the second algorithm as the algorithm C when determining, based on the first parameter, that the prediction space does not have the global spatial autocorrelation characteristic.

In a feasible embodiment, the second algorithm includes a machine learning algorithm, any method in a B-shade method cluster, any method in an MSN method cluster, or a stratified Kriging algorithm, and in an aspect of obtaining a second algorithm, the processor is specifically configured to:
perform area division on the prediction space to obtain a plurality of target areas; determine whether each of the plurality of target areas meets a preset condition; and determine the stratified Kriging algorithm as the second algorithm when each of the plurality of target areas meets the preset condition; or obtain a third algorithm and determine the third algorithm as the second algorithm when any one of the plurality of target areas does not meet the preset condition, where the third algorithm includes a machine learning algorithm, any method in the B-shade method cluster, or any method in the MSN method cluster.

In a feasible embodiment, the preset condition includes a first preset condition, a second preset condition, and a third preset condition, and in an aspect of determining the stratified Kriging algorithm as the second algorithm when each of the plurality of target areas meets the preset condition, the processor is specifically configured to:.

In a feasible embodiment, the first preset condition includes that the target area has a global spatial autocorrelation characteristic; and
the second preset condition includes that the target area includes a first sampling point; and the third preset condition includes that a quantity of first sampling points in the target area is greater than a preset quantity, or the quantity of first sampling points in the target area is greater than the preset quantity and wireless signal received strength of the first sampling point in the target area meets spatial second-order stationarity.

T dimension indicators used for any time of area division on the prediction space partially intersect or do not intersect with T dimension indicators used for any other time of area division on the prediction space.

According to a fifth aspect, an embodiment of this application provides a chip system. The chip system is applied to an electronic device. The chip system includes one or more interface circuits and one or more processors. The interface circuit and the processor are interconnected through a line. The interface circuit is configured to receive a signal from a memory of the electronic device, and send the signal to the processor, where the signal includes computer instructions stored in the memory. When the processor executes the computer instructions, the electronic device performs a part or all of the method in the first aspect or the second aspect.

According to a sixth aspect, an embodiment of this application provides a computer-readable storage medium. The computer storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, the processor is enabled to perform a part or all of the method in the first aspect or the second aspect.

According to a seventh aspect, an embodiment of this application provides a computer program product. The computer program product includes computer instructions. When the computer instructions are run on an electronic device, the electronic device is enabled to perform a part or all of the method in the first aspect or the second aspect.

These aspects or other aspects of this application are clearer and more comprehensible in descriptions of the following embodiments.

In the following the subject-matter of <FIG>, <FIG> and <FIG>-<NUM> and their descriptions is according to the invention as defined in the claims. The subject-matter of the rest of the description and figures is either not falling within the subject-matter for which protection is sought, or is not encompassed by the wording of the claims but is considered as useful for understanding the invention.

To describe the technical solutions in embodiments of this application or in the conventional technology more clearly, the following briefly introduces the accompanying drawings for describing embodiments or the conventional technology. It is clear that the accompanying drawings in the following descriptions show only some embodiments of this application, and a person of ordinary skill in the art may still derive other drawings from these accompanying drawings without creative efforts.

The following describes embodiments of this application with reference to the accompanying drawings.

<FIG> is a schematic diagram of an application scenario according to an embodiment of this application. As shown in <FIG>, in this application scenario, there is a base station <NUM>, a terminal <NUM>, and a prediction apparatus <NUM>.

The base station <NUM> may be a macro base station, a micro base station, a picocell base station, a remote radio unit, a repeater, or the like. The terminal <NUM> is an apparatus with a communication function, and may be a smartphone, a notebook computer, a tablet computer, an intelligent wearable device (such as a smart band, a smart watch, and smart glasses), an internet of things device, or the like.

As shown in <FIG>, the prediction apparatus <NUM> obtains wireless signal received strength and location information of a plurality of terminals <NUM> located in a coverage area of the base station <NUM>. For example, the wireless signal received strength and the location information may be obtained from data that is reported by the terminal <NUM> and carries location information of the terminal and wireless signal received strength of the terminal <NUM> at a location indicated by the location information, such as a minimization of drive tests (minimization of drive tests, MDT) result, a measurement report (measure report, MR), internet of things (The Internet of Things, IoT) data, or data obtained by a drive test device through sampling. Then, the prediction apparatus <NUM> obtains a target algorithm based on the wireless signal received strength and the location information of the plurality of terminals <NUM> and map information, and generates a wireless signal propagation model based on the target algorithm and the obtained wireless signal received strength and location information, that is, a signal coverage electromagnetic map shown in <FIG>.

Subsequently, network planning personnel or a network planning apparatus identifies a problem grid or a weak signal coverage area based on the obtained wireless signal propagation model, then performs a parameter optimization operation for the problem grid or the weak signal coverage area to obtain optimized parameters, and sets antenna parameters of the base station <NUM>, such as an uptilt angle, a downtilt angle, and a transmit power of a base station antenna, based on the optimized parameters. The foregoing method is repeated to continuously optimize the antenna parameters of the base station <NUM>, so that the base station <NUM> achieves a good signal coverage effect.

The following describes schematic diagrams of two system architectures according to embodiments of this application. As shown in <FIG>, a terminal uploads, to an operation platform through a base station, data that carries location information and wireless signal received strength of the terminal at a location indicated by the location information, such as an MDT result, an MR, or IoT data. The operation platform obtains a target interpolation algorithm based on the received location information and corresponding wireless signal received strength, and map data, and generates a wireless signal propagation model based on the target algorithm and the obtained wireless signal received strength and location information. The operation platform identifies a problem grid or a weak signal coverage area based on the obtained wireless signal propagation model, then performs a parameter optimization operation for the problem grid or the weak signal coverage area to obtain optimized parameters, and sends an instruction carrying the optimized parameters to the base station to control the base station to adjust antenna parameters of the base station based on the optimized parameters. The foregoing steps are repeated to achieve an objective of optimizing wireless signal coverage of the base station.

As shown in <FIG>, a terminal uploads, to an operation platform through a base station, data that carries location information and wireless signal received strength of the terminal at a location indicated by the location information, such as an MDT result, an MR, or IoT data. The operation platform uploads the received location information and corresponding wireless signal received strength to a cloud server. The cloud server obtains a target algorithm based on the received location information and corresponding wireless signal received strength, and map data, generates a wireless signal propagation model based on the target algorithm and the obtained wireless signal received strength and location information, and sends the wireless signal propagation model to the operation platform. The operation platform identifies a problem grid or a weak signal coverage area based on the obtained wireless signal propagation model, performs a parameter optimization operation for the problem grid or the weak signal coverage area to obtain optimized parameters, and sends an instruction carrying the optimized parameters to the base station to control the base station to adjust antenna parameters of the base station based on the optimized parameters. The foregoing steps are repeated to achieve an objective of optimizing wireless signal coverage of the base station.

The following specifically describes a specific process of predicting wireless signal propagation by the prediction apparatus, the operation platform, or the cloud server.

<FIG> is a schematic flowchart of a wireless signal propagation prediction method according to an embodiment of this application.

S301: Obtain location information of each of S first sampling points in prediction space and wireless signal received strength of a terminal at a location indicated by the location information, where S is an integer greater than <NUM>.

The wireless signal received strength and location information may be obtained from data that is reported by the terminal <NUM> and that carries location information of the terminal and wireless signal received strength of the terminal <NUM> at a location indicated by the location information, such as an MDT result, an MR, IoT data, or data obtained by a drive test device through random sampling.

S302: Obtain a first parameter of the prediction space through calculation based on location information of the S first sampling points and corresponding wireless signal received strength.

The first parameter of the prediction space indicates a degree of a global spatial autocorrelation characteristic of the prediction space. A larger value of the first parameter indicates a higher degree of the global spatial autocorrelation characteristic of the prediction space.

Specifically, calculation is performed for the location information of the S first sampling points and the corresponding wireless signal received strength based on a preset algorithm, to obtain the first parameter of the prediction space. Optionally, the preset algorithm may be a Moran's I algorithm, a spatial autocorrelation γ (Gamma) index algorithm, a join count statistics algorithm, a Geary's C algorithm, a Getis G algorithm, an Ord's G algorithm, or the like.

As shown in a in <FIG>, gray blocks have a global spatial autocorrelation characteristic, and blocks shown in b and c in <FIG> each do not have the global spatial autocorrelation characteristic.

S303: Obtain a target algorithm based on the first parameter of the prediction space, and generate a wireless signal propagation model of the prediction space based on the target algorithm, the location information of the S first sampling points, and the corresponding wireless signal received strength.

Optionally, whether the prediction space has the global spatial autocorrelation characteristic is determined based on a value relationship between the first parameter of the prediction space and a third preset threshold; and when the first parameter of the prediction space is less than the third preset threshold, it is determined that the prediction space does not have the global spatial autocorrelation characteristic; or when the first parameter of the prediction space is not less than the third preset threshold, it is determined that the prediction space has the global spatial autocorrelation characteristic.

Optionally, a value range of the first parameter is [-<NUM>, <NUM>]. When the first parameter falls within a first interval, it is determined that the prediction space has the global spatial autocorrelation characteristic. When the first parameter falls within a second interval, it is determined that the prediction space does not have the global spatial autocorrelation characteristic. The first interval and the second interval have no intersection. For example, the first interval may be [<NUM>, <NUM>], and the second interval is [-<NUM>, <NUM>).

Optionally, when it is determined, based on the first parameter of the prediction space, that the prediction space has the global spatial autocorrelation characteristic, a first algorithm is obtained, and the first algorithm is determined as the target algorithm; or when it is determined, based on the first parameter of the prediction space, that the prediction space does not have the global spatial autocorrelation characteristic, a second algorithm is obtained, and the second algorithm is determined as the target algorithm.

Optionally, the first algorithm includes a Bayesian hierarchical model (Bayesian hierarchical models, BHM) algorithm or any method in a Kriging method cluster, and the second algorithm includes a machine learning algorithm, any method in a biased sentinel hospital area disease estimation (biased sentinel hospital area disease estimation, B-shade) method cluster, any method in a means of surface with non-homogeneity (means of surface with non-homogeneity, MSN) method cluster, or a stratified Kriging algorithm.

The foregoing Kriging method cluster includes a series of evolved algorithms, such as ordinary Kriging (Ordinary Kriging), universal Kriging (Universal Kriging), co-Kriging (Co-Kriging), disjunctive Kriging (Disjunctive Kriging), and some hybrid algorithms combined with other algorithms, such as regression-Kriging (regression-Kriging), neural Kriging (neural Kriging), and Bayesian Kriging (Bayesian Kriging).

In a feasible embodiment, the second algorithm includes a machine learning algorithm, any method in a B-shade method cluster, any method in an MSN method cluster, or a stratified Kriging algorithm, and obtaining the second algorithm includes:
performing area division on the prediction space to obtain a plurality of target areas; and determining the stratified Kriging algorithm as the second algorithm when each of the plurality of target areas meets a preset condition; or obtaining a third algorithm and determining the third algorithm as the second algorithm when any one of the plurality of target areas does not meet the preset condition, where the third algorithm includes a machine learning algorithm, any method in the B-shade method cluster, or any method in the MSN method cluster.

Specifically, as shown in <FIG>, when it is determined, based on the first parameter of the prediction space, that the prediction space has the global spatial autocorrelation characteristic, whether the wireless signal received strength of the S first sampling points meets preset probability distribution is determined. In an example, whether the wireless signal received strength of the S first sampling points meets the preset probability distribution may be determined based on prior knowledge. If the wireless signal received strength of the S first sampling points meets the preset probability distribution, the BHM algorithm is determined as the first algorithm; or if the wireless signal received strength of the S first sampling points does not meet the preset probability distribution, any method in the Kriging method cluster is determined as the first algorithm.

Optionally, the preset probability distribution may be statistical distribution, such as normal distribution, Poisson distribution, binomial distribution, or Gaussian distribution, or may be spatial distribution.

Further, when it is determined, based on the first parameter, that the prediction space does not have the global spatial autocorrelation characteristic, area division is performed on the prediction space to obtain a plurality of target areas.

Optionally, performing area division on the prediction space to obtain a plurality of target areas includes:
performing area division on the prediction space based on T dimension indicators, to obtain the plurality of target areas.

It should be noted herein that the performing area division on the prediction space to obtain a plurality of target areas may be specifically performing area division on a map of the prediction space to obtain a plurality of target areas.

Optionally, the map of the prediction space may be a two-dimensional map, or may be a three-dimensional map. When the map of the prediction space is a two-dimensional map, the location information in this embodiment of this application is two-dimensional location information, for example, two-dimensional coordinates. When the map of the prediction space is a three-dimensional map, the location information in this embodiment of this application is three-dimensional location information, for example, three-dimensional coordinates.

Optionally, in an example, area division is performed on the prediction space based on T dimension indicators separately to obtain T first division maps, where the T first division maps are in a one-to-one correspondence with the T dimension indicators. Area boundary lines in the T first division maps are superimposed to obtain the plurality of target areas.

For example, it is assumed that the T dimension indicators include visible and invisible areas (including a visible area and an invisible area) and city functional areas (including a residential area, a commercial area, and an industrial area). The prediction space is divided based on the visible area and the invisible area to obtain a first map. Area boundary lines of the first map are shown in a in <FIG>, and the first map is divided into the visible area and the invisible area. The prediction space is divided based on the residential area, the commercial area, and the industrial area to obtain a second map, where area boundary lines of the second map are shown in b in <FIG>, and the second map is divided into the industrial area, the residential area, and the commercial area. Area boundary lines in the first map and the second map are superimposed to obtain a third map, where area boundary lines of the third map are shown in c in <FIG>, and the prediction space is divided into five areas based on the area boundary lines in c in <FIG>, as shown in c in <FIG>.

Optionally, in an example, the performing area division on the prediction space based on T dimension indicators specifically includes:
Area division is first performed on the prediction space based on any dimension indicator D<NUM> in the T dimension indicators, to obtain S<NUM> areas P<NUM>. Then, area division is performed on the S<NUM> areas P<NUM> based on any dimension indicator D<NUM> in T-<NUM> dimension indicators, to obtain S<NUM> areas P<NUM>, where the T-<NUM> dimension indicators are dimension indicators other than the dimension indicator D<NUM> in the T dimension indicators, and S<NUM> is not less than S<NUM>. Subsequently, area division is performed on the S<NUM> areas P<NUM> based on any dimension indicator D<NUM> in T-<NUM> dimension indicators, to obtain S<NUM> areas P<NUM>, where the T-<NUM> dimension indicators are dimension indicators other than the dimension indicator D<NUM> and the dimension indicator D<NUM> in the T dimension indicators, and S<NUM> is not less than S<NUM>. ST areas PT are obtained after area division is performed based on the T dimension indicators in the foregoing manner. The ST areas PT are the foregoing plurality of target areas.

For example, as shown in <FIG>, area division is first performed on the prediction space based on a first dimension indicator (for example, a visible area and an invisible area) to obtain a visible area and an invisible area, as shown in a in <FIG>; then area division is performed on the visible area and the invisible area based on a second dimension indicator (for example, a residential area and a non-residential area) to obtain four areas, as shown in b in <FIG>; and finally area division is performed on the non-residential area based on a third dimension indicator (for example, a commercial area and an industrial area) to obtain three areas, as shown in c in <FIG>.

In a feasible embodiment, it is determined, based on the location information of the S first sampling points, that the S first sampling points are approximately distributed regularly. For example, the S first sampling points are approximately distributed in a straight line, as shown in a in <FIG>. For another example, the S first sampling points are approximately distributed in a circle, as shown in b in <FIG>. For another example, the S first sampling points are approximately distributed in a triangle, as shown in c in <FIG>. Certainly, the S first sampling points may alternatively be distributed in another regular shape, such as a square or a rectangle. After it is determined that the S first sampling points are distributed regularly, a baseline is obtained, where the S first sampling points are located or approximately located on the baseline, or located in an area formed by the baseline. Then, area division is performed on the prediction space based on the baseline to obtain a second division map, where distances between points on each boundary line of an area in the map and corresponding points on the baseline are the same, as shown in <FIG>. Then, boundary lines in the second division map are superimposed on the boundary lines in the T first division maps, to obtain the plurality of target areas.

Optionally, in an example, performing area division on the prediction space to obtain a plurality of target areas includes:.

A plurality of dimension indicators used for any time of area division on the prediction space intersect or do not intersect with a plurality of dimension indicators used for any other time of area division on the prediction space, that is, a plurality of dimension indicators used for any time of area division on the prediction space may be partially the same as or completely different from a plurality of dimension indicators used for any other time of area division on the prediction space.

Optionally, a value range of the second parameter is [<NUM>, <NUM>]. The dimension indicators may further include a land use type, a road, a water system, a point of interest, and a building type, such as a geometric form, a material, a building age, and a floor area ratio.

It should be noted herein that, for a specific implementation process of performing area division on the prediction space based on the T dimension indicators to obtain the plurality of first areas, refer to the foregoing implementation process of performing area division on the prediction space based on the T dimension indicators to obtain the plurality of target areas, and details are not described herein again.

In an example, the area division specifically includes that the area division may be performed based on subjective experience, or may be performed based on an existing spatial computing method, for example, a k-means clustering algorithm, a density-based spatial clustering of application with noise (density-based spatial clustering of application with noise, DBSCAN) algorithm, or a Gaussian mixed model expectation-maximization (Gaussian Mixed Model Expectation-maximization, GMM-EM) algorithm. For example, clustering may be performed based on a building type, a geometric feature, a construction age, or the like.

It should be noted herein that, a higher significance degree of the spatial stratified heterogeneity of the areas obtained by performing area division on the prediction space indicates a smaller variance of wireless signal received strength of first sampling points located in a same area, and a larger variance of wireless signal received strength of first sampling points located in adjacent areas.

Further, as shown in <FIG>, after area division is performed on the prediction space to obtain the plurality of target areas, whether each of the plurality of target areas meets the first preset condition is determined. Optionally, the first preset condition includes that the target area has a global spatial autocorrelation characteristic. Specifically, for any one of the plurality of target areas, a third parameter of the target area is obtained through calculation based on location information of a first sampling point in the target area and corresponding wireless signal received strength, where the third parameter of the target area indicates a degree of a global spatial autocorrelation characteristic of the target area. When it is determined, based on the third parameter of the target area, that the target area does not have the global spatial autocorrelation characteristic, a machine learning algorithm is determined as the target algorithm. When it is determined, based on a third parameter of each of the plurality of target areas, that each target area has a global spatial autocorrelation characteristic, whether each of the plurality of target areas meets the second preset condition is determined.

It should be noted herein that, when the third parameter of the target area is calculated, if the target area does not include a first sampling point, it is considered by default that the area has the global spatial autocorrelation characteristic.

Optionally, the second preset condition includes that the target area includes a first sampling point. Specifically, whether each of the plurality of target areas includes a first sampling point is determined. If any one of the plurality of target areas does not include a first sampling point, any method in the B-shade method cluster is determined as the target algorithm; or if all of the plurality of target areas include a first sampling point, whether each of the plurality of target areas meets the third preset condition is determined. Optionally, the third preset condition includes that a quantity of first sampling points in the target area is greater than a preset quantity, or the quantity of first sampling points in the target area is greater than the preset quantity and wireless signal received strength of the first sampling point in the target area meets spatial second-order stationarity.

Specifically, whether a quantity of first sampling points included in each of the plurality of target areas is greater than the preset quantity is determined; and if a quantity of first sampling points included in any one of the plurality of target areas is not greater than the preset quantity, any method in the MSN method cluster is determined as the target algorithm; or if the quantity of first sampling points included in each of the plurality of target areas is greater than the preset quantity, the stratified Kriging algorithm is determined as the target algorithm.

Alternatively, whether a quantity of first sampling points in each of the plurality of target areas is greater than the preset quantity and whether wireless signal received strength of the first sampling point in the target area meets spatial second-order stationarity are determined; and if a quantity of first sampling points included in any one of the plurality of target areas is not greater than the preset quantity, or wireless signal received strength of a first sampling point in any one of the plurality of target areas does not meet spatial second-order stationarity, any method in the MSN method cluster is determined as the target algorithm; or if the quantity of first sampling points included in each of the plurality of target areas is greater than the preset quantity, and the wireless signal received strength of the first sampling point in the target area does not meet spatial second-order stationarity, the stratified Kriging algorithm is determined as the target algorithm.

It should be noted herein that generating a wireless signal propagation model of the prediction space based on the stratified Kriging algorithm, the location information of the S first sampling points, and the corresponding wireless signal received strength specifically includes: performing, for each of the plurality of target areas, interpolation calculation for location information of a first sampling point in the target area and corresponding wireless signal received strength by using any method in the Kriging method cluster, to obtain a wireless signal propagation model in each target area; or selecting, for each of the plurality of target areas, a same Kriging algorithm from the Kriging method cluster to perform interpolation calculation for location information of a first sampling point in the target area and corresponding wireless signal received strength, to obtain a wireless signal propagation model in each target area; and then obtaining the wireless signal propagation model of the prediction space based on the wireless signal propagation model in each of the plurality of target areas.

In an example, the obtaining the wireless signal propagation model of the prediction space based on the wireless signal propagation model in each of the plurality of target areas specifically includes: obtaining a wireless signal coverage electromagnetic map of each target area based on the wireless signal propagation model in each of the plurality of target areas, then splicing wireless signal coverage electromagnetic maps of the plurality of target areas to obtain a wireless signal coverage electromagnetic map of the prediction space, and finally obtaining the wireless signal propagation model of the prediction space based on the wireless signal coverage electromagnetic map of the prediction space.

It should be noted herein that, when the target algorithm is any method in the Kriging method cluster, the BHM algorithm, any method in the B-shade method cluster, any method in the MSN method cluster, or the stratified Kriging algorithm, generating the wireless signal propagation model based on the target algorithm, the location information of the sampling points, and the corresponding wireless signal received strength specifically refers to performing interpolation calculation on the location information of the sampling points and the corresponding wireless signal received strength based on the target algorithm, to obtain the wireless signal propagation model.

S304: Obtain wireless signal received strength of the terminal at any location in the prediction space based on the wireless signal propagation model of the prediction space.

Specifically, the wireless signal propagation model of the prediction space may be considered as a function in which wireless signal received strength varies with location information. If a piece of location information in the prediction space is given, wireless signal received strength of the terminal at a location indicated by the location information may be predicted based on the wireless signal propagation model.

It can be learned that, in the solution of this application, whether the prediction space meets a preset condition is determined based on sampling points in the prediction space, a suitable algorithm is selected based on whether the prediction space meets the preset condition, the wireless signal propagation model of the prediction space is generated based on the suitable algorithm, location information of the sampling points, and corresponding wireless signal received strength, and then the wireless signal received strength of the terminal at any location in the prediction space is obtained based on the wireless signal propagation model, so that precision of the wireless signal propagation model of the prediction space is improved, and precision of a wireless signal propagation prediction result is further improved.

The following specifically describes another specific implementation process of predicting wireless signal propagation by the prediction apparatus, the operation platform, or the cloud server.

<FIG> is a schematic flowchart of a wireless signal propagation model prediction method according to an embodiment of this application.

S801: Obtain location information of each of S first sampling points in prediction space and wireless signal received strength of a terminal at a location indicated by the location information, where S is an integer greater than <NUM>.

S802: Generate a first wireless signal propagation model based on location information of the S first sampling points and corresponding wireless signal received strength.

It should be noted herein that, for a specific process of generating the first wireless signal propagation model based on the location information of the S first sampling points and the corresponding wireless signal received strength, refer to related descriptions of steps S302 and S303, and details are not described herein again.

S803: Obtain check data of the prediction space, and obtain precision of the first wireless signal propagation model through calculation based on the check data.

Optionally, the check data may be drive test data, data reported by the terminal, or real data in another form.

Specifically, the drive test data includes a plurality of pieces of location information and first wireless signal received strength of the terminal at locations indicated by the plurality of pieces of location information. The plurality of pieces of location information are input into the first wireless signal propagation model for calculation, to obtain second wireless signal received strength corresponding to the plurality of pieces of location information. An error value is calculated based on the first wireless signal received strength and the second wireless signal received strength corresponding to the plurality of pieces of location information, where the error value indicates the precision of the first wireless signal propagation model, and a smaller error value indicates higher precision of the first wireless signal propagation model.

Optionally, the error value includes but is not limited to a mean squared error, a root mean squared error, a mean absolute error, or a standard deviation.

S804: Obtain one or more second areas when the precision of the first wireless signal propagation model is lower than a prediction precision expectation.

Specifically, when the precision of the first wireless signal propagation model is lower than the prediction precision expectation, a real wireless signal electromagnetic coverage map of the prediction space is obtained based on the check data of the prediction space, and then a predicted wireless signal electromagnetic coverage map is obtained based on the first wireless signal propagation model. Finally, the one or more second areas are obtained based on the predicted wireless signal electromagnetic coverage map and the real wireless signal electromagnetic coverage map. In the one or more second areas, an absolute value of a difference between first wireless signal received strength and second wireless signal received strength corresponding to same location information is greater than a second preset threshold.

S805: Obtain N second sampling points, and generate a target wireless signal propagation model based on location information of the S first sampling points and the N second sampling points and corresponding wireless signal received strength.

Location information of any one of the N second sampling points is different from location information of each of the S first sampling points, each of the N second sampling points is located in the second area, and N is an integer greater than <NUM>.

In a specific embodiment, the obtaining N second sampling points, and generating a target wireless signal propagation model based on location information of the S first sampling points and the N second sampling points and corresponding wireless signal received strength includes:.

The second convergence condition is different from the first convergence condition, and when i = <NUM>, the target sampling point set is an empty set.

Specifically, <FIG> includes the following steps.

S10A: Obtain Ni initial sampling points.

Location information of each of the Ni initial sampling points is different from that of the S first sampling points, each of the N initial sampling points is located in the second area, and Ni is an integer greater than <NUM>.

In a feasible embodiment, the obtaining Ni initial sampling points includes:
obtaining a plurality of first reference sampling point sets, where sampling points in the plurality of first reference sampling point sets are all located in the second area; obtaining a plurality of second reference sampling point sets based on the plurality of first reference sampling point sets and the S first sampling points, where sampling points in a jth second reference sampling point set in the plurality of second reference sampling point sets include the S first sampling points and a sampling point in a jth first reference sampling point set in the plurality of first reference sampling point sets; performing a first operation based on location information of a sampling point in each of the plurality of second reference sampling point sets and corresponding wireless signal received strength to obtain a plurality of reference wireless signal propagation models, where the plurality of reference wireless signal propagation models are in a one-to-one correspondence with the plurality of second reference sampling point sets; obtaining an error value of each of the plurality of reference wireless signal propagation models based on drive test data; and obtaining the Ni initial sampling points based on the error value, where the Ni initial sampling points are sampling points other than the S first sampling points in a second reference sampling point set corresponding to a smallest error value.

Specifically, a plurality of first reference sampling point sets may be obtained from a plurality of second areas in a plurality of sampling manners (for example, systematic sampling, random sampling, stratified sampling, and overall sampling). The plurality of first reference sampling point sets are in a one-to-one correspondence with the plurality of sampling manners.

Any set of sampling points in the plurality of first reference sampling point sets may be evenly obtained from the plurality of second areas. For example, if there are three second areas, six sampling points need to be obtained, and two sampling points are obtained from each of the three second areas. Alternatively, the sampling points may be obtained from the plurality of second areas according to a specific rule. For example, the sampling points may be obtained based on sizes of the plurality of second areas, and a larger quantity of sampling points are obtained from a larger second area. For another example, the sampling points may be obtained based on errors of the plurality of second areas, and a larger quantity of sampling points are obtained from a second area with a larger error compared with the check data.

It should be noted herein that, when a sampling point is obtained from the second area, location information of the sampling point is first determined, and then wireless signal received strength of the terminal at a location indicated by the location information is obtained based on the check data, or wireless signal received strength corresponding to the location information is obtained from data reported by the terminal located at a location indicated by the location information.

After the plurality of first reference sampling point sets are obtained, the S first sampling points are saved in the plurality of first reference sampling point sets to obtain the plurality of second reference sampling point sets. Then, for each of the plurality of second reference sampling point sets, an algorithm for the second reference sampling point set is obtained based on related descriptions in S302 and S303, and a wireless signal propagation model corresponding to the second reference sampling point set is generated based on the algorithm for the second reference sampling point set, location information in the second reference sampling point set, and corresponding wireless signal received strength.

After a wireless signal propagation model of each of the plurality of second reference sampling point sets is obtained, an error value of the wireless signal propagation model of each second reference sampling point set is calculated based on the check data. For a process of calculating the error value, refer to related descriptions of step S50A, and details are not described herein again. The Ni initial sampling points are obtained from the plurality of second reference sampling point sets based on error values of wireless signal propagation models of the plurality of second reference sampling point sets, where the Ni initial sampling points are sampling points other than the S first sampling points in the second reference sampling point set corresponding to the smallest error value.

Optionally, a difference between Ni and Ni-<NUM> may be the same as or different from a difference between Ni-<NUM> and Ni-<NUM>.

S20A: Perform random disturbance processing on the Ni initial sampling points to obtain Ni reference sampling points.

Random disturbance is performed on location information and/or corresponding wireless signal received strength of each of the Ni initial sampling points, to obtain the Ni reference sampling points.

S30A: Generate a wireless signal propagation model Mi based on a preset algorithm, the location information of the S first sampling points and the N second sampling points, and the corresponding wireless signal received strength.

The preset algorithm is an algorithm used when a wireless signal propagation model corresponding to the smallest error value is generated. The N second sampling points include the target sampling point set and the Ni reference sampling points.

S40A: Determine whether the wireless signal propagation model Mi meets a first convergence condition.

Optionally, the determining whether the wireless signal propagation model Mi meets a first convergence condition specifically includes:
inputting the plurality of pieces of location information in the check data into the first wireless signal propagation model for calculation, to obtain the second wireless signal received strength corresponding to the plurality of pieces of location information; calculating an error value based on the first wireless signal received strength and the second wireless signal received strength corresponding to the plurality of pieces of location information in the check data; and when the error value approaches a first value, determining that the wireless signal propagation model Mi meets the first convergence condition; or when the error value does not approach the first value, determining that the wireless signal propagation model Mi does not meet the first convergence condition.

When the wireless signal propagation model Mi meets the first convergence condition, S50A is performed; or when the signal propagation model Mi does not meet the first convergence condition, S20A to S40A are performed.

S50A: Determine whether the wireless signal propagation model Mi meets a second convergence condition.

Optionally, the determining whether the wireless signal propagation model Mi meets a first convergence condition specifically includes:
inputting the plurality of pieces of location information in the check data into the first wireless signal propagation model for calculation, to obtain the second wireless signal received strength corresponding to the plurality of pieces of location information; calculating an error value based on the first wireless signal received strength and the second wireless signal received strength corresponding to the plurality of pieces of location information in the check data; and when the error value approaches a second value, determining that the wireless signal propagation model Mi meets the second convergence condition; or when the error value does not approach the second value, determining that the wireless signal propagation model Mi does not meet the second convergence condition.

That the second convergence condition is different from the first convergence condition means that the second value is less than the first value.

When the wireless signal propagation model Mi meets the second convergence condition, S70A is performed; or when the signal propagation model Mi does not meet the second convergence condition, S60A is performed.

S60A: Save the Ni reference sampling points in the target sampling point set, and set i = i + <NUM>.

After setting i = i + <NUM>, steps S10A to S50A are performed.

S70A: Determine the wireless signal propagation model Mi as the target wireless signal propagation model.

S10B: Obtain Ni initial sampling points.

It should be noted herein that, for a specific process of obtaining the Ni initial sampling points, refer to related descriptions of the foregoing step S10A, and details are not described herein again.

S20B: Perform random disturbance processing on each sampling point in a sampling point set Ai-<NUM> to obtain a sampling point set A'i-<NUM>, and performing random disturbance processing on the Ni initial sampling points to obtain Ni reference sampling points.

Specifically, the performing random disturbance processing on each sampling point in a sampling point set Ai-<NUM> to obtain a sampling point set A'i-<NUM> specifically includes: performing random disturbance on location information and/or corresponding wireless signal received strength of each sampling point in the sampling point set Ai-<NUM> to obtain the sampling point set A'i-<NUM>.

The performing random disturbance processing on the Ni initial sampling points to obtain Ni reference sampling points specifically includes: performing random disturbance processing on location information and/or corresponding wireless signal received strength of each of the Ni initial sampling points to obtain the Ni reference sampling points.

S30B: Generate a wireless signal propagation model Mi based on a preset algorithm, the location information of the S first sampling points and the N second sampling points, and the corresponding wireless signal received strength.

S40B: Determine whether the wireless signal propagation model Mi meets a first convergence condition.

When the wireless signal propagation model Mi meets the first convergence condition, S50B is performed; or when the signal propagation model Mi does not meet the first convergence condition, S20B to S40B are performed.

S50B: Determine whether the wireless signal propagation model Mi meets a second convergence condition.

The second convergence condition is stricter than the first convergence condition, and when i = <NUM>, the sampling point set Ai-<NUM> is an empty set.

When the wireless signal propagation model Mi meets the second convergence condition, S70B is performed; or when the signal propagation model Mi does not meet the second convergence condition, S60B is performed.

S60B: Save the Ni reference sampling points in the sampling point set A'i-<NUM> to obtain a sampling point set Ai, and set i = i + <NUM>.

After setting i = i + <NUM>, steps S10B to S50B are performed.

S70B: Determine the wireless signal propagation model Mi as the target wireless signal propagation model.

It should be noted that, for a specific implementation process of S10B to S70B, refer to related descriptions of S10A to S70A, and details are not described herein again.

In a feasible embodiment, that the first wireless signal propagation model is generated based on the location information of the S first sampling points and the corresponding wireless signal received strength specifically includes:
calculating a first parameter of the prediction space based on the location information of the S first sampling points and the corresponding wireless signal received strength; obtaining an interpolation algorithm C based on the first parameter of the prediction space, where the first parameter indicates a degree of a global spatial autocorrelation characteristic of the prediction space, and a larger value of the first parameter indicates a higher degree of the global spatial autocorrelation characteristic of the prediction space; and obtaining the first wireless signal receiving model through calculation based on the interpolation algorithm C, the location information of the S first sampling points, and the corresponding wireless signal received strength.

It should be noted herein that, for a specific implementation process of obtaining the algorithm C based on the first parameter of the prediction space, refer to related descriptions of obtaining the target algorithm based on the first parameter in S302 and S303, and details are not described herein again.

S806: Obtain wireless signal received strength of the terminal at any location in the prediction space based on the target wireless signal propagation model.

Specifically, the target wireless signal propagation model may be considered as a function in which wireless signal received strength varies with location information. If a piece of location information in the prediction space is given, wireless signal received strength of the terminal at a location indicated by the location information may be predicted based on the wireless signal propagation model.

It can be learned that, in the solution of this embodiment, after the wireless signal propagation model of the prediction space is obtained through calculation based on the location information of the S first sampling points and the corresponding wireless signal received strength, when the precision of the wireless signal propagation model is lower than the prediction precision expectation, a plurality of second sampling points are obtained in an area with a large error in the prediction space, and then interpolation calculation is performed on location information of the S first sampling points and the plurality of sampling points and corresponding wireless signal received strength, to obtain a wireless signal propagation model with higher precision, so that a wireless signal propagation prediction result with higher accuracy can be obtained.

It should be noted herein that the method in this application may be applied to a scenario in which plane-shaped coverage information needs to be predicted based on spatial discrete points. For example, a sales status of retail stores in an entire area is predicted based on a sales status of a discrete retail store, and a traffic requirement of each base station in an area is predicted based on a traffic requirement of a discrete base station.

<FIG> is a schematic diagram of a structure of a wireless signal propagation prediction apparatus according to an embodiment of this application. As shown in <FIG>, the apparatus <NUM> includes:.

The obtaining unit <NUM> is further configured to obtain wireless signal received strength of the terminal at any location in the prediction space based on the wireless signal propagation model of the prediction space.

In a feasible embodiment, in an aspect of obtaining a target algorithm based on the first parameter of the prediction space, the obtaining unit <NUM> is specifically configured to:
obtain a first algorithm and determine the first algorithm as the target algorithm when determining, based on the first parameter, that the prediction space has the global spatial autocorrelation characteristic; or obtain a second algorithm and determine the second algorithm as the target algorithm when determining, based on the first parameter, that the prediction space has the global spatial autocorrelation characteristic.

In a feasible embodiment, the first algorithm includes any method in a Kriging method cluster or a BHM algorithm, and in an aspect of obtaining a first algorithm, the obtaining unit <NUM> is specifically configured to:
determine whether the wireless signal received strength of the S first sampling points meets preset probability distribution; and determine the BHM algorithm as the first algorithm if the wireless signal received strength of the S first sampling points meets the preset probability distribution; or determine any method in the Kriging method cluster as the first algorithm if the wireless signal received strength of the S first sampling points does not meet the preset probability distribution.

In a feasible embodiment, the second algorithm includes a machine learning algorithm, any method in a B-shade method cluster, any method in an MSN method cluster, or a stratified Kriging algorithm, and in an aspect of obtaining a second algorithm, the obtaining unit <NUM> is specifically configured to:
perform area division on the prediction space to obtain a plurality of target areas; and determine the stratified Kriging algorithm as the second algorithm when each of the plurality of target areas meets a preset condition; or obtain a third algorithm and determine the third algorithm as the second algorithm when any one of the plurality of target areas does not meet the preset condition, where the third algorithm includes a machine learning algorithm, any method in the B-shade method cluster, or any method in the MSN method cluster.

In a feasible embodiment, the preset condition includes a first preset condition, a second preset condition, and a third preset condition, and in an aspect of determining the stratified Kriging algorithm as the second algorithm when each of the plurality of target areas meets a preset condition, the obtaining unit <NUM> is specifically configured to:.

In a feasible embodiment, in an aspect of performing area division on the prediction space to obtain a plurality of target areas, the obtaining unit <NUM> is specifically configured to perform the following steps:.

It should be noted that the foregoing units (the obtaining unit <NUM>, the calculation unit <NUM>, and the generation unit <NUM>) are configured to perform related content of the foregoing steps S301 to S304, and details are not described herein again.

In this embodiment, the wireless signal propagation prediction apparatus <NUM> is presented in a form of units. The "unit" herein may be an application-specific integrated circuit (application-specific integrated circuit, ASIC), a processor and a memory that execute one or more software or firmware programs, an integrated logic circuit, and/or another device that can provide the foregoing functions. In addition, the obtaining unit <NUM>, the calculation unit <NUM>, and the generation unit <NUM> may be implemented by using a processor <NUM> of a wireless signal propagation prediction apparatus shown in <FIG>.

<FIG> is a schematic diagram of a structure of another wireless signal propagation prediction apparatus according to an embodiment of this application. As shown in <FIG>, the apparatus <NUM> includes:.

The obtaining unit <NUM> is further configured to obtain wireless signal received strength of the terminal at any location in the prediction space based on the target wireless signal propagation model.

In a feasible embodiment, in an aspect of obtaining N second sampling points, and generating a target wireless signal propagation model based on location information of the S first sampling points and the N second sampling points and corresponding wireless signal received strength, the obtaining unit <NUM> and generation unit <NUM> are specifically configured to perform the following steps:.

In a feasible embodiment, in an aspect of obtaining Ni initial sampling points, the obtaining unit <NUM> is specifically configured to:.

In a feasible embodiment, when the first wireless signal propagation model is generated based on the location information of the S first sampling points and the corresponding wireless signal received strength, the generation unit <NUM> is specifically configured to:
calculate a first parameter of the prediction space based on the location information of the S first sampling points and the corresponding wireless signal received strength; obtain an algorithm C based on the first parameter of the prediction space, where the first parameter indicates a degree of a global spatial autocorrelation characteristic of the prediction space; and generate the first wireless signal receiving model based on the algorithm C, the location information of the S first sampling points, and the corresponding wireless signal received strength.

In a feasible embodiment, in an aspect of obtaining an algorithm C based on the first parameter of the prediction space, the generation unit <NUM> is specifically configured to:
obtain a first algorithm and determine the first algorithm as the algorithm C when determining, based on the first parameter, that the prediction space has the global spatial autocorrelation characteristic; or obtain a second algorithm and determine the second algorithm as the algorithm C when determining, based on the first parameter, that the prediction space does not have the global spatial autocorrelation characteristic.

In a feasible embodiment, the first algorithm includes a Bayesian hierarchical model BHM algorithm or any method in a Kriging method cluster, and in an aspect of obtaining a first algorithm, the generation unit <NUM> is specifically configured to:
determine the BHM algorithm as the first algorithm when the wireless signal received strength of the S first sampling points meets preset probability distribution; or determine any method in the Kriging method cluster as the first algorithm when the wireless signal received strength of the S first sampling points does not meet the preset probability distribution.

In a feasible embodiment, the second algorithm includes a machine learning algorithm, any method in a B-shade method cluster, any method in an MSN method cluster, or a stratified Kriging algorithm, and in an aspect of obtaining a second algorithm, the generation unit <NUM> is specifically configured to:
perform area division on the prediction space to obtain a plurality of target areas; determine whether each of the plurality of target areas meets a preset condition; and determine the stratified Kriging algorithm as the second algorithm when each of the plurality of target areas meets the preset condition; or obtain a third algorithm and determine the third algorithm as the second algorithm when any one of the plurality of target areas does not meet the preset condition, where the third algorithm includes a machine learning algorithm, any method in the B-shade method cluster, or any method in the MSN method cluster.

In a feasible embodiment, the preset condition includes a first preset condition, a second preset condition, and a third preset condition, and in an aspect of determining the stratified Kriging algorithm as the second algorithm when each of the plurality of target areas meets the preset condition, the generation unit <NUM> is specifically configured to:.

In a feasible embodiment, in an aspect of performing area division on the prediction space to obtain a plurality of target areas, the generation unit <NUM> is specifically configured to:.

It should be noted that the foregoing units (the obtaining unit <NUM> and the generation unit <NUM>) are configured to perform related content of the foregoing steps S801 to S806, and details are not described herein again.

In this embodiment, the wireless signal propagation prediction apparatus <NUM> is presented in a form of units. The "unit" herein may be an application-specific integrated circuit (application-specific integrated circuit, ASIC), a processor and a memory that execute one or more software or firmware programs, an integrated logic circuit, and/or another device that can provide the foregoing functions. In addition, the obtaining unit <NUM> and the generation unit <NUM> may be implemented by using a processor <NUM> of a wireless signal propagation prediction apparatus shown in <FIG>.

As shown in <FIG>, the wireless signal propagation prediction apparatus <NUM> may be implemented by using a structure in <FIG>. The wireless signal propagation prediction apparatus <NUM> includes at least one processor <NUM>, at least one memory <NUM>, and at least one communications interface <NUM>. The processor <NUM>, the memory <NUM>, and the communications interface <NUM> are connected and communicate with each other through a communications bus.

The processor <NUM> may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (application-specific integrated circuit, ASIC), or one or more integrated circuits for controlling program execution of the foregoing solution.

The communications interface <NUM> is configured to communicate with another device or a communications network such as the Ethernet, a radio access network (RAN), or a wireless local area network (Wireless Local Area Networks, WLAN).

The memory <NUM> may be but is not limited to a read-only memory (read-only memory, ROM) or another type of static storage device capable of storing static information and instructions, a random access memory (random access memory, RAM) or another type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a compact disc read-only memory (Compact Disc Read-Only Memory, CD-ROM) or another compact disc storage, an optical disc storage (including a compact disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, and the like), a magnetic disk storage medium or another magnetic storage device, or any other medium that can be used to carry or store expected program code in a form of instructions or a data structure and can be accessed by a computer. The memory may exist independently, and is connected to the processor by using the bus. Alternatively, the memory may be integrated with the processor.

The memory <NUM> is configured to store application program code for executing the foregoing solutions, and the processor <NUM> controls the execution. The processor <NUM> is configured to execute the application program code stored in the memory <NUM>.

The code stored in the memory <NUM> may be used to perform any wireless signal propagation prediction method provided above, such as:.

It should be noted herein that, for a process of the wireless signal propagation prediction method, refer to related descriptions of steps S301 to S304 and steps S801 to S806, and details are not described herein again.

An embodiment of this application further provides a computer storage medium. The computer storage medium may store a program, and when the program is executed, some or all of the steps of any wireless signal propagation prediction method described in the foregoing method embodiments are performed.

It should be noted that, for brief description, the foregoing method embodiments are represented as a series of actions. However, a person skilled in the art should appreciate that this application is not limited to the described order of the actions, because based on this application, some steps may be performed in other orders or simultaneously. It should be further appreciated by a person skilled in the art that embodiments described in this specification all belong to example embodiments, and the related actions and modules are not necessarily required in this application.

In the foregoing embodiments, the descriptions of embodiments have respective focuses.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the described apparatus embodiments are merely examples. For example, division into the units is merely logical function division and may be other division in an actual implementation. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections may be implemented through some interfaces. The indirect couplings or communication connections between the apparatuses or units may be implemented in electronic or other forms.

When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, the integrated unit may be stored in a computer-readable memory. Based on such an understanding, the technical solutions of this application essentially, or the part contributing to the current technology, or all or some of the technical solutions may be implemented in the form of a software product. The computer software product is stored in a memory and includes several instructions for instructing a computer device (which may be a personal computer, a server, or a network device) to perform all or some of the steps of the methods described in embodiments of this application. The foregoing storage medium includes any medium that can store program code, such as a USB flash drive, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a removable hard disk, a magnetic disk, or an optical disc.

A person of ordinary skill in the art may understand that all or some of the steps of the methods in embodiments may be implemented by a program instructing related hardware. The program may be stored in a computer-readable memory. The memory may include a flash memory, a read-only memory (English: Read-Only Memory, ROM for short), a random access memory (English: Random Access Memory, RAM for short), a magnetic disk, an optical disc, or the like.

Claim 1:
A wireless signal propagation prediction method, wherein the method comprises:
obtaining location information of each of S first sampling points in prediction space and wireless signal received strength of a terminal at a location indicated by the location information, wherein S is an integer greater than <NUM>;
obtaining a first parameter of the prediction space through calculation based on location information of the S first sampling points and corresponding wireless signal received strength, wherein the first parameter is used to indicate a degree of a global spatial autocorrelation characteristic of the prediction space;
obtaining a target algorithm based on the first parameter, and generating a wireless signal propagation model of the prediction space based on the target algorithm, the location information of the S first sampling points, and the corresponding wireless signal received strength; and
obtaining wireless signal received strength of the terminal at any location in the prediction space based on the wireless signal propagation model of the prediction space.