CN108628973B - Agricultural data sharing system based on block chain - Google Patents
Agricultural data sharing system based on block chain Download PDFInfo
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- CN108628973B CN108628973B CN201810380542.4A CN201810380542A CN108628973B CN 108628973 B CN108628973 B CN 108628973B CN 201810380542 A CN201810380542 A CN 201810380542A CN 108628973 B CN108628973 B CN 108628973B
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Abstract
The invention provides an agricultural data sharing system based on a block chain, which comprises a data acquisition module, a transmission module, a data processing module, a key management distribution module and a block chain adaptation module, wherein the data acquisition module is used for acquiring data; the data acquisition module acquires crop data information and crop original owner identity information and sends the crop data information and the crop original owner identity information to the data processing module through the transmission module; the data processing module comprises a preprocessing unit, a clustering unit, an abnormality detection unit and a database, wherein the clustering unit is used for clustering the preprocessed crop data information; the abnormality detection unit is used for carrying out abnormality detection processing on the clustered crop data information; the key management distribution module is used for distributing a security key to the data information of each part in the system; the block chain adaptation module is used for receiving and sharing crop data information.
Description
Technical Field
The invention relates to the technical field of agricultural management, in particular to an agricultural data sharing system based on a block chain.
Background
With the improvement of living standard, people continuously pursue improvement of living quality, clothes and eating houses are used as important parts in the life of people, food safety and health are closely related to the health state of people, but the existing crop production and transaction has the following problems: firstly, production transaction information is blocked, and a third-party user is difficult to acquire original data of a crop growth process; secondly, transaction trust is difficult to establish between an acquirer and a grower (an original owner of crops), the existing crops are difficult to realize organic production, even if the acquirer or a third party wants to perform safe ecological detection on the crops, the acquirer or the third party can only perform random sampling detection generally, and the real detection purpose is difficult to achieve; third, it is difficult for the existing equipment systems and the like to provide a true objective pricing model for crops, and thus to achieve true transparent fairness in the market.
Disclosure of Invention
In order to solve the problems, the invention provides an agricultural data sharing system based on a block chain.
The purpose of the invention is realized by adopting the following technical scheme:
the agricultural data sharing system based on the block chain comprises a data acquisition module, a transmission module, a data processing module, a key management distribution module and a block chain adaptation module; the data acquisition module acquires crop data information and crop original owner identity information and sends the crop data information and the crop original owner identity information to the data processing module through the transmission module; the data processing module comprises a preprocessing unit, a clustering unit, an abnormality detection unit and a database; the preprocessing unit preprocesses the crop data information with a 0 value or a negative value, and replaces the 0 value or the negative value with a preset substitute value; the clustering unit is used for clustering the preprocessed crop data information; the anomaly detection unit is used for carrying out anomaly detection processing on the clustered crop data information, marking the abnormal crop data information, sending the original owner identity information of the crops and the processed crop data information to the database for storage, and sending the original owner identity information of the crops and the processed crop data information to the block chain adaptation module; the key management distribution module is used for distributing a security key to the data information of each part in the system; the block chain adaptation module is used for receiving and sharing crop data information.
Preferably, the data acquisition module comprises a timer, a crop growth remote sensing monitor, a GPS and a pesticide residue detector.
Preferably, the crop data information includes growth time of the crop, growth condition of the crop, pesticide residue of the crop, growth cost of the crop, growth location of the crop, market reference value of the crop, and average time of maturity of the crop.
Preferably, the receiving and sharing of crop data information by the blockchain adaptation module specifically includes:
(1) establishing a crop data partition block chain, and broadcasting data information after obtaining the key to the crop data partition block chain;
(2) establishing a regional block chain of the purchasing party, verifying the identity and purchasing the warehouse position information by the purchasing party, and broadcasting the identity information and the warehouse position information to the regional block chain of the purchasing party;
(3) and establishing a crop transaction whole block chain, connecting the purchasing party block chain with the crop data block chain through a consensus network, and establishing the crop transaction whole block chain, so that a third party can obtain related data information of crops by accessing the transaction whole block chain.
The invention has the beneficial effects that: the crop data information is ingeniously shared through the block chain, the technical barrier of information blocking of original agricultural product transaction is broken through, and original data are provided for a buyer and a third party to check the data information such as crop safety and health.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a system architecture connection block diagram of an exemplary embodiment of the present invention;
fig. 2 is a schematic structural diagram of a data processing module according to an exemplary embodiment of the present invention.
Reference numerals:
the system comprises a data acquisition module 1, a transmission module 2, a data processing module 3, a key management distribution module 4, a block chain adaptation module 5, a preprocessing unit 10, a clustering unit 20, an abnormality detection unit 30 and a database 40.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1 and fig. 2, the embodiment provides an agricultural data sharing system based on a blockchain, which includes a data acquisition module 1, a transmission module 2, a data processing module 3, a key management distribution module 4, and a blockchain adaptation module 5; the data acquisition module 1 acquires crop data information and crop original owner identity information and sends the crop data information and the crop original owner identity information to the data processing module 3 through the transmission module 2; the data processing module 3 comprises a preprocessing unit, a clustering unit, an abnormality detection unit and a database; the preprocessing unit preprocesses the crop data information with a 0 value or a negative value, and replaces the 0 value or the negative value with a preset substitute value; the clustering unit is used for clustering the preprocessed crop data information; the anomaly detection unit is used for carrying out anomaly detection processing on the clustered crop data information, marking the abnormal crop data information, sending the original owner identity information of the crops and the processed crop data information to the database for storage, and sending the original owner identity information of the crops and the processed crop data information to the block chain adaptation module 5; the key management distribution module 4 is used for distributing security keys to data information of each part in the system; the block chain adaptation module 5 is used for receiving and sharing crop data information.
In one embodiment, the data acquisition module 1 comprises a timer, a crop growth remote sensing monitor, a GPS and a pesticide residue detector.
The crop data information comprises the growth time of crops, the growth condition of the crops, the pesticide residue of the crops, the growth cost of the crops, the growth positions of the crops, the market reference value of the crops and the average time for the crops to mature.
In one embodiment, the blockchain adaptation module 5 receives and shares crop data information, including:
(1) establishing a crop data partition block chain, and broadcasting data information after obtaining the key to the crop data partition block chain;
(2) establishing a regional block chain of the purchasing party, verifying the identity and purchasing the warehouse position information by the purchasing party, and broadcasting the identity information and the warehouse position information to the regional block chain of the purchasing party;
(3) and establishing a crop transaction whole block chain, connecting the purchasing party block chain with the crop data block chain through a consensus network, and establishing the crop transaction whole block chain, so that a third party can obtain related data information of crops by accessing the transaction whole block chain.
According to the embodiment of the invention, the crop data information is shared skillfully through the block chain, the technical barrier of information blockage of the original agricultural product transaction is broken through, and the original data is provided for the purchasing party and the third party to check the data information such as the safety and health of the crop. The embodiment preprocesses the crop data information, and can prevent the 0 value or the negative value in the crop data information from influencing the subsequent crop data information clustering processing.
In an embodiment, the clustering unit 20 clusters the preprocessed crop data information, which specifically includes:
(1) extracting a set number of crop data information as a crop data information set, setting the crop data information set as Y, and determining the weight value of each dimension attribute value of the crop data information in the crop data information set Y;
(2) sorting the crop data information in the crop data information set Y according to the sequence from large attribute value to small attribute value with the largest weight value, and selecting the median as the first cluster center point Q1: calculating data information of other crops and the central point Q of the cluster1Similarity between the crop data information yiAnd cluster center point Q1If the similarity between the crop data information y and the crop data information y is greater than the set similarity threshold valueiIs assigned to the cluster center point Q1And marking is carried out;
(3) sorting the remaining unmarked crop data information according to the sequence from large to small of the attribute value with the largest weight value, and selecting the median as the central point Q of the next clusterλ+1Calculating data information of other crops and the central point Q of the clusterλ+1The similarity between them;
crop data information yjWhen not marked, if the crop data information yjAnd Qλ+1If the similarity between the crop data information y and the crop data information y is greater than the set similarity threshold valuejIs assigned to the cluster center point Qλ+1And marking is carried out; crop data information yjWhen marked, set crop data information yjAnd Qλ+1The similarity between them is Z (y)j,Qλ+1) Crop data information yjThe similarity with the cluster center point to which it is now assigned is Z (y)j,Qj0) Only when Z (y)j,Qλ+1)>Z(yj,Qj0) In time, crop data is sentMessage yjIs assigned to the cluster center point Qλ+1Otherwise, the marked crop data information y is not usedjAny operation is carried out;
(4) repeating (3) until all the crop data information are marked, and executing (5);
(5) if a cluster containing crop data information is found, deleting the crop data information of the cluster from a crop data information set Y, and switching to execute (2), otherwise, executing (6);
(6) and updating the cluster center point of each cluster to be the average value of all crop data information in the cluster, distributing each non-cluster center point to the cluster where the cluster center point with the highest similarity is located, and stopping the algorithm when all the cluster center points are not updated any more.
Wherein, it is assumed that the crop data information set Y ═ Y1,y2,…,ynAnd d, the dimensionality of each crop data information is beta, and the variation coefficient of the alpha-dimension attribute value of the crop data information in the crop data information set Y is obtained:
in the formula, yiaCrop data information Y being a set of crop data information YiA-th dimension of (a), 1, …, β;
setting the weight value of each dimension attribute value of the crop data information in the crop data information set Y according to the following formula:
in the formula, WaA weight value representing the a-th dimension attribute value of the crop data information in the crop data information set Y, a being 1, …, β, WaThe weighted value of the a-dimensional attribute value of the crop data information set by the expert, h is a set influence coefficient, and the value range of h is [0.80,0.90 ]]。
The embodiment sets a clustering mechanism aiming at the crop data information, and the mechanism can simply and quickly finish the clustering of the crop data information without pre-specifying the number of clusters; in this embodiment, the crop data information is sorted according to the descending order of the attribute values with the largest weight values, and the median is selected as the central point of each cluster, so that the selection of the central points of the clusters is more uniform, and the clustering effect on the crop data information is favorably improved.
In this embodiment, when a cluster including one piece of crop data information is found, the crop data information of the cluster is deleted from the crop data information set Y, so that the crop data information is no longer used as a cluster center point, and generation of an empty cluster or a cluster including only one piece of crop data information can be avoided.
The similarity between the crop data information and the cluster center point can be calculated by using the existing similarity function, for example, cosine similarity, pearson correlation coefficient, and the like are used for measurement.
In a preferred embodiment, crop data information y is setuAnd cluster center point QlThe calculation formula of the similarity between the two is as follows:
in the formula, Z (y)u,Ql) Representing crop data information yuAnd cluster center point QlSimilarity between, yuaRepresenting crop data information yuThe a-th dimension of the attribute value, ylaRepresents the cluster center point QlMax represents a maximum value, WaWeight value, W, of a-dimensional attribute value representing crop data information7And c is 1, …, and β are dimensions of the crop data information.
In the prior art, absolute distance is often used to measure the difference between two crop data information, such as euclidean distance, manhattan distance, etc., that is, the greater the distance between two crop data information is, the smaller the similarity between the two crop data information is, and vice versa, the greater the similarity is, but this distance measurement method usually involves all attributes of the object, and the importance of these attributes to the distance measurement is considered to be the same.
Compared with the prior art, the calculation formula of the similarity is innovatively set, different weight values are added to the attribute values in different dimensions through the calculation formula, and differences of crop data information in different dimensions can be distinguished.
Only when the attribute values of each dimension of a pair of crop data information are in the same range, the pair of crop data information are similar, so that the embodiment does not adopt a general distance formula, and measures the similarity by the maximum variation degree of the attribute values of all dimensions between two crop data information. Because the similarity of the embodiment only depends on the weighted attribute value ratio of the two crop data information in the same dimension, the similarity has dimension independence and is particularly suitable for clustering the crop data information collected by the system.
In an embodiment, the anomaly detection unit 30 performs anomaly point detection on the clustered crop data information, specifically including:
(1) if the number of the crop data information of one cluster is lower than a set number threshold after clustering, the cluster is regarded as an abnormal cluster, and all the crop data information in the abnormal cluster is regarded as abnormal crop data information;
(2) calculating the similarity between the cluster center points of other normal clusters and the cluster center point of the abnormal cluster;
(3) if the similarity between the cluster center point of an abnormal cluster and the cluster center point of a normal cluster is larger than a set cluster similarity threshold, the normal cluster is used as a cluster to be detected, and crop data information in the cluster to be detected is detected by using the crop data information of the abnormal cluster, and the method specifically comprises the following steps:
1) setting the crop data information set of the abnormal cluster as Yk＝{y1,y2,..,ykWill { y }1,y2,..,ykThe crop data information in theNormalizing, sorting the normalized crop data information in a descending order, performing reverse normalization on the sorted crop data information, and acquiring a median y in a crop data information set after the reverse normalizationmed；
2) Detecting crop data information in the cluster to be detected, if the crop data information in the cluster to be detected
When the following abnormal conditions are satisfied, crop data information is transmitted
Data information of crops considered as abnormal:
in the formula (I), the compound is shown in the specification,
representing crop data information
The a-th dimension of the attribute value, ymed,aRepresenting the median ymedA-th dimension of the attribute value, ZtIs another set similarity threshold, WaWeight value, W, of a-dimensional attribute value representing crop data information7And c is 1, …, and β are dimensions of the crop data information.
Because crop data information in the smaller-scale clusters is relatively loose and isolated relative to other crop data information, the data in the smaller-scale clusters are generally regarded as abnormal data in the prior art.
Based on this, the present embodiment performs anomaly detection on the clustered crop data information, and innovatively provides an anomaly condition for detecting whether the crop data information is abnormal, where the anomaly condition determines whether the crop data information is abnormal according to the similarity between the crop data information and the median of the abnormal cluster with the highest similarity, so that the method has a certain detection accuracy, and the detection method is simple and effective. Abnormal crop data information after clustering is detected, and the abnormal crop data information is marked, so that personnel who check the crop data information can determine the abnormal condition of the crop data information in time.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (4)
1. An agricultural data sharing system based on a block chain is characterized by comprising a data acquisition module, a transmission module, a data processing module, a key management distribution module and a block chain adaptation module; the data acquisition module acquires crop data information and crop original owner identity information and sends the crop data information and the crop original owner identity information to the data processing module through the transmission module; the data processing module comprises a preprocessing unit, a clustering unit, an abnormality detection unit and a database; the preprocessing unit preprocesses the crop data information with a 0 value or a negative value, and replaces the 0 value or the negative value with a preset substitute value; the clustering unit is used for clustering the preprocessed crop data information; the anomaly detection unit is used for carrying out anomaly detection processing on the clustered crop data information, marking the abnormal crop data information, sending the original owner identity information of the crops and the processed crop data information to the database for storage, and sending the original owner identity information of the crops and the processed crop data information to the block chain adaptation module; the key management distribution module is used for distributing a security key to the data information of each part in the system; the block chain adaptation module is used for receiving and sharing crop data information; the block chain adaptation module receives and shares crop data information, and specifically includes:
(1) establishing a crop data partition block chain, and broadcasting data information after obtaining the key to the crop data partition block chain;
(2) establishing a regional block chain of the purchasing party, verifying the identity and purchasing the warehouse position information by the purchasing party, and broadcasting the identity information and the warehouse position information to the regional block chain of the purchasing party;
(3) establishing a crop transaction whole block chain, connecting the purchasing party block chain with the crop data block chain through a consensus network, and establishing the crop transaction whole block chain, so that a third party can obtain related data information of crops by accessing the transaction whole block chain;
the abnormal detection unit carries out abnormal point detection on the crop data information after the clustering process, and specifically comprises the following steps:
(1) if the number of the crop data information of one cluster is lower than a set number threshold after clustering, the cluster is regarded as an abnormal cluster, and all the crop data information in the abnormal cluster is regarded as abnormal crop data information;
(2) calculating the similarity between the cluster center points of other normal clusters and the cluster center point of the abnormal cluster;
(3) if the similarity between the cluster center point of an abnormal cluster and the cluster center point of a normal cluster is larger than a set cluster similarity threshold value, taking the normal cluster as a cluster to be detected, and detecting crop data information in the cluster to be detected by using the crop data information of the abnormal cluster;
the clustering unit is used for clustering the preprocessed crop data information, and specifically comprises the following steps:
(1) extracting a set number of crop data information as a crop data information set, setting the crop data information set as Y, and determining the weight value of each dimension attribute value of the crop data information in the crop data information set Y;
(2) sorting the crop data information in the crop data information set Y according to the sequence from large attribute value to small attribute value with the largest weight value, and selecting the median as the first cluster center point Q1: calculating data information of other crops and the central point Q of the cluster1Similarity between the crop dataInformation yiAnd cluster center point Q1If the similarity between the crop data information y and the crop data information y is greater than the set similarity threshold valueiIs assigned to the cluster center point Q1And marking is carried out;
(3) sorting the remaining unmarked crop data information according to the sequence from large to small of the attribute value with the largest weight value, and selecting the median as the central point Q of the next clusterλ+1Calculating data information of other crops and the central point Q of the clusterλ+1The similarity between them;
crop data information yjWhen not marked, if the crop data information yjAnd Qλ+1If the similarity between the crop data information y and the crop data information y is greater than the set similarity threshold valuejIs assigned to the cluster center point Qλ+1And marking is carried out; crop data information yjWhen marked, set crop data information yjAnd Qλ+1The similarity between them is Z (y)j,Qλ+1) Crop data information yjThe similarity with the cluster center point to which it is now assigned is Z (y)j,Qj0) Only when Z (y)j,Qλ+1)>Z(yj,Qj0) Time, crop data information yjIs assigned to the cluster center point Qλ+1Otherwise, the marked crop data information y is not usedjAny operation is carried out;
(4) repeating (3) until all the crop data information are marked, and executing (5);
(5) if a cluster containing crop data information is found, deleting the crop data information of the cluster from a crop data information set Y, and switching to execute (2), otherwise, executing (6);
(6) updating the cluster center point of each cluster to be the average value of all crop data information in the cluster, distributing each non-cluster center point to the cluster where the cluster center point with the highest similarity is located, and stopping the algorithm when all the cluster center points are not updated any more;
wherein, it is assumed that the crop data information set Y ═ Y1,y2,…,ynData information of each cropThe dimensions of the data set are beta, and the variation coefficient of the a-dimensional attribute value of the crop data information in the crop data information set Y is obtained:
in the formula, yiaCrop data information Y being a set of crop data information YiA-th dimension of (a), 1, …, β;
setting the weight value of each dimension attribute value of the crop data information in the crop data information set Y according to the following formula:
in the formula, WaA weight value representing the a-th dimension attribute value of the crop data information in the crop data information set Y, a being 1, …, β, WaThe weighted value of the a-dimensional attribute value of the crop data information set by the expert, h is a set influence coefficient, and the value range of h is [0.80,0.90 ]]。
2. The block chain-based agricultural data sharing system according to claim 1, wherein the data acquisition module comprises a timer, a crop growth remote sensing monitor, a GPS and a pesticide residue detector.
3. The blockchain-based agricultural data sharing system according to claim 1, wherein the crop data information includes growth time of the crop, growth vigor of the crop, pesticide residue of the crop, growth cost of the crop, growth location of the crop, market reference price of the crop, and average time of maturity of the crop.
4. The system according to claim 1, wherein the detecting of the crop data information in the cluster to be detected by using the crop data information of the abnormal cluster comprises:
(1) setting the crop data information set of the abnormal cluster as Yk＝{y1,y2,..,ykWill { y }1,y2,..,ykNormalizing the crop data information in the data unit, sorting the normalized crop data information in a descending order, performing reverse normalization on the sorted crop data information, and acquiring a median y in a crop data information set after the reverse normalizationmed；
(2) Detecting crop data information in the cluster to be detected, if the crop data information in the cluster to be detected
When the following abnormal conditions are satisfied, crop data information is transmitted
Data information of crops considered as abnormal:
in the formula (I), the compound is shown in the specification,
representing crop data information
The a-th dimension of the attribute value, ymed,aRepresenting the median ymedA-th dimension of the attribute value, ZtIs another set similarity threshold, WaWeight value, W, of a-dimensional attribute value representing crop data informationcAnd c is 1, …, and β are dimensions of the crop data information.
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