CN115496111A - Electromagnetic spectrum data processing method, apparatus, device and medium - Google Patents
Electromagnetic spectrum data processing method, apparatus, device and medium Download PDFInfo
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Abstract
The embodiment of the application relates to an electromagnetic spectrum data processing method, an electromagnetic spectrum data processing device, electromagnetic spectrum data processing equipment and an electromagnetic spectrum data processing medium, wherein the method comprises the following steps: acquiring position data and grid resolution of a region range to be processed and monitoring sample data in the region range to be processed; rasterizing the range of the area to be processed to obtain rasterized data of the range of the area to be processed and the number of grids of the area Fan Weishan; performing rasterization merging processing on the monitoring sample data to obtain the processed monitoring sample data and the number of samples; judging an interpolation operation method according to the number of samples and the number of grids in the area range; and carrying out interpolation operation by using an interpolation operation method to obtain interpolation data corresponding to the grids in the range of the region to be processed. According to the method and the device, the electromagnetic spectrum distribution conditions of other unmonitored region positions can be rapidly and accurately deduced according to the existing monitoring node number and monitoring data distribution, and a good effect is achieved in the fitting degree with the actual condition.
Description
Technical Field
The present invention relates to the field of radio wave propagation, and in particular, to a method, an apparatus, a device, and a medium for processing electromagnetic spectrum data.
Background
In the field of radio frequency spectrum, an algorithm model is mainly realized by filling data of unmeasured position points through data interpolation according to a monitoring data synthesis region situation. The monitoring data interpolation algorithm is used for predicting the spectrum intensity of continuous position points on a geographical area by using a digitalized processing mode on spatially discrete spectrum monitoring data (including but not limited to fixed monitoring data, movable monitoring data, mobile monitoring data and drive test data) through an interpolation mode. The deployment and the test of the monitoring nodes in the actual environment are limited by conditions, and the predicted spectrum distribution condition and the actual condition fit degree cannot achieve a good effect, so that the problem that how to deduce and determine the accurate electromagnetic spectrum distribution condition of an unmonitored area through the existing monitoring node number and monitoring data distribution still needs to be solved urgently is solved.
Disclosure of Invention
In view of this, the embodiments of the present application provide an electromagnetic spectrum data processing method, apparatus, device and medium to solve at least one problem in the background art.
In a first aspect, an embodiment of the present application provides an electromagnetic spectrum data processing method, where the method includes:
acquiring position data and grid resolution of a region range to be processed and monitoring sample data in the region range to be processed; wherein the monitoring sample data comprises position data and a receiving power value of a sampling point;
rasterizing the area range to be processed according to the raster resolution and the position data of the area range to be processed and monitored to obtain rasterized data of the area range to be processed and the number of the Fan Weishan grids of the area; the rasterized data of the area range to be processed comprises position data of a raster in the area range to be processed;
rasterization merging processing is carried out on the monitoring sample data according to the rasterized data of the area range to be processed, and the processed monitoring sample data and the number of samples are obtained;
judging an interpolation operation method according to the number of the samples and the number of the grids in the area range;
and performing interpolation operation on the rasterized data of the range of the area to be processed and the processed monitoring sample data by using the interpolation operation method to obtain interpolation data corresponding to grids in the range of the area to be processed.
Further, the determining an interpolation operation method according to the number of samples and the number of grids in the area range specifically includes:
calculating a grid sample ratio according to the sample number and the area range grid number;
judging an interpolation operation method according to the number of the samples and the grid sample ratio;
further, the determining an interpolation operation method according to the number of samples and the grid sample ratio specifically includes:
when the number of the samples is less than 100, the interpolation operation method is a kriging interpolation method;
when the number of the samples is more than or equal to 100 and less than 200, judging the interpolation operation method according to the grid sample ratio;
when the number of the samples is more than or equal to 200 and less than 400, judging the interpolation operation method according to the grid sample ratio;
when the number of the samples is more than or equal to 400 and less than 800, judging the interpolation operation method according to the grid sample ratio;
and when the number of the samples is more than or equal to 800, the interpolation operation method is a thin plate spline interpolation method.
Further, when the number of samples is greater than or equal to 100 and less than 200, the determining the interpolation operation method according to the grid sample ratio specifically includes:
when the grid sample is larger than 800, the interpolation operation method is a kriging interpolation method;
when the grid sample ratio is more than 400 and less than or equal to 800, the interpolation operation method is a thin plate spline interpolation method;
when the grid sample ratio is less than or equal to 400, the interpolation operation method is an inverse distance interpolation method.
Further, when the number of samples is greater than or equal to 200 and less than 400, the interpolation operation method is judged according to the grid sample ratio;
when the grid sample is larger than 800, the interpolation operation method is a thin plate spline method;
when the grid sample ratio is greater than 400 and less than or equal to 800, the interpolation operation method is an inverse distance interpolation method;
when the grid sample ratio is less than or equal to 400, the interpolation operation method is a natural neighborhood interpolation method.
Further, when the number of samples is greater than or equal to 400 and less than 800, the interpolation operation method is judged according to the grid sample ratio;
when the grid sample is larger than 400, the interpolation operation method is a thin plate spline method;
when the grid sample ratio is less than or equal to 400, the interpolation operation method is a natural neighborhood interpolation method.
Further, the rasterizing and merging the monitoring sample data according to the rasterized data of the region range to be processed to obtain the processed monitoring sample data and the number of samples, and specifically includes:
determining corresponding sampling points in a grid according to the rasterization data of the region range to be processed and the position data of the sampling points in the monitoring sample data;
combining the receiving power values of corresponding sampling points in the same grid to obtain processed monitoring sample data and sample quantity; the processed monitoring sample data comprises position data of the grids and corresponding receiving power value data in the grids; the merging processing is to take the maximum value in the receiving power values of the sampling points in the grids as the corresponding receiving power value in the grids; the number of samples refers to the number of grids having corresponding reception power values.
On the other hand, an embodiment of the present application further provides an electromagnetic spectrum data processing apparatus, where the apparatus includes:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is configured to acquire position data of a region range to be processed, grid resolution and monitoring sample data in the region range to be processed; wherein the monitoring sample data comprises position data and a receiving power value of a sampling point;
the rasterization module is configured to perform rasterization processing on the to-be-processed area range according to the raster resolution and the position data of the to-be-processed monitoring area range to obtain rasterized data of the to-be-processed area range and the number of grids of the area Fan Weishan; the rasterization data of the range of the area to be processed comprises position data of grids in the range of the area to be processed;
the merging processing module is configured to perform rasterization merging processing on the monitoring sample data according to the rasterization data of the region range to be processed to obtain the processed monitoring sample data and the number of samples;
the judging module is configured to judge an interpolation operation method according to the number of the samples and the number of the grids of the area range;
and the interpolation operation module is configured to perform interpolation operation by using the interpolation operation method according to the rasterized data of the to-be-processed area range and the processed monitoring sample data to obtain interpolation data corresponding to grids in the to-be-processed area range.
On the other hand, the embodiment of the present application further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor executes the electromagnetic spectrum data processing method described above.
On the other hand, the embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the electromagnetic spectrum data processing method described above.
According to the electromagnetic spectrum data processing method, the device, the equipment and the medium, the relevance distribution characteristics of the spectrum data in the space dimension are analyzed through the number of samples and the number of grids, then the corresponding interpolation operation method is judged adaptively, and then the interpolation operation is carried out through the operation of monitoring sample data and the corresponding interpolation operation method, so that the electromagnetic spectrum data of other unmonitored region positions can be deduced. According to the method and the device, the electromagnetic spectrum distribution condition of other unmonitored region positions can be rapidly and accurately deduced according to the existing monitoring node number and monitoring data distribution, and a good effect is achieved in the fitting degree with the actual condition.
Drawings
Fig. 1 is a schematic flowchart of an electromagnetic spectrum data processing method according to an embodiment of the present application;
fig. 2 is a schematic flowchart illustrating a grid merging process of monitoring sample data according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart illustrating a method for determining interpolation according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating an interpolation operation method determination according to an embodiment of the present application;
FIG. 5 is a schematic diagram of an electromagnetic spectrum data processing apparatus according to an embodiment of the present application;
fig. 6 is a block diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the technical solution and advantages of the present invention more comprehensible, a detailed description is given below by way of specific examples. Wherein the figures are not necessarily to scale, and certain features may be exaggerated or minimized to more clearly show details of the features; unless defined otherwise, technical and scientific terms used herein have the same meaning as technical and scientific terms used in the technical field to which this application belongs.
Fig. 1 shows a method for processing electromagnetic spectrum data according to an embodiment of the present application, which is applied to a terminal for illustration, and it can be understood that the method can also be applied to a server, and can also be applied to a system including the terminal and the server, and is implemented by interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices, and the server can be implemented by an independent server or a server cluster formed by a plurality of servers. In the embodiment of the application, the method comprises the following steps:
s101, acquiring position data and grid resolution of a region range to be processed and monitoring sample data in the region range to be processed.
Specifically, the position data of the range of the area to be processed includes latitude and longitude data that can determine the range of the area to be processed. For example, if the range of the region to be processed is a rectangular region in the range of 19km × 13km, the position data corresponding to the range of the region to be processed includes longitude and latitude data of four vertices of the rectangular region. The grid resolution needs to be selected according to actual requirements, and is generally selected from 10 meters to 5000 meters. The monitoring sample data in the region to be processed specifically refers to spectrum monitoring data measured at an actual monitoring position point in the region to be processed, and includes but is not limited to fixed monitoring data, movable monitoring data, mobile monitoring data and road test data, and the monitoring sample data at least includes position data of a sampling point and a receiving power value of the sampling point. The method mainly analyzes the distribution characteristics of relevance of the spectrum data on the space latitude by monitoring the number of sampling points in the sample data, monitoring data distribution and density characteristics, so as to predict the electromagnetic spectrum distribution situation of an unmonitored area.
S102, rasterizing the to-be-processed area range according to the raster resolution and the position data of the to-be-processed monitoring area range to obtain rasterized data of the to-be-processed area range and the number of grids of the area Fan Weishan.
Specifically, the monitoring area to be processed is divided into grids according to the grid resolution, each grid is provided with corresponding grid position data, and accordingly rasterization data of the area range to be processed is obtained, namely the rasterization data of the area range to be processed includes all grids and corresponding position data thereof, wherein the position data of the grids includes corresponding longitude and latitude data at the grid positions. The number of cells in the area Fan Weishan refers to the total number of all cells after the rasterization process of the area to be monitored. Specifically, in the rasterization processing process, firstly, the grid resolution is required to be converted into a unit of longitude and latitude, wherein the longitude corresponds to the north-south direction, the latitude corresponds to the east-west direction, then, the to-be-processed area range is divided into corresponding grids according to the position data of the to-be-processed monitoring area range, and each grid has corresponding grid position data. It can be understood that the rasterization process is to divide the monitoring area range to be processed into grids in the east-west direction and the south-north direction, and the length in the east-west direction and the length in the south-north direction are not integral multiples of the grid resolution in general, so the grid size of the boundary area is generally smaller than that of the non-boundary area, that is, the total number of actual grids is larger than that calculated by theory. For example, if the range corresponding to the position data of the region to be processed is 19km (north-south distance) × 13km (east-west distance), and the grid resolution is 50m, the grid resolution needs to be converted into units in longitude and latitude, for example, the unit in longitude is 0.00045045045 degrees by dividing the grid resolution by 111000 meters, and then the region to be processed is divided into corresponding grids according to the position data of the region to be processed, so that the number of grids corresponding to 19km 13km is 392 km 311. Further, the grid resolution includes north-south grid resolution and east-west grid resolution, correspondingly, rasterization processing is performed on the area range to be processed according to the north-south grid resolution and the east-west grid resolution, and rasterized data of the area range to be processed and the number of grids of the area Fan Weishan are obtained; by setting the grid resolution in different directions, different spectrum data processing requirements can be flexibly met, and particularly, by flexibly setting the grid resolution for electromagnetic spectrum data with directivity, the speculative calculation can be more accurately and more quickly carried out.
S103, performing rasterization merging processing on the monitoring sample data according to the rasterization data of the region range to be processed to obtain the processed monitoring sample data and the number of samples.
Specifically, as shown in fig. 2, S103 includes:
s131, determining a grid corresponding to each sampling point according to the rasterization data of the region range to be processed and the position data of the sampling points in the monitoring sample data.
Specifically, longitude and latitude data in the position data of the sampling points are compared with position data of a grid in rasterization data of the range of the region to be processed, so that the grid position corresponding to each sampling point can be determined.
And S132, combining the receiving power values of the corresponding sampling points in the same grid to obtain the processed monitoring sample data and the number of the samples.
Specifically, the merging processing in the embodiment of the present application refers to comparing the received power values of all sampling points in the same grid, where a maximum value of the received power values is taken as a received power value corresponding to the grid, that is, the received power values in the corresponding grid area range are the same value at this time; the processed monitoring sample data includes position data of the grid and corresponding received power value data in the grid. After the merging processing, the region to be processed is divided into grids with received power value data and grids without received power value data, where the number of grids with received power value data is the number of samples, and the region range corresponding to the grids without received power value data is the region where the calculation of spectrum data needs to be estimated. For example, taking coordinate values x (meters) of the monitoring sample data in the east-west direction and coordinate values y (meters) in the north-south direction as an example, the raw acquisition data, that is, the monitoring sample data before merging processing, is shown in table 1.
TABLE 1
Each grid is 10 meters by 10 meters, the original collected data are merged, the maximum value of the receiving power of the sampling points in the same grid is taken as the corresponding receiving power value of the grid during merging, the monitoring sample data after merging processing as shown in the table 2 are obtained, and meanwhile, the number of the samples is 3.
TABLE 2
And S104, judging an interpolation operation method according to the number of samples and the number of grids in the area range.
Specifically, as shown in fig. 3, S104 includes:
and S141, calculating a grid sample ratio according to the number of samples and the number of grids in the area range.
Specifically, the grid sample ratio is obtained by calculating the ratio of the number of grids passing through the area Fan Weishan to the number of samples, and the grid sample ratio represents the density condition of sample data and reflects the dense and sparse conditions of the acquired monitoring data sample in the whole structure. Further, before S141, the method further includes determining whether the number of samples or the number of grids in the area range is equal to zero, and if so, the processing fails, and then the steps S101 to S103 need to be repeated, so as to process special abnormal situations, such as an unreasonable acquisition range of the monitoring sample data, an unreasonable setting of the grid resolution, and the like.
And S142, judging an interpolation operation method according to the number of the samples and the grid sample ratio.
Specifically, as shown in fig. 4, S142 includes:
s1421, when the number of samples is less than 100, the interpolation operation method is a Crigin interpolation method.
The kriging interpolation method has the characteristics of good approximation degree, strong calculation capability and wide application range, but the method consumes long time for processing under the condition of the number of samples. In the embodiment of the application, when the number of the samples is less than 100, the number of the samples is small, and the calculation result can be quickly and accurately obtained through a kriging interpolation method.
S1422, when the number of samples is greater than or equal to 100 and less than 200, determining an interpolation operation method according to the grid sample ratio.
When the number of samples is greater than or equal to 100 and less than 200, the number of samples is slightly larger, and at this time, the density degree of the samples is determined according to the grid samples, S1422 specifically includes:
and when the grid sample is larger than 800, the interpolation operation method is a kriging interpolation method. The grid sample ratio is larger than 800, which indicates that the samples are sparse at the moment, and a relatively good processing result can be obtained by using a kriging interpolation method under the condition of sparse samples.
When the grid sample ratio is greater than 400 and less than or equal to 800, the interpolation operation method is a thin-plate spline interpolation method. The deformation on the physical shape simulated by the thin-plate spline interpolation method can be approximated by the method, so that the method is widely applied to related applications such as face key point deformation. The method has the advantages that relatively good effect can be obtained on the electromagnetic spectrum data calculation by using the thin plate spline interpolation, and the method has certain disadvantage on the approximation degree of theory, but has great advantages on the aspects of calculation speed and calculation capacity. When the grid sample ratio is greater than 400 and less than or equal to 800, the sparsity is medium, and at this time, the calculation processing can be completed within the receiving time range by using the thin-plate spline interpolation method, and a good calculation result is obtained.
When the grid sample ratio is less than or equal to 400, the interpolation operation method is an inverse distance interpolation method. In the process of processing the spectrum data, the inverse distance interpolation method is not used for the self distance of the spatial position point, but is used for calculating the distance through subscripts in a rasterization result, meanwhile, the algorithm introduces power exponent configuration of a distance item, and the change trend of the electromagnetic spectrum data in a real scene can be better matched through changing the configuration. The index can be corrected through a data mining method and a machine learning processing mode, so that in an application specific scene environment, accumulated electromagnetic wave spectrum data are used as initial input, a more calculation effect can be obtained, and the inverse distance interpolation is suitable for scenes of observation point data sets which are uniformly distributed and dense enough to reflect local differences. When the grid sample ratio is smaller than 400, the sample density is higher, and the effect which is more consistent with the scene can be obtained by using the reverse distance interpolation.
S1423, when the number of samples is greater than or equal to 200 and less than 400, determining an interpolation operation method according to the grid sample ratio.
When the number of samples is greater than or equal to 200 and less than 400, the number of samples is medium, and at this time, the density degree of the samples is determined according to the grid sample ratio, S1423 specifically includes:
and when the grid sample is larger than 800, the interpolation operation method is a thin plate spline method. At the moment, the samples are sparse, and a relatively good processing result can be obtained through a thin plate spline method.
When the grid sample ratio is greater than 400 and less than or equal to 800, the interpolation operation method is an inverse distance interpolation method. At the moment, the number of samples and the density degree are both medium, and a relatively good processing result can be obtained by an inverse distance interpolation method.
When the grid sample ratio is less than or equal to 400, the interpolation operation method is a natural neighborhood interpolation method. The natural neighborhood method is an enhanced form of the Thiessen polygon method, has general expression on the approximation degree and better expression on the calculating capacity, is simpler and better accords with the inherent thinking of people. But is limited in use range, mainly suitable for scenes with small range area and low spatial variability. Since the processing method is relatively simple, the method has a better performance in terms of processing speed, and the sample size of a larger scene can be used. At the moment, the number of samples is medium, the density of the samples is high, a natural neighborhood interpolation mode is adopted, and the effect which is more accordant with the scene is obtained while the scene is efficiently processed.
S1424, when the number of samples is greater than or equal to 400 and less than 800, determining an interpolation operation method according to the grid sample ratio.
When the number of samples is greater than or equal to 400 and less than 800, the number of samples is greater, and at this time, the density degree of the samples is determined according to the grid sample ratio, S1424 specifically includes:
when the grid sample is larger than 400, the interpolation operation method is a thin plate spline method. At the moment, the samples are sparse, and a relatively good processing result can be obtained through a thin plate spline method.
When the grid sample ratio is less than or equal to 400, the interpolation operation method is a natural neighborhood interpolation method. At the moment, the number of samples is large, the density of the samples is large, a natural neighborhood interpolation mode is adopted, and the effect which is more accordant with the scene is obtained while the scene is efficiently processed.
S1425, when the number of samples is greater than or equal to 800, the interpolation operation method is a thin-plate spline interpolation method.
When the number of samples is greater than or equal to 800, the number of samples is large, the processing speed is limited, the processing speed and the estimation effect need to be balanced, and the thin-plate spline interpolation method is directly selected, so that the processing result meeting the application requirement can be obtained under the time-consuming requirement acceptable in the application situation.
And S105, performing interpolation operation by using an interpolation operation method according to the rasterized data of the range of the area to be processed and the processed monitoring sample data to obtain interpolation data corresponding to the grid in the range of the area to be processed.
Specifically, the rasterized data of the region range to be processed includes position data of a grid, all grid region ranges include a grid with received power value data and a blank grid without a received power value, and the corresponding processed monitoring sample data includes the position data of the grid and the received power value data in the grid. And performing interpolation operation by using an interpolation operation method according to the rasterized data of the range of the area to be processed and the processed monitoring sample data, so that the receiving power value in the blank grid can be calculated, namely the interpolation data corresponding to the grid in the range of the area to be processed is obtained.
According to the embodiment of the application, the number and the density degree of the samples are judged according to the number of the samples and the number of the grids, so that the relevance distribution characteristics of the spectrum data on the spatial dimension can be analyzed, then the corresponding interpolation operation method is judged in an adaptive manner, and then the interpolation operation is carried out according to the monitoring sample data and the corresponding interpolation operation method, so that the electromagnetic spectrum data of other unmonitored region positions can be deduced. According to the method and the device, the electromagnetic spectrum distribution condition of other unmonitored region positions can be rapidly and accurately deduced according to the existing monitoring node number and monitoring data distribution, and a good effect is achieved in the fitting degree with the actual condition.
The technical solutions and advantages of the embodiments of the present application will be further described below with reference to specific examples.
The received power coverage map is obtained by performing interpolation calculation in a scene area within a range of about 19km × 13km, wherein the scene area is rasterized according to the resolution of 50 meters, the size of the area is divided into 392 × 311 grids 121912, the scene area is sampled according to different modes, an interpolation processing method is selected according to the number of samples and the number of the grids, and the obtained data is shown in table 3. It should be noted that the number of sampling points in the table refers to the number of original sampling points, and the corresponding number of samples can be obtained only after the merging processing is performed.
TABLE 3
In another aspect, in one embodiment, as shown in fig. 5, there is provided an electromagnetic spectrum data processing apparatus including:
the acquisition module 101 is configured to acquire position data of a region range to be processed, grid resolution and monitoring sample data in the region range to be processed; wherein the monitoring sample data comprises position data and a receiving power value of a sampling point;
the rasterization module 102 is configured to perform rasterization processing on the to-be-processed area range according to the raster resolution and the position data of the to-be-processed monitoring area range to obtain rasterized data of the to-be-processed area range and the number of grids of the area Fan Weishan; the rasterization data of the range of the area to be processed comprises position data of grids in the range of the area to be processed;
the merging processing module 103 is configured to perform rasterization merging processing on the monitoring sample data according to the rasterized data of the region range to be processed, so as to obtain the processed monitoring sample data and the number of samples;
a judging module 104 configured to judge an interpolation operation method according to the number of samples and the number of grids of the area range;
the interpolation operation module 105 is configured to perform interpolation operation by using an interpolation operation method according to the rasterized data of the region range to be processed and the processed monitoring sample data, so as to obtain interpolation data corresponding to the grid within the region range to be processed.
For specific limitations of the electromagnetic spectrum data processing apparatus, reference may be made to the above limitations of the electromagnetic spectrum data processing method, which are not described herein again. The respective modules in the electromagnetic spectrum data processing apparatus described above may be entirely or partially implemented by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Fig. 6 is a block diagram of a computer device, which may be a terminal, according to an embodiment of the present disclosure, and an internal structure diagram of the computer device may be as shown in fig. 6. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an electromagnetic spectrum data processing method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the electronic equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an exemplary embodiment, there is also provided a computer device including a memory storing a computer program and a processor configured to execute the computer program to implement the electromagnetic wave spectrum data processing method as in the embodiment of the present disclosure.
In an exemplary embodiment, there is also provided a computer-readable storage medium on which a computer program is stored, which, when executed by a processor of a computer device, enables the computer device to execute an electromagnetic wave spectrum data processing method in the embodiments of the present disclosure. The computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It should be understood that the above embodiments are exemplary and are not intended to encompass all possible implementations encompassed by the claims. Various modifications and changes may also be made on the basis of the above embodiments without departing from the scope of the present disclosure. Likewise, various features of the above embodiments may be arbitrarily combined to form further embodiments of the present invention that may not be explicitly described. Therefore, the above examples only represent some embodiments of the present invention, and do not limit the scope of the present invention.
Claims (10)
1. A method of processing electromagnetic spectrum data, the method comprising:
acquiring position data and grid resolution of a to-be-processed area range and monitoring sample data in the to-be-processed area range; wherein the monitoring sample data comprises position data and a receiving power value of a sampling point;
rasterizing the area range to be processed according to the raster resolution and the position data of the area range to be processed and monitored to obtain rasterized data of the area range to be processed and the number of the Fan Weishan grids of the area; the rasterization data of the range of the area to be processed comprises position data of grids in the range of the area to be processed;
rasterizing and merging the monitoring sample data according to the rasterized data of the region range to be processed to obtain the processed monitoring sample data and the number of samples;
judging an interpolation operation method according to the number of the samples and the number of the grids in the area range;
and performing interpolation operation by using the interpolation operation method according to the rasterized data of the range of the area to be processed and the processed monitoring sample data to obtain interpolation data corresponding to grids in the range of the area to be processed.
2. The method for processing electromagnetic spectrum data according to claim 1, wherein the determining an interpolation operation method according to the number of samples and the number of grids in the area range specifically includes:
calculating a grid sample ratio according to the sample number and the area range grid number;
and judging an interpolation operation method according to the number of the samples and the grid sample ratio.
3. The method for processing electromagnetic spectrum data according to claim 2, wherein the determining an interpolation operation method according to the number of samples and the grid sample ratio specifically comprises:
when the number of the samples is less than 100, the interpolation operation method is a kriging interpolation method;
when the number of the samples is more than or equal to 100 and less than 200, judging the interpolation operation method according to the grid sample ratio;
when the number of the samples is more than or equal to 200 and less than 400, judging the interpolation operation method according to the grid sample ratio;
when the number of the samples is more than or equal to 400 and less than 800, judging the interpolation operation method according to the grid sample ratio;
and when the number of the samples is more than or equal to 800, the interpolation operation method is a thin plate spline interpolation method.
4. The electromagnetic spectrum data processing method according to claim 3, wherein the determining the interpolation operation method according to the grid sample ratio when the number of samples is greater than or equal to 100 and less than 200 specifically includes:
when the grid sample is larger than 800, the interpolation operation method is a kriging interpolation method;
when the grid sample ratio is more than 400 and less than or equal to 800, the interpolation operation method is a thin plate spline interpolation method;
when the grid sample ratio is less than or equal to 400, the interpolation operation method is an inverse distance interpolation method.
5. The electromagnetic spectrum data processing method according to claim 3, wherein the interpolation operation method is determined based on the grid sample ratio when the number of samples is 200 or more and less than 400;
when the grid sample is larger than 800, the interpolation operation method is a thin plate spline method;
when the grid sample ratio is greater than 400 and less than or equal to 800, the interpolation operation method is an inverse distance interpolation method;
when the grid sample ratio is less than or equal to 400, the interpolation operation method is a natural neighborhood interpolation method.
6. The electromagnetic spectrum data processing method according to claim 3, wherein the interpolation operation method is determined based on the grid sample ratio when the number of samples is 400 or more and less than 800;
when the grid sample is larger than 400, the interpolation operation method is a thin plate spline method;
when the grid sample ratio is less than or equal to 400, the interpolation operation method is a natural neighborhood interpolation method.
7. The method for processing electromagnetic spectrum data according to claim 1, wherein the rasterizing and merging processing is performed on the monitoring sample data according to the rasterizing data of the region range to be processed to obtain the processed monitoring sample data and the number of samples, and specifically includes:
determining a grid corresponding to each sampling point according to the rasterization data of the region range to be processed and the position data of the sampling point in the monitoring sample data;
combining the receiving power values of corresponding sampling points in the same grid to obtain processed monitoring sample data and sample quantity; the processed monitoring sample data comprises position data of the grid and corresponding received power value data in the grid; the merging processing is to take the maximum value of the receiving power values of the sampling points in the grids as the corresponding receiving power value in the grids; the number of samples refers to the number of grids with corresponding received power values.
8. An electromagnetic spectrum data processing apparatus, characterized in that the apparatus comprises:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is configured to acquire position data of a region range to be processed, grid resolution and monitoring sample data in the region range to be processed; wherein the monitoring sample data comprises position data and a receiving power value of a sampling point;
the rasterization module is configured to perform rasterization processing on the to-be-processed area range according to the raster resolution and the position data of the to-be-processed monitoring area range to obtain rasterized data of the to-be-processed area range and the number of grids of the area Fan Weishan; the rasterization data of the range of the area to be processed comprises position data of grids in the range of the area to be processed;
the data merging module is configured to perform rasterization merging processing on the monitoring sample data according to the rasterized data of the to-be-processed area range to obtain the processed monitoring sample data and the number of samples;
the judging module is configured to judge an interpolation operation method according to the number of the samples and the number of the grids of the area range;
and the interpolation operation module is configured to perform interpolation operation by using the interpolation operation method according to the rasterized data of the to-be-processed area range and the processed monitoring sample data to obtain interpolation data corresponding to grids in the to-be-processed area range.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the electromagnetic wave spectrum data processing method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the electromagnetic spectrum data processing method of any one of claims 1 to 7.
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| CN117092415B (en) * | 2023-10-18 | 2024-01-19 | 深圳市城市公共安全技术研究院有限公司 | Regional electromagnetic environment monitoring method, device, equipment and medium |
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