CN113487443B - Agricultural data trusted circulation platform based on data model - Google Patents
Agricultural data trusted circulation platform based on data model Download PDFInfo
- Publication number
- CN113487443B CN113487443B CN202110724882.6A CN202110724882A CN113487443B CN 113487443 B CN113487443 B CN 113487443B CN 202110724882 A CN202110724882 A CN 202110724882A CN 113487443 B CN113487443 B CN 113487443B
- Authority
- CN
- China
- Prior art keywords
- data
- agricultural
- model
- participant
- stations
- 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.)
- Active
Links
- 238000013499 data model Methods 0.000 title claims abstract description 102
- 238000000034 method Methods 0.000 claims description 12
- 238000000586 desensitisation Methods 0.000 claims description 10
- 238000013507 mapping Methods 0.000 claims description 8
- 238000011161 development Methods 0.000 abstract description 7
- 230000000694 effects Effects 0.000 abstract description 2
- 238000007726 management method Methods 0.000 description 10
- 240000008042 Zea mays Species 0.000 description 5
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 description 5
- 235000002017 Zea mays subsp mays Nutrition 0.000 description 5
- 235000005822 corn Nutrition 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000012795 verification Methods 0.000 description 5
- 238000012271 agricultural production Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 241000607479 Yersinia pestis Species 0.000 description 2
- 239000003337 fertilizer Substances 0.000 description 2
- 244000144972 livestock Species 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000004806 packaging method and process Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 102000004169 proteins and genes Human genes 0.000 description 2
- 108090000623 proteins and genes Proteins 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 244000105624 Arachis hypogaea Species 0.000 description 1
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 description 1
- 241000196324 Embryophyta Species 0.000 description 1
- 244000068988 Glycine max Species 0.000 description 1
- 235000010469 Glycine max Nutrition 0.000 description 1
- 241000238631 Hexapoda Species 0.000 description 1
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 241000209140 Triticum Species 0.000 description 1
- 235000021307 Triticum Nutrition 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000007429 general method Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 235000020232 peanut Nutrition 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 230000001360 synchronised 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; 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
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
Abstract
The invention relates to the technical field of agricultural digitization, in particular to an agricultural data trusted circulation platform based on a data model, which comprises a plurality of mutually communicated data stations, wherein the data stations are respectively connected with an agricultural participant, the agricultural participant accesses the agricultural data into the data stations, the data stations encrypt and store the agricultural data and verify the agricultural data, a data demand party of the agricultural participant formulates the data model, submits the data model to a corresponding data station for execution, obtains model data, and circulates the model data as trusted circulation data after associating the model data with an agricultural participant identifier, a data model identifier and standard time. The invention has the following substantial effects: the agricultural data of the agricultural participants are stored in the data station in an encrypted mode, and are circulated after being converted through the data model, so that the agricultural data are isolated from the outside, the privacy and the safety of the original agricultural data are guaranteed, the variety of the data model is various, the diversified data requirements are met, and the development of agriculture informatization is promoted.
Description
Technical Field
The invention relates to the technical field of agricultural digitization, in particular to an agricultural data trusted circulation platform based on a data model.
Background
The digital construction of rural areas is relatively backward, the information is not smooth, and the management is weak. The reliability of agricultural data is thus generally not high. Agriculture is a huge and complex industry with diverse types of participants and a huge number. For agricultural participants with different geographical areas, different crop types and different roles, the generated data and the required data are complex and various, and a large amount of information islands are formed. The circulation of the agricultural data is unsmooth, the cooperation among the related agricultural participants is seriously hindered, and the development of the agricultural productivity is limited. Agriculture is an important industry in China, and is related to civilian life and social stability. Therefore, researches on a technical scheme capable of enabling the agricultural data to be credible and smoothly flowing are urgently needed.
Chinese patent CN106934526a, publication date 2017, 7 month 7, discloses an agricultural production information management system, comprising: the system comprises a unit information management module for managing basic information of a user, a base management module for managing basic information of the user, a label management module for consulting user label distribution and activation conditions and performing statistical analysis, a data analysis module for consulting records of two-dimension code labels accessed by consumers and performing statistical analysis on the accessed records, an agricultural management module for managing purchasing agricultural information of the user, an agricultural operation management module for managing agricultural operation flow information of the user, a quality management module for managing agricultural product production system information of the user, a processing and packaging module for managing agricultural product processing and packaging information of the user, a sales and circulation management module for managing agricultural product logistics and sales information of the user and the like, and effectively helps enterprises, farmer professional cooperation and growers to record various information of agricultural products, and realizes electronic and comprehensive management of agricultural production information files. Although some agricultural data are electronically processed, the method is convenient for circulation of the agricultural data to a certain extent, but the method cannot guarantee safety and privacy of the data circulation process, cannot meet diversified data requirements, and is still insufficient for solving the problem of insufficient circulation degree of the agricultural data.
Disclosure of Invention
The invention aims to solve the technical problems that: the technical scheme of the trusted circulation of agricultural data is lacking at present. The agricultural data trusted circulation platform based on the data model is provided, and can meet diversified data requirements and is convenient to circulate under the condition that the privacy of agricultural data is guaranteed.
In order to solve the technical problems, the invention adopts the following technical scheme: the agricultural data trusted circulation platform based on the data model comprises a plurality of data stations which are communicated with each other, the plurality of data stations are respectively connected with an agricultural participant, the agricultural participant accesses the agricultural data into the data stations, the data stations encrypt and store the agricultural data and verify the agricultural data, a data model is formulated by a data demand party of the agricultural participant, the data model is submitted to a corresponding data station for execution, model data is obtained, and the model data is circulated as trusted circulation data after being related to an agricultural participant identifier, a data model identifier and standard time.
Preferably, the data model is built by the following method: according to the types of the agricultural participants, the data circulation requirements among the agricultural participants are exhausted; obtaining data and data format of a data source party and obtaining data and data format required by a data demand party; the data model is a model for converting data and data formats of a data source side into data and data formats required by a data demand side.
Preferably, the data station makes desensitization data of agricultural data of a plurality of agricultural participants, a data demand party in the agricultural participants makes a data model according to own data demands, the data model is a mapping and calculating combination for converting the desensitization data and the format into required data and the format, the data model is submitted to any one data station, the data station substitutes the desensitization data into the data model, if the data and the format required by the data demand party are obtained, unique identification is given to the data model, the data model is online after the abstract is associated, the data model is assigned according to the designated agricultural participants of the data demand party, the data station sends the online data model to other data stations where the designated agricultural participants are located, the data station calls out the agricultural data required by the designated agricultural participant data model, substitutes the data model, obtains model data, and associates the model data with the agricultural participant identification, the data model identification and standard time to obtain trusted data circulation.
Preferably, when the input data required by the data model is located in a plurality of data stations, the plurality of data stations construct a secure multiparty calculation to obtain the output of the data model, i.e. model data.
Preferably, the data model is associated with a permission identifier, the permission identifier defines a list of agricultural participants capable of obtaining output of the data model, when the data station provides the model data, whether the agricultural participants requesting to obtain the model data meet the permission identifier is verified, if yes, the model data are provided to the agricultural participants, otherwise, the model data are refused to be provided.
Preferably, each time the data station executes the data model, an execution record is established, the called-up agricultural data is packed and a hash value is extracted, the execution record comprises execution time, identification of the data model, a designated agricultural participant, the hash value of the called-up agricultural data and the hash value of the model data, and the newly added execution record is packed and stored periodically.
The invention has the following substantial effects: the agricultural data of the agricultural participants are stored in the data station in an encrypted mode and are verified, and the data is circulated after being converted through the data model, so that the agricultural data is isolated from the outside, the privacy and the safety of the original agricultural data are ensured, and the leakage of the agricultural data cannot occur; the data models are various in types and exactly match the various data requirements; the data model is self-formulated by the data demand party, after the data station is used for uploading the data model, the data demand party designates the data source party, so that the data performance of the data source party under the data model can be obtained under the condition that the original data of the data source party is not known, and the cooperation service can be developed; the data in the data station is stored and verified through the blockchain, and the execution record of the data model is stored and verified, so that the credibility of the data is ensured; therefore, the agricultural data trusted circulation platform which can meet diversified data requirements and effectively protect the safety and privacy of agricultural data is provided, and the development of informatization of agriculture is promoted.
Drawings
Fig. 1 is a schematic structural diagram of an agricultural data trusted circulation platform according to an embodiment.
FIG. 2 is a schematic diagram of a general method for creating a data model according to an embodiment.
Fig. 3 is a schematic diagram illustrating the execution of a data model submitted by the purchasing party of agricultural products according to the second embodiment.
FIG. 4 is a schematic diagram of the execution of a data model submitted by the agricultural regulatory agency of the second embodiment.
FIG. 5 is a schematic diagram of the execution of a data model submitted by a third bank according to an embodiment.
Wherein: 10. data demander 20, data station 30, data source side 201, planting data 202, data model 203, quality 204, yield 205, safe multiparty calculation 206, total yield of production area 207, real estate data 208, real estate data 209, asset data 210, credit line.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings.
Embodiment one:
Referring to fig. 1, an agricultural data trusted circulation platform based on a data model 202 includes a plurality of data stations 20 which are mutually communicated, the plurality of data stations 20 are respectively connected with an agricultural participant, the agricultural participant accesses the agricultural data into the data stations 20, the data stations 20 encrypt, store and verify the agricultural data, the data demand party 10 of the agricultural participant formulates the data model 202, submits the data model 202 to the corresponding data stations 20 for execution, obtains model data, and circulates the model data as trusted circulation data after associating the agricultural participant identification, the data model 202 identification and standard time.
The present embodiment provides a general data model 202 building method, and the method is used to build a mapping relationship or a calculation relationship between data by embedding specific data and data formats. If a certain data value of the data source 30 is limited, a mapping table between the limited value and the data required by the data consumer 10 may be directly established. The values of the crop planting modes include: open air planting, cold shed planting, warm shed planting and glass greenhouse planting. The data required by the data demander 10 is the peasant's resistance to low temperature disasters. The four values and the corresponding low-temperature disaster resistance capacity can be directly classified, and a mapping relation is established. Namely, the low-temperature disaster resistance capability of open-air planting and cold shed planting mapping is weak, and the low-temperature disaster resistance capability of greenhouse planting mapping is strong. The specific planting mode of farmers is not disclosed, and the agricultural disaster prevention department can know the resistance of the farmers in the local area to the low-temperature disasters.
Referring to fig. 2, a data model 202 is built by the following method: step A1) exhausting the data circulation requirements among the agricultural participants according to the types of the agricultural participants; step A2) obtaining data and data format of the data source 30; step A3) obtaining the data and data format required by the data demander 10; step A4) the data model 202 is a model for converting the data and data format of the data source 30 into the data and data format required by the data sink 10.
The data station 20 makes desensitization data of agricultural data of a plurality of agricultural participants, the data demand side 10 in the agricultural participants makes a data model 202 against the desensitization data according to own data demand, the data model 202 is a mapping and calculating combination for converting the desensitization data and format into required data and format, the data model 202 is submitted to any one data station 20, the data station 20 substitutes the desensitization data into the data model 202, if the data and format required by the data demand side 10 are obtained, unique identification is given to the data model 202 and a summary is associated, the data model 202 is online, according to the designated agricultural participants of the data demand side 10, the data station 20 sends the online data model 202 to other data stations 20 where the designated agricultural participants are located, the data station 20 calls out the agricultural data required by the designated agricultural participants, substitutes the data model 202 into the data model 202, obtains model data, associates the model data with the agricultural participant identification, the data model 202 identification and standard time, and then the data is used as trusted data circulation. When the input data required by the data model 202 is located at a plurality of data stations 20, the plurality of data stations 20 construct secure multiparty calculations 205, obtaining the output of the data model 202, i.e., model data.
The data model 202 is associated with a permission identity defining a list of agricultural participants who are able to obtain the output of the data model 202, and when the data station 20 provides the model data, it is verified whether the agricultural participants requesting to obtain the model data meet the permission identity, if so, the model data are provided to the agricultural participants, otherwise, the model data are refused to be provided. Each time the data station 20 executes the data model 202, an execution record is established, the called-up agricultural data is packed and a hash value is extracted, the execution record includes execution time, an identification of the data model 202, a designated agricultural participant, the called-up hash value of the agricultural data and the hash value of the model data, and the newly added execution record is periodically packed and stored.
The beneficial effects of this embodiment are: by encrypting and storing the agricultural data of the agricultural participants and verifying the agricultural data in the data station 20, the data is converted through the data model 202 and circulated, so that the agricultural data is isolated from the outside, the privacy and safety of the original agricultural data are ensured, and the leakage of the agricultural data cannot occur. The data model 202 is heterogeneous and exactly matches the diversified data requirements. The data model 202 is self-formulated by the data demand party 10, after the data station 20 puts the data model 202 on line, the data demand party 10 designates the data source party 30, so that the data performance of the data source party 30 under the data model 202 can be obtained without knowing the original data of the data source party 30, and the cooperation service can be developed. The data in the data station 20 is authenticated through the blockchain, and the execution record of the data model 202 is authenticated, so that the credibility of the data is ensured; therefore, the agricultural data trusted circulation platform which can meet diversified data requirements and effectively protect the safety and privacy of agricultural data is provided, and the development of informatization of agriculture is promoted.
Embodiment two:
In the embodiment, the agricultural data trusted circulation platform based on the data model 202 is used for pre-purchasing agricultural products, so that the field purchasing of the agricultural products is realized. In this embodiment, a plurality of data stations 20 are established, the plurality of data stations 20 are distributed in a rural area, each village maintains one data station 20, and farmers in the village access planting data 201 of the farmers into the data stations 20. Referring to fig. 3, if a farmer uses a planting greenhouse with a higher degree of automation, the controller of the planting greenhouse is connected to the data station 20. If ordinary greenhouse or open-air planting is adopted, the sensors including an environment temperature and humidity sensor, an illumination sensor, a rainfall sensor, an air speed sensor, a pest sensor and a growth sensor are installed, and the planted seed varieties, the planting areas, the geographical positions, the water fertilizers, the planting areas and the farmers are required to be input into the data station 20 through the data input device. The data station 20 synchronizes the data of the weather department to obtain weather data. Part of the rural areas have gradually begun to build informationized systems from which seed data, meteorological monitoring data, and soil monitoring data can be obtained. The data model 202 in this embodiment is a crop growth model that performs predictions of field crop quality 203 and yield 204. Many growth models are published by agricultural research institutions, i.e. growth models are known in the art. Such as: "[1] psilosis, yang Junyun, tan Jing, et al. Predicting the formation of high quality protein corn biological yield 204304 using a CERES corn growth model 302 [ J ]. Proc. Southwest agriculture, 2001, 23 (1): 1-3 ], provided a growth model 302 for screening high quality protein corn requirements. "[1] Li Wei ] A study of the difference in spring corn yield 204304 potential and yield 204304 from Jilin province based on the corn growth model 302 [ D ]. Jilin university, 2016." provides a growth model 302 that predicts yield 204304. Other agricultural products, such as soybeans, peanuts, rice, wheat, and the like, also have disclosed techniques for predicting yield 204 and quality 203.
According to planting data 201 accessed by farmers, a growth model calculates crop quality 203 and yield 204 of each planting area of each farmer, and the quality 203 and the yield 204 are related to the farmers and the planting areas and then are used as trusted data to circulate in limited agricultural product trading markets. The purchaser of the agricultural product can search the farmers meeting the quality 203 requirement according to the own demand to make bid purchase according to the information by registering account numbers and purchasing rights to the agricultural product trading market.
Referring to fig. 4, a plurality of data stations 20 in a crop producing area execute the data model 202, and the obtained model data, i.e., quality 203 and yield 204, are calculated in a multiparty manner to obtain the total yield 206 in the producing area. The total yield 204 is circulated to the agricultural regulatory authorities. The agricultural supervision department can estimate market supply conditions according to the trusted circulation information. If the yield 204 is too high, an early decision can be made as to whether the government has accepted the purchase to stabilize the price of the agricultural product. If the yield 204 is low, the reserve agricultural product can be prepared for leaving the warehouse to be put on the market as early as possible according to the trusted circulation data so as to prevent the market from being over-supplied and under-supplied.
It can be seen that, in this embodiment, although the planting data 201 such as seed variety, water and fertilizer condition, temperature and humidity, and plant diseases and insect pests used by each farmer is not always separated from the data station 20, and is not disclosed to the outside, the privacy is strong. However, other agricultural participants can obtain the wanted data, which is convenient for the development of the business.
By adopting the agricultural data trusted circulation platform, each participant can obtain the wanted data, the data of each participant can be kept private, and the flooding of the data is avoided, so that the real and fake differentiation is difficult.
Embodiment III:
With the development of the agricultural production to mechanization, modernization and intellectualization, the agricultural equipment used in the agricultural production process is more advanced and the functions are more and more rich. But at the same time the investment in earlier stages increases. Resulting in the need for some farmers to lend themselves to the bank.
In the conventional lending mode, farmers need to fill in lending application data, and banks need to verify the asset condition recorded in the application data. If the verification is wrong, risks may be brought to the bank. If farmers want a plurality of banks to try to apply for loans, the farmers need to fill in the same application data for many times, and the efficiency is quite low.
In this embodiment, the trusted circulation platform of agricultural data based on the data model 202 allows the user to access the data station 20 for more than a predetermined period of time, such as 2 years. In the course of 2 years, the user's planting data 201, annual planting area, yield 204, quality 203 and final benefit have been stored in the data station 20. When the farmer needs to apply for loan, the bank is informed. The bank then transmits the trust model it has developed, i.e. the data model 202 used in this embodiment, to the data station 20 to which the farmer is connected. Referring to fig. 5, the data station 20 retrieves the basic data of farmers including the property, vehicle, forest, current field crop, agricultural implement, livestock, cultivated land area, deposit, annual average income and securities, wherein the property, vehicle, forest, agricultural implement, livestock and cultivated land area are written into the data station 20 by the village committee organization and provide verification and assurance, or provide related documents issued by the town government or notarization. The best mode is that the information is directly synchronized by a blockchain-based agricultural big data system established by villages and towns. However, in view of the fact that large data systems have not been established in most rural areas, there is still a need to achieve this by means of conventional trust-providing means. The bank can also verify the asset condition by itself, and after verification, the bank should submit the verification result with a named verification report to the data station 20 where the farmer is located. Thus, other banks do not need to check, or the bank can determine by itself according to whether the assets are easy to change or not when the bank borrows next time, and the bank can not check any more if appropriate.
After the credit model of the bank is submitted to the data station 20, the credit limit 210 under the credit model is obtained. If the amount of the loan applied by the peasant household is not higher than the credit line 210, the bank can directly inform the peasant household to transact the loan contract signing and issuing business, which is very efficient and simple. In this process, the bank does not know what kind of real estate data 207 or real estate data 208 the farmer has in particular, nor does it know the deposit and securities asset data 209 of the farmer, but only knows the asset data 209 of the farmer, and under the rules formulated by itself, the credit line 210 can be obtained. The credit line 210 is a type of trusted circulation data.
Similarly, if a farmer applies for loans to multiple banks at the same time, the credit line that the farmer can obtain may be different in the trust model of the multiple banks. Farmers also select the most favored bank to conduct loan business.
In this embodiment, the farmer successfully applies for the loan and the bank also puts in the funds. However, during this process, the farmer's asset data 209 does not leave the data station 20, and remains private to the bank. But the bank can be trusted to know the credit line that the farmer's assets can give. Solves the trust problem of both parties and promotes the development of agriculture.
The above-described embodiment is only a preferred embodiment of the present invention, and is not limited in any way, and other variations and modifications may be made without departing from the technical aspects set forth in the claims.
Claims (2)
1. An agricultural data trusted circulation platform based on a data model is characterized in that,
The system comprises a plurality of data stations which are communicated with each other, wherein the plurality of data stations are respectively connected with an agricultural participant, the agricultural participant accesses the agricultural data into the data stations, the data stations encrypt and store the agricultural data and verify the agricultural data, a data model is formulated by a data demand party of the agricultural participant, the data model is submitted to a corresponding data station for execution, model data is obtained, and the model data is circulated as trusted circulation data after being related to an agricultural participant identifier, a data model identifier and standard time;
The data station makes desensitization data of agricultural data of a plurality of agricultural participants, a data demand party in the agricultural participants makes a data model according to own data demands, the data model is a mapping and calculating combination for converting the desensitization data and formats into required data and formats, the data model is submitted to any one data station, the data station substitutes the desensitization data into the data model, if the data and formats required by the data demand party are obtained, unique identification is given to the data model, the data model is uploaded after the abstract is associated, the data model is appointed by the data demand party, the data station sends the online data model to other data stations where the appointed agricultural participants are located according to the data demand party, the data station calls out the agricultural data required by the appointed agricultural participant's data model, substitutes the data model, obtains model data, and associates the model data with the agricultural participant identification, the data model identification and standard time and then uses the model data as trusted data circulation;
The data model is established by the following method:
According to the types of the agricultural participants, the data circulation requirements among the agricultural participants are exhausted;
obtaining data and data format of a data source party and obtaining data and data format required by a data demand party;
The data model is a model for converting data and data formats of a data source party into data and data formats required by a data demand party;
The data model is associated with a permission identifier, the permission identifier defines an agricultural participant list capable of obtaining output of the data model, when the data station provides model data, whether an agricultural participant requesting to obtain the model data accords with the permission identifier is verified, if so, the model data is provided for the agricultural participant, otherwise, the model data is refused to be provided;
And each time the data station executes the data model, an execution record is established, the called-out agricultural data is packed and a hash value is extracted, the execution record comprises the execution time, the identification of the data model, the designated agricultural participator, the hash value of the called-out agricultural data and the hash value of the model data, and the newly added execution record is packed and stored periodically.
2. The data model-based agricultural data trusted circulation platform of claim 1, wherein,
When the input data required by the data model are located in a plurality of data stations, the data stations construct secure multiparty computation to obtain the output of the data model, namely model data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110724882.6A CN113487443B (en) | 2021-06-29 | 2021-06-29 | Agricultural data trusted circulation platform based on data model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110724882.6A CN113487443B (en) | 2021-06-29 | 2021-06-29 | Agricultural data trusted circulation platform based on data model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113487443A CN113487443A (en) | 2021-10-08 |
| CN113487443B true CN113487443B (en) | 2024-04-30 |
Family
ID=77936526
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110724882.6A Active CN113487443B (en) | 2021-06-29 | 2021-06-29 | Agricultural data trusted circulation platform based on data model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113487443B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107194822A (en) * | 2017-05-25 | 2017-09-22 | 河南嘉禾智慧农业科技有限公司 | A kind of agricultural data shared system and method based on block chain |
| CN107633181A (en) * | 2017-09-12 | 2018-01-26 | 复旦大学 | The data model and its operation system of data-oriented opening and shares |
| CN109729168A (en) * | 2018-12-31 | 2019-05-07 | 浙江成功软件开发有限公司 | A kind of data share exchange system and method based on block chain |
| CN112347495A (en) * | 2020-11-15 | 2021-02-09 | 北京物资学院 | Trusted privacy intelligent service computing system and method based on block chain |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8505027B2 (en) * | 2005-12-22 | 2013-08-06 | Oracle Otc Subsidiary Llc | Elective data sharing between different implementations of a software product |
| US11423465B2 (en) * | 2019-01-07 | 2022-08-23 | Masters Choice | Systems and methods for facilitating agricultural transactions |
-
2021
- 2021-06-29 CN CN202110724882.6A patent/CN113487443B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107194822A (en) * | 2017-05-25 | 2017-09-22 | 河南嘉禾智慧农业科技有限公司 | A kind of agricultural data shared system and method based on block chain |
| CN107633181A (en) * | 2017-09-12 | 2018-01-26 | 复旦大学 | The data model and its operation system of data-oriented opening and shares |
| CN109729168A (en) * | 2018-12-31 | 2019-05-07 | 浙江成功软件开发有限公司 | A kind of data share exchange system and method based on block chain |
| CN112347495A (en) * | 2020-11-15 | 2021-02-09 | 北京物资学院 | Trusted privacy intelligent service computing system and method based on block chain |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113487443A (en) | 2021-10-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Adimassu et al. | Understanding determinants of farmers’ investments in sustainable land management practices in Ethiopia: Review and synthesis | |
| Ortega Álvarez et al. | MOPECO: an economic optimization model for irrigation water management | |
| US20130018586A1 (en) | Field and Crop Information Gathering System | |
| Baldos et al. | Understanding the spatial distribution of welfare impacts of global warming on agriculture and its drivers | |
| WO2001075706A1 (en) | Agricultural management system for providing agricultural solutions and enabling commerce | |
| Kuchimanchi et al. | Assessing differential vulnerability of communities in the agrarian context in two districts of Maharashtra, India | |
| KR102271036B1 (en) | Platform using cloud-based Big Data | |
| Roxburgh et al. | Ex-ante analysis of opportunities for the sustainable intensification of maize production in Mozambique | |
| Krejci et al. | Modeling food supply chains using multi-agent simulation | |
| Williams et al. | An investigation of farm-scale adaptation options for cotton production in the face of future climate change and water allocation policies in southern Queensland, Australia | |
| WO2019239422A1 (en) | System and method for digital crop lifecycle modeling | |
| Tesfaye et al. | Climate change adaptation measures by farm households in Gedeo zone, Ethiopia: An application of multivariate analysis approach | |
| Kassaye et al. | Evaluating the practices of climate-smart agriculture sustainability in Ethiopia using geocybernetic assessment matrix | |
| Gurung et al. | Profitability, marketing, and resource use efficiency of ginger production in Rukum west, Nepal | |
| CN113487443B (en) | Agricultural data trusted circulation platform based on data model | |
| Haensch et al. | Explaining permanent and temporary water market trade patterns within local areas in the southern Murray–Darling Basin | |
| Ziolkowska | Profitability of irrigation and value of water in Oklahoma and Texas agriculture | |
| CN103077474A (en) | Agricultural information management method assisted by map software | |
| Stephen et al. | Economics of rice production among beneficiaries of anchor borrowers programme in Gerie local government area of Adamawa State, Nigeria | |
| Karunanayaka et al. | Transforming agriculture supply chain with technology adoption-: A critical review of literature | |
| WO2022032385A1 (en) | A forestry method | |
| Masikati et al. | Integrated assessment of crop–livestock production systems beyond biophysical methods: role of systems simulation models | |
| Suthar et al. | The agriculture block chain: an overview | |
| Prasad et al. | Edge Computing and Blockchain in Smart Agriculture Systems | |
| Amwata | Effects of communal and individual land tenure systems on land use and food security in Kajiado District, Kenya |
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 | ||
| GR01 | Patent grant |