CN111291028A - High-speed industrial field oriented data acquisition system and method - Google Patents
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Abstract
The invention discloses a high-speed industrial field data acquisition-oriented system and a high-speed industrial field data acquisition-oriented method, wherein the system comprises an external interface matching module, an application data acquisition unit, a data classification extraction module, a data storage backup module and a server terminal, wherein the external interface matching module, the application data acquisition unit, the data classification extraction module, the data storage backup module and the server terminal are sequentially connected through an intranet; the method comprises the steps of connecting an external interface matching module with external equipment, collecting industrial equipment data by an application data collecting unit, monitoring data extracted by the application data collecting unit by a data classification extracting module and compared with rated data, storing all data by a central storage unit in a data storage backup module, actively requesting to be connected with a cloud server by a server terminal, and sending the data stored in the data storage backup module to the cloud server after the connection is successful.
Description
Technical Field
The invention relates to the field of industrial data, in particular to a high-speed industrial field data acquisition system and a high-speed industrial field data acquisition method.
Background
The data of the internet mainly come from network devices such as internet users and servers, and mainly include a large amount of text data, social data, multimedia data and the like, and the industrial data mainly comes from machine device data, industrial informatization data and industrial chain related data. From the type of data collection, not only basic data is covered, but also semi-structured user behavior data, reticular social relationship data, text or audio type user opinion and feedback data, periodic data collected by equipment and sensors, internet data obtained by a web crawler, and various types of data with potential significance in the future are gradually included.
Data acquisition is a difficult point in today's manufacturing industry. Production data of many enterprises mainly depend on traditional manual work mode, and artificial recording error and inefficiency appear easily in the acquisition process.
Some enterprises introduce related technical means and apply data acquisition systems, but because the systems themselves and the enterprises do not select the data acquisition systems most suitable for the enterprises, real-time, accurate and extensible management of information acquisition cannot be realized, and information fault occurs in each unit.
In the existing industrial data acquisition, only pure data is acquired and then transmitted into a cloud server, the data is redundant and repeated, so that the data volume is huge, systematic query cannot be performed, the bandwidth required by transmission is increased, and the cost for operating and maintaining the system is increased.
Disclosure of Invention
The invention aims to provide a high-speed industrial field data acquisition system and a high-speed industrial field data acquisition method, which are used for solving the problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
the system comprises an external interface matching module, an application data acquisition unit, a data classification extraction module, a data storage backup module and a server terminal, wherein the external interface matching module, the application data acquisition unit, the data classification extraction module, the data storage backup module and the server terminal are sequentially connected through an intranet.
According to the technical scheme: the external interface matching module comprises an adaptive interface sensing submodule and an industrial data authorization feedback submodule, the adaptive interface sensing submodule, the industrial data authorization feedback submodule and the application data acquisition unit are sequentially connected through an intranet, the adaptive interface sensing submodule is used for adapting to each external communication interface of the industrial equipment, and the industrial data authorization feedback submodule is used for enabling each adapted external communication interface to authorize data acquisition and simultaneously feed back data acquisition feedback data to the corresponding industrial equipment.
According to the technical scheme: the application data acquisition unit comprises an industrial equipment operation time acquisition submodule, an industrial equipment operation frequency acquisition submodule and an industrial equipment operation interval acquisition submodule, the industrial equipment operation time acquisition submodule, the industrial equipment operation frequency acquisition submodule and the industrial equipment operation interval acquisition submodule are respectively connected with the data classification extraction module through an internal network, the industrial equipment operation time acquisition submodule is used for acquiring the use time of the industrial equipment, the industrial equipment operation frequency acquisition submodule is used for acquiring the working frequency of external industrial equipment, and the industrial equipment operation interval acquisition submodule is used for acquiring the operation interval time of the external industrial equipment.
According to the technical scheme: the data classification extraction module comprises a normal data extraction submodule, an abnormal data extraction submodule and a data attribute classification submodule, the normal data extraction submodule and the abnormal data extraction submodule are respectively connected with the application data acquisition unit through an intranet, the data attribute classification submodule is respectively connected with the abnormal data extraction submodule and the application data acquisition unit through the intranet, the data classification extraction module is used for monitoring the comparison between the extracted data of the application data acquisition unit and rated data, the normal data extraction submodule is used for classifying the extracted normal data, the abnormal data extraction submodule is used for classifying the extracted data which shows abnormality, and the data attribute classification submodule is used for judging the extracted abnormal data attribute and analyzing the error value of the abnormal data.
According to the technical scheme: the data storage backup module comprises a central storage unit and a backup library, the central storage unit is used for storing all data, the backup library is used for backing up the stored data, and the central storage unit, the backup library and the data classification extraction module are connected through an intranet.
According to the technical scheme: the server terminal is used for actively requesting connection with the cloud server and sending the data stored in the data storage backup module to the cloud server after the connection is successful.
A high-speed industrial field oriented data acquisition method comprises the following steps:
s1: the industrial data authorization feedback sub-module enables each adaptive external communication interface to authorize data acquisition and simultaneously feeds back data acquisition feedback data to the corresponding industrial equipment;
s2: the industrial equipment operation time acquisition submodule acquires the working frequency of external industrial equipment, and the industrial equipment operation interval acquisition submodule acquires the operation interval time of the external industrial equipment;
s3: the data classification and extraction module is used for comparing extracted data of the application data acquisition unit with rated data for monitoring, the normal data extraction submodule classifies the extracted normal data, the abnormal data extraction submodule classifies the extracted data which shows abnormality, and the data attribute classification submodule judges the extracted abnormal data attribute and analyzes the error value of the abnormal data;
s4: storing all data by using a central storage unit in a data storage backup module, and backing up the stored data by using a backup library;
s5: and actively requesting to be connected with the cloud server by using the server terminal, and sending the data stored in the data storage backup module to the cloud server after the connection is successful.
According to the technical scheme: in step S3, the data classification and extraction module is used to compare the extracted data in the application data acquisition unit with the rated data for monitoring, the normal data extraction submodule classifies the extracted normal data, the abnormal data extraction submodule classifies the extracted data showing abnormality, and the data attribute classification submodule judges the extracted abnormal data attribute and analyzes the error value of the abnormal data, and the method further includes the following steps:
a1: extracting the abnormal data by using an abnormal data extraction submodule, and sending the extracted abnormal data to a data attribute classification submodule;
a2: judging the attribute of the abnormal data by using a data attribute classification submodule so as to automatically generate an error rate of the abnormal data;
a3: and analyzing the error rate of the abnormal data, and marking the analysis result.
According to the technical scheme: in step a3, the error rate of the abnormal data is analyzed, and the analysis result is marked, further comprising the following steps:
the data attribute classification submodule automatically calculates error rates of abnormal data by comparing with rated data, wherein the generated data error rates are respectively Z1、Z2、Z3、Z4、…、Zn-1、ZnSetting the standard interval value of the error rate as C and the rated error rate as W, and satisfying the following formula:
when the extracted abnormal data simultaneously satisfy the formula, the error rate is judged to be within the controllable range and does not satisfy the formula, the error rate of the abnormal data is marked in a key mode, and the server terminal sends the error rate to an external operator for processing.
Compared with the prior art, the invention has the beneficial effects that: the invention can screen and classify the industrial data, and is convenient for storing and sorting the data;
the industrial data authorization feedback sub-module enables each adaptive external communication interface to authorize data acquisition and simultaneously feeds back data acquisition feedback data to the corresponding industrial equipment;
the industrial equipment operation time acquisition submodule acquires the working frequency of external industrial equipment, and the industrial equipment operation interval acquisition submodule acquires the operation interval time of the external industrial equipment;
the data classification and extraction module is used for comparing extracted data of the application data acquisition unit with rated data for monitoring, the normal data extraction submodule classifies the extracted normal data, the abnormal data extraction submodule classifies the extracted data which shows abnormality, and the data attribute classification submodule judges the extracted abnormal data attribute and analyzes the error value of the abnormal data; extracting the abnormal data by using an abnormal data extraction submodule, and sending the extracted abnormal data to a data attribute classification submodule; judging the attribute of the abnormal data by using a data attribute classification submodule so as to automatically generate an error rate of the abnormal data; analyzing error rate of abnormal data, and marking the analysis result
Storing all data by using a central storage unit in a data storage backup module, and backing up the stored data by using a backup library;
and actively requesting to be connected with the cloud server by using the server terminal, and sending the data stored in the data storage backup module to the cloud server after the connection is successful.
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In order that the present invention may be more readily and clearly understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
FIG. 1 is a schematic diagram of a module structure of a high-speed industrial field data acquisition system according to the present invention;
FIG. 2 is a schematic diagram of the steps of a high-speed industrial field-oriented data acquisition method according to the present invention;
FIG. 3 is a detailed schematic diagram of step S3 of the high-speed industrial field data acquisition method according to the present invention;
fig. 4 is a schematic diagram of an implementation method for a high-speed industrial field data acquisition method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 4, in an embodiment of the present invention, a high-speed industrial field-oriented data acquisition system and a method thereof include an external interface matching module, an application data acquisition unit, a data classification extraction module, a data storage backup module, and a server terminal, where the external interface matching module, the application data acquisition unit, the data classification extraction module, the data storage backup module, and the server terminal are sequentially connected via an intranet.
According to the technical scheme: the external interface matching module comprises an adaptive interface sensing submodule and an industrial data authorization feedback submodule, the adaptive interface sensing submodule, the industrial data authorization feedback submodule and the application data acquisition unit are sequentially connected through an intranet, the adaptive interface sensing submodule is used for adapting to each external communication interface of the industrial equipment, and the industrial data authorization feedback submodule is used for enabling each adapted external communication interface to authorize data acquisition and simultaneously feed back data acquisition feedback data to the corresponding industrial equipment.
According to the technical scheme: the application data acquisition unit comprises an industrial equipment operation time acquisition submodule, an industrial equipment operation frequency acquisition submodule and an industrial equipment operation interval acquisition submodule, the industrial equipment operation time acquisition submodule, the industrial equipment operation frequency acquisition submodule and the industrial equipment operation interval acquisition submodule are respectively connected with the data classification extraction module through an internal network, the industrial equipment operation time acquisition submodule is used for acquiring the use time of the industrial equipment, the industrial equipment operation frequency acquisition submodule is used for acquiring the working frequency of external industrial equipment, and the industrial equipment operation interval acquisition submodule is used for acquiring the operation interval time of the external industrial equipment.
According to the technical scheme: the data classification extraction module comprises a normal data extraction submodule, an abnormal data extraction submodule and a data attribute classification submodule, the normal data extraction submodule and the abnormal data extraction submodule are respectively connected with the application data acquisition unit through an intranet, the data attribute classification submodule is respectively connected with the abnormal data extraction submodule and the application data acquisition unit through the intranet, the data classification extraction module is used for monitoring the comparison between the extracted data of the application data acquisition unit and rated data, the normal data extraction submodule is used for classifying the extracted normal data, the abnormal data extraction submodule is used for classifying the extracted data which shows abnormality, and the data attribute classification submodule is used for judging the extracted abnormal data attribute and analyzing the error value of the abnormal data.
According to the technical scheme: the data storage backup module comprises a central storage unit and a backup library, the central storage unit is used for storing all data, the backup library is used for backing up the stored data, and the central storage unit, the backup library and the data classification extraction module are connected through an intranet.
According to the technical scheme: the server terminal is used for actively requesting connection with the cloud server and sending the data stored in the data storage backup module to the cloud server after the connection is successful.
A high-speed industrial field oriented data acquisition method comprises the following steps:
s1: the industrial data authorization feedback sub-module enables each adaptive external communication interface to authorize data acquisition and simultaneously feeds back data acquisition feedback data to the corresponding industrial equipment;
s2: the industrial equipment operation time acquisition submodule acquires the working frequency of external industrial equipment, and the industrial equipment operation interval acquisition submodule acquires the operation interval time of the external industrial equipment;
s3: the data classification and extraction module is used for comparing extracted data of the application data acquisition unit with rated data for monitoring, the normal data extraction submodule classifies the extracted normal data, the abnormal data extraction submodule classifies the extracted data which shows abnormality, and the data attribute classification submodule judges the extracted abnormal data attribute and analyzes the error value of the abnormal data;
s4: storing all data by using a central storage unit in a data storage backup module, and backing up the stored data by using a backup library;
s5: and actively requesting to be connected with the cloud server by using the server terminal, and sending the data stored in the data storage backup module to the cloud server after the connection is successful.
According to the technical scheme: in step S3, the data classification and extraction module is used to compare the extracted data in the application data acquisition unit with the rated data for monitoring, the normal data extraction submodule classifies the extracted normal data, the abnormal data extraction submodule classifies the extracted data showing abnormality, and the data attribute classification submodule judges the extracted abnormal data attribute and analyzes the error value of the abnormal data, and the method further includes the following steps:
a1: extracting the abnormal data by using an abnormal data extraction submodule, and sending the extracted abnormal data to a data attribute classification submodule;
a2: judging the attribute of the abnormal data by using a data attribute classification submodule so as to automatically generate an error rate of the abnormal data;
a3: and analyzing the error rate of the abnormal data, and marking the analysis result.
According to the technical scheme: in step a3, the error rate of the abnormal data is analyzed, and the analysis result is marked, further comprising the following steps:
the data attribute classification submodule automatically calculates error rates of abnormal data by comparing with rated data, wherein the generated data error rates are respectively Z1、Z2、Z3、Z4、…、Zn-1、ZnSetting the standard interval value of the error rate as C and the rated error rate as W, and satisfying the following formula:
when the extracted abnormal data simultaneously satisfy the formula, the error rate is judged to be within the controllable range and does not satisfy the formula, the error rate of the abnormal data is marked in a key mode, and the server terminal sends the error rate to an external operator for processing.
Example 1: and defining conditions, comparing the data attribute classification sub-module with rated data to automatically calculate error rates of abnormal data, wherein the generated data error rates are respectively 17%, 21%, 19%, 23% and 31%, the standard interval value of the error rate is set to be 20%, the rated error rate is set to be 35%, and according to the formula:
calculating to obtain:
judging that the error rate is within a controllable range, and not processing the abnormal data;
example 2: limiting conditions, data attribute classification submodule and rated data comparison automatic calculationError rates of the abnormal data are 7%, 16%, 21%, 27% and 32% respectively, wherein the standard interval value of the error rate is set to be 20%, the rated error rate is set to be 35%, and according to the formula:
calculating to obtain:
and the interval value of the error does not meet the condition, the error rate of the abnormal data is marked in a key mode, and the server terminal sends the error rate to an external operator for processing.
Example 3: and defining conditions, comparing the data attribute classification sub-module with rated data to automatically calculate error rates of abnormal data, and generating data error rates of 18%, 20%, 23%, 29% and 36% respectively, wherein the standard interval value of the error rates is set to be 20%, the rated error rate is set to be 35%, and according to the formula:
calculating to obtain:
and the error rate of the abnormal data is marked in a key way, and the server terminal sends the error rate to an external operator for processing.
Example 4: and defining conditions, comparing the data attribute classification sub-module with rated data to automatically calculate error rates of abnormal data, and generating data error rates of 6%, 21%, 46%, 37% and 31% respectively, wherein the standard interval value of the error rates is set to be 20%, the rated error rate is set to be 35%, and according to the formula:
calculating to obtain:
the interval value of error and rated error rate do not meet the above-mentioned condition, and the abnormal data is treatedThe error rate of the server is marked in a key way, and the server terminal sends the error rate to an external operator for processing.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (9)
1. The utility model provides a data acquisition system towards high-speed industry scene which characterized in that: the system comprises an external interface matching module, an application data acquisition unit, a data classification extraction module, a data storage backup module and a server terminal, wherein the external interface matching module, the application data acquisition unit, the data classification extraction module, the data storage backup module and the server terminal are sequentially connected through an intranet.
2. The high-speed industrial field oriented data acquisition system according to claim 1, wherein: the external interface matching module comprises an adaptive interface sensing submodule and an industrial data authorization feedback submodule, the adaptive interface sensing submodule, the industrial data authorization feedback submodule and the application data acquisition unit are sequentially connected through an intranet, the adaptive interface sensing submodule is used for adapting to each external communication interface of the industrial equipment, and the industrial data authorization feedback submodule is used for enabling each adapted external communication interface to authorize data acquisition and simultaneously feed back data acquisition feedback data to the corresponding industrial equipment.
3. The high-speed industrial field oriented data acquisition system according to claim 1, wherein: the application data acquisition unit comprises an industrial equipment operation time acquisition submodule, an industrial equipment operation frequency acquisition submodule and an industrial equipment operation interval acquisition submodule, the industrial equipment operation time acquisition submodule, the industrial equipment operation frequency acquisition submodule and the industrial equipment operation interval acquisition submodule are respectively connected with the data classification extraction module through an internal network, the industrial equipment operation time acquisition submodule is used for acquiring the use time of the industrial equipment, the industrial equipment operation frequency acquisition submodule is used for acquiring the working frequency of external industrial equipment, and the industrial equipment operation interval acquisition submodule is used for acquiring the operation interval time of the external industrial equipment.
4. The high-speed industrial field oriented data acquisition system according to claim 1, wherein: the data classification extraction module comprises a normal data extraction submodule, an abnormal data extraction submodule and a data attribute classification submodule, the normal data extraction submodule and the abnormal data extraction submodule are respectively connected with the application data acquisition unit through an intranet, the data attribute classification submodule is respectively connected with the abnormal data extraction submodule and the application data acquisition unit through the intranet, the data classification extraction module is used for monitoring the comparison between the extracted data of the application data acquisition unit and rated data, the normal data extraction submodule is used for classifying the extracted normal data, the abnormal data extraction submodule is used for classifying the extracted data which shows abnormality, and the data attribute classification submodule is used for judging the extracted abnormal data attribute and analyzing the error value of the abnormal data.
5. The high-speed industrial field oriented data acquisition system according to claim 1, wherein: the data storage backup module comprises a central storage unit and a backup library, the central storage unit is used for storing all data, the backup library is used for backing up the stored data, and the central storage unit, the backup library and the data classification extraction module are connected through an intranet.
6. The high-speed industrial field oriented data acquisition system according to claim 1, wherein: the server terminal is used for actively requesting connection with the cloud server and sending the data stored in the data storage backup module to the cloud server after the connection is successful.
7. A high-speed industrial field oriented data acquisition method is characterized in that:
s1: the industrial data authorization feedback sub-module enables each adaptive external communication interface to authorize data acquisition and simultaneously feeds back data acquisition feedback data to the corresponding industrial equipment;
s2: the industrial equipment operation time acquisition submodule acquires the working frequency of external industrial equipment, and the industrial equipment operation interval acquisition submodule acquires the operation interval time of the external industrial equipment;
s3: the data classification and extraction module is used for comparing extracted data of the application data acquisition unit with rated data for monitoring, the normal data extraction submodule classifies the extracted normal data, the abnormal data extraction submodule classifies the extracted data which shows abnormality, and the data attribute classification submodule judges the extracted abnormal data attribute and analyzes the error value of the abnormal data;
s4: storing all data by using a central storage unit in a data storage backup module, and backing up the stored data by using a backup library;
s5: and actively requesting to be connected with the cloud server by using the server terminal, and sending the data stored in the data storage backup module to the cloud server after the connection is successful.
8. The high-speed industrial field-oriented data acquisition method according to claim 7, characterized in that: in step S3, the data classification and extraction module is used to compare the extracted data in the application data acquisition unit with the rated data for monitoring, the normal data extraction submodule classifies the extracted normal data, the abnormal data extraction submodule classifies the extracted data showing abnormality, and the data attribute classification submodule judges the extracted abnormal data attribute and analyzes the error value of the abnormal data, and the method further includes the following steps:
a1: extracting the abnormal data by using an abnormal data extraction submodule, and sending the extracted abnormal data to a data attribute classification submodule;
a2: judging the attribute of the abnormal data by using a data attribute classification submodule so as to automatically generate an error rate of the abnormal data;
a3: and analyzing the error rate of the abnormal data, and marking the analysis result.
9. The high-speed industrial field-oriented data acquisition method according to claim 8, characterized in that: in step a3, the error rate of the abnormal data is analyzed, and the analysis result is marked, further comprising the following steps:
the data attribute classification submodule automatically calculates error rates of abnormal data by comparing with rated data, wherein the generated data error rates are respectively Z1、Z2、Z3、Z4、…、Zn-1、ZnSetting the standard interval value of the error rate as C and the rated error rate as W, and satisfying the following formula:
when the extracted abnormal data simultaneously satisfy the formula, the error rate is judged to be within the controllable range and does not satisfy the formula, the error rate of the abnormal data is marked in a key mode, and the server terminal sends the error rate to an external operator for processing.
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| CN111726178B (en) * | 2020-07-02 | 2021-09-21 | 深圳市友恺通信技术有限公司 | Data transmission signal detection system and method based on radio module |
| CN112597139A (en) * | 2020-12-21 | 2021-04-02 | 江苏省未来网络创新研究院 | Data information acquisition method based on industrial internet |
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