CN110532329B - Intelligent bracelet data processing and sharing method based on block chain technology - Google Patents
Intelligent bracelet data processing and sharing method based on block chain technology Download PDFInfo
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Abstract
A data processing and sharing method for an intelligent bracelet based on a block chain technology relates to the technical field of intelligent bracelets and comprises the following steps: (S1) data acquisition: acquiring intelligent bracelet data from a data memory in an intelligent bracelet; (S2) data preprocessing: screening and cleaning the acquired data of the intelligent bracelet to acquire effective data; (S3) data classification and processing: classifying and processing the preprocessed intelligent bracelet data through a data mining algorithm; (S4) data sharing: the processed data are uploaded to the block chain through an encryption algorithm or a consensus algorithm, and the sharing of the data of the intelligent bracelet is realized through a wireless communication mode. The intelligent bracelet user data processing method and the intelligent bracelet user data processing system can acquire personal life information of the intelligent bracelet user, classify and store the information, and analyze the life law of the intelligent bracelet user through calculation, so that the daily work and rest conditions of the user are integrally grasped, the health and the life conditions of the user are favorably monitored, and the user data can be shared to family members, doctors and the like.
Description
Technical Field
The invention relates to the technical field of a block chain, in particular to an intelligent bracelet data processing and sharing method based on the block chain technology.
Background
The intelligent bracelet is a wearable intelligent device, and the intelligent bracelet can help a user to record real-time data such as exercise, sleep and diet in daily life usually, and synchronize the data with iOS or Android equipment, and plays a role in guiding healthy life through the data. Data generated by the smart band are usually wakened by vibration, tracked by sleep, monitored by sports, recorded by meals, measured by heart rate and the like. Along with the development of intelligence bracelet technique, the intelligence bracelet uses for everybody more and more extensively, and the data that the intelligence bracelet produced is also more and more, and how to manage the data of intelligence bracelet also becomes the problem that needs the solution at present urgently.
With the development of communication technologies, various communication technologies are gradually penetrating into digital processing technologies. The block chain is applied to various occasions of data processing due to the advantages of decentralization, openness, transparency, incapability of tampering and the like, and different service requirements can be realized in block chain platforms with different block chain nodes.
The intelligent bracelet in the prior art generally adopts a traditional data encryption mode, data loss is easily caused, and the intelligent bracelet in the prior art has problems in the aspect of charging, after the intelligent bracelet is used for a long time, a charging hole is enlarged due to the friction between the charging hole and the charging connector, so that the stability of the charging connector is reduced, the charging connector shakes to cause poor charging, and the conventional intelligent bracelet is difficult to grasp user personal information on the whole due to long-term use and incapability of storing and sharing a large amount of data for a long time. When the body of the user has problems, it is difficult to comprehensively measure the information of the user from the aspects of usual sleep tracking, exercise monitoring, diet recording and heart rate measurement.
Disclosure of Invention
Aiming at the defects of the prior art, the invention discloses an intelligent bracelet data processing and sharing method based on a block chain technology, which can acquire personal life information of an intelligent bracelet user, classify and store the information, and calculate and analyze the life law of the information, so that the daily work and rest conditions of the user can be grasped on the whole, the health and the life conditions of the user can be monitored, and the user data can be shared to family members, doctors and the like. The data sharing also adopts a block chain technology, and has high mutual confidence, data non-tampering, data traceability, interconnection and intercommunication, distributed storage, decentralization and the like.
The invention adopts the following technical scheme:
a method for processing and sharing data of an intelligent bracelet based on a block chain technology comprises the following steps:
(S1) data acquisition: acquiring intelligent bracelet data from a data memory in an intelligent bracelet;
(S2) data preprocessing: screening and cleaning the acquired data of the smart bracelet to acquire effective data;
(S3) data classification and processing: classifying and processing the preprocessed intelligent bracelet data through a data mining algorithm;
(S4) data sharing: the processed data is computationally transmitted to the block chain through an encryption algorithm or a consensus algorithm, and the data sharing of the intelligent bracelet is realized through a wireless communication mode.
As a further technical scheme of the invention, the data mining algorithm is a clustering algorithm and a random matrix theory algorithm.
As a further technical solution of the present invention, the clustering algorithm is any one of the following algorithms: a decision tree algorithm, a cluster classification algorithm, a BP neural network algorithm, a support vector machine algorithm, a VSM method, a Bayesian classification algorithm or a k-nearest neighbor algorithm.
As a further technical scheme of the invention, a decision tree algorithm is adopted in a classification algorithm of the intelligent bracelet data, and the decision tree algorithm comprises the following steps:
(1) training data: calculating the experience entropy of the data set from the selected big data group of the smart band, and selecting the feature with the largest information gain as the current splitting feature; wherein the calculation formula of the data set empirical entropy H (D) is as follows:
wherein i is the number of data, | D | is the number of all samples in the data set, | k is the number of categories of the target variable, | ckI is the number of samples in the classification, and when calculating, in all features, assuming a, the empirical conditional entropy H (D | a) of the feature a on the data set D is calculated, where the information gain formula for calculating the feature a is:
g (D, a) ═ H (D) — H (D | a) formula (2)
(2) Determining a root node: selecting a root node of the decision tree according to the empirical entropy calculated by the calculation formula; as shown in equations (1) and (2), when selecting the segmentation feature attributes of the current data set, an information gain degree is used for calculation; if the calculated information gain values are large, the greater the purity of return loss on the attribute feature, the attribute of the calculated large information gain value should be at the upper layer of the decision tree and taken as a root node;
(3) determining leaf nodes: selecting leaf nodes of the decision tree according to the calculated empirical entropy; selecting the attribute with smaller information gain value as a leaf node according to the method;
(4) establishing a data model: establishing a data model according to the root node and the leaf node determined by the method;
(5) constructing a decision tree: constructing a decision tree according to the data model; the constructed decision tree is in a tree structure, and the user target value is finally output;
(6) and operating the big data of the intelligent bracelet according to the constructed decision tree and outputting an operation result.
As a further technical solution of the present invention, the data processing method of the random matrix theory algorithm comprises:
constructing a random matrix theoretical model, calculating an initial matrix, substituting parameters expressed by the smart bracelet into a formula model according to the constructed matrix model, wherein the formula of the random matrix theoretical model is as follows:
wherein:
wherein: n represents the kind of data that influences the normal operating of intelligent bracelet, remembers data set Q ═ Q with these data kinds1，Q2，Q3……QNM represents the data types generated by the smart band, and these data types are memorized in the data set P ═ P1，P2，P3……PMDenoted as evaluation T, where the data set generated by the smart band is constructed as a matrix D1Wherein data elements P are collectedijThe measured value is measured at the time of j when the ith intelligent bracelet operates.
As a further technical scheme of the invention, the normalized matrix product eigenvalue D of the random matrix theory algorithmstdThe formula of (1) is:
Dstd＝[w1,w2,w3……wM+N]T(ii) a Formula (6)
Wherein DstdEvaluating a matrix model for the smart band data dependency, wherein w represents D1Minimum sum of matrices D2Matrix formed by matrix minima according to DstdAnd evaluating the influence factors of the normal operation of the intelligent bracelet according to the value.
As a further technical scheme of the invention, the data types influencing the normal operation of the intelligent bracelet comprise magnetic field, temperature, humidity, vibration, noise, current, voltage, ripple waves or harmonic waves, and the data types generated by the intelligent bracelet comprise user sleep time data, exercise time data, meal time data and heart rate measurement data.
As a further technical solution of the present invention, the data DstdWhen the value is between 0 and 30, the intelligent bracelet is considered to have no external interference and normally operate;
the data DstdWhen the value is between 30 and 50, the interference is considered to be very small, and the intelligent bracelet normally operates;
the data DstdWhen the value is greater than 50, the external interference is considered, and the operation of the intelligent bracelet is influenced;
as a further technical solution of the present invention, a software platform adopted by the blockchain is a hyper-hedger Fabric-based modular blockchain solution supporting platform, and a management system adopted by the blockchain includes a management application layer, a blockchain network, an access layer, and a department service system, and the method for implementing data sharing by the blockchain includes a data sending process and a data receiving process:
in the data transmission flow, the method comprises the following steps:
(1) the department service system packs data according to the service definition standard; the block chain management system is internally provided with intelligent bracelet organization business data, and then the business data are distributed;
(2) calling a data submission method of the SDK, and submitting a data attribution main body and packaged service data;
(3) the SDK inquires a public key of a data attribution main body on the node computer, and if the public key cannot be found, a data decryption center interface is called to obtain the public key;
(4) the SDK encrypts the packed service data by using the public key to generate encrypted packed service data;
(5) the SDK uses a Certification Authority (CA) private key signature of a service department together with the data attribution main body and the encrypted packed service data;
(6) and calling the interface of the node machine to submit data, and executing data saving action after the signature is verified successfully by the node machine.
In the data receiving process, the method comprises the following steps:
(1) the SDK signs the query request using C A private key of the business department;
(2) the SDK calls a node machine query interface, and the node machine executes a query action and returns encrypted service data after successfully verifying the signature;
(3) the SDK submits the encrypted service data to a decryption center through a hypertext transfer protocol over secure socket layer (HTTPS) channel for decryption to obtain decrypted service data;
(4) the SDK returns the decrypted service data.
As a further technical scheme of the invention, the wireless communication mode is a ZigBee wireless network, GPRS/CDMA wireless communication or Bluetooth communication mode.
Has the positive and beneficial effects that:
the intelligent bracelet data processing method and the intelligent bracelet data processing system realize the processing of the intelligent bracelet data through a decision tree algorithm and a random matrix theory algorithm, wherein various intelligent bracelet data are classified through the decision tree algorithm, leaf nodes and root nodes of a decision tree are selected according to the empirical entropy calculated by a user, a data model capable of quickly screening various data types of the intelligent bracelet is constructed, and the data screening capacity of the user is accelerated; the classification data screened by the user is further researched and analyzed through a random matrix theory algorithm, the correlation among various data of the intelligent bracelet is analyzed based on a statistical principle, the correlation among various data of the intelligent bracelet is extracted and analyzed through establishing a random matrix model, so that the relation among multi-level data in the intelligent bracelet is timely and accurately analyzed, the user can conveniently find the correlation among the data of the intelligent bracelet, the body problems caused by factors such as the physical condition, the eating habits and the like of the user are found in advance through the relation among the data, and the information of the user in all aspects is obtained through the data information of the intelligent bracelet;
according to the invention, data sharing and transmission are realized through the block chain technology, and the characteristics of decentralization, non-falsification, distributed common accounting, asymmetric encryption, data safe storage and the like of the block chain technology are utilized, so that the safe and credible sharing of the smart band is realized, and the interconnection and intercommunication of data among different people are realized.
Drawings
Fig. 1 is a schematic flow chart illustrating a method for processing and sharing data of an intelligent bracelet according to the invention;
FIG. 2 is a schematic diagram illustrating a decision tree algorithm in a method for processing and sharing data of an intelligent bracelet according to the invention;
FIG. 3 is a schematic diagram of an embodiment of a decision tree algorithm in a method for processing and sharing data of smart band based on a blockchain technique according to the present invention;
FIG. 4 is a schematic diagram illustrating a flow of a random matrix theory algorithm in the method for processing and sharing smart band data based on a blockchain technique according to the present invention;
fig. 5 is a schematic diagram illustrating a data sharing process in a method for processing and sharing smart band data based on a blockchain technique according to the present invention;
fig. 6 is a block chain architecture diagram in the method for processing and sharing data of an intelligent bracelet according to the block chain technology of the present invention;
fig. 7 is a schematic diagram of a block chain structure block header in the method for processing and sharing data of an intelligent bracelet according to the block chain technology of the present invention;
fig. 8 is a schematic diagram of a data architecture in a block chain in the method for processing and sharing data of an intelligent bracelet based on a block chain technology according to the present invention;
fig. 9 is a schematic diagram of a block chain Fabric platform architecture in the method for processing and sharing smart band data based on the block chain technology according to the present invention;
fig. 10 is a block chain node architecture diagram in the method for processing and sharing data of an intelligent bracelet based on the block chain technology.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, and it should be understood that the embodiments described herein are merely for purposes of illustration and explanation, and are not intended to limit the present invention.
As shown in fig. 1, a method for processing and sharing data of an intelligent bracelet based on a blockchain technology includes the following steps:
(S1) data acquisition: acquiring intelligent bracelet data from a data memory in an intelligent bracelet;
(S2) data preprocessing: screening and cleaning the acquired data of the smart bracelet to acquire effective data;
(S3) data classification and processing: classifying and processing the preprocessed intelligent bracelet data through a data mining algorithm; (S4) data sharing: the processed data is computationally transmitted to the block chain through an encryption algorithm or a consensus algorithm, and the data sharing of the intelligent bracelet is realized through a wireless communication mode.
Further, the data mining algorithm is a clustering algorithm and a random matrix theory algorithm.
Further, the clustering algorithm is any one of the following algorithms: a decision tree algorithm, a cluster classification algorithm, a BP neural network algorithm, a support vector machine algorithm, a VSM method, a Bayesian classification algorithm or a k-nearest neighbor algorithm.
As shown in fig. 2, further, the classification algorithm of the smart band data adopts a decision tree algorithm, and the decision tree algorithm includes the following steps:
(1) training data: calculating the experience entropy of a data set from the selected big data group of the smart band, selecting the feature with the largest information gain as the current splitting feature, and selecting the feature with the largest information gain as the current splitting feature; wherein the calculation formula of the data set empirical entropy H (D) is as follows:
wherein i is the number of data, | D | is the number of all samples in the data set, | k is the number of categories of the target variable, | ckI is the number of samples in the classification, and when calculating, in all features, assuming a, the empirical conditional entropy H (D | a) of the feature a on the data set D is calculated, where the information gain formula for calculating the feature a is:
g (D, a) ═ H (D) — H (D | a) formula (2)
(2) Determining a root node: selecting a root node of the decision tree according to the empirical entropy calculated by the calculation formula; as shown in equations (1) and (2), when selecting the segmentation feature attributes of the current data set, an information gain degree is used for calculation; if the calculated information gain values are large, the higher the purity of return loss on the attribute characteristic is, the attribute of the calculated large information gain value should be positioned at the upper layer of the decision tree and taken as a root node;
(3) determining leaf nodes: selecting leaf nodes of the decision tree according to the calculated experience entropy; selecting the attribute with smaller information gain value as a leaf node according to the method;
(4) establishing a data model: establishing a data model according to the root node and the leaf node determined by the method;
(5) constructing a decision tree: constructing a decision tree according to the data model; the constructed decision tree is in a tree structure, and the user target value is finally output;
(6) and operating the big data of the intelligent bracelet according to the constructed decision tree and outputting an operation result.
In order to better understand the above description, the data set of the smart band is given below, and the present invention is further described in conjunction with the data set. As shown in table 1:
TABLE 1 data sheet
Assuming Table 1 as D for a given data set, the minimum entropy decision tree is generated by selecting the optimal features according to the maximum information gain, and the information gains of the features A1, A2, A3, A4 and A5 on the data D are calculated, as shown in Table 1. D1 and D2, D3 in table 1 represent sample subsets having values of 1, 2 and 3 in each feature, respectively, and the data in table 1 can be calculated and counted according to the formula referred to above:
H(D)＝-8/15*log2(8/15)—7/15*log2(7/15)＝0.9968；
g(D,A1)＝H(D)-[8/15*H(D1)+7/15*H(D2)]＝0.2880；
g(D,A2)＝H(D)-[5/15*H(D1)+4/15*H(D2)+6/15*H(D3)]＝0.1398；
g(D,A3)＝H(D)-[3/15*H(D1)+12/15*H(D2)]＝0.0292；
g(D,A4)＝H(D)-[7/15*H(D1)+8/15*H(D2)]＝0.2880；
g(D,A5)＝H(D)-[6/15*H(D1)+4/15*H(D2)+5/15*H(D3)]＝0.4131；
according to the above calculation results, the information gain of the feature a5 is the largest, so a5 is selected as the root node. The samples are divided into 3 combinations according to the value of a5, S1 ═ {2,3,6,8,12,13}, S2 ═ {1,5,7,14}, and S3 ═ 4,9,10,11,15}, wherein the set S2 already belongs to the same class, does not need to be subdivided, and has become leaf nodes. Other root nodes and leaf nodes may be determined using similar methods. The number of constructed decisions is shown in fig. 3, and it can be seen intuitively from fig. 3 that the data to be selected is heart rate data of the elderly aged over 60 years old. Through the mathematical model constructed by fig. 3, the data that the user needs to analyze can be output. If the user wants to examine other data, different root nodes and leaf nodes can be set, such as sleep tracking data, motion monitoring data, meal recording data and the like, and the output data results are different when the root nodes and the leaf nodes are different.
According to the data processed by the decision tree model, further processing the data by using a random matrix theory algorithm, wherein the processing method comprises the following steps:
as shown in fig. 4, firstly, a random matrix theoretical model is constructed, an initial matrix is calculated, and parameters expressed by the smart bracelet are substituted into a formula model according to the constructed matrix model, wherein the formula of the random matrix theoretical model is as follows:
wherein:
wherein: n represents the kind of data that influences the normal operating of intelligent bracelet, remembers data set Q ═ Q with these data kinds1，Q2，Q3……QNM represents the data types generated by the smart band, and the data types are memorized in a data set P ═ P1，P2，P3……PMDenoted as evaluation T, where the data set generated by the smart band is constructed as a matrix D1Wherein data elements P are collectedijThe measured value is measured at the time of j when the ith intelligent bracelet operates.
Further, the normalized matrix product eigenvalue D of the stochastic matrix theory algorithmstdThe formula of (1) is:
Dstd＝[w1,w2,w3……wM+N]T(ii) a Formula (6)
Wherein DstdEvaluating a matrix model for the smart band data dependency, whereinw represents the minimum of the D1 matrix and D2
Matrix formed by matrix minima according to DstdAnd evaluating the influence factors of the normal operation of the intelligent bracelet according to the value.
Furthermore, the data types influencing the normal operation of the intelligent bracelet comprise a magnetic field, temperature, humidity, vibration, noise, current, voltage, ripple waves or harmonic waves, and the data types generated by the intelligent bracelet comprise user sleep time data, exercise time data, meal time data and heart rate measurement data.
The harmonic waves, the magnetic field, the vibration, the temperature and the humidity which affect the normal operation of the intelligent bracelet are respectively tested, and various different data are used as a group of data sets. The data shown in Table 2 were obtained.
TABLE 2 data sheet
Using the above formula, the following calculations can be made:
even if equations (4) and (5) are substituted into the data, respectively, it is found that:
then D is1And D2The data are respectively substituted into the formula (3) to calculate, and the D is finally calculated by the formula (6)stdAs a result of (2), when data DstdWhen the value is between 0 and 30, the intelligent bracelet is considered to have no external interference and normally operate; the data DstdWhen the value is between 30 and 50, the interference is considered to be very small, and the intelligent bracelet normally operates; the data DstdWhen the value is greater than 50, the external interference is considered, and the operation of the intelligent bracelet is influenced; based on this, can realize processing and analysis to intelligent bracelet data.
Further, the software platform adopted by the blockchain is a hyper-hedger Fabric-based modular blockchain solution supporting platform, wherein as shown in fig. 6, the management system adopted by the blockchain comprises a management application layer, a blockchain network, an access layer and a department service system, and the above-mentioned transmission and sharing after data processing are realized through the blockchain management system.
In the block chain architecture, as shown in fig. 8, a 5-layer architecture is adopted, which is, from bottom to top, a data layer, a network layer, a consensus layer, an excitation layer, and an intelligent contract layer. In a data layer, the calculation of the data of the smart band can be realized, in a specific embodiment, an encryption algorithm is adopted to encrypt and decrypt the received data, and the adopted encryption algorithm is DES, 3DES, Blowfish encryption algorithm, Twofish encryption algorithm, IDEA, RC6 or CAST 5. In the network layer, which typically uses a P2P network, each node stores all transaction records in full in the form of blockchains. The incentive layer includes an issuing mechanism and an incentive mechanism. In the consensus layer, a workload attestation mechanism can be implemented. At the application layer, the application of data is implemented by the client.
The method for realizing data sharing through the block chain comprises the following steps:
as shown in fig. 5, it includes a data transmission flow and a data reception flow.
In the data transmission flow, the method comprises the following steps:
(1) the department service system packs data according to the service definition standard; the intelligent bracelet organization business data is arranged in the block chain management system, and then the business data are distributed;
(2) calling a data submission method of the SD K, and submitting a data attribution main body and packaged service data;
(3) the SD K inquires a public key of a data attribution main body on the node machine, and if the public key cannot be found, a data decryption center interface is called to obtain the public key;
(4) the SDK encrypts the packed service data by using the public key to generate encrypted packed service data;
(5) the SDK uses a Certification Authority (CA) private key signature of a service department together with the data attribution main body and the encrypted packed service data;
(6) and calling a node machine interface to submit data, and executing data storage action after the node machine successfully verifies the signature.
In the data receiving process, the method comprises the following steps:
(1) the SDK signs the query request using C A private key of the business department;
(2) the SDK calls a node machine query interface, and the node machine executes a query action and returns encrypted service data after successfully verifying the signature;
(3) the SDK submits the encrypted service data to a decryption center through a hypertext transfer protocol over secure socket layer (HTTPS) channel to be decrypted to obtain decrypted service data;
(4) the SDK returns the decrypted service data.
In data transmission, the wireless communication mode is a ZigBee wireless network, GPRS/CDMA wireless communication or Bluetooth communication mode.
As shown in fig. 7, the blockchain is called a distributed database that is commonly maintained by multiple parties, decentralized, traceable, and not falsifiable, and is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Request data in a certain period can be packed into a data block (block) through a cryptographic technology, and the data block is connected into a chain structure for storage according to time sequence by using a Hash fingerprint. A data block is typically composed of two parts, a block header and a block body. The block header usually stores data such as the version number of the system, the hash value of the previous block, the merkle root, and the timestamp, and the block body contains detailed request data. Taking a bitcoin as an example, the block head of the bitcoin also stores data such as random numbers for mining and the like in addition to the above information, and the block body stores specific transaction data.
As shown in fig. 9, the blockchain platform is a hyper-leader Fabric-based modular blockchain solution support platform. The Fabric platform is an alliance chain structure, supports an intelligent contract technology, does not depend on tokens when the system operates, can support about hundred transactions per second and basically meets the requirement of cross-organization transactions of digital assets between alliances. In addition, the Fabric adopts a modular architecture, wherein a consensus algorithm and the like can be used as a pluggable module for a user to select. Meanwhile, the method can lead a user to redesign and develop a specific module according to the self requirement, so that the Fabric is selected as a block chain foundation platform of the digital asset transaction system. The Fabric mainly comprises member service modules (Membership Services), block chain service modules (Blockchain Services) and chain code service modules (Chaincode Services). The member service module mainly provides functions of member registration, identity management, transaction examination and the like, and performs mechanism registration authentication and transaction authentication through a registration certificate issuing mechanism (ECA) and a transaction authentication center (TCA). The block chain service module is mainly responsible for point-to-point communication between nodes, consensus, and the storage of account book data. The chain code service module provides intelligent contract service, provides a safe contract running environment and the like. Meanwhile, the platform realizes asynchronous communication through an Event Stream (Event Stream) between all the components.
As shown in fig. 10, the block link points are connected in a chain manner, that is, the block link points are connected and communicated with each other through a block chain network, and the nodes are connected in a chain manner, so that information interaction between different nodes can be realized. When data are shared, the data of the intelligent bracelets are alternated and shared through different data nodes.
Although specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that these specific embodiments are merely illustrative and that various omissions, substitutions and changes in the form of the detail of the methods and systems described above may be made by those skilled in the art without departing from the spirit and scope of the invention. For example, it is within the scope of the present invention to combine the steps of the above-described methods to perform substantially the same function in substantially the same way to achieve substantially the same result. Accordingly, the scope of the invention is to be limited only by the following claims.
Claims (6)
1. A method for processing and sharing data of an intelligent bracelet based on a block chain technology is characterized in that: the method comprises the following steps:
(S1) data acquisition: acquiring intelligent bracelet data from a data memory in an intelligent bracelet;
(S2) data preprocessing: screening and cleaning the acquired data of the smart bracelet to acquire effective data; the data mining algorithm is a clustering algorithm and a random matrix theory algorithm; the clustering algorithm is any one of the following algorithms: a decision tree algorithm, a cluster classification algorithm, a BP neural network algorithm, a support vector machine algorithm, a VSM method, a Bayesian classification algorithm or a k-nearest neighbor algorithm; the classification algorithm of the intelligent bracelet data adopts a decision tree algorithm, and the decision tree algorithm comprises the following steps:
(1) training data: calculating the experience entropy of the data set from the selected big data group of the smart band, and selecting the feature with the largest information gain as the current splitting feature; wherein the calculation formula of the data set empirical entropy H (D) is as follows:
wherein i is the number of data, | D | is the number of all samples in the data set, | k is the number of categories of the target variable, | ckI is the number of samples in the classification, and when calculating, in all features, assuming a, the empirical conditional entropy H (D | a) of the feature a on the data set D is calculated, where the information gain formula for calculating the feature a is:
g (D, a) ═ H (D) — H (D | a) formula (2)
(2) Determining a root node: selecting a root node of the decision tree according to the experience entropy calculated by the calculation formula; as shown in equations (1) and (2), when selecting the segmentation feature attributes of the current data set, an information gain degree is used for calculation; if the calculated information gain values are large, the higher the purity of return loss on the attribute characteristic is, the attribute of the calculated large information gain value should be positioned at the upper layer of the decision tree and taken as a root node;
(3) determining leaf nodes: selecting leaf nodes of the decision tree according to the calculated empirical entropy; selecting the attribute with smaller information gain value as a leaf node according to the method;
(4) establishing a data model: establishing a data model according to the root node and the leaf node determined by the method;
(5) constructing a decision tree: constructing a decision tree according to the data model; the constructed decision tree is in a tree structure, and the user target value is finally output;
(6) operating big data of the intelligent bracelet according to the constructed decision tree, and outputting an operation result;
the data processing method of the random matrix theory algorithm comprises the following steps:
constructing a random matrix theoretical model, calculating an initial matrix, substituting parameters expressed by the intelligent bracelet into a formula model according to the constructed matrix model, wherein the formula of the random matrix theoretical model is as follows:
wherein:
wherein: n represents the kind of data that influences the normal operating of intelligent bracelet, remembers data set Q ═ Q with these data kinds1，Q2，Q3……QNM represents the data types generated by the smart band, and these data types are written as a data set P ═ P1，P2，P3……PMDenoted as evaluation T, where the data set generated by the smart band is constructed as a matrix D1WhereinAggregate data element PijThe measured value is measured at the time of j when the ith intelligent bracelet operates; d2The data types influencing the normal operation of the intelligent bracelet comprise a magnetic field, temperature, humidity, vibration, noise, current, voltage, ripple waves or harmonic waves;
(S3) data classification and processing: classifying and processing the preprocessed intelligent bracelet data through a data mining algorithm;
(S4) data sharing: the processed data is computationally transmitted to the block chain through an encryption algorithm or a consensus algorithm, and the data sharing of the intelligent bracelet is realized through a wireless communication mode.
2. The method of claim 1, wherein the method comprises: normalized matrix product eigenvalue D of the random matrix theory algorithmstdThe formula of (1) is:
Dstd＝[w1,w2,w3……wM+N]T(ii) a Formula (6)
Wherein DstdEvaluating a matrix model for the smart band data dependency, wherein w represents D1Sum of matrix minimums D2Matrix formed by matrix minima according to DstdAnd evaluating the influence factors of the normal operation of the intelligent bracelet according to the value.
3. The method of claim 1, wherein the method comprises: the data types influencing the normal operation of the intelligent bracelet comprise a magnetic field, temperature, humidity, vibration, noise, current, voltage, ripple waves or harmonic waves, and the data types generated by the intelligent bracelet comprise user sleep time data, exercise time data, meal time data and heart rate measurement data.
4. The method of claim 2, wherein the method comprises:
the data DstdWhen the value is between 0 and 30, the intelligent bracelet is considered to have no external interference and normally operate;
the data DstdWhen the value is between 30 and 50, the interference is considered to be very small, and the intelligent bracelet normally operates;
the data DstdWhen the value is greater than 50, then consider that there is external interference, intelligent bracelet operation receives the influence.
5. The method of claim 1, wherein the method comprises: the software platform adopted by the blockchain is a HyperLegendr Fabric-based modular blockchain solution supporting platform, and the management system adopted by the block chain comprises a management application layer, a block chain network, an access layer and a department service system which are respectively a data layer, a network layer, a consensus layer, an excitation layer and an intelligent contract layer from bottom to top, the data layer can realize the calculation of the intelligent bracelet data, and the received data is encrypted and decrypted by adopting an encryption algorithm, the adopted encryption algorithm is DES, 3DES, Blowfish encryption algorithm, Twofish encryption algorithm, IDEA, RC6 or CAST5, in the network layer, which typically uses a P2P network, each node stores all of the transaction records in full, blockchain fashion, in the incentive layer, which includes an issuing mechanism and an incentive mechanism, in a consensus layer, a workload attestation mechanism can be implemented, the method for realizing data sharing by the block chain comprises a data sending process and a data receiving process:
in the data transmission flow, the method comprises the following steps:
(1) the department service system packs data according to the service definition standard; the intelligent bracelet organization business data is arranged in the block chain management system, and then the business data are distributed;
(2) calling a data submission method of the SDK, and submitting a data attribution main body and packaged service data;
(3) the SDK inquires a public key of a data attribution main body on the node machine, and if the public key cannot be found, a data decryption center interface is called to obtain the public key;
(4) the SDK encrypts the packed service data by using the public key to generate encrypted packed service data;
(5) the SDK uses the authentication authorization private key signature of a service department together with the data attribution main body and the encrypted packed service data;
(6) calling a node machine interface to submit data, and executing a data storage action after the node machine successfully verifies the signature; in the data receiving process, the method comprises the following steps:
(1) the SDK uses a CA private key of a service department to sign the query request;
(2) the SDK calls a node machine query interface, and the node machine executes a query action and returns encrypted service data after successfully verifying the signature;
(3) the SDK submits the encrypted service data to a decryption center through a hypertext transfer protocol channel based on a secure socket layer to decrypt and obtain decrypted service data;
(4) the SDK returns the decrypted service data.
6. The method of claim 1, wherein the method comprises: the wireless communication mode is a ZigBee wireless network, GPRS or CDMA wireless communication or Bluetooth communication mode, and the communication data information is connected into a chain structure for storage according to time sequence by using the Hash fingerprint.
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