Intelligent medical information pushing system and method based on big data

Abstract

The invention discloses an intelligent medical information pushing system and method based on big data, relates to the technical field of intelligent medical treatment, and solves the problem of low transmission efficiency caused by insufficient consideration of node processing characteristics and data type differences in an original information pushing process. The optimal path is intelligently screened based on the transmission path characteristics, the node processing capability, the transfer state and the data type difference are fully considered, the transmission time consumption is effectively shortened, and the medical information timeliness is improved; data encryption is carried out on information in the transmission process, an innovative hierarchical encryption mechanism depends on Hash mapping and dynamic recombination of information segments, encryption hierarchies are enhanced along with progressive increase of transmission nodes, data full-link safety is guaranteed, decryption is guaranteed to be accurate and efficient, the requirement for rapid and safe circulation of information in an intelligent medical scene is met in an all-round mode, and the safety of the intelligent medical scene is improved. And a technical base is built for medical collaboration and data sharing.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent medical treatment, and specifically to an intelligent medical information push system and method based on big data. Background Technique

[0002] Among intelligent medical networks, information push not only includes pushing medical information to relevant personnel, but also includes the information push process between multiple system nodes. In the actual push process, each node performs information push according to the set transmission protocol to complete the real-time sharing process of information within the network;

[0003] Under the background of the booming development of intelligent medical treatment, the efficient transmission and security guarantee of medical data have become key challenges. Existing medical information push technologies often face problems such as rough transmission path planning, insufficient consideration of node processing characteristics and data type differences, resulting in low transmission efficiency and delaying the diagnosis and treatment time; at the same time, traditional encryption methods mostly use fixed keys or single encryption levels, which are difficult to adapt to the complex and changeable transmission node environment in medical networks, and the risk of data leakage is relatively high; in addition, medical data covers multiple formats such as text and images, and the processing capabilities of different nodes vary widely. There is an urgent need for a solution that can dynamically adapt to the network topology, accurately optimize the transmission path, and strengthen the full-link encryption to meet the urgent need for real-time and secure interaction of massive information in the intelligent medical scenario. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides an intelligent medical information push system and method based on big data, which solves the problem of low transmission efficiency caused by insufficient consideration of node processing characteristics and data type differences in the original information push process.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent medical information push method based on big data includes the following steps:

[0006] Step 1: Confirm the network nodes associated with this intelligent medical network from big data, and generate a network topology diagram belonging to this intelligent medical network based on the connection methods between different network nodes;

[0007] Step 2: Based on the confirmed push information, confirm the push party and the push recipient, then confirm the nodes associated with the push party and the push recipient from the network topology diagram, and confirm the transmission path based on the topological relationship of the associated nodes in the network topology diagram. Then, based on the different transmission characteristics associated with different transmission paths, select the best path from multiple transmission paths. The specific method is as follows:

[0008] Based on the determined push party and the push recipient, confirm the associated nodes from the network topology diagram, record the push party associated node as the main node, and record the push recipient associated node as the secondary node;

[0009] Lock the transmission path existing between the main node and the secondary node from the network topology diagram:

[0010] If there is only one set of transmission paths, mark this transmission path as the optimal path;

[0011] If there are multiple sets of transmission paths, confirm the transmission characteristics associated with each different transmission path:

[0012] For the case where there are only the main node and the secondary node in the transmission path: Confirm different types of relevant information in the push message, and record the confirmed relevant information belonging to different types as single-category information. From the historical data associated with the main node, confirm the processing rate of the main node for processing relevant information of the same type. Perform an average process on the confirmed several sets of processing rates, lock the characteristic rate, and based on the information capacity of the single-category information, use: Information capacity ÷ Characteristic rate = Single-category processing time to confirm the single-category processing time associated with the corresponding single-category information. Then, confirm and sum up the single-category processing times associated with different single-category information in this push message, lock the total time characteristic of this transmission path and record it as the transmission characteristic of this transmission path;

[0013] For the case where there is a relay in the transmission path: Confirm the interaction moments of the main node and the secondary node, use this interaction moment as the reference moment, and identify whether there is other data information that has not been processed by the relay before the reference moment:

[0014] If there is, confirm the different single-category information belonging to different data types existing in the other data information, and based on the historical processing data of the relay, confirm the characteristic rate associated with the relay for different single-category information. Based on the data capacity and characteristic rate of different single-category information, lock the required duration for the relay to process the other data information, and record it as a type of characteristic time. Then, confirm the single-category information of different data types in this transmission information, use the same processing method as above to confirm the characteristic rate, so as to lock the total duration required for the transmission information to complete the data processing process, and record it as the second type of characteristic time. Sum up the first type of characteristic time and the second type of characteristic time, and lock the transmission characteristic belonging to this transmission path;

[0015] If not, directly determine the second type of characteristic time, and use the determined second type of characteristic time as the transmission characteristic of this transmission path;

[0016] For the case where there is no relay in the transmission path: Use the other nodes existing between the main node and the secondary node as pending nodes, and determine different single-category information based on the different types of relevant information associated with the push message. Then, based on the historical data associated with different pending nodes, confirm the characteristic rate V corresponding to different single-category information for different pending nodes k, where k represents different undetermined nodes, and for multiple groups of characteristic rates V corresponding to the same group of single-class information k , select V k The undetermined node associated with max is used as the processing node for this single-class information, and based on the capacity of the single-class information and V k max locks the processing time, then sums up the different processing times associated with different single-class information, locks the total time characteristic, and takes the total time characteristic as the transmission characteristic of this transmission path;

[0017] Based on the different transmission characteristics confirmed for different transmission paths, select the minimum value from multiple groups of transmission characteristics, and take the transmission path corresponding to the minimum value as the best path associated with this push information;

[0018] Step 3: Based on the confirmed best path, perform push processing on the push information, and during the push process, encrypt the information at each node, and based on the specific encryption processing process, complete the information push work of the push information. The specific method is as follows:

[0019] Confirm the primary node and the secondary node from the confirmed best path, and based on the sorting method of the best path, confirm the other nodes existing between the primary node and the secondary node, and record the other nodes existing inside as transit nodes;

[0020] Push the push information from the primary node to the first group of transit nodes: Denote the push information as an information segment, divide this information segment into two information segments, denote the data capacities associated with different classification information as R1 and R2, then based on the preset hash value mapping table, confirm the hash values associated with R1 and R2, and sort and integrate the two confirmed groups of hash values before and after to confirm the encryption key for this push information at this stage. During the push process, place the information segment originally at the back between the front information segments. After the subsequent transit nodes receive the push information sent by the primary node, they will decrypt it. According to the two groups of data capacities R1 and R2 associated in the protocol, confirm the two groups of hash values associated with the corresponding capacities, then randomly combine the two groups of hash values before and after to confirm the decryption key, decrypt this type of push information, and according to the decryption process, re-exchange the two information segments to make them return to the original correct positions;

[0021] Push the push information from the first group of relay nodes to subsequent nodes: Make the two existing information segments act as the main information segment, divide a single main information segment into two information segments, determine the data capacity of dividing the main information segment into two information segments, and adopt the same processing method as above to confirm its encryption key. Then, swap the positions of the two information segments associated with the main information segment, push the information segments after the position swap is completed. After the push is completed, confirm four groups of hash values based on the four groups of data capacity associated in the protocol, and randomly combine the four groups of hash values to decrypt this push information until the decryption is correct and then stop. Then, reorganize the four information segments according to the correct sorting method of the four groups of hash values to obtain the correct sorting method;

[0022] When performing push transmission at each subsequent node, based on the main information segment determined by the previous node, perform specific division of the information segment, synchronously confirm the encryption key, and then perform position swap transmission after confirming the encryption key to complete the corresponding push encryption transmission process. After the transmission process is completed, the subsequent node directly performs decryption.

[0023] Preferably, a big data-based intelligent medical information push system includes:

[0024] A topology map confirmation end, which confirms the network nodes associated with this intelligent medical network from big data, and generates a network topology map belonging to this intelligent medical network based on the connection methods between different network nodes;

[0025] An optimal path selection end, which based on the confirmed push information, confirms the push party and the pushed party, confirms the nodes associated with the push party and the pushed party from the network topology map, and confirms the transmission path based on the topological relationship of the associated nodes in the network topology map. Then, based on the different transmission characteristics associated with different transmission paths, selects the optimal path from multiple transmission paths;

[0026] A push encryption processing end, which based on the confirmed optimal path, performs push processing on the push information, and during the push process, encrypts the information at each node, and completes the information push work of the push information based on the specific encryption processing process.

[0027] The present invention provides a big data-based intelligent medical information push system and method. Compared with the prior art, it has the following beneficial effects:

[0028] The present invention intelligently selects the optimal path based on the transmission path characteristics, fully considers the node processing capabilities, transit status, and data type differences, effectively shortens the transmission time, and improves the timeliness of medical information;

[0029] By encrypting the information during the transmission process, the innovative hierarchical encryption mechanism relies on hash mapping and dynamic recombination of information segments, and strengthens the encryption level with the transmission nodes, ensuring the security of the entire data link and the accuracy and efficiency of decryption, meeting the requirements of fast and secure information flow in the intelligent medical scenario in all aspects, and building a solid technical foundation for medical collaboration and data sharing. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic flowchart of the method of the present invention;

[0031] Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] The First Embodiment

[0034] Please refer to Figure 1 , this application provides a big data-based intelligent medical information push method, including the following steps:

[0035] Step 1: Confirm the network nodes associated with this intelligent medical network from the big data, generate a network topology diagram belonging to this intelligent medical network based on the connection methods between different network nodes, and mark the internal nodes of the network topology diagram based on the ownership of the network nodes. Specifically, for a medical network, there are multiple different network nodes, and different network nodes include different terminal devices with network connections such as servers, relay terminals, routers, service devices, computer devices, etc. There are different node connection methods between each terminal device, and the network topology diagram of the corresponding intelligent medical network can be confirmed based on the specific data flow process during the corresponding connection process;

[0036] Among them, the specific processing method for generating the network topology diagram is:

[0037] Based on several network nodes associated within the intelligent medical network, confirm the connection methods between the corresponding network nodes from the big data, use the network nodes as individual topology nodes, connect the topology nodes associated with the network nodes with data transmission, and perform connection processing on several topology nodes in sequence to confirm the network topology diagram associated with the corresponding intelligent medical network;

[0038] Specifically, there is a data transmission connection between different network nodes. Based on the specific connection method, the topological relationship between the corresponding network nodes can be determined, and the corresponding network topology map can be generated accordingly.

[0039] Step 2: Based on the confirmed push information, confirm the push party and the pushed party. Then, confirm the nodes associated with the push party and the pushed party from the network topology map, and confirm the transmission path based on the topological relationship of the associated nodes in the network topology map. Then, based on the different transmission characteristics associated with different transmission paths, select the best path from multiple transmission paths. Specifically, during the use of the path, there may or may not be an intermediate end. Then, according to the corresponding processing process, the specific path associated in the optimal state can be confirmed. When data is transmitted based on this best path subsequently, the fastest transmission effect can be achieved, and the transmission time can be fully reduced.

[0040] Among them, the specific method for selecting the best path is as follows:

[0041] Based on the determined push party and the pushed party, confirm the associated nodes from the network topology map. Denote the push party associated node as the primary node and the pushed party associated node as the secondary node.

[0042] From the network topology map, lock the transmission path existing between the primary node and the secondary node:

[0043] If there is only one set of transmission paths, mark this transmission path as the best path;

[0044] If there are multiple sets of transmission paths, confirm the transmission characteristics associated with each different transmission path:

[0045] For the case where only the primary node and the secondary node exist in the transmission path: Confirm different types of relevant information in the push message (by different types, it means data in different formats, such as text format, picture format, etc., different data information), and record the confirmed relevant information belonging to different types as single-class information. From the historical data associated with the primary node, confirm the processing rate of the primary node for processing relevant information of the same type. Perform an average processing on the confirmed several groups of processing rates, lock the characteristic rate, and based on the information capacity of the single-class information, use: Information capacity ÷ Characteristic rate = Single-class processing time to confirm the single-class processing time associated with the corresponding single-class information. Then, confirm and sum up the single-class processing times associated with different single-class information in this push message one by one, lock the total time characteristic of this transmission path and record it as the transmission characteristic of this transmission path. Specifically, when the node itself processes data, it generally performs specific conversions of data formats. During the data transmission between systems, it is generally necessary to convert the corresponding data into binary-encoded data and then transmit it based on the corresponding transmission path. And when the corresponding node performs data processing, it only occupies a small part of the memory for the processing process, so the processing process is generally relatively slow because most of the memory inside the corresponding node needs to maintain its normal operation;

[0046] For the case where there is a relay in the transmission path (by relay, it means a data relay processing end. Such a port belongs to a relevant module that only performs data conversion and does not participate in any operations. It is mainly used for data transmission between different nodes. All the operating memory inside it is used for data processing and conversion, so the processing rate of this relay is relatively fast): Confirm the interaction moment between the primary node and the secondary node, and use this interaction moment as the reference moment to identify whether there is other data information that has not been processed by the relay before the reference moment (because the relay is always in the data processing state, so it is very likely that at the corresponding moment, the relay is still in the state of processing other data information):

[0047] If there is, then confirm the different single-class information belonging to different data types existing in the other data information, and based on the historical processing data of the relay, confirm the characteristic rate associated with the relay for different single-class information (the same way as confirming the characteristic rate when only the primary node and the secondary node exist in the transmission path). Based on the data capacity and characteristic rate of different single-class information, lock the required duration for the relay to process the other data information, and record it as one type of characteristic time. Then, confirm the single-class information of different data types in this transmission information, and use the same processing method as above to confirm the characteristic rate, so as to lock the total duration required for the transmission information to complete the data processing process, and record it as the second type of characteristic time. Sum up the first type of characteristic time and the second type of characteristic time to lock the transmission characteristic belonging to this transmission path;

[0048] If not, directly determine the time of the secondary features and use the determined time of the secondary features as the transmission feature of this transmission path. Specifically, for the case where there is a relay node, assume A is the main node and B is the secondary node, and the relay node is C. Then the transmission path can be expressed as A-C-B. This is just a set of examples, and there may be other nodes in the actual path. When A transmits data to B, the corresponding push information is associated. Such push information needs to be relayed by C. Before C processes it, there may be other data information that has not been processed, or there may be no other data information. Therefore, through a specific identification and processing process, confirm the conversion time associated with the corresponding relay node during the data conversion process, and conduct a unified confirmation of the time to lock the specific transmission feature associated with the corresponding transmission path;

[0049] For the case where there is no relay node in the transmission path: regard the other nodes existing between the main node and the secondary node as undetermined nodes, and determine different single-category information based on different types of relevant information associated with the push information. Then, based on the historical data associated with different undetermined nodes, confirm the characteristic rate V of different undetermined nodes for different single-category information k , where k represents different undetermined nodes. For multiple groups of characteristic rates V corresponding to the same set of single-category information k , select the undetermined node associated with V k max as the processing node for this single-category information, and lock the processing time based on the capacity of the single-category information and V k max. Then sum the different processing times associated with different single-category information to lock the total time feature, and use the total time feature as the transmission feature of this transmission path. Specifically, assume E and F are the other nodes existing in the middle of the path. Then assume the transmission path is A-E-F-B, where there are three different single-category information in the push information, and each different single-category information has different processing rates in E and F. Select the maximum value from the existing multiple groups of processing rates, and use the node corresponding to the maximum value as the processing node for the corresponding single-category information. Based on the corresponding characteristic rate and the actual information capacity data, confirm the processing time, and then lock the corresponding transmission feature according to the confirmed total processing time;

[0050] Based on the different transmission features confirmed for different transmission paths, select the minimum value from multiple groups of transmission features, and use the transmission path corresponding to the minimum value as the best path associated with this push information. After specific numerical confirmation, this best path is the path with the fastest efficiency in the subsequent data transmission and processing process. Then it can effectively reduce the data transmission time to a great extent, so as to achieve a better data transmission effect.

[0051] Step 3: Based on the confirmed optimal path, perform push processing on the push information. During the push process, encrypt the information at each node, and based on the specific encryption process, complete the information push work of the push information. Specifically, during the encryption process, according to the specific number of nodes in the corresponding path, encrypt such push information. The transmission process between each node will perform relevant encryption to achieve a better encryption effect and ensure that the push information will not be leaked. The specific processing method for information encryption is as follows:

[0052] Identify the primary node and the secondary node from the confirmed optimal path, and based on the sorting method of the optimal path, identify the other nodes existing between the primary node and the secondary node, and denote the other nodes existing inside as transit nodes;

[0053] Push the push information from the primary node to the first group of transit nodes: Denote the push information as an information segment, divide this information segment into two information segments, denote the data capacities associated with different classification information as R1 and R2, then based on the preset hash value mapping table, identify the hash values associated with R1 and R2, and sort and integrate the two groups of identified hash values before and after to identify the encryption key of this push information at this stage. During the push process, place the information segment originally at the back between the front information segments. After the subsequent transit nodes receive the push information sent by the primary node, they will decrypt it. According to the two groups of data capacities R1 and R2 associated in the protocol, identify the two groups of hash values associated with the corresponding capacities, and then randomly combine the two groups of hash values before and after to identify the decryption key, decrypt such push information (if it fails the first time, execute it again. The process here can decrypt at most twice), and based on the decryption process, re-exchange the two information segments to make them return to the original correct positions. Assume: A is divided into A1 - A2, and the original sorting method is A1 - A2. During transmission, it is adjusted to A2 - A1. Specifically, a push information A is divided into two classification information A1 and A2, with A1 sorted in front and A2 sorted behind. The data capacity associated with the corresponding classification information A1 is R1, and the data capacity associated with A2 is R2. Its hash value mapping table is a preset table, and each specific parameter corresponds to a hash value. Assume 1 corresponds to 11, 2 corresponds to 121, then when the capacity value R1 is 12, the corresponding hash value is 11121. Similarly, a group of hash values can be identified for R2. Then, sort the two groups of hash values before and after to identify the encryption key of this push node;

[0054] Push the push information from the first group of relay nodes to the subsequent nodes: Make the two existing information segments act as the main information segment, divide a single main information segment into two information segments (so the original two information segments are divided into four information segments, one information segment corresponding to two information segments), determine the data capacity of dividing the main information segment into two information segments, and adopt the same processing method as above to confirm its encryption key. Then, swap the positions of the two information segments associated with the main information segment, push the information segments after the position swap is completed. After the push is completed, confirm four groups of hash values according to the four groups of data capacities associated in the protocol, and randomly combine the four groups of hash values to decrypt this push information until the decryption is correct and then stop. Then, according to the correct sorting method of the four groups of hash values, recombine the four information segments to obtain the correct sorting method;

[0055] When performing push transmission at each subsequent node, according to the main information segment determined by the previous node, make specific divisions of the information segments, and synchronously confirm the encryption key. After confirming the encryption key, perform position swap transmission, complete the corresponding push encryption transmission process, and after the transmission process is completed, the subsequent node directly performs decryption.

[0056] Understand in combination with an example: Taking A - E - F - B as an example, its push information is H. When transmitting from node A to node E, H is divided into H1 and H2, with H1 in the front and H2 in the back. The capacity corresponding to H1 is R1, and the capacity corresponding to H2 is R2. The R1 corresponds to a string of hash values Z1, and H2 corresponds to a string of hash values Z2. Combine the two to confirm the encryption key Z1Z2. When transmitting, it is transmitted in the form of H2 - H1. After the transmission is completed, perform decryption. Confirm the hash values Z1 and Z2 associated with R1 and R2. First, decrypt with Z2Z1, the decryption is incorrect, then decrypt with Z1Z2, the decryption is correct. Then, arrange the information segment with the capacity corresponding to Z1 in the front and the Z2 information segment in the back to complete the decryption process and combine it into the correct sorting method;

[0057] When node E transmits to node F, H1 is divided into H11 and H12, and H2 is divided into H21 and H22. The H11 corresponds to a group of data capacities R11, the H12 corresponds to a group of data capacities R12, the H21 corresponds to a group of data capacities R21, and the H22 corresponds to a group of data capacities R22. Then, the same method as above can be adopted to confirm the corresponding hash values, arrange the hash values, confirm the encryption key, and then swap the positions of the information segments. The original position is: H11 - H12 - H21 - H22. After the swap, it is H12 - H11 - H22 - H21. Then, perform decryption. Based on the random combination method of the hash values, confirm the decryption process, and according to the decryption process, recombine the associated information segments to obtain the correct sorting method;

[0058] And so on. For each subsequent pair of nodes, it is necessary to divide the original information segment, swap them after division, and synchronously confirm the hash value to achieve a better encryption effect.

[0059] Second Embodiment

[0060] Combined with Figure 2 , a smart medical information push system based on big data, comprising:

[0061] A topology map confirmation end, which confirms the network nodes associated with this smart medical network from big data, and generates a network topology map belonging to this smart medical network based on the connection methods between different network nodes;

[0062] An optimal path selection end, which confirms the push party and the push recipient based on the confirmed push information, confirms the nodes associated with the push party and the push recipient from the network topology map, confirms the transmission path based on the topological relationship of the associated nodes in the network topology map, and then selects the optimal path from multiple transmission paths based on the different transmission characteristics associated with different transmission paths.

[0063] A push encryption processing end, which performs push processing on the push information based on the confirmed optimal path, encrypts the information at each node during the push process, and completes the information push work of the push information based on the specific encryption processing process.

[0064] Some of the data in the above formula are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0065] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for pushing intelligent medical information based on big data, characterized in that: The following steps are involved: Step 1: Identify the network nodes associated with the smart medical network from the big data, and generate a network topology diagram belonging to the smart medical network based on the connection mode between different network nodes; Step 2: confirm the pusher and the pushed party based on the confirmed push information, then confirm the nodes associated with the pusher and the pushed party in the network topology map, and confirm the transmission path based on the topological relationship of the associated nodes in the network topology map, and then select the best path from multiple transmission paths based on different transmission characteristics associated with different transmission paths; Step 3: Based on the confirmed optimal path, the push information is pushed, and in the push process, the information is encrypted at each node, and based on the specific encryption processing process, the information push work of the push information is completed.

2. According to the big data-based smart medical information push method of claim 1, it is characterized in that: In the step 1, in the network topology diagram, the internal nodes of the network topology diagram need to be marked based on the party to which the network nodes belong.

3. The method for pushing intelligent medical information based on big data according to claim 1, characterized in that: In step 1, the specific processing method for generating the network topology diagram is: Based on several network nodes associated with the smart medical network, the connection mode between the corresponding network nodes is confirmed from big data, and the network node is used as a single topological node to connect the topological nodes associated with the network node with data transmission. Several topological nodes are connected in turn to confirm the network topology diagram associated with the corresponding smart medical network.

4. The method for pushing intelligent medical information based on big data according to claim 1, characterized in that: In step 2, the specific method of confirming the transmission characteristics corresponding to different transmission paths is: Based on the determined pusher and pushee, the associated nodes are confirmed from the network topology diagram, and the node associated with the pusher is recorded as the primary node, and the node associated with the pushee is recorded as the secondary node; From the network topology diagram, identify the transmission path between the primary node and the secondary node: If there is only one set of transmission paths, then this transmission path is marked as the best path; If there are multiple transmission paths, confirm the transmission characteristics associated with each different transmission path: For the case where there are only primary nodes and secondary nodes in the transmission path: confirm the related information of different types in the pushed information, and record the confirmed related information of different types as single-category information; confirm the processing rate of the primary node for processing related information of the same type from the historical data associated with the primary node; average the confirmed groups of processing rates, lock the characteristic rate, and based on the information capacity of the single-category information, use: information capacity ÷ characteristic rate = single-category processing time to confirm the single-category processing time associated with the corresponding single-category information; then confirm and sum the single-category processing times associated with different single-category information of this pushed information one by one; lock the total time characteristics of this transmission path and record them as the transmission characteristics of this transmission path.

5. The method for pushing intelligent medical information based on big data according to claim 4, characterized in that: In the case where there is a transfer end in the transmission path: confirm the interaction time of the primary node and the secondary node, use this interaction time as the reference time, and identify whether there is other data information that has not been processed by the transfer end before the reference time: If it exists, confirm the different single types of information belonging to different data types in other data information, and based on the historical processing data of the transfer end, confirm the characteristic rate associated with the different single types of information at the transfer end. Based on the data capacity and characteristic rate of the different single types of information, lock the time required for the transfer end to process other data information, record it as the first type of characteristic time, and then confirm the single types of information of different data types in this transmission information. Use the same processing method as above to confirm the characteristic rate, so as to lock the total time required for the transmission information to complete the data processing process, record it as the second type of characteristic time, sum the first type of characteristic time and the second type of characteristic time, and lock the transmission characteristics belonging to this transmission path; If it does not exist, the second type of characteristic time is directly determined, and the determined second type of characteristic time is used as the transmission characteristic of this transmission path.

6. The method for pushing intelligent medical information based on big data according to claim 5, characterized in that: For the case where there is no transfer end in the transmission path: other nodes between the primary node and the secondary node are taken as pending nodes, and different single types of information are determined based on different types of related information associated with the pushed information, and then based on the historical data associated with different pending nodes, the characteristic rates V corresponding to different single types of information of different pending nodes are confirmed. k , where k represents different pending nodes, for multiple sets of feature rates V corresponding to the same set of single-class information k , select V k The pending node associated with max is used as the processing node for this single-class information, and based on the capacity of the single-class information and V k Max locks the processing time, then sums up the different processing times associated with different types of information, locks the total time feature, and uses the total time feature as the transmission feature of this transmission path.

7. The method for pushing intelligent medical information based on big data according to claim 6, characterized in that: In the step 2, based on the different transmission characteristics confirmed by the different transmission paths, a minimum value is selected from the multiple groups of transmission characteristics, and the transmission path corresponding to the minimum value is used as the best path associated with the push information.

8. The method for pushing intelligent medical information based on big data according to claim 1, characterized in that: In step 3, the specific method of encrypting information at each node is as follows: Confirm the main node and the secondary node from the confirmed best path, and confirm other nodes existing in the main node and the secondary node according to the sorting method of the best path, and record other nodes existing inside as transit nodes; Push the push information from the master node to the first group of transfer nodes: record the push information as an information segment, divide the information segment into two information segments, record the data capacity associated with different classified information as R1 and R2, and then confirm the hash values ​​associated with R1 and R2 based on the preset hash value mapping table, and sort and integrate the two confirmed hash values ​​in front and back order, confirm the encryption key of this push information at this stage, and during the push process, place the original information segment located at the back end between the front end information segments. After the subsequent transfer node receives the push information sent by the master node, it will decrypt it, and confirm the two groups of hash values ​​associated with the corresponding capacity based on the two groups of data capacities R1 and R2 associated in the protocol, and then randomly combine the two groups of hash values ​​in front and back order, confirm the decryption key, decrypt this type of push information, and according to the decryption process, re-swap the two information segments to their original correct positions; Push the push information from the first group of transit nodes to the subsequent nodes: make the two existing information segments act as the main information segments, divide the single main information segment into two information segments, determine the data capacity of the two information segments divided by the main information segment, and use the same processing method as above to confirm its encryption key, then swap the positions of the two information segments associated with the main information segment, push the information segments after the position swap is completed, and after the push is completed, confirm the four groups of hash values ​​according to the four groups of data capacity associated in the protocol, and randomly combine the four groups of hash values ​​to decrypt the push information until the decryption is correct, and then reorganize the four information segments according to the correct sorting method of the four groups of hash values ​​to obtain the correct sorting method; When each subsequent node performs push transmission, it divides the information segment specifically according to the main information segment determined by the previous node, and simultaneously confirms the encryption key. After confirming the encryption key, it performs position exchange transmission to complete the corresponding push encryption transmission process. After completing the transmission process, the subsequent node directly decrypts.

9. A smart medical information push system based on big data, the system is operated according to a smart medical information push method based on big data according to any one of claims 1 to 8, characterized in that: include: The topology confirmation end confirms the network nodes associated with the smart medical network from the big data, and generates a network topology belonging to the smart medical network based on the connection mode between different network nodes; The best path selection end confirms the pusher and the pushed party based on the confirmed push information, confirms the nodes associated with the pusher and the pushed party from the network topology map, confirms the transmission path based on the topological relationship of the associated nodes in the network topology map, and then selects the best path from multiple transmission paths based on different transmission characteristics associated with different transmission paths; The push encryption processing end pushes the push information based on the confirmed optimal path, and encrypts the information at each node during the push process, and completes the information push work based on the specific encryption processing process.

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