CN108564260A - Appraisal procedure for industrial process mass data processing and storage - Google Patents
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Abstract
The invention discloses a kind of appraisal procedures for industrial process mass data processing and storage, it includes S1：Structured modeling is carried out to industrial data acquisition system；S2：The expected performance index of industrial data processing and storing process is calculated according to model；S3：Establish the strategy of mass data processing layer, transport layer and accumulation layer；S4：Procedure parameter related with performance indicator is extracted from database；S5：Calculate actual performance index；S6：Expected performance index and actual performance index are compared, industrial process properties are assessed.The advantageous effect that the present invention obtains is：By the assessment to properties, the efficiency of entire data acquisition storing process is improved after contributing to and saves memory space.
Description
Technical field
It is especially a kind of for industrial process sea the present invention relates to computer data acquiring, transimission and storage technical field
Measure the appraisal procedure of data processing and storage.
Background technology
In industrial Internet of Things, diversified sensor is widely deployed in industrial production environment, large scale industry data
Acquisition system requires data sampling and processing and storage to have high concurrent and high real-time.Traditional industrial data acquisition system
It needs to spend more resource and time when extracting mass data, processing data, and data storage efficiency is relatively low, number
There are problems that data fusion ability, expansion capability, general-purpose capability and flexibility shortcoming according to acquisition system, these all reduce whole
The performance indicator of a system, while increasing the operation load of system.
In following industrial data acquisition system, trend below will produce：(1) need the data volume acquired huge；
(2) need the data class acquired various；(3) by mass data critical-path analysis, and result is fed back in production.For existing solid
The data acquisition system of mould-fixed to the acquisition of the magnanimity of data record high concurrent and data, processing and storage capacity not
Foot, can by adjusting industrial process mass data processing, transimission and storage during links different mechanisms come into
Row optimization, makes industrial mass data collection system more flexibly be applicable in.
Therefore, the optimization for the mass data collection process based on industry spot that there is an urgent need for a kind of executing evaluation method.
Invention content
In view of the drawbacks described above of the prior art, it is an object of the invention to provide one kind being used for industrial process mass data
The appraisal procedure of processing and storage is helped to improve the efficiency of entire data acquisition storing process and saved and deposited by assessment
Store up space.
Realized the purpose of the present invention is technical solution in this way, one kind for industrial process mass data processing and
The appraisal procedure of storage, is as follows：
S1：Structured modeling is carried out to industrial data acquisition system；
S2：The expected performance index of industrial data processing and storing process is calculated according to model；
S3：Establish the strategy of mass data processing layer, transport layer and accumulation layer；
S4：Procedure parameter related with performance indicator is extracted from database；
S5：Calculate actual performance index；
S6：Expected performance index and actual performance index are compared, data processing and memory mechanism are assessed.
Further, the step S1 is as follows：
S11：Define the critical processes in industrial process data collecting flowchart；
S12：Specify the time between industrial process data collecting flowchart and spatial relationship；
S13：Each critical processes in data processing, transimission and storage are modeled respectively.
Further, the step S2 is as follows：
S21：Determining influences the property indices of industrial process data processing and storage；
S22：Key parameter is optimized and extracted by the model to critical processes, according to key parameter and performance indicator
Between mathematical formulae calculate expected performance index, or directly define expected performance index according to demand.
Further, the strategy that mass data processing layer is established in the step S3 is as follows：
S31：Judge the data type received from each sensor, after being classified by common industrial data structure by
Distributed treatment；
S32：According to the data format requirement of industry spot, the place of initial data and industrial process reference standard data is established
Reason rule, is arranged the standard output format of different classes of data；
S33：Magnanimity initial data in industrial system is subjected to data correctness test, data filtering, data category set
At the output of, Data Format Transform.
Further, the strategy that mass data distributed transmission layer is established in the step S3 is as follows：
S34：Judge that the data-signal type of industrial data, digital signal distribution base band mode are transmitted；Analog signal is then divided
It is transmitted with wide band system；
S35：The data bit size for judging industrial data, sets the threshold value of data bit, and data bit uses if being more than threshold value
Parallel transmission mode；Otherwise serial transmission mode is used；
S36：Judge the transmission direction of industrial data, the unidirectional then selection simplex communication of flow direction of data；The stream of data
To transmitting in two directions, full-duplex communication is selected.
Further, the strategy that mass data distributed storage layer is established in the step S3 is as follows：
S37：Judge that the size of different classes of industrial data amount, data volume then suitably extend greatly storage space；Otherwise it fits
When reduction storage space；
S38：The logical construction for establishing each database for storing data, is arranged storage organization and the side of database
Method；
S39：It establishes distributed relation database and stores different classes of data.
Further, the step S4 includes：
S41：Definition mining target, i.e., with the relevant procedure parameter of industrial process；
S42：Data in database are screened, determine key parameter；
S43：It is different according to structures such as the correlation rule of different classes of data, classification prediction, clustering, time series patterns
Data mining model；
S44：Procedure parameter is extracted according to key parameter；
S45：The database for establishing procedure parameter, for evaluating mass data collection storing process in industrial process.
Further, real process performance indicator is calculated in the step S5 to be as follows：
S51：The extraction process parameter from the process parameter data library in step S45；
S52：Actual performance index is calculated according to the mathematical relationship between procedure parameter and performance indicator.
Further, the appraisal procedure further includes having step 7), and the result assessed according to step 6) adjusts next period
Data processing and memory mechanism, until industrial data acquisition system properties reach requirement.
Further, the step S7 includes：
S71：If performance indicator difference=actual performance index-expected performance index；Actual performance index=J ', it is expected that property
Energy index=J, performance indicator difference=Δ J, Δ J=J '-J, performance indicator difference are vector；
S72：The required precision for each flow for being handled and being stored according to industrial data sets property indices phasor difference
The threshold value of Δ J1, Δ J2, Δ J3 ... Δs Jn；
S73：It sets industrial process and adjusts the period, the performance indicator for calculating an Industry restructuring period is poor, if a certain performance
Index error is more than threshold value, then related industrial flow implementation meets condition to explanation with performance indicator difference；
S74：If a certain performance indicator difference is less than setting value, the related industry with performance indicator difference of next period is adjusted
The relevant tupe of flow and mechanism；
S75：By adjustment the period repeat step S3-S7, improve data analysis layer, transport layer and accumulation layer strategy, until
All actual performance index errors are met the requirements.
By adopting the above-described technical solution, the present invention has the advantage that：The present invention can improve entire data and adopt
The efficiency for collecting storing process by the orderly classification of data, is excavated, and saves memory space, and magnanimity number in industrial process is optimized
According to acquisition storing process, prepare for next data analysis.
Other advantages, target and the feature of the present invention will be illustrated in the following description to a certain extent, and
And to a certain extent, based on will be apparent to those skilled in the art to investigating hereafter, Huo Zheke
To be instructed from the practice of the present invention.The target and other advantages of the present invention can be wanted by following specification and right
Book is sought to realize and obtain.
Description of the drawings
The description of the drawings of the present invention is as follows：
Fig. 1 is the flow diagram of the present invention.
Specific implementation mode
The invention will be further described with reference to the accompanying drawings and examples.
Embodiment：As shown in Figure 1；A kind of appraisal procedure for industrial process mass data processing and storage, with wind-force
For parameter acquisition process as example, specific implementation step is as follows during power-plant operation：
S1：PMSG permanent magnet synchronous wind generator equipment is chosen to be modeled as parameter acquisition system；
Step S1 is as follows：Determine the critical processes in wind power equipment data acquisition, i.e. initial data
Each apparatus from wind power plant, specifies the time between parameter acquisition flow and spatial relationship, can utilize imitative
True software is to the transmission process of the universal transport interface in parameter acquisition, signal condition, digital-to-analogue conversion process and the parameter of parameter
Storing process carries out simulation modeling.
S2：The expected performance index of industrial data processing and storing process is calculated according to model；
Step S2 is as follows：The property indices for determining affecting parameters data processing and storage, by right
The model of above-mentioned critical processes optimizes and extracts characteristic, constitutes the formula of mathematical between performance indicator, meter
Expected performance index is calculated, or directly defines expected performance index according to demand.
For the performance indicator of the wind power equipment data collecting system generally comprise precision, noise, resolution ratio, sample rate,
Non-linearization, transmission speed, memory capacity, storage speed.
S3：Establish the strategy of mass data processing layer, transport layer and accumulation layer；
(a) strategy for establishing mass data processing is as follows：
S31：Judge the data type received from each sensor, after being classified by common industrial data structure by
Distributed treatment, will physically disperse and identical mass data is concentrated and is uniformly processed in real time again in logic, and wind-powered electricity generation is set
The data type of standby acquisition has generally comprised wind speed, angle, switching value, pressure, temperature, electric current, voltage and power etc.；
S32：According to the data format requirement at wind power generating set scene, initial data and industrial process reference standard are established
The processing rule of data, is arranged the standard output format of different classes of data；
S33：Collected magnanimity initial data is subjected to data correctness test, data filtering, data classification ensemble, number
It is exported according to format conversion.
(b) strategy for establishing mass data transfers is as follows：
S34：Judge the data-signal type of industrial data, such as switching value (0 or 1) is digital signal, then directly distributes
Base band mode is transmitted；Electric current, voltage are analog signal, then distribute wide band system transmission；It can be upper in this acquisition system
Distribution base band mode is transmitted after analog signal is carried out digital-to-analogue conversion by the Data processing stated by certain method, then in computer
Middle progress DA conversions show its electric current, voltage value；
S35：Judge the data bit size of Various types of data, such as the threshold value for setting data bit is counted as sixteen-bit binary number
Parallel transmission mode is used if being more than threshold value according to position；Otherwise serial transmission mode is used；
S36：Judge the transmission direction of Various types of data, the unidirectional then selection simplex communication of flow direction of data；The stream of data
To transmitting in two directions, full-duplex communication is selected.It should be noted that some parameters with controlling are after treatment
It can feed back on certain components and act, then must use full-duplex communication.
(c) strategy for establishing mass data storage is as follows：
S37：Judge that the size of different classes of industrial data amount, data volume then suitably extend greatly storage space；Otherwise it fits
When reduction storage space；
S38：The logical construction for establishing each database for storing data, is arranged storage organization and the side of database
Method；Certain data of wind power plant are continuous and carry timing, need to consider when memory module is arranged；
S39：It establishes distributed relation database and stores different classes of data.
S4：Procedure parameter related with performance indicator is extracted from database；
Step S4 includes：S41：Definition mining target, i.e., with the relevant process of wind power equipment acquisition system performance indicator
Parameter；
S42：Various types of data in database is screened, determines key parameter；
S43：It is different according to structures such as the correlation rule of different classes of data, classification prediction, clustering, time series patterns
Data mining model；
S44：Procedure parameter is extracted according to key parameter；
S45：Establish the database of procedure parameter so as to subsequent extracted useful information evaluate industrial process in mass data adopt
Collect storing process.
S5：Calculate actual performance index；
S51：The extraction process parameter from the process parameter data library in step S45；
S52：Actual performance index is calculated according to the mathematical relationship between procedure parameter and performance indicator.
S6：Expected performance index and actual performance index are compared, data processing and memory mechanism are assessed
S7：Data processing and the memory mechanism for adjusting next period, until industrial data acquisition system properties reach
It is required that；
Step S7 includes：S71：The concept for introducing performance indicator difference, if actual performance index=J ', expected performance index
=J, performance indicator difference=Δ J, Δ J=J '-J, performance indicator difference are vector；
S72：The required precision for each flow for being handled and being stored according to industrial data sets property indices phasor difference
The threshold value of Δ J1, Δ J2, Δ J3 ... Δs Jn；If desired data transmission bauds is 400KB/S, data transmission bauds is set
Performance indicator difference Δ J=-30；
S73：It sets industrial process and adjusts the period, the performance indicator for calculating an Industry restructuring period is poor, i.e. real data
Transmission speed must not be less than 370KB/S.If a certain performance indicator difference is greater than the set value, actual data transfer speed is 396KB/S,
Δ J=-6 ＞ -30.Then related industrial flow implementation meets condition to explanation with performance indicator difference；
S74：If a certain performance indicator difference is less than setting value, i.e. actual transmission speed is 285KB/S, Δ J=-115 ＜-
30, then adjust the relevant tupe of industrial flow related with transmission speed this performance indicator difference of next period and mechanism；
S75：By adjustment the period repeat step S3-S7, improve data analysis layer, transport layer and accumulation layer strategy, until
All properties index error is met the requirements.
It should be understood that the part that this specification does not elaborate belongs to the prior art.
Finally illustrate, the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although with reference to compared with
Good embodiment describes the invention in detail, it will be understood by those of ordinary skill in the art that, it can be to the skill of the present invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of the technical program, should all be covered in the present invention
Right in.
Claims (10)
1. a kind of appraisal procedure for industrial process mass data processing and storage, which is characterized in that the method step is such as
Under：
S1：Structured modeling is carried out to industrial data acquisition system；
S2：The expected performance index of industrial data processing and storing process is calculated according to model；
S3：Establish the strategy of mass data processing layer, transport layer and accumulation layer；
S4：Procedure parameter related with performance indicator is extracted from database；
S5：Calculate actual performance index；
S6：Expected performance index and actual performance index are compared, data processing and memory mechanism are assessed.
2. being used for the appraisal procedure of industrial process mass data processing and storage as described in claim 1, which is characterized in that institute
Step S1 is stated to be as follows：
S11：Define the critical processes in industrial process data collecting flowchart；
S12：Specify the time between industrial process data collecting flowchart and spatial relationship；
S13：Each critical processes in data processing, transimission and storage are modeled respectively.
3. being used for the appraisal procedure of industrial process mass data processing and storage as described in claim 1, which is characterized in that institute
Step S2 is stated to be as follows：
S21：Determining influences the property indices of industrial process data processing and storage；
S22：Key parameter is optimized and extracted by the model to critical processes, according between key parameter and performance indicator
Mathematical formulae calculate expected performance index, or directly define expected performance index according to demand.
4. being used for the appraisal procedure of industrial process mass data processing and storage as described in claim 1, which is characterized in that institute
It states and establishes the strategy of mass data processing layer in step S3 and be as follows：
S31：The data type received from each sensor is judged, by distribution after being classified by common industrial data structure
Formula processing；
S32：According to the data format requirement of industry spot, the processing for establishing initial data and industrial process reference standard data is advised
Then, the standard output format of different classes of data is set；
S33：Magnanimity initial data in industrial system is subjected to data correctness test, data filtering, data classification ensemble, number
It is exported according to format conversion.
5. being used for the appraisal procedure of industrial process mass data processing and storage as described in claim 1, which is characterized in that institute
It states and establishes the strategy of mass data distributed transmission layer in step S3 and be as follows：
S34：Judge that the data-signal type of industrial data, digital signal distribution base band mode are transmitted；Analog signal then distributes width
Band mode is transmitted；
S35：The data bit size for judging industrial data sets the threshold value of data bit, and data bit is if parallel more than being used if threshold value
Transmission mode；Otherwise serial transmission mode is used；
S36：Judge the transmission direction of industrial data, the unidirectional then selection simplex communication of flow direction of data；The flow direction of data exists
It is transmitted in both direction, selects full-duplex communication.
6. being used for the appraisal procedure of industrial process mass data processing and storage as described in claim 1, which is characterized in that institute
It states and establishes the strategy of mass data distributed storage layer in step S3 and be as follows：
S37：Judge that the size of different classes of industrial data amount, data volume then suitably extend greatly storage space；Otherwise suitably subtract
Small storage space；
S38：The logical construction for establishing each database for storing data, is arranged the storage organization and method of database；
S39：It establishes distributed relation database and stores different classes of data.
7. being used for the appraisal procedure of industrial process mass data processing and storage as described in claim 1, which is characterized in that institute
Stating step S4 includes：
S41：Definition mining target, i.e., with the relevant procedure parameter of industrial process；
S42：Data in database are screened, determine key parameter；
S43：According to different data of structure such as the correlation rule of different classes of data, classification prediction, clustering, time series patterns
Mining model；
S44：Procedure parameter is extracted according to key parameter；
S45：The database for establishing procedure parameter, for evaluating mass data collection storing process in industrial process.
8. being used for the appraisal procedure of industrial process mass data processing and storage as claimed in claim 7, which is characterized in that institute
Calculating real process performance indicator in step S5 is stated to be as follows：
S51：The extraction process parameter from the process parameter data library in step S45；
S52：Actual performance index is calculated according to the mathematical relationship between procedure parameter and performance indicator.
9. being used for the appraisal procedure of industrial process mass data processing and storage as described in claim 1, which is characterized in that institute
It further includes having step 7) to state appraisal procedure, and the result assessed according to step 6) adjusts data processing and the storage machine in next period
System, until industrial data acquisition system properties reach requirement.
10. being used for the appraisal procedure of industrial process mass data processing and storage as claimed in claim 9, which is characterized in that
The step S7 includes：
S71：If performance indicator difference=actual performance index-expected performance index；Actual performance index=J ', expected performance refer to
Mark=J, performance indicator difference=Δ J, Δ J=J '-J, performance indicator difference are vector；
S72：The required precision setting property indices phasor difference Δ J1 for each flow for being handled and being stored according to industrial data,
The threshold value of Δ J2, Δ J3 ... Δs Jn；
S73：It sets industrial process and adjusts the period, the performance indicator for calculating an Industry restructuring period is poor, if a certain performance indicator
Difference is more than threshold value, then related industrial flow implementation meets condition to explanation with performance indicator difference；
S74：If a certain performance indicator difference is less than setting value, next period and the poor related industrial flow of the performance indicator are adjusted
Relevant tupe and mechanism；
S75：Repeat step S3-S7 by the adjustment period, improve data analysis layer, transport layer and accumulation layer strategy, until all
Actual performance index error is met the requirements.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109743356A (en) * | 2018-12-07 | 2019-05-10 | 南京邮电大学 | Industry internet collecting method and device, readable storage medium storing program for executing and terminal |
| CN113015942A (en) * | 2018-12-28 | 2021-06-22 | 西门子股份公司 | Data analysis method, device and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103400205A (en) * | 2013-07-30 | 2013-11-20 | 国家电网公司 | System and method for managing energy efficiency of power distribution network on basis of reactive optimized coordination control |
| CN105243285A (en) * | 2015-11-10 | 2016-01-13 | 广州西麦科技股份有限公司 | Big data health forecast system |
| CN105468756A (en) * | 2015-11-30 | 2016-04-06 | 浪潮集团有限公司 | Design and implementation method of mass data processing system |
| CN106874483A (en) * | 2017-02-20 | 2017-06-20 | 山东鲁能软件技术有限公司 | A kind of device and method of the patterned quality of data evaluation and test based on big data technology |
-
2018
- 2018-03-29 CN CN201810272564.9A patent/CN108564260A/en not_active Withdrawn
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103400205A (en) * | 2013-07-30 | 2013-11-20 | 国家电网公司 | System and method for managing energy efficiency of power distribution network on basis of reactive optimized coordination control |
| CN105243285A (en) * | 2015-11-10 | 2016-01-13 | 广州西麦科技股份有限公司 | Big data health forecast system |
| CN105468756A (en) * | 2015-11-30 | 2016-04-06 | 浪潮集团有限公司 | Design and implementation method of mass data processing system |
| CN106874483A (en) * | 2017-02-20 | 2017-06-20 | 山东鲁能软件技术有限公司 | A kind of device and method of the patterned quality of data evaluation and test based on big data technology |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109743356A (en) * | 2018-12-07 | 2019-05-10 | 南京邮电大学 | Industry internet collecting method and device, readable storage medium storing program for executing and terminal |
| CN109743356B (en) * | 2018-12-07 | 2021-07-20 | 南京邮电大学 | Industrial internet data acquisition method and device, readable storage medium and terminal |
| CN113015942A (en) * | 2018-12-28 | 2021-06-22 | 西门子股份公司 | Data analysis method, device and system |
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Application publication date: 20180921 |