CN115660696A - Animal individual tracing consensus method with data verification function - Google Patents
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Abstract
The invention discloses an animal individual source tracing consensus method with a data verification function, and belongs to the technical field of block chain consensus. According to the method, the culture data measurement value is verified through a time sequence model based on an XGboost algorithm and a data verification algorithm, an exception handling mechanism is added, an exception source is judged, and abnormal Internet of things equipment or animal individuals are checked in time; determining a leader node according to the priority coefficient of the calculation node, packaging the block by the leader node and initiating block verification; and finally, according to the block verification information, the computing node feeds back a block verification result through a practical Byzantine fault-tolerant algorithm, and the block passing the verification is written into the main chain. Compared with the traditional culture data maintenance system, the invention has the innovation points that the safety and the accuracy of culture data maintenance are ensured and the automation degree of the culture system is improved through the multiple verification mechanisms of the XGboost algorithm-based data verification, the exception handling mechanism, the node priority coefficient competition mode and the practical Byzantine fault-tolerant algorithm.
Description
Technical Field
The invention relates to the technical field of machine learning and block chaining, in particular to an animal individual source tracing consensus method with a data verification function.
Background
The block chain is a centralization shared general ledger which combines data blocks into a specific data structure in a chain mode according to time sequence and is ensured in a cryptographic mode in a non-falsification and non-forgery way. A decentralized infrastructure and distributed computing paradigm for verifying and storing data using a cryptographically chained block structure, generating and updating data using a distributed node consensus algorithm, programming and manipulating data using automated script code (smart contracts). The core advantage of the blockchain technology is decentralization, consensus can be achieved in a distributed system with nodes not needing to trust each other by means of data encryption, timestamps, distributed consensus, economic incentives and the like, point-to-point transaction, coordination and cooperation based on decentralization credit are achieved, and therefore a solution is provided for solving the problems of high cost, low efficiency, unsafe data storage and the like of a centralization mechanism. At present, the mainstream consensus mechanisms include a workload certification mechanism (POW), a rights and interests certification mechanism (POS), a stock authorization certification mechanism (DPOS), a practical byzantine fault-tolerant mechanism (PBFT), and the like, but most of the current consensus mechanisms have the defects of excessive resource consumption, low efficiency, poor adaptability, and the like.
Disclosure of Invention
In order to solve the defects of high labor cost, irregular data updating, easiness in tampering, unretraceable historical data and the like of the traditional animal husbandry breeding mode, the invention provides an animal individual source tracing consensus method with a data verification function, which is simple and efficient in consensus process and can effectively overcome the defects of low data safety, unretraceable historical data and the like in the animal husbandry breeding process.
In order to achieve the purpose, the invention adopts the following technical scheme: an animal individual source tracing consensus method with a data verification function is characterized by comprising the following steps:
step 1: the data nodes acquire individual breeding data and transmit the individual breeding data to the computing nodes, the computing nodes acquire unique individual identification marks through an individual identification method, and the identification marks are used for corresponding data and individuals, inquiring and comparing subsequent related data and the like. The data nodes are internet of things equipment, the computing nodes are equipment with programming and computing capabilities, and the breeding data comprise data related to individual growth such as weight, body temperature, colony house temperature and colony house humidity;
step 2: the calculation node predicts the individual real-time data according to the individual identity identification and the data type to obtain an individual real-time data expected value, calculates a coefficient which is similar to the expected value and is obtained from a measured value transmitted by the data node according to a preset rule, verifies the breeding data transmitted by the data node in the step 1, and judges whether the data is abnormal or not. The data prediction model is obtained by taking historical culture data as a data set and training based on an XGboost algorithm;
and step 3: when abnormal data occur, the computing node judges the data abnormal source according to a preset rule, wherein the data abnormal source comprises data node abnormality and individual abnormality early warning. And if the data node is abnormal, reporting the number of the data node to a system administrator, and if the individual is abnormal, obtaining an individual health score according to an individual health score algorithm and feeding the individual health score back to the system. Finally, the computing node packs the data to generate a transaction;
and 4, step 4: the system calculates the priority coefficient of each computing node according to a preset rule and selects one computing node as a leader node;
and 5: the leader node verifies the transaction according to a preset rule, confirms the identity and transaction format of the calculation node, packs the transaction generation block after verification passes, broadcasts the block to the network and simultaneously initiates block verification;
step 6: and the computing node verifies the blocks according to a preset rule and feeds back verification results to the system, and when the system receives a certain number of block output verification passing feedback results, the block output is proved to be successful, and an intelligent contract is called to finish the block output.
Further, in step 2, the preset rule is:
1. the calculation node calculates the similar coefficient of the measured value and the expected value transmitted by the data node according to a similar coefficient formula;
2. inputting the similar coefficients into a similar threshold function;
3. and (3) verifying the cultivation data transmitted by the data nodes in the step (1) through a data verification algorithm.
Further, the formula of the proximity coefficient is as follows:
where x represents a measure of the real-time data of the individual, x ＇ Represents the expected value of the individual real-time data, mu represents the adjustment coefficient, and S represents the coefficient for the measured value of the individual real-time data to be close to the expected value.
Further, the close threshold function is:
where γ is shown as the individual real-time data anomaly threshold and F (S) represents the proximity threshold function.
Further, the data verification algorithm is as follows:
1. when data is updated, if the node C is calculated i Fails to receive the corresponding data node D i The transmitted data generates a link error, and the node C is calculated i And a data node D i Communication abnormity occurs;
2. computing node C i Obtaining expected value x of real-time data of individual through individual identity identification and data prediction module ＇ ；
3. Computing node C i Obtaining the predicted value x of the individual real-time data by calculation according to the formula 1 ＇ Judging the individual real-time data measured value x as valid data or abnormal data according to a function value of a similar threshold function F (S) and a similar coefficient S of the measured value x;
4. when the function value of F (S) is 0, the individual real-time data measured value x is valid data, and when the function value of F (S) is 1, the individual real-time data measured value x is abnormal data;
5. when x is valid data, transaction T i The early warning position of (1) 0; when x is abnormal data, transaction T i And position 1 is pre-warned.
Further, in step 3, the preset rule is:
1. when the data is abnormal data, the computing node triggers early warning and records the data node identification and the individual identity identification of the data;
2. the computing node computes an abnormal coefficient according to the abnormal coefficient formula and judges an abnormal source through an early warning function;
3. the anomaly coefficient formula is:
wherein D ij As a data node D i Transaction T generated by packaging transmitted data j N is a data node D i The total number of data transmitted;
4. the early warning function is:
wherein F i (E) The data node early warning function is adopted, and delta is an early warning threshold value;
5. when the early warning function value is 0, the data node D is represented i A program failure occurs; when the early warning function value is 0, the data node is normal, and at the moment, the individual health score needs to be calculated.
Further, in step 3, the individual health evaluation algorithm is as follows:
1. establishing an evaluation factor set, which comprises the following specific steps:
and establishing a hierarchical index system by taking the state parameters of the individual breeding data maintained by the system as evaluation factors. The target layer individual health score can be decomposed into Q evaluation items, and then the Q evaluation items are continuously decomposed into index layers;
2. determining the weight of each stage of evaluation factors
；
3. Determining the state parameter of the index layer, wherein if the data is normal, the state parameter is 0, and if the data is abnormal, the state parameter is 1;
4. the formula for calculating the individual health score is as follows:
wherein H represents the individual health score, n represents the number of evaluation items in the item layer, and W i Weight, W, of evaluation item i j Denotes S i Weight of each index in (1), T j Denotes S i The state parameters of the indexes.
Further, in step 4, the preset rule is:
1. calculating the priority coefficient of each calculation node according to a priority coefficient formula, wherein the priority coefficient formula is as follows:
wherein beta is a node priority coefficient, alpha is the number of days for which the node joins the network, B is the number of times for which the node successfully generates the block, sigma is a very small number to prevent the condition that the node priority coefficient is 0, T is an average time interval, and T is the time interval between the calculation node and the last participation in consensus;
2. adopting a roulette algorithm to select a leader node;
further, the roulette algorithm is as follows:
according to the priority coefficient R of the computing node i And calculating the selected probability algorithm:
wherein R is i Representing the priority coefficient of the ith computing node, n representing the total number of candidate nodes, R k Computing node D k The priority coefficient of (2);
a cumulative probability algorithm for all the computing nodes selected as leader nodes:
the leader node is selected according to the roulette algorithm, i.e., at [0,1]Generating a random number r, Q on the interval i-1 <r≤Q i Then node D is calculated i Successful election as leader, e.g. r<Q 1 Then node D is calculated 1 And if the leader node is successfully selected, the step is continuously executed in a period.
Further, in step 5, the preset rule is:
1. the leader node verifies the identity of the trading confirmation node through an asymmetric encryption algorithm;
2. the leader node calls a transaction verification program to verify whether the computing node normally packages the transaction;
3. the leader node packs the transaction generation block;
4. the leader node broadcasts the packed blocks and block hashes to the network and initiates block validation.
Further, in step 6, the preset rule is:
1. the computing node confirms the identity of the computing node of the packaging transaction through an asymmetric encryption algorithm;
2. the calculation node calls a transaction verification program to verify the block and returns a verification result to the system;
3. when the system receives the result that the verification of the block exceeding the lambda is passed, the block is proved to be successful, and the system calls an intelligent contract to finish the block output;
4. if the block is successfully output, the computing node which fails in block output verification is a program exception;
5. if the block is failed to be output, the leader node generates program abnormity, and the system reselects the leader node for repeated operation;
6. and giving corresponding rewards to the leader node which successfully delivers the blocks and the computing node which is correctly verified by the blocks, and carrying out program inspection on the leader node which fails to deliver the blocks and the computing node which fails to verify the blocks.
The invention has the beneficial effects that: the novel animal individual source tracing consensus method with the data verification function is provided, the traditional animal husbandry centralized management mode is changed through characteristics such as a prediction model based on a time sequence, facial recognition, block chain decentralized and consensus mechanism, the labor cost of animal husbandry breeding is reduced, the defects that manual data maintenance is easy to tamper, the historical data updating process cannot be traced, individual abnormity cannot be found in time and the like are overcome, a brand new operation mode that the individual data maintenance process is recorded in the whole process, the individual is abnormally fed back in time is provided, and all data information is stamped, traceable and not tampered; according to the invention, the individual identification technology can adopt a face identification technology, and the individual real-time image is used for identifying the unique identity of the individual at any time to obtain all data related to the individual, so that the labor cost is greatly reduced; secondly, by taking individual historical growth data as a data set, predicting the expected value of the individual real-time breeding data based on an XGboost time sequence model, verifying the measured value of the individual data in real time and identifying an abnormal source according to the provided algorithm, and realizing the automation of data maintenance. Thirdly, calculating the individual health score according to the proposed algorithm, and carrying out health assessment on individuals with possible abnormalities, so that timely feedback of individual abnormalities is realized, and the timeliness of individual abnormality discovery is improved; and fourthly, the computing node verifies the block output by using a Byzantine fault-tolerant algorithm (PBFT) to ensure that the block output is correct. And fifthly, the data maintenance adopts an intelligent contract technology, and an intelligent contract script language is automatically executed, so that the data maintenance process is transparent, accurate and easy to trace.
Drawings
Fig. 1 is a diagram of an algorithm structure according to the present invention, and fig. 2 is a flowchart of the present invention.
Detailed Description
The invention provides an animal individual source tracing consensus method with a data verification function, and for the purpose, technical scheme and advantages of the invention to be more clearly understood, the invention is further described in detail with reference to specific examples.
The block chain type in the invention selects the alliance chain, and the system has the advantages of determinacy, consistency, observability, verifiability, high efficiency, real-time property and the like by applying the intelligent contract.
An animal individual source tracing consensus method with a data verification function is applied to a livestock breeding system based on a block chain, and the livestock breeding system comprises a node management module, a data acquisition module, a data prediction module, a data analysis module, an early warning processing module and a database.
The node management module is used for managing the data nodes and the computing nodes;
the data acquisition module is used for acquiring various required data information;
the data prediction module is used for predicting the individual real-time data expected value;
the data analysis module is used for intelligently summarizing and analyzing the big data according to the related parameters of the historical data;
the early warning processing module is used for processing early warning feedback;
the database is used for storing individual information.
The specific process of maintaining the breeding data is described in detail by taking the weight of the deer individual as an example, wherein the individual identification obtains the unique identity of the individual through an image identification method.
1. In the embodiment of the invention, the weight data of deer are acquired by data nodes-Internet of things equipment according to a certain time period, and the data are transmitted to computing nodes, wherein the computing nodes have computing and programming capabilities, the number Y =3 of the data nodes, the number M =10 of the computing nodes and the number N =10 of individual deer are acquired, in the process of updating the weight data of the deer, the data nodes transmit the weight data and the individual real-time images to the computing nodes, the computing nodes recognize unique identification marks of the deer through the images and obtain expected values of real-time deer breeding data through a data prediction module, and the measured values of the weight data and the expected values of the real-time data are shown in a table 1:
table 1: measured and expected values
2. The data node data is received by the computing node, and because all the data nodes return data information, the communication between the computing node and the data nodes is not abnormal,
the calculation node calculates according to formula 1 to obtain a similar coefficient between the real-time data measurement value and the predicted value, in this example, the adjustment coefficient μ =0.1 of the similar coefficient formula, and the calculated similar coefficient is shown in table 2:
the measured value is verified by calculating the function value of the similar threshold function according to the formula 2, in this example, the threshold γ =0.25 of the similar threshold function, and the function value of the similar threshold of the data to be verified is calculated as shown in table 3:
table 2: coefficient of closeness of measured value to expected value
Table 3: proximity threshold function value of data to be verified
<xnotran> 1 , , 3 9, , 0,0, 0,0, 0,0, 0,0, 1,0. </xnotran>
3. Due to triggering of an early warning mechanism, the abnormal coefficient of the data node 3 is calculated by the computing node according to a formula 3 and is 0.33, the abnormal coefficient is input into an early warning function in a formula 4 to judge an abnormal data source, in the example, the early warning threshold value delta =0.6 of the early warning function, the early warning function value is 1, the data node 3 has no program fault, and the individual health score of the deer with the identity mark of 9 needs to be calculated according to an individual health scoring algorithm.
4. Calculating the individual health score of the deer with the identity mark of 9 according to an individual health score algorithm:
(1) Establishing an evaluation factor set, decomposing a target layer into two evaluation items which are respectively an environment factor set S in the embodiment of the invention 1 And an individual data factor set S 2 And then solving two evaluation items to an index layer, wherein the final evaluation factor set is shown in table 4:
table 4: set of evaluation factors
(2) Determining the weight of each level of evaluation factors;
the evaluation factor weights in this example are shown in table 5:
table 5: evaluation factor weight
(3) Determining the state parameters of the index layer, and obtaining the state parameters of the individual index layer of the deer with the identity mark of 9 through inquiry, wherein the state parameters are shown in a table 6:
table 6: state parameter of index layer
(4) And (3) obtaining the individual health score of the deer with the identity mark of 9 according to a formula 5, and feeding back the identity mark and the health score of the deer to the early warning processing module.
5. The priority coefficients of all the calculation nodes are calculated according to formula 6, and the result is shown in table 7:
table 7: calculating node priority coefficients
Election leader node, calculate 10 computing nodes and getSelected probability P i Respectively as follows:
0.081，0.084,0.195，0.068，0.102，0.072,0.150,0.123,0.067,0.057，
cumulative probability Q i Respectively as follows: 0.081,0.165,0.360,0.428,.0530,0.602,0.752,0.875,0.942,1,
random number r =0.65, so the computing node with i =7 becomes the leader.
6. After the leader node completes verification of the transaction, the transaction is packaged to generate a block and block verification is initiated, and verification results returned by all the computing nodes with λ =1/2 in the block verification threshold in the example are shown in table 8:
table 8: verifying feedback results
At the moment, the proportion of the verification result of the block output is 9/10, the block output is successful, and the leader node and the computing node which successfully output the block are rewarded, wherein the computing node 10 may have an abnormal block output verification program.
Claims (7)
1. The individual animal source tracing consensus method with the data verification function is characterized by mainly comprising the following steps of:
step 1: the data node acquires individual breeding data and transmits the individual breeding data to the computing node, the computing node acquires an individual unique identity through an individual identification method, and the identity is used for the correspondence of the data and the individual and the inquiry, comparison and the like of subsequent related data;
the data nodes are internet of things equipment, the computing nodes are equipment with programming and computing capabilities, and the breeding data comprise data related to individual growth such as weight, body temperature, colony house temperature and colony house humidity;
step 2: the calculation node predicts the individual real-time data according to the individual identity identification and the data type to obtain an individual real-time data expected value, calculates a coefficient which is similar to the expected value and is obtained from a measured value transmitted by the data node according to a preset rule, verifies the breeding data transmitted by the data node in the step 1, and judges whether the data is abnormal or not;
the data prediction model is obtained by taking historical breeding data as a data set and training the historical breeding data based on an XGboost algorithm;
and step 3: when abnormal data occur, the computing node judges a data abnormal source according to a preset rule, wherein the data abnormal source comprises data node abnormality and individual abnormality early warning; if the data nodes are abnormal, reporting the data node numbers to a system administrator, if the individual abnormal early warning is performed, obtaining an individual health score according to an individual health score algorithm and feeding the individual health score back to the system, and finally packaging the data by a computing node to generate a transaction;
and 4, step 4: the system calculates the priority coefficient of each computing node according to a preset rule and selects one computing node as a leader node;
and 5: the leader node verifies the transaction according to a preset rule, confirms the identity and transaction format of the computing node, packs the transaction generation block after the verification passes, broadcasts the block to the network and simultaneously initiates block verification;
step 6: and the computing node verifies the blocks according to a preset rule and feeds back verification results to the system, and when the system receives a certain number of block output verification passing feedback results, the block output is proved to be successful, and an intelligent contract is called to finish the block output.
2. The individual animal source tracing consensus method with data verification function as claimed in claim 1, wherein: in step 2, the preset rule is as follows:
(1) The calculation node calculates the similar coefficient of the measured value and the expected value transmitted by the data node according to a similar coefficient formula;
the formula of the similar coefficient is as follows:
where x represents a measure of the real-time data of the individual, x ＇ Representing the expected value of the individual real-time data, mu representing the adjustment factorS represents a coefficient of closeness of the measured value of the individual real-time data to the expected value,
(2) Inputting the close coefficients into a close threshold function;
the near threshold function is:
where γ is shown as the individual real-time data anomaly threshold, F (S) represents a near threshold function,
(3) And (3) verifying the cultivation data transmitted by the data nodes in the step (1) through a data verification algorithm.
3. The individual animal source tracing consensus method with data verification function as claimed in claim 2, wherein: the data verification algorithm is as follows:
(1) When data is updated, if the node C is calculated i Fails to receive the corresponding data node D i The transmitted data generates a link error, and the node C is calculated i And a data node D i Communication abnormity occurs;
(2) Computing node C i Obtaining expected value x of real-time data of individual through individual identity identification and data prediction module ＇ ；
(3) Computing node C i Calculating to obtain an individual real-time data predicted value x according to formula 1 ＇ Judging the individual real-time data measured value x as valid data or abnormal data according to a function value of a similar threshold function F (S) and a similar coefficient S of the measured value x;
(4) When the function value of F (S) is 0, the individual real-time data measured value x is valid data, and when the function value of F (S) is 1, the individual real-time data measured value x is abnormal data;
(5) When x is valid data, transaction T i The early warning position of (1) 0; when x is abnormal data, transaction T i And (5) early warning position 1.
4. The individual animal source tracing consensus method with data verification function as claimed in claim 1, wherein: in step 3, the preset rule is as follows:
(1) When the data is abnormal data, the computing node triggers early warning and records the data node identification and the individual identity identification of the data;
(2) The computing node computes an abnormal coefficient according to the abnormal coefficient formula, and judges an abnormal source through an early warning function;
(3) The anomaly coefficient formula is:
wherein D ij As a data node D i Transaction T generated by packaging transmitted data j N is a data node D i The total number of data transmitted is,
(4) The early warning function is:
wherein F i (E) Is a data node early warning function, delta is an early warning threshold,
(5) When the early warning function value is 0, indicating that the data node i has a program fault; when the early warning function value is 0, the data node is normal, and at the moment, the individual health score needs to be calculated.
5. The individual animal source tracing consensus method with data verification function according to claim 1, wherein: in step 3, the individual health evaluation algorithm is as follows:
(1) Establishing an evaluation factor set, which comprises the following specific steps:
taking the state parameters of the individual breeding data maintained by the system as evaluation factors to establish a hierarchical index system;
the target layer individual health score can be decomposed into Q evaluation items, and then the Q evaluation items are continuously decomposed into index layers;
(2) Determining the weight of each stage of evaluation factors
；
(3) Determining the state parameter of the index layer, wherein if the data is normal, the state parameter is 0, and if the data is abnormal, the state parameter is 1;
(4) The formula for calculating the individual health score is as follows:
wherein H represents the individual health score, n represents the number of evaluation items in the item layer, and W i Weight, W, representing evaluation item i j Denotes S i Weight of each index in (1), T j Denotes S i The state parameters of the indexes.
6. The animal individual source-tracing consensus method with data verification function as claimed in claim 1, wherein: in step 4, the preset rule is as follows:
(1) Calculating the priority coefficient of each calculation node according to a priority coefficient formula, wherein the priority coefficient formula is as follows:
wherein beta is a node priority coefficient, alpha is the number of days for which the node joins the network, B is the number of times for which the node successfully generates the block, sigma is a very small number to prevent the condition that the node priority coefficient is 0, T is an average time interval, and T is the time interval between the calculation node and the last participation in consensus;
(2) And adopting a roulette algorithm to select the leader node.
7. The individual animal source tracing consensus method with data verification function according to claim 1, wherein: in step 6, the preset rule is as follows:
(1) The computing node confirms the identity of the computing node of the packaging transaction through an asymmetric encryption algorithm;
(2) The calculation node calls a transaction verification program to verify the block and returns a verification result to the system;
(3) When the system receives the result that the verification of the block exceeding the lambda is passed, the block is proved to be successful, and the system calls an intelligent contract to finish the block output;
(4) If the block is successfully output, the computing node which fails in block output verification is a program exception;
(5) If the block is failed to be output, the leader node generates program abnormity, and the system reselects the leader node for repeated operation;
(6) And giving corresponding rewards to the leader node which successfully delivers the blocks and the computing node which is correctly verified by the blocks, and carrying out program inspection on the leader node which fails to deliver the blocks and the computing node which fails to verify the blocks.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111461793A (en) * | 2020-04-27 | 2020-07-28 | 吉林省桥王智能科技有限公司 | Integral chain consensus method based on activity probability selection |
| CN117035802A (en) * | 2023-04-19 | 2023-11-10 | 吉林农业科技学院 | Consensus method for predicting animal health based on capacity demonstration double test |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111461793A (en) * | 2020-04-27 | 2020-07-28 | 吉林省桥王智能科技有限公司 | Integral chain consensus method based on activity probability selection |
| CN111461793B (en) * | 2020-04-27 | 2023-10-10 | 吉林省桥王智能科技有限公司 | Integration chain consensus method based on liveness probability selection |
| CN117035802A (en) * | 2023-04-19 | 2023-11-10 | 吉林农业科技学院 | Consensus method for predicting animal health based on capacity demonstration double test |
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