Spectrum coverage map construction method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a spectrum coverage map construction method and device, electronic equipment and a storage medium, and the construction method comprises the steps: delimiting a collection range according to base station communication parameters of a target base station, setting a collection path in the collection range, and gradually obtaining the signal receiving power from the target base station based on the collection path; performing gridding processing on the acquisition range to obtain a plurality of measurement grids and a plurality of prediction grids; for a measurement grid intersected with the acquisition path, aggregating the signal receiving power in the measurement grid to obtain a signal power parameter in the measurement grid; the prediction grids are filled based on the signal power parameters in the measurement grids, prediction signal parameters of all the prediction grids are obtained, and a spectrum coverage map in the collection range is obtained according to the signal power parameters of the measurement grids and the prediction signal parameters of the prediction grids; according to the method provided by the embodiment of the invention, the data acquisition difficulty is reduced, the acquisition cost is low, and the map construction efficiency is high.

Description

Technical Field

[0001] The present application relates to, but is not limited to, the field of wireless communication technology, and in particular to a spectrum coverage map construction method, device, electronic device and storage medium. Background Art

[0002] In modern wireless communication networks, the evaluation of network coverage quality is crucial to optimizing network performance. Intelligent network optimization refers to the simultaneous adjustment and optimization of large-scale base station antennas to ensure that the optimized network has high performance. The core of intelligent network optimization is to intelligently adjust the parameters of large-scale base stations and predict the network coverage after parameter adjustment. The network coverage prediction is an important feedback on the performance of parameter adjustment.

[0003] In the related art, traditional network coverage testing methods mainly involve the equipment performing traversal measurements within a preset range centered on a base station. These methods have the disadvantages of high cost, low efficiency, and long time consumption. Summary of the invention

[0004] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0005] The embodiments of the present application provide a spectrum coverage map construction method, device, electronic device and storage medium, which can reduce the difficulty of data collection, reduce the collection cost, and increase the efficiency of map construction.

[0006] To achieve the above-mentioned purpose, the first aspect of an embodiment of the present application proposes a method for constructing a spectrum coverage map, including: defining a collection range according to the base station communication parameters of the target base station, setting a collection path within the collection range, and gradually acquiring the signal receiving power from the target base station based on the collection path; gridding the collection ranges in different measurement planes respectively to obtain multiple measurement grids and multiple prediction grids, wherein the measurement grids intersect with the collection path, and the prediction grids do not intersect with the collection path; for the measurement grids intersecting with the collection path, aggregating the signal receiving power within the measurement grids to obtain the signal power parameters within the measurement grids; based on the signal power parameters within the measurement grids, filling the prediction grids to obtain the predicted signal parameters of each of the prediction grids, and obtaining the spectrum coverage map within the collection range according to the signal power parameters of the measurement grids and the predicted signal parameters of the prediction grids.

[0007] In one embodiment, the prediction grids are filled based on the signal power parameters within the measurement grids to obtain predicted signal parameters of each prediction grid, including: defining prediction ranges based on the centers of each prediction grid, and predicting the corresponding prediction grids according to the signal power parameters of each measurement grid within the prediction range to obtain multiple initial prediction parameters; and weighting the initial prediction parameters obtained by predicting each measurement grid based on the distance between the measurement grid and the prediction grid to obtain predicted signal parameters of the prediction grid.

[0008] In one embodiment, a formula for determining the initial prediction parameter of the prediction grid based on the signal power parameter of the measurement grid is: ; in, represents the signal power parameter of the measurement grid, represents the distance between the measurement grid and the target base station, represents the distance between the measurement grid and the prediction grid, represents the initial prediction parameters of the prediction grid.

[0009] In one embodiment, the prediction grids are filled based on the signal power parameters in the measurement grids to obtain predicted signal parameters of each prediction grid, and also includes: training a signal prediction model based on the signal power parameters in the measurement grids in the current acquisition plane and the predicted signal parameters of the filled prediction grids, as well as the signal power parameters in the measurement grids in other acquisition planes; and predicting the predicted signal parameters of the unfilled prediction grids using the trained signal prediction model.

[0010] In one embodiment, the prediction grid is filled based on the signal power parameters in the measurement grid to obtain the predicted signal parameters of each prediction grid, and also includes: in the measurement planes at other measurement heights, the predicted signal parameters of each prediction grid are predicted by the trained signal prediction model to obtain a spectrum coverage map in three-dimensional space.

[0011] In one embodiment, the gridding processing of the acquisition range includes: obtaining the longitude and latitude coordinates of the boundary of the acquisition range, and converting the longitude and latitude coordinates into UTM coordinates; dividing the measurement area into multiple grids, determining the UTM area in which the acquisition range is located according to the UTM coordinates, and determining the grid index of each of the grids in the measurement area based on the easting coordinate and the northing coordinate in the UTM coordinates.

[0012] In one embodiment, the grid index includes an easting index and a northing index, and determining the grid index of each grid in the measurement area based on the easting coordinate and the northing coordinate in the UTM coordinate includes: Determine the easting index of the grid: ; Determine the north index of the grid: ; in, represents the easting index, easting represents the easting coordinate of the grid, represents the northing index, northerning represents the northing coordinate of the grid, size represents the side length of the grid, % represents the modulo operation, and int() represents taking the integer part.

[0013] To achieve the above-mentioned purpose, the second aspect of the present application proposes a spectrum coverage map construction device, including: a measurement module, which is used to define a collection range according to the base station communication parameters of the target base station, set a collection path within the collection range, and gradually obtain the signal receiving power from the target base station based on the collection path; an area division module, which is used to grid the collection range in different measurement planes respectively to obtain multiple measurement grids and multiple prediction grids, wherein the measurement grid intersects with the collection path, and the prediction grid does not intersect with the collection path; a data aggregation module, which is used to aggregate the signal receiving power within the measurement grid that intersects with the collection path to obtain the signal power parameter within the measurement grid; a prediction module, which is used to fill the prediction grid based on the signal power parameter within the measurement grid to obtain the predicted signal parameters of each of the predicted grids, and obtain the spectrum coverage map within the collection range according to the signal power parameters of the measurement grid and the predicted signal parameters of the prediction grid.

[0014] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the spectrum coverage map construction method described in the first aspect when executing the computer program.

[0015] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium, and the storage medium stores a computer program. When the computer program is executed by a processor, the spectrum coverage map construction method described in the first aspect is implemented.

[0016] The embodiments of the present application include at least the following beneficial effects: within the acquisition range, the acquisition path is traversed and the signal receiving power is obtained one by one. Since the measurement grid intersects with the acquisition path, the measurement grid includes at least one signal receiving power. By aggregating the signal receiving power in the measurement grid, the signal power parameter is obtained, thereby ensuring the integrity and validity of the data in the measurement area; on some of the measurement planes, there are usually obstacles such as buildings and trees in the prediction grid, which makes it impossible for the measurement equipment to directly measure the signal receiving power in the prediction grid, and the acquisition path cannot intersect with the prediction grid. Therefore, there is no signal receiving power obtained by measurement in the prediction grid. Based on the signal power parameters in the measurement grid in the same measurement plane, the predicted signal parameters in the prediction grid are predicted to obtain a complete spectrum coverage map; the spectrum coverage map construction method proposed in the embodiment of the present application can construct a map of all-round base station signal coverage on the ground or high altitude where there are obstacles. The map construction method proposed in the embodiment of the present application only needs to collect signals from the measurement grids within the acquisition range, and fill the predicted grids where the signals cannot be collected with the collected signal data, thereby reducing the difficulty of data collection, reducing the collection cost, and increasing the efficiency of map construction.

[0017] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0019] Figure 1 A schematic diagram of an optional flow chart of a spectrum coverage map construction method provided in an embodiment of the present application; Figure 2 A schematic diagram of an optional process of dividing a grid provided in an embodiment of the present application; Figure 3 An optional schematic diagram of a physical model prediction process provided in an embodiment of the present application; Figure 4 A schematic diagram of an optional flow chart for performing predictions using the signal prediction model provided in an embodiment of the present application; Figure 5 An optional schematic diagram of signal acquisition provided in an embodiment of the present application; Figure 6 A schematic diagram of an optional structure of a circular collection path provided in an embodiment of the present application; Figure 7 A schematic diagram of an optional structure of a tic-tac-toe collection path provided in an embodiment of the present application; Figure 8 An optional structural diagram of the collection path planning provided in the embodiment of the present application; Fig. 9 A schematic diagram of another optional flow chart of the spectrum coverage map construction method provided in the embodiment of the present application; Fig.10 An optional structural diagram of a spectrum coverage map construction device provided in this embodiment; Fig.11 A schematic diagram of an optional hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0021] In the description of this application, “several” means one or more, “more” means more than two, “greater than”, “less than”, “exceed”, etc. are understood to exclude the number, and “above”, “below”, “within”, etc. are understood to include the number.

[0022] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims or the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0023] To facilitate understanding of the technical solutions provided in the embodiments of the present application, some key terms used in the embodiments of the present application are explained here: The Universal Transverse Mercator (UTM) grid system is a plane rectangular coordinate system. This coordinate grid system and the projection it is based on have been widely used in topographic maps, as a reference grid for satellite imagery and natural resource databases, and other applications that require precise positioning. In the UTM system, the earth's surface area between 84 degrees north latitude and 80 degrees south latitude is divided into north-south longitudinal zones (projection zones) at 6 degrees of longitude. These projection zones are numbered from 1 to 60 starting from the 180th meridian and going east. Each zone is divided into quadrilaterals with a latitude difference of 8 degrees. The horizontal row of quadrilaterals starts at 80 degrees south latitude. They are labeled in sequence with letters C to X (excluding I and O) (the Xth row includes all land areas in the northern hemisphere from 72 degrees to 84 degrees north latitude, a total of 12 degrees). Each quadrilateral is labeled with a combination of numbers and letters. The reference grid is read rightward and upward. Each quadrilateral is divided into a number of cells with a side length of 1,000,000 meters, which are labeled with a combination of letters. In each projection zone, the meridian at the center of the zone is assigned a horizontal coordinate value of 500,000 meters. The coordinate value of the marker for the equator in the northern hemisphere is 0, and for the southern hemisphere it is 10,000,000 meters, decreasing southwards. The distance between the main lines of the UTM grid on large-scale maps is generally 1 km, so the UTM system is sometimes called a league. Because the UTM system uses the Transverse Mercator projection, the scale factor is constant along each north-south grid line (the grid line in the center of the zone is the meridian) and variable in the east-west direction. The scale factor along the central grid line of each UTM grid should be 0.99960, and at the edge of the widest part of the north-south longitudinal line (the equator), including the overlap of the zone, about 363 kilometers from the center point, the scale factor is 1.00158.

[0024] Machine Learning (ML) is a multi-disciplinary subject that involves probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in studying how computers simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications are spread across all areas of artificial intelligence. Machine learning and deep learning usually include artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and teaching learning.

[0025] In the related art, traditional network coverage testing methods mainly involve the equipment performing traversal measurements within a preset range centered on a base station. These methods have the disadvantages of high cost, low efficiency, and long time consumption.

[0026] Based on this, an embodiment of the present application provides a spectrum coverage map construction method, device, electronic device and storage medium, and the spectrum coverage map construction method includes: defining a collection range according to the base station communication parameters of the target base station, setting a collection path within the collection range, and gradually acquiring the signal receiving power from the target base station based on the collection path; gridding the collection ranges in different measurement planes respectively to obtain multiple measurement grids and multiple prediction grids, wherein the measurement grids intersect with the collection path, and the prediction grids do not intersect with the collection path; for the measurement grids intersecting with the collection path, aggregating the signal receiving power within the measurement grids to obtain the signal power parameters within the measurement grids; based on the signal power parameters within the measurement grids, filling the prediction grids to obtain the predicted signal parameters of each prediction grid, and obtaining the spectrum coverage map within the collection range according to the signal power parameters of the measurement grids and the predicted signal parameters of the prediction grids; this method can reduce the difficulty of data collection, has low collection costs, and has high efficiency in map construction.

[0027] The spectrum coverage map construction method, device, electronic device and storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the spectrum coverage map construction method in the embodiments of the present application is described.

[0028] The spectrum coverage map construction method provided in the embodiment of the present application relates to the field of computer technology. The spectrum coverage map construction method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the spectrum coverage map construction method, etc., but is not limited to the above forms.

[0029] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0030] The embodiments of the present application are further described below in conjunction with the accompanying drawings.

[0031] like Figure 1 As shown, Figure 1 An optional flow chart of a spectrum coverage map construction method provided in an embodiment of the present application, the spectrum coverage map construction method can be executed by a server, or can also be executed by a terminal, or can also be executed by a server in conjunction with a terminal, and the spectrum coverage map construction method includes but is not limited to the following steps S110 to S140: Step S110, defining a collection range according to the base station communication parameters of the target base station, setting a collection path within the collection range, and gradually acquiring the signal receiving power from the target base station based on the collection path; Step S120, performing grid processing on the acquisition ranges in different measurement planes respectively to obtain a plurality of measurement grids and a plurality of prediction grids, wherein the measurement grids intersect with the acquisition path, and the prediction grids do not intersect with the acquisition path; Step S130, for the measurement grids intersecting with the acquisition path, aggregating the signal receiving power in the measurement grids to obtain the signal power parameters in the measurement grids; Step S140, based on the signal power parameters in the measurement grid, the prediction grid is filled to obtain the predicted signal parameters of each prediction grid, and the spectrum coverage map within the acquisition range is obtained according to the signal power parameters of the measurement grid and the predicted signal parameters of the prediction grid.

[0032] It can be understood that, according to the preset collection frequency of the equipment for collection, a plurality of measurement points are equally divided on the collection path. After the collection range is gridded, the measurement grid intersecting the collection path includes at least one measurement point. After traversing each measurement point on the collection path, each measurement point measures a corresponding signal receiving power, that is, the measurement grid includes at least one signal receiving power. By aggregating the signal receiving power in the measurement grid, the signal power parameter is obtained, thereby ensuring the integrity and validity of the data in the measurement area. On some of the measurement planes, there are usually obstacles such as buildings and trees in the prediction grid, which makes it impossible for the measurement equipment to directly measure the signal received power in the prediction grid. The signal receiving power is measured, so the predicted grid does not have the signal receiving power obtained by the measurement, and the predicted signal parameters in the predicted grid are predicted based on the signal power parameters in the measurement grid in the same measurement plane, so as to obtain a complete spectrum coverage map; the spectrum coverage map construction method proposed in the embodiment of the present application can construct a map of all-round base station signal coverage on the ground or high altitude where there are obstacles. The map construction method proposed in the embodiment of the present application only needs to collect signals from the measurement grids within the collection range, and fills the predicted grids that cannot collect signals with the collected signal data, thereby reducing the difficulty of data collection, reducing the collection cost, and increasing the efficiency of map construction.

[0033] Reference Figure 5 As shown, when the collection range is on the ground, vehicle-mounted signal collection equipment and handheld signal collection equipment can be used to collect and analyze the signal receiving power within the collection range. When the collection range is at high altitude, a signal collector can be mounted on a drone, and the drone can fly within the collection range according to a prescribed path to collect the signal receiving power.

[0034] After collecting the signal receiving power at each measurement point, the signal receiving power in the measurement grid is statistically analyzed to calculate the signal power parameters in the measurement grid. The signal power parameters include but are not limited to the sum of the signal receiving power, the number of signal receiving power, the mean signal receiving power, the maximum signal receiving power, the minimum signal receiving power and the extreme value of the signal receiving power.

[0035] Reference Figure 2 As shown, in one embodiment of the present application, Figure 1 Step S120 in the embodiment includes but is not limited to the following steps S210 to S220: Step S210, obtaining the longitude and latitude coordinates of the boundary of the acquisition range, and converting the longitude and latitude coordinates into UTM coordinates; Step S220, dividing the measurement area into a plurality of grids, determining the UTM area where the acquisition range is located according to the UTM coordinates, and determining the grid index of each grid in the measurement area based on the easting coordinate and the northing coordinate in the UTM coordinates.

[0036] Get the longitude and latitude coordinates of the target base station, determine the longitude and latitude interval of the collection range, clarify the boundaries of the collection range, and divide the collection range into multiple small grid cells according to the preset grid length. Each grid cell represents a grid, and each grid will have a unique UTM coordinate. Determine the relative position of the easting coordinate and the northing coordinate in the 100,000-meter square in the UTM area, and further calculate the index of each grid, and sort them according to the easting coordinate (Easting) and northing coordinate (Northing) of each grid UTM coordinate. Specifically, the easting coordinate of the grid represents the coordinate of the grid at the easternmost position in the east, and the northing coordinate of the grid represents the coordinate of the grid at the northernmost position in the north.

[0037] Specifically, the calculation formula of the east index of the grid is: ; The calculation formula for the north index of the grid is: ; in, Represents the easting index, easting represents the easting coordinate of the grid, Indicates the north index, northerning indicates the north coordinate of the grid, size indicates the side length of the grid, % indicates the remainder operation, and int() indicates taking the integer part.

[0038] In addition, the grid index of each grid also includes the vertical grid in the vertical direction. After delimiting the vertical range in space, different measurement heights are set in the vertical direction. Based on the division of the measurement height, the vertical index of the grid in each measurement plane is determined from bottom to top. After the grid index calculation is completed, the grid index of each grid can be expressed as ( , , ),in, represents the easting index, Indicates the northbound index. Indicates the vertical index.

[0039] Reference Figures 6 to 8As shown in the figure, after the collection area is divided and multiple grids are formed, it is necessary to identify and mark buildings, trees, no-fly zones and other areas in the measurement area, which are inaccessible to vehicle-mounted signal collection equipment and drones for measurement. For these unmeasurable grids, we exclude them from the collection task. After excluding the above grids, we will plan appropriate collection paths for the remaining measurable grids according to their layout and shape. The design of the collection path can take a variety of forms, including circular, square or tic-tac-toe shapes. These paths can be carried out in the same horizontal plane, in the same vertical plane, or across multiple horizontal or vertical planes for measurement to ensure the comprehensiveness and efficiency of the collection work.

[0040] Exemplarily, the coordinates of the target base station within the measurement range are marked as , the flight altitude of the drone includes , and , set the flight radius of the drone to R. When the flight path of the drone is set to a circle, the calculation formula for the coordinates of each measurement point on the collection path is: , i takes values ​​of 1, 2, and 3; when the flight path of the drone is set to a tic-tac-toe shape, its horizontal flight path is specifically from arrive and from arrive , the vertical path is specifically from arrive and from arrive , the diagonal path is specifically from arrive and from arrive .

[0041] After setting the collection path and collecting the signal receiving power at each measuring point through the vehicle-mounted signal collection equipment or the unmanned aerial vehicle, the grid containing the measuring point is determined as the measurement grid, the measurement grid intersects with the collection path, and at least one signal receiving power is collected within the range included in the measurement grid; the grid not containing the measurement point is determined as the prediction grid, the prediction grid usually includes obstacles, so the prediction grid does not intersect with the collection path, and no signal receiving power is collected within the range included in the prediction grid. Accordingly, in a measurement plane at any measurement height, the measurement grid and the prediction grid in the above embodiment are equivalent to the grid in this embodiment. After calculating the east index and the north index of each grid, each measurement grid and each prediction grid also obtains the corresponding east index and north index.

[0042] Reference Figure 3 As shown, in one embodiment of the present application, Figure 1Step S140 in the embodiment includes but is not limited to the following steps S310 to S320: Step S310, defining prediction ranges based on the centers of the prediction grids, predicting the corresponding prediction grids according to the signal power parameters of the measurement grids within the prediction ranges, and obtaining a plurality of initial prediction parameters; Step S320 , based on the distance between the measurement grid and the prediction grid, weighted processing is performed on the initial prediction parameters obtained by prediction of each measurement grid to obtain the prediction signal parameters of the prediction grid.

[0043] After calculating the signal power parameters of each measurement grid, a prediction range of a preset radius is defined with the midpoint of the prediction grid as the center. The preset prediction radius can be one grid, that is, the prediction range is the outer eight grids centered on the prediction grid. The preset prediction radius can also be two grids, that is, the prediction range is the outer 24 grids centered on the prediction grid.

[0044] The process of predicting the initial prediction parameters of the prediction grid by the signal power parameters of the measurement grid uses the free space propagation model to estimate the attenuation of the signal at different locations. Free space is defined as an infinite space with zero conductivity and isotropic uniform and lossless radio wave propagation. In this ideal space, radio waves propagate at the speed of light in a vacuum, and there are no reflection, refraction, diffraction, dispersion, and absorption of radio waves. When there are no obstacles between the transmitter and the receiver, that is, there is a direct path, the free space model can be used to predict the strength of the received signal. The formula for the received signal power propagation under the free space propagation model is: ; in, is the received signal power in dBm, is the transmission power of the target base station, in dBm, is the receiving antenna gain of the target base station in dBi, d is the transmission distance between the base station and the receiving point in meters, f is the frequency, and c is the speed of light.

[0045] After setting the prediction radius, the number of measurement grids within the prediction range is obtained. When the number of measurement grids is greater than or equal to the preset number threshold, the distance between the focus of each measurement grid within the prediction range and the midpoint of the prediction grid located in the center is calculated in turn. The signal power prediction formula between the two points is derived based on the received signal power propagation formula under the free space propagation model: ; in, represents the initial prediction parameters calculated from one of the measurement grids on the prediction grid, represents the signal power parameter of the measurement grid, Indicates the distance between the measurement grid and the target base station, Represents the distance between the measured grid and the predicted grid.

[0046] After predicting the prediction grids through the measurement grids in turn and obtaining multiple initial prediction parameters, the weight value of each initial prediction parameter is determined according to the number of measurement grids and the distance between the measurement grid and the prediction grid. That is, the initial prediction parameters are weighted according to the distance between the corresponding measurement grid and the prediction grid, and finally the prediction grid is filled to obtain the prediction signal parameters.

[0047] In addition, when the number of measurement grids within the prediction range is less than a preset threshold, the signal prediction model is trained using the signal power parameters in the measurement grids and the predicted signal parameters in the filled prediction grids, and the unfilled prediction grids are predicted using the signal prediction model.

[0048] For example, refer again to Figure 8 As shown, assuming that the quantity threshold is 7 measurement grids, for the prediction grid A6, the prediction radius is set to 1, then the grids within the prediction range include A1, A2, A3, A5, A7, A9, A10 and A11, and the measurement grids include A1, A2, A3, A5, A9, A10 and A11, a total of seven. Therefore, the prediction grid A6 is predicted by measuring the grids A1, A2, A3, A5, A9, A10 and A11 using the signal power prediction formula; for the prediction grid A7, the prediction radius is set to 1, then the grids within the prediction range include A2, A3, A4, A6, A8, A10, A11 and A12, and the measurement grids include A2, A3, A4, A10, A11 and A12, a total of six. Therefore, the prediction grid A7 will be predicted by the trained signal prediction model.

[0049] Reference Figure 4 As shown, in one embodiment of the present application, the method for predicting by using the signal prediction model includes but is not limited to steps S410 to S420: Step S410, training a signal prediction model based on the signal power parameters in the measurement grid and the predicted signal parameters of the filled prediction grid; Step S420, predicting the prediction signal parameters of the unfilled prediction grids using the trained signal prediction model.

[0050] Specifically, the signal prediction model proposed in this embodiment uses a gradient boosting tree for data analysis and training, and shuffles the signal power parameters in the measurement grid and the predicted signal parameters of the filled prediction grid, and divides them into a training set and a test set. The input features of the signal prediction model include the midpoint coordinates of the grid, and the output result is the predicted signal parameter of the prediction grid. The signal prediction model is trained through the training set, and the test set is used to determine whether the prediction performance of the signal prediction model meets expectations. In a specific embodiment, referring to Fig. 9 As shown, first, in one of the acquisition planes, the signal power parameters of the grids are measured and the prediction grids are filled using the signal power prediction formula; then, a training data set in three-dimensional space is constructed using the signal power parameters of the measured grids in the acquisition plane and the predicted signal parameters of the filled prediction grids, as well as the signal power parameters of the measured grids in other acquisition planes. The signal prediction model is trained using the training data set to obtain a signal prediction model that can predict each prediction grid on a three-dimensional scale. The prediction grids that have not been filled in the above-mentioned acquisition plane and the prediction grids in other acquisition planes are predicted using the trained signal prediction model, thereby obtaining a spectrum coverage map in three-dimensional space.

[0051] In addition, refer to Fig.10 The present application also provides a spectrum coverage map construction device 1000, comprising: The measurement module 1001 is used to define a collection range according to the base station communication parameters of the target base station, set a collection path within the collection range, and gradually obtain the signal receiving power from the target base station based on the collection path; The area division module 1002 is used to perform grid processing on the acquisition ranges in different measurement planes to obtain multiple measurement grids and multiple prediction grids, wherein the measurement grid intersects with the acquisition path, and the prediction grid does not intersect with the acquisition path; The data aggregation module 1003 is used to aggregate the signal receiving power in the measurement grid intersecting with the acquisition path to obtain the signal power parameter in the measurement grid; The prediction module 1004 is used to fill the prediction grids based on the signal power parameters in the measurement grids to obtain the predicted signal parameters of each prediction grid, and obtain the spectrum coverage map within the acquisition range according to the signal power parameters of the measurement grids and the predicted signal parameters of the prediction grids.

[0052] The above-mentioned spectrum coverage map construction device 1000 and spectrum coverage map construction method are based on the same inventive concept, which will not be described in detail herein.

[0053] In addition, refer to Fig.11 , Fig.11The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes: The processor 1101 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application; The memory 1102 may be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1102 may store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1102, and the processor 1101 calls and executes the spectrum coverage map construction method of the embodiment of the present application, for example, executing the above-described Figure 1 Steps S110 to S140 of the method, Figure 2 Steps S210 to S220 of the method, Figure 3 Steps S310 to S320 of the method, Figure 4 Steps S410 to S420 of the method; Input / output interface 1103, used to implement information input and output; The communication interface 1104 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.); A bus 1105 that transmits information between various components of the device (e.g., the processor 1101, the memory 1102, the input / output interface 1103, and the communication interface 1104); The processor 1101 , the memory 1102 , the input / output interface 1103 and the communication interface 1104 are connected to each other in communication within the device via the bus 1105 .

[0054] The embodiment of the present application also provides a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned spectrum coverage map construction method, for example, to execute the above-described Figure 1 Steps S110 to S140 of the method, Figure 2Steps S210 to S220 of the method, Figure 3 Steps S310 to S320 of the method, Figure 4 The method comprises steps S410 to S420.

[0055] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0056] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0057] It can be understood by those skilled in the art that Figures 1 to 4 The technical solutions shown in the figure do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figure, or a combination of certain steps, or different steps.

[0058] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0059] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0060] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0061] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0062] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0063] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0064] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0065] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0066] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for constructing a spectrum coverage map, characterized in that: include: Delimiting a collection range according to base station communication parameters of a target base station, setting a collection path within the collection range, and gradually acquiring a signal receiving power from the target base station based on the collection path; Performing grid processing on the acquisition ranges in different measurement planes respectively to obtain a plurality of measurement grids and a plurality of prediction grids, wherein the measurement grids intersect with the acquisition path, and the prediction grids do not intersect with the acquisition path; For the measurement grid intersecting with the acquisition path, aggregating the signal receiving power in the measurement grid to obtain a signal power parameter in the measurement grid; Based on the signal power parameters in the measurement grid, the prediction grid is filled to obtain the predicted signal parameters of each prediction grid, and the spectrum coverage map within the acquisition range is obtained according to the signal power parameters of the measurement grid and the predicted signal parameters of the prediction grid.

2. The method for constructing a spectrum coverage map according to claim 1, characterized in that: The filling of the prediction grid based on the signal power parameter in the measurement grid to obtain the prediction signal parameter of each prediction grid includes: Defining prediction ranges based on the centers of the prediction grids, respectively, and predicting the corresponding prediction grids according to the signal power parameters of the measurement grids within the prediction ranges, respectively, to obtain a plurality of initial prediction parameters; Based on the distance between the measurement grid and the prediction grid, the initial prediction parameters predicted by each of the measurement grids are weighted to obtain the prediction signal parameters of the prediction grid.

3. The method for constructing a spectrum coverage map according to claim 2, characterized in that: The formula for determining the initial prediction parameters of the prediction grid based on the signal power parameters of the measurement grid is: ； in, represents the signal power parameter of the measurement grid, represents the distance between the measurement grid and the target base station, represents the distance between the measurement grid and the prediction grid, represents the initial prediction parameters of the prediction grid.

4. The method for constructing a spectrum coverage map according to claim 1, characterized in that: The filling of the prediction grid based on the signal power parameter in the measurement grid to obtain the prediction signal parameter of each prediction grid also includes: Training a signal prediction model based on the signal power parameters in the measurement grid in the current acquisition plane, the predicted signal parameters of the filled prediction grid, and the signal power parameters in the measurement grids in other acquisition planes; The predicted signal parameters of the unfilled prediction grid are predicted using the trained signal prediction model.

5. The method for constructing a spectrum coverage map according to claim 4, characterized in that: The filling of the prediction grid based on the signal power parameter in the measurement grid to obtain the prediction signal parameter of each prediction grid also includes: In the measurement planes at the remaining measurement heights, the predicted signal parameters of each prediction grid are predicted using the trained signal prediction model to obtain a spectrum coverage map in a three-dimensional space.

6. The method for constructing a spectrum coverage map according to claim 1, characterized in that: The gridding process of the acquisition range includes: Obtaining the longitude and latitude coordinates of the boundary of the acquisition range, and converting the longitude and latitude coordinates into UTM coordinates; The measurement area is divided into a plurality of grids, the UTM area in which the acquisition range is located is determined according to the UTM coordinates, and the grid index of each of the grids in the measurement area is determined based on the easting coordinate and the northing coordinate in the UTM coordinates.

7. The method for constructing a spectrum coverage map according to claim 6, characterized in that: The grid index includes an easting index and a northing index, and determining the grid index of each grid in the measurement area based on the easting coordinate and the northing coordinate in the UTM coordinate includes: Determine the easting index of the grid: ； Determine the north index of the grid: ； in, represents the easting index, easting represents the easting coordinate of the grid, represents the north index, northerning represents the north coordinate of the grid, size represents the side length of the grid, % represents the modulo operation, and int() represents taking the integer part.

8. A spectrum coverage map construction device, characterized in that: include: A measurement module, configured to define a collection range according to a base station communication parameter of a target base station, set a collection path within the collection range, and gradually acquire a signal receiving power from the target base station based on the collection path; A region division module, used for performing grid processing on the acquisition ranges in different measurement planes respectively to obtain a plurality of measurement grids and a plurality of prediction grids, wherein the measurement grids intersect with the acquisition path, and the prediction grids do not intersect with the acquisition path; A data aggregation module, configured to aggregate the signal receiving power in the measurement grid intersecting with the acquisition path to obtain a signal power parameter in the measurement grid; A prediction module is used to fill the prediction grid based on the signal power parameters in the measurement grid to obtain the predicted signal parameters of each prediction grid, and to obtain the spectrum coverage map within the acquisition range according to the signal power parameters of the measurement grid and the predicted signal parameters of the prediction grid.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the spectrum coverage map construction method according to any one of claims 1 to 7 when executing the computer program.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the spectrum coverage map construction method according to any one of claims 1 to 7 is implemented.

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