CN115454993A - Spatial electromagnetic environment characteristic data processing method with space-time information - Google Patents
Spatial electromagnetic environment characteristic data processing method with space-time information Download PDFInfo
- Publication number
- CN115454993A CN115454993A CN202210890106.8A CN202210890106A CN115454993A CN 115454993 A CN115454993 A CN 115454993A CN 202210890106 A CN202210890106 A CN 202210890106A CN 115454993 A CN115454993 A CN 115454993A
- Authority
- CN
- China
- Prior art keywords
- data
- electromagnetic
- electromagnetic environment
- information
- characteristic data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
Abstract
The application discloses a spatial electromagnetic environment characteristic data processing method with space-time information, which comprises the following steps: inputting electromagnetic environment data; analyzing and extracting key fields and numerical values of the recorded electromagnetic environment data to form an original record of electromagnetic characteristic data; performing multi-level binary coding on the position information in the original record, combining and coding the obtained binary sequence to generate a data space index for describing the original record of the electromagnetic characteristic data, and storing the data space index in association with the original record of the electromagnetic characteristic data; converting the acquisition time of the original record of the electromagnetic characteristic data into world standard timestamp information, and storing the world standard timestamp information in association with the original record of the electromagnetic characteristic data; and taking the data space index and the timestamp information as space-time indexes, associating with the original record of the electromagnetic characteristic data to generate an electromagnetic environment characteristic data file, and storing according to an HDF5 file format. Through the structural processing, the problems of large data volume, low query efficiency, low data value density and the like of the electromagnetic environment are solved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a spatial electromagnetic environment characteristic data processing method with space-time information.
Background
The electromagnetic environment data is mainly characterized in that: the electromagnetic signal radiation equipment has the advantages of multiple types, large quantity, wide distribution space, uneven signal density and high occupancy rate in time, space and frequency spectrum ranges; secondly, the development and change are fast, the spatial distribution is uneven, the timeliness is strong, and the antagonism is strong; thirdly, the signal use in the parameter domains such as time domain, space domain, frequency domain, polarization domain, modulation domain, etc. is more and more complex in breadth and depth.
The management and application of electromagnetic environment data have not been a good solution, a large number of electromagnetic environment characteristics are collected and monitored, and then are simply stored on a flow table, scientific classification management and system analysis are not performed on original data, and basic electromagnetic data are not refined into more useful electromagnetic information. In addition, the acquired basic electromagnetic environment data also lacks an effective and standard characterization method, so that the problems of low data query efficiency, difficult association, poor timeliness and the like exist when the electromagnetic data is analyzed and applied, and the business requirements of signal analysis and processing cannot be met.
From the application requirement of electromagnetic environment information, the characterization of the spatial electromagnetic environment needs to quantitatively describe the types, attributes, distribution, aging and other conditions of various electromagnetic signals in a specific area, form an electromagnetic environment element sample space, support the analysis, induction and summarization of spatial electromagnetic environment characteristics, and reasonably predict the possible future electromagnetic environment, so as to achieve the final purpose of mastering and controlling the electromagnetic environment.
In the prior art, a description method for spatial electromagnetic environment characteristics is mainly described according to typical physical quantities of an electromagnetic domain, such as frequency, power, field strength, waveform, polarization and other basic parameters, and is designed and recorded through a relational table, and data does not reflect the spatial-temporal characteristics and frequency-using equipment characteristics of the electromagnetic environment. The data management is mainly implemented by applying a traditional database mode, typically SQL Server, oracle and the like, while the spatial electromagnetic environment data is usually multi-dimensional time-varying mass data, has higher timeliness and spatiality, has more mass data concurrence scenes, and particularly can not meet the requirements of the traditional data management method and the traditional storage mode when large data analysis is carried out.
Disclosure of Invention
The purpose of this application lies in: the method is characterized in that the spatial electromagnetic environment characteristic data is subjected to structural processing, global unique spatial position index and world standard time are given to electromagnetic information, so that structural electromagnetic data are formed, the problems of large data volume, multi-source isomerism, low query efficiency, low data value density, data isolated island, difficulty in maintenance and the like of the current electromagnetic environment are solved, and a bottom layer supporting technology is provided for constructing the spatial electromagnetic environment big data.
The technical scheme of the application is as follows: the method for processing the spatial electromagnetic environment characteristic data with the space-time information comprises the following steps: step 1, performing data interface adaptation according to the data type of electromagnetic environment data, and inputting corresponding electromagnetic environment data through an adapted data interface; step 2, analyzing and extracting key fields and numerical values in the recorded electromagnetic environment data, sequentially storing the key fields and the numerical values into a container map, and forming original records of electromagnetic characteristic data one by one; step 3, performing multi-level binary encoding on the position information in each extracted electromagnetic characteristic data original record, combining and encoding binary sequences obtained by the multi-level binary encoding to generate a data space index of the corresponding electromagnetic characteristic data original record, and storing the data space index and the electromagnetic characteristic data original record in a container map in a correlated manner; step 4, converting the acquisition time corresponding to each electromagnetic characteristic data original record into a world standard time stamp to serve as time stamp information of the corresponding electromagnetic characteristic data original record, and storing the time stamp information and the electromagnetic characteristic data original record in a container map in a correlated manner; and 5, taking the data space index and the timestamp information as a space-time index of each piece of electromagnetic environment characteristic data, associating the space-time index and the timestamp information with corresponding electromagnetic characteristic data original records, sorting according to a preset data structure to form an electromagnetic environment characteristic data set, then carrying out structural design on the electromagnetic environment characteristic data, storing the electromagnetic environment characteristic data as an HDF5 file capable of supporting high-speed concurrent retrieval and block IO, and generating an electromagnetic environment characteristic data file with space-time characteristics.
In any one of the above technical solutions, further, step 2 further includes: screening and cleaning the original records of the electromagnetic characteristic data, eliminating redundant and error data, and extracting characteristic parameters of the screened and cleaned original records of the electromagnetic characteristic data in a data processing mode, wherein the data processing mode at least comprises time frequency analysis, wavelet transformation and parameter estimation, and the characteristic parameters at least comprise signal carrier frequency, modulation pattern, waveform parameters, harmonic parameters and stray parameters.
In any one of the above technical solutions, further, in step 3, the position information at least includes latitude information and longitude information, and the combining and encoding of the binary sequence obtained by the multilevel binary encoding specifically includes: step 31, placing the binary sequence corresponding to the longitude information into even digits, placing the binary sequence corresponding to the latitude information into odd digits, and performing sequence combination to generate a longitude and latitude coding string; and step 32, coding the longitude and latitude coding strings according to a preset digit by looking up a Base32 coding table to generate a data space index.
In any of the above technical solutions, further, the key field at least includes: the longitude, latitude and height of the position information, the data acquisition time, the acquired electromagnetic spectrum data, the electromagnetic radiation source information, the environmental background noise information, the abnormal frequency point data, the signal modulation type and the signal pulse density are represented.
In any one of the above technical solutions, further, the step 1 specifically includes: combing an original electromagnetic environment data source to be input, determining a data structure of the electromagnetic environment data source and uploading a hardware interface; and compiling corresponding interface programs for electromagnetic environment data of various sources and types, and performing interface adaptation according to data types to finish data entry.
In any of the above technical solutions, further, the data interface includes a structured data interface and an unstructured data interface.
In any of the above technical solutions, further, the electromagnetic environment data source at least includes: electromagnetic environment acquisition equipment, electronic equipment operating mode acquisition equipment and database data.
The beneficial effect of this application is:
the technical scheme in the application can provide a basic data processing method for scientific storage, high-speed concurrent retrieval and big data mining of electromagnetic environment characteristic data. The method can be applied to systems such as space electromagnetic environment information monitoring and management, space electromagnetic environment data centers, radio equipment frequency planning and management, electromagnetic spectrum combat command and the like, is an efficient and complete management method for space electromagnetic characteristic data, and can provide a solution for effective storage, classification, rapid query and management of mass data.
By the aid of the electromagnetic environment characteristic data file generated by the technical scheme, big data analysis can be rapidly and effectively carried out on the electromagnetic environment, existing electromagnetic environment acquisition and monitoring recorded electromagnetic data can be mastered, and sensing, understanding and controlling capabilities of electromagnetic spatial characteristics are improved.
Drawings
The advantages of the above and/or additional aspects of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow diagram of a method of processing spatial electromagnetic environment characteristic data with spatio-temporal information according to an embodiment of the present application;
fig. 2 is a schematic diagram of data flow according to one embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the present application can be more clearly understood, the present application will be described in further detail below with reference to the accompanying drawings and detailed description. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited by the specific embodiments disclosed below.
As shown in fig. 1 and fig. 2, the present embodiment provides a method for processing spatial electromagnetic environment characteristic data with spatiotemporal information, the method comprising:
step 1, carrying out data interface adaptation according to the data type of the electromagnetic environment data, and recording corresponding electromagnetic environment data through the adapted data interface.
Specifically, electromagnetic environment data acquired by electromagnetic environment acquisition equipment (such as spectrum monitoring equipment, electronic reconnaissance equipment and the like), past electromagnetic environment data stored in a database and electromagnetic environment data acquired in other ways need to be recorded into a data processing computer, and data interface adaptation is performed on different types of data to ensure correct data reading and recording.
Firstly, combing an original electromagnetic environment data source to be recorded, determining a data structure of the electromagnetic environment data source and uploading a hardware interface. Wherein, the source of the original electromagnetic environment data to be recorded comprises:
1) Electromagnetic environment collection equipment mainly includes: military and civil electromagnetic spectrum monitoring equipment, electronic information reconnaissance equipment and electromagnetic signal acquisition equipment. The data uploading interface of such devices is usually a serial port or an ethernet port;
2) Electronic equipment operating mode collection equipment mainly includes: the device comprises a state monitoring and working condition acquisition device of equipment such as broadcasting equipment, communication equipment, base stations and radars. The data uploading interface of such devices is usually a serial port or an ethernet port;
3) Database data, mainly comprising: electromagnetic spectrum historical data stored by a national or local radio regulatory agency and electromagnetic spectrum historical data monitored by each frequency tube unit of the military. The data can be accessed through the internet access after being connected with the database.
Secondly, writing corresponding interface programs for electromagnetic environment data of various sources and types, and performing interface adaptation according to the data types to complete data entry. The data interface mainly comprises a structured data interface and an unstructured data interface:
1) The data interface comprises a structured data interface, a data processing module and a data processing module, wherein the structured data mainly refers to data which is realized by logically expressing a two-dimensional table structure and comprises data acquisition;
2) The system comprises an unstructured data interface, wherein the unstructured data interface refers to reading interfaces of various documents, pictures, XML, HTML and report data which describe electromagnetic environment acquisition tasks and sites.
It should be noted that the present embodiment does not limit the writing implementation of the interface program.
And 2, analyzing and extracting key fields and corresponding numerical values in the recorded electromagnetic environment data, sequentially storing the key fields and the corresponding numerical values into a container map, and forming original records of the electromagnetic characteristic data one by one. Wherein, the key field at least includes: the longitude, latitude and altitude of the position information, the data acquisition time, the acquired electromagnetic spectrum data, the electromagnetic radiation source information (such as the information of the electromagnetic radiation source position/azimuth/signal modulation parameters and the like), the environmental background noise information, the abnormal frequency point data, the signal modulation type and the signal pulse density are represented.
Specifically, an original electromagnetic characteristic data original record with the structural characteristic is formed after the original electromagnetic characteristic original record is classified and sorted according to a defined data structure, the data record has two-dimensional relational data characteristics, once the data record has the structural characteristic, the data can be conveniently stored and managed, and the efficiency of data retrieval and query can be greatly improved.
For example, the efficiency can be improved by tens of thousands of times compared with the implementation mode of analyzing and then retrieving from the text of massive original records, such as fast retrieval according to the acquisition time, retrieval according to the frequency range, retrieval according to the type of the signal transmitting equipment and the like.
Further, the step 2 further comprises: screening and cleaning the original records of the electromagnetic characteristic data, eliminating redundant and error data, and extracting characteristic parameters of the screened and cleaned original records of the electromagnetic characteristic data in a data processing mode, wherein the data processing mode at least comprises time frequency analysis, wavelet transformation, parameter estimation and other processing, and the characteristic parameters at least comprise signal carrier frequency, modulation pattern, waveform parameters, harmonic parameters, stray parameters and other characteristic parameters. Through the process, the value density of the data in the original record of the electromagnetic characteristic data is improved.
Specifically, by screening and cleaning the original records of the electromagnetic characteristic data stored in the container map one by one, checking the multi-source data, comparing the data, combining and eliminating wrong and redundant data, performing time-frequency analysis, wavelet transformation, parameter estimation and other processing on the low-value original records, extracting characteristic parameters such as signal carrier, modulation, waveform, harmonic wave, stray and the like, improving the value density of the data, ensuring the accuracy of each original record of the electromagnetic characteristic data, and being beneficial to reducing the data quantity of invalid, repeated and low-value data in data processing.
The multi-source data mainly refers to electromagnetic environment data, pictures or videos which are obtained by monitoring, collecting or other modes aiming at a specific position, space or frequency utilization equipment.
And 3, performing multi-level binary coding on the position information in each extracted electromagnetic characteristic data original record, combining and coding binary sequences obtained by the multi-level binary coding to generate a data space index of the corresponding electromagnetic characteristic data original record, and storing the data space index in a container map in association with the electromagnetic characteristic data original record.
Further, in step 3, the position information at least includes latitude information and longitude information, and the combining and encoding of the binary sequence obtained by the multilevel binary encoding specifically includes:
step 31, placing the binary sequence corresponding to the longitude information into even digits, placing the binary sequence corresponding to the latitude information into odd digits, and performing sequence combination to generate a longitude and latitude coding string;
and step 32, coding the longitude and latitude coding strings according to a preset digit by looking up a Base32 coding table to generate a data space index.
In this embodiment, taking the beijing tiananmen square (latitude: 39.9096045, longitude: 116.3972282) as an example, the GeoHash algorithm is adopted to perform binary coding and combination on the position information of the beijing tiananmen square, and the specific process is as follows:
1) The latitude interval of the earth is [ -90,90], binary coding is carried out on the latitude 39.9096045 in sequence, and the following results are obtained:
a) Dividing the interval [ -90,90] into an interval [ -90,0 ] and [0,90],39.9096045 in a right interval, and taking 1;
b) Dividing the interval [0,90] into an interval [0,45 ] and [45,90],39.9096045, and taking 0 in the left interval;
c) Dividing a section [0,45) into a section (0,22.5) and a section [22.5,45), and taking 1 in the right section 39.9096045;
d) Dividing the interval [22.5,45) into an interval [22.5,33.75) and [33.75,45), and taking 1 in the right interval from 39.9096045;
e) Dividing the interval [33.75,45) into an interval [33.75,39.375) and [39.375,45), and taking 1 in the right interval from 39.9096045;
f) Dividing the interval [39.375,45) into an interval [39.375,42.188) and [42.188,45), and taking 0 in 39.9096045 in the left interval;
g) Dividing the interval [39.375,42.188) into an interval [39.375,40.7815) and [40.7815,42.188 ], and taking 0 in 39.9096045 in the left interval;
h) Dividing the interval [39.375,40.7815) into an interval [39.375,40.07825) and [40.07825,40.7815), and taking 0 in 39.9096045 in the left interval;
i) Dividing the interval [39.375,40.07825) into an interval [39.375,39.726625) and [39.726625,40.07825), and taking 1 in the right interval from 39.9096045;
j) Dividing the interval [39.726625,40.07825) into an interval [39.726625,39.9024375) and [39.9024375,40.07825), and taking 1 in the right interval from 39.9096045;
2) Sorting 0 or 1 data obtained by binary encoding the latitude to obtain a binary sequence 1011100011, wherein the length of the sequence can be determined according to the required data precision;
3) Similarly, the longitude interval of the earth is [ -180,180], and the following result is obtained by continuously performing binary coding on the longitude 116.3972282:
a) Dividing the interval [ -180,180] into intervals [ -180,0) and [0,180], and taking 1 for 116.3972282 in the right interval;
b) Dividing the interval [0,180] into intervals [0,90] and [90,180],116.3972282 in the right interval, and taking 1;
c) Dividing the interval [90,180] into an interval [90,135 ] and [135,180], and taking 0 for 116.3972282 in the left interval;
d) Dividing the interval [90,135) into an interval [90,112.5) and [112.5,135), and taking 1 in the right interval from 116.3972282;
e) Dividing the interval [112.5,135) into an interval [112.5,123.75) and [123.75,135), and taking 0 in 116.3972282 in the left interval;
f) Dividing the interval [112.5,123.75) into an interval [112.5,118.125) and [118.125,123.75), and taking 0 in 116.3972282 in the left interval;
g) Dividing the interval [112.5,118.125) into an interval [112.5,118.125) and [112.5,118.125), and taking 1 in the right interval from 116.3972282;
h) Dividing the interval [115.3125,118.125) into an interval [115.3125,116.71875) and [116.71875,118.125), and taking 0 in 116.3972282 in the left interval;
i) Dividing the interval [115.3125,116.71875) into an interval [115.3125,116.015625) and [116.015625,116.71875), and taking 1 in the right interval from 116.3972282;
j) Dividing the interval [116.015625,116.71875) into an interval [116.015625,116.3671875) and [116.3671875,116.71875), and taking 1 in the right interval from 116.3972282;
4) Arranging the 0 or 1 data obtained by carrying out binary coding on the longitude to obtain a binary sequence 11010 01011;
5) Combining the longitude and latitude binary sequences obtained in the processes 1) to 4) according to the manner of longitude at even number and latitude at odd number, wherein the generated longitude and latitude coding string is as follows as shown in table 1: 11100 11101 00100 01111.
TABLE 1
6) And (3) coding the combined longitude and latitude coding string according to the length of 5 bits/group by using a Base32 coding table, wherein the process is shown in table 2, and the GeoHash code of the longitude and latitude is obtained to be wx4g, which is shown in table 3.
TABLE 2
TABLE 3
| Longitude and latitude coding string | 11100 | 11101 | 00100 | 01111 |
| 10 system number | 28 | 29 | 4 | 15 |
| Base32 coding | w | x | 4 | g |
It should be noted that, the above process is described by taking the GeoHash algorithm as an example, and the coding length only takes 20 bits, and actually, the division may be continued continuously by increasing the coding length by a bisection method to obtain a coding value with more bits in the longitude (or latitude) degree, so as to narrow the area range represented by the GeoHash value and more accurately reflect the position of the longitude and latitude coordinate.
And 4, converting the acquisition time corresponding to each electromagnetic characteristic data original record into a world standard time stamp to serve as the time stamp information of the corresponding electromagnetic characteristic data original record, and storing the time stamp information and the electromagnetic characteristic data original record into a container map in a correlated manner.
Specifically, the acquisition time corresponding to each original record of the electromagnetic characteristic data is read, and the record is carried out according to the format of year \ month \ day \ hour \ minute \ second, that is, YYYY: MM: DD: HH: MM: and (7) SS.
Because the acquisition time of the data is generally local time, in order to avoid the influence of a time zone, the uniformity of the data time reference is ensured, the efficiency of data retrieval is improved, and the acquisition time is converted into a universal time stamp (UTC _ timestamp), namely, the whole second data.
Using beijing time as an example, UTC _ timestamp = local _ timestamp (beijing local time) -8 × 60 (whole seconds for beijing time).
Finally, the universal time stamp (UTC _ timestamp) is written into the container map as the time stamp information of the piece of data record.
And 5, taking the data space index and the timestamp information as a space-time index of each piece of electromagnetic environment characteristic data, associating the space-time index and the timestamp information with corresponding electromagnetic characteristic data original records, sorting according to a preset data structure to form an electromagnetic environment characteristic data set, then carrying out structural design on the electromagnetic environment characteristic data, storing the electromagnetic environment characteristic data as an HDF5 file capable of supporting high-speed concurrent retrieval and block IO, and generating an electromagnetic environment characteristic data file with space-time characteristics.
Specifically, the electromagnetic characteristic data original records with data space index and timestamp information are sorted according to a designed preset data structure to form a space electromagnetic environment characteristic data set with space-time information. Because the HDF5 data file manages all data sets in a packet form, and can support subset fragmentation and partial IO, even if TB in the file size can also achieve fast loading and data retrieval, it is a very effective solution to apply the HDF5 file for storage and management for a large number of data records generated in the previous 4 steps and massive historical data that have been generated in the past.
After structural design and storage are carried out according to the format requirements of the HDF5 data file, the H5Grid _ EM _ data file can be generated, and the file serves as an electromagnetic environment characteristic data resource and can provide a complete bottom layer data support and algorithm training data source for electromagnetic environment big data analysis.
The data structure designed according to the HDF5 file format sequentially comprises the following data structures: data spatial index data _ ID, data acquisition time stamp (UTC time), acquisition point position coordinates (position), geographic information (geographic information), weather information (weather), cultural information (culture), basic electromagnetic data (basic electromagnetic data), signal characteristic information (signal character), radiation source signal information (emitter information), acquisition monitoring equipment information (monitoring equipment information), spectrum resource statistics (spectrum resource statistics), and the like.
The definition of each part in the structure is as follows:
1) Data space index data _ ID: the grid position code of the current data original record is used as the retrieval main value ID of the data and is character type data;
2) Timestamp information (UTC time): the current data acquisition time is counted according to the whole second by taking 1 month and 1 day zero time in 1970 as a starting point, and the current data acquisition time is integer data;
3) Radiation source signal (acquisition point) position coordinates (position): acquiring spatial position information of a point, describing the spatial position information by longitude, latitude and height data, and obtaining double-precision floating point data;
4) Geographic information (geographic information): a geographic feature used to describe a current spatial location, comprising:
landform information: mainly refers to the topographic features of the earth surface, such as ice, sea water sea _ water, fresh water fresh _ water, land, sand land, rock, concrete, grassland grass, shrub, forest, other, for enumerating class data, type expansion can be carried out according to the actual landform situation;
5) Weather information (weather): the method is used for describing meteorological features corresponding to the current spatial position and time, and comprises the following steps:
a) The climate characteristics, such as yin (cloudy), sunny (sunny), rain (rain), snow (snowy) and the like, are enumeration type data;
b) Meteorological data such as temperature (temperature), humidity (humidity), wind-force (wind-force), wind speed (wind speed), air pressure (air pressure), etc., which are single-precision floating point data;
6) Human information (culture): the human information used for describing the present spatial position includes:
a) A zip code, which can be described by district codes of province, city, and county, as character type data;
b) Administrative district name (district), which is described by a 12-bit statistic with a division code, and is character type data;
c) Administrative attributes (administrative attributes), such as cities, suburbs, wildlands, etc., are enumerated type data;
d) Population density (population density), which describes the number of people per square kilometer (people/km 2) currently in use for correlating electromagnetic activity with population, as integer data;
7) Basic electromagnetic data (basic electromagnetic data): basic electromagnetic environment characteristic data used for describing the current spatial position, including:
a) Environmental noise floor (ambient noise level): recording a background noise value of a full frequency band, and recording by using historical average noise power, frequency point VS noise power and single-precision floating point number;
b) Current service (spectrum service): the frequency utilization situation and the frequency band (shaping data) VS service type (enumeration data) which are currently planned and allocated to broadcast, television, mobile communication base stations, trunking communication, navigation, other civil radio applications and the like are indicated;
8) Signal characteristic information (signal character): electromagnetic signal features used to characterize a current spatial location, comprising:
a) Spectrum information (spectrum): the device is used for recording signal frequency spectrum and power data, frequency point VS signal power and single-precision floating point type data under the current space-time condition;
b) Waveform Information (IQ): the device is used for recording signal waveforms under the current time-space condition, and is described by IQ data, and double-precision floating point data;
c) Modulation information (modulation): the method is used for recording the modulation type and the modulation keying number of signals appearing in the current frequency spectrum, such as FM, AM, ASK, FSK, PSK, QAM, MSK and the like, and the signals are character type data;
9) Radiation source signal information (emitter information)
A) Radiation source picture (picture): the system is used for recording the image information of a radiation source object in the picture formats of jpg, png and the like;
b) Radiation source position information (location): the system is used for recording the accurate position information of a radiation source in the current space-time grid, and is described by longitude, latitude and height data, and is double-precision floating point data;
c) Radiation source orientation information (direction): the system is used for recording the space angle information of the radiation source relative to the acquisition point, wherein the azimuth angle takes the positive north direction as 0 degree, and the clockwise direction is positive; the pitch angle takes a horizontal plane as 0 degree of pitch, the upward direction is positive, and the azimuth angle and the pitch angle are single-precision floating point type data;
d) Radiation source type (type): the system is used for recording the signal types of radiation sources, such as radar, communication, navigation, broadcasting, base stations and the like, and is enumeration data;
e) Radiation source signal parameters (character): the system comprises a radiation source, a signal processing module and a signal processing module, wherein the radiation source is used for recording specific parameters of a radiation source signal, including carrier frequency, signal power, signal bandwidth, modulation pattern, antenna gain, beam width, polarization and the like, and the specific parameters are single-precision floating point data and enumeration data;
f) Radiation source operating mode (mode): the system is used for recording the working state of the current radiation source signal, wherein the working state is enumeration data, 0 is a shutdown state, and 1 is a startup state;
10 Monitoring equipment information (monitoring equipment information): the device information and task information for recording and executing the collection monitoring or perception task are used, and the method comprises the following steps:
a) Capture device picture (picture): the system is used for recording the image information of the real object of the acquisition equipment, and is in picture formats of jpg, png and the like;
b) Collecting monitoring equipment information: name (name), device model (type), serial number (ID), validity time (valid time), etc., as character type data;
c) Collecting monitoring task types (types), such as environment monitoring, signal collection, information reconnaissance, interference countermeasure and the like, which are character type data;
11 Spectrum resource statistics (spectrum resource statistics)
A) Spectrum occupancy statistics (occupancy): the method is used for describing the occupation condition of the current frequency resource, and the frequency band VS occupies/is idle and is data of a floating point type and a Boolean type;
b) Anomaly frequency statistics (anomaly frequency): the frequency information used for recording the abnormity or violation, including frequency range and power, is single-precision floating point data.
In this embodiment, the data structure of the generated H5Grid _ EM _ data file is designed as follows:
the electromagnetic environment characteristic data are structurally designed according to the requirement of an HDF5 file format, an electromagnetic characteristic data description method under the HDF5 format standard is formed, so that the data storage has the advantages of higher retrieval efficiency, more flexible data IO mode and almost no file size limitation compared with the traditional binary file, the file access of big data (TB level) is supported, and compared with the traditional binary file, the method has the advantages of high-efficiency IO, block loading and high-speed concurrent retrieval.
The technical scheme of the present application is described in detail above with reference to the accompanying drawings, and the present application provides a spatial electromagnetic environment characteristic data processing method with spatio-temporal information, which includes: step 1, performing data interface adaptation according to the data type of electromagnetic environment data, and inputting corresponding electromagnetic environment data through an adapted data interface; step 2, analyzing and extracting key fields and numerical values in the recorded electromagnetic environment data, sequentially storing the key fields and the numerical values into a container map, and forming original records of electromagnetic characteristic data one by one; step 3, performing multi-level binary encoding on the position information in each extracted electromagnetic characteristic data original record, combining and encoding binary sequences obtained by the multi-level binary encoding to generate a data space index of the corresponding electromagnetic characteristic data original record, and storing the data space index and the electromagnetic characteristic data original record in a container map in a correlated manner; step 4, converting the acquisition time corresponding to each electromagnetic characteristic data original record into a world standard time stamp to serve as time stamp information of the corresponding electromagnetic characteristic data original record, and storing the time stamp information and the electromagnetic characteristic data original record in a container map in a correlated manner; and 5, taking the data space index and the timestamp information as a space-time index of each piece of electromagnetic environment characteristic data, associating the space-time index and the timestamp information with corresponding electromagnetic characteristic data original records, sorting according to a preset data structure to form an electromagnetic environment characteristic data set, then carrying out structural design on the electromagnetic environment characteristic data, storing the electromagnetic environment characteristic data as an HDF5 file capable of supporting high-speed concurrent retrieval and block IO, and generating an electromagnetic environment characteristic data file with space-time characteristics. According to the technical scheme, the spatial electromagnetic environment characteristic data are subjected to structural processing, global unique spatial position index and world standard time are given to electromagnetic information, structured electromagnetic data are formed, the problems that the current electromagnetic environment data volume is large, multi-source isomerism is high, the query efficiency is low, the data value density is not high, data islanding exists, maintenance is difficult and the like are solved, and a bottom layer supporting technology is provided for constructing the spatial electromagnetic environment big data.
The steps in the present application may be sequentially adjusted, combined, and subtracted according to actual requirements.
The units in the device can be merged, divided and deleted according to actual requirements.
Although the present application has been disclosed in detail with reference to the accompanying drawings, it is to be understood that such description is merely illustrative and not restrictive of the application of the present application. The scope of the present application is defined by the appended claims and may include various modifications, adaptations, and equivalents of the invention without departing from the scope and spirit of the application.
Claims (7)
1. A method for processing space electromagnetic environment characteristic data with space-time information is characterized by comprising the following steps:
step 1, performing data interface adaptation according to the data type of electromagnetic environment data, and inputting corresponding electromagnetic environment data through an adapted data interface;
step 2, analyzing and extracting key fields and numerical values in the recorded electromagnetic environment data, sequentially storing the key fields and the numerical values into a container map, and forming original records of electromagnetic characteristic data one by one;
step 3, performing multi-level binary coding on the position information in each extracted electromagnetic characteristic data original record, combining and coding the binary sequence obtained by the multi-level binary coding to generate a data space index of the corresponding electromagnetic characteristic data original record, and storing the data space index in a container map in association with the electromagnetic characteristic data original record;
step 4, converting the acquisition time corresponding to each electromagnetic characteristic data original record into a world standard timestamp to serve as timestamp information of the corresponding electromagnetic characteristic data original record, and storing the timestamp information and the electromagnetic characteristic data original record into a container map in a correlated manner;
and 5, taking the data space index and the timestamp information as a space-time index of each piece of electromagnetic environment characteristic data, associating the space-time index and the timestamp information with corresponding electromagnetic characteristic data original records, sorting according to a preset data structure to form an electromagnetic environment characteristic data set, then carrying out structural design on the electromagnetic environment characteristic data, storing the electromagnetic environment characteristic data as an HDF5 file capable of supporting high-speed concurrent retrieval and block IO, and generating an electromagnetic environment characteristic data file with space-time characteristics.
2. The method for processing the spatial electromagnetic environment characteristic data with spatiotemporal information according to claim 1, wherein the step 2 further comprises:
screening and cleaning original records of electromagnetic characteristic data, eliminating redundant and error data, and extracting characteristic parameters of the screened and cleaned original records of the electromagnetic characteristic data in a data processing mode, wherein the data processing mode at least comprises time frequency analysis, wavelet transformation and parameter estimation, and the characteristic parameters at least comprise signal carrier frequency, modulation pattern, waveform parameters, harmonic parameters and stray parameters.
3. The method for processing space-time information characteristic data of an electromagnetic environment according to claim 1, wherein in step 3, the position information at least includes latitude information and longitude information, and the combining and encoding of binary sequences obtained by multilevel binary encoding specifically includes:
step 31, placing the binary sequence corresponding to the longitude information into even digits, placing the binary sequence corresponding to the latitude information into odd digits, and performing sequence combination to generate a longitude and latitude coding string;
and step 32, coding the longitude and latitude coding strings according to a preset digit number in a form of table lookup to generate a data space index.
4. The method for processing spatiotemporal information possessing spatial electromagnetic environment characteristic data according to claim 1, characterized in that said key fields comprise at least: the longitude, latitude and height of the position information, the data acquisition time, the acquired electromagnetic spectrum data, the electromagnetic radiation source information, the environmental background noise information, the abnormal frequency point data, the signal modulation type and the signal pulse density are represented.
5. The method for processing the spatial electromagnetic environment characteristic data with spatiotemporal information according to any one of claims 1 to 4, characterized in that the step 1 specifically comprises:
combing an original electromagnetic environment data source to be input, determining a data structure of the electromagnetic environment data source and uploading a hardware interface;
and compiling corresponding interface programs for electromagnetic environment data of various sources and types, and performing interface adaptation according to data types to finish data entry.
6. The method for spatial electromagnetic environment characterization data with spatio-temporal information according to claim 5, wherein the data interface comprises a structured data interface and an unstructured data interface.
7. The method for spatial electromagnetic environment characterization data with spatiotemporal information according to claim 5, wherein the electromagnetic environment data sources comprise at least: electromagnetic environment acquisition equipment, electronic equipment operating mode acquisition equipment and database data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210890106.8A CN115454993A (en) | 2022-07-27 | 2022-07-27 | Spatial electromagnetic environment characteristic data processing method with space-time information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210890106.8A CN115454993A (en) | 2022-07-27 | 2022-07-27 | Spatial electromagnetic environment characteristic data processing method with space-time information |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115454993A true CN115454993A (en) | 2022-12-09 |
Family
ID=84297344
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210890106.8A Pending CN115454993A (en) | 2022-07-27 | 2022-07-27 | Spatial electromagnetic environment characteristic data processing method with space-time information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115454993A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4276597A (en) * | 1974-01-17 | 1981-06-30 | Volt Delta Resources, Inc. | Method and apparatus for information storage and retrieval |
| CN112148807A (en) * | 2020-09-28 | 2020-12-29 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | Electromagnetic environment field data warehouse construction method |
| WO2021072874A1 (en) * | 2019-10-14 | 2021-04-22 | 平安科技（深圳）有限公司 | Dual array-based location query method and apparatus, computer device, and storage medium |
| CN113297395A (en) * | 2021-07-08 | 2021-08-24 | 中国人民解放军国防科技大学 | Spatio-temporal multi-modal mixed data processing method, correlation method and indexing method |
-
2022
- 2022-07-27 CN CN202210890106.8A patent/CN115454993A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4276597A (en) * | 1974-01-17 | 1981-06-30 | Volt Delta Resources, Inc. | Method and apparatus for information storage and retrieval |
| WO2021072874A1 (en) * | 2019-10-14 | 2021-04-22 | 平安科技（深圳）有限公司 | Dual array-based location query method and apparatus, computer device, and storage medium |
| CN112148807A (en) * | 2020-09-28 | 2020-12-29 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | Electromagnetic environment field data warehouse construction method |
| CN113297395A (en) * | 2021-07-08 | 2021-08-24 | 中国人民解放军国防科技大学 | Spatio-temporal multi-modal mixed data processing method, correlation method and indexing method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110264709B (en) | Method for predicting traffic flow of road based on graph convolution network | |
| CN105045858B (en) | Taxi pickup point based on ballot recommends method | |
| Monerie et al. | On the range of future Sahel precipitation projections and the selection of a sub-sample of CMIP5 models for impact studies | |
| Klein et al. | PAIRS: A scalable geo-spatial data analytics platform | |
| Zhao et al. | Detecting region outliers in meteorological data | |
| CN109782373A (en) | A kind of sand-dust storm forecast method based on improved Naive Bayesian-CNN multiple target sorting algorithm | |
| CN104778248A (en) | Network information management system for comprehensive meteorological observation operation monitoring system | |
| CN114611941A (en) | Landslide disaster evaluation method and system considering space dangerousness | |
| US9612315B2 (en) | Methods of and systems for extracting patterns of human extent and density from geographically anchored radio signal sources | |
| CN110826454B (en) | Remote sensing image change detection method and device | |
| Maoyi et al. | How well does MPAS-atmosphere simulate the characteristics of the Botswana High? | |
| CN116720984B (en) | Intelligent electric power auditing method based on satellite map technology | |
| CN115454993A (en) | Spatial electromagnetic environment characteristic data processing method with space-time information | |
| CN110309166B (en) | Traceable geographic elevation data completion method | |
| CN114840673A (en) | Multi-source heterogeneous marine environment data integration method based on NetCDF | |
| CN113239076A (en) | Geographic information inquiry management platform based on three-dimensional image | |
| CN114722276A (en) | Data management and analysis method for smart city service | |
| CN111339446B (en) | Interest point mining method and device, electronic equipment and storage medium | |
| Xiao et al. | A spatialization method for grain yield statistical data: A study on winter wheat of Shandong Province, China | |
| CN109885705A (en) | A kind of three-dimensional simulation environment protection geographic information system | |
| Chang et al. | A seamless approach for evaluating climate models across spatial scales | |
| Li et al. | Biophysical impact of multiple surface forcings on land surface temperature over eastern China | |
| Al Mohit et al. | Investigation of cluster-based cyclone track pattern within the Bay of Bengal | |
| CN111782596B (en) | Radio monitoring data processing method based on high-performance hybrid computing | |
| CN114897630B (en) | Vegetation growth optimum temperature estimation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |