CN116757849A - Asset management system and method based on block chain - Google Patents
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Abstract
The invention provides an asset management system and method based on a blockchain, which relate to the technical field of data management and are used for acquiring the holding asset information of a first node, generating a target client image and carrying out similar client optimization to acquire a target client node. When transaction request information is received, an intelligent encryption module is activated and converted into a temporary network breaking state, a recommended key pair is obtained by optimizing, a secondary mapping code is carried out, the recommended key pair is sent to a corresponding node and a channel is constructed for carrying out asset transaction, the technical problem that safety is insufficient because the safety verification path in the asset transaction process is short and the effectiveness and completeness of risk avoidance cannot be guaranteed is solved, the locking of transaction objects is carried out through user attribute feature analysis, the safety asset transaction of both transaction parties is carried out through a secondary encryption mode, namely asymmetric key and key code conversion, and the network risk is avoided to the maximum extent in the transaction process is solved.
Description
Technical Field
The invention relates to the technical field of data management, in particular to an asset management system and method based on a blockchain.
Background
The traditional asset management mode mainly relies on banks, securities companies and the like to perform asset management, has a certain single point failure risk, is prone to losing, tampering and other security risks, combines block chains to perform asset data management, and has the characteristics of decentralization and the like to perform risk avoidance to a certain extent, but has certain technical limitations. In the prior art, asset management based on blockchain has the defects that the effectiveness and completeness of risk avoidance cannot be ensured due to the fact that the safety verification path in the asset transaction process is short, so that the safety is insufficient.
Disclosure of Invention
The application provides an asset management system and method based on a blockchain, which are used for solving the technical problem that the safety is insufficient because the safety verification path in the asset transaction process is shorter and the effectiveness and completeness of risk avoidance cannot be ensured in the prior art.
In view of the above, the present application provides a blockchain-based asset management system and method.
In a first aspect, the present application provides a blockchain-based asset management method, the method comprising:
acquiring holding asset information of a first node of a first blockchain, wherein the holding asset information comprises asset type characteristics, asset pricing characteristics and asset manufacturer characteristics;
Generating a target customer representation by customer demand analysis of the holding asset information, wherein the target customer representation is determined by correlation analysis of the asset type feature, the asset price tag feature and the asset vendor feature;
according to the target customer portrait, traversing the first blockchain to perform similar customer optimization, acquiring a target customer node, and recommending the asset held by the first node to a visual user interface of the target customer node;
when receiving transaction request information of the target client node for the asset information, activating an intelligent encryption module to convert the intelligent encryption module into a temporary off-network state, wherein the intelligent encryption module comprises a key matching sub-module and a key mapping sub-module;
in the key matching sub-module, optimizing an asymmetric key pair based on a key dislocation optimizing algorithm to obtain a recommended key pair, wherein the recommended key pair comprises an encryption key and a decryption key;
performing secondary mapping on the encryption key in the key mapping sub-module to generate a first mapping code, and performing secondary mapping on the decryption key to generate a second mapping code;
And converting the intelligent encryption module into a networking state, transmitting the first mapping code and the second mapping code to the first node and the target client node, and constructing a first transaction channel to conduct asset transaction.
In a second aspect, the present application provides a blockchain-based asset management system, the system comprising:
the information acquisition module is used for acquiring the holding asset information of the first node of the first blockchain, wherein the holding asset information comprises asset type characteristics, asset price marking characteristics and asset manufacturer characteristics;
the portrait generation module is used for generating a target customer portrait by carrying out customer demand analysis on the holding asset information, wherein the target customer portrait is determined by carrying out correlation analysis on the asset type characteristics, the asset price characteristics and the asset manufacturer characteristics;
the optimizing recommending module is used for traversing the first blockchain to conduct similar client optimizing according to the target client portrait, obtaining target client nodes and recommending the assets held by the first nodes to a visual user interface of the target client nodes;
The state conversion module is used for activating an intelligent encryption module when receiving transaction request information of the target client node for the asset information, and converting the intelligent encryption module into a temporary network disconnection state, wherein the intelligent encryption module comprises a key matching sub-module and a key mapping sub-module;
the key optimizing module is used for optimizing the asymmetric key pair based on a key dislocation optimizing algorithm in the key matching sub-module to obtain a recommended key pair, wherein the recommended key pair comprises an encryption key and a decryption key;
the mapping code generation module is used for carrying out secondary mapping on the encryption key in the key mapping sub-module to generate a first mapping code, and carrying out secondary mapping on the decryption key to generate a second mapping code;
and the channel transaction module is used for converting the intelligent encryption module into a networking state, transmitting the first mapping code and the second mapping code to the first node and the target client node, and constructing a first transaction channel to perform asset transaction.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
according to the asset management method based on the blockchain, which is provided by the embodiment of the application, the holding asset information of the first node of the first blockchain is obtained, the client demand analysis is carried out to generate the target client portrait, the first blockchain is traversed to carry out similar client optimization according to the target client portrait, the target client node is obtained, and the recommendation of the holding asset of the first node is carried out. When the transaction request information of the target client node for the asset information is received, an intelligent encryption module is activated and converted into a temporary network breaking state, an asymmetric key pair is optimized based on a key dislocation optimizing algorithm in a key matching sub-module, a recommended key pair is obtained, secondary mapping is conducted on the encryption key and the decryption key in a key mapping sub-module, and a first mapping code and a second mapping code are generated. The intelligent encryption module is converted into a networking state, the first mapping code and the second mapping code are sent to the first node and the target client node, a first transaction channel is constructed for carrying out asset transaction, the problem that in the prior art, because the safety verification path in the asset transaction process is short, the effectiveness and completeness of risk avoidance cannot be guaranteed, the technical problem of insufficient safety is caused, the transaction object is locked through carrying out user attribute feature analysis, the safety asset transaction of both transaction parties is carried out through carrying out a two-level encryption mode, namely asymmetric keys and key code conversion, and the network risk is maximally avoided aiming at the transaction process.
Drawings
FIG. 1 is a flow chart of a blockchain-based asset management method according to the present application.
FIG. 2 is a schematic diagram of a target customer representation generation flow in a blockchain-based asset management method of the present application.
FIG. 3 is a schematic diagram of a blockchain-based asset management system in accordance with the present application.
Reference numerals illustrate: the system comprises an information acquisition module 11, an image generation module 12, an optimizing recommendation module 13, a state conversion module 14, a key optimizing module 15, a mapping code generation module 16 and a channel transaction module 17.
Detailed Description
The application provides an asset management system and method based on a blockchain, which are used for acquiring the information of a first node of a holding asset, generating a target client image and carrying out similar client optimization to acquire a target client node. When transaction request information is received, an intelligent encryption module is activated and converted into a temporary network breaking state, a recommended key pair is obtained by optimizing, secondary mapping coding is carried out, the recommended key pair is sent to a corresponding node, and a channel is constructed to carry out asset transaction, so that the technical problem that safety is insufficient because the safety verification path in the asset transaction process is short and the effectiveness and completeness of risk avoidance cannot be guaranteed in the prior art is solved.
Example 1
As shown in FIG. 1, the present application provides a blockchain-based asset management method, the method comprising:
s10: acquiring holding asset information of a first node of a first blockchain, wherein the holding asset information comprises asset type characteristics, asset pricing characteristics and asset manufacturer characteristics;
the traditional asset management mode mainly relies on banks, securities companies and the like to perform asset management, has a certain single-point failure risk, is easy to lose, tamper and other security risks, and combines a blockchain to perform asset data management, so that the risk can be reduced to a certain extent, but the technology is not perfect, and still has a certain disadvantage. According to the asset management method based on the blockchain, the user attribute feature analysis is carried out to lock the transaction object, the two-level encryption mode, namely the asymmetric key and the key code conversion is carried out to carry out the security asset transaction of the two transaction parties, and the network risk is avoided to the maximum extent in the transaction process.
The first blockchain is a target blockchain to be subjected to asset management, the first node is any node in the first blockchain, the first node corresponds to a target enterprise, and the blockchain nodes and the enterprise have a one-to-one mapping relation. Acquiring asset type characteristics of the first node, such as bond assets, patent assets, business assets, technical assets, and the like; determining conversion proportions of assets and currencies for different asset types, and determining the asset pricing features based thereon; and determining the product operation direction, audience characteristics, qualification characteristics and the like of the first node as the characteristics of the asset manufacturer. And taking the asset type characteristic, the asset pricing characteristic and the asset manufacturer characteristic as the holding asset information of the enterprise mapped by the first node. And carrying out customer matching analysis by taking the holding asset information as a reference.
S20: generating a target customer representation by customer demand analysis of the holding asset information, wherein the target customer representation is determined by correlation analysis of the asset type feature, the asset price tag feature and the asset vendor feature;
further, as shown in fig. 2, by performing customer requirement analysis on the holding asset information, a target customer portrait is generated, where the target customer portrait is determined by performing correlation analysis on the asset type feature, the asset price feature and the asset vendor feature, and the application S20 further includes:
s21: searching in a plurality of blockchains according to the asset type characteristics to generate transaction history data;
s22: performing client association on the transaction history data according to the asset price marking characteristics to generate a first associated client and a first support degree, wherein the first associated client is a client with price greater than or equal to the asset price marking characteristics and successful transaction, and the first support degree refers to price marking frequency proportion greater than or equal to the asset price marking characteristics;
s23: performing client association on the transaction history data according to the asset manufacturer characteristics to generate a second associated client and a second support degree, wherein the second associated client is a client with the same asset manufacturer characteristics and successful transaction, and the second support degree refers to the triggering proportion of the asset manufacturer characteristics;
S24: and constructing the target customer portrait according to the first association customer, the first support degree, the second association customer and the second support degree.
Further, the method S24 further includes constructing the target customer representation according to the first association customer, the first support degree, the second association customer, and the second support degree:
s241: when the first support degree is greater than or equal to a first support degree threshold value and the second support degree is greater than or equal to a second support degree threshold value, setting attribute tag information of the first associated client and the second associated client as the target client portrait;
s242: when the first support degree is greater than or equal to the first support degree threshold value and the second support degree is smaller than the second support degree threshold value, setting the attribute tag information of the first associated client as the target client portrait;
s243: and when the second support degree is greater than or equal to the second support degree threshold value and the first support degree is smaller than the first support degree threshold value, setting the attribute tag information of the second associated client as the target client portrait.
And carrying out customer demand analysis based on the holding asset information, carrying out overall analysis by taking the asset type characteristics, the asset price marking characteristics and the asset manufacturer characteristics as criteria and combining transaction history data, screening relevant customers which meet a support degree threshold and have the same characteristics, extracting attribute labels of the relevant customers, and setting the relevant customers as target customer portraits, wherein the target customer portraits are highly representative.
Specifically, the asset type feature is used as an index, searching is carried out in the plurality of blockchains, transaction information conforming to the asset type feature is called in a preset time interval, namely, bordered in a historical time range of a current time node, and transaction historical data is generated regularly. Further carrying out client association analysis based on the transaction historical data by taking the asset price feature as a benchmark, specifically traversing each transaction historical data, carrying out transaction price identification and checking with the asset price feature, screening clients with transaction price greater than or equal to the asset price feature and successful transaction as the first association clients of the first node; and further counting the total transaction frequency and the transaction frequency of which the transaction price is greater than or equal to the asset price marking characteristic, and calculating the ratio of the total transaction frequency and the transaction frequency to obtain the price marking frequency ratio as the first support degree.
Similarly, based on the characteristics of the asset manufacturer, carrying out customer association analysis in combination with the transaction history data, specifically, carrying out identification and extraction of characteristics of the asset manufacturer of a transaction party on each transaction history record in the transaction history records, respectively checking the characteristics of the asset manufacturer with the characteristics of the first node, screening customers with the same characteristics of the asset manufacturer and successful transactions, as the second association customers, preferably, configuring characteristic similarity tolerance, namely, an allowable deviation interval for measuring the consistency degree of the characteristics of the manufacturer, which is set by a person skilled in the art, if the checked characteristics of the asset manufacturer meet the characteristic similarity tolerance, indicating that the customers are highly similar to the enterprises of the first node, and adding the customers into the second association customers; and further calculating the ratio of the same clients to the total clients of the asset manufacturer, namely the triggering ratio of the features of the asset manufacturer, as the second support.
And determining a client attribute tag corresponding to the asset information of the first node based on the first association client, the first support degree, the second association client and the second support degree. Specifically, the first support degree threshold value and the second support degree threshold value are set, that is, the critical support degree of the representativeness of the associated customer is measured for different dimensions, which is set by a person skilled in the art in a user-defined manner, the first support degree and the first support degree threshold value are checked, and the second support degree threshold value are checked. And if the first support degree is greater than or equal to the first support degree threshold value and the second support degree threshold value is greater than or equal to the second support degree threshold value, indicating that universality exists between the first association client and the second association client, and the first association client and the second association client have a certain audience representativeness, extracting attribute tag information of the first association client and the second association client, and setting the attribute tag information as the target client portrait.
Similarly, when the first support degree is greater than or equal to the first support degree threshold and the second support degree is smaller than the second support degree threshold, the feature universality of the second associated client is indicated to be insufficient, the corresponding attribute label is not representative, the attribute label information of the first associated client is extracted, and the target client portrait is set. And when the second support degree is larger than or equal to the second support degree threshold and the first support degree is smaller than the first support degree threshold, indicating that the attribute label of the first associated client is not representative, extracting attribute label information of the second associated client, and setting the attribute label information as the target client portrait. And carrying out association analysis and attribute tag screening of transaction clients by combining historical transaction data so as to ensure the universality and actual transaction fit of the determined target client tags.
S30: according to the target customer portrait, traversing the first blockchain to perform similar customer optimization, acquiring a target customer node, and recommending the asset held by the first node to a visual user interface of the target customer node;
s40: when receiving transaction request information of the target client node for the asset information, activating an intelligent encryption module to convert the intelligent encryption module into a temporary off-network state, wherein the intelligent encryption module comprises a key matching sub-module and a key mapping sub-module;
and the target customer portraits are universal labels conforming to the purposes of the transaction customer classes of the first nodes, the first block chain is traversed, label matching is carried out on the basis of the target customer portraits node by node, link nodes similar to the target customer labels are screened, and the link nodes are extracted to serve as the target customer nodes. Further, recommending the asset held by the first node on the visual interface of the target client node, for example, orderly characterizing the asset held by the first node, determining a pushing form, and generating a display popup window on the visual interface of each target client node for display.
Further, the intelligent encryption module is an execution module for performing transaction key matching, the recommendation of the first holding asset is performed on the target client node, and when transaction request information of the target client node for the holding asset information is received, the intelligent encryption module is activated to perform matching of the transaction encryption key and the transaction decryption key. The intelligent encryption module comprises the key matching sub-module and the key mapping sub-module, wherein mapping corresponding relations exist among the sub-modules, and keys of corresponding types are respectively stored and used for carrying out matching determination on transactions. With the activation of the intelligent encryption module, the intelligent encryption module is synchronously switched to a temporary network disconnection state, so that network security risk problems such as information leakage, tampering and interception can be effectively avoided, and the security of key matching is improved.
S50: in the key matching sub-module, optimizing an asymmetric key pair based on a key dislocation optimizing algorithm to obtain a recommended key pair, wherein the recommended key pair comprises an encryption key and a decryption key;
further, in the key matching sub-module, based on a key dislocation optimizing algorithm, an asymmetric key pair is optimized, and a recommended key pair is obtained, where the recommended key pair includes an encryption key and a decryption key, and the S50 of the present application further includes:
S51: constructing a key dislocation optimizing fitness function:
wherein ,characterizing the fitness of the choice of key pair, +.>Characterization of the i-th key pair in t time from the current time onwards>Frequency of choice, +.>Characterizing the minimum selected frequency in t time from the current time onwards,/for>Characterizing the maximum selected frequency in t time from the current time onwards,/for>Characterization of the last Key pair +.>Selecting the current duration of distance +.> and />The weight coefficient is preset;
s52: and according to the key dislocation optimizing fitness function, carrying out minimum fitness optimizing in the key matching submodule, and selecting the recommended key pair, wherein the recommended key pair comprises an encryption key and a decryption key.
Further, according to the key dislocation optimizing fitness function, minimum fitness optimizing is performed in the key matching submodule, the recommended key pair is selected, and the recommended key pair includes an encryption key and a decryption key, and the application S52 further includes:
s521: the key matching sub-module is provided with a first memory and a first processor, wherein the first processor is used for executing a key dislocation optimizing algorithm, and the first memory is used for storing a key pair;
S522: extracting M key pairs of a first period from the first memory, carrying out minimum fitness optimization based on the key dislocation optimization fitness function, and generating a first period recommended key pair when iteration is carried out for preset times;
s523: setting the M key pairs in the first period as first period tabu key pairs;
s524: extracting second periodic M key pairs from the first memory based on the first periodic tabu key pairs, wherein the second periodic M key pairs are different from the first periodic tabu key pairs;
s525: performing minimum fitness optimization on the M key pairs in the second period based on the key dislocation optimization fitness function to obtain a second period recommended key pair;
s526: and when the cycle period meets the preset period, comparing a plurality of cycle recommended key pairs to perform minimum fitness optimization, and acquiring the recommended key pairs, wherein the recommended key pairs comprise encryption keys and decryption keys.
Based on the key matching sub-module, combining the key dislocation optimizing algorithm to perform optimizing iteration of the asymmetric key so as toAnd maximally guaranteeing the security of the key, and acquiring the recommended key pair comprising the encryption key and the decryption key so as to maximally guarantee the transaction security. Specifically, the key dislocation optimizing fitness function is constructed, namely, a criterion for assisting in key pairing optimizing is constructed, the expression is as follows, , wherein ,/>Characterizing the fitness of the choice of key pair, +.>Characterization of the i-th key pair in t time from the current time onwards>Frequency of choice, +.>Characterizing the minimum selected frequency in t time from the current time onwards,/for>Characterizing the maximum selected frequency in t time from the current time onwards,/for>Characterization of the last Key pair +.>Selecting the current duration of distance +.> and />For the preset weight coefficient, the parameters can be obtained based on the data statistics mode, and can be regarded as known parameters. And combining the key dislocation optimizing fitness function, and carrying out fitness calculation and optimizing iteration aiming at the key pair so as to determine the optimal key pair.
Specifically, the key matching sub-module is provided with the first memory for storing the key pair and the first processor for executing a key dislocation optimizing algorithm, wherein the first period is an optimizing period of the key pair, and the period time limit and the period range can be set in a self-defined manner based on a person skilled in the art. And based on the first memory, retrieving and extracting M key pairs in the first period, namely the optimizing range of the first period. Based on the M key pairs, randomly extracting a pair, and combining the key dislocation optimizing fitness function to calculate the selected fitness as a first fitness; randomly extracting a pair based on the M key pairs, calculating and selecting fitness as the second fitness, and correcting the first fitness and the second fitness by taking the minimum fitness as a response target, so as to determine the current optimal fitness; repeating the fitness calculation step to perform optimizing iteration of the current optimal fitness, and taking the key pair corresponding to the current optimal fitness, which is determined by iteration, as the first period recommended key pair when the preset iteration times are met.
Meanwhile, the M key pairs in the first period are set as the first period tabu key pairs, and tabu locking is carried out on the first period tabu key pairs so as to avoid the follow-up optimization falling into local optimum. Further, M key pairs different from the first periodic tabu key pair are extracted as the second periodic M key pairs based on the first memory. And similarly, combining the key dislocation optimizing fitness function, carrying out optimizing iteration based on the minimum fitness on the M key pairs in the second period, determining the key pair corresponding to the minimum fitness, recommending the key pair as the second period, and selecting the minimum fitness so as to ensure the low frequency of the key pair and improve the safety coefficient. And meanwhile, setting the M key pairs in the second period as second period emergency key pairs.
Wherein, the tabu key pair has tabu time, can be based on the user-defined setting of the person skilled in the art, regard the setting moment of the tabu key pair as the initial moment, after meeting the tabu time, carry on the tabu release of the corresponding tabu key, namely it is regarded as the optimizing range of the key pair again. Repeating the above mode to perform cyclic optimization on the periodic key pairs until the cyclic period meets the preset period, performing positive sequencing and integration on the determined plurality of periodic recommended key pairs from large to small, and selecting the key pair corresponding to the minimum fitness as the recommended key pair, wherein the recommended key pair comprises the encryption key and the decryption key, the encryption key and the decryption key have asymmetry, the encryption key is a public key, the decryption key is a private key, and the public key encryption and the private key decryption are performed to maximize the guarantee of transaction security.
S60: performing secondary mapping on the encryption key in the key mapping sub-module to generate a first mapping code, and performing secondary mapping on the decryption key to generate a second mapping code;
s70: and converting the intelligent encryption module into a networking state, transmitting the first mapping code and the second mapping code to the first node and the target client node, and constructing a first transaction channel to conduct asset transaction.
Further, the second mapping is performed on the encryption key in the key mapping sub-module to generate a first mapping code, and the second mapping is performed on the decryption key to generate a second mapping code, which is further included in the present application S60: the key mapping submodule maps each bit character of the codes and the keys to have a unique corresponding relation.
Further, the method further includes performing a second mapping on the encryption key in the key mapping sub-module to generate a first mapping code, performing a second mapping on the decryption key to generate a second mapping code, and before the method further includes:
s61: when the first updating period is met, updating the unique corresponding relation between the mapping code and the key character in the key mapping submodule to generate a mapping code table;
S62: and updating the mapping coding table to a preset user node of the first blockchain based on the short message of the operator.
In order to further guarantee the security in key transmission, avoid revealing the risk. And performing code conversion again aiming at the generated secret key, wherein the secret key mapping submodule is embedded with the mapping coding table which has timeliness updating property so as to weaken the formation of a mapping rule and ensure the randomness of the code. When the first updating period is met, the unique corresponding relation between the mapping code and the key character in the key mapping submodule is updated, for example, conversion codes corresponding to the key character, namely, adjustment of the unique corresponding relation, arrangement rules of the coding sequence and the like, so that the mapping code table is ensured to have certain novelty and difficult to be used, and the updated codes and the key character are mapped and associated, so that the mapping code table is generated. Further, based on the operator short message, updating the mapping coding table to the preset user node of the first blockchain, wherein the preset user node is a user node for executing the transaction, the user node receives the mapping coding table, decodes and converts the second-level mapping coding to determine a decryption key, acquires transaction content based on the decryption key, and can maximally ensure the transaction security of the blockchain through secondary encryption.
Further, in the key mapping submodule, based on the mapping coding table, performing mapping conversion of coding on the encryption key to generate the first mapping code; and synchronizing the mapping conversion of the decryption key to generate the second mapping code. After the key coding is completed, the intelligent encryption module is converted into a networking state, a network environment for information transmission is provided, the first mapping coding is sent to the first node, and the second mapping coding is sent to the target client node. And constructing the first transaction channel aiming at the first node and the target client node to finish asset transaction, wherein the first transaction channel is a temporary channel constructed aiming at a transaction time zone.
The asset management method based on the block chain has the following technical effects:
1. and carrying out customer demand analysis on the holding asset information, determining target customer portraits with characteristic relevance, and carrying out transaction customer locking through similarity analysis. And converting the intelligent encryption module into a temporary network breaking state, and carrying out optimizing matching of an asymmetric key by combining a key dislocation optimizing algorithm, so that possible network information countermeasure risks can be avoided.
2. Based on a two-level encryption mode, namely asymmetric key optimizing matching and key coding conversion, safety asset transaction of both transaction parties is carried out based on the two-level encryption mode, and network risk is avoided to the maximization of the transaction process.
Example two
Based on the same inventive concept as the blockchain-based asset management method in the previous embodiments, as shown in fig. 3, the present application provides a blockchain-based asset management system, including:
an information acquisition module 11, where the information acquisition module 11 is configured to acquire holding asset information of a first node of a first blockchain, where the holding asset information includes an asset type feature, an asset pricing feature, and an asset vendor feature;
a portrayal generation module 12, wherein the portrayal generation module 12 is configured to generate a target client portrayal by performing a client demand analysis on the holding asset information, and the target client portrayal is determined by performing a correlation analysis on the asset type feature, the asset price feature and the asset vendor feature;
the optimizing recommending module 13 is used for traversing the first blockchain to perform similar client optimizing according to the target client portrait, acquiring a target client node and recommending the asset held by the first node to a visual user interface of the target client node;
The state conversion module 14 is configured to activate an intelligent encryption module when receiving transaction request information of the target client node for the asset information, and convert the intelligent encryption module into a temporary offline state, where the intelligent encryption module includes a key matching sub-module and a key mapping sub-module;
the key optimizing module 15 is configured to optimize an asymmetric key pair based on a key dislocation optimizing algorithm in the key matching sub-module, and obtain a recommended key pair, where the recommended key pair includes an encryption key and a decryption key;
the mapping code generating module 16 is configured to perform secondary mapping on the encryption key in the key mapping sub-module to generate a first mapping code, and perform secondary mapping on the decryption key to generate a second mapping code;
the channel transaction module 17 is configured to convert the intelligent encryption module into a networking state, send the first mapping code and the second mapping code to the first node and the target client node, and construct a first transaction channel to perform asset transaction.
Further, the image generation module 12 further includes:
the data retrieval module is used for retrieving in a plurality of blockchains according to the asset type characteristics to generate transaction history data;
the first customer association module is used for carrying out customer association on the transaction history data according to the asset price marking characteristics to generate a first associated customer and a first support degree, wherein the first associated customer is a customer with price marking greater than or equal to the asset price marking characteristics and successful transaction, and the first support degree refers to a price marking frequency proportion greater than or equal to the asset price marking characteristics;
the second client association module is used for carrying out client association on the transaction history data according to the characteristics of the asset manufacturer to generate a second associated client and a second support degree, wherein the second associated client is a client with the same characteristics of the asset manufacturer and successful transaction, and the second support degree refers to the triggering proportion of the characteristics of the asset manufacturer;
and the target customer portrait construction module is used for constructing the target customer portrait according to the first associated customer, the first support degree, the second associated customer and the second support degree.
Further, the target customer portrait construction module further includes:
the target customer portrait setting module is used for setting attribute tag information of the first associated customer and the second associated customer as the target customer portrait when the first support degree is larger than or equal to a first support degree threshold value and the second support degree is larger than or equal to a second support degree threshold value;
the second-class target customer portrait setting module is used for setting the attribute label information of the first associated customer as the target customer portrait when the first support degree is larger than or equal to the first support degree threshold value and the second support degree is smaller than the second support degree threshold value;
and the three-category target customer portrait setting module is used for setting the attribute label information of the second associated customer as the target customer portrait when the second support degree is larger than or equal to the second support degree threshold value and the first support degree is smaller than the first support degree threshold value.
Further, the key optimizing module 15 further includes:
The function construction module is used for constructing a key dislocation optimizing fitness function:
wherein ,characterizing the fitness of the choice of key pair, +.>Characterization of the i-th key pair in t time from the current time onwards>Frequency of choice, +.>Characterizing the minimum selected frequency in t time from the current time onwards,/for>Characterizing the maximum selected frequency in t time from the current time onwards,/for>Characterization of the last Key pair +.>Selecting the current duration of distance +.> and />The weight coefficient is preset;
and the recommended key pair acquisition module is used for carrying out minimum fitness optimization in the key matching sub-module according to the key dislocation optimization fitness function, and selecting the recommended key pair, wherein the recommended key pair comprises an encryption key and a decryption key.
Further, the recommended key pair obtaining module further includes:
the analysis module is used for the key matching sub-module and is provided with a first memory and a first processor, wherein the first processor is used for executing a key dislocation optimizing algorithm, and the first memory is used for storing a key pair;
the first periodic recommended key pair generation module is used for extracting M key pairs of a first period from the first memory, carrying out minimum fitness optimization based on the key dislocation optimization fitness function, and generating the first periodic recommended key pair when the preset number of iterations is reached;
The first periodic tabu key pair setting module is used for setting the first periodic M key pairs as first periodic tabu key pairs;
a key pair extraction module, configured to extract second period M key pairs from the first memory based on the first period tabu key pairs, where the second period M key pairs are different from the first period tabu key pairs;
the second periodic recommended key pair acquisition module is used for carrying out minimum fitness optimization on the second periodic M key pairs based on the key dislocation optimization fitness function to acquire a second periodic recommended key pair;
and the key pair optimizing module is used for comparing a plurality of cycle recommended key pairs to perform minimum adaptability optimization when the cycle period meets the preset period, and acquiring the recommended key pair, wherein the recommended key pair comprises an encryption key and a decryption key.
Further, the map code generation module 16 further includes: the key mapping submodule maps each bit character of the codes and the keys to have a unique corresponding relation.
Further, the map code generation module 16 further includes:
the mapping coding table generation module is used for updating the unique corresponding relation between the mapping codes and the key characters in the key mapping submodule when the first updating period is met, so as to generate a mapping coding table;
and the node updating module is used for updating the mapping coding table to the preset user node of the first block chain based on the short message of the operator.
From the foregoing detailed description of the asset management method based on the blockchain, those skilled in the art will clearly understand that the asset management system and method based on the blockchain in this embodiment, for the apparatus disclosed in the embodiments, since the apparatus corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant points refer to the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A blockchain-based asset management system, for use with an asset trading platform, comprising:
the information acquisition module is used for acquiring the holding asset information of the first node of the first blockchain, wherein the holding asset information comprises asset type characteristics, asset price marking characteristics and asset manufacturer characteristics;
the portrait generation module is used for generating a target customer portrait by carrying out customer demand analysis on the holding asset information, wherein the target customer portrait is determined by carrying out correlation analysis on the asset type characteristics, the asset price characteristics and the asset manufacturer characteristics;
the optimizing recommending module is used for traversing the first blockchain to conduct similar client optimizing according to the target client portrait, obtaining target client nodes and recommending the assets held by the first nodes to a visual user interface of the target client nodes;
the state conversion module is used for activating an intelligent encryption module when receiving transaction request information of the target client node for the asset information, and converting the intelligent encryption module into a temporary network disconnection state, wherein the intelligent encryption module comprises a key matching sub-module and a key mapping sub-module;
The key optimizing module is used for optimizing the asymmetric key pair based on a key dislocation optimizing algorithm in the key matching sub-module to obtain a recommended key pair, wherein the recommended key pair comprises an encryption key and a decryption key;
the mapping code generation module is used for carrying out secondary mapping on the encryption key in the key mapping sub-module to generate a first mapping code, and carrying out secondary mapping on the decryption key to generate a second mapping code;
and the channel transaction module is used for converting the intelligent encryption module into a networking state, transmitting the first mapping code and the second mapping code to the first node and the target client node, and constructing a first transaction channel to perform asset transaction.
2. The system of claim 1, wherein the representation generation module further comprises:
the data retrieval module is used for retrieving in a plurality of blockchains according to the asset type characteristics to generate transaction history data;
the first customer association module is used for carrying out customer association on the transaction history data according to the asset price marking characteristics to generate a first associated customer and a first support degree, wherein the first associated customer is a customer with price marking greater than or equal to the asset price marking characteristics and successful transaction, and the first support degree refers to a price marking frequency proportion greater than or equal to the asset price marking characteristics;
The second client association module is used for carrying out client association on the transaction history data according to the characteristics of the asset manufacturer to generate a second associated client and a second support degree, wherein the second associated client is a client with the same characteristics of the asset manufacturer and successful transaction, and the second support degree refers to the triggering proportion of the characteristics of the asset manufacturer;
and the target customer portrait construction module is used for constructing the target customer portrait according to the first associated customer, the first support degree, the second associated customer and the second support degree.
3. The system of claim 2, wherein the target customer representation construction module further comprises:
the target customer portrait setting module is used for setting attribute tag information of the first associated customer and the second associated customer as the target customer portrait when the first support degree is larger than or equal to a first support degree threshold value and the second support degree is larger than or equal to a second support degree threshold value;
the second-class target customer portrait setting module is used for setting the attribute label information of the first associated customer as the target customer portrait when the first support degree is larger than or equal to the first support degree threshold value and the second support degree is smaller than the second support degree threshold value;
And the three-category target customer portrait setting module is used for setting the attribute label information of the second associated customer as the target customer portrait when the second support degree is larger than or equal to the second support degree threshold value and the first support degree is smaller than the first support degree threshold value.
4. The system of claim 1, wherein the key optimizing module further comprises:
the function construction module is used for constructing a key dislocation optimizing fitness function:
；
wherein ,characterizing the fitness of the choice of key pair, +.>Characterization of the i-th key pair in t time from the current time onwards>Frequency of choice, +.>Characterizing the minimum selected frequency in t time from the current time onwards,/for>Characterizing the maximum selected frequency in t time from the current time onwards,/for>Characterization of the last Key pair +.>Selecting the current duration of distance +.> and />The weight coefficient is preset;
and the recommended key pair acquisition module is used for carrying out minimum fitness optimization in the key matching sub-module according to the key dislocation optimization fitness function, and selecting the recommended key pair, wherein the recommended key pair comprises an encryption key and a decryption key.
5. The system of claim 4, wherein the recommended key pair acquisition module further comprises:
the analysis module is used for the key matching sub-module and is provided with a first memory and a first processor, wherein the first processor is used for executing a key dislocation optimizing algorithm, and the first memory is used for storing a key pair;
the first periodic recommended key pair generation module is used for extracting M key pairs of a first period from the first memory, carrying out minimum fitness optimization based on the key dislocation optimization fitness function, and generating the first periodic recommended key pair when the preset number of iterations is reached;
the first periodic tabu key pair setting module is used for setting the first periodic M key pairs as first periodic tabu key pairs;
a key pair extraction module, configured to extract second period M key pairs from the first memory based on the first period tabu key pairs, where the second period M key pairs are different from the first period tabu key pairs;
the second periodic recommended key pair acquisition module is used for carrying out minimum fitness optimization on the second periodic M key pairs based on the key dislocation optimization fitness function to acquire a second periodic recommended key pair;
And the key pair optimizing module is used for comparing a plurality of cycle recommended key pairs to perform minimum adaptability optimization when the cycle period meets the preset period, and acquiring the recommended key pair, wherein the recommended key pair comprises an encryption key and a decryption key.
6. The system of claim 1, wherein the map code generation module further comprises: the key mapping submodule maps each bit character of the codes and the keys to have a unique corresponding relation.
7. The system of claim 6, wherein the map code generation module further comprises:
the mapping coding table generation module is used for updating the unique corresponding relation between the mapping codes and the key characters in the key mapping submodule when the first updating period is met, so as to generate a mapping coding table;
and the node updating module is used for updating the mapping coding table to the preset user node of the first block chain based on the short message of the operator.
8. The asset management method based on the blockchain is characterized by being applied to an asset transaction platform and comprising the following steps of:
acquiring holding asset information of a first node of a first blockchain, wherein the holding asset information comprises asset type characteristics, asset pricing characteristics and asset manufacturer characteristics;
Generating a target customer representation by customer demand analysis of the holding asset information, wherein the target customer representation is determined by correlation analysis of the asset type feature, the asset price tag feature and the asset vendor feature;
according to the target customer portrait, traversing the first blockchain to perform similar customer optimization, acquiring a target customer node, and recommending the asset held by the first node to a visual user interface of the target customer node;
when receiving transaction request information of the target client node for the asset information, activating an intelligent encryption module to convert the intelligent encryption module into a temporary off-network state, wherein the intelligent encryption module comprises a key matching sub-module and a key mapping sub-module;
in the key matching sub-module, optimizing an asymmetric key pair based on a key dislocation optimizing algorithm to obtain a recommended key pair, wherein the recommended key pair comprises an encryption key and a decryption key;
performing secondary mapping on the encryption key in the key mapping sub-module to generate a first mapping code, and performing secondary mapping on the decryption key to generate a second mapping code;
And converting the intelligent encryption module into a networking state, transmitting the first mapping code and the second mapping code to the first node and the target client node, and constructing a first transaction channel to conduct asset transaction.
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