CN109981203A - Radio signal monitoring system and monitoring method based on machine learning - Google Patents
Radio signal monitoring system and monitoring method based on machine learning Download PDFInfo
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- CN109981203A CN109981203A CN201910214533.2A CN201910214533A CN109981203A CN 109981203 A CN109981203 A CN 109981203A CN 201910214533 A CN201910214533 A CN 201910214533A CN 109981203 A CN109981203 A CN 109981203A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 48
- 238000010801 machine learning Methods 0.000 title claims abstract description 15
- 238000000034 method Methods 0.000 title claims abstract description 14
- 238000012806 monitoring device Methods 0.000 claims abstract description 22
- 238000001228 spectrum Methods 0.000 claims description 15
- 230000002159 abnormal effect Effects 0.000 claims description 7
- 238000012552 review Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims 1
- 238000005516 engineering process Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- LHCPRYRLDOSKHK-UHFFFAOYSA-N 7-deaza-8-aza-adenine Chemical compound NC1=NC=NC2=C1C=NN2 LHCPRYRLDOSKHK-UHFFFAOYSA-N 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04H—BROADCAST COMMUNICATION
- H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
- H04H60/29—Arrangements for monitoring broadcast services or broadcast-related services
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- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- Emergency Alarm Devices (AREA)
Abstract
The invention discloses radio signal monitoring systems and monitoring method based on machine learning, monitoring system includes acquisition terminal, monitoring device, cloud platform, device end, alarm module, Back Administration Module, the signal acquisition terminal, alarm module, Back Administration Module are all electrically connected with monitoring device, the cloud platform, device end are all wirelessly connected with monitoring device, the monitoring device includes CPU, memory module, CPU connection memory module, the device end install APP.Whole process of the present invention is automatically controllable, and signal identification speed is fast, accuracy rate is high, supports the monitoring system of alarm, is suitble to unified platform deployment, anomaly is from passively to actively.The present invention can accomplish unattended automatic carry out radio monitoring, alleviate the work load of radio monitoring personnel, improve radio monitoring ability, so that radio monitoring is accomplished round-the-clock, wide-band monitoring, filled up the blank in radio monitoring field.
Description
Technical field
The present invention relates to radio signal monitoring systems and monitoring method based on machine learning.
Background technique
As industrialization and information-based fusion deepen continuously, digital economy is becoming the new engine of economic development, this is all
It is unable to do without the application of radio technology, the guarantee of more too busy to get away frequency spectrum resource and order.Radio monitoring is as radio control
Key technology support, possess various dimensions, sufficient covering power, and have certain analysis and decision ability, realize intelligence
Monitoring promotes monitoring capability, and to further maintenance airwaves order, service economy and social development have vital work
With.But radio control is facing the challenge of new situations, traditional monitoring, comparison, analysis method are difficult to adapt to new situations
Requirement.Meanwhile the generation information technologies such as big data, cloud computing and artificial intelligence are constantly mature, provide for monitoring
New thinking.
Traditional radio monitoring, the discovery for abnormal signal, relies primarily on personal monitoring's examination or template characteristic is sentenced
The means such as not.By taking such as black broadcast monitoring as an example, monitoring personnel's software is first monitored frequency range interested, then according to general
It monitors resulting data to compare with given data empirical data, and analyzes abnormal signal, compared with station database is compared
Whether to quasi- abnormal signal, screening finally by monitoring is to confirm black broadcast.Black broadcast case is investigated and prosecuted in conjunction with all parts of the country strike,
More black broadcast are according to report or to complain and know, non-monitored system actively monitoring finds and presents.Obviously, it is this mainly according to
Manually condition triggering and according to the method for set process, relatively passively, efficiency is limited, and effect can not by work accumulation by
Gradually improve.Black broadcast is mostly to play in night to morning, and certain black broadcast escape inspection by the way of not timing, indefinite frequency
It surveys, only judges that abnormal signal has very big uncertainty using manpower lookup.
Summary of the invention
The purpose of the present invention is overcoming the shortcomings of in existing product, the radio signal monitoring system based on machine learning is provided
System and monitoring method.
In order to achieve the above object, the present invention is achieved by the following technical solutions:
Radio signal based on machine learning monitors system, including acquisition terminal, monitoring device, cloud platform, equipment end
End, alarm module, Back Administration Module, the signal acquisition terminal, alarm module, Back Administration Module are all electric with monitoring device
Property connection, the cloud platform, device end be all wirelessly connected with monitoring device, and the monitoring device includes CPU, memory module,
CPU connection memory module, the device end install APP.
The acquisition terminal includes wireless signal acquiring module, WIFI module, GPS module.
The alarm module includes sound alarm module, light alarm module.
The device end is PC machine, laptop or tablet computer.
The memory module obtains radio signal for storing.
The monitoring method of radio signal monitoring system based on machine learning, includes the following steps:
Step 1:CPU is sampled by data set of the acquisition terminal to broadcast band standard signal and history swept-frequency signal
And learn, frequency spectrum detector is generated, while CPU samples history audio data by acquisition terminal and learns to obtain language
Sound data, noise data, to generate audio frequency detector；
Step 2: acquisition terminal carries out extract real-time to radio signal, to obtain real time data；
Step 3: whether normally frequency spectrum detector compares and analyzes real time data, if real time data is normal, monitors
Equipment demodulates the real time data, and otherwise real time data is labeled as black broadcast singal by CPU；
Step 4: normally whether the real time data after demodulation compared and analyzed again by audio frequency detector, if audio is examined
It surveys device and detects that the real time data after the demodulation is abnormal, then the real time data after demodulation is labeled as black broadcast singal by CPU, no
Then jump out；
Step 5: black broadcast singal is sent to Back Administration Module by monitoring device, manually by Back Administration Module to this
Black broadcast singal carries out review identification, if the signal is really black broadcast singal, which is sent to by monitoring device
Cloud platform or device end, while device end, alarm module are alarmed, otherwise send the signal to frequency spectrum detector and
Audio frequency detector updates frequency spectrum detector and audio frequency detector.
Beneficial effects of the present invention are as follows: the present invention starts with from data analysis, in conjunction with frequency spectrum detector and audio frequency detector,
Various dimensions study, manual examination and verification, finally constitute data closed loop, and whole process is automatically controllable, and signal identification speed is fast, accuracy rate is high,
It supports the monitoring system of alarm, is suitble to unified platform deployment, anomaly is from passively to actively.The present invention can accomplish nobody
Automatic carry out radio monitoring on duty, alleviates the work load of radio monitoring personnel, improves radio monitoring ability, make
Radio monitoring accomplishes round-the-clock, wide-band monitoring, has filled up the blank in radio monitoring field.
Detailed description of the invention
Fig. 1 is system block diagram of the invention.
Specific embodiment
Technical solution of the present invention is described further with reference to the accompanying drawings of the specification:
As shown in Figure 1, the radio signal based on machine learning monitors system, including acquisition terminal 2, monitoring device 1, cloud
Platform 3, device end 4, alarm module 6, Back Administration Module 5, the acquisition terminal 2, alarm module 6, Back Administration Module 5
It is all electrically connected with monitoring device 1, the cloud platform 3, device end 4 are all wirelessly connected with monitoring device 1, the monitoring device
1 includes CPU11, memory module 12, the CPU11 connection memory module 12, the installation of device end 4 APP.The acquisition is eventually
End 2 includes wireless signal acquiring module 21, WIFI module 22, GPS module 23.It is, the alarm module 6 includes audible alarm
Module 61, light alarm module 62.It is, the device end 4 is PC machine, laptop or tablet computer.It is, institute
State memory module 12 for store acquisition radio signal.
The monitoring method of radio signal monitoring system based on machine learning, includes the following steps:
Step 1:CPU11 is carried out by data set of the acquisition terminal 2 to broadcast band standard signal and history swept-frequency signal
It samples and learns, generate frequency spectrum detector, while CPU11 is sampled and learnt to history audio data by acquisition terminal 2
Voice data, noise data are obtained, to generate audio frequency detector；
Step 2: acquisition terminal 2 carries out extract real-time to radio signal, to obtain real time data；
Step 3: whether normally frequency spectrum detector compares and analyzes real time data, if real time data is normal, monitors
Equipment 1 demodulates the real time data, and otherwise real time data is labeled as black broadcast singal by CPU11；Step 4: after demodulation
Normally whether real time data compared and analyzed again by audio frequency detector, if audio frequency detector detects the reality after the demodulation
When data it is abnormal, then CPU11 by the real time data after demodulation be labeled as black broadcast singal, otherwise jump out；
Step 5: black broadcast singal is sent to Back Administration Module 5 by monitoring device 1, manually passes through Back Administration Module 5
Review identification is carried out to the black broadcast singal, if the signal is really black broadcast singal, monitoring device 1 sends out the black broadcast singal
Cloud platform 3 or device end 4 are given, while device end 4, alarm module 6 are alarmed, and frequency spectrum is otherwise sent the signal to
Detector and audio frequency detector update frequency spectrum detector and audio frequency detector.
The present invention starts with from data analysis, learns in conjunction with frequency spectrum detector and audio frequency detector, various dimensions, manual examination and verification,
Data closed loop is finally constituted, whole process is automatically controllable, and signal identification speed is fast, accuracy rate is high, supports the monitoring system of alarm, fits
Integrated Platform deployment is closed, anomaly is from passively to actively.The present invention can accomplish unattended automatic carry out radio prison
It surveys, alleviates the work load of radio monitoring personnel, improve radio monitoring ability, radio monitoring is made to accomplish whole day
It waits, the monitoring of wide-band, has filled up the blank in radio monitoring field.
It should be noted that listed above is only a kind of specific embodiment of the invention.It is clear that the invention is not restricted to
Upper embodiment, can also be there are many deforming, in short, those skilled in the art can directly lead from present disclosure
Out or all deformations for associating, it is considered as protection scope of the present invention.
Claims (6)
1. the radio signal based on machine learning monitors system, which is characterized in that including acquisition terminal (2), monitoring device
(1), cloud platform (3), device end (4), alarm module (6), Back Administration Module (5), the acquisition terminal (2), alarm mould
Block (6), Back Administration Module (5) all with monitoring device (1) be electrically connected, the cloud platform (3), device end (4) all with prison
Measurement equipment (1) is wirelessly connected, and the monitoring device (1) includes CPU (11), memory module (12), CPU (11) the connection storage
Module (12), the device end (4) install APP.
2. the radio signal based on machine learning monitors system according to claim 1, which is characterized in that the acquisition is eventually
Holding (2) includes wireless signal acquiring module (21), WIFI module (22), GPS module (23).
3. the radio signal based on machine learning monitors system according to claim 1, which is characterized in that the alarm mould
Block (6) includes sound alarm module (61), light alarm module (62).
4. the radio signal based on machine learning monitors system according to claim 1, which is characterized in that the equipment is whole
Holding (4) is PC machine, laptop or tablet computer.
5. the radio signal based on machine learning monitors system according to claim 1, which is characterized in that the storage mould
Block (12) obtains radio signal for storing.
6. the monitoring method of the radio signal monitoring system based on machine learning, is based on being based on engineering described in claim 1
The radio signal of habit monitors system, includes the following steps:
Step 1:CPU (11) is carried out by the data set of acquisition terminal (2) to broadcast band standard signal and history swept-frequency signal
It samples and learns, generate frequency spectrum detector, while CPU (11) samples simultaneously history audio data by acquisition terminal (2)
Study is to obtain voice data, noise data, to generate audio frequency detector；
Step 2: acquisition terminal (2) carries out extract real-time to radio signal, to obtain real time data；
Step 3: whether normally frequency spectrum detector compares and analyzes real time data, if real time data is normal, monitoring device
(1) real time data is demodulated, otherwise real time data is labeled as black broadcast singal by CPU (11)；
Step 4: normally whether the real time data after demodulation compared and analyzed again by audio frequency detector, if audio frequency detector
Real time data after detecting the demodulation is abnormal, then the real time data after demodulation is labeled as black broadcast singal by CPU (11), no
Then jump out；
Step 5: black broadcast singal is sent to Back Administration Module (5) by monitoring device (1), manually passes through Back Administration Module
(5) review identification is carried out to the black broadcast singal, if the signal is really black broadcast singal, monitoring device (1) is by the black broadcast
Signal is sent to cloud platform (3) or device end (4), while device end (4), alarm module (6) are alarmed, and otherwise should
Signal is sent to frequency spectrum detector and audio frequency detector, updates frequency spectrum detector and audio frequency detector.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910214533.2A CN109981203A (en) | 2019-03-20 | 2019-03-20 | Radio signal monitoring system and monitoring method based on machine learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910214533.2A CN109981203A (en) | 2019-03-20 | 2019-03-20 | Radio signal monitoring system and monitoring method based on machine learning |
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| Publication Number | Publication Date |
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| CN109981203A true CN109981203A (en) | 2019-07-05 |
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| CN201910214533.2A Pending CN109981203A (en) | 2019-03-20 | 2019-03-20 | Radio signal monitoring system and monitoring method based on machine learning |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106100777A (en) * | 2016-05-27 | 2016-11-09 | 西华大学 | Broadcast support method based on speech recognition technology |
| CN106888061A (en) * | 2017-04-12 | 2017-06-23 | 云南大学 | A kind of intelligent radio power utilization monitoring device and method |
| CN106936517A (en) * | 2015-12-28 | 2017-07-07 | 镇江高科信息科技有限公司 | A kind of automatic recognition system and its method of abnormal radio signal |
| CN107276707A (en) * | 2017-06-08 | 2017-10-20 | 国家无线电监测中心 | A kind of black automation of broadcast continuity analysis method and Weigh sensor device based on multi-attribute analysis |
| CN107800498A (en) * | 2017-11-30 | 2018-03-13 | 安徽汇鑫电子有限公司 | The unattended embedded monitoring device of synchronized broadcast |
-
2019
- 2019-03-20 CN CN201910214533.2A patent/CN109981203A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106936517A (en) * | 2015-12-28 | 2017-07-07 | 镇江高科信息科技有限公司 | A kind of automatic recognition system and its method of abnormal radio signal |
| CN106100777A (en) * | 2016-05-27 | 2016-11-09 | 西华大学 | Broadcast support method based on speech recognition technology |
| CN106888061A (en) * | 2017-04-12 | 2017-06-23 | 云南大学 | A kind of intelligent radio power utilization monitoring device and method |
| CN107276707A (en) * | 2017-06-08 | 2017-10-20 | 国家无线电监测中心 | A kind of black automation of broadcast continuity analysis method and Weigh sensor device based on multi-attribute analysis |
| CN107800498A (en) * | 2017-11-30 | 2018-03-13 | 安徽汇鑫电子有限公司 | The unattended embedded monitoring device of synchronized broadcast |
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| TA01 | Transfer of patent application right |
Effective date of registration: 20191023 Address after: 311100 Zhejiang Province Hangzhou Yuhang District Wuchang Street Higher Education Road 970-1 7 Building 3 Room 302 Applicant after: Hangzhou Jiaxin Shitong Electronic Technology Co., Ltd. Applicant after: NINGBO RADIO MONITORING STATION IN ZHEJIANG PROVINCE Address before: 311100 Zhejiang Province Hangzhou Yuhang District Wuchang Street Higher Education Road 970-1 7 Building 3 Room 302 Applicant before: Hangzhou Jiaxin Shitong Electronic Technology Co., Ltd. |
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Application publication date: 20190705 |
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