CN105678629A - Planting industry problem solution system based on internet of things - Google Patents
Planting industry problem solution system based on internet of things Download PDFInfo
- Publication number
- CN105678629A CN105678629A CN201511031796.8A CN201511031796A CN105678629A CN 105678629 A CN105678629 A CN 105678629A CN 201511031796 A CN201511031796 A CN 201511031796A CN 105678629 A CN105678629 A CN 105678629A
- Authority
- CN
- China
- Prior art keywords
- information
- model
- word bank
- data
- planting
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 claims description 38
- 239000002689 soil Substances 0.000 claims description 36
- 230000008569 process Effects 0.000 claims description 20
- 230000035558 fertility Effects 0.000 claims description 18
- 230000002452 interceptive effect Effects 0.000 claims description 18
- 239000003337 fertilizer Substances 0.000 claims description 17
- 230000003068 static effect Effects 0.000 claims description 11
- 230000005540 biological transmission Effects 0.000 claims description 8
- 239000000575 pesticide Substances 0.000 claims description 7
- 235000013399 edible fruits Nutrition 0.000 claims description 6
- 238000003306 harvesting Methods 0.000 claims description 5
- 239000000203 mixture Substances 0.000 claims description 5
- 230000009897 systematic effect Effects 0.000 claims description 5
- 241000607479 Yersinia pestis Species 0.000 claims description 4
- 201000010099 disease Diseases 0.000 claims description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 4
- 239000000470 constituent Substances 0.000 claims description 3
- 230000005611 electricity Effects 0.000 claims description 3
- 230000000361 pesticidal effect Effects 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims description 3
- 241000894007 species Species 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 230000003993 interaction Effects 0.000 abstract description 7
- 238000001914 filtration Methods 0.000 abstract 1
- 241000196324 Embryophyta Species 0.000 description 25
- 238000007726 management method Methods 0.000 description 15
- 230000008901 benefit Effects 0.000 description 14
- 238000012216 screening Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 5
- 239000000463 material Substances 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000007613 environmental effect Effects 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 208000031968 Cadaver Diseases 0.000 description 2
- 230000009471 action Effects 0.000 description 2
- 230000003466 anti-cipated effect Effects 0.000 description 2
- 235000013339 cereals Nutrition 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000004720 fertilization Effects 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 235000021049 nutrient content Nutrition 0.000 description 2
- 235000015097 nutrients Nutrition 0.000 description 2
- 238000009331 sowing Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 230000026676 system process Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 244000221633 Brassica rapa subsp chinensis Species 0.000 description 1
- 241001269238 Data Species 0.000 description 1
- 238000012271 agricultural production Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/9032—Query formulation
- G06F16/90332—Natural language query formulation or dialogue systems
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Mathematical Physics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Marine Sciences & Fisheries (AREA)
- Animal Husbandry (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- Agronomy & Crop Science (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computational Linguistics (AREA)
- Mining & Mineral Resources (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a planting industry problem solution system based on internet of things. The system is embedded in a cloud platform, and the cloud platform is composed of a data layer, a user interaction layer and a logic control layer, wherein the data layer comprises a standard knowledge base, an experience knowledge base, an expert database and a price information base, the cloud platform searches or filters the information of the user interaction layer and the data layer by a searching engine or a filtering engine in the logic control layer, and then the searched and filtered information is operated to obtain a model guiding the agricultural planting. The planting industry problem solution system of the present invention assembles the data layer, the user interaction layer and the logic control layer fully by utilizing the cloud platform, determines the agricultural planting model correspondingly, in particular a strategy model, and determines a yield priority model, a quality priority model and a cost priority model as the important means of the planting problem solution.
Description
Technical field
The invention belongs to agriculture field, be specifically designed the service system of agricultural planting and operation and management, particularly relate to a kind of plant husbandry Resolving probiems system based on Internet of Things.
Background technology
The solution system of various crop problem or model disclosed in current document, such as, a kind of agriculture (woods) industry intelligence fertilization system described by Chinese Patent Application No. 201410165091.4, it establishes the data of a Land in Regional Land soil fertility Back ground Information spatial database and calculate center, this center is integrated with soil investigation, land fertility is evaluated, corps nutrient, woods crop alimentary, soil position monitor technology and soil testing and fertilizer recommendation technology, transmit information to the interaction platform of the substation in particular locality foundation or plantation side, it is run through terminal, touch screen terminal or mobile terminal enquiry are to the relevant quality in local soil, nutrient content level, soil, one's respective area soil characteristic, environmental condition, the productive potentialities target of crop and the best harvest time, and fertilizer application formula or fertilising solution are provided, set up experts database and fertilizer supply business link, meet agriculture (woods) industry high-yield and high-efficiency to produce and management needs.
Chinese Patent Application No. 201410165085.9 describes a kind of Maninot esculenta crantz. intelligence fertilization system, its data setting up a Land in Regional Land soil fertility Back ground Information spatial database calculate center, this center is integrated with soil investigation, land fertility is evaluated, corps nutrient, soil position monitor technology and soil testing and fertilizer recommendation technology, transmit information to the interaction platform of the substation in particular locality foundation or plantation side, it is run through terminal, touch screen terminal or mobile terminal enquiry are to the relevant quality in local Maninot esculenta crantz. soil, nutrient content level, soil, one's respective area soil characteristic, environmental condition, the productive potentialities target of Maninot esculenta crantz., growing state and the best harvest time, and fertilizer application formula or fertilising solution are provided, set up experts database and fertilizer supply business link, meet the needs of Maninot esculenta crantz. high-yield and high-efficiency production management
Chinese Patent Application No. 201310750814.2 which depict a kind of method formulating the accurate Cultural plan of crop by smart mobile phone and GPS, it loads agriculture district essential information data by smart mobile phone, GPS receiver device location field is utilized at agricultural production scene, select the field cultivation management stage, and input the crop varieties type of field, yield target and cultivation management action data or real-time growth of cereal crop seedlings data, the crop management knowledge model module being processed program by smart mobile phone carries out computing decision-making, generates accurate cultivation management scheme.Mainly include step: a data steps for importing, agriculture district essential information data are imported smart mobile phone; One GPS receiver step, receives gps satellite text by GPS receiver device; One satellite message analyzing step, resolves gps satellite text to extract the positional information of current field; One current field positioning step in map vector, according to current positional information, processes program by smart mobile phone and positions in current agriculture district map vector, highlight and be currently located field; One current field cultivation management stage selects step, in the crop growthing development stage according to current field, selects the corresponding field cultivation management stage on smart mobile phone; One data input step, if field management phase is before sowing or transplanting, then selects or the input long-term cropping variety type of field, yield target and cultivation management action data; If field management phase is growing mid-term, then input field long-term cropping variety type, yield target and real-time growth of cereal crop seedlings data; One computing steps in decision-making, smart mobile phone process program carries out computing decision-making, generates sowing or transplants cultivation management scheme that is front and that grow mid-term; One step display, shows output cultivation management scheme on smart mobile phone visual interface
Solution system disclosed at present does not solve problem on general frame, particular without the communication data of Erecting and improving, more forms the model of concrete solution problem not over system, it is achieved the abundant combination of static information and multidate information. For solving above-mentioned technical problem, the present invention is by general frame design solution system, it is achieved thereby that the another effective scheme of agricultural planting Resolving probiems.
Summary of the invention
For solving above-mentioned technical problem, the invention provides a kind of plant husbandry Resolving probiems system based on Internet of Things, the data that data acquisition system in this system Internet of Things provides, the data that each common platform provides, then all related datas in system are carried out computing, computing carries out based on multidate information and timely demand information, and calculating process adopts modelling to carry out.
Concrete, the invention provides a kind of plant husbandry Resolving probiems system based on Internet of Things, it is characterized in that this system is embedded in cloud platform, cloud platform is made up of data Layer, user's alternation of bed, logic control layer, wherein data Layer includes standard knowledge storehouse, Experiential Knowledge Database, experts database and pricing information storehouse, cloud platform is by the search engine in logic control layer or filter engine, the information of the information of user's alternation of bed, data Layer scanned for or filters, then search and the information computing again filtered being obtained instructing the model of agricultural planting.
Preferably, in above-mentioned solution system, the model of agricultural planting includes Policy model, life cycle model, kind model and price expectation model.
Policy model refers to the model that the requirement of plantation result controls planting process content according to grower, and it determines the scheme compositions such as varieties of plant in planting process, Cultivate administration, plantation goods and materials kind with result for leading.
Life cycle model be the periodicity of the growth stage with crop to determine the model of the measure adopted required for each cycle, control as guiding with the growth cycle of crop, it is determined that the Cultivate administration in different crop cycles and the plantation scheme compositions such as goods and materials kind.
Kind model is that it is with Fruit variety for guiding, with planting environment for according to the scheme determining Fruit variety with environmental factors for according to the model determining varieties of plant.
Price expectation model is to determine with elements of market for foundation to put into the model that in human and material resources and way to manage, involved price selects in planting process, it is to be chosen as guiding with Input Factors price in planting process, with elements of market for foundation, it is determined that the scheme of Input Factors price in planting process.
Preferably, in above-mentioned solution system, Policy model includes yield priority model, the quality of priority model and cost priority model, life cycle model include planting front preparation model, initial planting model, plantation model in mid-term and harvest time model.
Yield optimization model is, with grower, for ultimate requirement, output demand is determined the model that each key element of planting process of raising yield is determined, it determines the association scheme of each key element that can improve yield in planting process with maximum production for target.
Quality optimization model is, with grower, for ultimate requirement, quality of agricultural product requirement is determined the model that each key element of planting process of raising quality is determined, it determines the association scheme of each key element that can improve quality in planting process with the maximum target that turns to of quality.
Cost optimization model is to determine, for ultimate requirement, the model that each key element of planting process that reduction plantation puts into is determined to proportion of crop planting input is minimum with grower, for target, it determines that can reduce each factor price in planting process puts into the association scheme of selection mode so that planting process cost is minimum.
Preferably, in above-mentioned solution system, standard knowledge storehouse includes kind word bank, pest and disease damage word bank, fertilizer product word bank, Pesticidal products word bank, weather information word bank, mechanical information word bank, area information word bank, soil fertility word bank, hydrographic information word bank.
Preferably, in above-mentioned solution system, experts database includes expert's information, expert's exchange of information, expert develop programs information, expert problem information, typical problem storehouse.
Preferably, in above-mentioned solution system, pricing information storehouse includes historical price information word bank and price expectation word bank, historical price information word bank includes seed, agricultural machinery, pesticide, fertilizer, agricultural facility, electricity, water, the historical information such as artificial, price expectation word bank by historical price information data, pricing information external information by predicting the price trend data obtained.
Preferably, in above-mentioned solution system, classify with kind in pricing information storehouse.
Preferably, in above-mentioned solution system, user's alternation of bed is made up of three partial informations, Part I includes planting conditions and selects interactive information, planting cost to select interactive information, plantation policy selection interactive information, Part II includes Fruit variety interactive information, and Part III includes plantation stage selection interactive information, Plant plane selects interactive information.
Preferably, in above-mentioned solution system, the part data of data Layer and user's alternation of bed are provided by Internet of things system, described Internet of things system includes information collecting device and information transmission equipment, information collecting device includes light energy collecting device, temperature collection equipment, wind-force collecting device, image collection device, crops component collection equipment, soil constituent collecting device, sound wave collecting device, humidity collecting device, composition of air collecting device, work machine equipment operation information collecting device, one or more in planting information collecting device, information transmission equipment includes wireless information transfer equipment, one or both in cable information transmission equipment etc.
Present invention also offers above-mentioned plant husbandry Resolving probiems systematic difference method, the method comprises the steps:
The first step: inputting plant husbandry problem to logic control layer, logic control layer determines the type setting up search engine or filter engine according to problem, and the data in integrated data Layer set up corresponding engine;
Second step: engine determines the static factor and the dynamic factor of solution problem according to user's alternation of bed data and plant husbandry problem, and computing simultaneously is static factor and dynamic factors;
3rd step: obtained the guidance program of plant husbandry Resolving probiems by static factor and dynamic factors computing, guidance program is input to Experiential Knowledge Database simultaneously.
Preferably, in above-mentioned plant husbandry Resolving probiems systematic difference method, the type of search engine or filter engine includes but not limited to crop varieties filter engine, fertilizer variety filter engine, pesticide species filter engine etc.
Preferably, in above-mentioned plant husbandry Resolving probiems systematic difference method, engine is kind filter engine, sets up the area information word bank of engine application data layer Plays knowledge base, soil fertility word bank, and price expectation word bank in pricing information storehouse and historical price information word bank.
Preferably, in above-mentioned plant husbandry Resolving probiems systematic difference method, applicating history pricing information word bank assesses the cost input data.
In said system and method, the Data Source in standard knowledge storehouse relies primarily on collection documents and materials and official's data of agricultural relevant departments. After computer disposal, form standardized data base.
Each word bank in standard knowledge storehouse can be independent for cloud computing engine provide data, after the algorithm process that cloud computing engine is corresponding. Act on each functional unit, for instance: crops life cycle management module, price forecast system etc.
Standard knowledge storehouse is loaded into data by the mode obtained with manual entry automatically, and interacts with Experiential Knowledge Database and experts database, constantly improves and self-teaching. Ultimately form the big data cloud of an Agro-chemistry service.
Affect Different Crop and be subject to the elements affect of different word banks. Such as: Brassica campestris L. ssp.chinensis can receive the impact of the key elements such as such as region, pest and disease damage, fertilizer product, soil fertility, meteorology, pesticide.
The area information word bank region characteristic by variety of crops, filters out the crops being adapted at the desired ecological region planting of peasant household's current region or peasant household. The result of screening is stored in primary election storehouse, carries out next step screening in order to filter engine.
In said system and method, soil fertility word bank provides different soil types to select for peasant household. After peasant household selects, system is by kind higher for income when being adapted at current soil according to Benefit Model automatic screening. System will set an income threshold values, will be eliminated lower than the kind of this threshold values. This word bank provides soil pool comprehensive fertility factor, soil types and selected fertilizer type is combined, then through certain formula. Form the Variation of Integrated Soil Fertility value of a unit are, and convert the factor value between 0-1 to by corresponding operation function. This factor value and Variation of Integrated Soil Fertility.
In said system and method, the data of Experiential Knowledge Database collect the related data of user automatically by the business function module of cloud platform, and according to corresponding data model, are stored in Experiential Knowledge Database.
It is by the data automatically the got related algorithm through semanteme/filter engine that the data of Experiential Knowledge Database process, and splits data into valid data and invalid data. And valid data are carried out continuous comparison with the data in standard knowledge storehouse, constantly revises. Ultimately form the valid data that error rate is minimum.
Experiential Knowledge Database adopts the mode that big data cloud calculates, and the data in standard knowledge storehouse carry out the verification of empirical formula so that it is the data provided the user are more accurate, more efficient.
In said system and method, price information system is according to inside and outside influence factors such as conventional historical price information data, kind, weather, crop cycles.Adopt corresponding model algorithm, it was predicted that the price of following certain period of time and price trend. And the algorithm according to operation result correction model.
The business model of price information system is mainly as follows:
1) by technology such as web crawlers, announced price is automatically obtained. By analysis, this historical information is synchronized in historical price subsystem, and sets up corresponding index according to absolute time top-stitching by the means such as duplicate removal, it is simple to price forecast system uses.
(2) reserved manual extraction interface, the data of manual entry can contrast with the error range value of historical data and setting, and the data that absolute error is bigger will be abandoned automatically by system.
(3) price expectation subsystem was served as theme with the time, and the kind being different sets up corresponding forecast model. Utilize this model and all various factors affecting price dynamically to analyze, finally export the price on timeline basis and price trend information.
In said system and method, the interaction logic in user's alternation of bed is divided into two steps:
The first step is to select planting conditions to determine selection strategy, then carries out Fruit variety, specific as follows:
(1) user selects the planting conditions such as the region of proportion of crop planting, soil fertility condition, plantation area weather conditions.
(2) select the benefit strategies such as yield preferential, the quality of priority, cost priority, the benefit Policy model of correspondence after selection, can be formed.
(3) in conjunction with planting conditions and strategy, system meeting automatic screening kind of maximizing the benefits under conditions present with strategy, and recommend three kinds of optimum to select for user. Certainly, user can also select more kind.
Second step is the selection carrying out scheme according to kind and Crop Stage, specific as follows:
(1) have selected after crop varieties, just enter crop life cycle. System can automatically generate solution corresponding to this stage according to Policy model with the characteristic of kind.
(2) have selected different Policy models, system can provide the scheme of correspondence. Such as: if selected for " the quality of priority model ", then adopt and disclosure satisfy that the solution improving crop quality.
(3) in the different life stage of plantation, user can change benefit Policy model.
(4) user can be that any life cycle phase of this crop sets benefit Policy model, and generates solution corresponding to current generation.
In said system and method, each model has income and controls function, and it carries out screening contrast by the input required for the anticipated price of crops grown per area and unit are and selects to satisfy condition the kind of optimum.
In said system and method, each model has meteorological factor, the data in meteorological storehouse carries out mining analysis, and passes through Internet of Things or meteorological data interface to the monitoring of real-time weather in region, and by corresponding operation function, produce a factor between 0-1. This factor is probably nonlinear with meteorological relation.
In said system and method, each model has input cost factor, carries out statistical analysis by unit cultivated area is put into, and adopting corresponding operation function, the cost calculating a factor between 1-0, this factor value and input is inversely proportional to, putting into more high, factor value is more low.
In said system and method, each model has business computing mode, which enables to the system maximizing the benefits according to Benefit Model, the factor generating the meteorology of correspondence, Variation of Integrated Soil Fertility, input cost of intelligence, and the factor of correspondence and corresponding business datum is found by these factors. Again through plan template mechanism, generate the corresponding guidance program to crops life cycle operation.
In said system and method, each model has standardization issue processing mode, namely when user's problematic needs consulting time, first system can recommend Intelligence Consulting System, by to qualificationss such as kind, region, soil and keywords, retrieving coupling or similar the problems and solutions in typical problem storehouse.If user does not find the solution of correspondence in typical problem storehouse. System also will provide the user expert consulting system.
In said system and method, each model has expert/wound visitor's issue handling mode, and namely when user puts question to and cannot find answer in typical problem storehouse, system can recommend user to use expert/wound visitor's consulting system. At this time needing user to fill in corresponding questionnaire according to question template, system processes questionnaire content by analysis, provides different processing modes. If questionnaire content logically cannot solve or user's wrong report, system by automatically by ignore user request and to feedback. If questionnaire content legality, then according to expert corresponding to the key information match of questionnaire or wound visitor, and can be notified by APP pushed information; After expert/wound visitor is by Resolving probiems, system can be automatically fed to user.
In said system and method, the effect of experts database is when user's request does not obtain the solution mated in standard knowledge storehouse, user can select to experts database application for help, system can automatically retrieve the expert in experts database according to user's request, and recommends most suitable expert to provide the user help. The business model of experts database includes: (1) analyze user's request (2) recommend optimum expert (3) to promote expert and user link up (4) expert and according to practical situation and provide the user solution (5) system according to normalized template and solution and standard knowledge storehouse carry out contrast revise (6) output solution.
Beneficial effect
The plant husbandry Resolving probiems system of the present invention make use of cloud platform, data Layer, user's alternation of bed and logic control layer are fully gathered, and determine the model of agricultural planting accordingly, especially Policy model, specify that yield priority model, the quality of priority model and the cost priority model important means as plantation Resolving probiems.
The system of the present invention achieve standard knowledge storehouse, pricing information storehouse, experts database etc. dynamically and static data fully in conjunction with use, achieving the high accuracy in judging dynamic environment and dynamic anthropic factor and high efficiency, reliable platform has been built in the solution for agricultural planting problem.
Accompanying drawing explanation
Fig. 1 plant husbandry Resolving probiems system construction drawing
Fig. 2 plant husbandry Resolving probiems system data Rotating fields
Fig. 3 agricultural planting model structure figure
Detailed description of the invention
Embodiment 1
A kind of plant husbandry Resolving probiems system based on Internet of Things, this system is embedded in cloud platform, and cloud platform is made up of data Layer, user's alternation of bed, logic control layer.
Wherein data Layer includes standard knowledge storehouse, Experiential Knowledge Database, experts database and pricing information storehouse.
Standard knowledge storehouse includes kind word bank, pest and disease damage word bank, fertilizer product word bank, Pesticidal products word bank, weather information word bank, mechanical information word bank, area information word bank, soil fertility word bank, hydrographic information word bank. Experts database includes expert's information, expert's exchange of information, expert develop programs information, expert problem information, typical problem storehouse. Pricing information storehouse includes historical price information word bank and price expectation word bank, and historical price information word bank includes seed, agricultural machinery, pesticide, fertilizer, agricultural facility, electricity, water, artificial historical information. The price trend data that price expectation word bank is obtained by prediction by historical price information data, pricing information external information.
The information of the information of user's alternation of bed, data Layer, by the search engine in logic control layer or filter engine, is scanned for or filters by cloud platform, then obtains instructing the model of agricultural planting by search and the information computing again filtered.
The model of agricultural planting is Policy model, life cycle model, kind model and price expectation model.Policy model includes yield priority model, the quality of priority model and cost priority model, life cycle model include planting front preparation model, initial planting model, plantation model in mid-term and harvest time model.
User's alternation of bed is made up of three partial informations, Part I includes planting conditions and selects interactive information, planting cost to select interactive information, plantation policy selection interactive information, Part II includes Fruit variety interactive information, and Part III includes plantation stage selection interactive information, Plant plane selects interactive information.
The part data of data Layer and user's alternation of bed are provided by Internet of things system, described Internet of things system includes information collecting device and information transmission equipment, information collecting device includes light energy collecting device, temperature collection equipment, wind-force collecting device, image collection device, crops component collection equipment, soil constituent collecting device, sound wave collecting device, humidity collecting device, composition of air collecting device, work machine equipment operation information collecting device, planting information collecting device, and information transmission equipment is wireless information transfer equipment.
The operational approach of said system is:
The first step: inputting plant husbandry problem to logic control layer, logic control layer determines the type setting up search engine according to problem, and the data in integrated data Layer set up corresponding engine;
Second step: engine determines the static factor and the dynamic factor of solution problem according to user's alternation of bed data and plant husbandry problem, and computing simultaneously is static factor and dynamic factors;
3rd step: obtained the guidance program of plant husbandry Resolving probiems by static factor and dynamic factors computing, guidance program is input to Experiential Knowledge Database simultaneously.
Types of search engine is crop varieties filter engine, fertilizer variety filter engine and pesticide species filter engine.
Interaction logic in user's alternation of bed is divided into two steps:
The first step:
(1) user selects the region of proportion of crop planting, soil fertility condition, plantation area weather conditions.
(2) select that yield is preferential, the quality of priority, cost priority strategy, the benefit Policy model of correspondence after selection, can be formed.
(3) in conjunction with planting conditions and strategy, system automatic screening is the kind of maximizing the benefits under conditions present with strategy, and recommends three kinds of optimum to select for user.
Second step:
(1) have selected after crop varieties, just enter crop life cycle. System can automatically generate solution corresponding to this stage according to Policy model with the characteristic of kind.
(2) have selected different Policy models, system can provide the scheme of correspondence. Have selected " the quality of priority model ", adopt and disclosure satisfy that the solution improving crop quality.
(3) in the different life stage of plantation, user can change benefit Policy model.
(4) user can be that any life cycle phase of this crop sets benefit Policy model, and generates solution corresponding to current generation.
Each model has income and controls function, and it carries out screening contrast by the input required for the anticipated price of crops grown per area and unit are and selects to satisfy condition the kind of optimum.
Each model has meteorological factor, the data in meteorological storehouse carries out mining analysis, and passes through Internet of Things or meteorological data interface to the monitoring of real-time weather in region, and by corresponding operation function, produce a factor between 0-1. This factor is probably nonlinear with meteorological relation.
Each model has input cost factor, carries out statistical analysis by unit cultivated area is put into, and adopts corresponding operation function, and the cost calculating a factor between 1-0, this factor value and input is inversely proportional to, and puts into more high, and factor value is more low.
Each model has business computing mode, which enables to the system maximizing the benefits according to Benefit Model, the factor generating the meteorology of correspondence, Variation of Integrated Soil Fertility, input cost of intelligence, and the factor of correspondence and corresponding business datum is found by these factors. Again through plan template mechanism, generate the corresponding guidance program to crops life cycle operation.
Each model has standardization issue processing mode, namely when user's problematic needs consulting time, first system can recommend Intelligence Consulting System, by to qualificationss such as kind, region, soil and keywords, retrieving coupling or similar the problems and solutions in typical problem storehouse. If user does not find the solution of correspondence in typical problem storehouse. System also will provide the user expert consulting system.
Each model has expert/wound visitor's issue handling mode, and namely when user puts question to and cannot find answer in typical problem storehouse, system can recommend user to use expert/wound visitor's consulting system. At this time needing user to fill in corresponding questionnaire according to question template, system processes questionnaire content by analysis, provides different processing modes. If questionnaire content logically cannot solve or user's wrong report, system by automatically by ignore user request and to feedback. If questionnaire content legality, then according to expert corresponding to the key information match of questionnaire or wound visitor, and can be notified by APP pushed information; After expert/wound visitor is by Resolving probiems, system can be automatically fed to user.
The effect of experts database is when user's request does not obtain the solution mated in standard knowledge storehouse, user selects to experts database application for help, system can automatically retrieve the expert in experts database according to user's request, and recommends most suitable expert to provide the user help. The business model of experts database includes: (1) analyze user's request (2) recommend optimum expert (3) to promote expert and user link up (4) expert and according to practical situation and provide the user solution (5) system according to normalized template and solution and standard knowledge storehouse carry out contrast revise (6) output solution.
Structure and the operation of above-mentioned plant husbandry Resolving probiems system is understood by the plant husbandry Resolving probiems system construction drawing of Fig. 1, the plant husbandry Resolving probiems system data Rotating fields of Fig. 2 and the agricultural planting model structure figure of Fig. 3.
Claims (13)
1. the plant husbandry Resolving probiems system based on Internet of Things, this system is embedded in cloud platform, cloud platform is made up of data Layer, user's alternation of bed, logic control layer, wherein data Layer includes standard knowledge storehouse, Experiential Knowledge Database, experts database and pricing information storehouse, cloud platform is by the search engine in logic control layer or filter engine, the information of the information of user's alternation of bed, data Layer scanned for or filters, then search and the information computing again filtered being obtained instructing the model of agricultural planting.
2. solution system according to claim 1, wherein the model of agricultural planting includes Policy model, life cycle model, kind model and price expectation model.
3. solution system according to claim 1, wherein Policy model includes yield priority model, the quality of priority model and cost priority model, life cycle model include planting front preparation model, initial planting model, plantation model in mid-term and harvest time model.
4. the solution system according to any one of claim 1-3, wherein standard knowledge storehouse includes kind word bank, pest and disease damage word bank, fertilizer product word bank, Pesticidal products word bank, weather information word bank, mechanical information word bank, area information word bank, soil fertility word bank, hydrographic information word bank.
5. the solution system according to any one of claim 1-4, wherein experts database includes expert's information, expert's exchange of information, expert develop programs information, expert problem information, typical problem storehouse.
6. the solution system according to any one of claim 1-5, wherein pricing information storehouse includes historical price information word bank and price expectation word bank, historical price information word bank includes seed, agricultural machinery, pesticide, fertilizer, agricultural facility, electricity, water, the price historical information such as artificial, price expectation word bank by historical price information data, pricing information external information by predicting the price trend data obtained.
7. solution system according to claim 6, wherein classify with kind in pricing information storehouse.
8. the solution system according to any one of claim 1-7, user's alternation of bed is made up of three partial informations, Part I includes planting conditions and selects interactive information, planting cost to select interactive information, plantation policy selection interactive information, Part II includes Fruit variety interactive information, and Part III includes plantation stage selection interactive information, Plant plane selects interactive information.
9. the solution system according to any one of claim 1-8, wherein the part data of data Layer and user's alternation of bed are provided by Internet of things system, described Internet of things system includes information collecting device and information transmission equipment, information collecting device includes light energy collecting device, temperature collection equipment, wind-force collecting device, image collection device, crops component collection equipment, soil constituent collecting device, sound wave collecting device, humidity collecting device, composition of air collecting device, work machine equipment operation information collecting device, one or more in planting information collecting device, information transmission equipment includes wireless information transfer equipment, one or both in cable information transmission equipment etc.
10. the plant husbandry Resolving probiems systematic difference method described in claim 1, the method comprises the steps:
The first step: inputting plant husbandry problem to logic control layer, logic control layer determines the type setting up search engine or filter engine according to problem, and the data in integrated data Layer set up corresponding engine;
Second step: engine determines the static factor and the dynamic factor of solution problem according to user's alternation of bed data and plant husbandry problem, and computing simultaneously is static factor and dynamic factors;
3rd step: obtained the guidance program of plant husbandry Resolving probiems by static factor and dynamic factors computing, guidance program is input to Experiential Knowledge Database simultaneously.
11. application process according to claim 10, wherein the type of search engine or filter engine includes but not limited to crop varieties filter engine, fertilizer variety filter engine, pesticide species filter engine etc.
12. application process according to claim 11, engine is kind filter engine, sets up the area information word bank of engine application data layer Plays knowledge base, soil fertility word bank, and price expectation word bank in pricing information storehouse and historical price information word bank.
13. application process according to claim 12, wherein applicating history pricing information word bank assesses the cost input data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201511031796.8A CN105678629A (en) | 2015-12-31 | 2015-12-31 | Planting industry problem solution system based on internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201511031796.8A CN105678629A (en) | 2015-12-31 | 2015-12-31 | Planting industry problem solution system based on internet of things |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105678629A true CN105678629A (en) | 2016-06-15 |
Family
ID=56298520
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201511031796.8A Pending CN105678629A (en) | 2015-12-31 | 2015-12-31 | Planting industry problem solution system based on internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105678629A (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106599136A (en) * | 2016-11-30 | 2017-04-26 | 深圳前海弘稼科技有限公司 | Method and device for guiding agricultural production based on big data |
| CN106650996A (en) * | 2016-10-17 | 2017-05-10 | 宁德师范学院 | Intelligent agricultural product production management system |
| CN106686054A (en) * | 2016-11-18 | 2017-05-17 | 深圳市芭田生态工程股份有限公司 | Service system of using strategy model to solve plantation problem |
| CN106682995A (en) * | 2016-12-08 | 2017-05-17 | 内蒙古蒙草生态环境（集团）股份有限公司 | Grassland ecological industry big data platform system |
| CN106713414A (en) * | 2016-11-18 | 2017-05-24 | 深圳市芭田生态工程股份有限公司 | Service system solving plantation problems on basis of life-cycle model |
| CN107133838A (en) * | 2017-03-22 | 2017-09-05 | 无锡中科富农物联科技有限公司 | A kind of Knowledge based engineering means of agricultural production Method of Commodity Recommendation |
| CN111539621A (en) * | 2020-04-21 | 2020-08-14 | 生态环境部华南环境科学研究所 | Solution method and system for farmland non-point source and heavy metal pollution prevention and control |
| CN112231559A (en) * | 2020-10-13 | 2021-01-15 | 黑龙江省农业科学院绥化分院 | Crop breeding method |
| CN113837602A (en) * | 2021-09-23 | 2021-12-24 | 清远市巨劲科技有限公司 | Intelligent agricultural planting service system based on agricultural big data platform |
| CN114430536A (en) * | 2022-04-06 | 2022-05-03 | 广东邦盛北斗科技股份公司 | Agricultural Internet of things method and system based on Beidou positioning module and cloud platform |
| CN116307403A (en) * | 2023-05-12 | 2023-06-23 | 湖北泰跃卫星技术发展股份有限公司 | Planting process recommendation method and system based on digital village |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103701850A (en) * | 2013-11-27 | 2014-04-02 | 中国农业科学院农业信息研究所 | Farming almanac cloud management system and method |
| CN103927686A (en) * | 2014-04-23 | 2014-07-16 | 广西力源宝科技有限公司 | Intelligent fertilization system for pine trees |
| CN104635694A (en) * | 2015-01-08 | 2015-05-20 | 沈阳远大智能高科农业有限公司 | Intelligent agricultural early warning system |
| CN104732328A (en) * | 2015-02-03 | 2015-06-24 | 天津市农村工作委员会信息中心 | Agricultural Internet of Things platform system |
| CN104794528A (en) * | 2015-01-05 | 2015-07-22 | 胡宝清 | Rocky desertification governance model case library management system and rocky desertification governance model case library management method |
-
2015
- 2015-12-31 CN CN201511031796.8A patent/CN105678629A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103701850A (en) * | 2013-11-27 | 2014-04-02 | 中国农业科学院农业信息研究所 | Farming almanac cloud management system and method |
| CN103927686A (en) * | 2014-04-23 | 2014-07-16 | 广西力源宝科技有限公司 | Intelligent fertilization system for pine trees |
| CN104794528A (en) * | 2015-01-05 | 2015-07-22 | 胡宝清 | Rocky desertification governance model case library management system and rocky desertification governance model case library management method |
| CN104635694A (en) * | 2015-01-08 | 2015-05-20 | 沈阳远大智能高科农业有限公司 | Intelligent agricultural early warning system |
| CN104732328A (en) * | 2015-02-03 | 2015-06-24 | 天津市农村工作委员会信息中心 | Agricultural Internet of Things platform system |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106650996A (en) * | 2016-10-17 | 2017-05-10 | 宁德师范学院 | Intelligent agricultural product production management system |
| CN106686054A (en) * | 2016-11-18 | 2017-05-17 | 深圳市芭田生态工程股份有限公司 | Service system of using strategy model to solve plantation problem |
| CN106713414A (en) * | 2016-11-18 | 2017-05-24 | 深圳市芭田生态工程股份有限公司 | Service system solving plantation problems on basis of life-cycle model |
| CN106599136A (en) * | 2016-11-30 | 2017-04-26 | 深圳前海弘稼科技有限公司 | Method and device for guiding agricultural production based on big data |
| CN106682995A (en) * | 2016-12-08 | 2017-05-17 | 内蒙古蒙草生态环境（集团）股份有限公司 | Grassland ecological industry big data platform system |
| CN107133838A (en) * | 2017-03-22 | 2017-09-05 | 无锡中科富农物联科技有限公司 | A kind of Knowledge based engineering means of agricultural production Method of Commodity Recommendation |
| CN111539621A (en) * | 2020-04-21 | 2020-08-14 | 生态环境部华南环境科学研究所 | Solution method and system for farmland non-point source and heavy metal pollution prevention and control |
| CN112231559A (en) * | 2020-10-13 | 2021-01-15 | 黑龙江省农业科学院绥化分院 | Crop breeding method |
| CN113837602A (en) * | 2021-09-23 | 2021-12-24 | 清远市巨劲科技有限公司 | Intelligent agricultural planting service system based on agricultural big data platform |
| CN114430536A (en) * | 2022-04-06 | 2022-05-03 | 广东邦盛北斗科技股份公司 | Agricultural Internet of things method and system based on Beidou positioning module and cloud platform |
| CN114430536B (en) * | 2022-04-06 | 2022-07-19 | 广东邦盛北斗科技股份公司 | Agricultural Internet of things method and system based on Beidou positioning module and cloud platform |
| CN116307403A (en) * | 2023-05-12 | 2023-06-23 | 湖北泰跃卫星技术发展股份有限公司 | Planting process recommendation method and system based on digital village |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105678629A (en) | Planting industry problem solution system based on internet of things | |
| Aryal et al. | Understanding factors associated with agricultural mechanization: A Bangladesh case | |
| CN110134733B (en) | Land management system and method based on GIS system and big data analysis | |
| Khoshnevisan et al. | Comparison of energy consumption and GHG emissions of open field and greenhouse strawberry production | |
| US20200250593A1 (en) | Yield estimation in the cultivation of crop plants | |
| US20160309646A1 (en) | Agronomic systems, methods and apparatuses | |
| CN105260791A (en) | Planting plan optimization system and method based on agricultural Internet of Things and big data analysis | |
| CN104852989A (en) | A smart agriculture monitoring system based on Web of Things | |
| CN104521699A (en) | Field intelligent irrigation on-line control management method | |
| CN114202438A (en) | Sky and ground integrated agricultural remote sensing big data system based on wisdom agriculture | |
| CN104424390A (en) | Irrigation area monitoring method and device | |
| CN113657751A (en) | Wisdom agricultural is produced and is melted comprehensive service platform | |
| Lin et al. | Intelligent greenhouse system based on remote sensing images and machine learning promotes the efficiency of agricultural economic growth | |
| US20200245525A1 (en) | Yield estimation in the cultivation of crop plants | |
| CN114359745A (en) | Information processing method, storage medium, and electronic device | |
| Kwanmuang et al. | Small-scale farmers under Thailand’s smart farming system | |
| Anand et al. | Applications of Internet of Things (IoT) in agriculture: The need and implementation | |
| CN106803208A (en) | Agriculture block number is according to pair system and its matching method | |
| CN105574780A (en) | Service system based on user-customized requirement settlement | |
| CN106713414A (en) | Service system solving plantation problems on basis of life-cycle model | |
| CN115907211A (en) | Ecological wisdom farm | |
| Sahu et al. | A study on weather based crop prediction system using big data analytics and machine learning | |
| Lin et al. | The Design and Application of Intelligent Agricultural Greenhouse in Big Data Era | |
| Aleksiev et al. | AI shaping agriculture: smart technologies for smart production in Bulgaria | |
| Zhou et al. | Present situation and development prospect of the digital orchard technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160615 |