CN112330238A - Improved practical Byzantine consensus method based on food supply chain subject credit - Google Patents
Improved practical Byzantine consensus method based on food supply chain subject credit Download PDFInfo
- Publication number
- CN112330238A CN112330238A CN202010781396.3A CN202010781396A CN112330238A CN 112330238 A CN112330238 A CN 112330238A CN 202010781396 A CN202010781396 A CN 202010781396A CN 112330238 A CN112330238 A CN 112330238A
- Authority
- CN
- China
- Prior art keywords
- node
- food
- consensus
- credit
- main
- 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.)
- Granted
Links
- 235000013305 food Nutrition 0.000 title claims abstract description 100
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000008569 process Effects 0.000 claims abstract description 17
- 230000007246 mechanism Effects 0.000 claims abstract description 14
- 244000005700 microbiome Species 0.000 claims description 9
- 238000004519 manufacturing process Methods 0.000 claims description 7
- 230000001105 regulatory effect Effects 0.000 claims description 4
- 230000001360 synchronised effect Effects 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 4
- 230000007613 environmental effect Effects 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 7
- 239000003795 chemical substances by application Substances 0.000 description 5
- 230000001717 pathogenic effect Effects 0.000 description 4
- 244000052616 bacterial pathogen Species 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 230000000813 microbial effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000006227 byproduct Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013524 data verification Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 208000015181 infectious disease Diseases 0.000 description 1
- 210000000554 iris Anatomy 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 244000000010 microbial pathogen Species 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 235000021067 refined food Nutrition 0.000 description 1
- 230000004936 stimulating effect Effects 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
-
- 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
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
-
- 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
- G06Q30/00—Commerce
- G06Q30/018—Certifying business or products
- G06Q30/0185—Product, service or business identity fraud
-
- 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
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0609—Buyer or seller confidence or verification
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Entrepreneurship & Innovation (AREA)
- Computer Security & Cryptography (AREA)
- Bioethics (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- General Health & Medical Sciences (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Preparation And Processing Of Foods (AREA)
- Food Preservation Except Freezing, Refrigeration, And Drying (AREA)
Abstract
The invention discloses an improved practical Byzantine consensus method based on food supply chain main body credit, which is characterized in that a main node selection mechanism based on credit randomly selects candidate main nodes to become main nodes, the main nodes need to prepay a certain credit value before consensus, if the consensus is completed, the main nodes return and give a certain reward, and if the consensus fails, the prepayment credit is deducted. The method dynamically adjusts the credit degree of the participating main body and the food reliability degree on the supply chain, and before practical Byzantine consensus, the node expansion and the main node selection are all based on the credit mechanism, so that the participating main body is stimulated to speak credit, the food reliability degree reference is provided for consumers, the consensus efficiency is improved, the safety of the main node is enhanced, the algorithm is more in line with the application scene, and the original limitation is overcome. By improving the consensus method, the consensus efficiency is improved, the safety of the main node is enhanced, and the method is more suitable for a food tracing process based on a block chain technology, so that merchants are more honest and consumers are more relieved.
Description
Technical Field
The invention designs a reward and punishment mechanism and a consensus method in a block chain technology, and relates to a method for evaluating credit of a main body and reliability of food in the reward and punishment mechanism; in the aspect of consensus algorithm, a node expansion mechanism and a main node selection mechanism are involved.
Background
More and more arouse people to the concern of food quality. However, the food quality problem is related to the safety and the personal interests of each consumer, and both the manufacturing enterprises and the consumers want to know the information about the raw materials and sources of the final food or the consumers, so as to ensure the reliability and safety of the food and know the quality level [1 ].
Although the national food and drug quality supervision bureau highly attaches importance to the prevention and treatment of related problems, the problems of food safety are still difficult to treat because fishes which are missed in the net always exist. In order to meet the actual requirements on safe food on one hand and meet the supervision requirements of supervision and management departments on the other hand, a new supervision mode and technical means must be applied to the food supervision field, the whole process of a food supply chain is recorded on the case, once food safety problems occur, specific links and specific persons in charge can be accurately tracked in time, the process of the whole supply chain is completely transparent, and participants on the chain take care of the food safety problems and dare not to falsify the food safety problems [2 ]. The food safety tracing can enable a consumer and a related supervision department to obtain accurate streaming information from the information, the processing of food in each link can be embodied on the system through a user, the person responsible for each link is recorded in the system, and the person responsible for the food safety accident can be effectively positioned and traced to the person responsible for the problem, so that the supervision and control effects are realized. Therefore, the establishment of a decentralized, traceable and monitorable food traceability system has great significance.
However, various information technologies such as two-dimensional bar codes, geographic information systems, Web services, RFID, irises, and the like are used in the current food safety traceability system. However, these databases are centralized databases, and information is easily tampered in the recording process, and cannot be true and credible. The existing two-dimensional code anti-counterfeiting technology has the problems that the two-dimensional code is simple to generate and easy to copy, and the information scanned by the counterfeit two-dimensional code is still real commodity information, so that the anti-counterfeiting traceability requirement required by products cannot be met. The RFID tag has the problems of high price, easy electromagnetic interference on read-write data, difficult encryption of tag data, easy change of data and the like, cannot ensure the safety and reliability of the data, and is difficult to guarantee the credible tracking of the data of products in the production, processing, packaging, supply, sale and other links. The control modes of the tracing system are centralized, the unit responsible for tracing data acquisition work is simplified, so that the centralization of data and the system is caused, the data tampering difficulty is low, the data cannot be identified after tampering, and the data integrity cannot be verified. Such a situation leads to a low degree of information trust provided by the regulatory authorities and consumers for the unofficial leading traceability platform, and affects the implementation effect of the traceability system.
In the food safety tracing problem, the quality and safety of food reaching the hands of consumers are guaranteed. However, the whole social economy develops rapidly, so that people feel fidgety, only want to earn fast money, many food production enterprises do not consider the integrity two characters, the quality of the produced and processed food is not required, and merchants are lacked to actively and actively guarantee the food quality safety, the food reliability and the integrity value of the merchants. Meanwhile, the consensus mechanism is one of core basic technologies of the block chain technology and plays a vital role in a food tracing system based on the block chain. The practical Byzantine fault-tolerant consensus algorithm PBFT is used as a consensus algorithm which is used more in the block chain of the existing alliance, has the advantages of low energy consumption, high throughput and the like, and has the defects of high requirement on bandwidth, fixed node number, lack of dynamics and the like. The food tracing model has high expansibility, a plurality of nodes in a network and high limitation of PBFT.
Disclosure of Invention
The invention aims to provide an improved practical Byzantine consensus method based on food supply chain main body belief, aiming at the problems of merchant dishonest, food quality safety and the defects of fixed nodes and lack of dynamic property of a practical Byzantine fault-tolerant consensus algorithm. The invention dynamically adjusts the credit degree of the participating main body and the food reliability degree on the supply chain, and before the practical Byzantine consensus, the node expansion and the main node selection are all based on the credit mechanism, thereby stimulating the participating main body to speak the credit and providing the food reliability reference for the consumer, improving the consensus efficiency, enhancing the safety of the main node, leading the algorithm to better accord with the application scene of the subject and overcoming the original limitation.
The main idea for realizing the invention is as follows: setting an initialization credit value for each main body in the food tracing process, performing credit evaluation on each main body by dynamically adjusting the credit values of the participants, setting initial reliability for all batches of food by a supervision department, and adjusting the reliability of the food on a chain by corresponding strategies. Then, based on the main body credit mechanism, a practical Byzantine consensus algorithm is improved, in the aspect of node expansion, a new user needs to apply to a supervision department first, and the new user is approved by the supervision department and then joins a network to form a common node; after the common node operates for a period of time, the credit value of the common node reaches a certain threshold value, the common node is applied to become a consensus node, the common node becomes the consensus node after passing the verification of the common node, the common node applies for data synchronization to the candidate main node, and when the synchronous data of the candidate main node exceeding 1/2 are consistent, the synchronization is successful; the consensus node has a certain period, and needs to be added again after the period is over; in the aspect of the main node selection mechanism, the main node selection mechanism is based on credit, namely, a node which exceeds a threshold value and has the highest block height is selected from the consensus nodes to be used as a candidate main node, then the candidate main node is randomly selected to be used as the main node, the main node needs to prepay a certain credit value before consensus is carried out, if the consensus is completed, the main node returns and gives a certain reward, and if the consensus fails, the prepay credit is deducted.
According to the main thought, the specific implementation of the method comprises the following steps:
step 1: setting participation subject initialization credit value S0
Setting an initialization credit value S for each participant producer, carrier, agent and seller in the food tracing process0Dividing into 95 portions;
step 2: calculating a subject credit value Si
According to the corresponding situation, the initial credit value S in step 10Adding or subtracting the scores corresponding to the situations, and dynamically adjusting the final credit values of the manufacturers, the transporters, the agents and the sellers of the participating main bodies to be recorded as Si(i denotes the participating subjects during the transportation of the corresponding food). (and specifies that the score 90 < SiThe merchant with the grade A less than or equal to 100 is the grade A, and the merchant with the grade S less than 80iGrade B is not less than 90, and grade S is not less than 70i80 or less is C grade, 60 or more is SiD is 70 or less, S isi60 or less is the E grade, wherein A>B>C>D>E-level, i.e., a-level is highest, representing the merchant with the highest credit value, most trustworthy).
And step 3: calculating the reliability R of cold transport of foodi
According to a formula of the growth quantity of microorganisms in the food, calculating the reliability R of the food from production, transportation, agency to sale in i logistics linksi；
And 4, step 4: setting food initial reliability F0
For all batches of food, the regulatory authority sets an initial reliability F0100 points are obtained;
and 5: calculating food reliability on a food chain Fi
From the initial reliability F in step 40The credit value S of the participating subject obtained according to the step 2iAnd the reliability R calculated by the influence of environmental factors such as temperature, humidity, time and the like of the food in the transportation process in the step 3iCorrespondingly adding or subtracting, calculating final food reliability Fi(i indicates the participating subjects during the transportation of the corresponding food). (and specifies that the fraction 90 < FiThe merchant with the grade A less than or equal to 100 and the grade F less than 80iGrade B is not less than 90, grade F is more than 70i80 or less is C grade, 60 or more is FiD is 70 or less, F isi60 or less is the E grade, wherein A>B>C>D>The grade E, namely the grade A with the highest grade, represents the highest reliability of the food, and can refer to the purchase with the highest reliability).
Step 6: node expansion before consensus process
Before a new merchant prepares to join a block chain network, the new merchant needs to apply for a supervision department, and the new merchant joins the network after approval by the supervision department to become a common node; after the common node operates for a period of time, the credit value of the common node is S in step 2iReach a certain threshold, i.e. B-rating (80 < S)iLess than or equal to 90), namely applying for becoming a consensus node, forming the consensus node after the verification of the original consensus node is passed, and applying for data synchronization to the candidate main node, wherein when the synchronization data of the candidate main node exceeding 1/2 are consistent, the synchronization is successful; the consensus node has a certain period, and after the period is finished, the merchant is required to join the consensus node again;
and 7: selection of Byzantine consensus algorithm master node
In the consensus node in step 6, the node which exceeds the threshold and has the highest block height is selected as the candidate main node, then the candidate main node is randomly selected to become the main node, and the main node (namely a certain merchant which becomes the main node) needs to prepay a certain credit value before performing consensus (S)iMinus 10 points), returning and giving a certain reward (Si +10 points + reward) if the consensus is completed, and deducting the prepaid credit (S) if the consensus failsiTotal score becomes Si-10)。
Compared with the prior art, the invention has the following obvious advantages and beneficial effects: the invention provides an improved and practical Byzantine consensus method based on food supply chain subject credit. According to the method, the credit degree of the participating main body and the food reliability on the supply chain are dynamically adjusted, and the main node is conditionally selected based on the main body credit extended node before practical Byzantine consensus, so that the credit participation of the main body is stimulated, the food reliability reference is provided for consumers, the consensus efficiency is improved, the safety of the main node is enhanced, the success rate of the consensus process is improved, the original limitation is overcome, and the method is more suitable for food tracing scenes.
Drawings
FIG. 1 is a flow chart of the subject credit and food reliability assessment in accordance with the present invention.
Fig. 2 is a flow chart of node expansion and master node selection based on principal credit according to the present invention.
FIG. 3: the growth rate of the microorganisms is related to the temperature.
FIG. 4: the consensus process diagram of the practical Byzantine consensus algorithm.
Detailed Description
The technical solution of the present invention is further described with reference to the accompanying drawings, in which fig. 1 is a flowchart for evaluating the credit of a subject and the reliability of food, and fig. 2 is a flowchart for selecting a node based on the credit of a subject and a master node.
Step 1: setting participation subject initialization credit value S0
Setting an initialization credit value S for each participant producer, carrier, agent and seller in the food tracing process0Score 95.
Step 2: calculating a subject credit value Si
Starting from the initial credit value S in step 1, according to the following four conditions0Adding or subtracting the scores of the corresponding conditions, and dynamically adjusting the final credit values of the participating main body manufacturers, the transporters, the agents and the sellers to be recorded as Si(i represents the participating subject during the transportation of the corresponding food). (and specifies that the score 90 < SiThe merchant with the grade of A being less than or equal to 100 and the grade of S being more than 80iGrade B is not less than 90, and grade S is not less than 70i80 or less is C grade, 60 or more is SiD is 70 or less, S isi60 or less is the E grade, wherein A>B>C>D>E-level, i.e., a-level is highest, representing the merchant with the highest credit value and the most trustworthy value).
Firstly, the supervision process finds that the main body producer, the carrier, the agent and the seller make fake, and reduces corresponding credit according to the fake making degree;
when the downstream main body of the supply chain carries out data verification on the upstream main body, submitting reporting transaction to the supervision chain after data tampering is found;
when a food safety accident occurs, tracing all participants on a tracing chain, namely a supply chain where the participants are located, positioning the participants to a responsibility source main body according to corresponding food numbers, reducing the credit of the participants according to the accident level, and performing credit adjustment to all downstream participants related to the batch of food to a certain extent;
and fourthly, when the flow of the whole supply chain is finished and the supervision department does not find illegal operation, giving proper credit score to the main body on the supply chain.
And step 3: calculating the reliability R of cold transport of foodi
According to a formula of the growth quantity of microorganisms in the food, calculating the reliability R of the food from production, transportation, agency to sale in i logistics linksi。
For temperature control in food supply processes, the lower the number of pathogenic microorganisms, the higher its reliability. According to the formula for the number of microorganisms growing in the food product:
in the formula: n is a radical oftIs the microbial concentration at time t (cfu/g); n is a radical of0Is the microbial concentration at the start time (cfu/g); b is a parameter suitable for experimental data; Δ T ═ T-TminWherein T is the temperature (. degree. C.) at which the microorganism grows, TminIs the zero growth temperature of the microorganism [3]. Wherein the relationship curve of the growth rate of the microorganism and the temperature is approximately shown in figure 3:
stipulating: the sum of the safety and reliability of the food and the possible pathogenic rate (infection rate) is 1; ② when the concentration of pathogenic bacteria in the food is not more than 1cfu/g, the food safety reliability is 1; thirdly, when pathogenic bacteria in the food reach or exceed pathogenic concentration (health standard published by the state), namely once the pathogenic concentration exceeds the health standard, the food cannot be eaten, and the safety and reliability of the logistics link is 0 at the moment. According to the above specification, let NDRepresenting the pathogenic concentration of microorganisms, and defining the safety and reliability of the food logistics link at the moment t as follows:
Rt＝1-lgNt/lgND (2)
the formula (1) and the formula (2) can be used for obtaining:
in the formula (3)
Equation (3) can be abbreviated as:
Rt＝1-dΔT2t,0≤Rt≤1 (4)
the food logistics link, including production, transportation, agency, sale, etc., follows that the output of the previous link is the input of the next link, and the safety and reliability of the food after passing through i logistics units is formula 5[3 ]:
and 4, step 4: setting food initial reliability F0
For all batches of food, the regulatory authority sets an initial reliability F0100 points.
And 5: calculating food reliability on a food chain Fi
The adjustment range of the food safety reliability is obtained according to the credit value Si of the participating subject accounting for 30% and the safety reliability Ri of the relevant information of the food in the supply process accounting for 70%. From the initial reliability F in step 40The credit value S of the participating subject obtained according to the step 2iAnd step 3, obtaining the reliability R influenced by environmental factors such as temperature, time and the like of the food in the transportation processiCorrespondingly adding or subtracting, calculating final food reliability Fi(i indicates the participating subjects during the transportation of the corresponding food). (and specifies that the fraction 90 < FiThe merchant with the grade A less than or equal to 100 and the grade F less than 80iGrade B is not less than 90, grade F is more than 70i80 or less is C grade, 60 or more is FiD is 70 or less, F isi60 or less is the E grade, wherein A>B>C>D>The highest grade E, i.e. grade a, means that the food is the most reliable, most credible,reference may be made to a purchase).
Step 6: node expansion
When a new department prepares to join the network, the new department applies for the supervision department, and the new department joins the network after approval to become a common node; after the common node operates for a period of time, the credit value of the common node reaches a certain threshold value, the common node is applied to become a consensus node, the common node becomes the consensus node after passing the verification of the common node, the common node applies for data synchronization to the candidate main node, and when the synchronous data of the candidate main node exceeding 1/2 are consistent, the synchronization is successful; the consensus node has a certain period, and the node needs to be added again after the period is over.
And 7: master node selection
The consensus process for the practical Byzantine consensus algorithm is shown in FIG. 4, where C is the client and N is the client0～N3Representing service nodes, in particular, N0Is a master node, N3Is a failed node. When the consensus node is selected, the node which exceeds the threshold value and has the highest block height is selected as the candidate main node, then the candidate main node is randomly selected as the main node, the main node needs to prepay a certain credit value before consensus is carried out, if the consensus is completed, the main node returns and gives a certain reward, and if the consensus fails, the prepay credit is deducted.
Through the improved practical Byzantine consensus method, the consensus efficiency is improved, the safety of the main node is enhanced, and meanwhile, the method is more suitable for a food tracing process based on a block chain technology, so that merchants are more honest and consumers are more relieved.
Claims (2)
1. An improved practical Byzantine consensus method based on food supply chain subject credit, characterized in that: setting an initialization credit value for each main body in the food tracing process, performing credit evaluation on each main body by dynamically adjusting the credit values of the participants, setting initial reliability for all batches of food by a supervision department, and adjusting the reliability of the food on a chain by corresponding strategies; then, based on the main body credit mechanism, a practical Byzantine consensus algorithm is improved, in the aspect of node expansion, a new user needs to apply to a supervision department first, and the new user is approved by the supervision department and then joins a network to form a common node; after the common node operates for a period of time, the credit value of the common node reaches a certain threshold value, the common node is applied to become a consensus node, the common node becomes the consensus node after passing the verification of the common node, the common node applies for data synchronization to the candidate main node, and when the synchronous data of the candidate main node exceeding 1/2 are consistent, the synchronization is successful; the consensus node has a certain period, and needs to be added again after the period is over; in the aspect of the main node selection mechanism, the main node selection mechanism is based on credit, namely, a node which exceeds a threshold value and has the highest block height is selected from consensus nodes to serve as a candidate main node, then the candidate main node is randomly selected to serve as the main node, the main node needs to prepay a certain credit value before consensus is carried out, if the consensus is completed, a certain reward is returned and given, and if the consensus fails, the prepayment credit is deducted.
2. The improved practical byzantine consensus method based on food supply chain subject credit of claim 1, wherein: the specific implementation comprises the following steps:
step 1: setting participation subject initialization credit value S0
Setting an initialization credit value S for each participant producer, carrier, agent and seller in the food tracing process0；
Step 2: calculating a subject credit value Si
According to the corresponding situation, the initial credit value S in step 10Adding or subtracting the scores corresponding to the situations, and dynamically adjusting the final credit values of the participating main body manufacturers, the transporters, the agents and the sellers to be recorded as SiAnd i represents a participating subject in the corresponding food transportation process;
and step 3: calculating the reliability R of cold transport of foodi
According to a formula of the growth quantity of microorganisms in the food, calculating the reliability R of the food from four links of production, transportation, agency to sale and after the food passes through i logistics linksi；
And 4, step 4: setting food initial reliability F0
For all batches of food, the regulatory authority setsInitial reliability F0；
And 5: calculating food reliability on a food chain Fi
From the initial reliability F in step 40The credit value S of the participating subject obtained according to the step 2iAnd 3, the reliability R of the calculation is influenced by the environmental factors of temperature, humidity and time of the food in the transportation processiCorrespondingly adding or subtracting, calculating final food reliability Fi；
Step 6: node expansion before consensus process
Before a new merchant prepares to join a block chain network, the new merchant needs to apply for a supervision department, and the new merchant joins the network after approval by the supervision department to become a common node; after the common node operates for a period of time, the credit value of the common node is S in step 2iWhen a certain threshold value is reached, the common node is applied to become a common node, the common node is verified to be a common node by the original common node, data synchronization is applied to the candidate main node, and when the synchronous data of the candidate main node exceeding 1/2 are consistent, the synchronization is successful; the consensus node has a certain period, and after the period is finished, the merchant needs to join again;
and 7: selection of Byzantine consensus algorithm master node
And (6) selecting the node which exceeds the threshold value and has the highest block height from the consensus nodes in the step 6 as a candidate main node, then randomly selecting the candidate main node to become the main node, wherein a certain credit value needs to be prepaid by a certain merchant which becomes the main node before consensus is carried out, if the consensus is finished, returning is carried out and a certain reward is given, and if the consensus fails, the prepaid credit is deducted.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010781396.3A CN112330238B (en) | 2020-08-06 | 2020-08-06 | Improved practical Bayesian-preemption consensus method based on food supply chain main body credit |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010781396.3A CN112330238B (en) | 2020-08-06 | 2020-08-06 | Improved practical Bayesian-preemption consensus method based on food supply chain main body credit |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112330238A true CN112330238A (en) | 2021-02-05 |
| CN112330238B CN112330238B (en) | 2024-06-21 |
Family
ID=74303880
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010781396.3A Active CN112330238B (en) | 2020-08-06 | 2020-08-06 | Improved practical Bayesian-preemption consensus method based on food supply chain main body credit |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112330238B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114448980A (en) * | 2022-01-06 | 2022-05-06 | 上海应用技术大学 | Improvement method of PBFT algorithm for Internet of things |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019222993A1 (en) * | 2018-05-25 | 2019-11-28 | 北京大学深圳研究生院 | Blockchain consensus method based on trust relationship |
| CN110516965A (en) * | 2019-08-27 | 2019-11-29 | 北京工商大学 | The credible retrospect model of oil and foodstuffs full supply chain and construction method based on block chain |
| CN110677485A (en) * | 2019-09-30 | 2020-01-10 | 大连理工大学 | Dynamic layered Byzantine fault-tolerant consensus method based on credit |
| CN110796547A (en) * | 2019-10-30 | 2020-02-14 | 桂林电子科技大学 | Improved practical Byzantine fault-tolerant system based on alliance block chain |
| CN111431940A (en) * | 2020-04-28 | 2020-07-17 | 安徽农业大学 | Block chain technology-based dry fruit supply chain information tamper-proof implementation method |
-
2020
- 2020-08-06 CN CN202010781396.3A patent/CN112330238B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019222993A1 (en) * | 2018-05-25 | 2019-11-28 | 北京大学深圳研究生院 | Blockchain consensus method based on trust relationship |
| CN110516965A (en) * | 2019-08-27 | 2019-11-29 | 北京工商大学 | The credible retrospect model of oil and foodstuffs full supply chain and construction method based on block chain |
| CN110677485A (en) * | 2019-09-30 | 2020-01-10 | 大连理工大学 | Dynamic layered Byzantine fault-tolerant consensus method based on credit |
| CN110796547A (en) * | 2019-10-30 | 2020-02-14 | 桂林电子科技大学 | Improved practical Byzantine fault-tolerant system based on alliance block chain |
| CN111431940A (en) * | 2020-04-28 | 2020-07-17 | 安徽农业大学 | Block chain technology-based dry fruit supply chain information tamper-proof implementation method |
Non-Patent Citations (1)
| Title |
|---|
| 周杰;李文敬;: "基于云计算的物流区块链共识算法研究", 计算机工程与应用, no. 19, 1 October 2018 (2018-10-01) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114448980A (en) * | 2022-01-06 | 2022-05-06 | 上海应用技术大学 | Improvement method of PBFT algorithm for Internet of things |
| CN114448980B (en) * | 2022-01-06 | 2023-09-01 | 上海应用技术大学 | Improvement method of PBFT algorithm for Internet of things |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112330238B (en) | 2024-06-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106022792A (en) | Block-chain-based food security tracing method and system | |
| CN107103480A (en) | Supply chain management method based on block chain with sovereign right | |
| CN114596094A (en) | Block chain-based carbon general excitation method and system | |
| Xiao | Supervision Strategy Analysis on Price Discrimination of E‐Commerce Company in the Context of Big Data Based on Four‐Party Evolutionary Game | |
| Boyer et al. | Changes in Beef Packers' Market Power after the Livestock Mandatory Price Reporting Act: An Agent‐based Auction | |
| CN112330238A (en) | Improved practical Byzantine consensus method based on food supply chain subject credit | |
| Wang et al. | Research on cooperation strategy of enterprises’ quality and safety in food supply chain | |
| CN113793209B (en) | Intelligent litigation system based on blockchain | |
| Lenhart | The earned income tax credit and food insecurity | |
| CN110472435A (en) | A kind of warrant quantitative evaluation and warrant based on block chain is from being in harmony transaction processing system | |
| CN110728511B (en) | Commodity transaction method and system based on block chain | |
| CN113392379A (en) | Online knowledge sharing method based on block chain intelligent contracts | |
| Li et al. | Game analysis of social capital violations and government regulation in public–private partnership risk sharing | |
| CN111475777A (en) | Block chain intelligent contract upgrading method | |
| CN114741734B (en) | Drug anti-counterfeiting traceability cloud chain data multi-party safe computing method | |
| Fajriyah et al. | The effect of social media instagram promotion on the sale of mariposa novel | |
| CN115170256A (en) | Microgrid power transaction method, system, equipment and storage medium based on block chain and edge calculation | |
| Yang et al. | Bidding process in online auctions and winning strategy: Rate equation approach | |
| Nwe | The Border Trade on Economic Growth in Myanmar | |
| CN116776300B (en) | Copyright article protection circulation system and protection circulation method based on NFT technology | |
| Ianchovichina | Duty Drawbacks, Competitiveness, and Growth: Are Duty Drawbacks Worth the Hassle? | |
| Ma et al. | Research on protection of the agricultural products quality safety based on evolution game from the perspective of the supply chain | |
| Xie et al. | [Retracted] IAPE Exploration under the International Communication Environment Based on Big Data Analysis of Social Network | |
| Deffains et al. | Class actions, compliance and moral cost | |
| Rehman et al. | Dynamics of effective portfolio diversification among EFA markets: a heterogeneous panel analysis |
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 | ||
| GR01 | Patent grant |