CN113377806B - Embedded machine learning artificial intelligence data analysis processing system - Google Patents
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Abstract
The invention discloses an embedded machine learning artificial intelligence data analysis processing system, which comprises a signal acquisition module, wherein the signal acquisition module is connected with a manager decision module and a system decision module, the manager decision module and the system decision module are jointly connected with a credibility judgment module, the credibility judgment module is connected with a judgment result output module, a prompt module and a signal library, the signal acquisition module is also connected with a machine identification judgment module through an intelligent learning system, and the machine identification judgment module is connected with an error reporting recording module and a result reference module.
Description
Technical Field
The invention relates to an embedded machine learning artificial intelligence data analysis processing system, and belongs to the field of radio signal analysis systems.
Background
In the radio technology, a wavelength band refers to a division of the electromagnetic spectrum, for example, a long-wave, short-wave, ultrashort-wave band, etc., for a radio signal. The other is the division of the working frequency range of the devices such as the transmitter and the receiver, after the radio signals are collected, the radio identification processing is too inconvenient due to the existence of various frequency bands, so that the problem that the radio is difficult to analyze, identify and process exists.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an embedded machine learning artificial intelligence data analysis processing system, so that the technical problems are solved.
In order to achieve the purpose, the invention adopts the technical scheme that: the utility model provides an embedded machine learning artificial intelligence data analysis processing system, includes, its characterized in that, signal acquisition module is connected with administrator's decision module and system decision module, administrator's decision module and system decision module are connected with credibility judgment module jointly, credibility judgment module is connected with judgement result output module, suggestion module and signal library, signal acquisition module still connects machine discernment judgment module through intelligent learning system, machine discernment judgment module is connected with error reporting record module and result reference module, wherein:
the signal acquisition module is used for collecting radio signals;
the manager decision module is used for manually identifying the type of the radio signal;
the system decision module automatically identifies a result of the radio signal;
the reliability judging module judges the accuracy of the identification result of the radio signal, if the reliability judging module judges the accuracy of the identification result of the radio signal, the judgment result output module outputs the correct identification result of the radio signal, and if the reliability judging module judges the accuracy of the identification result of the radio signal, the prompting module sends out a prompt;
the machine identification judging module judges the identification result of the intelligent learning system, if the identification result is correct, the result reference module outputs the identification result, and if the identification result is wrong, the error reporting and recording module records the wrong result.
Furthermore, the signal library comprises a manual updating module and a manual input module, the manual input module is connected with the port of the prompting module, the manual updating module is used for manually updating the radio signals and the matching frequency bands in the signal library, and the manual input module is used for manually inputting the radio signals and the matching frequency bands into the signal library.
Furthermore, the intelligent learning system comprises a machine learning module and a machine training module, wherein the machine learning module is used for machine learning frequency band identification of the radio signals, and the machine training module is used for applying the identification model learned by the machine learning module to identify the frequency band of the radio signals.
Further, the machine learning module comprises a judgment observation module, an error calculation module, a human management module and a learning model updating module, wherein the judgment observation module is connected to the error calculation module and the learning model updating module, the human management module is connected to the error calculation module, the judgment observation module is used for observing the decision process of the system decision module so as to drive the learning model updating module to update the calculation model, the error calculation module is used for calculating the error rate of an error identification part in the system decision module, and the human management module is used for judging whether the data of the error part is used for machine learning of the learning model updating module or not by human.
Further, the machine training module comprises a model base, the model base is connected with a model management module and a learning model invoking module, the learning model invoking module is connected with a return calculation module, the model base is matched with the model management module to perform input and deletion operations on the learning model, the learning model invoking module is used for invoking the learning model in the model base, and the return calculation module is used for calculating the accuracy of the learning model in recognizing the radio signal.
Further, the system decision module includes a signal filtering module, a signal conversion module, a feature extraction module, and a result transmission module, where the signal filtering module is configured to filter radio signals outside a preset carrier frequency range and outside a preset bandwidth range, the signal conversion module converts the radio signals into digital signals, the feature extraction module is configured to extract physical features of each of the digital signals to ensure a frequency band, and the result transmission module transmits an identified result.
Furthermore, the judgment result output module and the result reference module are connected with a comprehensive comparison module together, and the comprehensive comparison module is used for comparing the recognition results of the intelligent learning system and the system decision module.
Furthermore, the comprehensive comparison module comprises a result comparison module, the result comparison module is connected with a divergence ratio calculation module, the divergence ratio calculation module is connected with an abnormal result temporary storage module, the abnormal result temporary storage module is connected with a periodic deletion module, the periodic deletion module is connected with a retrieval module, the retrieval module is connected with a display module, the comparison module is used for comparing the identification results of the intelligent learning system and the system decision module, the divergence ratio calculation module is used for calculating different rates between the two, the abnormal result temporary storage module is used for temporarily caching different results, the periodic deletion module is used for periodically and automatically deleting the temporary storage results, the retrieval module is used for retrieving the temporarily stored results, and the display module is used for displaying the retrieved results.
The invention has the beneficial effects that:
1. through the administrator decision module and the system decision module which are arranged, the administrator decision module is used for manually identifying the type of a radio signal, the signal filtering module is used for filtering radio signals outside a preset carrier frequency range and outside a preset bandwidth range, the signal conversion module converts the radio signals into digital signals, the characteristic extraction module is used for extracting physical characteristics of each digital signal to ensure a frequency band, the result sending module sends out an identified result, systematic self-judgment can be carried out on the radio information, an expert can also be used as an administrator to carry out artificial judgment, the system can identify the radio signal, the expert can also be enabled to identify, and the function of analyzing and identifying the frequency band of the radio signal is realized.
2. The intelligent learning system is arranged, the machine learning module is used for machine learning frequency band identification of radio signals, the machine training module is used for applying an identification model learned by the machine learning module to identify radio signal frequency bands, the judgment observation module is used for observing a decision process of the system decision module to drive the learning model updating module to update a calculation model, the error calculation module is used for calculating the error rate of an error identification part in the system decision module, the artificial management module is used for artificially judging whether error part data are used for machine learning of the learning model updating module, the model base is matched with the model management module to perform input and deletion operation on the learning model, the learning model calling module is used for calling the learning model in the model base, the return calculation module is used for calculating the accuracy of the learning model for identifying the radio signals, and the system can perform self iterative updating to identify the frequency bands while identifying the system.
3. Through the comprehensive comparison module, the comparison module is used for comparing the recognition results of the intelligent learning system and the system decision module, the divergence ratio calculation module is used for calculating different ratios between the recognition results and the system decision module, the abnormal result temporary storage module is used for temporarily caching different results, the periodic deletion module is used for periodically and automatically deleting the temporary storage results, the retrieval module is used for retrieving the temporary storage results, the display module is used for displaying the retrieved results, the recognition results of machine learning and the automatic recognition results can be compared, and the system evaluation in the later period is facilitated.
4. The credibility judgment module and the machine identification judgment module are arranged, so that credibility judgment can be carried out on the identification results made by the system decision module, the manager decision module and the intelligent learning system, the machine identification judgment module judges the identification result of the intelligent learning system, if the judgment result is correct, the result reference module outputs the identification result, if the judgment result is wrong, the error report recording module records the error result, the manual updating module is used for manually updating the radio signal and the matching frequency band in the signal library, the manual input module is used for manually inputting the radio signal and the matching frequency band into the signal library, and the missing identification frequency band can be timely reminded, so that a worker can timely fill in the system.
Drawings
FIG. 1 is a schematic diagram of the embedded machine learning artificial intelligence data analysis processing system of the present invention;
FIG. 2 is a schematic diagram of a system decision module of the embedded machine learning artificial intelligence data analysis processing system according to the present invention;
FIG. 3 is a schematic diagram of a machine learning module of the embedded machine learning artificial intelligence data analysis processing system of the present invention;
FIG. 4 is a schematic diagram of a machine training module of the embedded machine learning artificial intelligence data analysis processing system of the present invention;
fig. 5 is a schematic diagram of a comprehensive comparison module of the embedded machine learning artificial intelligence data analysis processing system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood, however, that the detailed description herein of specific embodiments is intended to illustrate the invention and not to limit the scope of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, and the terminology used herein in the description of the present invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the present invention.
As shown in fig. 1-5, an embedded machine learning artificial intelligence data analysis processing system, including signal acquisition module, signal acquisition module is connected with administrator's decision-making module and system decision-making module, administrator's decision-making module and system decision-making module are connected with credibility judgment module jointly, credibility judgment module is connected with judged result output module, suggestion module and signal library, signal acquisition module still passes through intelligent learning system connection machine discernment judgment module, machine discernment judgment module is connected with error report record module and result reference module, wherein:
the signal acquisition module is used for collecting radio signals;
the administrator decision module is used for manually identifying the type of the radio signal;
the system decision module automatically identifies a result of the radio signal;
the reliability judging module judges the accuracy of the identification result of the radio signal, if the judgment result is correct, the judgment result output module outputs the correct identification result of the radio signal, and if the judgment result is wrong, the prompting module sends a prompt;
the machine identification judging module judges the identification result of the intelligent learning system, if the identification result is correct, the result reference module outputs the identification result, and if the identification result is wrong, the error reporting and recording module records the wrong result.
The signal library comprises a manual updating module and a manual input module, the manual input module is connected with a port of the prompting module, the manual updating module is used for manually updating radio signals and matching frequency bands in the signal library, and the manual input module is used for manually inputting the radio signals and the matching frequency bands into the signal library.
The intelligent learning system comprises a machine learning module and a machine training module, wherein the machine learning module is used for machine learning frequency band identification of radio signals, and the machine training module is used for applying an identification model learned by the machine learning module to identify the frequency band of the radio signals.
The machine learning module comprises a judgment observation module, an error calculation module, a man-made management module and a learning model updating module, wherein the judgment observation module is connected to the error calculation module and the learning model updating module, the man-made management module is connected to the error calculation module, the judgment observation module is used for observing the decision process of the system decision module so as to drive the learning model updating module to update the calculation model, the error calculation module is used for carrying out error rate calculation on an error identification part in the system decision module, and the man-made management module is used for judging whether error part data is used for machine learning of the learning model updating module or not.
The machine training module comprises a model base, the model base is connected with a model management module and a learning model calling module, the learning model calling module is connected with a return calculation module, the model base is matched with the model management module to input and delete the learning model, the learning model calling module is used for calling the learning model in the model base, and the return calculation module is used for calculating the accuracy of the learning model for recognizing the radio signal.
The system decision module comprises a signal filtering module, a signal conversion module, a feature extraction module and a result sending module, wherein the signal filtering module is used for filtering radio signals outside a preset carrier frequency range and a preset bandwidth range, the signal conversion module is used for converting the radio signals into digital signals, the feature extraction module is used for extracting physical features of each digital signal to ensure a frequency band, and the result sending module is used for sending an identified result.
The judgment result output module and the result reference module are connected with a comprehensive comparison module together, and the comprehensive comparison module is used for comparing the recognition results of the intelligent learning system and the system decision module.
The comprehensive comparison module comprises a result comparison module, the result comparison module is connected with a divergence rate calculation module, the divergence rate calculation module is connected with an abnormal result temporary storage module, the abnormal result temporary storage module is connected with a periodic deletion module, the periodic deletion module is connected with a retrieval module, the retrieval module is connected with a display module, the comparison module is used for comparing the identification results of the intelligent learning system and the system decision module, the divergence rate calculation module is used for calculating different rates between the abnormal result temporary storage module and the system decision module, the periodic deletion module is used for temporarily caching different results, the periodic deletion module is used for periodically and automatically deleting the temporary stored results, the retrieval module is used for retrieving the temporarily stored results, and the display module is used for displaying the retrieved results.
The system comprises a manager decision module and a system decision module, wherein the manager decision module is used for manually identifying the type of a radio signal, a signal filtering module is used for filtering radio signals outside a preset carrier frequency range and outside a preset bandwidth range, a signal conversion module is used for converting the radio signals into digital signals, a characteristic extraction module is used for extracting physical characteristics of each digital signal to ensure a frequency band, a result sending module is used for sending an identified result, the system can automatically judge the radio information, and an expert can be used as a manager for artificial judgment; through the arranged intelligent learning system, the machine learning module is used for machine learning frequency band identification of radio signals, the machine training module is used for applying an identification model learned by the machine learning module to identify a radio signal frequency band, the decision process of the observation module used for observing the system decision module is judged to drive the learning model updating module to update a calculation model, the error calculation module is used for calculating the error rate of an error identification part in the system decision module, and the artificial management module is used for artificially judging whether the data of the error part is used for machine learning of the learning model updating module, the model base is matched with the model management module to input and delete the learning model, the learning model retrieving module is used for retrieving the learning model in the model base, the return calculation module is used for calculating the accuracy of the learning model for identifying the radio signals, and for the function of identifying local intelligent machine learning of the radio signals, the system can be automatically updated in an iterative manner to identify the frequency band while the system identifies; through the arranged comprehensive comparison module, the comparison module is used for comparing the recognition results of the intelligent learning system and the system decision module, the divergence ratio calculation module is used for calculating different rates between the two, the abnormal result temporary storage module is used for temporarily caching different results, the periodic deletion module is used for periodically and automatically deleting the temporary storage results, the retrieval module is used for retrieving the temporary storage results, the display module is used for displaying the retrieval results, the recognition results of machine learning and the automatic recognition results can be compared, and the later-stage system evaluation is facilitated; the credibility judgment module and the machine identification judgment module are arranged, so that credibility judgment can be carried out on the identification results made by the system decision module, the manager decision module and the intelligent learning system, the machine identification judgment module judges the identification result of the intelligent learning system, if the judgment result is correct, the result reference module outputs the identification result, if the judgment result is wrong, the error report recording module records the error result, the manual updating module is used for manually updating the radio signal and the matching frequency band in the signal library, the manual input module is used for manually inputting the radio signal and the matching frequency band into the signal library, and the missing identification frequency band can be timely reminded, so that a worker can timely fill in the system.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents or improvements made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (1)
1. The utility model provides an embedded machine learning artificial intelligence data analysis processing system which characterized in that, signal acquisition module is connected with administrator's decision-making module and system's decision-making module, administrator's decision-making module and system's decision-making module are connected with credibility judging module jointly, credibility judging module is connected with judged result output module, suggestion module and signal library, signal acquisition module still connects machine identification judging module through intelligent learning system, machine identification judging module is connected with error report record module and result reference module, wherein:
the signal acquisition module is used for collecting radio signals;
the administrator decision module is used for manually identifying the type of the radio signal;
the system decision module automatically identifies a result of the radio signal;
the reliability judging module judges the accuracy of the identification result of the radio signal, if the judgment result is correct, the judgment result output module outputs the correct identification result of the radio signal, and if the judgment result is wrong, the prompting module sends a prompt;
the machine identification judging module judges the identification result of the intelligent learning system, if the identification result is correct, the result reference module outputs the identification result, and if the identification result is wrong, the error reporting and recording module records the wrong result;
the signal library comprises a manual updating module and a manual input module, the manual input module is connected with a port of the prompting module, the manual updating module is used for manually updating radio signals and matching frequency bands in the signal library, and the manual input module is used for manually inputting the radio signals and the matching frequency bands into the signal library;
the intelligent learning system comprises a machine learning module and a machine training module, wherein the machine learning module is used for machine learning frequency band identification of radio signals, and the machine training module is used for applying an identification model learned by the machine learning module to identify the frequency band of the radio signals;
the machine learning module comprises a judgment observation module, an error calculation module, a man-made management module and a learning model updating module, wherein the judgment observation module is connected with the error calculation module and the learning model updating module, the man-made management module is connected with the error calculation module, the judgment observation module is used for observing the decision process of the system decision module so as to drive the learning model updating module to update the calculation model, the error calculation module is used for calculating the error rate of an error identification part in the system decision module, and the man-made management module is used for judging whether the error part data is used for machine learning of the learning model updating module or not by man;
the machine training module comprises a model base, the model base is connected with a model management module and a learning model invoking module, the learning model invoking module is connected with a return calculation module, the model base is matched with the model management module to perform input and deletion operations on a learning model, the learning model invoking module is used for invoking the learning model in the model base, and the return calculation module is used for calculating the accuracy of the learning model in recognizing radio signals;
the system decision module comprises a signal filtering module, a signal conversion module, a feature extraction module and a result sending module, wherein the signal filtering module is used for filtering radio signals outside a preset carrier frequency range and outside a preset bandwidth range, the signal conversion module is used for converting the radio signals into digital signals, the feature extraction module is used for extracting physical features of each digital signal to ensure a frequency band, and the result sending module is used for sending out an identified result;
the judgment result output module and the result reference module are commonly connected with a comprehensive comparison module, and the comprehensive comparison module is used for comparing the identification results of the intelligent learning system and the system decision module;
the comprehensive comparison module comprises a result comparison module, the result comparison module is connected with a divergence ratio calculation module, the divergence ratio calculation module is connected with an abnormal result temporary storage module, the abnormal result temporary storage module is connected with a periodic deletion module, the periodic deletion module is connected with a retrieval module, the retrieval module is connected with a display module, the comparison module is used for comparing the identification results of the intelligent learning system and the system decision module, the divergence ratio calculation module is used for calculating different rates between the abnormal result temporary storage module and the system decision module, the periodic deletion module is used for temporarily caching different results, the periodic deletion module is used for periodically and automatically deleting the temporary storage results, the retrieval module is used for retrieving the temporary storage results, and the display module is used for displaying the retrieved results.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106096789A (en) * | 2016-06-22 | 2016-11-09 | 华东师范大学 | A kind of based on machine learning techniques can be from the abnormal industry control security protection of perception and warning system |
| CN111948954A (en) * | 2020-09-08 | 2020-11-17 | 兰州工业学院 | Intelligent home system based on internet control |
Family Cites Families (5)
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| CN110213010B (en) * | 2019-04-28 | 2020-07-07 | 浙江大学 | Unmanned aerial vehicle detection system and method based on multi-channel radio frequency signals |
| KR102281515B1 (en) * | 2019-07-23 | 2021-07-26 | 엘지전자 주식회사 | Artificial intelligence apparatus for recognizing speech of user using personalized language model and method for the same |
| US11481675B2 (en) * | 2019-08-21 | 2022-10-25 | National Taiwan University | Identity recognition system based on compressed signals and method thereof |
| US11526725B2 (en) * | 2019-12-06 | 2022-12-13 | Bank Of America Corporation | Attention-based layered neural network architecture for explainable and high-performance AI processing |
| CN112257592B (en) * | 2020-10-22 | 2024-07-09 | 北京博识创智科技发展有限公司 | Signal identification self-learning method based on artificial intelligence |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111948954A (en) * | 2020-09-08 | 2020-11-17 | 兰州工业学院 | Intelligent home system based on internet control |
Non-Patent Citations (1)
| Title |
|---|
| 基于人工智能下语音识别方法与装置及系统的研究;熊晓倩;《科技资讯》;20180123(第03期);全文 * |
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