CN110135817A - Data processing method and device applied to agriculture Internet of Things - Google Patents
Data processing method and device applied to agriculture Internet of Things Download PDFInfo
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- CN110135817A CN110135817A CN201910432923.7A CN201910432923A CN110135817A CN 110135817 A CN110135817 A CN 110135817A CN 201910432923 A CN201910432923 A CN 201910432923A CN 110135817 A CN110135817 A CN 110135817A
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- 238000003672 processing method Methods 0.000 title claims abstract description 11
- 238000012545 processing Methods 0.000 claims abstract description 30
- 238000002360 preparation method Methods 0.000 claims abstract description 27
- 238000000605 extraction Methods 0.000 claims abstract description 22
- 238000007405 data analysis Methods 0.000 claims abstract description 20
- 238000012544 monitoring process Methods 0.000 claims abstract description 17
- 238000004422 calculation algorithm Methods 0.000 claims description 17
- 238000000034 method Methods 0.000 claims description 16
- 238000003062 neural network model Methods 0.000 claims description 14
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- 238000012271 agricultural production Methods 0.000 description 1
- 238000009360 aquaculture Methods 0.000 description 1
- 244000144974 aquaculture Species 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
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Abstract
The invention discloses a kind of data processing methods and device applied to agriculture Internet of Things, including target data acquisition module, data preparation module, data characteristics extraction module, data analysis module, data locating module, monitoring modular, warning module and central processing unit.The present invention realizes the automatic removing of colliding data and high remainder evidence while realizing target data oriented acquisition, improves the reliability of data source.The extraction that data characteristics is carried out based on MapReduce, is converted into having valuable data available for the mass data of numerous and complicated multiplicity, improves the analysis efficiency of large-scale data.
Description
Technical field
The present invention relates to agriculture internet of things data acquisition process fields, and in particular to a kind of number applied to agriculture Internet of Things
According to processing method and processing device.
Background technique
Agriculture Internet of Things is concrete application of the technology of Internet of things in agricultural production, operation, management and service, is used each
The awareness apparatus such as class sensor, vision collecting terminal carry out field planting, facilities horticulture, livestock and poultry cultivation, aquaculture, agricultural product
The acquisition of the field data in the fields such as logistics realizes that Agricultural Information is multiple dimensioned by establishing data transmission and format conversion method
Reliable transmission；Since above-mentioned data information data amount is larger, real-time transmission data is more, therefore, often will appear numerical value conflict,
It is situations such as data redundancy, not high so as to cause collected sensing data reliability, due to each in the transmission process of simultaneity factor
The problem of module working condition, is easy to cause the appearance of wrong data again, further reduced the reliability of system.
Summary of the invention
To solve the above problems, the present invention provides the data processing methods and device that are applied to agriculture Internet of Things.
To achieve the above object, the technical scheme adopted by the invention is as follows:
Data processing equipment applied to agriculture Internet of Things, comprising:
Target data acquisition module, interior imputation method editor module, for the acquisition of various target datas, and by collected data
It is sent to data preparation module；
Data preparation module for searching existing redundant content and conflict content between the data, and will be removed corresponding
Redundant content and conflict content；
Data characteristics extraction module carries out the extraction of characteristic using MapReduce to the data after the completion of arrangement；
Data analysis module is completed the assessment of corresponding data based on the characteristic using neural network model, and exports and comment
Estimate result；
Data locating module, based on the characteristic be arrange after the completion of data find suitable position in the database,
And similarity number strong point is found for it, establish its relationship between similarity number strong point；
Monitoring modular is deployed in processing unit in the form of static jar packet, for being carried out at data by way of script recording
Target data acquisition module, data preparation module, data characteristics extraction module and data analysis module workflow during reason
The recording of number of passes evidence, and the flow data based on recording realizes the assessment of each module working condition, sends assessment result to
Central processor；
Warning module, the control command for being sent according to central processing unit export corresponding warning information；
Central processing unit, for coordinating above-mentioned module work.
Further, the neural network model uses PCA-BP neural network model.
Further, the warning module includes
Phonetic warning module is corresponding for being carried out according to the assessment result of data analysis module or the assessment result of monitoring modular
The broadcasting of phonetic warning caveat；
Short message warning module, for carrying out the transmission of early warning short message by way of short message editing, transmitted short message is at least wrapped
Include current corresponding assessment result.
Further, further includes:
Graphic plotting module, for generating various curve graphs according to the data after the completion of arrangement based on the curve graph template chosen.
Further, the data preparation module is using EKA algorithm and AKF algorithm process conflict content.
Further, the redundant content is purged using redundancy function.
Further, assessment of the monitoring modular based on BP neural network model realization working condition, and target data
Acquisition module, data preparation module, data characteristics extraction module and data analysis module one BP neural network mould of each correspondence
Type.
The present invention also provides a kind of data processing methods applied to agriculture Internet of Things, are realized based on above system, packet
Include following steps:
S1, the target data obtained as needed are called in preset algorithm data-base corresponding by algorithm editor module
Algorithm realizes the acquisition of target data, and sends data preparation module for collected data；
S2, existing redundant content and conflict content between the data are searched by data preparation module, and phase will be removed
The redundant content and conflict content answered；
S3, the extraction that using MapReduce the data after the completion of arrangement are carried out with characteristic；
S4, the assessment for completing corresponding data based on the characteristic using neural network model, and export assessment result；
S5, to find suitable position in the database after the completion of arranging, and find similarity number strong point for it, establish itself and phase
Relationship between likelihood data point.
Further, it in entire data handling procedure, is deployed in data processing equipment in the form of static jar packet
Monitoring modular using script record mode carry out target data acquisition module in data handling procedure, data preparation module, number
According to the recording of characteristic extracting module and data analysis module workflow data, and the assessment of each module working condition is completed,
Central processing unit is sent by assessment result.
Further, the central processing unit is exported corresponding based on the assessment result of data analysis module and monitoring modular
Control command carries out early warning to warning module.
The invention has the following advantages:
1) the automatic removing that colliding data and high remainder evidence are realized while realizing target data oriented acquisition, improves number
According to the reliability in source.
2) mass data of numerous and complicated multiplicity is converted into having value by the extraction that data characteristics is carried out based on MapReduce
Data available, improve the analysis efficiency of large-scale data.
3) it is monitored the arrangement of module by way of static state jar packet, it is whole to realize target data acquisition module, data
That manages module, data characteristics extraction module and data analysis module operating state data stays shelves, convenient for the retrospect of working condition
Management；Target data acquisition module, data preparation module, data characteristics extraction module and data analysis module are realized simultaneously
The monitoring of working condition further improves the reliability of system.
Detailed description of the invention
Fig. 1 is the system block diagram for the data processing equipment that the embodiment of the present invention is applied to agriculture Internet of Things.
Fig. 2 is the flow chart for the data processing method that the embodiment of the present invention is applied to agriculture Internet of Things.
Specific embodiment
In order to which objects and advantages of the present invention are more clearly understood, the present invention is carried out with reference to embodiments further
It is described in detail.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to limit this hair
It is bright.
Embodiment 1
As shown in Figure 1, the embodiment of the invention provides a kind of data processing equipments applied to agriculture Internet of Things, comprising:
Target data acquisition module, interior imputation method editor module, for the acquisition of various target datas, and by collected data
It is sent to data preparation module；
Data preparation module for searching existing redundant content and conflict content between the data, and will be removed corresponding
Redundant content and conflict content；The data preparation module is using EKA algorithm and AKF algorithm process conflict content；It is described superfluous
Remaining content is purged using redundancy function；Specifically, in redundancy function, respectively by k1And k2In know
Know element and takes out e1And e2, so by e1And e2In X, Y and relationship R taking-up be compared and compare xe respectively1, xe2, ye1,
ye2, by the element entry deletion with identical content, and retain original relationship r value, relationship merged with not deleted item；
Data characteristics extraction module carries out the extraction of characteristic using MapReduce to the data after the completion of arrangement；
Data analysis module completes the assessment of corresponding data using PCA-BP neural network model based on the characteristic, and
Export assessment result；
Data locating module, based on the characteristic be arrange after the completion of data find suitable position in the database,
And similarity number strong point is found for it, establish its relationship between similarity number strong point；The data locating module is based on facet skill
Art realizes that data position, and by calculating the facet distance between different data term data is accurately positioned；In location data,
Corresponding term is selected under the constraint of known facet, and the description to required data is completed with this, if chosen successfully, is returned
Return corresponding data；If selection is unsuccessful, system will calculate the similar of term according to synonymicon and concept distance map
Property, form new location information；
Monitoring modular is deployed in processing unit in the form of static jar packet, for being carried out at data by way of script recording
Target data acquisition module, data preparation module, data characteristics extraction module and data analysis module workflow during reason
The recording of number of passes evidence, and the flow data based on recording realizes the assessment of each module working condition, sends assessment result to
Central processor；
Warning module, the control command for being sent according to central processing unit export corresponding warning information；
Graphic plotting module, for generating various curve graphs according to the data after the completion of arrangement based on the curve graph template chosen；
Central processing unit, for coordinating above-mentioned module work.
In the present embodiment, the warning module includes
Phonetic warning module is corresponding for being carried out according to the assessment result of data analysis module or the assessment result of monitoring modular
The broadcasting of phonetic warning caveat；
Short message warning module, for carrying out the transmission of early warning short message by way of short message editing, transmitted short message is at least wrapped
Include current corresponding assessment result.
In the present embodiment, assessment of the monitoring modular based on BP neural network model realization working condition, and number of targets
According to acquisition module, data preparation module, data characteristics extraction module and data analysis module one BP neural network of each correspondence
Model.
Embodiment 2
As shown in Fig. 2, the embodiment of the invention provides a kind of data processing methods applied to agriculture Internet of Things, including walk as follows
It is rapid:
S1, the target data obtained as needed are called in preset algorithm data-base corresponding by algorithm editor module
Algorithm realizes the acquisition of target data, and sends data preparation module for collected data；
S2, existing redundant content and conflict content between the data are searched by data preparation module, and phase will be removed
The redundant content and conflict content answered；
S3, the extraction that using MapReduce the data after the completion of arrangement are carried out with characteristic；
S4, the assessment for completing corresponding data based on the characteristic using PCA-BP neural network model, and export assessment knot
Fruit；
S5, to find suitable position in the database after the completion of arranging, and find similarity number strong point for it, establish itself and phase
Relationship between likelihood data point, specifically, realizing that data are positioned based on facet technology, by between calculating different data term
Data are accurately positioned in facet distance；In location data, corresponding term is selected under the constraint of known facet, has been come with this
The description of data needed in pairs returns to corresponding data if chosen successfully；If selection is unsuccessful, system is by basis
Synonymicon and concept distance map calculate the similitude of term, form new location information.
In the present embodiment, in entire data handling procedure, being deployed in data processing equipment in the form of static jar packet
Interior monitoring modular using script record mode carry out target data acquisition module in data handling procedure, data preparation module,
The recording of data characteristics extraction module and data analysis module workflow data, and complete commenting for each module working condition
Estimate, sends central processing unit for assessment result.
In the present embodiment, the data preparation module is using EKA algorithm and AKF algorithm process conflict content；The redundancy
Content is purged using redundancy function；Specifically, in redundancy function, respectively by k1And k2In knowledge
Element takes out e1And e2, so by e1And e2In X, Y and relationship R taking-up be compared and compare xe respectively1, xe2, ye1, ye2,
By the element entry deletion with identical content, and retain original relationship r value, relationship is merged with not deleted item.
In the present embodiment, the central processing unit is exported based on the assessment result of data analysis module and monitoring modular and is corresponded to
Control command to warning module carry out early warning.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the principle of the present invention, it can also make several improvements and retouch, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (10)
1. being applied to the data processing equipment of agriculture Internet of Things characterized by comprising
Target data acquisition module, interior imputation method editor module, for the acquisition of various target datas, and by collected data
It is sent to data preparation module；
Data preparation module for searching existing redundant content and conflict content between the data, and will be removed corresponding
Redundant content and conflict content；
Data characteristics extraction module carries out the extraction of characteristic using MapReduce to the data after the completion of arrangement；
Data analysis module is completed the assessment of corresponding data based on the characteristic using neural network model, and exports and comment
Estimate result；
Data locating module, based on the characteristic be arrange after the completion of data find suitable position in the database,
And similarity number strong point is found for it, establish its relationship between similarity number strong point；
Monitoring modular is deployed in processing unit in the form of static jar packet, for being carried out at data by way of script recording
Target data acquisition module, data preparation module, data characteristics extraction module and data analysis module workflow during reason
The recording of number of passes evidence, and the flow data based on recording realizes the assessment of each module working condition, sends assessment result to
Central processor；
Warning module, the control command for being sent according to central processing unit export corresponding warning information；
Central processing unit, for coordinating above-mentioned module work.
2. the data processing equipment as described in claim 1 for being applied to agriculture Internet of Things, which is characterized in that the neural network
Model uses PCA-BP neural network model.
3. the data processing equipment as described in claim 1 for being applied to agriculture Internet of Things, which is characterized in that the warning module
Including
Phonetic warning module is corresponding for being carried out according to the assessment result of data analysis module or the assessment result of monitoring modular
The broadcasting of phonetic warning caveat；
Short message warning module, for carrying out the transmission of early warning short message by way of short message editing, transmitted short message is at least wrapped
Include current corresponding assessment result.
4. the data processing equipment as described in claim 1 for being applied to agriculture Internet of Things, which is characterized in that further include:
Graphic plotting module, for generating various curve graphs according to the data after the completion of arrangement based on the curve graph template chosen.
5. the data processing equipment as described in claim 1 for being applied to agriculture Internet of Things, which is characterized in that the data preparation
Module is using EKA algorithm and AKF algorithm process conflict content.
6. the data processing equipment as described in claim 1 for being applied to agriculture Internet of Things, which is characterized in that the redundant content
It is purged using redundancy function.
7. the data processing equipment as described in claim 1 for being applied to agriculture Internet of Things, which is characterized in that the monitoring modular
Based on the assessment of BP neural network model realization working condition, and target data acquisition module, data preparation module, data characteristics
Extraction module and data analysis module one BP neural network model of each correspondence.
8. being applied to the data processing method of agriculture Internet of Things, which comprises the steps of:
S1, the target data obtained as needed are called in preset algorithm data-base corresponding by algorithm editor module
Algorithm realizes the acquisition of target data, and sends data preparation module for collected data；
S2, existing redundant content and conflict content between the data are searched by data preparation module, and phase will be removed
The redundant content and conflict content answered；
S3, the extraction that using MapReduce the data after the completion of arrangement are carried out with characteristic；
S4, the assessment for completing corresponding data based on the characteristic using neural network model, and export assessment result；
S5, to find suitable position in the database after the completion of arranging, and find similarity number strong point for it, establish itself and phase
Relationship between likelihood data point.
9. the data processing method as claimed in claim 8 for being applied to agriculture Internet of Things, it is characterised in that: entire data processing
In the process, by be deployed in the form of static jar packet the monitoring modular in data processing equipment using in a manner of script recording into
Target data acquisition module, data preparation module, data characteristics extraction module and data analyze mould in row data handling procedure
The recording of block workflow data, and the assessment of each module working condition is completed, central processing unit is sent by assessment result.
10. the data processing method as claimed in claim 9 for being applied to agriculture Internet of Things, it is characterised in that: the centre
It manages device and corresponding control command is exported to warning module progress early warning based on the assessment result of data analysis module and monitoring modular.
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| CN201910432923.7A CN110135817A (en) | 2019-05-23 | 2019-05-23 | Data processing method and device applied to agriculture Internet of Things |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170006135A1 (en) * | 2015-01-23 | 2017-01-05 | C3, Inc. | Systems, methods, and devices for an enterprise internet-of-things application development platform |
| CN108776700A (en) * | 2018-06-08 | 2018-11-09 | 新疆林科院森林生态研究所 | A kind of Forest Eco-station data processing system based on technology of Internet of things |
| CN109246088A (en) * | 2018-08-20 | 2019-01-18 | 田金荣 | A kind of big data security system based on financial service management |
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2019
- 2019-05-23 CN CN201910432923.7A patent/CN110135817A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170006135A1 (en) * | 2015-01-23 | 2017-01-05 | C3, Inc. | Systems, methods, and devices for an enterprise internet-of-things application development platform |
| CN108776700A (en) * | 2018-06-08 | 2018-11-09 | 新疆林科院森林生态研究所 | A kind of Forest Eco-station data processing system based on technology of Internet of things |
| CN109246088A (en) * | 2018-08-20 | 2019-01-18 | 田金荣 | A kind of big data security system based on financial service management |
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