CN105023108B - A kind of acquisition methods and system to marking data - Google Patents
A kind of acquisition methods and system to marking data Download PDFInfo
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Abstract
The present invention provides a kind of acquisition methods and system to marking data, the method includes：Data acquisition module receive client by network transmission come Potency data, and be forwarded to data build module；Data build module and carry out persistence operation to the Potency data received；After Potency data runs up to certain amount grade, data structure module starts to classify to all data of persistence；It is that such Potency data is built accordingly to marking model according to the request after receiving the asking mark of the Potency data for a certain type that client is sent by data acquisition module；Data filtering module is sent to data computation analysis module according to marking after request is filtered operation to the Potency data of the type of persistence；Data computation analysis module carries out data training to the Potency data by filter operation received, therefrom rejects after being not suitable for the data to marking model, is obtained by calculating mark model to marking data using remaining Potency data.
Description
Technical field
The present invention relates to industrial circle more particularly to a kind of acquisition methods and system to marking data.
Background technology
With the fast development of Internet technology, data analysis causes the very big concern of information industry circle, main former
Because being that can be widely used there are mass data, and there is an urgent need to convert the data into useful information and knowledge.Together
Shi Qiye often collects a large amount of data, these data cover the important informations such as production, operation, buying, but due to letter
Breath overload and Un-structured, the policymaker of enterprise are unable to fully utilize these huge data resources, are only capable of using therein one
Fraction, this may lead to incorrect decision, or even decision error occur.And by data mining technology, enterprise has the ability completely
From immense data ocean, comprehensive and valuable information and knowledge are excavated, is used as decision support, and then formed
The exclusive competitive advantage of enterprise, the information and knowledge of acquisition can be widely applied to various applications, including：Equipment management, production control
System, market analysis, engineering design etc..
Each production equipment has the theoretical maximum production capacity of oneself, and it is no any dry to realize that this production capacity must assure that
It disturbs and mass loss.Certainly, this requirement can not possibly be reached in actual production, many factors can all lead to the drop of device efficiency
It is low, such as equipment fault, equipment adjustment, product specification replacement, the low, raw material quality of equipment performance performance etc..Existing production is set
Standby efficiency debugs substantially only through the experience or designing institute of engineer, can not understand industry general level, there are no
Method optimizes equipment by data analysis.
Problems solved by the invention is the level and industry for how understanding the production equipment of factory residing for the industry
Best level and average level reduce energy consumption to further increase the efficiency of production equipment.
Invention content
Present invention seek to address that problem as described above.It is an object of the present invention to provide in a kind of solution problem above
It is any one it is a kind of to mark data acquisition methods and system.Specifically, the present invention, which provides, to get to marking number
According to rear, according to the status of energy consumption for the current production equipment that the data fully understand.
According to the first aspect of the invention, the acquisition methods that the present invention provides a kind of to marking data, including：
Data acquisition module receive client by network transmission come Potency data, and be forwarded to data build module；
The data structure module carries out persistence operation to the Potency data received；It is run up in Potency data
After certain amount grade, the data structure module starts to classify to all data of persistence；Receiving the client
It is such efficiency according to the request after the asking mark of the Potency data for a certain type sent by data acquisition module
Data structure is accordingly to marking model；
Data filtering module is filtered operation according to described pair of mark request to the Potency data of the type of persistence
After be sent to data computation analysis module, the filter operation includes：Unified coding and denoising；
The data computation analysis module carries out data training to the Potency data by filter operation received, therefrom
It rejects after being not suitable for the data to marking model, using remaining Potency data, is calculated by described pair of mark model, obtained pair
Mark data.
Further, the method further includes：
Described pair obtained mark data result is changed by the data computation analysis module to be needed to show form to target,
The client is sent to be shown by the data acquisition module.
Further,
The data acquisition module is made of load-balanced server and more than one Web server；The load is equal
The Potency data is distributed to wherein by weighing apparatus after receiving the Potency data that the client is sent, according to preset distribution policy
One Web server.
Further,
The data structure module carries out persistence operation to the Potency data received, specifically includes：The number
Such as judge that the Potency data received is structural data according to structure module, is then persisted to the Potency data
MySQL database cluster, while detaching strategy according to principal and subordinate and detaching Potency data, master library provides insertion, deletes, update behaviour
Make, inquiry operation is provided from library；Such as judge that the Potency data received is unstructured data, then passes through the roads mongo
One be persisted to the Potency data by server in shard server clusters.
Further,
One be persisted to the Potency data by mongo routing servers in shard server clusters, tool
Body includes：
Two mapping relations are pre-saved in the mongo routing servers, the first mapping relations are key assignments (key)
The mapping relations in section and Potency data block (chunk), the second mapping relations are Potency data block (chunk) and place fragment section
The mapping relations of point (i.e. shard servers)；
The mongo routing servers are after receiving the Potency data that the data structure module is sent, according to predetermined calculation
Method calculates the Potency data, using the result being calculated as key assignments, according to first mapping relationship searching pair
The Potency data block answered recycles the Potency data agllutination found to close the corresponding shard clothes of second mapping relationship searching
The Potency data, is then persisted in the corresponding shard servers by business device.
Further, the method further includes：
When the Potency data to be persisted in corresponding shard servers, the mongo routing servers are also sentenced
Whether disconnected current Potency data block to be written is beyond restriction size；If it was exceeded, being divided to row at two the Potency data block
It is written again after Potency data block.
Further,
Described pair mark model calculation formula be：The sum of products of the yield of each product and the practical Potency data of the product
Divided by the sum of products of the yield of each product and the optimal Potency data of the product.
According to the present invention the and aspect, the acquisition system that the present invention provides a kind of to marking data, including：
Data acquisition module, for receive client by network transmission come Potency data, and be forwarded to data structure
Module；
The data build module, for carrying out persistence operation to the Potency data received；It is additionally operable to imitating
After energy data accumulation to certain amount grade, start to classify to all data of persistence；It is additionally operable to receiving the client
It is such effect according to the request after the asking mark of the Potency data for a certain type that end is sent by data acquisition module
Energy data structure is accordingly to marking model；
Data filtering module, for being filtered to the Potency data of the type of persistence according to described pair of mark request
Data computation analysis module is sent to after operation, the filter operation includes：Unified coding and denoising；
The data computation analysis module, for carrying out data instruction to the Potency data by filter operation received
Practice, therefrom reject after being not suitable for the data to marking model, using remaining Potency data, is counted by described pair of mark model
It calculates, obtains to marking data.
Further,
The data computation analysis module, which is additionally operable to described pair obtained mark data result being changed into, to be needed to target exhibition
Existing form, the client is sent to be shown by the data acquisition module.
Further,
The data acquisition module is made of load-balanced server and more than one Web server；The load is equal
Weighing apparatus is used for after receiving the Potency data that the client is sent, and is distributed to the Potency data according to preset distribution policy
One of Web server.
Further,
The data structure module is used to carry out persistence operation to the Potency data received, specifically includes：Institute
It is structural data to state the Potency data that data structure module is used to such as judge to receive, then holds the Potency data
Longization arrives MySQL database cluster, while detaching strategy according to principal and subordinate and detaching Potency data, and master library, which provides, to be inserted into, deletes, more
New operation provides inquiry operation from library；It is unstructured data to be additionally operable to the Potency data for judging to receive such as, then leads to
Cross one that the Potency data is persisted in shard server clusters by mongo routing servers.
Further,
Described pair mark model calculation formula be：The sum of products of the yield of each product and the practical Potency data of the product
Divided by the sum of products of the yield of each product and the optimal Potency data of the product.
The present invention is based on Internet technology, in conjunction with industrial equipment relevant feature, solves in the past excellent to equipment effectiveness
The problem of change is had no way of doing it realizes the same industry to mark, tracks the data of links in production (such as：Energy consumption, yield, power generation
Amount, running rate, loss etc.), according to the algorithm integrated in system, the status of energy consumption of current device is predicted, it accordingly can be dynamic
Optimize the production schedule, and production task can be prepared parallel, to greatest extent using production production capacity, to improve the yield in workshop
And production efficiency；All data in relation to producing can be all stored in database, and data can be called, analyze at any time；And
And the decision of management will be more support with data, to eliminate way to manage by rule of thumb, management be made more to have science
Property.
Being described below for exemplary embodiment is read with reference to the drawings, other property features of the invention and advantage will
It is apparent from.
Description of the drawings
It is incorporated into specification and the attached drawing of a part for constitution instruction shows the embodiment of the present invention, and with
Principle for explaining the present invention together is described.In the drawings, similar reference numeral is for indicating similar element.Under
Attached drawing in the description of face is some embodiments of the present invention, rather than whole embodiments.Those of ordinary skill in the art are come
It says, it without creative efforts, can be obtain other attached drawings according to these attached drawings.
Fig. 1 schematically illustrates system architecture diagram；
Fig. 2 schematically illustrates the detail flowchart of system operation；
Specific implementation mode
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
The every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.It needs
Illustrate, in the absence of conflict, the features in the embodiments and the embodiments of the present application mutually can be combined arbitrarily.
This system be based on MySQL and mongoDB servers, using efficient Java programming languages, by efficient NIO frames
Frame realizes the major functions such as high speed data transfer analysis.This system can be divided into several function modules according to service logic, respectively
Data exchange can be effectively performed in mutual interface between module.As shown in Figure 1, this system includes mainly：Data acquisition module,
Data build module, data filtering module and data computation analysis module.It is fitted close between these modules, ensures in flow
The realization of creation data analysis system.In specific implementation, data acquisition module can by load-balanced server and one with
On Web server composition, data structure module, data filtering module and data computation analysis module can then be integrated in same
In application server.
As shown in Fig. 2, the detailed process of system operation includes：
Step 1：Data acquisition module receive client by network transmission come Potency data；
Wherein, Potency data can be entered by computer or mobile device in client by plant data acquisition personnel.
In specific implementation, data acquisition module can be made of load-balanced server and more than one Web server, load balancing
The Potency data is distributed to one of them by device after receiving the Potency data that client is sent, according to preset distribution policy
Web server, in this way can to avoid all Potency data concentration of transmissions to a server when caused data congestion problem.
Step 2：Data acquisition module is if it is judged that above-mentioned Potency data is structural data or unstructured data, then
The Potency data can be forwarded to data structure module；
Step 3：Data build module and carry out persistence operation to the Potency data received；
The process of data persistence is the process of data accumulation, wherein carries out the specific packet of persistence operation to Potency data
It includes：Such as judge that the Potency data received is structural data, then the Potency data can be persisted to MySQL database collection
Group, while detaching strategy according to principal and subordinate and detaching Potency data, master library provides insertion, deletes, update operation, and inquiry is provided from library
Operation, to ensure that read and write abruption reaches the best impact of performance；Such as judge that the Potency data received is unstructured data,
Can be then persisted to the Potency data by mongo routing servers in shard server clusters one.
In specific implementation, the Potency data is persisted in shard server clusters by mongo routing servers
One process include：Two mapping relations are pre-saved in mongo routing servers, the first mapping relations are key assignments
(key) mapping relations in section and Potency data block (chunk), the second mapping relations are Potency data block (chunk) and institute
In the mapping relations of fragment node (i.e. shard servers).Mongo routing servers are receiving the effect sent of data structure module
After energy data, the Potency data is calculated according to pre-defined algorithm (such as modulus rems), the result being calculated is made
The Potency data agllutination found is recycled to close second according to the corresponding Potency data block of the first mapping relationship searching for key assignments
Then the Potency data is persisted in the corresponding shard servers by the corresponding shard servers of mapping relationship searching.
In follow-up realize, such as receive the reading or the request for updating certain Potency data that data structure module is sent, then it can be according to phase
Same flow, in conjunction with reading the Potency data and anti-in the first mapping relations and the second mapping relations to corresponding shard servers
Feedback.
Preferably, when being persisted to Potency data in the corresponding shard servers, mongo routing servers are also
Current Potency data block (chunk) to be written can be judged whether beyond restriction size.If it was exceeded, by the Potency data block
It is divided to row at being written again after two Potency data blocks (chunk).
Step 4：After Potency data runs up to certain amount grade, data structure module starts all data to persistence
Classify；In asking mark for the Potency data for a certain type for receiving client and being sent by data acquisition module
Afterwards, it is built accordingly to mark model for such Potency data according to the request, such as is energy consumption, yield, productivity or loss structure
It builds to marking model；
Step 5：Data filtering module carried out the Potency data of the type of persistence mark request according to above-mentioned
It is sent to data computation analysis module after filter operation, the filter operation includes：Unified coding and denoising.Wherein：Unified coding
Refer to that the Potency data of coded format inconsistent (e.g., Gbk, Big5) is unified for a certain specific format (such as utf-8 formats)；It goes
It refers to determining the coding range for needing to handle word to make an uproar, and is carried out at division by incongruent data and as a segmentation boundary
Reason, word segmentation processing etc.；
Step 6：Data computation analysis module carries out data training to the Potency data by filter operation received, from
After middle rejecting is not suitable for the data to marking model, using remaining Potency data, mark model is calculated by above-mentioned, is obtained
To marking data, and result is changed into and needs to show form to target, browsing is finally presented in the form of chart or report
On device or mobile client.Wherein, as a result may include：Industry average potency level, the optimal level of performance of industry, this factory work as
Preceding level of performance etc..
For example, for firing system, need Potency data training extracting production line number, fuel by data training
Type, kiln diameter, kiln length, clinker process power consumption, clinker synthesis power consumption, sinter leaching heat consumption, previous year clinker, on
1 year rotary kiln running rate, firebrick loss, rotary kiln coefficient of reliability, 28 days intensity of clinker, clinker spare part maintenance cost or
These data formats such as waste heat power generation, to achieve the purpose that post-processing.Since system passes through Mysql and MongoDB data
Library stores data, and index data is retrieved by B-tree mode, but for the analysis of big data quantity, actual conditions
It is to be stored according to row.Data packet is made of the size separation that data are arranged according to 64K, and metadata is stored in Knowledge Grid,
When an inquiry, which is submitted, to be executed, the optimizer in data computation analysis module can be inquired by Knowledge Grid, from data
The data needed for query results are extracted in packet, according to marking model finally such as efficiency optimal value, the following efficiency desired value
Etc. data calculate.
Illustrate the calculation of energy consumption data with an example below.
In order to compare the energy consumption level in each cement plant, system needs to calculate one of cement plant to marking data, and formula is such as
Under：
Wherein EI is indicated to marking data, PiIndicate the yield of product i, (country is generally divided into three to the quantity of n expression products
Kind：32.5MPa、42.5MPa、52.5MPa.Foreign countries are generally divided into：OPC, SRC, PPC), EiIndicate the actual consumption of product i,
EiBPIndicate the optimal energy consumption of product i.
Mark data are indicated with the comparing result to the mark actual energy consumption index in cement plant and the energy consumption index in mark post cement plant.
According to definition, mark post cement plant is 1 to mark data, and practical cement plant to marking data in 0~1 range, index is bigger
Indicate that gap is smaller between mark post enterprise, therefore cement plant can judge the level residing for oneself, Yi Ji according to the index
The space that can be promoted in terms of efficiency.
Predict that the cement output in this year on the production line of one, cement plant, system use sorting algorithm, it is necessary first to from number
Include according to the data extracted in library：It is height above sea level where cement plant, design production capacity, kiln diameter, raw material milling process power consumption, broken
Broken process power consumption, mill types, cement milling process power consumption, cement output upper one year, ball consumption, firing heat consumption, clinker upper one year
Yield.After having extracted data, 4/5 using total data is used as inspection set as training set, 1/5, then uses C4.5 (a kind of
Data mining algorithm) algorithm progress model training.Model training reuses inspection data the set pair analysis model after terminating and is assessed,
The cement output in cement plant can be predicted if model accuracy is met the requirements.
The daily operation of production equipment can all generate a large amount of production management data, these data are huge for enterprise
Precious deposits, enterprise work personnel to these data carry out analyzing processing, it can be deduced that the much information in terms of production passes through
It is compared with industry average data, the support of data can be provided for workshop management.After mark is compared in data by efficiency,
The case where production information will be unimpeded, and enterprise can in time, accurately grasp production equipment, it is dynamic to adjust production management plan
Slightly.
Descriptions above can combine implementation individually or in various ways, and these variants all exist
Within protection scope of the present invention.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations.Although
Present invention has been described in detail with reference to the aforementioned embodiments, it will be understood by those of ordinary skill in the art that：It still may be used
With technical scheme described in the above embodiments is modified or equivalent replacement of some of the technical features；
And these modifications or replacements, various embodiments of the present invention technical solution that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (11)
1. a kind of acquisition methods to marking data, including：
Data acquisition module receive client by network transmission come Potency data, and be forwarded to data build module；
The data structure module carries out persistence operation to the Potency data received；It is run up to centainly in Potency data
After the order of magnitude, the data structure module starts to classify to all data of persistence；Pass through receiving the client
The Potency data for a certain type that data acquisition module is sent to mark ask after, according to the request be such Potency data
Structure is accordingly to marking model；
Data filtering module is sent out after being filtered operation to the Potency data of the type of persistence according to described pair of mark request
Data computation analysis module is given, the filter operation includes：Unified coding and denoising；
The data computation analysis module carries out data training to the Potency data by filter operation received, therefrom rejects
After being not suitable for the data to marking model, using remaining Potency data, is calculated, obtained to marking number by described pair of mark model
Include energy consumption, yield, generated energy, running rate, loss according to, wherein the Potency data,
Wherein, the data structure module carries out persistence operation to the Potency data received, specifically includes：As judged
It is unstructured data to go out the Potency data that receives, then by mongo routing servers by the Potency data persistence
To one in shard server clusters,
Wherein, one be persisted to the Potency data by mongo routing servers in shard server clusters,
It specifically includes：
Two mapping relations are pre-saved in the mongo routing servers, the first mapping relations are section and the effect of key assignments
The mapping relations of energy data block, the second mapping relations are the mapping relations of Potency data block and place fragment node；
The mongo routing servers are after receiving the Potency data that the data structure module is sent, according to pre-defined algorithm pair
The Potency data is calculated, corresponding according to first mapping relationship searching using the result being calculated as key assignments
Potency data block recycles the Potency data agllutination found to close the corresponding shard servers of second mapping relationship searching,
Then the Potency data is persisted in the corresponding shard servers.
2. the method as described in claim 1, which is characterized in that further include：
Described pair obtained mark data result is changed by the data computation analysis module to be needed to show form to target, is passed through
The data acquisition module is sent to the client to be shown.
3. the method as described in claim 1, it is characterised in that：
The data acquisition module is made of load-balanced server and more than one Web server；The load equalizer
After receiving the Potency data that the client is sent, which is distributed to by one of them according to preset distribution policy
Web server.
4. the method as described in claim 1, it is characterised in that：
The data structure module carries out persistence operation to the Potency data received, further includes specifically：The data
Structure module such as judges that the Potency data received is structural data, then is persisted to the Potency data
MySQL database cluster, while detaching strategy according to principal and subordinate and detaching Potency data, master library provides insertion, deletes, update behaviour
Make, inquiry operation is provided from library.
5. method as claimed in claim 4, which is characterized in that further include：
When the Potency data to be persisted in corresponding shard servers, the mongo routing servers also judge to work as
Whether preceding Potency data block to be written is beyond restriction size；If it was exceeded, being divided to row at two efficiency the Potency data block
It is written again after data block.
6. the method as described in claim 1, it is characterised in that：
Described pair mark model calculation formula be：The sum of products of the yield of each product and the practical Potency data of the product divided by
The sum of products of the yield of each product and the optimal Potency data of the product.
7. a kind of acquisition system to marking data, including：
Data acquisition module, for receive client by network transmission come Potency data, and be forwarded to data structure module；
The data build module, for carrying out persistence operation to the Potency data received；It is additionally operable in efficiency number
After running up to certain amount grade, start to classify to all data of persistence；It is additionally operable to receive the client logical
Cross the Potency data for a certain type that data acquisition module is sent to mark ask after, according to the request be such efficiency number
According to structure accordingly to marking model；
Data filtering module, for being filtered operation to the Potency data of the type of persistence according to described pair of mark request
After be sent to data computation analysis module, the filter operation includes：Unified coding and denoising；
The data computation analysis module, for carrying out data training to the Potency data by filter operation received, from
After middle rejecting is not suitable for the data to marking model, using remaining Potency data, is calculated, obtained by described pair of mark model
To marking data, wherein the Potency data includes energy consumption, yield, generated energy, running rate, loss,
Wherein, the data structure module is used to carry out persistence operation to the Potency data received, specifically includes：Institute
It is unstructured data to state the Potency data that data structure module is used to such as judge to receive, then is route by mongo
The Potency data is persisted to one in shard server clusters by server,
Wherein, one be persisted to the Potency data by mongo routing servers in shard server clusters,
It specifically includes：
Two mapping relations are pre-saved in the mongo routing servers, the first mapping relations are section and the effect of key assignments
The mapping relations of energy data block, the second mapping relations are the mapping relations of Potency data block and place fragment node；
The mongo routing servers are after receiving the Potency data that the data structure module is sent, according to pre-defined algorithm pair
The Potency data is calculated, corresponding according to first mapping relationship searching using the result being calculated as key assignments
Potency data block recycles the Potency data agllutination found to close the corresponding shard servers of second mapping relationship searching,
Then the Potency data is persisted in the corresponding shard servers.
8. system as claimed in claim 7, it is characterised in that：
The data computation analysis module, which is additionally operable to described pair obtained mark data result being changed into, to be needed to show shape to target
Formula is sent to the client to be shown by the data acquisition module.
9. system as claimed in claim 7, it is characterised in that：
The data acquisition module is made of load-balanced server and more than one Web server；The load equalizer
For after receiving the Potency data that the client is sent, the Potency data to be distributed to wherein according to preset distribution policy
One Web server.
10. system as claimed in claim 7, it is characterised in that：
The data structure module is used to carry out persistence operation to the Potency data received, further includes specifically：It is described
The Potency data that data structure module is used to such as judge to receive is structural data, then the Potency data is lasting
Change to MySQL database cluster, while detaching strategy according to principal and subordinate and detaching Potency data, master library provides insertion, deletes, update
Operation provides inquiry operation from library.
11. system as claimed in claim 7, it is characterised in that：
Described pair mark model calculation formula be：The sum of products of the yield of each product and the practical Potency data of the product divided by
The sum of products of the yield of each product and the optimal Potency data of the product.
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| CN102645915A (en) * | 2011-09-17 | 2012-08-22 | 深圳市善能科技有限公司 | Energy-saving method and system for monitoring, diagnosing and controlling energy efficiency |
| CN103713632A (en) * | 2014-01-08 | 2014-04-09 | 广西云涌科技有限公司 | User-side energy efficiency diagnosing and analyzing system |
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