CN116418430A - Electromagnetic spectrum situation drawing method and system - Google Patents
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Abstract
The application discloses an electromagnetic spectrum situation drawing method and system. Firstly, carrying out regional division by combining GIS data and using an image processing technology, gathering the positions with more consistent wave propagation characteristics, and then processing electromagnetic spectrum data monitored by gridding each region, so as to realize inversion reconstruction from scattered point radio monitored electromagnetic spectrum information to regional electromagnetic spectrum information. In the inversion reconstruction process, K-means clustering, nonnegative matrix factorization, radiation source space propagation loss function reconstruction based on an asymmetric structure masking self-encoder and other technologies are specifically applied. And rendering similar thermodynamic diagram is carried out on the regional signal power spectrum distribution obtained by inversion in the GIS, so that the distribution prediction and visual display of electromagnetic spectrum information in a monitoring region in space are realized, and the utilization efficiency of electromagnetic spectrum monitoring data is improved.
Description
Technical Field
The application relates to the technical field of radio monitoring, in particular to an electromagnetic spectrum situation drawing method and system.
Background
The development of information communication evolves towards the integration of space and ground, and the dynamic perception, accurate analysis, visual presentation and efficient utilization of important sensitive regions, hot focus attention directions and complex strange electromagnetic environments are important components of the electromagnetic spectrum monitoring and management development strategy of the army and civilian. The electromagnetic spectrum map is based on GIS (Geographic Information System ), comprehensively utilizes the technologies of electromagnetic calculation, signal processing, data mining, machine learning, database and the like, aims to intuitively present invisible and untouched radio signals on a familiar electronic map so as to achieve the purposes of multidimensional visual display, intelligent analysis, development and utilization of electromagnetic environment, such as time-frequency-space-energy, and the like, and is an innovative application of technologies of big data, artificial intelligence and the like in the field of radio management.
For the construction of an electromagnetic spectrum map, the core is that regional electromagnetic spectrum situation results are obtained by inversion from electromagnetic spectrum monitoring information of isolated and scattered points through an electromagnetic spectrum situation drawing method, and visual display is carried out on a GIS. However, compared with a large amount of spectrum monitoring data generated by radio monitoring, the utilization rate of the current electromagnetic spectrum management and information service systems to the spectrum monitoring data still appears to be low, the electromagnetic power distribution application in the monitoring area mainly stays in the monitoring stage of the frequency spectrum of the isolated site, and a relatively effective means and method are not available for monitoring the continuous distribution and change conditions of the frequency spectrum of the frequency band monitoring signal and the frequency point of interest monitoring large signal in the area without the monitoring sensor outside the monitoring site.
At present, electromagnetic spectrum situation reconstruction and drawing methods can be divided into two main types, namely model driving and data driving. The model-driven spectrum situation reconstruction method mainly utilizes radiation source information and natural environment information, models and deduces spectrum space distribution based on an electromagnetic basic theory, and an electromagnetic model used in the engineering at present is mostly based on fitting of a large amount of measured data, and is called an empirical or semi-empirical model, and commonly used empirical models comprise an Egli model, an Okumura-Hata model, an ITU-R model, a COST231 model and the like. Empirical or semi-empirical models often have the advantage of simple parameters and low computational effort. However, the model can only obtain the path loss of electric wave propagation, has poor prediction capability on the electromagnetic environment under specific environment and complex terrain conditions, has low precision, cannot truly reflect the electromagnetic wave propagation condition under an actual link, and is difficult to be applied to the reconstruction of the spectrum situation under the complex surface environment. In order to solve the electromagnetic environment prediction problem in a specific environment, various deterministic methods are sequentially proposed, wherein the deterministic method is a method for solving an equation to be solved through initial conditions and boundary conditions based on Maxwell's equations or various simplified forms thereof. The model-driven spectrum situation reconstruction method has stronger spectrum space reconstruction capability in theory, but the algorithm is complex to realize, the reconstruction accuracy is strongly dependent on exact boundary information, the algorithm lacks a real-time correction link, and the method has great limitation in engineering practice.
The data-driven spectrum situation reconstruction method is mainly characterized in that unknown spectrum distribution is complemented based on known spectrum data in a signal processing mode, and the basic principle of the complement is based on the correlation and continuity of spectrum situations in multiple dimensions such as space, time and frequency. The data-driven spectrum situation reconstruction method can be divided into two types, namely a parameterized model and a non-parameterized model: the parameterized model is mainly based on the situation superposition principle of frequency spectrum, and the application of the model often needs to know accurate information of the radiation sources in advance, such as the number, power, spatial distribution and the like of the radiation sources, and is mainly applied to a frequency spectrum situation reconstruction task under the condition of knowing the accurate positions of the radiation sources. Typical parameterized models include compressed sensing, dictionary learning, bayesian models, and the like. The non-parameterized model generally carries out spectrum situation reconstruction through some sparse recovery interpolation algorithm, does not depend on priori radiation source information, and can be well suitable for unknown radiation sources or mobile radiation source scenes. The main current non-parametric model methods include a kriging method, a radial basis function, a kernel method, matrix completion, tensor completion and the like. The parameterized model and the non-parameterized model are both preset that spectrum data are continuous in an unknown area and have self-similarity, but when the area has stronger 'shielding' and 'multipath' effects, abnormal oscillation can occur in the spectrum space distribution of the area, so that the accurate reconstruction of the spectrum space is difficult to realize by a data-driven spectrum situation reconstruction method, and more densely sampled data need to be acquired in the area to overcome the problem to remedy the disadvantage.
Therefore, how to more effectively draw the electromagnetic spectrum situation is a problem to be solved.
Disclosure of Invention
In view of this, the application provides an electromagnetic spectrum situation drawing method, which is based on an asymmetric structure masking self-encoder and can draw electromagnetic spectrum situations more effectively.
The application provides an electromagnetic spectrum situation drawing method, which comprises the following steps:
obtaining geographic information system data of a target region;
performing region self-adaptive partitioning on the target region based on the geographic information system data to obtain a plurality of partitioned monitoring regions;
carrying out regional electromagnetic spectrum monitoring on each monitoring area, and recording electromagnetic spectrum monitoring information in a monitoring result tensor, wherein the electromagnetic spectrum monitoring information comprises geographic position information and electromagnetic spectrum information;
performing matrix expansion on the monitoring result tensor to form a monitoring result matrix;
performing non-negative matrix factorization on the monitoring result matrix to obtain a space propagation loss function matrix of the point radiation source;
modeling and constructing the space propagation loss function matrix by adopting a masking self-encoder with an asymmetric structure, and outputting a modeling and constructing result;
obtaining a spectrum situation reconstruction result of each monitoring area based on the modeling construction result and the estimation of the radiation source power spectrum;
splicing the spectrum situation reconstruction results of each monitoring area to obtain the overall electromagnetic spectrum situation of the target area;
and carrying out thermodynamic diagram rendering on the overall electromagnetic spectrum situation of the target region on a geographic information system.
Preferably, the method further comprises:
and constructing an observation matrix corresponding to each monitoring area according to the geographical position information of the monitoring point.
Preferably, the method further comprises:
and carrying out K-means clustering on the monitoring result matrix to obtain the target class number.
Preferably, modeling the spatial propagation loss function matrix using an asymmetric-structured masked self-encoder, and outputting a modeling construction result, including:
performing space grid division on the monitoring area to obtain a space grid division result;
recording a monitoring grid reserved block corresponding to the space propagation loss function matrix according to the space grid division result and the observation matrix;
vectorizing and stacking the monitoring grid reserved blocks to form vectorized representation;
constructing an encoder by adopting an image transducer model;
encoding the vectorized representation using the encoder, outputting an encoding result;
zero filling and supplementing are carried out on the coding result based on the observation matrix, and a supplementing result is output;
constructing a decoder by adopting an image transducer model;
decoding the complementary result by using the decoder, and reconstructing a reconstructed output of a vectorized space propagation loss function matrix;
and (3) matrixing the reconstruction output, and outputting a modeling construction result.
Preferably, the performing area adaptive partitioning on the target region based on the geographic information system data to obtain a plurality of partitioned monitoring areas includes:
and according to the surface roughness, performing region self-adaptive partitioning on the target region based on the geographic information system data by adopting an image processing method to obtain a plurality of partitioned monitoring regions.
The application also discloses an electromagnetic spectrum situation drawing system, comprising:
the acquisition module is used for acquiring geographic information system data of the target region;
the partitioning module is used for performing region self-adaptive partitioning on the target region based on the geographic information system data to obtain a plurality of partitioned monitoring regions;
the electromagnetic spectrum monitoring module is used for carrying out regional electromagnetic spectrum monitoring on each monitoring area and recording electromagnetic spectrum monitoring information in a monitoring result tensor, wherein the electromagnetic spectrum monitoring information comprises geographic position information and electromagnetic spectrum information;
the matrixing expansion module is used for matrixing expansion of the monitoring result tensor to form a monitoring result matrix;
the non-negative matrix factorization module is used for performing non-negative matrix factorization on the monitoring result matrix to obtain a space propagation loss function matrix of the point radiation source;
the masking self-encoder of the asymmetric structure carries out modeling construction on the space propagation loss function matrix and outputs modeling construction results;
the calculation module is used for obtaining a spectrum situation reconstruction result of each monitoring area based on the modeling construction result and the estimation of the radiation source power spectrum;
the splicing module is used for splicing the spectrum situation reconstruction results of each region to obtain the overall electromagnetic spectrum situation of the target region;
and the rendering module is used for performing thermodynamic diagram rendering on the overall electromagnetic spectrum situation of the target region on a geographic information system.
Preferably, the system further comprises:
and the construction module is used for constructing an observation matrix corresponding to each monitoring area according to the geographical position information of the monitoring point positions.
Preferably, the system further comprises:
and the clustering module is used for carrying out K-means clustering on the monitoring result matrix to obtain the target class number.
Preferably, the masking self-encoder of the asymmetric structure is specifically used for:
performing space grid division on the monitoring area to obtain a space grid division result;
recording a monitoring grid reserved block corresponding to the space propagation loss function matrix according to the space grid division result and the observation matrix;
vectorizing and stacking the monitoring grid reserved blocks to form vectorized representation;
constructing an encoder by adopting an image transducer model;
encoding the vectorized representation using the encoder, outputting an encoding result;
zero filling and supplementing are carried out on the coding result based on the observation matrix, and a supplementing result is output;
constructing a decoder by adopting an image transducer model;
decoding the complementary result by using the decoder, and reconstructing a reconstructed output of a vectorized space propagation loss function matrix;
and (3) matrixing the reconstruction output, and outputting a modeling construction result.
Preferably, the partitioning module is specifically configured to, when performing region adaptive partitioning on the target region based on the geographic information system data to obtain a plurality of partitioned monitoring regions:
and according to the surface roughness, performing region self-adaptive partitioning on the target region based on the geographic information system data by adopting an image processing method to obtain a plurality of partitioned monitoring regions.
In summary, the application discloses an electromagnetic spectrum situation drawing method, when an electromagnetic spectrum situation is required to be drawn, geographic information system data of a target region is firstly obtained, then region self-adaptive partitioning is carried out on the target region based on the geographic information system data, and a plurality of partitioned monitoring regions are obtained; carrying out regional electromagnetic spectrum monitoring on each monitoring area, and recording electromagnetic spectrum monitoring information in a monitoring result tensor, wherein the electromagnetic spectrum monitoring information comprises geographic position information and electromagnetic spectrum information; matrix expansion is carried out on the monitoring result tensor to form a monitoring result matrix; performing non-negative matrix factorization on the monitoring result matrix to obtain a space propagation loss function matrix of the point radiation source; modeling construction is carried out on the space propagation loss function matrix by adopting a masking self-encoder with an asymmetric structure, and a modeling construction result is output; splicing the spectrum situation reconstruction results of each monitoring area to obtain the overall electromagnetic spectrum situation of the target area; and carrying out thermodynamic diagram rendering on the overall electromagnetic spectrum situation of the target region on a geographic information system. According to the method, firstly, geographical information system data are combined to conduct regional division, the positions with more consistent electric wave propagation characteristics are aggregated together, then electromagnetic spectrum data monitored by gridding of each monitoring region are processed to achieve inversion reconstruction from scattered point radio monitoring electromagnetic spectrum information to regional electromagnetic spectrum information, regional signal power spectrum obtained through inversion is distributed in the geographical information system to conduct rendering similar to thermodynamic diagram, and accordingly distribution prediction and visual display of the electromagnetic spectrum information in the monitoring region in space are achieved, and utilization efficiency of the electromagnetic spectrum monitoring data is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an embodiment 1 of an electromagnetic spectrum situation drawing method disclosed in the present application;
fig. 2 is a schematic diagram of embodiment 2 of an electromagnetic spectrum situation drawing method disclosed in the present application;
fig. 3 is a schematic structural diagram of an embodiment 1 of an electromagnetic spectrum situation mapping system disclosed in the present application;
fig. 4 is a schematic structural diagram of an embodiment 2 of an electromagnetic spectrum situation mapping system disclosed in the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, which is a flowchart of an embodiment 1 of an electromagnetic spectrum situation drawing method disclosed in the present application, the method may include the following steps:
s101, acquiring geographic information system data of a target region;
when an electromagnetic spectrum situation needs to be drawn, firstly, geographic information system data of a target region, including longitude, latitude, elevation, geology and other data, is acquired;
s102, carrying out region self-adaptive partitioning on a target region based on geographic information system data to obtain a plurality of partitioned monitoring regions;
after the geographic information system data of the target region is acquired, further carrying out region self-adaptive partitioning on the target region according to the acquired geographic information system data to obtain a plurality of partitioned monitoring regions { ζ1, ζ2, …, ζN }; by adaptively partitioning the area of the object lower than the object, the areas with similar topography (such as similar flat areas, areas with the same vegetation, areas with the same ice and snow, areas with the same town topography, etc.) are divided together, so that the electric wave propagation process in each area is ensured to have similar characteristics.
S103, carrying out regional electromagnetic spectrum monitoring on each monitoring area, and recording electromagnetic spectrum monitoring information in a monitoring result tensor, wherein the electromagnetic spectrum monitoring information comprises geographic position information and electromagnetic spectrum information;
and then, carrying out regional electromagnetic spectrum monitoring on each monitoring region ζi, and recording geographic position information such as longitude and latitude of each monitoring point position and electromagnetic spectrum information such as frequency (f), time (t) and power (p). Wherein electromagnetic spectrum monitoring information is recorded in a monitoring result tensor Gi.
S104, performing matrix expansion on the monitoring result tensor to form a monitoring result matrix;
then, the result tensor Gi is subjected to matrixing expansion to form a monitoring result matrix Xi;
s105, performing non-negative matrix factorization on the monitoring result matrix to obtain a space propagation loss function matrix of the point radiation source;
then, performing non-negative matrix factorization on the monitoring result matrix Xi to obtain a space propagation loss function matrix Si of the point radiation source;
specifically, the formula can be used
Obtaining a space propagation loss function matrix Si of the point radiation source; where Ci represents an estimate of the power spectrum of the radiation source, L i ×R i Representing the dimensions of the matrix Si, R i ×N i Representing the dimensions of matrix Ci.
S106, modeling construction is carried out on the space propagation loss function matrix by adopting a masking self-encoder with an asymmetric structure, and a modeling construction result is output;
then, a modeling construction is performed on the space propagation loss function matrix Si by adopting a masking self-encoder with an asymmetric structure, and a modeling construction result is output
S107, obtaining a spectrum situation reconstruction result of each monitoring area based on the modeling construction result and the estimation of the radiation source power spectrum;
then, the construction result is modeled
And an estimate Ci representing the power spectrum of the radiation source, the result of the reconstruction of the regional spectrum situation being obtained by matrix multiplication>
S108, splicing the spectrum situation reconstruction results of each monitoring area to obtain the overall electromagnetic spectrum situation of the target area;
then, splicing the spectrum situation reconstruction results of each monitoring area
Obtaining the overall electromagnetic spectrum situation of the target region>
And S109, performing thermodynamic diagram rendering on the overall electromagnetic spectrum situation of the target region on the geographic information system.
Finally, on the geographic information system, the overall electromagnetic spectrum situation of the target region is displayed
Thermodynamic diagram rendering is performed.
In summary, the method and the device for processing the electromagnetic spectrum information in the geographic information system firstly combine geographic information system data to carry out regional division, gather the positions with more consistent electric wave propagation characteristics together, then process the electromagnetic spectrum data monitored by gridding of each monitoring region to realize inversion reconstruction from scattered point radio monitored electromagnetic spectrum information to regional electromagnetic spectrum information, and carry out rendering of similar thermodynamic diagrams on regional signal power spectrum distribution obtained by inversion in the geographic information system, thereby realizing distribution prediction and visual display of the electromagnetic spectrum information in the monitoring region in space, and further improving the utilization efficiency of the electromagnetic spectrum monitoring data.
As shown in fig. 2, which is a schematic diagram of embodiment 2 of an electromagnetic spectrum situation mapping method disclosed in the present application, the method may include the following steps:
step 1, obtaining geographic information system data of a target region;
when an electromagnetic spectrum situation needs to be drawn, firstly, geographic information system data of a target region, including longitude, latitude, elevation, geology and other data, is acquired;
step 2, carrying out region self-adaptive partitioning on the target region based on geographic information system data to obtain a plurality of partitioned monitoring regions;
after the geographic information system data of the target region are acquired, further performing region self-adaptive partitioning on the target region by adopting an image processing method according to the surface roughness and the acquired geographic information system data to obtain a plurality of partitioned monitoring regions { ζ1, ζ2, …, ζN }; by adaptively partitioning the area of the object lower than the object, the areas with similar topography (such as similar flat areas, areas with the same vegetation, areas with the same ice and snow, areas with the same town topography, etc.) are divided together, so that the electric wave propagation process in each area is ensured to have similar characteristics.
Step 3, carrying out regional electromagnetic spectrum monitoring on each monitoring area, and recording electromagnetic spectrum monitoring information in a monitoring result tensor, wherein the electromagnetic spectrum monitoring information comprises geographic position information and electromagnetic spectrum information;
and carrying out regional electromagnetic spectrum monitoring on each monitoring region ζi, and recording geographic position information such as longitude and latitude of each monitoring point position and electromagnetic spectrum information such as frequency (f), time (t) and power (p). Wherein electromagnetic spectrum monitoring information is recorded in a monitoring result tensor Gi.
Step 4, constructing an observation matrix Mi corresponding to each region ζi according to the geographical position information of the monitoring point, wherein when the Mi element takes 1, the position of the region is provided with a monitoring node, and when the Mi element takes 0, the position of the region is provided with no monitoring node;
step 5, matrixing and expanding the monitoring result tensor Gi to form a monitoring result matrix Xi;
and 6, performing K-means clustering on Xi, firstly setting a larger class number R0 in the clustering process, then combining smaller classes in the clustering process, and determining a final class number (target class number) Ri after clustering by a plurality of rounds of nearest neighbor criteria.
Step 7, performing non-negative matrix factorization on the monitoring result matrix Xi to obtain a space propagation loss function matrix Si of the point radiation source;
specifically, the formula can be used
Obtaining a space propagation loss function matrix Si of the point radiation source; where Ci represents an estimate of the power spectrum of the radiation source, L i ×R i Representing the dimensions of the matrix Si, R i ×N i Representing the dimensions of matrix Ci.
Step 8, modeling the space propagation loss function matrix Si by adopting a masking self-encoder with an asymmetric structure, and outputting a modeling construction result
Specifically comprises the steps 9 to 18;
step 9, carrying out space grid division on the monitoring area, wherein the grid division size can be equal to the size of the monitoring area
For example, for a region to be monitored of 100m '100m, the monitoring grid size may be 1m'1m;
step 10, combining space grid division and an observation matrix Mi, and recording a monitoring grid reserved block corresponding to Si;
step 11, vectorizing and stacking the reserved monitoring grid blocks to form vectorized representation Ui;
step 12, constructing an encoder f (-) by adopting an image transducer model;
step 13, using f (·) to encode the Ui representation, outputting as
Step 14, combining the observation matrix Mi, for
Zero padding and supplementing to generate->
Step 15, constructing a decoder g (-) by adopting an image transducer model;
step 16, using decoder g (·) pair
Decoding is performed, and a reconstruction output of the vectorized Si is reconstructed>
Step 17, pairing
Forming->
And as an asymmetric structural mask for Si, reconstructing output from the encoder;
step 18, using MSE error distortion in the training stage when using a transducer model to construct f (-) and g (-);
step 19, obtaining a spectrum situation reconstruction result of each monitoring area based on the modeling construction result and the estimation of the radiation source power spectrum;
modeling construction results
And an estimate Ci representing the power spectrum of the radiation source, obtaining a reconstruction result of the regional spectrum situation by matrix multiplication>
Step 20, splicing the spectrum situation reconstruction results of each monitoring area to obtain the overall electromagnetic spectrum situation of the target area;
splicing the spectrum situation reconstruction results of each monitoring area
Obtaining the overall electromagnetic spectrum situation of the target region>
And step 21, performing thermodynamic diagram rendering on the overall electromagnetic spectrum situation of the target region on the geographic information system.
Finally, on the geographic information system, the overall electromagnetic spectrum situation of the target region is displayed
Thermodynamic diagram rendering is performed.
In summary, the deep learning method in the image processing field is introduced into the electromagnetic spectrum situation reconstruction and drawing process, so that the advantages of the data-driven deep generation model in processing the spatial electromagnetic spectrum monitoring information are fully exerted, and the spatial distribution prediction and visualization of scattered point electromagnetic spectrum monitoring information in the monitoring area are realized; compared with the traditional model driven electromagnetic spectrum situation reconstruction method, the method does not need to know the information of the position, the power and the antenna radiation characteristics of the electromagnetic radiation source in advance, and compared with the tensor complement electromagnetic spectrum situation reconstruction method, the drawn electromagnetic spectrum situation precision is higher.
As shown in fig. 3, a schematic structural diagram of an embodiment 1 of an electromagnetic spectrum situation mapping system is disclosed in the present application, where the system may include:
an acquiring module 301, configured to acquire geographic information system data of a target region;
the partitioning module 302 is configured to perform area adaptive partitioning on the target area based on the geographic information system data, so as to obtain a plurality of partitioned monitoring areas;
an electromagnetic spectrum monitoring module 303, configured to perform regional electromagnetic spectrum monitoring on each monitoring area, and record electromagnetic spectrum monitoring information in a monitoring result tensor, where the electromagnetic spectrum monitoring information includes geographic location information and electromagnetic spectrum information;
the matrixing expansion module 304 is configured to matrix-expand the monitoring result tensor to form a monitoring result matrix;
the non-negative matrix factorization module 305 is configured to perform non-negative matrix factorization on the monitoring result matrix to obtain a spatial propagation loss function matrix of the point radiation source;
the masking self-encoder 306 of the asymmetric structure carries out modeling construction on the space propagation loss function matrix and outputs a modeling construction result;
a calculation module 307, configured to obtain a spectrum situation reconstruction result of each monitoring area based on the modeling construction result and the estimation of the radiation source power spectrum;
a splicing module 308, configured to splice the spectrum situation reconstruction result of each region to obtain an overall electromagnetic spectrum situation of the target region;
and the rendering module 309 is configured to perform thermodynamic diagram rendering on the overall electromagnetic spectrum situation of the target region on the geographic information system.
The working principle of the electromagnetic spectrum situation drawing system disclosed in this embodiment is the same as that of the above electromagnetic spectrum situation drawing method embodiment 1, and will not be described here again.
As shown in fig. 4, a schematic structural diagram of an embodiment 2 of an electromagnetic spectrum situation mapping system is disclosed in the present application, where the system may include:
an acquisition module 401, configured to acquire geographic information system data of a target region;
the partition module 402 is configured to perform area adaptive partition on the target area based on the geographic information system data by adopting an image processing method according to the surface roughness, so as to obtain a plurality of partitioned monitoring areas;
an electromagnetic spectrum monitoring module 403, configured to perform regional electromagnetic spectrum monitoring on each monitoring area, and record electromagnetic spectrum monitoring information in a monitoring result tensor, where the electromagnetic spectrum monitoring information includes geographic location information and electromagnetic spectrum information;
a construction module 404 for constructing an observation matrix corresponding to each monitoring area according to the geographical position information of the monitoring point
A matrixing expansion module 405, configured to matrix-expand the monitoring result tensor to form a monitoring result matrix;
the clustering module 406 is configured to perform K-means clustering on the monitoring result matrix to obtain a target class number;
a non-negative matrix factorization module 407, configured to perform non-negative matrix factorization on the monitoring result matrix to obtain a spatial propagation loss function matrix of the point radiation source;
the masking self-encoder 408 with an asymmetric structure performs space grid division on the monitored area to obtain a space grid division result; recording a monitoring grid reserved block corresponding to the space propagation loss function matrix according to a space grid division result and the observation matrix; vectorizing and stacking the monitoring grid reserved blocks to form vectorized representation; constructing an encoder by adopting an image transducer model; encoding the vectorized representation by using an encoder, and outputting an encoding result; zero filling and supplementing are carried out on the coding result based on the observation matrix, and a supplementing result is output; constructing a decoder by adopting an image transducer model; decoding the complementary result by using the decoder, and reconstructing a reconstructed output of a vectorized space propagation loss function matrix; matrix arrangement is carried out on reconstruction output, and modeling construction results are output;
a calculation module 409, configured to obtain a spectrum situation reconstruction result of each monitoring area based on the modeling construction result and the estimation of the radiation source power spectrum;
a splicing module 410, configured to splice the spectrum situation reconstruction result of each region to obtain an overall electromagnetic spectrum situation of the target region;
and the rendering module 411 is configured to perform thermodynamic diagram rendering on the overall electromagnetic spectrum situation of the target region on the geographic information system.
The working principle of the electromagnetic spectrum situation drawing system disclosed in this embodiment is the same as that of the above electromagnetic spectrum situation drawing method embodiment 2, and will not be described herein.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The electromagnetic spectrum situation drawing method is characterized by comprising the following steps of:
obtaining geographic information system data of a target region;
performing region self-adaptive partitioning on the target region based on the geographic information system data to obtain a plurality of partitioned monitoring regions;
carrying out regional electromagnetic spectrum monitoring on each monitoring area, and recording electromagnetic spectrum monitoring information in a monitoring result tensor, wherein the electromagnetic spectrum monitoring information comprises geographic position information and electromagnetic spectrum information;
performing matrix expansion on the monitoring result tensor to form a monitoring result matrix;
performing non-negative matrix factorization on the monitoring result matrix to obtain a space propagation loss function matrix of the point radiation source;
modeling and constructing the space propagation loss function matrix by adopting a masking self-encoder with an asymmetric structure, and outputting a modeling and constructing result;
obtaining a spectrum situation reconstruction result of each monitoring area based on the modeling construction result and the estimation of the radiation source power spectrum;
splicing the spectrum situation reconstruction results of each monitoring area to obtain the overall electromagnetic spectrum situation of the target area;
and carrying out thermodynamic diagram rendering on the overall electromagnetic spectrum situation of the target region on a geographic information system.
2. The method as recited in claim 1, further comprising:
and constructing an observation matrix corresponding to each monitoring area according to the geographical position information of the monitoring point.
3. The method as recited in claim 2, further comprising:
and carrying out K-means clustering on the monitoring result matrix to obtain the target class number.
4. A method according to claim 3, wherein modeling the spatial propagation loss function matrix using an asymmetric structured mask self-encoder and outputting modeling results comprises:
performing space grid division on the monitoring area to obtain a space grid division result;
recording a monitoring grid reserved block corresponding to the space propagation loss function matrix according to the space grid division result and the observation matrix;
vectorizing and stacking the monitoring grid reserved blocks to form vectorized representation;
constructing an encoder by adopting an image transducer model;
encoding the vectorized representation using the encoder, outputting an encoding result;
zero filling and supplementing are carried out on the coding result based on the observation matrix, and a supplementing result is output;
constructing a decoder by adopting an image transducer model;
decoding the complementary result by using the decoder, and reconstructing a reconstructed output of a vectorized space propagation loss function matrix;
and (3) matrixing the reconstruction output, and outputting a modeling construction result.
5. The method according to any one of claims 1-4, wherein the performing the region adaptive partitioning on the target region based on the geographic information system data to obtain a plurality of partitioned monitoring regions includes:
and according to the surface roughness, performing region self-adaptive partitioning on the target region based on the geographic information system data by adopting an image processing method to obtain a plurality of partitioned monitoring regions.
6. An electromagnetic spectrum situation mapping system, comprising:
the acquisition module is used for acquiring geographic information system data of the target region;
the partitioning module is used for performing region self-adaptive partitioning on the target region based on the geographic information system data to obtain a plurality of partitioned monitoring regions;
the electromagnetic spectrum monitoring module is used for carrying out regional electromagnetic spectrum monitoring on each monitoring area and recording electromagnetic spectrum monitoring information in a monitoring result tensor, wherein the electromagnetic spectrum monitoring information comprises geographic position information and electromagnetic spectrum information;
the matrixing expansion module is used for matrixing expansion of the monitoring result tensor to form a monitoring result matrix;
the non-negative matrix factorization module is used for performing non-negative matrix factorization on the monitoring result matrix to obtain a space propagation loss function matrix of the point radiation source;
the masking self-encoder of the asymmetric structure carries out modeling construction on the space propagation loss function matrix and outputs modeling construction results;
the calculation module is used for obtaining a spectrum situation reconstruction result of each monitoring area based on the modeling construction result and the estimation of the radiation source power spectrum;
the splicing module is used for splicing the spectrum situation reconstruction results of each region to obtain the overall electromagnetic spectrum situation of the target region;
and the rendering module is used for performing thermodynamic diagram rendering on the overall electromagnetic spectrum situation of the target region on a geographic information system.
7. The system of claim 6, further comprising:
and the construction module is used for constructing an observation matrix corresponding to each monitoring area according to the geographical position information of the monitoring point positions.
8. The system of claim 7, further comprising:
and the clustering module is used for carrying out K-means clustering on the monitoring result matrix to obtain the target class number.
9. The system according to claim 8, wherein the masking self-encoder of the asymmetric structure is specifically configured to, in performing modeling construction of the spatial propagation loss function matrix and outputting a modeling construction result:
performing space grid division on the monitoring area to obtain a space grid division result;
recording a monitoring grid reserved block corresponding to the space propagation loss function matrix according to the space grid division result and the observation matrix;
vectorizing and stacking the monitoring grid reserved blocks to form vectorized representation;
constructing an encoder by adopting an image transducer model;
encoding the vectorized representation using the encoder, outputting an encoding result;
zero filling and supplementing are carried out on the coding result based on the observation matrix, and a supplementing result is output;
constructing a decoder by adopting an image transducer model;
decoding the complementary result by using the decoder, and reconstructing a reconstructed output of a vectorized space propagation loss function matrix;
and (3) matrixing the reconstruction output, and outputting a modeling construction result.
10. The system according to any one of claims 6-9, wherein the partitioning module, when performing the region adaptive partitioning of the target region based on the geographic information system data, is specifically configured to:
and according to the surface roughness, performing region self-adaptive partitioning on the target region based on the geographic information system data by adopting an image processing method to obtain a plurality of partitioned monitoring regions.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117092415A (en) * | 2023-10-18 | 2023-11-21 | 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 |
| CN118036738A (en) * | 2024-03-05 | 2024-05-14 | 中国人民解放军国防大学联合作战学院 | Soldier chess situation display and control method, server and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117092415A (en) * | 2023-10-18 | 2023-11-21 | 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 |
| 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 |
| CN118036738A (en) * | 2024-03-05 | 2024-05-14 | 中国人民解放军国防大学联合作战学院 | Soldier chess situation display and control method, server and storage medium |
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