CN112446796A - Intelligent agricultural monitoring management system and management method - Google Patents
Intelligent agricultural monitoring management system and management method Download PDFInfo
- Publication number
- CN112446796A CN112446796A CN202011271632.3A CN202011271632A CN112446796A CN 112446796 A CN112446796 A CN 112446796A CN 202011271632 A CN202011271632 A CN 202011271632A CN 112446796 A CN112446796 A CN 112446796A
- Authority
- CN
- China
- Prior art keywords
- growth environment
- regulation
- data
- environment data
- cloud platform
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 57
- 238000007726 management method Methods 0.000 title claims description 29
- 238000003860 storage Methods 0.000 claims abstract description 44
- 230000001105 regulatory effect Effects 0.000 claims abstract description 40
- 230000001276 controlling effect Effects 0.000 claims abstract description 13
- 238000000513 principal component analysis Methods 0.000 claims abstract description 10
- 238000000034 method Methods 0.000 claims abstract description 8
- 239000013598 vector Substances 0.000 claims description 36
- 238000012545 processing Methods 0.000 claims description 27
- 238000004140 cleaning Methods 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000010606 normalization Methods 0.000 claims description 7
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000013500 data storage Methods 0.000 claims description 3
- 238000000354 decomposition reaction Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 230000003993 interaction Effects 0.000 claims description 3
- 238000012271 agricultural production Methods 0.000 description 4
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910002092 carbon dioxide Inorganic materials 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000003750 conditioning effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000008020 evaporation Effects 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2477—Temporal data queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y10/00—Economic sectors
- G16Y10/05—Agriculture
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y20/00—Information sensed or collected by the things
- G16Y20/10—Information sensed or collected by the things relating to the environment, e.g. temperature; relating to location
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/10—Detection; Monitoring
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/20—Analytics; Diagnosis
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/30—Control
- G16Y40/35—Management of things, i.e. controlling in accordance with a policy or in order to achieve specified objectives
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Agronomy & Crop Science (AREA)
- Mathematical Physics (AREA)
- Fuzzy Systems (AREA)
- Computational Linguistics (AREA)
- Economics (AREA)
- Marine Sciences & Fisheries (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Human Resources & Organizations (AREA)
- Mining & Mineral Resources (AREA)
- Animal Husbandry (AREA)
- Toxicology (AREA)
- Environmental & Geological Engineering (AREA)
- Quality & Reliability (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Biomedical Technology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an intelligent agricultural monitoring and management system which comprises a growth environment monitoring module and a growth environment regulating module at an agricultural crop end, and a cloud platform control module and a cloud platform storage module at a management platform end. The method and the device perform principal component analysis on a plurality of growth environment data categories which are inconsistent with the optimal growth environment data in the growth environment data, select a plurality of growth environment data categories which have the largest influence on the environment to be processed in sequence, avoid mutual conflict among the growth environment data categories while regulating the growth environment data to the optimal growth environment data, and the environment regulating and controlling module regulates and controls the environment in sequence, and once the real-time growth environment data is recovered to the optimal growth environment data, the execution of subsequent regulating and controlling instructions is stopped, so that the environment regulating and controlling efficiency is effectively improved.
Description
Technical Field
The invention relates to the technical field of intelligent agriculture, in particular to an intelligent agricultural monitoring management system and a management method.
Background
The intelligent agriculture is an advanced stage of agricultural production, integrates emerging internet, mobile internet, cloud computing and internet of things, realizes intelligent perception, intelligent early warning, intelligent decision, intelligent analysis and expert online guidance of an agricultural production environment by depending on various sensing nodes (environment temperature and humidity, soil moisture, carbon dioxide, images and the like) and a wireless communication network deployed on an agricultural production field, and provides accurate planting, visual management and intelligent decision for agricultural production.
Although the current wisdom agriculture can adjust the environment for the crops to grow and provide the best growing environment for the crops, certain defects exist in the adjusting process of the growing environment, some influencing factors contained in the growth environment category often conflict with each other, for example, humidity and sunshine exceed the optimum environment state, and the growth environment needs to be humidified and regulated by sunlight, when the humidity of the growth environment is regulated, humidification water vapor needs to be sprayed into the environment through a humidifying device, but the humidification water vapor can further block the sunlight on the surface of crops after being dispersed, and can accelerate the evaporation of steam and further aggravate the environmental humidity to reduce when increasing sunshine, then sunshine and humidity control process conflict each other, if simple and rough only cut apart into single factor with the factor of growing environment classification and handle, can cause and consider one another, greatly reduced is to the regulation and control efficiency of growing environment.
Disclosure of Invention
The invention aims to provide an intelligent agricultural monitoring and management system, which aims to solve the technical problems that in the prior art, the regulation and control efficiency of the growth environment is greatly reduced because the factors of the growth environment are simply and roughly divided into single factors for processing, and the factors are considered.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a smart agricultural monitoring and management system comprises a growth environment monitoring module and a growth environment regulating module at an agricultural crop end, and a cloud platform control module and a cloud platform storage module at a management platform end;
the growth environment monitoring module is used for monitoring growth environment data of crops in real time and uploading the growth environment data to the cloud platform control module and the cloud platform storage module in real time;
the cloud platform control module is used for receiving growth environment data uploaded by the growth environment monitoring module in real time, evaluating the growth environment data in real time, calling a regulation and control scheme for regulating the growth environment data stored in the cloud platform storage module to recover to the optimal growth environment data if the growth environment data is not consistent with the optimal growth environment data, sending a regulation and control instruction for starting regulation and control to the growth environment regulation and control module according to the regulation and control scheme, and sending a regulation and control instruction for stopping regulation and control to the growth environment regulation and control module when the growth environment data uploaded by the growth environment monitoring module received by the cloud platform control module is recovered to the optimal growth environment data in the process of regulating the growth environment data by the growth environment regulation and control module;
the cloud platform storage module is used for storing and regulating various regulation and control schemes which are inconsistent with the best growth environment data and the growth environment data stored according to time sequence, and providing storage and reading functions for the external module;
and the growth environment regulation and control module is used for receiving the regulation and control instruction sent by the cloud platform control module and regulating and controlling the growth environment data according to the regulation and control instruction so as to maintain the growth environment of the crops to be optimal.
As a preferred scheme of the present invention, the cloud platform control module specifically judges the growth environment data in real time, and comprises the following steps:
a1, comparing the real-time growth environment data with optimal growth environment data, recording the growth environment data types which are not consistent with the optimal growth environment data in the growth environment data, and acquiring historical time sequence data sets corresponding to all the growth environment data types in the T time period from a cloud platform storage module;
a2, preprocessing data in the historical time sequence data set, and decomposing the preprocessed historical time sequence data set into a feature vector form according to time sequence;
a3, performing principal component analysis on the historical time sequence data set in the form of the feature vector, and selecting the former p growth environment data categories from the growth environment data categories as principal component categories influencing the growth environment data;
and A4, taking the real-time growth environment data corresponding to the first p main component categories as growth environment data categories needing preferential control, and calling corresponding control schemes from the cloud platform storage module according to the growth environment data categories needing preferential control.
As a preferred aspect of the present invention, the data in the historical time series data set are arranged according to a time series, and the time series and the data correspond to each other one to one.
As a preferred aspect of the present invention, in a2, the specific steps of preprocessing the data in the historical time series data set and decomposing the data into a feature vector form are as follows:
a201, performing data cleaning on data in a historical time series data set, wherein the data cleaning comprises repeated item processing, missing item processing and abnormal item processing;
a202, carrying out numerical value normalization processing on data in a historical time sequence data set after data cleaning;
a203, decomposing the history time sequence data set after the numerical value normalization into a characteristic vector form according to time sequence, wherein the history time sequence data set is marked as { t1:[x1,y1,z1],t2:[x2,y2,z2],t3:[x3,y3,z3],…,tn:[xn,yn,zn]Where t is1,t2,t3,…,tnExpressed as n times, T, formed by dividing a T periodnThe data values of the growing environment data category x, y and z at the nth time point are respectively represented by xn, yn and zn in the T time period, and the historical time sequence data set in the form of a feature vector is marked as { [ x1, y1 and z1]；[x2,y2,z2]；[x3,y3,z3]；…；[xn,yn,zn]And f, wherein x1-xn, y1-yn, z1-zn are vector columns, and x1, y1 and z1 are vector rows.
As a preferred embodiment of the present invention, in a3, the specific step of performing principal component analysis on a historical time series data set in the form of a feature vector is as follows:
a301, sequentially comparing historical time series data set { [ x1, y1, z1] in a feature vector form; [ x2, y2, z2 ]; [ x3, y3, z3 ]; …, respectively; subtracting the row mean value from each row of [ xn, yn, zn ] } to perform decentralized processing;
a302, performing decentralized processing on a historical time series data set in a feature vector form { [ x1, y1, z1 ]; [ x2, y2, z2 ]; [ x3, y3, z3 ]; …, respectively; performing covariance matrix calculation to obtain a growth environment data category characteristic covariance matrix mark k [ i x i ] of the historical time sequence data set, wherein i is the number of vector rows and columns;
a303, calculating eigenvalues and eigenvectors of a growth environment data category characteristic covariance matrix k [ i x i ] through SVD (singular value decomposition), and respectively obtaining i eigenvalues and eigenvectors;
and A304, sorting the i eigenvectors from large to small according to the corresponding i eigenvalues, keeping the growth environment data category time sequences corresponding to the i eigenvectors, selecting p growth environment data categories corresponding to the p eigenvectors sorted in the front as main component categories, and taking the i-p growth environment data categories corresponding to the remaining i-p eigenvectors as standby categories.
As a preferred embodiment of the present invention, in a4, the specific steps of the cloud platform control module invoking the corresponding regulation and control scheme from the cloud platform storage module according to the type of the growth environment data that is preferentially regulated and controlled include:
a401, the cloud platform control module respectively calls p regulation and control schemes corresponding to p growth environment data categories which are preferentially regulated and i-p standby regulation and control schemes corresponding to i-p standby growth environment data categories from a cloud platform storage module, and sorts the p regulation and control schemes according to the sequence of the p growth environment data categories;
a402, the cloud platform control module sequentially sends the p regulation and control schemes to the growth environment regulation and control module according to the time interval t:
when the growth environment regulation module executes b (b < p) regulation schemes, the cloud platform control module receives real-time growth environment data from the growth environment monitoring module and recovers the most optimal growth environment data, and the cloud platform control module stops sending the rest p-b regulation instructions to the growth environment regulation module;
when the growth environment regulation and control module sequentially executes p regulation and control schemes, the cloud platform control module receives real-time growth environment data from the growth environment monitoring module and does not recover to optimal growth environment data, the cloud platform control module simultaneously sends the i-p standby regulation and control schemes to the growth environment regulation and control module, and the growth environment regulation and control module simultaneously executes i-p regulation and control instructions.
As a preferred scheme of the present invention, the growth environment monitoring module includes various sensors for collecting growth environment data, the growth environment regulation and control module includes various terminal devices for regulating growth environment data, the cloud platform control module and the cloud platform storage module are established in a distributed data processing system constructed by a plurality of servers and a computing host for performing operation processing and data storage, and the growth environment monitoring module, the cloud platform control module, the cloud platform storage module and the growth environment regulation and control module perform data exchange and service interaction through network communication.
As a preferable aspect of the present invention, the present invention provides a management method according to the intelligent agricultural monitoring management system, including the steps of:
s1, the growth environment monitoring module monitors growth environment data in real time, uploads the crop growth environment data to the cloud platform control module and the cloud platform storage module in real time, and the step goes to S2 and S4;
s2, the cloud platform control module receives the growth environment data uploaded by the growth environment monitoring module in real time and judges the growth environment data in real time;
s201, when the growth environment data is located in the optimal environment threshold range, the cloud platform control module sends a regulation and control instruction for stopping regulation and control to the growth environment regulation and control module;
s202, when the growth environment data exceed the optimal growth environment data range, the cloud platform control module calls a regulation and control scheme corresponding to the regulation and control growth environment data stored in the cloud platform storage module, and sends a regulation and control instruction for starting regulation and control to the growth environment regulation and control module according to the regulation and control scheme;
s3, go to step S5;
s4, the cloud platform storage module stores growth environment data uploaded by the growth environment monitoring module in real time;
s5, the growth environment regulation and control module receives the regulation and control instruction sent by the cloud platform control module, and conducts regulation and control operation on growth environment data according to the regulation and control instruction, and the step is switched to S1.
Compared with the prior art, the invention has the following beneficial effects:
the method and the device perform principal component analysis on a plurality of growth environment data categories which are inconsistent with the optimal growth environment data in the growth environment data, select a plurality of growth environment data categories which have the largest influence on the environment to be processed in sequence, avoid mutual conflict among the growth environment data categories while regulating the growth environment data to the optimal growth environment data, and the environment regulating and controlling module regulates and controls the environment in sequence, and once the real-time growth environment data is recovered to the optimal growth environment data, the execution of subsequent regulating and controlling instructions is stopped, so that the environment regulating and controlling efficiency is effectively improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a block diagram of a smart agriculture monitoring and management system according to an embodiment of the present invention;
fig. 2 is a flowchart of a management method of the intelligent agricultural monitoring and management system according to an embodiment of the present invention.
The reference numerals in the drawings denote the following, respectively:
1-growth environment monitoring module; 2-a growth environment regulation module; 3-a cloud platform control module; 4-cloud platform storage module.
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.
As shown in fig. 1, the invention provides an intelligent agricultural monitoring and management system, which comprises a growth environment monitoring module 1 and a growth environment regulating module 2 at an agricultural crop end, a cloud platform control module 3 and a cloud platform storage module 4 at a management platform end;
the growth environment monitoring module 1 is used for monitoring growth environment data of crops in real time and uploading the growth environment data to the cloud platform control module 3 and the cloud platform storage module 4 in real time;
the cloud platform control module 3 is used for receiving growth environment data uploaded by the growth environment monitoring module 1 in real time, evaluating the growth environment data in real time, calling a regulation and control scheme for regulating the growth environment data stored in the cloud platform storage module 4 to restore to the optimal growth environment data if the growth environment data does not accord with the optimal growth environment data, sending a regulation and control instruction for starting regulation and control to the growth environment regulation and control module 2 according to the regulation and control scheme, and sending a regulation and control instruction for stopping regulation and control to the growth environment regulation and control module 2 when the growth environment data uploaded by the growth environment monitoring module 1 received by the cloud platform control module 3 is restored to the optimal growth environment data in the process of regulating the growth environment data by the growth environment regulation and control module 2;
the cloud platform storage module 4 is used for storing and regulating various regulation and control schemes which are inconsistent with the best growth environment data and the growth environment data stored according to time sequence, and providing storage and reading functions for the external module;
and the growth environment regulation and control module 2 is used for receiving the regulation and control instruction sent by the cloud platform control module 3 and regulating and controlling the growth environment data according to the regulation and control instruction so as to maintain the growth environment of the crops to be optimal.
The growth environment data category of the growth environment data includes, but is not limited to, temperature, humidity, and sunlight, and the growth environment data is a specific numerical value of the temperature, the humidity, and the sunlight monitored by the growth environment monitoring module 1.
The cloud platform control module 3 carries out the concrete steps of real-time judgment on growth environment data:
a1, comparing the real-time growth environment data with the optimal growth environment data, recording the growth environment data types which are not consistent with the optimal growth environment data in the growth environment data, and acquiring historical time sequence data sets corresponding to all the growth environment data types in the T time period from the cloud platform storage module 4;
the T time period can be set automatically in actual use.
A2, preprocessing data in the historical time sequence data set, and decomposing the preprocessed historical time sequence data set into a feature vector form according to time sequence;
a3, performing principal component analysis on the historical time sequence data set in the form of the feature vector, and selecting the former p growth environment data categories from the growth environment data categories as principal component categories influencing the growth environment data;
and A4, taking the real-time growth environment data corresponding to the first p main component categories as growth environment data categories needing preferential control, and calling corresponding control schemes from the cloud platform storage module 4 according to the growth environment data categories needing preferential control.
The data in the historical time sequence data set are arranged according to the time sequence, and the time sequence corresponds to the data one by one.
In a2, the specific steps of preprocessing the data in the historical time series data set and decomposing the data into a feature vector form are as follows:
a201, data cleaning is carried out on data in a historical time series data set, wherein the data cleaning comprises repeated item processing, missing item processing and abnormal item processing; repeating the treatment: traversing a historical time sequence data set, deleting repeated items at all times in the historical time sequence data set until a growing environment data type only corresponds to one data at one time, and ensuring that all the data have uniqueness at the time;
and (3) missing item treatment: all growing environment data types with missing items in the historical time sequence data set are extracted independently, and data of all growing environment data types at the moment corresponding to the missing items are deleted;
abnormal item processing: and (4) all the growth environment data categories with abnormal items in the historical time series data set are extracted independently, and the growth environment data categories with abnormal items are deleted.
A202, carrying out numerical value normalization processing on data in a historical time sequence data set after data cleaning;
the growth environment data corresponding to various growth environment data types are not consistent, the growth environment data of the growth environment data types range from tens to tens of thousands, and subsequent principal component analysis is directly performed by using the original data, which can cause the result to have errors, so that normalization processing is required.
Mapping original growth environment data with large differences into a [0,1] range, and if the type of the growth environment data is x, the maximum value of the growth environment data in the growth environment data type in the T time period is xmax, and the minimum value of the growth environment data in the T time period is xmin, marking the mapped growth environment data type as x1 ═ x-xmin)/(xmax-xmin).
A203, decomposing the history time sequence data set after the numerical value normalization into a characteristic vector form according to time sequence, wherein the history time sequence data set is marked as { t1:[x1,y1,z1],t2:[x2,y2,z2],t3:[x3,y3,z3],…,tn:[xn,yn,zn]Where t is1,t2,t3,…,tnExpressed as n times, T, formed by dividing a T periodnThe data values of the growing environment data category x, y and z at the nth time point are respectively represented by xn, yn and zn in the T time period, and the historical time sequence data set in the form of a feature vector is marked as { [ x1, y1 and z1]；[x2,y2,z2]；[x3,y3,z3]；…；[xn,yn,zn]Wherein x1-xn, y1-yn, z1-zn are vector columns,x1, y1, z1 are vector rows.
For further understanding of the growth environment data types x, y and z, when the growth environment data types x, y and z are mapped to a real scene, x represents temperature, y represents humidity, and z represents sunshine, the growth environment data types are synchronously added or deleted when temperature, humidity and sunshine are added or deleted, and data values of temperature, humidity and sunshine at the same moment are taken as a feature vector.
In a3, the specific steps of principal component analysis of the historical time series data set in the form of feature vectors are as follows:
a301, sequentially comparing historical time series data set { [ x1, y1, z1] in a feature vector form; [ x2, y2, z2 ]; [ x3, y3, z3 ]; …, respectively; subtracting the row mean value from each row of [ xn, yn, zn ] } to perform decentralized processing;
a302, performing decentralized processing on a historical time series data set in a feature vector form { [ x1, y1, z1 ]; [ x2, y2, z2 ]; [ x3, y3, z3 ]; …, respectively; performing covariance matrix calculation to obtain a growth environment data category characteristic covariance matrix mark k [ i x i ] of the historical time sequence data set, wherein i is a vector row and column number, and i is a constant which is the same as the vector column number in the historical time sequence data set in the characteristic vector form;
a303, calculating eigenvalues and eigenvectors of a growth environment data category characteristic covariance matrix k [ i x i ] through SVD (singular value decomposition), and respectively obtaining i eigenvalues and eigenvectors;
and A304, sorting the i eigenvectors from large to small according to the corresponding i eigenvalues, keeping the growth environment data category time sequences corresponding to the i eigenvectors, selecting p growth environment data categories corresponding to the p eigenvectors sorted in the front as main component categories, and taking the i-p growth environment data categories corresponding to the remaining i-p eigenvectors as standby categories.
In a4, the specific steps of the cloud platform control module 3 calling the corresponding regulation and control scheme from the cloud platform storage module 4 according to the type of the preferentially regulated growth environment data are as follows:
a401, the cloud platform control module 3 respectively calls p regulation and control schemes corresponding to p growth environment data categories which are preferentially regulated and i-p standby regulation and control schemes corresponding to i-p standby growth environment data categories from the cloud platform storage module 4, and sorts the p regulation and control schemes according to the sequence of the p growth environment data categories;
a402, the cloud platform control module 3 sends the p regulation and control schemes to the growth environment regulation and control module 2 in sequence according to the long interval t:
when the growth environment regulation and control module 2 executes b (b < p) regulation and control schemes, the cloud platform control module 3 receives real-time growth environment data from the growth environment monitoring module 1 and recovers the best growth environment data, and the cloud platform control module 3 stops sending the rest p-b regulation and control instructions to the growth environment regulation and control module 2, so that the regulation and control time is further saved, and the regulation and control efficiency is improved;
when the growth environment regulation and control module 2 executes p regulation and control schemes in sequence, the cloud platform control module 3 receives real-time growth environment data from the growth environment monitoring module 1 and does not recover to optimal growth environment data, the cloud platform control module 3 simultaneously sends i-p standby regulation and control schemes to the growth environment regulation and control module 2, and the growth environment regulation and control module 2 executes i-p regulation and control instructions simultaneously.
P is less than or equal to the value i, the values p and t can be set automatically in actual use, and p main component categories already contain main factors influencing the environment, so that the growth environment data corresponding to p main component categories are preferentially regulated and controlled to cause chain reaction to cause synchronous change of the growth environment data of other growth environment data categories so as to recover the growth environment data range, the growth environment data corresponding to each growth environment data category is prevented from being regulated and controlled, the regulation and control time is effectively shortened, and mutual conflict between regulation and control of the growth environment data categories is avoided.
Growth environment monitoring module 1 contains all kinds of sensors that carry out the collection to growth environment data, wherein the sensor includes but not limited to temperature sensor, humidity transducer and sunshine sensor, the growth environment data classification that the sensor corresponds the collection is the temperature, humidity and sunshine, growth environment regulation and control module 2 includes all kinds of terminal equipment who adjusts growth environment data, terminal equipment and growth environment data classification one-to-one, including temperature regulation equipment, humidity regulation equipment and sunshine adjusting device, can add or delete the growth environment data classification of regulation and control according to the environmental conditioning needs in the in-service use, corresponding sensor and terminal equipment add or delete in step.
The cloud platform control module 3 and the cloud platform storage module 4 are established in a distributed data processing system constructed by a plurality of servers and a computing host to perform operation processing and data storage, and the growth environment monitoring module 1, the cloud platform control module 3, the cloud platform storage module 4 and the growth environment regulation and control module 2 perform data exchange and service interaction through network communication.
As shown in fig. 2, based on the structure of the above intelligent agriculture monitoring and management system, the present invention provides a management method, which includes the following steps:
s1, the growth environment monitoring module monitors growth environment data in real time, uploads the crop growth environment data to the cloud platform control module and the cloud platform storage module in real time, and the step goes to S2 and S4;
s2, the cloud platform control module receives the growth environment data uploaded by the growth environment monitoring module in real time and judges the growth environment data in real time;
s201, when the growth environment data is located in the optimal environment threshold range, the cloud platform control module sends a regulation and control instruction for stopping regulation and control to the growth environment regulation and control module;
s202, when the growth environment data exceed the optimal growth environment data range, the cloud platform control module calls a regulation and control scheme corresponding to the regulation and control growth environment data stored in the cloud platform storage module, and sends a regulation and control instruction for starting regulation and control to the growth environment regulation and control module according to the regulation and control scheme;
s3, go to step S5;
s4, the cloud platform storage module stores growth environment data uploaded by the growth environment monitoring module in real time;
s5, the growth environment regulation and control module receives the regulation and control instruction sent by the cloud platform control module, and conducts regulation and control operation on growth environment data according to the regulation and control instruction, and the step is switched to S1.
The method and the device perform principal component analysis on a plurality of growth environment data categories which are inconsistent with the optimal growth environment data in the growth environment data, select a plurality of growth environment data categories which have the largest influence on the environment to be processed in sequence, avoid mutual conflict among the growth environment data categories while regulating the growth environment data to the optimal growth environment data, and the environment regulating and controlling module regulates and controls the environment in sequence, and once the real-time growth environment data is recovered to the optimal growth environment data, the execution of subsequent regulating and controlling instructions is stopped, so that the environment regulating and controlling efficiency is effectively improved.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (8)
1. The utility model provides an wisdom agricultural control management system which characterized in that: the system comprises a growth environment monitoring module (1) and a growth environment regulating and controlling module (2) at an crop end, a cloud platform control module (3) and a cloud platform storage module (4) at a management platform end;
the growth environment monitoring module (1) is used for monitoring growth environment data of crops in real time and uploading the growth environment data to the cloud platform control module (3) and the cloud platform storage module (4) in real time;
the cloud platform control module (3) is used for receiving growth environment data uploaded by the growth environment monitoring module (1) in real time, evaluating the growth environment data in real time, calling a regulation and control scheme for regulating the growth environment data stored in the cloud platform storage module (4) to restore to the optimal growth environment data if the growth environment data is not consistent with the optimal growth environment data, sending a regulation and control instruction for starting regulation and control to the growth environment regulation and control module (2) according to the regulation and control scheme, and sending a regulation and control instruction for stopping regulation and control to the growth environment regulation and control module (2) when the growth environment data uploaded by the growth environment monitoring module (1) received by the cloud platform control module (3) is restored to the optimal growth environment data in the process of regulating the growth environment data by the growth environment regulation and control module (2);
the cloud platform storage module (4) is used for storing and regulating various regulation and control schemes which are inconsistent with the best growth environment data and the growth environment data stored according to time sequence, and providing storage and reading functions for the external module;
and the growth environment regulation and control module (2) is used for receiving the regulation and control instruction sent by the cloud platform control module (3) and regulating and controlling the growth environment data according to the regulation and control instruction so as to maintain the growth environment of the crops at the optimum level.
2. The intelligent agricultural monitoring and management system of claim 1, wherein: the cloud platform control module (3) is used for judging the growth environment data in real time:
a1, comparing the real-time growth environment data with the optimal growth environment data, recording the growth environment data types which are not consistent with the optimal growth environment data in the growth environment data, and acquiring historical time sequence data sets corresponding to all the growth environment data types in the T time period from a cloud platform storage module (4);
a2, preprocessing data in the historical time sequence data set, and decomposing the preprocessed historical time sequence data set into a feature vector form according to time sequence;
a3, performing principal component analysis on the historical time sequence data set in the form of the feature vector, and selecting the former p growth environment data categories from the growth environment data categories as principal component categories influencing the growth environment data;
a4, taking the real-time growth environment data corresponding to the first p main component categories as growth environment data categories needing preferential control, and calling corresponding control schemes from the cloud platform storage module (4) according to the growth environment data categories needing preferential control.
3. The intelligent agricultural monitoring and management system of claim 2, wherein: and the data in the historical time sequence data set are arranged according to time sequences, and the time sequences correspond to the data one by one.
4. The intelligent agricultural monitoring and management system of claim 3, wherein: in the step a2, the specific steps of preprocessing the data in the historical time series data set and decomposing the data into a feature vector form are as follows:
a201, performing data cleaning on data in a historical time series data set, wherein the data cleaning comprises repeated item processing, missing item processing and abnormal item processing;
a202, carrying out numerical value normalization processing on data in a historical time sequence data set after data cleaning;
a203, decomposing the history time sequence data set after the numerical value normalization into a characteristic vector form according to time sequence, wherein the history time sequence data set is marked as { t1:[x1,y1,z1],t2:[x2,y2,z2],t3:[x3,y3,z3],…,tn:[xn,yn,zn]Where t is1,t2,t3,…,tnExpressed as n times, T, formed by dividing a T periodnThe data values of the growing environment data category x, y and z at the nth time point are respectively represented by xn, yn and zn in the T time period, and the historical time sequence data set in the form of a feature vector is marked as { [ x1, y1 and z1]；[x2,y2,z2]；[x3,y3,z3]；…；[xn,yn,zn]And f, wherein x1-xn, y1-yn, z1-zn are vector columns, and x1, y1 and z1 are vector rows.
5. The intelligent agricultural monitoring and management system of claim 4, wherein: in the step a3, the specific steps of performing principal component analysis on the historical time series data set in the form of feature vectors are as follows:
a301, sequentially comparing historical time series data set { [ x1, y1, z1] in a feature vector form; [ x2, y2, z2 ]; [ x3, y3, z3 ]; …, respectively; subtracting the row mean value from each row of [ xn, yn, zn ] } to perform decentralized processing;
a302, performing decentralized processing on a historical time series data set in a feature vector form { [ x1, y1, z1 ]; [ x2, y2, z2 ]; [ x3, y3, z3 ]; …, respectively; performing covariance matrix calculation to obtain a growth environment data category characteristic covariance matrix mark k [ i x i ] of the historical time sequence data set, wherein i is the number of vector rows and columns;
a303, calculating eigenvalues and eigenvectors of a growth environment data category characteristic covariance matrix k [ i x i ] through SVD (singular value decomposition), and respectively obtaining i eigenvalues and eigenvectors;
and A304, sorting the i eigenvectors from large to small according to the corresponding i eigenvalues, keeping the growth environment data category time sequences corresponding to the i eigenvectors, selecting p growth environment data categories corresponding to the p eigenvectors sorted in the front as main component categories, and taking the i-p growth environment data categories corresponding to the remaining i-p eigenvectors as standby categories.
6. The intelligent agricultural monitoring and management system of claim 5, wherein: in the step a4, the specific steps of the cloud platform control module (3) calling the corresponding regulation and control scheme from the cloud platform storage module (4) according to the type of the preferentially regulated growth environment data are as follows:
a401, the cloud platform control module (3) respectively calls p regulation and control schemes corresponding to p growth environment data categories which are preferentially regulated and controlled and i-p standby regulation and control schemes corresponding to i-p standby growth environment data categories from the cloud platform storage module (4), and sorts the p regulation and control schemes according to the sequence of the p growth environment data categories;
a402, the cloud platform control module (3) sequentially sends p regulation schemes to the growth environment regulation module (2) according to a long interval t:
when the growth environment regulation and control module (2) executes b (b < p) regulation and control schemes, the cloud platform control module (3) receives real-time growth environment data from the growth environment monitoring module (1) and recovers the optimal growth environment data, and the cloud platform control module (3) stops sending the rest p-b regulation and control instructions to the growth environment regulation and control module (2);
when the growth environment regulation and control module (2) sequentially executes p regulation and control schemes, the cloud platform control module (3) receives real-time growth environment data from the growth environment monitoring module (1) and does not recover to the optimal growth environment data, the cloud platform control module (3) simultaneously sends the i-p standby regulation and control schemes to the growth environment regulation and control module (2), and the growth environment regulation and control module (2) simultaneously executes i-p regulation and control instructions.
7. The intelligent agricultural monitoring and management system of claim 6, wherein the growth environment monitoring module (1) comprises various sensors for collecting growth environment data, the growth environment regulation and control module (2) comprises various terminal devices for regulating growth environment data, the cloud platform control module (3) and the cloud platform storage module (4) are established in a distributed data processing system constructed by a plurality of servers and a computing host for operation processing and data storage, and the growth environment monitoring module (1), the cloud platform control module (3), the cloud platform storage module (4) and the growth environment regulation and control module (2) perform data exchange and service interaction through network communication.
8. A management method of the intelligent agricultural monitoring and management system according to any one of claims 1 to 7, comprising the following steps:
s1, the growth environment monitoring module monitors growth environment data in real time, uploads the crop growth environment data to the cloud platform control module and the cloud platform storage module in real time, and the step goes to S2 and S4;
s2, the cloud platform control module receives the growth environment data uploaded by the growth environment monitoring module in real time and judges the growth environment data in real time;
s201, when the growth environment data is located in the optimal environment threshold range, the cloud platform control module sends a regulation and control instruction for stopping regulation and control to the growth environment regulation and control module;
s202, when the growth environment data exceed the optimal growth environment data range, the cloud platform control module calls a regulation and control scheme corresponding to the regulation and control growth environment data stored in the cloud platform storage module, and sends a regulation and control instruction for starting regulation and control to the growth environment regulation and control module according to the regulation and control scheme;
s3, go to step S5;
s4, the cloud platform storage module stores growth environment data uploaded by the growth environment monitoring module in real time;
s5, the growth environment regulation and control module receives the regulation and control instruction sent by the cloud platform control module, and conducts regulation and control operation on growth environment data according to the regulation and control instruction, and the step is switched to S1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011271632.3A CN112446796A (en) | 2020-11-13 | 2020-11-13 | Intelligent agricultural monitoring management system and management method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011271632.3A CN112446796A (en) | 2020-11-13 | 2020-11-13 | Intelligent agricultural monitoring management system and management method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112446796A true CN112446796A (en) | 2021-03-05 |
Family
ID=74737333
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011271632.3A Withdrawn CN112446796A (en) | 2020-11-13 | 2020-11-13 | Intelligent agricultural monitoring management system and management method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112446796A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114169526A (en) * | 2021-12-15 | 2022-03-11 | 宏景科技股份有限公司 | Method, system, storage medium and equipment for accurately regulating and controlling crop growth |
| CN115793749A (en) * | 2022-12-05 | 2023-03-14 | 河北泽润信息科技有限公司 | Intelligent greenhouse environment control system and method based on cloud computing |
-
2020
- 2020-11-13 CN CN202011271632.3A patent/CN112446796A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114169526A (en) * | 2021-12-15 | 2022-03-11 | 宏景科技股份有限公司 | Method, system, storage medium and equipment for accurately regulating and controlling crop growth |
| CN115793749A (en) * | 2022-12-05 | 2023-03-14 | 河北泽润信息科技有限公司 | Intelligent greenhouse environment control system and method based on cloud computing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112446796A (en) | Intelligent agricultural monitoring management system and management method | |
| Manoharan | Supervised Learning for Microclimatic parameter Estimation in a Greenhouse environment for productive Agronomics | |
| CN107219759B (en) | Greenhouse environment control method and device | |
| CN113791650B (en) | Ozone concentration adjusting method and system | |
| CN116301138B (en) | Intelligent supervision system of agricultural greenhouse based on sunlight greenhouse | |
| CN113298537A (en) | Rice full-chain quality information intelligent detection system and method based on Internet of things | |
| CN110545529A (en) | Warmhouse booth environmental monitoring and management service system | |
| CN115604301A (en) | Planting environment monitoring system based on artificial intelligence | |
| CN115685862A (en) | Intelligent agricultural detection system based on edge calculation | |
| CN116562623A (en) | Real-time planting risk assessment system suitable for velvet mushroom is planted | |
| CN110057764B (en) | Pesticide application safety management warning device and method | |
| CN115348281A (en) | Automatic plant planting method and system based on self-optimization | |
| CN114372408A (en) | Short-term power load prediction method and device based on chaotic sparrow search algorithm | |
| CN109116827B (en) | Solar greenhouse water and fertilizer integrated irrigation control method and device based on Internet of things | |
| CN114818888A (en) | Soil composition data fusion method and system based on multi-channel Kalman filtering | |
| US20230157231A1 (en) | Methods for artificial pollination and apparatus for doing the same | |
| CN116703083B (en) | Controllable agricultural cultivation management system and method based on artificial intelligence | |
| CN113349188A (en) | Lawn and forage grass precise weeding method based on cloud weeding spectrum | |
| CN115361424B (en) | Medical equipment monitoring method and system based on data analysis of Internet of things | |
| CN115994692B (en) | Intelligent river and lake management platform based on 5G and big data | |
| US20230309464A1 (en) | Method and apparatus for automated crop recipe optimization | |
| CN112330294A (en) | Intelligent agricultural control system and control method based on cloud computing | |
| CN116523182A (en) | Ecological garden construction and production management method, system and storage medium | |
| CN114982580A (en) | Intelligent flower auxiliary cultivation method and system based on image recognition | |
| Wickramaarachchi et al. | Real-time greenhouse environmental conditions optimization using neural network and image processing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20210305 |
|
| WW01 | Invention patent application withdrawn after publication |