Blockchain Method and Medical, Healthcare and Elderly Care Transaction System Based on Deep Learning and Information Hiding

Abstract

A blockchain method and a medical, healthcare, and elderly care trading system based on deep learning and information hiding, including: a node registration request step; a registration information confirmation step; a registration consensus step; a registration consensus achievement step; a feature acquisition step; an institutional information hiding and sending step; a user information hiding and sending step. Through the above methods, systems, and robots, multiple platform nodes verify the conditions of the registered nodes to reach a consensus, making the successfully registered nodes more trustworthy; by using user characteristics and institutional characteristics as the input of the deep learning model and the data and information to be shared as the expected output of the deep learning model, users and institutions can hide information and authenticate their own identities through their own characteristics, making the sending of information and the sharing of data safer and more convenient.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence, and particularly to a blockchain method and a medical, health and elderly care trading system based on deep learning and information hiding. Background Art

[0002] In the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art: Existing blockchain systems often lack strict professional authentication during registration, which is insufficient for professional systems such as medical, health and elderly care. At the same time, data transmission in existing blockchain systems consumes a lot of resources, has low efficiency, and lacks an efficient information hiding method.

[0003] Therefore, the prior art still needs to be improved and developed. Summary of the Invention

[0004] Based on this, it is necessary to provide a blockchain method and a medical, health and elderly care trading system based on deep learning and information hiding to solve the problems of low efficiency of existing blockchain systems, especially low data transmission efficiency and low security in medical, health and elderly care trading systems.

[0005] In a first aspect, an embodiment of the present invention provides a blockchain method, and the method includes:

[0006] Node registration request step: Obtain a preset type, where the preset type includes a platform or an institution or a user; the platform node obtains a request from a to-be-registered node to register a node of the preset type; the request includes the spatial location of the to-be-registered node, the spatial range applied by the to-be-registered node for jurisdiction, and the management priority applied by the to-be-registered node; the platform node obtains the location information and basic information of the to-be-registered node. If the basic information of the to-be-registered node meets the registration conditions of the preset type node, a quasi-consent message indicating that the to-be-registered node is to be consented to become a platform node is sent to the to-be-registered node, and the quasi-consent message has been encrypted with the private key of the platform node.

[0007] Registration information confirmation step: If the to-be-registered node successfully obtains the previous preset number of the quasi-consent messages, obtain the hash value of the latest block in the preset type blockchain as the hash value of the previous block, pack the hash value of the previous block, the basic information of the to-be-registered node, and the previous preset number of the quasi-consent messages into a to-be-inserted block to be inserted into the preset type blockchain, and broadcast the to-be-inserted block to all nodes in the blockchain set after encrypting it with the private key of the to-be-registered node; each node in the blockchain set obtains the broadcast to-be-inserted block, and first decrypts the to-be-inserted block with the public key of the to-be-registered node that broadcasts the to-be-inserted block.

[0008] Registration consensus step: If any node in the blockchain set receives the to-be-inserted block broadcast by no more than a preset number of nodes, select one of the to-be-inserted blocks, extract the hash value of the previous block from the to-be-inserted block, and match it with the hash values of the latest blocks of each candidate preset type of blockchain stored by the any blockchain node. If the match fails, discard the to-be-inserted block. If the match succeeds, after extracting a preset number of the proposed consent messages that have arrived from the block, decrypt the preset number of the proposed consent messages that have arrived using the public key of the platform node that sent the proposed consent message, and determine whether the proposed consent message is a valid proposed consent message. If the verification passes, insert the to-be-inserted block into each candidate preset type of blockchain, and encrypt the to-be-inserted block with the private key of the any node and then broadcast it again to each node in the blockchain set;

[0009] Registration consensus achievement step: If any node in the blockchain set receives the to-be-inserted block broadcast by more than a preset number of nodes, select one of the to-be-inserted blocks, extract the hash value of the previous block from the to-be-inserted block, and match it with the hash values of the latest blocks of each candidate preset type of blockchain stored by the any node. If the match fails, discard the to-be-inserted block. If the match succeeds, insert the to-be-inserted block into each candidate preset type of blockchain.

[0010] Preferably, the method further includes:

[0011] User demand priority determination step: Obtain the rules for judging the priority of each type of user demand through user data, obtain user data, and calculate the priority of each type of user demand according to the rules; when the judgment of the priority of each type of user demand fails according to the rules, input the user data into the deep learning model for predicting the priority of each type of user demand for calculation, and use the output of the model as the priority of each type of user demand of the user; Deep learning model construction step for predicting the priority of each type of user demand: Use the user data with known priority of each type of user demand and the known priority of each type of user demand as input and expected output pairs to train and test the deep learning model to obtain the deep learning model for predicting the priority of each type of user demand;

[0012] Steps for determining the service priority of an institution: Obtain the rules for judging the priority of each type of service of the institution through institutional data, obtain institutional data, and calculate the priority of each type of service of the institution according to the rules; when the judgment of the priority of each type of service fails according to the rules, input the institutional data into the deep learning model for predicting the priority of each type of service for calculation, and use the output of the model as the priority of each type of service of the institution; Steps for constructing the deep learning model for predicting the priority of each type of service: Use the institutional data with known priorities of each type of service and the known priorities of each type of service as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the priority of each type of service;

[0013] Steps for determining the docking priority of the platform: Obtain the rules for judging the docking priority of each type of the platform through platform data, obtain platform data, and calculate the docking priority of each type of the platform according to the rules; when the judgment of the docking priority of each type of fails according to the rules, input the platform data into the deep learning model for predicting the docking priority of each type of service for calculation, and use the output of the model as the docking priority of each type of the platform; Steps for constructing the deep learning model for predicting the docking priority of each type of service: Use the platform data with known docking priorities of each type of service and the known docking priorities of each type of service as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the docking priority of each type of service.

[0014] Preferably, the method further includes:

[0015] Steps for a user node to publish a requirement: The user node publishes the requirement in a broadcast manner to the nodes in the blockchain set, and the requirement has been encrypted with the private key of the user node;

[0016] Steps for an institution node to publish a service: The institution node publishes the available service in a broadcast manner to the nodes in the blockchain set, and the available service has been encrypted with the private key of the institution node;

[0017] Platform node order transfer steps: The platform node receives multiple requirements from multiple user nodes and multiple available services from multiple institutional nodes. It decrypts the multiple requirements with the multiple public keys of the multiple user nodes respectively, and decrypts the multiple available services with the multiple public keys of the multiple institutional nodes respectively. The platform node matches the multiple requirements with the multiple available services to obtain multiple successfully matched requirements and services. If multiple requirements match the same service, it obtains the priorities of the multiple requirements, sorts the multiple requirements according to the priorities, and only retains the requirement with the highest priority. If multiple services match the same requirement, it obtains the priorities of the multiple services, sorts the multiple services according to the priorities, and only retains the service with the highest priority. It feeds back the service information corresponding to the successfully matched and retained requirement and the institutional node information to the user node corresponding to the requirement, and the service information has been encrypted with the private key of the institutional node.

[0018] Institutional node order receiving steps: The institutional node receives multiple requirements from multiple user nodes, decrypts the multiple requirements with the multiple public keys of the multiple user nodes respectively. The institutional node matches the multiple requirements with the idle services of the institutional node to obtain multiple successfully matched requirements and services. If multiple requirements match the same service, it obtains the priorities of the multiple requirements, sorts the multiple requirements according to the priorities, and only retains the requirement with the highest priority. It feeds back the service information corresponding to the successfully matched and retained requirement and the institutional node information to the user node corresponding to the requirement, and the service information has been encrypted with the private key of the institutional node.

[0019] Feedback information receiving steps: The user node receives the feedback information on the published requirement within the preset time. If no feedback information is received, it re-executes the feedback information receiving steps. If it receives multiple pieces of feedback service information and institutional node information on the published requirement, it decrypts the multiple pieces of feedback service information with the multiple public keys of the multiple feedback institutional nodes respectively to obtain the multiple pieces of service information, obtains the priorities of the multiple services, sorts the multiple services according to the priorities, and only retains the service with the highest priority. It takes the service information corresponding to the successfully matched and retained service and the institutional node as the successfully matched service and institutional node for the requirement, and sends a confirmation message to the institutional node to accept the service. After receiving the confirmation message, the institutional node packages the requirement, user node, service, institutional node, and transaction information and adds it as the latest block to the transaction blockchain, and the institutional node provides the service for the requirement of the user node.

[0020] Preferably, the method further includes:

[0021] Feature acquisition step: The institutional node acquires the characteristics of the requester corresponding to the requirement; the user node acquires the characteristics of the service provider corresponding to the service.

[0022] Institutional information hiding and sending step: When the institutional node sends the information to be hidden to the user node, the characteristics of the requester and the random information are used as inputs, the information to be hidden is used as the expected output, and the random information is adjusted by testing and reverse generating the input data through the deep learning model to obtain the preferred information; the characteristics of the requester and the preferred information are used as inputs, the output information is calculated through the deep learning model, the output information is compared with the information to be hidden to obtain the difference information; the preferred information and the difference information are sent to the user node; after receiving the preferred information and the difference information, the user node uses the characteristics of the requester and the preferred information as inputs, calculates the output information through the deep learning model, synthesizes the output information with the difference information to obtain the information to be hidden; the requirement, user node, service, institutional node, information of the requester and the service provider, information of the information sender and the receiver, preferred information and difference information are packaged and added to the transaction blockchain as the latest block.

[0023] User information hiding and sending step: When the user node sends the information to be hidden to the institutional node, the characteristics of the service provider and the random information are used as inputs, the information to be hidden is used as the expected output, and the random information is adjusted by testing and reverse generating the input data through the deep learning model to obtain the preferred information; the characteristics of the service provider and the preferred information are used as inputs, the output information is calculated through the deep learning model, the output information is compared with the information to be hidden to obtain the difference information; the preferred information and the difference information are sent to the user node; after receiving the preferred information and the difference information, the user node uses the characteristics of the service provider and the preferred information as inputs, calculates the output information through the deep learning model, synthesizes the output information with the difference information to obtain the information to be hidden; the requirement, user node, service, institutional node, information of the requester and the service provider, information of the information sender and the receiver, preferred information and difference information are packaged and added to the transaction blockchain as the latest block.

[0024] In a second aspect, an embodiment of the present invention provides a blockchain system, and the system includes:

[0025] Node registration request module: Obtain a preset type, which includes a platform or an institution or a user; the platform node obtains a request for the to-be-registered node to register a node of the preset type; the request includes the spatial location of the to-be-registered node, the spatial range applied for jurisdiction by the to-be-registered node, and the management priority applied for by the to-be-registered node; the platform node obtains the location information and basic information of the to-be-registered node. If the basic information of the to-be-registered node meets the registration conditions for nodes of the preset type, a quasi-consent message indicating that the platform node intends to consent to the to-be-registered node becoming a platform node is sent to the to-be-registered node, and the quasi-consent message has been encrypted with the private key of the platform node.

[0026] Registration information confirmation module: If the to-be-registered node successfully obtains the previous preset number of the quasi-consent messages, it obtains the hash value of the latest block in the preset type blockchain as the hash value of the previous block, packs the hash value of the previous block, the basic information of the to-be-registered node, and the previous preset number of the quasi-consent messages into a block to be inserted and inserts it into the preset type blockchain, and broadcasts the block to be inserted to all nodes in the blockchain set after encrypting it with the private key of the to-be-registered node; each node in the blockchain set obtains the broadcast block to be inserted, and first decrypts the block to be inserted with the public key of the to-be-registered node that broadcasts the block to be inserted.

[0027] Registration consensus module: If any node in the blockchain set receives the block to be inserted broadcast by no more than the preset number of nodes, it selects one of the blocks to be inserted, extracts the hash value of the previous block from the block to be inserted, and matches it with the hash value of the latest block of each candidate preset type blockchain stored in the any blockchain node. If the match fails, the block to be inserted is discarded. If the match is successful, after extracting the previous preset number of the quasi-consent messages from the block, the previous preset number of the quasi-consent messages are decrypted through the public key of the platform node that sends the quasi-consent message, and it is judged whether the quasi-consent message is a quasi-consent message. If the verification passes, the block to be inserted is inserted into each candidate preset type blockchain, and the block to be inserted is encrypted with the private key of the any node and then broadcast to each node in the blockchain set again.

[0028] Registration consensus achievement module: If any node in the blockchain set receives the block to be inserted broadcast by more than the preset number of nodes, it selects one of the blocks to be inserted, extracts the hash value of the previous block from the block to be inserted, and matches it with the hash value of the latest block of each candidate preset type blockchain stored in the any node. If the match fails, the block to be inserted is discarded. If the match is successful, the block to be inserted is inserted into each candidate preset type blockchain.

[0029] Preferably, the system further includes:

[0030] User requirement priority determination module: Obtain the rules for determining the priority of each type of user requirement by judging user data, obtain user data, and calculate the priority of each type of user requirement according to the rules; when the determination of the priority of each type of user requirement fails according to the rules, input the user data into the deep learning model for predicting the priority of each type of user requirement for calculation, and use the output of the model as the priority of each type of user requirement of the user; Deep learning model construction module for predicting the priority of each type of user requirement: Use the user data with known priority of each type of user requirement and the known priority of each type of user requirement as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the priority of each type of user requirement;

[0031] Institutional service priority determination module: Obtain the rules for determining the priority of each type of institutional service by judging institutional data, obtain institutional data, and calculate the priority of each type of institutional service according to the rules; when the determination of the priority of each type of institutional service fails according to the rules, input the institutional data into the deep learning model for predicting the priority of each type of institutional service for calculation, and use the output of the model as the priority of each type of institutional service of the institution; Deep learning model construction module for predicting the priority of each type of institutional service: Use the institutional data with known priority of each type of institutional service and the known priority of each type of institutional service as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the priority of each type of institutional service;

[0032] Platform service priority determination module: Obtain the rules for determining the priority of each type of platform docking by judging platform data, obtain platform data, and calculate the priority of each type of platform docking according to the rules; when the determination of the priority of each type of platform docking fails according to the rules, input the platform data into the deep learning model for predicting the priority of each type of platform docking for calculation, and use the output of the model as the priority of each type of platform docking of the platform; Deep learning model construction module for predicting the priority of each type of platform docking: Use the platform data with known priority of each type of platform docking and the known priority of each type of platform docking as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the priority of each type of platform docking.

[0033] Preferably, the system further includes:

[0034] User node demand publishing module: The user node publishes the demand in the form of broadcasting to the nodes in the blockchain set, and the demand has been encrypted with the private key of the user node;

[0035] Institutional node service publishing module: The institutional node publishes the available services in the form of broadcasting to the nodes in the blockchain set, and the available services have been encrypted with the private key of the institutional node;

[0036] Platform Node Order Transfer Module: The platform node receives multiple requirements from multiple user nodes and multiple available services from multiple institutional nodes. It decrypts the multiple requirements with the multiple public keys of the multiple user nodes respectively, and decrypts the multiple available services with the multiple public keys of the multiple institutional nodes respectively. The platform node matches the multiple requirements with the multiple available services to obtain multiple successfully matched requirements and services. If multiple requirements match the same service, it obtains the priorities of the multiple requirements, sorts the multiple requirements according to the priorities, and only retains the requirement with the highest priority. If multiple services match the same requirement, it obtains the priorities of the multiple services, sorts the multiple services according to the priorities, and only retains the service with the highest priority. It feeds back the service information corresponding to the successfully matched and retained requirement and the institutional node information to the user node corresponding to the requirement, and the service information has been encrypted with the private key of the institutional node.

[0037] Institutional Node Order Receiving Module: The institutional node receives multiple requirements from multiple user nodes, decrypts the multiple requirements with the multiple public keys of the multiple user nodes respectively. The institutional node matches the multiple requirements with the idle services of the institutional node to obtain multiple successfully matched requirements and services. If multiple requirements match the same service, it obtains the priorities of the multiple requirements, sorts the multiple requirements according to the priorities, and only retains the requirement with the highest priority. It feeds back the service information corresponding to the successfully matched and retained requirement and the institutional node information to the user node corresponding to the requirement, and the service information has been encrypted with the private key of the institutional node.

[0038] Feedback Information Receiving Module: The user node receives the feedback information on the published requirement within a preset time. If no feedback information is received, it re-executes the feedback information receiving module. If it receives multiple pieces of feedback service information and institutional node information on the published requirement, it decrypts the multiple pieces of feedback service information with the multiple public keys of the multiple feedback institutional nodes respectively to obtain the multiple pieces of service information, obtains the priorities of the multiple services, sorts the multiple services according to the priorities, and only retains the service with the highest priority. It takes the service information corresponding to the successfully matched and retained service and the institutional node as the service and institutional node for which the requirement is successfully matched, and sends a confirmation message to the institutional node to accept the service. After receiving the confirmation message, the institutional node packages the requirement, user node, service, institutional node, and transaction information and adds it as the latest block to the transaction blockchain, and the institutional node provides the service for the requirement of the user node.

[0039] Preferably, the system further includes:

[0040] Feature acquisition module: The institutional node acquires the characteristics of the requester corresponding to the requirement; the user node acquires the characteristics of the service provider corresponding to the service;

[0041] Institutional information hiding and sending module: When the institutional node sends the information to be hidden to the user node, taking the requester characteristics and random information as input, taking the information to be hidden as the expected output, testing through a deep learning model and reversely generating input data to adjust the random information to obtain the optimized information; taking the requester characteristics and the optimized information as input, calculating through the deep learning model to obtain the output information, comparing the output information with the information to be hidden to obtain the difference information; sending the optimized information and the difference information to the user node; after the user node receives the optimized information and the difference information, taking the requester characteristics and the optimized information as input, calculating through the deep learning model to obtain the output information, synthesizing the output information with the difference information to obtain the information to be hidden; packaging the requirement, user node, service, institutional node, requester and service provider information, information sender and receiver information, optimized information and difference information and adding them as the latest block to the transaction blockchain;

[0042] User information hiding and sending module: When the user node sends the information to be hidden to the institutional node, taking the service provider characteristics and random information as input, taking the information to be hidden as the expected output, testing through a deep learning model and reversely generating input data to adjust the random information to obtain the optimized information; taking the service provider characteristics and the optimized information as input, calculating through the deep learning model to obtain the output information, comparing the output information with the information to be hidden to obtain the difference information; sending the optimized information and the difference information to the user node; after the user node receives the optimized information and the difference information, taking the service provider characteristics and the optimized information as input, calculating through the deep learning model to obtain the output information, synthesizing the output information with the difference information to obtain the information to be hidden; packaging the requirement, user node, service, institutional node, requester and service provider information, information sender and receiver information, optimized information and difference information and adding them as the latest block to the transaction blockchain.

[0043] In a third aspect, an embodiment of the present invention provides an artificial intelligence device, and the system includes the device of any one of the modules in the second aspect embodiment.

[0044] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and characterized in that when the program is executed by a processor, the steps of the method of any one of the first aspect embodiments are implemented.

[0045] Fifth aspect, an embodiment of the present invention provides a robot system, including a memory, a processor, and an artificial intelligence robot program stored on the memory and executable on the processor, characterized in that when the processor executes the program, the steps of the method according to any one of the embodiments of the first aspect are implemented.

[0046] Sixth aspect, an embodiment of the present invention provides a medical, healthcare and elderly care trading system, including a medical, healthcare and elderly care system, characterized in that the medical, healthcare and elderly care system implements the steps of the method according to any one of the embodiments of the first aspect.

[0047] The blockchain method and the medical, healthcare and elderly care trading system based on deep learning and information hiding provided in this embodiment include: a node registration request step; a registration information confirmation step; a registration consensus step; a registration consensus achievement step; a feature acquisition step; an institutional information hiding and sending step; a user information hiding and sending step. Through the above methods, systems and robots, multiple platform nodes verify the conditions of the registered nodes to reach a consensus, making the successfully registered nodes more credible; by using user features and institutional features as the input of the deep learning model and the data and information to be shared as the expected output of the deep learning model, users and institutions can hide information and authenticate their own identities through their own features, making the sending of information and the sharing of data safer and more convenient. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a module diagram of the artificial intelligence system provided by the embodiment of the present invention;

[0049] Figure 2 It is a module diagram of the artificial intelligence system provided by the embodiment of the present invention;

[0050] Figure 3 It is a module diagram of the artificial intelligence system provided by the embodiment of the present invention;

[0051] Figure 4 It is a module diagram of the artificial intelligence system provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The following describes in detail the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention.

[0053] I. Basic Embodiment of the Present Invention

[0054] First aspect, an embodiment of the present invention provides a blockchain method, the method including: a node registration request step;

[0055] Registration information confirmation step; registration consensus step; registration consensus achievement step. Technical effect: By having multiple platform nodes verify the conditions of the registered nodes to reach a consensus, the successfully registered nodes become more trustworthy.

[0056] In a preferred embodiment, the method further includes: user demand priority determination step; institutional service priority determination step; platform service priority determination step. Technical effect: By using rules and deep learning models to determine the priorities of user demands, institutional services, and platform services, it lays a foundation for selecting appropriate demands and services for transactions.

[0057] In a preferred embodiment, the method further includes: user node demand publication step; institutional node service publication step; platform node order transfer step; institutional node order acceptance step; feedback information reception step. Technical effect: By having institutional nodes and platform nodes jointly participate in the order acceptance and transfer of transactions in a fair competition manner, the best-quality services can be provided to users.

[0058] In a preferred embodiment, the method further includes: feature acquisition step; institutional information hiding and sending step; user information hiding and sending step. Technical effect: By using user features and institutional features as inputs to the deep learning model and the data and information to be shared as the expected outputs of the deep learning model, users and institutions can hide information and authenticate their identities through their own features, making information sending and data sharing safer and more convenient.

[0059] In a second aspect, an embodiment of the present invention provides a blockchain system, as Figure 1 shown, the system includes: node registration request module; registration information confirmation module; registration consensus module; registration consensus achievement module.

[0060] In a preferred embodiment, as Figure 2 shown, the system further includes: user demand priority determination module; institutional service priority determination module; platform service priority determination module.

[0061] In a preferred embodiment, as Figure 3 shown, the system further includes: user node demand publication module; institutional node service publication module; platform node order transfer module; institutional node order acceptance module; feedback information reception module.

[0062] In a preferred embodiment, as Figure 4 shown, the system further includes: feature acquisition module; institutional information hiding and sending module; user information hiding and sending module.

[0063] In a third aspect, an embodiment of the present invention provides an artificial intelligence device, and the system includes the modules of the system according to any one of the embodiments in the second aspect.

[0064] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. It is characterized in that when the program is executed by a processor, the steps of the method according to any one of the embodiments in the first aspect are implemented.

[0065] In a fifth aspect, an embodiment of the present invention provides a robot system, including a memory, a processor, and an artificial intelligence robot program stored on the memory and executable on the processor. It is characterized in that when the processor executes the program, the steps of the method according to any one of the embodiments in the first aspect are implemented.

[0066] In a sixth aspect, an embodiment of the present invention provides a medical, healthcare, and elderly care transaction system, including a medical, healthcare, and elderly care system. It is characterized in that the medical, healthcare, and elderly care system implements the steps of the method according to any one of the embodiments in the first aspect.

[0067] II. Preferred Embodiments of the Present Invention

[0068] (I) Key Issues

[0069] How to achieve secure sharing of medical, healthcare, and elderly care data across users, scenarios, and institutions to improve the security, credibility, and privacy of information sharing?

[0070] (II) Key Technologies

[0071] Through blockchain and information hiding technologies, secure sharing of medical, healthcare, and elderly care data across users, scenarios, and institutions is achieved.

[0072] (III) Technical Highlights

[0073] A secure sharing technology for medical, healthcare, and elderly care data across users, scenarios, and institutions that can improve the security, credibility, and privacy of information sharing.

[0074] (IV) Technical Summary Solution

[0075] The user terminal and the server of the medical and health care service institution are used as nodes to establish the medical and health care service industry blockchain. Different types of nodes have different priority permissions. The institution node can apply for certification, publish available services, and disclose service quality, etc. The user node can publish requirements, apply for services, and query service quality, etc. The institution node and the user node have scene attributes at the same time, including medical scenes, rehabilitation scenes, elderly care scenes and various combinations thereof, institutional service scenes, community service scenes, home service scenes and various combinations thereof. When publishing information, the nodes of the blockchain can choose to disclose a part of the information to the nodes that meet the preset conditions, and hide the nodes that do not meet the preset conditions. For the information that needs to be hidden, before the information is released, the features of the nodes that meet the preset conditions (such as the user's face information) and random secret information are used as input, and the original information is used as the expected output. Through the information hiding deep learning model, the optimal secret information and prediction error are obtained, and the optimal secret information and prediction error are uploaded to the chain. Nodes that meet the preset conditions download the optimal secret information and prediction error from the chain, input their own node characteristics and optimal secret information into the information hiding deep learning model, obtain the predicted information, and then correct the predicted information according to the prediction error to obtain the original information, which not only realizes information hiding, but also does not require users to manually decrypt.

[0076] (V) Detailed technical plan

[0077] The blockchain set includes platform blockchain, service blockchain, user blockchain, and transaction blockchain. The platform blockchain includes platform blocks, which run on platform nodes. The service blockchain includes institutional blocks, which run on institutional nodes. The user blockchain includes user blocks, which run on user nodes. If the blockchain set can be used for medical and health care, the platform blockchain, service blockchain, and user blockchain are respectively the medical and health care platform blockchain, the medical and health care service blockchain, and the medical and health care user blockchain. If the blockchain set can be used for the modern service industry, the platform blockchain, service blockchain, and user blockchain are respectively the modern service industry platform blockchain, the modern service industry service blockchain, and the modern service industry user blockchain.

[0078] Platform node construction steps:

[0079] 1. Preset the initial node of the platform. The spatial attributes of the platform node include its own spatial location. The management attributes of the platform node include the spatial scope under its jurisdiction. For the purpose of local management, the spatial location of the platform node is within the spatial scope under its jurisdiction. The initial node of the platform broadcasts its information including the node code to the blockchain set.

[0080] 2 The coding of the platform node includes the organization code of the supporting unit and co-construction unit of the platform node or the project code of the national project on which it is based; the transaction rights of the platform node include the review of platform authentication, user authentication, organization authentication, and lower-level platform authentication, the docking of demand and service, and the allocation of rights of user nodes, organization nodes, and lower-level platform nodes. The service docking types of the platform node include medical service docking, health service docking, and elderly care service docking.

[0081] 3 The initial node of the platform includes one running node and multiple mirror nodes. The data and status of the mirror node are consistent with the running node. When the running node is damaged, one of the mirror nodes is changed to the running node, and a new mirror node is added. If the health care platform is aimed at users on the earth, the spatial scope of the initial node of the platform is the earth; if the health care platform is aimed at users in a certain country, the spatial scope of the initial node of the platform is the country; if the health care platform is aimed at users in a certain region, the spatial scope of the initial node of the platform is the region.

[0082] 4 When the node to be registered needs to register the platform node, it broadcasts the request to register the platform node to the nodes in the blockchain set.

[0083] 5 The platform node obtains the request of the node to be registered (the node to be registered includes a user node or an institutional node or other node) to register as a platform node; the request includes the spatial location of the node to be registered, the spatial scope of the node to be registered, and the management priority of the node to be registered; the platform node obtains the location information and basic information of the node to be registered, and if the basic information of the node to be registered meets the preset platform node registration conditions, it sends a message of intended approval to the node to be registered to become a platform node to the node to be registered, and the intended approval message has been encrypted with the private key of the platform node. (The purpose of not restricting whether the location of the node to be registered belongs to the jurisdiction of the platform node is to ensure that the platform is not limited to the expansion of the location range. Even if the node applied for exceeds the jurisdiction of the platform, it can still be expanded, so that the health care platform is not restricted by geographical location. For example, even if the jurisdiction of the initial node of the platform is China, if a foreign node registration can still be successful, then the platform has expanded to a foreign country. So what is the purpose of the node's jurisdiction? Its purpose is to focus only on the scope of its own jurisdiction when connecting needs with services. Such focus can improve the quality of service)

[0084] If the node to be registered successfully obtains the pre-set number of the aforesaid intended consent messages that arrive first, it obtains the hash value of the latest block in the platform blockchain as the hash value of the previous block, packages the hash value of the previous block, the basic information of the node to be registered, and the pre-set number of the aforesaid intended consent messages into a block to be inserted and inserts it into the platform blockchain, and broadcasts the block to be inserted to all nodes in the blockchain set after encrypting it with the private key of the node to be registered. Each node in the blockchain set obtains the broadcast block to be inserted and first decrypts the block to be inserted with the public key of the node to be registered that broadcasts the block to be inserted.

[0085] If any node in the blockchain set receives the block to be inserted broadcast by no more than the pre-set number (if the node receives the block to be inserted broadcast by k nodes, it means that except for the node to be registered, k - 1 nodes have successfully verified the block to be inserted. Since it does not exceed the pre-set number, further verification and broadcasting are still needed) of nodes (judge whether the hash values of the blocks to be inserted in each broadcast are the same. If they are the same, it means that the block to be inserted is broadcast by multiple nodes), it selects one of the blocks to be inserted, extracts the hash value of the previous block from the block to be inserted, and matches it with the hash value of the latest block of each candidate platform blockchain stored by the any blockchain node (each node stores the blockchains ranked in the top pre-set number in terms of length, such as the longest blockchain, the second-longest blockchain, and the third-longest blockchain, and broadcasts the length and hash value of the blockchain regularly. If the longest length of the blockchain received in the broadcast exceeds the length of the blockchain stored by the first node, and the longest-length blockchain is broadcast by more than the pre-set number of nodes, the shortest blockchain in each node is replaced with the longest-length blockchain). If the match fails, it discards the block to be inserted. If the match is successful, after extracting the pre-set number of the aforesaid intended consent messages from the block, it decrypts the pre-set number of the aforesaid intended consent messages with the public key of the platform node that sends the intended consent message, and judges whether the intended consent message is an intended consent message. If the verification passes, it inserts the block to be inserted into each candidate platform blockchain, and broadcasts the block to be inserted to each node in the blockchain set again after encrypting it with the private key of the any node.

[0086] If the any node receives the block to be inserted broadcast by more than the pre-set number (since it exceeds the pre-set number, further verification and broadcasting are not needed) of nodes, it selects one of the blocks to be inserted, extracts the hash value of the previous block from the block to be inserted, and matches it with the hash value of the latest block of each candidate platform blockchain stored by the any node. If the match fails, it discards the block to be inserted. If the match is successful, it inserts the block to be inserted into each candidate platform blockchain.

[0087] 6Through the above steps, the platform nodes can be expanded autonomously, so that there will be no bottleneck at the platform node level. Except for the first-level platform nodes which are preset, the rest of the platform nodes are registered and expanded autonomously. The registration of nodes is verified by multiple platform nodes at the same time, which is conducive to the fairness of platform node registration and ensures the quality of platform nodes.

[0088] 7 The blockchain uses nearby broadcasting, local relay, and global update methods to improve the efficiency of the blockchain.

[0089] User node self-registration steps;

[0090] The user's ID number is used as the code of the user node; the transaction rights of the user node include user authentication, publishing requirements, and querying service data. If the blockchain set is a medical and health care blockchain set, the demand types of the user node include medical needs, health needs, and elderly care needs.

[0091] 1 When the node to be registered needs to register a user node, it broadcasts the request to register the user node to the nodes in the blockchain set.

[0092] 2. The platform node obtains a request from a node to be registered (the node to be registered includes a user node or an institution node or other node) to register a user node; the request includes the spatial location of the node to be registered, the spatial scope of jurisdiction applied for by the node to be registered, and the management priority applied for by the node to be registered; the platform node obtains the location information and basic information of the node to be registered, and if the basic information of the node to be registered meets the preset user node registration conditions and the location information of the node to be registered meets the preset spatial jurisdiction of the platform node, then a proposed consent message is sent to the node to be registered, indicating that the node to be registered is to be approved to become a user node, and the proposed consent message has been encrypted with the private key of the platform node.

[0093] If the node to be registered successfully obtains the pre-set number of intended consent messages, it obtains the hash value of the latest block in the user blockchain as the hash value of the previous block, and packages the hash value of the previous block, the basic information of the node to be registered, and the pre-set number of intended consent messages into a block to be inserted into the user blockchain, and encrypts it with the private key of the node to be registered and broadcasts the block to be inserted to all nodes in the blockchain set. Each node in the blockchain set obtains the broadcasted block to be inserted, and first decrypts the block to be inserted with the public key of the node to be registered that broadcasts the block to be inserted.

[0094] If any node in the blockchain set receives the to-be-inserted block broadcast by no more than a preset number of nodes (if a node that has received k broadcasts of the to-be-inserted block, it means that except for the to-be-registered node, k - 1 other nodes have successfully verified the to-be-inserted block. Since the number does not exceed the preset number, further verification and broadcasting are still required), then select one of the to-be-inserted blocks, extract the hash value of the previous block from the to-be-inserted block, and match it with the hash value of the latest block of each candidate user blockchain stored by the any blockchain node. If the match fails, discard the to-be-inserted block. If the match is successful, after extracting a preset number of the proposed consent messages that have arrived from the block, decrypt the preset number of the proposed consent messages that have arrived using the public key of the platform node that sent the proposed consent message, and determine whether the proposed consent message is a valid proposed consent message. If the verification passes, insert the to-be-inserted block into each candidate user blockchain, and encrypt the to-be-inserted block with the private key of the any node and then broadcast it again to each node in the blockchain set.

[0095] If any node receives the to-be-inserted block broadcast by more than a preset number of nodes (since the number exceeds the preset number, further verification and broadcasting are not required), then select one of the to-be-inserted blocks, extract the hash value of the previous block from the to-be-inserted block, and match it with the hash value of the latest block of each candidate user blockchain stored by the blockchain node. If the match fails, discard the to-be-inserted block. If the match is successful, insert the to-be-inserted block into each candidate user blockchain.

[0096] 3 Through the above steps, the user nodes can expand autonomously, so that no bottleneck will be formed at the user node level. By having multiple platform nodes verify the registration of nodes simultaneously, this is conducive to the fairness of user node registration and ensures the quality of user nodes.

[0097] Autonomous registration steps for institutional nodes;

[0098] The institutional code of the institutional node is used as the encoding of the institutional node; the trading permissions of the institutional node include institutional authentication, service publication, and service data publication. If the blockchain set is a medical, health, and elderly care blockchain set, then the service types of the institutional node include medical services, health services, and elderly care services.

[0099] 1 When a to-be-registered node needs to register an institutional node, it broadcasts a request to register the institutional node to the nodes in the blockchain set.

[0100] The platform node obtains a request from a node to be registered (the node to be registered includes a user node, an institutional node, or other nodes) to register as an institutional node; the request includes the spatial location of the node to be registered, the spatial scope applied for jurisdiction by the node to be registered, and the management priority applied for by the node to be registered. The platform node obtains the location information and basic information of the node to be registered. If the basic information of the node to be registered meets the preset institutional node registration conditions, and the location information of the node to be registered meets the spatial jurisdiction scope of the preset platform node, a quasi-consent message indicating that the platform node intends to consent to the node to be registered becoming an institutional node is sent to the node to be registered, and the quasi-consent message has been encrypted with the private key of the platform node.

[0101] If the node to be registered successfully obtains a preset number of the previously arrived quasi-consent messages, it obtains the hash value of the latest block in the institutional blockchain as the hash value of the previous block, packs the hash value of the previous block, the basic information of the node to be registered, and the preset number of the previously arrived quasi-consent messages into a block to be inserted and inserts it into the institutional blockchain, and then broadcasts the block to be inserted to all nodes in the blockchain set after encrypting it with the private key of the node to be registered. Each node in the blockchain set obtains the broadcast block to be inserted. First, it decrypts the block to be inserted with the public key of the node to be registered that broadcasts the block to be inserted.

[0102] If any node in the blockchain set receives the block to be inserted broadcast by no more than a preset number (if it receives the block to be inserted broadcast by k nodes, it means that except for the node to be registered, k - 1 other nodes have successfully verified the block to be inserted. Since it does not exceed the preset number, further verification and broadcasting are still required) of nodes (judge whether the hash values of the blocks to be inserted in each broadcast are the same. If they are the same, it means that the block to be inserted is broadcast by multiple nodes), it selects one of the blocks to be inserted, extracts the hash value of the previous block from the block to be inserted, and matches it with the hash value of the latest block of each candidate institutional blockchain stored by the any blockchain node. If the match fails, the block to be inserted is discarded. If the match is successful, after extracting the preset number of the previously arrived quasi-consent messages from the block, it decrypts the preset number of the previously arrived quasi-consent messages with the public key of the platform node that sends the quasi-consent message, and judges whether the quasi-consent message is a quasi-consent message. If the verification passes, the block to be inserted is inserted into each candidate institutional blockchain, and the block to be inserted is encrypted with the private key of the any node and then broadcast to each node in the blockchain set again.

[0103] If any of the nodes receives the to-be-inserted block broadcast by more than a preset number of nodes (since the preset number is exceeded, there is no need to continue verification and broadcasting), then select one of the to-be-inserted blocks, extract the hash value of the previous block from the to-be-inserted block, and match it with the hash value of the latest block of each candidate institutional blockchain stored by the blockchain node. If the match fails, discard the to-be-inserted block. If the match is successful, insert the to-be-inserted block into each candidate institutional blockchain.

[0104] 3 Through the above steps, institutional nodes can expand autonomously, so that bottlenecks will not form at the institutional node level. By multiple platform nodes verifying the registration of nodes simultaneously, this is conducive to the fairness of institutional node registration and ensures the quality of institutional nodes.

[0105] Determine the priority steps;

[0106] 1 Obtain the rules for judging the priority of various needs of users through user data, obtain user data, and calculate the medical need priority, health need priority, and elderly care need priority of the user according to the rules, as the medical need priority, health need priority, and elderly care need priority of the user node. When the judgment of the medical need priority fails according to the rules, input the user data into the deep learning model for predicting the medical need priority for calculation, and use the output of the model as the medical need priority of the user. When the judgment of the health need priority fails according to the rules, input the user data into the deep learning model for predicting the health need priority for calculation, and use the output of the model as the health need priority of the user. When the judgment of the elderly care need priority fails according to the rules, input the user data into the deep learning model for predicting the elderly care need priority for calculation, and use the output of the model as the elderly care need priority of the user. Before that, it is necessary to construct the model first. Specifically: Use the user data with known medical need priority and the known medical need priority as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the medical need priority. Use the user data with known health need priority and the known health need priority as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the health need priority. Use the user data with known elderly care need priority and the known elderly care need priority as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the elderly care need priority.

[0107] 2 Obtain the rules for determining the priority of various services of an institution through institutional data, obtain the institutional data, and calculate the medical service priority, health service priority, and elderly care service priority of the institution according to the rules, as the medical service priority, health service priority, and elderly care service priority of the institution node. When the judgment of the medical service priority fails according to the rules, input the institutional data into the deep learning model for predicting the medical service priority for calculation, and use the output of the model as the medical service priority of the institution. When the judgment of the health service priority fails according to the rules, input the institutional data into the deep learning model for predicting the health service priority for calculation, and use the output of the model as the health service priority of the institution. When the judgment of the elderly care service priority fails according to the rules, input the institutional data into the deep learning model for predicting the elderly care service priority for calculation, and use the output of the model as the elderly care service priority of the institution. Before that, it is necessary to construct the model first. Specifically: Use the institutional data with known medical service priorities and the known medical service priorities as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the medical service priority. Use the institutional data with known health service priorities and the known health service priorities as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the health service priority. Use the institutional data with known elderly care service priorities and the known elderly care service priorities as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the elderly care service priority.

[0108] 3 Obtain the rules for judging the docking priorities of various services of the platform based on platform data. Obtain the platform data. According to the rules, calculate the medical service docking priority, health service docking priority, and elderly care service docking priority of the platform, which are used as the medical service docking priority, health service docking priority, and elderly care service docking priority of the platform node. When the judgment of the medical service docking priority fails according to the rules, input the platform data into the deep learning model for predicting the medical service docking priority for calculation, and use the output of the model as the medical service docking priority of the platform. When the judgment of the health service docking priority fails according to the rules, input the platform data into the deep learning model for predicting the health service docking priority for calculation, and use the output of the model as the health service docking priority of the platform. When the judgment of the elderly care service docking priority fails according to the rules, input the platform data into the deep learning model for predicting the elderly care service docking priority for calculation, and use the output of the model as the elderly care service docking priority of the platform. Before that, it is necessary to construct the model first. Specifically: Use the platform data with known medical service docking priorities and the known medical service docking priorities as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the medical service docking priority. Use the platform data with known health service docking priorities and the known health service docking priorities as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the health service docking priority. Use the platform data with known elderly care service docking priorities and the known elderly care service docking priorities as input and expected output to train and test the deep learning model to obtain the deep learning model for predicting the elderly care service docking priority.

[0109] 4 The most frequent transaction of the user node is to publish demands, the most frequent transaction of the institution node is to publish services, and the most frequent transactions of the platform node are the docking of demands and services and node registration.

[0110] Steps for selecting demands and services according to priorities;

[0111] 1 Steps for the user node to publish demands: The user node publishes demands in the way of broadcasting to the nodes in the blockchain set, and the demands have been encrypted with the private key of the user node;

[0112] 2 Steps for the institution node to publish services: The institution node publishes the available services in the way of broadcasting to the nodes in the blockchain set, and the available services have been encrypted with the private key of the institution node;

[0113] 3 Platform Node Order Transfer Steps: The platform node receives multiple requirements from multiple user nodes and multiple available services from multiple institutional nodes, decrypts the multiple requirements with the multiple public keys of the multiple user nodes respectively, decrypts the multiple available services with the multiple public keys of the multiple institutional nodes respectively, the platform node matches the multiple requirements with the multiple available services, and obtains multiple successfully matched requirements and multiple services; if multiple requirements match the same service, obtain the priorities of the multiple requirements, sort the multiple requirements according to the priorities, and only keep the requirement with the highest priority; if multiple services match the same requirement, obtain the priorities of the multiple services, sort the multiple services according to the priorities, and only keep the service with the highest priority; feedback the service information corresponding to the successfully matched and retained requirement and the institutional node information to the user node corresponding to the requirement, and the service information has been encrypted with the private key of the institutional node;

[0114] 4 Institutional Node Order Receiving Steps: The institutional node receives multiple requirements from multiple user nodes, decrypts the multiple requirements with the multiple public keys of the multiple user nodes respectively, the institutional node matches the multiple requirements with the idle services of the institutional node, and obtains multiple successfully matched requirements and multiple services; if multiple requirements match the same service, obtain the priorities of the multiple requirements, sort the multiple requirements according to the priorities, and only keep the requirement with the highest priority; feedback the service information corresponding to the successfully matched and retained requirement and the institutional node information to the user node corresponding to the requirement, and the service information has been encrypted with the private key of the institutional node;

[0115] 5 Feedback Information Receiving Steps: The user node receives the feedback information on the published requirement within a preset time (the feedback information may come from the institutional node or the platform node), if no feedback information is received, re-execute the feedback information receiving steps; if multiple pieces of feedback service information and institutional node information on the published requirement are received, decrypt the multiple pieces of feedback service information with the multiple public keys of the multiple feedback institutional nodes respectively to obtain the multiple pieces of service information, obtain the priorities of the multiple services, sort the multiple services according to the priorities, and only keep the service with the highest priority; use the service information corresponding to the successfully matched and retained service and the institutional node as the service and institutional node for which the requirement is successfully matched, and send a confirmation message for accepting the service to the institutional node; after receiving the confirmation message, the institutional node packages the requirement, user node, service, institutional node, and transaction information and adds it as the latest block to the transaction blockchain, and the institutional node provides the service for the requirement of the user node.

[0116] Steps of information hiding;

[0117] The steps of providing the service for the said requirement include:

[0118] The institutional node obtains the requester characteristics corresponding to the requirement (the requester characteristics include the face characteristics of the requester); the user node obtains the service provider characteristics corresponding to the service (the requester characteristics include the face characteristics of the service provider);

[0119] When the institutional node sends the information to be hidden to the user node, taking the requester characteristics and random information as the input, taking the information to be hidden as the expected output, testing through a deep learning model and reverse generating the input data to adjust the random information to obtain the optimal information; taking the requester characteristics and the optimal information as the input, calculating through the deep learning model to obtain the output information, comparing the output information with the information to be hidden to obtain the difference information. Sending the optimal information and the difference information to the user node; after receiving the optimal information and the difference information, the user node takes the requester characteristics and the optimal information as the input, calculates through the deep learning model to obtain the output information, synthesizes the output information with the difference information to obtain the information to be hidden. Packing the requirement, user node, service, institutional node, requester and service provider information, information sender and receiver information, optimal information and difference information and adding them as the latest block to the transaction blockchain.

[0120] When the user node sends the information to be hidden to the institutional node, taking the service provider characteristics and random information as the input, taking the information to be hidden as the expected output, testing through a deep learning model and reverse generating the input data to adjust the random information to obtain the optimal information; taking the service provider characteristics and the optimal information as the input, calculating through the deep learning model to obtain the output information, comparing the output information with the information to be hidden to obtain the difference information. Sending the optimal information and the difference information to the user node; after receiving the optimal information and the difference information, the user node takes the service provider characteristics and the optimal information as the input, calculates through the deep learning model to obtain the output information, synthesizes the output information with the difference information to obtain the information to be hidden. Packing the requirement, user node, service, institutional node, requester and service provider information, information sender and receiver information, optimal information and difference information and adding them as the latest block to the transaction blockchain.

[0121] The above-described embodiments merely represent several implementation manners of the present invention. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

Claims

1. A blockchain method, characterized in that, The method includes: Node registration request step: Obtain a preset type, where the preset type includes a platform or an institution or a user; the platform node obtains a request from the node to be registered to register a preset type of node; the request includes the spatial location of the node to be registered, the spatial range applied for by the node to be registered for jurisdiction, and the management priority applied for by the node to be registered; the platform node obtains the location information and basic information of the node to be registered. If the basic information of the node to be registered meets the registration conditions for the preset type of node, a quasi-consent message indicating that the platform node intends to consent to the node to be registered becoming a platform node is sent to the node to be registered, and the quasi-consent message has been encrypted with the private key of the platform node. Registration information confirmation step: If the node to be registered successfully obtains the preset number of the previously arrived quasi-consent messages, it obtains the hash value of the latest block in the preset type of blockchain as the hash value of the previous block, packs the hash value of the previous block, the basic information of the node to be registered, and the preset number of the previously arrived quasi-consent messages into a block to be inserted and inserts it into the preset type of blockchain, and then broadcasts the block to be inserted to all nodes in the blockchain set after encrypting it with the private key of the node to be registered; each node in the blockchain set obtains the broadcast block to be inserted. First, it decrypts the block to be inserted with the public key of the node to be registered that broadcasts the block to be inserted. Registration consensus step: If any node in the blockchain set receives the block to be inserted broadcast by no more than the preset number of nodes, it selects one of the blocks to be inserted, extracts the hash value of the previous block from the block to be inserted, and matches it with the hash value of the latest block of each candidate preset type of blockchain stored by the any blockchain node. If the match fails, the block to be inserted is discarded. If the match is successful, after extracting the preset number of the previously arrived quasi-consent messages from the block, the preset number of the previously arrived quasi-consent messages are decrypted through the public key of the platform node that sends the quasi-consent message, and it is determined whether the quasi-consent message is a quasi-consent message. If the verification passes, the block to be inserted is inserted into each candidate preset type of blockchain, and the block to be inserted is encrypted with the private key of the any node and then broadcast to each node in the blockchain set again. Registration consensus achievement step: If any node in the blockchain set receives the block to be inserted broadcast by more than the preset number of nodes, it selects one of the blocks to be inserted, extracts the hash value of the previous block from the block to be inserted, and matches it with the hash value of the latest block of each candidate preset type of blockchain stored by the any node. If the match fails, the block to be inserted is discarded. If the match is successful, the block to be inserted is inserted into each candidate preset type of blockchain.

2. The blockchain method according to claim 1, wherein The method further includes: Steps for determining user requirement priorities: Obtain the rules for judging the priorities of each type of user requirements through user data, obtain user data, and calculate the priorities of each type of the user's requirements according to the rules; when the judgment of the priorities of each type of requirements fails according to the rules, input the user data into the deep learning model for predicting the priorities of each type of requirements for calculation, and use the output of the model as the priorities of each type of the user's requirements; Steps for constructing the deep learning model for predicting the priorities of each type of requirements: Use the user data with known priorities of each type of requirements and the known priorities of each type of requirements as input and expected output to train and test the deep learning model, and obtain the deep learning model for predicting the priorities of each type of requirements; Steps for determining institutional service priorities: Obtain the rules for judging the priorities of each type of institutional services through institutional data, obtain institutional data, and calculate the priorities of each type of the institution's services according to the rules; when the judgment of the priorities of each type of services fails according to the rules, input the institutional data into the deep learning model for predicting the priorities of each type of services for calculation, and use the output of the model as the priorities of each type of the institution's services; Steps for constructing the deep learning model for predicting the priorities of each type of services: Use the institutional data with known priorities of each type of services and the known priorities of each type of services as input and expected output to train and test the deep learning model, and obtain the deep learning model for predicting the priorities of each type of services; Steps for determining platform service priorities: Obtain the rules for judging the priorities of each type of platform docking through platform data, obtain platform data, and calculate the priorities of each type of the platform's docking according to the rules; when the judgment of the priorities of each type of docking fails according to the rules, input the platform data into the deep learning model for predicting the priorities of each type of docking for calculation, and use the output of the model as the priorities of each type of the platform's docking; Steps for constructing the deep learning model for predicting the priorities of each type of docking: Use the platform data with known priorities of each type of docking and the known priorities of each type of docking as input and expected output to train and test the deep learning model, and obtain the deep learning model for predicting the priorities of each type of docking.

3. The blockchain method according to claim 1, wherein The method further includes: Steps for a user node to publish requirements: The user node publishes requirements in a way of broadcasting to the nodes in the blockchain set, and the requirements have been encrypted with the private key of the user node; Steps for an institutional node to publish services: The institutional node publishes the available services in a way of broadcasting to the nodes in the blockchain set, and the available services have been encrypted with the private key of the institutional node; Platform node order transfer steps: The platform node receives multiple requirements from multiple user nodes and multiple available services from multiple institutional nodes, decrypts the multiple requirements with the multiple public keys of the multiple user nodes respectively, decrypts the multiple available services with the multiple public keys of the multiple institutional nodes respectively, the platform node matches the multiple requirements with the multiple available services, and obtains multiple successfully matched requirements and services; If multiple requirements match the same service, obtain the priorities of the multiple requirements, sort the multiple requirements according to the priorities, and only retain the requirement with the highest priority; If multiple services match the same requirement, obtain the priorities of the multiple services, sort the multiple services according to the priorities, and only retain the service with the highest priority; Feed back the service information corresponding to the successfully matched and retained requirement and the institutional node information to the user node corresponding to the requirement, and the service information has been encrypted with the private key of the institutional node; Institutional node order receiving steps: The institutional node receives multiple requirements from multiple user nodes, decrypts the multiple requirements with the multiple public keys of the multiple user nodes respectively, the institutional node matches the multiple requirements with the idle services of the institutional node, and obtains multiple successfully matched requirements and services; If multiple requirements match the same service, obtain the priorities of the multiple requirements, sort the multiple requirements according to the priorities, and only retain the requirement with the highest priority; Feed back the service information corresponding to the successfully matched and retained requirement and the institutional node information to the user node corresponding to the requirement, and the service information has been encrypted with the private key of the institutional node; Feedback information receiving steps: The user node receives the feedback information on the published requirement within the preset time. If no feedback information is received, the feedback information receiving steps are re-executed; If multiple pieces of feedback service information and institutional node information on the published requirement are received, decrypt the multiple pieces of feedback service information with the multiple public keys of the multiple feedback institutional nodes respectively to obtain the multiple pieces of service information, obtain the priorities of the multiple services, sort the multiple services according to the priorities, and only retain the service with the highest priority; Take the service information corresponding to the successfully matched and retained service and the institutional node as the successfully matched service and institutional node for the requirement, and send a confirmation message to the institutional node to accept the service; After receiving the confirmation message, the institutional node packs the requirement, user node, service, institutional node, and transaction information and adds it as the latest block to the transaction blockchain, and the institutional node provides the service for the requirement of the user node.

4. The blockchain method according to claim 1, wherein The method further includes: Feature acquisition steps: The institutional node acquires the feature of the demander corresponding to the requirement; The user node acquires the feature of the service provider corresponding to it; Steps for hiding and sending institutional information: When an institutional node sends information to be hidden to a user node, it takes the requester's characteristics and random information as inputs, the information to be hidden as the expected output, tests through a deep learning model, and reversely generates input data to adjust the random information to obtain optimized information; takes the requester's characteristics and the optimized information as inputs, calculates through the deep learning model to obtain output information, compares the output information with the information to be hidden to obtain difference information; sends the optimized information and the difference information to the user node; after receiving the optimized information and the difference information, the user node takes the requester's characteristics and the optimized information as inputs, calculates through the deep learning model to obtain output information, synthesizes the output information with the difference information to obtain the information to be hidden; packs the requirements, user node, service, institutional node, requester and service provider information, information sender and receiver information, optimized information and difference information and adds them as the latest block to the transaction blockchain; Steps for hiding and sending user information: When a user node sends information to be hidden to an institutional node, it takes the service provider's characteristics and random information as inputs, the information to be hidden as the expected output, tests through a deep learning model, and reversely generates input data to adjust the random information to obtain optimized information; takes the service provider's characteristics and the optimized information as inputs, calculates through the deep learning model to obtain output information, compares the output information with the information to be hidden to obtain difference information; sends the optimized information and the difference information to the user node; after receiving the optimized information and the difference information, the user node takes the service provider's characteristics and the optimized information as inputs, calculates through the deep learning model to obtain output information, synthesizes the output information with the difference information to obtain the information to be hidden; packs the requirements, user node, service, institutional node, requester and service provider information, information sender and receiver information, optimized information and difference information and adds them as the latest block to the transaction blockchain.

5. A blockchain system, characterized in that, The system includes: Node registration request module: Obtain a preset type, where the preset type includes platform or institution or user; the platform node obtains a request from a node to be registered to register a node of the preset type; the request includes the spatial location of the node to be registered, the spatial range applied by the node to be registered for jurisdiction, and the management priority applied by the node to be registered; the platform node obtains the location information and basic information of the node to be registered. If the basic information of the node to be registered meets the registration conditions of the preset type of node, it sends a quasi-consent message indicating that it quasi-agrees for the node to be registered to become a platform node to the node to be registered, and the quasi-consent message has been encrypted with the private key of the platform node; Registration Information Confirmation Module: If the node to be registered successfully obtains the pre-set number of the aforesaid proposed consent messages that have arrived, it obtains the hash value of the latest block in the pre-set type of blockchain as the hash value of the previous block, packs the hash value of the previous block, the basic information of the node to be registered, and the pre-set number of the aforesaid proposed consent messages into a block to be inserted and inserts it into the pre-set type of blockchain, and broadcasts the block to be inserted to all nodes in the blockchain set after encrypting it with the private key of the node to be registered; Each node in the blockchain set obtains the broadcast block to be inserted, and first decrypts the block to be inserted with the public key of the node to be registered that broadcasts the block to be inserted; Registration Consensus Module: If any node in the blockchain set receives the block to be inserted broadcast by no more than the pre-set number of nodes, it selects one of the blocks to be inserted, extracts the hash value of the previous block from the block to be inserted, and matches it with the hash value of the latest block of each candidate pre-set type of blockchain stored by the any blockchain node. If the match fails, it discards the block to be inserted. If the match is successful, after extracting the pre-set number of the aforesaid proposed consent messages from the block, it decrypts the pre-set number of the aforesaid proposed consent messages through the public key of the platform node that sends the proposed consent message, and determines whether the proposed consent message is a proposed consent message. If the verification passes, it inserts the block to be inserted into each candidate pre-set type of blockchain, and broadcasts the block to be inserted to each node in the blockchain set again after encrypting it with the private key of the any node; Registration Consensus Reached Module: If any node in the blockchain set receives the block to be inserted broadcast by more than the pre-set number of nodes, it selects one of the blocks to be inserted, extracts the hash value of the previous block from the block to be inserted, and matches it with the hash value of the latest block of each candidate pre-set type of blockchain stored by the any node. If the match fails, it discards the block to be inserted. If the match is successful, it inserts the block to be inserted into each candidate pre-set type of blockchain.

6. The blockchain system according to claim 5, characterized in that, The system further includes: User Requirement Priority Determination Module: Obtains the rules for judging the priority of each type of user requirement through user data, obtains user data, and calculates the priority of each type of user requirement according to the rules; When the judgment of the priority of each type of user requirement fails according to the rules, the user data is input into the deep learning model for predicting the priority of each type of user requirement for calculation, and the output of the model is used as the priority of each type of user requirement of the user; Deep Learning Model Construction Module for Predicting the Priority of Each Type of User Requirement: Uses the user data with the known priority of each type of user requirement, the known priority of each type of user requirement as the input and expected output to train and test the deep learning model, and obtains the deep learning model for predicting the priority of each type of user requirement; Institutional service priority determination module: Obtain the rules for judging the priority of each type of service of an institution through institutional data, obtain institutional data, and calculate the priority of each type of service of the institution according to the rules; when the judgment of the priority of each type of service fails according to the rules, input the institutional data into the deep learning model for predicting the priority of each type of service for calculation, and use the output of the model as the priority of each type of service of the institution; Deep learning model construction module for predicting the priority of each type of service: Use the institutional data with known priority of each type of service and the known priority of each type of service as input and expected output to train and test the deep learning model, and obtain the deep learning model for predicting the priority of each type of service; Platform service priority determination module: Obtain the rules for judging the docking priority of each type of the platform through platform data, obtain platform data, and calculate the docking priority of each type of the platform according to the rules; when the judgment of the docking priority of each type of service fails according to the rules, input the platform data into the deep learning model for predicting the docking priority of each type of service for calculation, and use the output of the model as the docking priority of each type of service of the platform; Deep learning model construction module for predicting the docking priority of each type of service: Use the platform data with known docking priority of each type of service and the known docking priority of each type of service as input and expected output to train and test the deep learning model, and obtain the deep learning model for predicting the docking priority of each type of service.

7. The blockchain system according to claim 5, characterized in that, The system further includes: User node demand publishing module: The user node publishes the demand in a way of broadcasting to the nodes in the blockchain set, and the demand has been encrypted with the private key of the user node; Institutional node service publishing module: The institutional node publishes the available services in a way of broadcasting to the nodes in the blockchain set, and the available services have been encrypted with the private key of the institutional node; Platform node order transfer module: The platform node receives multiple demands from multiple user nodes and multiple available services from multiple institutional nodes, decrypts the multiple demands with the public keys of the multiple user nodes respectively, decrypts the multiple available services with the public keys of the multiple institutional nodes respectively, the platform node matches the multiple demands with the multiple available services, and obtains multiple successfully matched demands and services; if multiple demands match the same service, obtain the priorities of the multiple demands, sort the multiple demands according to the priorities, and only keep the demand with the highest priority; if multiple services match the same demand, obtain the priorities of the multiple services, sort the multiple services according to the priorities, and only keep the service with the highest priority; feedback the service information corresponding to the successfully matched and retained demand and the institutional node information to the user node corresponding to the demand, and the service information has been encrypted with the private key of the institutional node; Institutional Node Order Receiving Module: The institutional node receives multiple requirements from multiple user nodes, decrypts the multiple requirements respectively using the multiple public keys of the multiple user nodes, matches the multiple requirements with the idle services of the institutional node, and obtains multiple successfully matched requirements and multiple services; if multiple requirements are matched with the same service, obtain the priorities of the multiple requirements, sort the multiple requirements according to the priorities, and only retain the requirement with the highest priority; feedback the service information corresponding to the successfully matched and retained requirements and the institutional node information to the user node corresponding to the requirement, and the service information has been encrypted using the private key of the institutional node. Feedback Information Receiving Module: The user node receives the feedback information on the published requirement within a preset time. If no feedback information is received, the feedback information receiving module is re-executed; if multiple pieces of feedback service information and institutional node information on the published requirement are received, decrypt the multiple pieces of feedback service information respectively using the public keys of the multiple feedback institutional nodes to obtain the multiple pieces of service information, obtain the priorities of the multiple services, sort the multiple services according to the priorities, and only retain the service with the highest priority; use the service information corresponding to the successfully matched and retained service and the institutional node as the service and institutional node for which the requirement is successfully matched, and send a confirmation message accepting the service to the institutional node; after the institutional node receives the confirmation message, package the requirement, user node, service, institutional node, transaction information and add it as the latest block to the transaction blockchain, and the institutional node provides the service for the requirement of the user node.

8. The blockchain system according to claim 5, wherein The system further includes: Feature Acquisition Module: The institutional node acquires the requester features corresponding to the requirement; the user node acquires the service provider features corresponding to the service. Institutional Information Hiding and Sending Module: When the institutional node sends information that needs to be hidden to the user node, use the requester features and random information as the input, use the information that needs to be hidden as the expected output, test through a deep learning model and reverse generate the input data to adjust the random information to obtain the optimal information; use the requester features and the optimal information as the input, calculate through the deep learning model to obtain the output information, compare the output information with the information that needs to be hidden to obtain the difference information; send the optimal information and the difference information to the user node; after the user node receives the optimal information and the difference information, use the requester features and the optimal information as the input, calculate through the deep learning model to obtain the output information, synthesize the output information with the difference information to obtain the information that needs to be hidden; package the requirement, user node, service, institutional node, requester and service provider information, information sender and receiver information, optimal information and difference information and add it as the latest block to the transaction blockchain. User Information Hiding and Sending Module: When the user node sends the information to be hidden to the institutional node, it takes the service provider feature and random information as input, the information to be hidden as the expected output, tests through a deep learning model and reversely generates input data to adjust the random information to obtain the optimal information; takes the service provider feature and the optimal information as input, calculates through the deep learning model to obtain the output information, compares the output information with the information to be hidden to obtain the difference information; sends the optimal information and the difference information to the user node; after receiving the optimal information and the difference information, the user node takes the service provider feature and the optimal information as input, calculates through the deep learning model to obtain the output information, synthesizes the output information with the difference information to obtain the information to be hidden; packs the requirements, user node, service, institutional node, requester and service provider information, information sender and receiver information, optimal information and difference information and adds them as the latest block to the transaction blockchain.

9. A medical, healthcare and elderly care trading system, including a medical, healthcare and elderly care system, characterized in that, The medical, health and elderly care system implements the steps of the method according to any one of claims 1-4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method according to any one of claims 1-4.

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