CN113128291A - Data acquisition system and method - Google Patents
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Abstract
The invention discloses a data acquisition system and a method, which relate to the technical field of data acquisition, and the system comprises: the system comprises a data acquisition node system, a data acquisition unit, a communication management unit and a data processing unit, wherein the data acquisition node system, the data acquisition unit, the communication management unit and the data processing unit are deployed at nodes of a cloud computing system; the data acquisition node system is connected with the data processing unit; the data acquisition unit section is in signal connection with the data processing unit; the node system is connected with a cloud server through the communication management unit; the data acquisition node system comprises a receiver and a transmitter, wherein the receiver is connected with the transmitter; the receiver is connected with the data processing unit; the method has the advantages of high data acquisition efficiency and high data acquisition efficiency.
Description
Technical Field
The invention relates to the technical field of data acquisition, in particular to a data acquisition system and a data acquisition method.
Background
Data acquisition is generally explained in two different ways: one is the process of collecting, identifying and selecting data from a data source. The other is the recording process of the digitization, electronic scanning system, and the encoding process of the content and attributes.
The data acquisition system comprises: the system comprises functional modules of visual report definition, audit relationship definition, examination and approval and release of reports, data filling, data preprocessing, data review, comprehensive query statistics and the like. By networking and digitalizing information acquisition, the coverage range of data acquisition is enlarged, and the comprehensiveness, timeliness and accuracy of auditing work are improved; finally, the modernization, program standardization, decision scientification and service networking of related business work management are realized.
Data acquisition, also known as data acquisition, utilizes a device to acquire data from outside the system and input it to an interface within the system. Data acquisition techniques are widely used in various fields. Such as a camera and a microphone, are data acquisition tools.
The collected data are various physical quantities such as temperature, water level, wind speed, pressure, etc. which have been converted into electrical signals, and may be analog quantities or digital quantities. The acquisition is generally a sampling mode, that is, the same point data is repeatedly acquired at certain time intervals (called sampling period). The acquired data are mostly instantaneous values, but also characteristic values within a certain period of time. Accurate data measurements are the basis for data acquisition. The data measurement method includes contact and non-contact, and the detection elements are various. No matter which method and element, the data correctness is ensured on the premise of not influencing the state of the object to be measured and the measurement environment. The data collection is very broad, and comprises the collection of planar continuous physical quantities. In computer-aided drawing, mapping, designing, the process of digitizing a graphic or image may also be referred to as data acquisition, where geometric (or physical, e.g., grayscale) data is acquired.
Today, the internet industry is rapidly developing, data acquisition is widely applied to the internet and distributed fields, and the data acquisition field is changed significantly. First, intelligent data acquisition systems in distributed control applications have been under great development both at home and abroad. Secondly, the number of bus-compatible data collection cards is increasing, as are the number of data collection systems compatible with personal computers. Various data acquisition machines are published at home and abroad, and bring data acquisition into a brand new era.
For most manufacturing enterprises, automatic data acquisition of measuring instruments is always a troublesome matter, even if the instruments have interfaces such as RS232/485, the instruments are still used and measured, and meanwhile, the instruments are manually recorded on paper and finally input into a PC for processing, so that the work is heavy, the accuracy of the data cannot be guaranteed, and the data obtained by managers often lags behind by one or two days; for on-site bad product information and relevant yield data, how to realize high-efficiency, concise and real-time data acquisition is a big problem.
Disclosure of Invention
In view of this, the present invention provides a data acquisition system and method, which has the advantages of high data acquisition efficiency and high data acquisition efficiency.
In order to achieve the purpose, the invention adopts the following technical scheme:
a data acquisition system, the system comprising: the system comprises a data acquisition node system, a data acquisition unit, a communication management unit and a data processing unit, wherein the data acquisition node system, the data acquisition unit, the communication management unit and the data processing unit are deployed at nodes of a cloud computing system; the data acquisition node system is connected with the data processing unit; the data acquisition unit section is in signal connection with the data processing unit; the node system is connected with a cloud server through the communication management unit; the data acquisition node system comprises a receiver and a transmitter, wherein the receiver is connected with the transmitter; the receiver is connected with the data processing unit; the transmitter is connected with the communication management unit; the communication management unit comprises a power supply system, and the power supply system comprises a power supply management chip and a power supply power control module; the power supply power control module comprises a POE power transformer and a photoelectric coupler, and the output end of the power supply management chip is connected with the input end of the POE power transformer; the output end of the photoelectric coupler is connected with the feedback end of the power management chip; the data acquisition unit comprises an information acquisition device and an information transmitter, the information acquisition device is connected with the information transmitter, and the information transmitter is used for transmitting the information acquired by the information acquisition device to the data processing unit.
Further, the data processing unit includes: the device comprises a data preprocessing unit, a data specification unit and a data standardization unit; the data preprocessing is used for sequentially removing the unique attribute, processing the missing value and detecting the abnormal value from the data information; the data protocol unit is used for carrying out protocol processing on the data after data preprocessing, so that the data after protocol processing are irrelevant pairwise, but original information can be kept as much as possible; and the data standardization unit scales the data after the protocol processing according to a proportion so as to enable the data to fall into a small specific interval.
Further, the data acquisition unit is a biological feature recognition device; the biological characteristic recognition device is specifically a fingerprint module, or an iris monitoring module, or a face recognition module, or a voiceprint recognition module.
Further, the voiceprint recognition module comprises: the voice print recognition system comprises a spectrogram conversion subunit, a CNN (voice over Internet network) voiceprint feature extraction subunit, a CNN parameter subunit, a user voiceprint feature model library subunit and a voiceprint feature spectrum matching decoding subunit, wherein external sound is input into the spectrogram conversion subunit, the spectrogram conversion subunit converts the external sound and inputs a conversion result into the CNN voiceprint feature extraction subunit, the CNN voiceprint feature extraction subunit extracts CNN parameters from the CNN parameter subunit, performs voiceprint feature extraction by combining the conversion result and inputs an extraction result into the voiceprint feature spectrum matching decoding subunit, and in the voiceprint feature spectrum matching decoding subunit, the extraction result and the user voiceprint features in the user voiceprint feature model library are matched, decoded and recognized, and the output of the recognition result is an identity recognition result.
Furthermore, the connection mode of the communication management unit and the cloud server at least comprises one of the following modes, namely GSM, GPRS, 3G, 4G, WIFI, Bluetooth, NFC or wired connection communication mode.
A method of data acquisition, the method performing the steps of: step 1: carrying out data acquisition and carrying out data processing on the acquired data; step 2: sending the processed data; the data processing comprises: sequentially carrying out unique attribute removal, missing value processing and abnormal value detection processing; carrying out protocol processing on the data after data preprocessing, so that the data after protocol processing are irrelevant pairwise, but original information can be kept; and scaling the data after the reduction processing to make the data fall into a specific interval.
Further, when data acquisition is performed, the acquired data is as follows: fingerprint data information or voiceprint data information or iris data information or face information; the method for acquiring the voiceprint data information comprises the following steps: carrying out short-time frame division on external sound, and reading in voice data according to frames; detecting the voice data, judging whether the voice data is a voice frame or a non-voice frame, if so, entering the next step, otherwise, ending the process; performing frequency conversion, namely performing Fast Fourier Transform (FFT) on the voice frame to form voice spectrum data; storing the voice frequency spectrum data in a matrix mode, wherein the row of the matrix is a time frame sequence, the column of the matrix is a frequency sequence, and the matrix is a two-dimensional time-frequency spectrogram; the time-frequency spectrogram is subjected to voiceprint feature extraction in CNN to obtain voiceprint features; carrying out voiceprint feature matching identification on the voiceprint features and the user voiceprint features; and outputting the recognition result.
Compared with the prior art, the invention has the following beneficial effects: the data acquisition system of the invention has high efficiency. After the collected data are preprocessed, the effectiveness of the collected data is ensured.
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The invention is described in further detail below with reference to the following figures and detailed description:
fig. 1 is a schematic system structure diagram of a data acquisition system according to an embodiment of the present invention.
Fig. 2 is a schematic method flow diagram of a data acquisition method according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
Please refer to fig. 1. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions of the present invention, so that the present invention has no technical significance. In addition, the terms such as "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and changes or modifications in the relative relationship may be made without substantial technical changes and modifications.
Example 1
A data acquisition system, the system comprising: the system comprises a data acquisition node system, a data acquisition unit, a communication management unit and a data processing unit, wherein the data acquisition node system, the data acquisition unit, the communication management unit and the data processing unit are deployed at nodes of a cloud computing system; the data acquisition node system is connected with the data processing unit; the data acquisition unit section is in signal connection with the data processing unit; the node system is connected with a cloud server through the communication management unit; the data acquisition node system comprises a receiver and a transmitter, wherein the receiver is connected with the transmitter; the receiver is connected with the data processing unit; the transmitter is connected with the communication management unit; the communication management unit comprises a power supply system, and the power supply system comprises a power supply management chip and a power supply power control module; the power supply power control module comprises a POE power transformer and a photoelectric coupler, and the output end of the power supply management chip is connected with the input end of the POE power transformer; the output end of the photoelectric coupler is connected with the feedback end of the power management chip; the data acquisition unit comprises an information acquisition device and an information transmitter, the information acquisition device is connected with the information transmitter, and the information transmitter is used for transmitting the information acquired by the information acquisition device to the data processing unit.
Specifically, data acquisition refers to a process of automatically acquiring information from analog and digital units to be tested, such as sensors and other devices to be tested. The data acquisition system is a flexible and user-defined measurement system implemented in conjunction with computer-based measurement software and hardware products.
The purpose of data acquisition is to measure physical phenomena such as voltage, current, temperature, pressure or sound. The data collection based on PC, through the combination of modularization hardware, application software and computer, measure. Although the data acquisition system has different definitions according to different application requirements, the purposes of acquiring, analyzing and displaying information of each system are the same. The data acquisition system integrates signals, sensors, actuators, signal conditioning, data acquisition equipment and application software.
Example 2
On the basis of the above embodiment, the data processing unit includes: the device comprises a data preprocessing unit, a data specification unit and a data standardization unit; the data preprocessing is used for sequentially removing the unique attribute, processing the missing value and detecting the abnormal value from the data information; the data protocol unit is used for carrying out protocol processing on the data after data preprocessing, so that the data after protocol processing are irrelevant pairwise, but original information can be kept as much as possible; and the data standardization unit scales the data after the protocol processing according to a proportion so as to enable the data to fall into a small specific interval.
Example 3
On the basis of the previous embodiment, the data acquisition unit is a biological feature recognition device; the biological characteristic recognition device is specifically a fingerprint module, or an iris monitoring module, or a face recognition module, or a voiceprint recognition module.
Data preprocessing refers to some processing performed on data before main processing. For example, before most geophysical areal observation data are subjected to conversion or enhancement processing, the irregularly distributed measurement network is firstly subjected to interpolation processing and conversion processing into regular network processing, so that the calculation of a computer is facilitated. In addition, for some section measurement data, such as seismic data preprocessing, vertical stacking, rearrangement, channel header addition, editing, resampling, multi-path editing and the like are available.
Example 4
On the basis of the previous embodiment, the voiceprint recognition module comprises: the voice print recognition system comprises a spectrogram conversion subunit, a CNN (voice over Internet network) voiceprint feature extraction subunit, a CNN parameter subunit, a user voiceprint feature model library subunit and a voiceprint feature spectrum matching decoding subunit, wherein external sound is input into the spectrogram conversion subunit, the spectrogram conversion subunit converts the external sound and inputs a conversion result into the CNN voiceprint feature extraction subunit, the CNN voiceprint feature extraction subunit extracts CNN parameters from the CNN parameter subunit, performs voiceprint feature extraction by combining the conversion result and inputs an extraction result into the voiceprint feature spectrum matching decoding subunit, and in the voiceprint feature spectrum matching decoding subunit, the extraction result and the user voiceprint features in the user voiceprint feature model library are matched, decoded and recognized, and the output of the recognition result is an identity recognition result.
Example 5
On the basis of the above embodiment, the connection mode of the communication management unit and the cloud server at least comprises one of the following modes, namely GSM, GPRS, 3G, 4G, WIFI, Bluetooth, NFC or wired connection communication mode.
Example 6
A method of data acquisition, the method performing the steps of: step 1: carrying out data acquisition and carrying out data processing on the acquired data; step 2: sending the processed data; the data processing comprises: sequentially carrying out unique attribute removal, missing value processing and abnormal value detection processing; carrying out protocol processing on the data after data preprocessing, so that the data after protocol processing are irrelevant pairwise, but original information can be kept; and scaling the data after the reduction processing to make the data fall into a specific interval.
In particular, the original data should be audited mainly from the aspects of completeness and accuracy. The integrity check mainly checks whether units or individuals to be checked have omission or not and whether all the check items or indexes are complete or not. The accuracy audit mainly comprises two aspects: firstly, whether the data material truly reflects the objective actual condition and whether the content accords with the reality is checked; and secondly, checking whether the data has errors or not, calculating whether the data is correct or not, and the like. The method for checking the data accuracy mainly comprises logic check and calculation check. The logical check mainly checks whether the data is in accordance with the logic, whether the content is reasonable and whether the items or the numbers have the phenomenon of mutual contradiction. The calculation check is to check whether each item of data in the questionnaire has errors in calculation results and calculation methods, and is mainly used for checking quantitative (numerical type) data.
For the second-hand data obtained through other channels, besides checking the integrity and accuracy of the second-hand data, the applicability and timeliness of the data should be emphasized. Second-hand data can come from a variety of sources, some of which may have been obtained through special investigation for a particular purpose or have been processed as required for a particular purpose. For the user, it should be clear first of all the source of the data, the aperture of the data and the related background data, so as to determine whether these data meet the needs of self-analysis and research, whether rework and so on are needed, and the hard cover can not be handled blindly. In addition, the timeliness of the data is also checked, and for some problems with strong timeliness, if the acquired data is too late, the significance of research may be lost. In general, the most up-to-date statistics should be used as much as possible. After the data is audited, the data is confirmed to be suitable for actual needs, and further processing and arrangement are necessary.
Example 7
On the basis of the above embodiment, when data acquisition is performed, the acquired data is: fingerprint data information or voiceprint data information or iris data information or face information; the method for acquiring the voiceprint data information comprises the following steps: carrying out short-time frame division on external sound, and reading in voice data according to frames; detecting the voice data, judging whether the voice data is a voice frame or a non-voice frame, if so, entering the next step, otherwise, ending the process; performing frequency conversion, namely performing Fast Fourier Transform (FFT) on the voice frame to form voice spectrum data; storing the voice frequency spectrum data in a matrix mode, wherein the row of the matrix is a time frame sequence, the column of the matrix is a frequency sequence, and the matrix is a two-dimensional time-frequency spectrogram; the time-frequency spectrogram is subjected to voiceprint feature extraction in CNN to obtain voiceprint features; carrying out voiceprint feature matching identification on the voiceprint features and the user voiceprint features; and outputting the recognition result.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional units, and in practical applications, the functions may be distributed by different functional units according to needs, that is, the units or steps in the embodiments of the present invention are further decomposed or combined, for example, the units in the foregoing embodiment may be combined into one unit, or may be further decomposed into multiple sub-units, so as to complete all or part of the functions described above. The names of the units and steps involved in the embodiments of the present invention are only for distinguishing the units or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage unit and the processing unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative elements, method steps, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the software elements, method steps, and corresponding programs may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus/unit.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (7)
1. A data acquisition system, characterized in that the system comprises: the system comprises a data acquisition node system, a data acquisition unit, a communication management unit and a data processing unit, wherein the data acquisition node system, the data acquisition unit, the communication management unit and the data processing unit are deployed at nodes of a cloud computing system; the data acquisition node system is connected with the data processing unit; the data acquisition unit section is in signal connection with the data processing unit; the node system is connected with a cloud server through the communication management unit; the data acquisition node system comprises a receiver and a transmitter, wherein the receiver is connected with the transmitter; the receiver is connected with the data processing unit; the transmitter is connected with the communication management unit; the communication management unit comprises a power supply system, and the power supply system comprises a power supply management chip and a power supply power control module; the power supply power control module comprises a POE power transformer and a photoelectric coupler, and the output end of the power supply management chip is connected with the input end of the POE power transformer; the output end of the photoelectric coupler is connected with the feedback end of the power management chip; the data acquisition unit comprises an information acquisition device and an information transmitter, the information acquisition device is connected with the information transmitter, and the information transmitter is used for transmitting the information acquired by the information acquisition device to the data processing unit.
2. The system of claim 1, wherein the data processing unit comprises: the device comprises a data preprocessing unit, a data specification unit and a data standardization unit; the data preprocessing is used for sequentially removing the unique attribute, processing the missing value and detecting the abnormal value from the data information; the data protocol unit is used for carrying out protocol processing on the data after data preprocessing, so that the data after protocol processing are irrelevant pairwise, but original information can be kept as much as possible; and the data standardization unit scales the data after the protocol processing according to a proportion so as to enable the data to fall into a small specific interval.
3. The system of claim 2, wherein the data acquisition unit is a biometric identification device; the biological characteristic recognition device is specifically a fingerprint module, or an iris monitoring module, or a face recognition module, or a voiceprint recognition module.
4. The system of claim 3, wherein the voiceprint recognition module comprises: the voice print recognition system comprises a spectrogram conversion subunit, a CNN (voice over Internet network) voiceprint feature extraction subunit, a CNN parameter subunit, a user voiceprint feature model library subunit and a voiceprint feature spectrum matching decoding subunit, wherein external sound is input into the spectrogram conversion subunit, the spectrogram conversion subunit converts the external sound and inputs a conversion result into the CNN voiceprint feature extraction subunit, the CNN voiceprint feature extraction subunit extracts CNN parameters from the CNN parameter subunit, performs voiceprint feature extraction by combining the conversion result and inputs an extraction result into the voiceprint feature spectrum matching decoding subunit, and in the voiceprint feature spectrum matching decoding subunit, the extraction result and the user voiceprint features in the user voiceprint feature model library are matched, decoded and recognized, and the output of the recognition result is an identity recognition result.
5. The system of claim 4, wherein the communication management unit is connected to the cloud server in a manner selected from the group consisting of GSM, GPRS, 3G, 4G, WIFI, Bluetooth, NFC, and wired communication.
6. A data acquisition method based on the system of one of claims 1 to 5, characterized in that the method performs the following steps: step 1: carrying out data acquisition and carrying out data processing on the acquired data; step 2: sending the processed data; the data processing comprises: sequentially carrying out unique attribute removal, missing value processing and abnormal value detection processing; carrying out protocol processing on the data after data preprocessing, so that the data after protocol processing are irrelevant pairwise, but original information can be kept; and scaling the data after the reduction processing to make the data fall into a specific interval.
7. The method of claim 6, wherein the data collected during the data collection is: fingerprint data information or voiceprint data information or iris data information or face information; the method for acquiring the voiceprint data information comprises the following steps: carrying out short-time frame division on external sound, and reading in voice data according to frames; detecting the voice data, judging whether the voice data is a voice frame or a non-voice frame, if so, entering the next step, otherwise, ending the process; performing frequency conversion, namely performing Fast Fourier Transform (FFT) on the voice frame to form voice spectrum data; storing the voice frequency spectrum data in a matrix mode, wherein the row of the matrix is a time frame sequence, the column of the matrix is a frequency sequence, and the matrix is a two-dimensional time-frequency spectrogram; the time-frequency spectrogram is subjected to voiceprint feature extraction in CNN to obtain voiceprint features; carrying out voiceprint feature matching identification on the voiceprint features and the user voiceprint features; and outputting the recognition result.
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