CN116403671A - Data risk identification method applied to medical scene - Google Patents
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Abstract
The invention relates to a data risk identification method applied to a medical scene, which comprises the following steps: determining the number of nodes for storing medical treatment data according to a request instruction, when the number of nodes is 1, locking the node for storing the medical treatment data as a single node, judging whether the medical treatment data can be sent to a requester according to the safety coefficient value of the single node and the characteristic identification of the requester for initiating the request instruction, when the number of nodes is greater than 1, determining all nodes comprising the medical treatment data to obtain a node set, ordering each node in the node set according to the priority of the node to obtain a node stream, and selecting a response server for extracting the medical treatment data of the node stream, wherein the safety level of the response server is highest, and extracting the medical treatment data from the node stream by utilizing the response server.
Description
Technical Field
The invention relates to the technical field of safety protection of medical data, in particular to a data risk identification method applied to medical scenes.
Background
The medical treatment data is put into use as early as the end of 20 th century, is stored in a computer, records the processes of occurrence, development and treatment prognosis of personal or social diseases, and has high medical value. As such, medical visit data has been a major goal of information theft. Along with the rapid development of the Internet, the medical treatment data of the internal network of the hospital gradually goes to the public network, however, the information security protection of the medical industry starts later, and the accident of the leakage of the medical treatment data is endless.
In order to improve the safety of medical treatment data, a method commonly used at present is to construct a blockchain based on the medical treatment data by using a blockchain technology, namely a plurality of nodes cooperatively control the medical treatment data.
The blockchain-based medical treatment data management can effectively improve the safety and identify the operation with the safety threat, but does not consider whether a requester requesting to acquire the medical data has the request qualification, so that the phenomenon of stealing requester information to acquire the medical data frequently occurs.
Disclosure of Invention
The invention provides a data risk identification method applied to medical scenes, which mainly aims to improve the safety of acquiring medical treatment data.
In order to achieve the above object, the present invention provides a data risk identification method applied in a medical scene, including:
receiving a request instruction of medical treatment data, wherein the medical treatment data is pre-stored in one or more nodes of a blockchain;
determining the number of nodes for storing the medical treatment data according to the request instruction, and locking the node for storing the medical treatment data as a single node when the number of nodes is 1;
according to the history storage record of the single node, calculating to obtain the safety coefficient value of the single node, and rejecting the request instruction when the safety coefficient value of the single node is smaller than or equal to a preset safety threshold value;
when the safety coefficient value of the single node is larger than the safety threshold value, calculating to obtain the characteristic identification of the requester initiating the request instruction;
verifying the validity of the requester based on the feature identifier, rejecting the request instruction when the requester is illegal, and extracting medical treatment data from a single node and sending the medical treatment data to the requester when the requester is legal;
when the node number is greater than 1, determining all nodes comprising medical treatment data to obtain a node set;
sequencing each node in the node set according to the node priority to obtain a node stream;
and selecting a response server for extracting the medical treatment data of the node flow, wherein the security level of the response server is highest, and extracting the medical treatment data from the node flow by using the response server.
Optionally, the calculating, according to the history of the single node, a security coefficient value of the single node includes:
receiving a calculation expiration date for calculating the security coefficient value of the single node, and acquiring a storage record of each historically stored data of the single node according to the calculation expiration date, wherein the storage result of each storage record comprises a successful storage data and a failed storage data, and when each storage result of the successful storage data or the failed storage data is recorded, whether other nodes of the block chain agree to execute the storage operation of the single node or not is recorded, wherein the opinion comprises agreement to execute the storage operation and disagreement to execute the storage operation;
counting the number of nodes which are agreed to execute the storage operation by other nodes of the blockchain when the storage data is successful, obtaining the number of nodes success, and counting the number of nodes which are not agreed to execute the storage operation by other nodes of the blockchain when the storage data is failed, obtaining the number of nodes failure;
and calculating the security coefficient value of the single node according to the node success number and the node failure number.
Optionally, the calculating, according to the node success number and the node failure number, a security coefficient value of a single node includes:
the security coefficient value of the single node is calculated according to the following formula:
wherein ,
security coefficient value representing a single node,>
representing the storage result as the number of successful storage data, < >>
Representing the storage result as the number of storage data failures, < >>
Indicate->
Node success number when stripe storage data is successful, +.>
Indicate->
The number of node failures when the stripe fails to store data, +.>
Indicate->
Total number of nodes involved when bar storage data is successful, < >>
Indicate->
Stripe storage data failureTotal number of nodes involved.
Optionally, the rejecting the request instruction includes:
generating a forced transfer instruction of medical treatment data, and extracting the medical treatment data stored in a single node according to the forced transfer instruction;
when medical treatment data stored in a single node are successfully extracted, a storage result of successful storage data is generated in a history storage record of the single node, and the number of successful nodes corresponding to the storage result is the number of all nodes of the blockchain;
the method comprises the steps of restoring the storage results of all node numbers with the node success number being the blockchain to a historical storage record of a single node, generating a security mechanism upgrading instruction of the single node to a node manager after the restoring operation is completed, and simultaneously storing the extracted medical treatment data to other nodes of the blockchain, wherein the other nodes are different from the single node;
and when the extracted medical treatment data is successfully stored in other nodes of the blockchain, rejecting the request instruction, and initiating a reminding instruction to a requester of the request instruction while rejecting the request instruction, wherein the reminding instruction comprises reminding the requester to reinitiate the request instruction.
Optionally, the calculating obtains the characteristic identifier of the requester who initiates the request instruction, including:
the intelligent gateway and the hardware device where the applicant is located are started, wherein the hardware device comprises a face collector and a fingerprint collector;
collecting the face and the fingerprint of the request person by using the face collector and the fingerprint collector to obtain the request face and the request fingerprint;
acquiring gateway parameters of the intelligent gateway and receiving a password input to the intelligent gateway by a requester;
and calculating according to the request face, the request fingerprint, the gateway parameters and the password to obtain the characteristic identification of the request person.
Optionally, the calculating according to the face, the request fingerprint, the gateway parameter and the password to obtain the feature identifier of the requester includes:
calculating to obtain the characteristic identification of the applicant according to the following formula:
wherein ,
representing the requestor +.>
Is characterized by->
Representing the requestor +.>
Password of->
Represents the IP address where the single node is located, +.>
Representing the requestor +.>
Is to request face, is to be strapped with>
Representing the requestor +.>
Wherein>
and />
Are composed of matrix data in two-dimensional form, +.>
Indicate->
Personal intelligenceGateway parameters of gateway, ">
Representing exclusive OR operation, ++>
Representing a string concatenation operation.
Optionally, the verifying the validity of the applicant based on the feature identifier includes:
transmitting the characteristic identification into a blockchain, wherein the blockchain further comprises a verification node;
after the feature identification is successfully sent to the blockchain, automatically triggering the verification node;
the verification node is utilized to acquire the sender address of the feature identifier, and the sender address is requested to acquire the password of the requester;
comparing the password pre-stored in the blockchain of the requester after the password of the requester is obtained, and judging that the requester is illegal when the obtained password is different from the password pre-stored in the blockchain;
when the obtained password is the same as the password pre-stored in the blockchain, re-acquiring the face and the fingerprint of the requesting person to obtain the verification face and the verification fingerprint;
obtaining the verification identification of the requester based on the verification face, the verification fingerprint, the gateway parameters and the password calculation;
judging whether the errors of the verification mark and the characteristic mark are smaller than a preset error threshold value, and judging that the requester is legal when the errors of the verification mark and the characteristic mark are smaller than the error threshold value;
and judging that the requester is illegal when the error between the verification mark and the characteristic mark is larger than or equal to an error threshold value.
Optionally, the sorting is performed on each node in the node set according to the node priority, so as to obtain a node flow, including:
measuring the flow value required to be consumed by each node in unit time;
calculating the consumed time of each node in the node flow when responding to a task by utilizing a pre-constructed virtual machine;
and sequencing the priority of the nodes according to the length of the consumed time to obtain a node stream, wherein the nodes in the node stream gradually reduce the consumed time when responding to a task from left to right.
Optionally, the calculating, by using the pre-built virtual machine, the time consumed by each node in the node stream when responding to a task includes:
the time spent is calculated according to the following formula:
wherein ,
indicate->
The individual node is at->
Time consumption calculated on the individual virtual machines, < >>
Weight of formula for time consuming calculation>
Indicate->
In the process of responding to the task, the flow value required to be consumed in unit time of each node is +.>
Indicate->
The software and hardware level quantization values of the virtual machines.
Optionally, the selecting a response server for extracting medical treatment data of the node stream includes:
determining all response servers that can serve medical visit data of the extracted node stream;
sequentially acquiring hardware parameters and software parameters of each response server, wherein the hardware parameters comprise CPU frequency and memory, and the software parameters comprise bandwidth values and power consumption values;
removing response servers which do not meet the extraction of the medical treatment data according to the hardware parameters and the software parameters;
and selecting the response server with the lowest number of times of server crashes in the history record from the rest response servers, wherein the security level of the response server is highest.
In order to solve the problems described in the background art, the embodiment of the invention firstly receives a request instruction of medical treatment data, wherein the medical treatment data is stored in one or more nodes of a blockchain in advance, and the node number for storing the medical treatment data is determined according to the request instruction. Importantly, the essential difference between the embodiment of the invention and the traditional method is that instead of directly extracting the medical treatment data in response to the request instruction of the medical treatment data, the node number storing the medical treatment data is determined first, so that only 1 node stores the medical treatment data, and a plurality of nodes store the medical treatment data separately, and the essential difference is that the medical treatment data is extracted. Therefore, when the node number is 1, the node for storing the medical treatment data is locked to be a single node, and the safety coefficient value of the single node and the characteristic identification of the requester are obtained through calculation, and the safety of the node and the requester is repeatedly verified, so that the safety of the extracted medical treatment data is ensured. In addition, when the number of nodes is greater than 1, all nodes comprising medical treatment data are determined to obtain a node set, and each node is sequenced according to the priority of the node to obtain a node stream, wherein the function of the node stream is to optimize the extraction time of the extracted medical treatment data and prevent the leakage of the medical treatment data caused by overlong extracted medical treatment data. Therefore, the data risk identification method applied to the medical scene can improve the safety of acquiring the medical treatment data.
Drawings
Fig. 1 is a flow chart of a data risk identification method applied in a medical scenario according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, a flow chart of a data risk identification method applied in a medical scene according to an embodiment of the invention is shown. In this embodiment, the data risk identification method applied in a medical scene includes:
s1, receiving a request instruction of medical treatment data, wherein the medical treatment data is stored in one or more nodes of a blockchain in advance.
It should be noted that the request instruction is generally issued by medical related demanding personnel, such as scientific researchers, doctors in hospitals or related patients. Illustratively, a scientific research staff engaged in a hospital of the small Zhang Wei, the hospital hopes that the small sheet can put forward a more reasonable medication scheme aiming at thyroid medication, and the small sheet firstly wants to acquire all medical treatment data about thyroid in the hospital, so as to initiate a request instruction of the thyroid medical treatment data.
It should be explained that the medical visit data is stored in advance in one or more nodes of the blockchain according to the size of the medical visit data.
S2, determining the number of nodes for storing the medical treatment data according to the request instruction, and locking the node for storing the medical treatment data as a single node when the number of nodes is 1.
Illustratively, as described above for the small Zhang Xiangyao, thyroid visit data is acquired and the hospital's thyroid visit data is found to be of a small data volume, so all of the thyroid visit data is stored in one node in the blockchain, and thus the node is referred to as a single node.
S3, according to the historical storage record of the single node, calculating to obtain the safety coefficient value of the single node, and when the safety coefficient value of the single node is smaller than or equal to a preset safety threshold value, rejecting the request instruction.
In detail, the calculating, according to the history storage record of the single node, the security coefficient value of the single node includes:
receiving a calculation expiration date for calculating the security coefficient value of the single node, and acquiring a storage record of each historically stored data of the single node according to the calculation expiration date, wherein the storage result of each storage record comprises a successful storage data and a failed storage data, and when each storage result of the successful storage data or the failed storage data is recorded, whether other nodes of the block chain agree to execute the storage operation of the single node or not is recorded, wherein the opinion comprises agreement to execute the storage operation and disagreement to execute the storage operation;
counting the number of nodes which are agreed to execute the storage operation by other nodes of the blockchain when the storage data is successful, obtaining the number of nodes success, and counting the number of nodes which are not agreed to execute the storage operation by other nodes of the blockchain when the storage data is failed, obtaining the number of nodes failure;
and calculating the security coefficient value of the single node according to the node success number and the node failure number.
For example, assuming that the set calculation deadline is 2023, 2 nd and 10 th, it indicates that all history storage records of a single node are acquired before 2023, 2 nd and 10 th, and 100 storage records are assumed, wherein 90 storage data succeed and 10 storage data fail, so that the number of nodes which agree or disagree to store when each storage data succeed or fails needs to be counted, and the number of node successes or the number of node failures corresponding to each storage data succeed or storage data fail is obtained.
Further, the calculating, according to the node success number and the node failure number, a security coefficient value of a single node includes:
the security coefficient value of the single node is calculated according to the following formula:
wherein ,
security coefficient value representing a single node,>
representing the storage result as the number of successful storage data, < >>
Representing the storage result as the number of storage data failures, < >>
Indicate->
Node success number when stripe storage data is successful, +.>
Indicate->
The number of node failures when the stripe fails to store data, +.>
Indicate->
Total number of nodes involved when bar storage data is successful, < >>
Indicate->
The total number of nodes involved in the failure of the stripe to store data.
According to the calculation, the security coefficient value of the single node can be obtained, and the security coefficient value of the single node is compared with a preset security threshold, when the comparison result is smaller than or equal to the preset security threshold, the security coefficient of the single node is lower, and at the moment, if the data stored in the single node is called again, the risk of data leakage is easy to be caused, so in detail, the request instruction is rejected, and the method comprises the following steps:
generating a forced transfer instruction of medical treatment data, and extracting the medical treatment data stored in a single node according to the forced transfer instruction;
when medical treatment data stored in a single node are successfully extracted, a storage result of successful storage data is generated in a history storage record of the single node, and the number of successful nodes corresponding to the storage result is the number of all nodes of the blockchain;
the method comprises the steps of restoring the storage results of all node numbers with the node success number being the blockchain to a historical storage record of a single node, generating a security mechanism upgrading instruction of the single node to a node manager after the restoring operation is completed, and simultaneously storing the extracted medical treatment data to other nodes of the blockchain, wherein the other nodes are different from the single node;
and when the extracted medical treatment data is successfully stored in other nodes of the blockchain, rejecting the request instruction, and initiating a reminding instruction to a requester of the request instruction while rejecting the request instruction, wherein the reminding instruction comprises reminding the requester to reinitiate the request instruction.
It can be understood that, when the security coefficient value of the single node is smaller than or equal to the security threshold, the embodiment of the invention transfers the medical treatment data stored by the single node to other nodes of the blockchain for security, and in order to improve the storage security of the single node, the embodiment of the invention also initiates a security mechanism upgrade instruction of the single node to the node manager. Further, after the transfer of the medical treatment data is successfully completed, the embodiment of the invention rejects the request instruction and reminds the requester to reinitiate the request instruction so as to acquire the medical treatment data again.
And S4, calculating to obtain the characteristic identification of the requester initiating the request instruction when the security coefficient value of the single node is larger than the security threshold value.
In detail, the calculating obtains the characteristic identifier of the requester who initiates the request instruction, including:
the intelligent gateway and the hardware device where the applicant is located are started, wherein the hardware device comprises a face collector and a fingerprint collector;
collecting the face and the fingerprint of the request person by using the face collector and the fingerprint collector to obtain the request face and the request fingerprint;
acquiring gateway parameters of the intelligent gateway and receiving a password input to the intelligent gateway by a requester;
and calculating according to the request face, the request fingerprint, the gateway parameters and the password to obtain the characteristic identification of the request person.
For example, the small-sized doctor uses the computer of the hospital to request to acquire thyroid gland treatment data in the hospital, so that an intelligent gateway supporting the computer of the hospital to access the internet and hardware equipment with face recognition and fingerprint collection functions are required to be provided.
Further, the gateway parameters of the intelligent gateway include, but are not limited to, a device number of the gateway device, an access permission number, an IP address, etc. And the password input by the applicant to the intelligent gateway can be, but not limited to, the identity card account number of the applicant, the number of the applicant in the hospital, and the like.
In addition, the calculating according to the request face, the request fingerprint, the gateway parameter and the password to obtain the characteristic identifier of the request includes:
calculating to obtain the characteristic identification of the applicant according to the following formula:
wherein ,
representing the requestor +.>
Is characterized by->
Representing the requestor +.>
Password of->
Represents the IP address where the single node is located, +.>
Representing the requestor +.>
Is to request face, is to be strapped with>
Representing the requestor +.>
Wherein>
and />
Are composed of matrix data in two-dimensional form, +.>
Indicate->
Gateway parameters of the individual intelligent gateway,>
representing exclusive OR operation, ++>
Representing a string concatenation operation.
According to the above, the feature identifier having a strict correspondence with the requester can be obtained by the face, the fingerprint, the gateway parameter and the password having the specific identifier.
And S5, verifying the validity of the requester based on the characteristic identifier, refusing the request instruction when the requester is illegal, and extracting medical treatment data from a single node and sending the medical treatment data to the requester when the requester is legal.
In detail, the verifying the validity of the applicant based on the feature identification includes:
transmitting the characteristic identification into a blockchain, wherein the blockchain further comprises a verification node;
after the feature identification is successfully sent to the blockchain, automatically triggering the verification node;
the verification node is utilized to acquire the sender address of the feature identifier, and the sender address is requested to acquire the password of the requester;
comparing the password pre-stored in the blockchain of the requester after the password of the requester is obtained, and judging that the requester is illegal when the obtained password is different from the password pre-stored in the blockchain;
when the obtained password is the same as the password pre-stored in the blockchain, re-acquiring the face and the fingerprint of the requesting person to obtain the verification face and the verification fingerprint;
obtaining the verification identification of the requester based on the verification face, the verification fingerprint, the gateway parameters and the password calculation;
judging whether the errors of the verification mark and the characteristic mark are smaller than a preset error threshold value, and judging that the requester is legal when the errors of the verification mark and the characteristic mark are smaller than the error threshold value;
and judging that the requester is illegal when the error between the verification mark and the characteristic mark is larger than or equal to an error threshold value.
In the embodiment of the invention, in order to prevent someone from maliciously stealing the password of the original requester, after the verification node is started, the password of the requester is acquired through the verification node, and the password of the requester is acquired through a question-answer mode, namely, the secret of the requester is acquired firstly, the secret is transmitted to the requester, and the requester is required to acquire the password of the requester again after the secret is acquired.
Further, when the password comparison is consistent (i.e. the password is passed), the hardware device is started again to acquire the face and the fingerprint of the requester again, and the method aims to prevent the requester from helping the requester to download the data after logging in, so that the risk of data leakage is improved. The generation process of the verification identifier is the same as that of the feature identifier, and is not described in detail herein.
And S6, when the number of the nodes is greater than 1, determining all the nodes comprising the medical treatment data to obtain a node set.
It can be understood that when the number of nodes is greater than 1, it indicates that the medical treatment data needs to be split and stored in different blockchain nodes due to the reasons of improving the security or excessively large data volume of the medical treatment data, so that all the nodes storing the medical treatment data need to be determined first, and a node set is obtained.
In addition, it should be emphasized that when the number of nodes is greater than 1, verification is also performed on the validity of the applicant, where the verification method is the same as the verification method when the number of nodes is equal to 1, and no further description is given here.
And S7, sequencing each node in the node set according to the node priority to obtain a node stream.
It should be explained that, in order to improve the extraction efficiency of subsequently extracting medical treatment data, the embodiment of the present invention needs to perform sorting on a node set, specifically, perform sorting on each node in the node set according to a node priority, to obtain a node stream, including:
measuring the flow value required to be consumed by each node in unit time;
calculating the consumed time of each node in the node flow when responding to a task by utilizing a pre-constructed virtual machine;
and sequencing the priority of the nodes according to the length of the consumed time to obtain a node stream, wherein the nodes in the node stream gradually reduce the consumed time when responding to a task from left to right.
It should be explained that the virtual machine may simulate the consumed time required for each node to respond to a data storage instruction, a data extraction instruction, etc. according to the node attribute information.
Further, the calculating, by using the pre-constructed virtual machine, the time consumed by each node in the node stream when responding to a task includes:
the time spent is calculated according to the following formula:
wherein ,
indicate->
The individual node is at->
Time consumption calculated on the individual virtual machines, < >>
Weight of formula for time consuming calculation>
Indicate->
In the process of responding to the task, the flow value required to be consumed in unit time of each node is +.>
Indicate->
The software and hardware level quantization values of the virtual machines.
From the above description, it is known that by the time consumed for the response task of each node, ordering can be performed for each node in the node set, thereby obtaining the node set.
S8, selecting a response server for extracting the medical treatment data of the node flow, wherein the security level of the response server is highest, and extracting the medical treatment data from the node flow by using the response server.
In an embodiment of the present invention, the selecting a response server for extracting medical treatment data of the node flow includes:
determining all response servers that can serve medical visit data of the extracted node stream;
sequentially acquiring hardware parameters and software parameters of each response server, wherein the hardware parameters comprise CPU frequency and memory, and the software parameters comprise bandwidth values and power consumption values;
removing response servers which do not meet the extraction of the medical treatment data according to the hardware parameters and the software parameters;
and selecting the response server with the lowest number of times of server crashes in the history record from the rest response servers, wherein the security level of the response server is highest.
In summary, the embodiment of the invention improves the security of medical treatment data acquisition from multiple angles of node selection, sorting, server selection and the like.
In order to solve the problems described in the background art, the embodiment of the invention firstly receives a request instruction of medical treatment data, wherein the medical treatment data is stored in one or more nodes of a blockchain in advance, and the node number for storing the medical treatment data is determined according to the request instruction. Importantly, the essential difference between the embodiment of the invention and the traditional method is that instead of directly extracting the medical treatment data in response to the request instruction of the medical treatment data, the node number storing the medical treatment data is determined first, so that only 1 node stores the medical treatment data, and a plurality of nodes store the medical treatment data separately, and the essential difference is that the medical treatment data is extracted. Therefore, when the node number is 1, the node for storing the medical treatment data is locked to be a single node, and the safety coefficient value of the single node and the characteristic identification of the requester are obtained through calculation, and the safety of the node and the requester is repeatedly verified, so that the safety of the extracted medical treatment data is ensured. In addition, when the number of nodes is greater than 1, all nodes comprising medical treatment data are determined to obtain a node set, and each node is sequenced according to the priority of the node to obtain a node stream, wherein the function of the node stream is to optimize the extraction time of the extracted medical treatment data and prevent the leakage of the medical treatment data caused by overlong extracted medical treatment data. Therefore, the data risk identification method applied to the medical scene can improve the safety of acquiring the medical treatment data.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A data risk identification method applied in a medical scene, the method comprising:
receiving a request instruction of medical treatment data, wherein the medical treatment data is pre-stored in one or more nodes of a blockchain;
determining the number of nodes for storing the medical treatment data according to the request instruction, and locking the node for storing the medical treatment data as a single node when the number of nodes is 1;
according to the history storage record of the single node, calculating to obtain the safety coefficient value of the single node, and rejecting the request instruction when the safety coefficient value of the single node is smaller than or equal to a preset safety threshold value;
when the safety coefficient value of the single node is larger than the safety threshold value, calculating to obtain the characteristic identification of the requester initiating the request instruction;
verifying the validity of the requester based on the feature identifier, rejecting the request instruction when the requester is illegal, and extracting medical treatment data from a single node and sending the medical treatment data to the requester when the requester is legal;
when the node number is greater than 1, determining all nodes comprising medical treatment data to obtain a node set;
sequencing each node in the node set according to the node priority to obtain a node stream;
and selecting a response server for extracting the medical treatment data of the node flow, wherein the security level of the response server is highest, and extracting the medical treatment data from the node flow by using the response server.
2. The method for identifying data risk in a medical scenario according to claim 1, wherein the calculating the security coefficient value of the single node according to the historic storage record of the single node comprises:
receiving a calculation expiration date for calculating the security coefficient value of the single node, and acquiring a storage record of each historically stored data of the single node according to the calculation expiration date, wherein the storage result of each storage record comprises a successful storage data and a failed storage data, and when each storage result of the successful storage data or the failed storage data is recorded, whether other nodes of the block chain agree to execute the storage operation of the single node or not is recorded, wherein the opinion comprises agreement to execute the storage operation and disagreement to execute the storage operation;
counting the number of nodes which are agreed to execute the storage operation by other nodes of the blockchain when the storage data is successful, obtaining the number of nodes success, and counting the number of nodes which are not agreed to execute the storage operation by other nodes of the blockchain when the storage data is failed, obtaining the number of nodes failure;
and calculating the security coefficient value of the single node according to the node success number and the node failure number.
3. The method for identifying data risk in medical situations according to claim 2, wherein the calculating the security coefficient value of a single node according to the node success number and the node failure number comprises:
the security coefficient value of the single node is calculated according to the following formula:
wherein ,
security coefficient value representing a single node,>
representing the storage result as the number of successful storage data, < >>
Representing the storage result as the number of storage data failures, < >>
Indicate->
Node success number when stripe storage data is successful, +.>
Indicate->
The number of node failures when the stripe fails to store data, +.>
Indicate->
Total number of nodes involved when bar storage data is successful, < >>
Indicate->
The total number of nodes involved in the failure of the stripe to store data.
4. The method for identifying data risk in a medical scenario according to claim 3, wherein said rejecting said request instruction comprises:
generating a forced transfer instruction of medical treatment data, and extracting the medical treatment data stored in a single node according to the forced transfer instruction;
when medical treatment data stored in a single node are successfully extracted, a storage result of successful storage data is generated in a history storage record of the single node, and the number of successful nodes corresponding to the storage result is the number of all nodes of the blockchain;
the method comprises the steps of restoring the storage results of all node numbers with the node success number being the blockchain to a historical storage record of a single node, generating a security mechanism upgrading instruction of the single node to a node manager after the restoring operation is completed, and simultaneously storing the extracted medical treatment data to other nodes of the blockchain, wherein the other nodes are different from the single node;
and when the extracted medical treatment data is successfully stored in other nodes of the blockchain, rejecting the request instruction, and initiating a reminding instruction to a requester of the request instruction while rejecting the request instruction, wherein the reminding instruction comprises reminding the requester to reinitiate the request instruction.
5. The method for identifying data risk in a medical scenario of claim 4, wherein the calculating obtains a characteristic identifier of a requestor initiating a request instruction, comprising:
the intelligent gateway and the hardware device where the applicant is located are started, wherein the hardware device comprises a face collector and a fingerprint collector;
collecting the face and the fingerprint of the request person by using the face collector and the fingerprint collector to obtain the request face and the request fingerprint;
acquiring gateway parameters of the intelligent gateway and receiving a password input to the intelligent gateway by a requester;
and calculating according to the request face, the request fingerprint, the gateway parameters and the password to obtain the characteristic identification of the request person.
6. The method for recognizing data risk in medical situations according to claim 5, wherein the calculating the feature identifier of the requesting person according to the requesting face, the request fingerprint, the gateway parameter and the password comprises:
calculating to obtain the characteristic identification of the applicant according to the following formula:
wherein ,
representing the requestor +.>
Is characterized by->
Representing the requestor +.>
Password of->
Represents the IP address where the single node is located, +.>
Representing the requestor +.>
Is to request face, is to be strapped with>
Representing the requestor +.>
Wherein>
and />
Are composed of matrix data in two-dimensional form, +.>
Indicate->
Gateway parameters of the individual intelligent gateway,>
representing exclusive OR operation, ++>
Representing a string concatenation operation.
7. The method for identifying data risk in a medical scenario of claim 6, wherein verifying the validity of the requestor based on the characteristic identification comprises:
transmitting the characteristic identification into a blockchain, wherein the blockchain further comprises a verification node;
after the feature identification is successfully sent to the blockchain, automatically triggering the verification node;
the verification node is utilized to acquire the sender address of the feature identifier, and the sender address is requested to acquire the password of the requester;
comparing the password pre-stored in the blockchain of the requester after the password of the requester is obtained, and judging that the requester is illegal when the obtained password is different from the password pre-stored in the blockchain;
when the obtained password is the same as the password pre-stored in the blockchain, re-acquiring the face and the fingerprint of the requesting person to obtain the verification face and the verification fingerprint;
obtaining the verification identification of the requester based on the verification face, the verification fingerprint, the gateway parameters and the password calculation;
judging whether the errors of the verification mark and the characteristic mark are smaller than a preset error threshold value, and judging that the requester is legal when the errors of the verification mark and the characteristic mark are smaller than the error threshold value;
and judging that the requester is illegal when the error between the verification mark and the characteristic mark is larger than or equal to an error threshold value.
8. The method for identifying data risk in a medical scenario of claim 7, wherein said performing a ranking on each node in the set of nodes according to a node priority to obtain a node flow comprises:
measuring the flow value required to be consumed by each node in unit time;
calculating the consumed time of each node in the node flow when responding to a task by utilizing a pre-constructed virtual machine;
and sequencing the priority of the nodes according to the length of the consumed time to obtain a node stream, wherein the nodes in the node stream gradually reduce the consumed time when responding to a task from left to right.
9. The method for identifying data risk in a medical scenario of claim 8, wherein calculating the time spent by each node in the node stream in response to a task using the pre-built virtual machine comprises:
the time spent is calculated according to the following formula:
wherein ,
indicate->
The individual node is at->
Time consumption calculated on the individual virtual machines, < >>
Weight of formula for time consuming calculation>
Represent the first/>
In the process of responding to the task, the flow value required to be consumed in unit time of each node is +.>
Indicate->
The software and hardware level quantization values of the virtual machines.
10. The method for data risk identification in a medical setting of claim 9, wherein the selecting a response server for extracting medical visit data for the node stream comprises:
determining all response servers that can serve medical visit data of the extracted node stream;
sequentially acquiring hardware parameters and software parameters of each response server, wherein the hardware parameters comprise CPU frequency and memory, and the software parameters comprise bandwidth values and power consumption values;
removing response servers which do not meet the extraction of the medical treatment data according to the hardware parameters and the software parameters;
and selecting the response server with the lowest number of times of server crashes in the history record from the rest response servers, wherein the security level of the response server is highest.
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