CN111770189A - Networking type medical big data grading transmission method and system - Google Patents
Networking type medical big data grading transmission method and system Download PDFInfo
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Abstract
The invention relates to the technical field of big data transmission, in particular to a networking type medical big data grading transmission method and system. According to the method, when the user big data is acquired, network environment detection is carried out on each medical terminal in the first medical interactive network, then the big data interface information of each target medical terminal is extracted, then the topological structure of the second medical interactive network is determined, and the level of each target medical terminal is determined. And secondly, respectively converting each big data set in the big data of the user into a big data code and generating an incidence matrix between the encryption authority information corresponding to each big data set and the signature information of the big data code converted from the big data set. And finally, carrying out privacy big data identification on each big data set based on the incidence matrix so as to encrypt and send the big data set. The method and the device can ensure that the medical terminal can only obtain part of big data in the big data of the user according to the authority level of the medical terminal, and further avoid the disclosure of the private big data in the big data of the user.
Description
Technical Field
The disclosure relates to the technical field of big data transmission, in particular to a networking type medical big data hierarchical transmission method and system.
Background
With the rapid development of internet technology and digital communication technology, more and more industries rely on the internet and digital communication to generate metamorphism, and the operation efficiency is greatly improved. Taking the medical industry as an example, digital medical treatment and information medical treatment provide a lot of convenience for patients and hospitals, and many defects of traditional medical treatment are overcome. At present, taking electronic medical big data as an example, the method can enable patients and doctors to break geographic limits and barriers, thereby realizing a flexible on-line medical seeking mode. The online medical visit is realized by large data transmission to realize communication and communication between patients and doctors (such as patient consultation, doctor inquiry and the like). However, during the transmission of large data for online medical treatment, it is difficult for the related art to keep some private large data of the patient secret, which may cause the leakage of the private large data of the patient.
Disclosure of Invention
In order to solve the technical problems in the related art, the present disclosure provides a method and a system for internet-based hierarchical transmission of medical big data.
The invention provides a networking type medical big data hierarchical transmission method, which is applied to a big data transmission server communicated with a user terminal and a plurality of medical terminals, and comprises the following steps:
when user big data uploaded by a user terminal is acquired, network environment detection is carried out on each medical terminal in the first medical interactive network;
counting m target medical terminals detected through the network environment in the first medical interactive network and extracting big data interface information of each target medical terminal; indicating the m target medical terminals to carry out communication connection according to the big data interface information to form a second medical interactive network; extracting a topological structure of the second medical interactive network, and determining the level of each target medical terminal according to the topological structure;
respectively converting each big data set in the user big data into big data codes; the big data codes can be completely output on each target medical terminal, the output format of the big data codes converted by each big data set is the same as the storage format of the big data set in the big data transmission server, and each big data code has signature information for uniquely determining the big data code;
generating an incidence matrix between the encryption authority information corresponding to each big data set and the signature information of the big data codes converted from the big data sets;
performing privacy big data identification on each big data set based on the incidence matrix to obtain target privacy big data corresponding to each big data set and privacy authority of the target privacy big data; encrypting the big data sets according to the privacy authority to obtain a big data encryption packet corresponding to each big data set; and packaging the big data encryption packet to obtain an encrypted compression packet, and sequentially sending the encrypted compression packet to each target medical terminal according to the size sequence of the hierarchy where each target medical terminal is located.
Optionally, the counting m target medical terminals detected through the network environment in the first medical interaction network and extracting big data interface information of each target medical terminal specifically includes:
counting terminal name information corresponding to m target medical terminals detected by the network environment in the first medical interactive network; the terminal name information is used for representing the equipment model of each target medical terminal;
generating a terminal identifier containing the terminal name information; the terminal identification is used for representing a unique identification corresponding to the target medical terminal;
mapping the terminal identification to the network environment based on the corresponding relation between the detection information of the target medical terminal in the network environment corresponding to the first medical interactive network and the terminal name information;
transmitting the current environment parameters obtained after the terminal identification is mapped to the network environment to each target medical terminal;
extracting the terminal identification to obtain an identifier, and determining terminal request information matched with the terminal identification based on the identifier and a corresponding relation between detection information and terminal name information in a network environment corresponding to the first medical interactive network;
and receiving response information fed back by each target medical terminal based on the current environment parameters, interleaving and coding a first information sequence of the response information and a second information sequence corresponding to terminal request information matched with the terminal identifier of each target medical terminal to obtain a third information sequence corresponding to each target medical terminal, and performing deserialization operation on the third information sequence to obtain big data interface information corresponding to each target medical terminal.
Optionally, the converting each big data set in the user big data into a big data code respectively includes:
acquiring a big data set distribution track of the user big data, and determining a correlation coefficient between each big data set of the user big data under a bipolar coding type and each big data set of the user big data under the unipolar coding type according to a big data set of the user big data under the unipolar coding type and the big data set capacity of the user big data under the unipolar coding type under the condition that the big data contains the unipolar coding type based on the big data set distribution track;
migrating a big data set of the user big data under the bipolar coding class associated with a big data set under the unipolar coding class to the unipolar coding class based on the correlation coefficient; if the user big data comprises a plurality of big data sets in the bipolar coding category, determining a correlation coefficient between the user big data and each big data set in the bipolar coding category based on the big data sets of the user big data in the unipolar coding category and the big data set capacity of the user big data, and marking each big data set in the bipolar coding category according to the correlation coefficient between the big data sets to obtain a target big data set; migrating the target big data set to the unipolar coding category;
and respectively coding the big data sets under the unipolar coding category and the big data sets under the bipolar coding category to obtain big data codes corresponding to each big data set in the user big data.
Optionally, generating an association matrix between the encryption authority information corresponding to each big data set and the signature information of the big data code converted from the big data set specifically includes:
determining encryption authority information corresponding to each big data set and a digital string information set corresponding to each big data set; the encryption authority information and the digital string information set respectively comprise a plurality of information fields with different structure parameters, and the digital string information set is used for representing the signature information;
extracting first field description information of each big data set in one information field of the encryption authority information, and determining the information field with the minimum structure parameter in the digital string information set as a target information field;
inputting the first field description information into the target information field according to preset input address information, obtaining second field description information in the target information field, and determining the association relationship between the encryption authority information and the digital string information set based on the first field description information and the second field description information; and determining an incidence matrix between the encryption authority information and the signature information according to the incidence relation.
Optionally, performing privacy big data identification on each big data set based on the incidence matrix to obtain target privacy big data corresponding to each big data set and privacy permission of the target privacy big data, and specifically includes:
extracting the private big data in each big data set based on the incidence matrix to obtain at least one private big data packet;
screening key privacy big data in the at least one privacy big data packet to obtain key privacy big data corresponding to each big data set; the key privacy big data is used for representing big data information with the privacy level larger than a preset privacy level in the privacy big data packet;
identifying the privacy big data in the at least one privacy big data packet based on the key privacy big data to obtain target privacy big data corresponding to each big data set;
and identifying the access authority corresponding to the target privacy big data based on the key privacy big data to obtain the privacy authority of the target privacy big data.
The invention also provides a networking type medical big data hierarchical transmission system, which comprises a big data transmission server, a user terminal and a plurality of medical terminals; the big data transmission server is communicated with the user terminal and each medical terminal;
the user terminal is configured to:
uploading the user big data to the big data transmission server;
the big data transmission server is used for:
when the user big data is obtained, network environment detection is carried out on each medical terminal in the first medical interactive network;
counting m target medical terminals detected through the network environment in the first medical interactive network and extracting big data interface information of each target medical terminal; indicating the m target medical terminals to carry out communication connection according to the big data interface information to form a second medical interactive network; extracting a topological structure of the second medical interactive network, and determining the level of each target medical terminal according to the topological structure;
respectively converting each big data set in the user big data into big data codes; the big data codes can be completely output on each target medical terminal, the output format of the big data codes converted by each big data set is the same as the storage format of the big data set in the big data transmission server, and each big data code has signature information for uniquely determining the big data code;
generating an incidence matrix between the encryption authority information corresponding to each big data set and the signature information of the big data codes converted from the big data sets;
performing privacy big data identification on each big data set based on the incidence matrix to obtain target privacy big data corresponding to each big data set and privacy authority of the target privacy big data; encrypting the big data sets according to the privacy authority to obtain a big data encryption packet corresponding to each big data set; and packaging the big data encryption packet to obtain an encrypted compression packet, and sequentially sending the encrypted compression packet to each target medical terminal according to the size sequence of the hierarchy where each target medical terminal is located.
Optionally, the big data transmission server is specifically configured to:
counting terminal name information corresponding to m target medical terminals detected by the network environment in the first medical interactive network; the terminal name information is used for representing the equipment model of each target medical terminal;
generating a terminal identifier containing the terminal name information; the terminal identification is used for representing a unique identification corresponding to the target medical terminal;
mapping the terminal identification to the network environment based on the corresponding relation between the detection information of the target medical terminal in the network environment corresponding to the first medical interactive network and the terminal name information;
transmitting the current environment parameters obtained after the terminal identification is mapped to the network environment to each target medical terminal;
extracting the terminal identification to obtain an identifier, and determining terminal request information matched with the terminal identification based on the identifier and a corresponding relation between detection information and terminal name information in a network environment corresponding to the first medical interactive network;
and receiving response information fed back by each target medical terminal based on the current environment parameters, interleaving and coding a first information sequence of the response information and a second information sequence corresponding to terminal request information matched with the terminal identifier of each target medical terminal to obtain a third information sequence corresponding to each target medical terminal, and performing deserialization operation on the third information sequence to obtain big data interface information corresponding to each target medical terminal.
Optionally, the big data transmission server is specifically configured to:
acquiring a big data set distribution track of the user big data, and determining a correlation coefficient between each big data set of the user big data under a bipolar coding type and each big data set of the user big data under the unipolar coding type according to a big data set of the user big data under the unipolar coding type and the big data set capacity of the user big data under the unipolar coding type under the condition that the big data contains the unipolar coding type based on the big data set distribution track;
migrating a big data set of the user big data under the bipolar coding class associated with a big data set under the unipolar coding class to the unipolar coding class based on the correlation coefficient; if the user big data comprises a plurality of big data sets in the bipolar coding category, determining a correlation coefficient between the user big data and each big data set in the bipolar coding category based on the big data sets of the user big data in the unipolar coding category and the big data set capacity of the user big data, and marking each big data set in the bipolar coding category according to the correlation coefficient between the big data sets to obtain a target big data set; migrating the target big data set to the unipolar coding category;
and respectively coding the big data sets under the unipolar coding category and the big data sets under the bipolar coding category to obtain big data codes corresponding to each big data set in the user big data.
Optionally, the big data transmission server is specifically configured to:
determining encryption authority information corresponding to each big data set and a digital string information set corresponding to each big data set; the encryption authority information and the digital string information set respectively comprise a plurality of information fields with different structure parameters, and the digital string information set is used for representing the signature information;
extracting first field description information of each big data set in one information field of the encryption authority information, and determining the information field with the minimum structure parameter in the digital string information set as a target information field;
inputting the first field description information into the target information field according to preset input address information, obtaining second field description information in the target information field, and determining the association relationship between the encryption authority information and the digital string information set based on the first field description information and the second field description information; and determining an incidence matrix between the encryption authority information and the signature information according to the incidence relation.
Optionally, the big data transmission server is specifically configured to:
extracting the private big data in each big data set based on the incidence matrix to obtain at least one private big data packet;
screening key privacy big data in the at least one privacy big data packet to obtain key privacy big data corresponding to each big data set; the key privacy big data is used for representing big data information with the privacy level larger than a preset privacy level in the privacy big data packet;
identifying the privacy big data in the at least one privacy big data packet based on the key privacy big data to obtain target privacy big data corresponding to each big data set;
and identifying the access authority corresponding to the target privacy big data based on the key privacy big data to obtain the privacy authority of the target privacy big data.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects.
The invention provides a networking type medical big data hierarchical transmission method and a networking type medical big data hierarchical transmission system. And secondly, respectively converting each big data set in the big data of the user into a big data code and generating an incidence matrix between the encryption authority information corresponding to each big data set and the signature information of the big data code converted from the big data set. And finally, carrying out privacy big data identification on each big data set based on the incidence matrix so as to encrypt and send the big data set. Therefore, when the target medical terminal receives the encrypted compressed packet, partial big data encrypted packets can be decrypted according to a preset decryption protocol, and corresponding target privacy big data are obtained. Therefore, when the user big data is issued to the medical terminals at different levels, the medical terminals can be ensured to only obtain part of the big data in the user big data according to the authority levels of the medical terminals, and further the leakage of the privacy big data in the user big data is avoided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic communication architecture diagram of a networked medical big data hierarchical transmission system according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for transmitting networked medical big data in a hierarchical manner according to an embodiment of the present invention.
Fig. 3 is a block diagram of a networked medical big data hierarchical transmission device according to an embodiment of the present invention.
Fig. 4 is a schematic hardware structure diagram of a large data transmission server according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
In order to solve the technical problem that privacy big data of a patient are difficult to keep secret when a doctor is on line, the embodiment of the invention provides a networking type medical big data grading transmission method and a networking type medical big data grading transmission system, which can analyze and identify the user big data uploaded by a user terminal, so that privacy big data with different privacy grades in the user big data are determined, and then the privacy big data are encrypted. Therefore, when the user big data is issued to the medical terminals at different levels, the medical terminals can be ensured to only obtain part of the user big data according to the authority levels of the medical terminals. Therefore, the leakage of the privacy big data in the user big data can be avoided.
To achieve the above purpose, firstly, a schematic diagram of a communication architecture of the networked hierarchical transmission system 100 of medical big data as shown in fig. 1 is provided. The networked hierarchical medical big data transmission system 100 can comprise a big data transmission server 110, a user terminal 120 and a plurality of medical terminals 130. Wherein the big data transmission server 110 communicates with the user terminal 120 and each medical terminal 130.
With continued reference to fig. 1, the plurality of medical terminals 130 are at different levels, and in the present embodiment, the levels are used to represent the security level of the big data of the medical terminals 130 in the hospital system. Further, the user terminal 120 may be a mobile phone, an intelligent electronic device, and the like, and the medical terminal 130 may be a desktop computer, a notebook computer, and the like, which is not limited herein.
On the basis of the above, please refer to fig. 2 in combination, a flowchart of a networked hierarchical transmission method for medical big data is provided, which may be applied to the big data transmission server 110 in fig. 1, where the big data transmission server 110 specifically performs the following steps S110 to S150 when implementing the above method.
Step S110, when the big user data uploaded by the user terminal is obtained, network environment detection is carried out on each medical terminal in the first medical interactive network.
In this embodiment, the user big data is used to represent medical big data information of a patient corresponding to the user terminal, a plurality of medical terminals communicating with the big data transmission server communicate with each other to form the first medical interactive network, and the network environment detection includes timeliness detection of a communication encryption protocol, stability detection of a network communication parameter, and disturbance detection of a big data transmission link.
Step S120, counting m target medical terminals detected through the network environment in the first medical interactive network and extracting big data interface information of each target medical terminal; indicating the m target medical terminals to carry out communication connection according to the big data interface information to form a second medical interactive network; and extracting the topological structure of the second medical interactive network, and determining the level of each target medical terminal according to the topological structure.
In the present embodiment, m is a positive integer and is smaller than n. n is the total number of medical terminals 130 communicating with the big data transmission server 110. The big data interface information is used for representing the communication address big data of the target medical terminal. The topological structure comprises a plurality of topological nodes, the topological nodes are connected through node links, and each topological node corresponds to one target medical terminal.
Step S130, respectively converting each big data set in the user big data into big data codes.
In this embodiment, the big data codes can be completely output on each target medical terminal, each big data set is converted into a big data code with the same output format as the big data set stored in the big data transmission server, and each big data code has signature information for uniquely determining the big data code.
Step S140 is to generate an association matrix between the encryption authority information corresponding to each big data set and the signature information of the big data code converted from the big data set.
In this embodiment, the encryption permission information includes a privacy level of each big data set on the user terminal level, and the incidence matrix is used to characterize encryption consistency between the encryption permission information and the signature information.
S150, carrying out privacy big data identification on each big data set based on the incidence matrix to obtain target privacy big data corresponding to each big data set and privacy authority of the target privacy big data; encrypting the big data sets according to the privacy authority to obtain a big data encryption packet corresponding to each big data set; and packaging the big data encryption packet to obtain an encrypted compression packet, and sequentially sending the encrypted compression packet to each target medical terminal according to the size sequence of the hierarchy where each target medical terminal is located.
In this embodiment, when each target medical terminal receives the encrypted compressed packet, it may decrypt a part of the big data encrypted packet in the encrypted compressed packet according to a decryption protocol established with the big data transmission server 110 in advance to obtain corresponding target private big data.
The following advantageous effects can be achieved when the method described in the above steps S110 to S150 is performed: firstly, when big data of a user is obtained, network environment detection is carried out on each medical terminal in the first medical interactive network, then m target medical terminals in the first medical interactive network are counted, big data interface information of each target medical terminal is extracted, a topological structure of the second medical interactive network is determined, and a level where each target medical terminal is located is determined based on the topological structure. And secondly, respectively converting each big data set in the big data of the user into a big data code and generating an incidence matrix between the encryption authority information corresponding to each big data set and the signature information of the big data code converted from the big data set. And finally, carrying out privacy big data identification on each big data set based on the incidence matrix so as to encrypt and send the big data set. Therefore, when the target medical terminal receives the encrypted compressed packet, partial big data encrypted packets can be decrypted according to a preset decryption protocol, and corresponding target privacy big data are obtained. Therefore, when the user big data is issued to the medical terminals at different levels, the medical terminals can be ensured to only obtain part of the big data in the user big data according to the authority levels of the medical terminals, and further the leakage of the privacy big data in the user big data is avoided.
In specific implementation, in order to accurately obtain big data interface information corresponding to each target medical terminal, the statistics of m target medical terminals detected through the network environment in the first medical interactive network and the extraction of the big data interface information of each target medical terminal described in step S120 may specifically include the contents described in the following steps S1201 to S1206.
Step S1201, counting terminal name information corresponding to m target medical terminals detected by the network environment in the first medical interactive network; the terminal name information is used for representing the equipment model of each target medical terminal.
Step S1202, generating a terminal identifier containing the terminal name information; the terminal identification is used for representing the unique identification corresponding to the target medical terminal.
Step S1203, mapping the terminal identifier to the network environment based on a corresponding relationship between the detection information of the target medical terminal in the network environment corresponding to the first medical interactive network and the terminal name information.
Step S1204, the current environment parameter obtained after the terminal identification is mapped to the network environment is transmitted to each target medical terminal.
Step S1205, extracting the terminal identification to obtain an identifier, and determining terminal request information matched with the terminal identification based on the identifier and the corresponding relation between the detection information and the terminal name information in the network environment corresponding to the first medical interactive network.
Step S1206, receiving response information fed back by each target medical terminal based on the current environment parameters, interleaving and coding a first information sequence of the response information and a second information sequence corresponding to terminal request information matched with the terminal identification of each target medical terminal to obtain a third information sequence corresponding to each target medical terminal, and performing deserialization operation on the third information sequence to obtain big data interface information corresponding to each target medical terminal.
And executing the contents described in the steps S1201-S1206, firstly counting the terminal name information, generating a terminal identifier containing the terminal name information, further mapping the terminal identifier to a network environment, and transmitting the obtained current environment parameters to each target medical terminal. Second, the identifier is extracted and terminal request information is determined. And finally, receiving the response information, interleaving and coding a first information sequence of the response information and a second information sequence corresponding to the terminal request information matched with the terminal identification to obtain a third information sequence, and performing deserialization operation on the third information sequence to obtain big data interface information corresponding to each target medical terminal. By the design, the large data interface information corresponding to each target medical terminal can be accurately obtained.
In specific implementation, in order to accurately obtain the big data codes and avoid interference by third-party big data in the coding process, the step S130 may specifically include the following contents described in steps S1301 to S1303, where each big data set in the user big data is converted into a big data code.
Step S1301, obtaining a big data set distribution track of the user big data, and determining, according to a big data set of the user big data in the unipolar coding type and a big data set capacity thereof, a correlation coefficient between each big data set of the user big data in the bipolar coding type and each big data set of the user big data in the unipolar coding type when it is determined that the user big data includes the unipolar coding type based on the big data set distribution track.
Step S1302, migrating a big data set of the user big data under the bipolar coding category and associated with a big data set under the unipolar coding category to the unipolar coding category based on the association coefficient; if the user big data comprises a plurality of big data sets in the bipolar coding category, determining a correlation coefficient between the user big data and each big data set in the bipolar coding category based on the big data sets of the user big data in the unipolar coding category and the big data set capacity of the user big data, and marking each big data set in the bipolar coding category according to the correlation coefficient between the big data sets to obtain a target big data set; migrating the target big data set to the unipolar coding category.
Step S1303, respectively encoding the big data sets in the unipolar encoding category and the big data sets in the bipolar encoding category to obtain a big data code corresponding to each big data set in the user big data.
Executing the content described in the steps S1301 to S1303, obtaining a distribution track of the big data set, determining a correlation coefficient between each big data set of the big data of the user in the bipolar coding category and each big data set of the unipolar coding category according to the big data set and the capacity of the big data set thereof when the big data of the user is determined to contain the unipolar coding category based on the distribution track of the big data set, transferring the big data sets in the bipolar coding category and in the unipolar coding category to the unipolar coding category based on the correlation coefficient, and further coding the big data set in the unipolar coding category and the big data set in the bipolar coding category to obtain big data codes. In this way, by setting the correlation coefficient between each big data set under the bipolar coding category and each big data set under the unipolar coding category, the big data sets under the unipolar coding category and the big data sets under the bipolar coding category are further encoded respectively to obtain big data codes, so that the big data codes can be accurately obtained, and meanwhile, the interference of third-party big data in the coding process is avoided.
In a specific implementation, in order to ensure the accuracy of the association between the encryption right information and the signature information and thereby improve the efficiency of the association between the encryption right information and the signature information, step S140 describes generating an association matrix between the encryption right information corresponding to each big data set and the signature information encoded by the big data into which the big data set is converted, and specifically may further include the following steps S1401 to S1403.
Step 1401, determining encryption authority information corresponding to each big data set and a number string information set corresponding to each big data set; the encryption authority information and the digital string information set respectively comprise a plurality of information fields with different structure parameters, and the digital string information set is used for representing the signature information.
Step S1402, extracting first field description information of each big data set in one of the information fields of the encryption authority information, and determining the information field having the smallest structure parameter in the number string information set as a target information field.
Step S1403, inputting the first field description information into the target information field according to preset input address information, obtaining second field description information in the target information field, and determining an association relationship between the encryption permission information and the digital string information set based on the first field description information and the second field description information; and determining an incidence matrix between the encryption authority information and the signature information according to the incidence relation.
And executing the contents described in the steps S1401 to S1403, determining encryption authority information and a digital string information set, where the encryption authority information and the digital string information set respectively include a plurality of information fields with different structure parameters, inputting the extracted first field description information into a target information field in the digital string information set according to preset input address information to obtain second field description information, further determining an association relationship between the encryption authority information and the digital string information set, and determining an association matrix between the encryption authority information and the signature information according to the association relationship. By the design, the association accuracy between the encryption authority information and the signature information can be ensured, the association efficiency between the encryption authority information and the signature information can be greatly improved,
in a specific implementation, in order to prevent the target privacy big data from being leaked, the privacy big data identification is performed on each big data set based on the association matrix in step S150, so as to obtain the target privacy big data corresponding to each big data set and the privacy authority of the target privacy big data, which may specifically include the following contents described in step S1501 to step S1504.
S1501, extracting the private big data in each big data set based on the incidence matrix to obtain at least one private big data packet.
S1502, screening the key privacy big data in the at least one privacy big data packet to obtain key privacy big data corresponding to each big data set; the key privacy big data is used for representing big data information of which the privacy level is greater than a preset privacy level in the privacy big data packet.
S1503, identifying the privacy big data in the at least one privacy big data packet based on the key privacy big data to obtain target privacy big data corresponding to each big data set.
S1504, identifying the access authority corresponding to the target privacy big data based on the key privacy big data to obtain the privacy authority of the target privacy big data.
Executing the contents described in the steps S1501 to S1504, extracting the privacy big data in each big data set based on the incidence matrix, screening the key privacy big data in the obtained at least one privacy big data packet to obtain the key privacy big data, and further identifying the privacy big data and the access authority based on the key privacy big data to obtain the target privacy big data and the privacy authority of the target privacy big data. By the design, the target privacy big data can be effectively prevented from being leaked by setting the privacy authority.
In specific implementation, in order to ensure the security problem in the process of encrypting and compressing the packet for transmission, the step S150 of encapsulating the big data encrypted packet to obtain the encrypted and compressed packet, and sequentially transmitting the encrypted and compressed packet to each target medical terminal according to the size order of the hierarchy where each target medical terminal is located may further include the following contents described in step S1505-step S1507.
In step S1505, before the big data encrypted packet is encapsulated, the big data in the big data encrypted packet is corrected according to a preset correction method to obtain a corrected big data encrypted packet.
Step S1506, obtaining a first key sequence of the big data encryption packet, and encapsulating the corrected big data encryption packet according to the first key sequence and a preset encapsulation manner to obtain an encrypted compressed packet.
Step S1507, a second key sequence corresponding to the encrypted compressed packet is searched, and the encrypted compressed packet is sequentially sent to each target medical terminal according to the second key sequence and the size sequence of the hierarchy where each target medical terminal is located.
Executing the contents described in steps S1505-S1507, according to the corrected big data encryption packet obtained by correcting before encapsulating the big data encryption packet and the obtained first key sequence, the integrity and accuracy of the big data encryption packet can be ensured, and further, the encrypted compressed packets are sequentially transmitted to each target medical terminal according to the size order of the hierarchy where each target medical terminal is located, so that the security of the encrypted compressed packets in the transmission process can be ensured.
Based on the same inventive concept, the invention also provides a networking type medical big data hierarchical transmission system, which comprises a big data transmission server, a user terminal and a plurality of medical terminals; the big data transmission server is communicated with the user terminal and each medical terminal;
the user terminal is configured to:
uploading the user big data to the big data transmission server;
the big data transmission server is used for:
when the user big data is obtained, network environment detection is carried out on each medical terminal in the first medical interactive network;
counting m target medical terminals detected through the network environment in the first medical interactive network and extracting big data interface information of each target medical terminal; indicating the m target medical terminals to carry out communication connection according to the big data interface information to form a second medical interactive network; extracting a topological structure of the second medical interactive network, and determining the level of each target medical terminal according to the topological structure;
respectively converting each big data set in the user big data into big data codes; the big data codes can be completely output on each target medical terminal, the output format of the big data codes converted by each big data set is the same as the storage format of the big data set in the big data transmission server, and each big data code has signature information for uniquely determining the big data code;
generating an incidence matrix between the encryption authority information corresponding to each big data set and the signature information of the big data codes converted from the big data sets;
performing privacy big data identification on each big data set based on the incidence matrix to obtain target privacy big data corresponding to each big data set and privacy authority of the target privacy big data; encrypting the big data sets according to the privacy authority to obtain a big data encryption packet corresponding to each big data set; and packaging the big data encryption packet to obtain an encrypted compression packet, and sequentially sending the encrypted compression packet to each target medical terminal according to the size sequence of the hierarchy where each target medical terminal is located.
Optionally, the big data transmission server is specifically configured to:
counting terminal name information corresponding to m target medical terminals detected by the network environment in the first medical interactive network; the terminal name information is used for representing the equipment model of each target medical terminal;
generating a terminal identifier containing the terminal name information; the terminal identification is used for representing a unique identification corresponding to the target medical terminal;
mapping the terminal identification to the network environment based on the corresponding relation between the detection information of the target medical terminal in the network environment corresponding to the first medical interactive network and the terminal name information;
transmitting the current environment parameters obtained after the terminal identification is mapped to the network environment to each target medical terminal;
extracting the terminal identification to obtain an identifier, and determining terminal request information matched with the terminal identification based on the identifier and a corresponding relation between detection information and terminal name information in a network environment corresponding to the first medical interactive network;
and receiving response information fed back by each target medical terminal based on the current environment parameters, interleaving and coding a first information sequence of the response information and a second information sequence corresponding to terminal request information matched with the terminal identifier of each target medical terminal to obtain a third information sequence corresponding to each target medical terminal, and performing deserialization operation on the third information sequence to obtain big data interface information corresponding to each target medical terminal.
Optionally, the big data transmission server is specifically configured to:
acquiring a big data set distribution track of the user big data, and determining a correlation coefficient between each big data set of the user big data under a bipolar coding type and each big data set of the user big data under the unipolar coding type according to a big data set of the user big data under the unipolar coding type and the big data set capacity of the user big data under the unipolar coding type under the condition that the big data contains the unipolar coding type based on the big data set distribution track;
migrating a big data set of the user big data under the bipolar coding class associated with a big data set under the unipolar coding class to the unipolar coding class based on the correlation coefficient; if the user big data comprises a plurality of big data sets in the bipolar coding category, determining a correlation coefficient between the user big data and each big data set in the bipolar coding category based on the big data sets of the user big data in the unipolar coding category and the big data set capacity of the user big data, and marking each big data set in the bipolar coding category according to the correlation coefficient between the big data sets to obtain a target big data set; migrating the target big data set to the unipolar coding category;
and respectively coding the big data sets under the unipolar coding category and the big data sets under the bipolar coding category to obtain big data codes corresponding to each big data set in the user big data.
Optionally, the big data transmission server is specifically configured to:
determining encryption authority information corresponding to each big data set and a digital string information set corresponding to each big data set; the encryption authority information and the digital string information set respectively comprise a plurality of information fields with different structure parameters, and the digital string information set is used for representing the signature information;
extracting first field description information of each big data set in one information field of the encryption authority information, and determining the information field with the minimum structure parameter in the digital string information set as a target information field;
inputting the first field description information into the target information field according to preset input address information, obtaining second field description information in the target information field, and determining the association relationship between the encryption authority information and the digital string information set based on the first field description information and the second field description information; and determining an incidence matrix between the encryption authority information and the signature information according to the incidence relation.
Optionally, the big data transmission server is specifically configured to:
extracting the private big data in each big data set based on the incidence matrix to obtain at least one private big data packet;
screening key privacy big data in the at least one privacy big data packet to obtain key privacy big data corresponding to each big data set; the key privacy big data is used for representing big data information with the privacy level larger than a preset privacy level in the privacy big data packet;
identifying the privacy big data in the at least one privacy big data packet based on the key privacy big data to obtain target privacy big data corresponding to each big data set;
and identifying the access authority corresponding to the target privacy big data based on the key privacy big data to obtain the privacy authority of the target privacy big data.
In addition to the above, referring to fig. 3, the present invention further provides a networked medical big data hierarchical transmission device 200, applied to a big data transmission server communicating with a user terminal and a plurality of medical terminals, the device comprising:
the network environment detection module 210 is configured to perform network environment detection on each medical terminal in the first medical interactive network when user big data uploaded by the user terminal is acquired;
a medical terminal counting module 220, configured to count m target medical terminals detected through the network environment in the first medical interaction network and extract big data interface information of each target medical terminal; indicating the m target medical terminals to carry out communication connection according to the big data interface information to form a second medical interactive network; extracting a topological structure of the second medical interactive network, and determining the level of each target medical terminal according to the topological structure;
a big data set conversion module 230, configured to convert each big data set in the user big data into a big data code; the big data codes can be completely output on each target medical terminal, the output format of the big data codes converted by each big data set is the same as the storage format of the big data set in the big data transmission server, and each big data code has signature information for uniquely determining the big data code;
the incidence matrix generating module 240 is configured to generate an incidence matrix between the encryption permission information corresponding to each big data set and the signature information of the big data code converted from the big data set;
the privacy big data identification module 250 is configured to perform privacy big data identification on each big data set based on the incidence matrix to obtain target privacy big data corresponding to each big data set and privacy permission of the target privacy big data; encrypting the big data sets according to the privacy authority to obtain a big data encryption packet corresponding to each big data set; and packaging the big data encryption packet to obtain an encrypted compression packet, and sequentially sending the encrypted compression packet to each target medical terminal according to the size sequence of the hierarchy where each target medical terminal is located.
On the basis, please refer to fig. 4 in combination, which provides a big data transmission server 110, including a processor 111, and a memory 112 and a bus 113 connected to the processor 111; wherein, the processor 111 and the memory 112 complete the communication with each other through the bus 113; the processor 111 is used to call program instructions in the memory 112 to perform the above-described method.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. A networking type medical big data hierarchical transmission method is applied to a big data transmission server which is communicated with a user terminal and a plurality of medical terminals, and the method comprises the following steps:
when user big data uploaded by a user terminal is acquired, network environment detection is carried out on each medical terminal in the first medical interactive network;
counting m target medical terminals detected through the network environment in the first medical interactive network and extracting big data interface information of each target medical terminal; indicating the m target medical terminals to carry out communication connection according to the big data interface information to form a second medical interactive network; extracting a topological structure of the second medical interactive network, and determining the level of each target medical terminal according to the topological structure;
respectively converting each big data set in the user big data into big data codes; the big data codes can be completely output on each target medical terminal, the output format of the big data codes converted by each big data set is the same as the storage format of the big data set in the big data transmission server, and each big data code has signature information for uniquely determining the big data code;
generating an incidence matrix between the encryption authority information corresponding to each big data set and the signature information of the big data codes converted from the big data sets;
performing privacy big data identification on each big data set based on the incidence matrix to obtain target privacy big data corresponding to each big data set and privacy authority of the target privacy big data; encrypting the big data sets according to the privacy authority to obtain a big data encryption packet corresponding to each big data set; and packaging the big data encryption packet to obtain an encrypted compression packet, and sequentially sending the encrypted compression packet to each target medical terminal according to the size sequence of the hierarchy where each target medical terminal is located.
2. The method for hierarchically transmitting networked medical big data according to claim 1, wherein counting m target medical terminals detected through the network environment in the first medical interactive network and extracting big data interface information of each target medical terminal specifically comprises:
counting terminal name information corresponding to m target medical terminals detected by the network environment in the first medical interactive network; the terminal name information is used for representing the equipment model of each target medical terminal;
generating a terminal identifier containing the terminal name information; the terminal identification is used for representing a unique identification corresponding to the target medical terminal;
mapping the terminal identification to the network environment based on the corresponding relation between the detection information of the target medical terminal in the network environment corresponding to the first medical interactive network and the terminal name information;
transmitting the current environment parameters obtained after the terminal identification is mapped to the network environment to each target medical terminal;
extracting the terminal identification to obtain an identifier, and determining terminal request information matched with the terminal identification based on the identifier and a corresponding relation between detection information and terminal name information in a network environment corresponding to the first medical interactive network;
and receiving response information fed back by each target medical terminal based on the current environment parameters, interleaving and coding a first information sequence of the response information and a second information sequence corresponding to terminal request information matched with the terminal identifier of each target medical terminal to obtain a third information sequence corresponding to each target medical terminal, and performing deserialization operation on the third information sequence to obtain big data interface information corresponding to each target medical terminal.
3. The networked medical big data hierarchical transmission method according to claim 1, wherein the step of converting each big data set in the user big data into big data codes respectively comprises:
acquiring a big data set distribution track of the user big data, and determining a correlation coefficient between each big data set of the user big data under a bipolar coding type and each big data set of the user big data under the unipolar coding type according to a big data set of the user big data under the unipolar coding type and the big data set capacity of the user big data under the unipolar coding type under the condition that the big data contains the unipolar coding type based on the big data set distribution track;
migrating a big data set of the user big data under the bipolar coding class associated with a big data set under the unipolar coding class to the unipolar coding class based on the correlation coefficient; if the user big data comprises a plurality of big data sets in the bipolar coding category, determining a correlation coefficient between the user big data and each big data set in the bipolar coding category based on the big data sets of the user big data in the unipolar coding category and the big data set capacity of the user big data, and marking each big data set in the bipolar coding category according to the correlation coefficient between the big data sets to obtain a target big data set; migrating the target big data set to the unipolar coding category;
and respectively coding the big data sets under the unipolar coding category and the big data sets under the bipolar coding category to obtain big data codes corresponding to each big data set in the user big data.
4. The method for hierarchically transmitting networked medical big data according to claim 1, wherein generating an association matrix between encryption authority information corresponding to each big data set and signature information encoded by big data converted from the big data set specifically comprises:
determining encryption authority information corresponding to each big data set and a digital string information set corresponding to each big data set; the encryption authority information and the digital string information set respectively comprise a plurality of information fields with different structure parameters, and the digital string information set is used for representing the signature information;
extracting first field description information of each big data set in one information field of the encryption authority information, and determining the information field with the minimum structure parameter in the digital string information set as a target information field;
inputting the first field description information into the target information field according to preset input address information, obtaining second field description information in the target information field, and determining the association relationship between the encryption authority information and the digital string information set based on the first field description information and the second field description information; and determining an incidence matrix between the encryption authority information and the signature information according to the incidence relation.
5. The networked medical big data graded transmission method according to claim 1, wherein privacy big data identification is performed on each big data set based on the incidence matrix, so as to obtain target privacy big data corresponding to each big data set and privacy authority of the target privacy big data, and specifically includes:
extracting the private big data in each big data set based on the incidence matrix to obtain at least one private big data packet;
screening key privacy big data in the at least one privacy big data packet to obtain key privacy big data corresponding to each big data set; the key privacy big data is used for representing big data information with the privacy level larger than a preset privacy level in the privacy big data packet;
identifying the privacy big data in the at least one privacy big data packet based on the key privacy big data to obtain target privacy big data corresponding to each big data set;
and identifying the access authority corresponding to the target privacy big data based on the key privacy big data to obtain the privacy authority of the target privacy big data.
6. The networked medical big data hierarchical transmission system is characterized by comprising a big data transmission server, a user terminal and a plurality of medical terminals; the big data transmission server is communicated with the user terminal and each medical terminal;
the user terminal is configured to:
uploading the user big data to the big data transmission server;
the big data transmission server is used for:
when the user big data is obtained, network environment detection is carried out on each medical terminal in the first medical interactive network;
counting m target medical terminals detected through the network environment in the first medical interactive network and extracting big data interface information of each target medical terminal; indicating the m target medical terminals to carry out communication connection according to the big data interface information to form a second medical interactive network; extracting a topological structure of the second medical interactive network, and determining the level of each target medical terminal according to the topological structure;
respectively converting each big data set in the user big data into big data codes; the big data codes can be completely output on each target medical terminal, the output format of the big data codes converted by each big data set is the same as the storage format of the big data set in the big data transmission server, and each big data code has signature information for uniquely determining the big data code;
generating an incidence matrix between the encryption authority information corresponding to each big data set and the signature information of the big data codes converted from the big data sets;
performing privacy big data identification on each big data set based on the incidence matrix to obtain target privacy big data corresponding to each big data set and privacy authority of the target privacy big data; encrypting the big data sets according to the privacy authority to obtain a big data encryption packet corresponding to each big data set; and packaging the big data encryption packet to obtain an encrypted compression packet, and sequentially sending the encrypted compression packet to each target medical terminal according to the size sequence of the hierarchy where each target medical terminal is located.
7. The system according to claim 6, wherein said big data transmission server is specifically configured to:
counting terminal name information corresponding to m target medical terminals detected by the network environment in the first medical interactive network; the terminal name information is used for representing the equipment model of each target medical terminal;
generating a terminal identifier containing the terminal name information; the terminal identification is used for representing a unique identification corresponding to the target medical terminal;
mapping the terminal identification to the network environment based on the corresponding relation between the detection information of the target medical terminal in the network environment corresponding to the first medical interactive network and the terminal name information;
transmitting the current environment parameters obtained after the terminal identification is mapped to the network environment to each target medical terminal;
extracting the terminal identification to obtain an identifier, and determining terminal request information matched with the terminal identification based on the identifier and a corresponding relation between detection information and terminal name information in a network environment corresponding to the first medical interactive network;
and receiving response information fed back by each target medical terminal based on the current environment parameters, interleaving and coding a first information sequence of the response information and a second information sequence corresponding to terminal request information matched with the terminal identifier of each target medical terminal to obtain a third information sequence corresponding to each target medical terminal, and performing deserialization operation on the third information sequence to obtain big data interface information corresponding to each target medical terminal.
8. The system according to claim 6, wherein said big data transmission server is specifically configured to:
acquiring a big data set distribution track of the user big data, and determining a correlation coefficient between each big data set of the user big data under a bipolar coding type and each big data set of the user big data under the unipolar coding type according to a big data set of the user big data under the unipolar coding type and the big data set capacity of the user big data under the unipolar coding type under the condition that the big data contains the unipolar coding type based on the big data set distribution track;
migrating a big data set of the user big data under the bipolar coding class associated with a big data set under the unipolar coding class to the unipolar coding class based on the correlation coefficient; if the user big data comprises a plurality of big data sets in the bipolar coding category, determining a correlation coefficient between the user big data and each big data set in the bipolar coding category based on the big data sets of the user big data in the unipolar coding category and the big data set capacity of the user big data, and marking each big data set in the bipolar coding category according to the correlation coefficient between the big data sets to obtain a target big data set; migrating the target big data set to the unipolar coding category;
and respectively coding the big data sets under the unipolar coding category and the big data sets under the bipolar coding category to obtain big data codes corresponding to each big data set in the user big data.
9. The system according to claim 6, wherein said big data transmission server is specifically configured to:
determining encryption authority information corresponding to each big data set and a digital string information set corresponding to each big data set; the encryption authority information and the digital string information set respectively comprise a plurality of information fields with different structure parameters, and the digital string information set is used for representing the signature information;
extracting first field description information of each big data set in one information field of the encryption authority information, and determining the information field with the minimum structure parameter in the digital string information set as a target information field;
inputting the first field description information into the target information field according to preset input address information, obtaining second field description information in the target information field, and determining the association relationship between the encryption authority information and the digital string information set based on the first field description information and the second field description information; and determining an incidence matrix between the encryption authority information and the signature information according to the incidence relation.
10. The system according to claim 6, wherein said big data transmission server is specifically configured to:
extracting the private big data in each big data set based on the incidence matrix to obtain at least one private big data packet;
screening key privacy big data in the at least one privacy big data packet to obtain key privacy big data corresponding to each big data set; the key privacy big data is used for representing big data information with the privacy level larger than a preset privacy level in the privacy big data packet;
identifying the privacy big data in the at least one privacy big data packet based on the key privacy big data to obtain target privacy big data corresponding to each big data set;
and identifying the access authority corresponding to the target privacy big data based on the key privacy big data to obtain the privacy authority of the target privacy big data.
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