Blockchain-based secure transaction method and apparatus

Abstract

The application discloses a kind of based on blockchain's safe transaction method and device, it is related to blockchain technical field, the method includes: according to the historical transaction data of bank determines multiple customer relationships;Customer relationship is classified, obtains multiple customer relationship categories;According to the historical transaction data of customer relationship category determines the risk probability of each dimension;According to risk probability determines safe relationship category;According to safe relationship category, the safe transaction counterparty corresponding to customer is determined, its information is pushed to customer, and after confirmation of customer, it and confirmation information are uploaded to blockchain;Before the network signal intensity of customer is less than predetermined value, multiple safe transaction counterparty information is issued to the mobile terminal of this customer;When less than predetermined value, whether the safe transaction counterparty is confirmed by mobile terminal according to current transaction data transaction counterparty, if yes, the current transaction of the customer is supported by mobile terminal.The application can improve transaction security when network signal is weak.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a blockchain-based secure transaction method and device. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. No statement herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] Currently, banking transactions require an internet connection for risk control and resource allocation, and the bank's control functions, including identity verification, reside on its servers. When the network signal is weak, a customer's mobile device may be unable to perform risk control and, consequently, transactions. Summary of the Invention

[0004] An embodiment of the present invention provides a secure transaction method based on blockchain, the method comprising:

[0005] determining a plurality of customer relationships based on the bank's historical transaction data, wherein the customer relationships include transaction parties and transaction counterparties;

[0006] Classify customer relationships and obtain multiple customer relationship categories;

[0007] For each customer relationship category, determine the risk probability of each dimension corresponding to that customer relationship category based on its historical transaction data;

[0008] Determine the security relationship category based on the risk probability corresponding to each dimension;

[0009] For each customer of the bank, determine the corresponding security counterparty based on the security relationship category;

[0010] Push the customer's corresponding security counterparty information to the customer. After the customer confirms, upload the customer's confirmation information and the security counterparty information to the blockchain;

[0011] Before the customer's network signal strength is less than a predetermined value, the information of multiple secure transaction counterparties corresponding to the customer is sent to the customer's mobile terminal; when the network signal strength of the customer's mobile terminal is less than a predetermined value, the mobile terminal confirms whether the transaction counterparty is a secure transaction counterparty based on the customer's current transaction data. If so, the mobile terminal supports the customer's current transaction.

[0012] An embodiment of the present invention further provides a blockchain-based secure transaction device, comprising:

[0013] A customer relationship determination module, configured to determine a plurality of customer relationships based on the bank's historical transaction data, wherein the customer relationships include transaction parties and transaction counterparties;

[0014] A classification module is used to classify customer relationships and obtain multiple customer relationship categories;

[0015] A risk probability determination module is used to determine, for each customer relationship category, the risk probability of each dimension corresponding to the customer relationship category based on the historical transaction data of the customer relationship category;

[0016] A security relationship category determination module is used to determine the security relationship category based on the risk probability corresponding to each dimension;

[0017] A security counterparty determination module is used to determine, for each customer of the bank, the security counterparty corresponding to the customer based on the security relationship category;

[0018] An information push module is used to push the information of the customer's corresponding security counterparty to the customer. After the customer confirms, the customer's confirmation information and the information of the security counterparty are uploaded to the blockchain;

[0019] The information sending module is used to send the information of multiple secure trading counterparties corresponding to the customer to the customer's mobile terminal before the customer's network signal strength is less than a predetermined value; when the network signal strength of the customer's mobile terminal is less than the predetermined value, the mobile terminal confirms whether the trading counterparty is a secure trading counterparty based on the customer's current transaction data. If so, the mobile terminal supports the customer's current transaction.

[0020] An embodiment of the present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned blockchain-based secure transaction method when executing the computer program.

[0021] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned blockchain-based secure transaction method.

[0022] An embodiment of the present invention also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the above-mentioned blockchain-based secure transaction method.

[0023] In the embodiment of the present invention, compared with the technical solution in the prior art in which when the network signal is weak, the customer's mobile terminal may not be able to authenticate due to the weak network signal, and thus cannot conduct transactions, the present invention determines multiple customer relationships based on the bank's historical transaction data, wherein the customer relationships include transaction parties and transaction counterparties; classifies the customer relationships to obtain multiple customer relationship categories; for each customer relationship category, determines the risk probability of each dimension corresponding to the customer relationship category based on the historical transaction data of the customer relationship category; determines the security relationship category based on the risk probability corresponding to each dimension; for each customer of the bank, determines the customer's corresponding security transaction counterparty based on the security relationship category; and The information of the corresponding secure transaction counterparty is pushed to the customer, and after the customer confirms, the customer's confirmation information and the information of the secure transaction counterparty are uploaded to the blockchain; before the customer's network signal strength is less than a predetermined value, the multiple secure transaction counterparty information corresponding to the customer is sent to the customer's mobile terminal; when the network signal strength of the customer's mobile terminal is less than a predetermined value, the mobile terminal confirms whether the transaction counterparty is a secure transaction counterparty based on the customer's current transaction data. If so, the mobile terminal supports the customer's current transaction. Through the above series of operations, the present invention provides the secure transaction counterparty information to the mobile terminal. When the signal is weak, transactions can be directly performed based on the secure transaction counterparty without waiting for the network signal to become stronger. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0025] Figure 1 The process of secure transaction method based on blockchain in the embodiment of the present invention Figure 1 ;

[0026] Figure 2 The process of secure transaction method based on blockchain in the embodiment of the present invention Figure 2 ;

[0027] Figure 3 The process of secure transaction method based on blockchain in the embodiment of the present invention Figure 3 ;

[0028] Figure 4 The process of secure transaction method based on blockchain in the embodiment of the present invention Figure 4 ;

[0029] Figure 5The process of secure transaction method based on blockchain in the embodiment of the present invention Figure 5 ;

[0030] Figure 6 The process of secure transaction method based on blockchain in the embodiment of the present invention Figure 6 ;

[0031] Figure 7 The process of secure transaction method based on blockchain in the embodiment of the present invention Figure 7 ;

[0032] Figure 8 This is a structural block diagram of a secure transaction device based on blockchain in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0034] Based on the problems existing in the prior art, this invention proposes a secure transaction method based on blockchain, the execution subject is the bank side, the specific process is as follows Figure 1 As shown, the method includes:

[0035] Step 101: Determine multiple customer relationships based on historical transaction data of a bank, where the customer relationships include transaction parties and transaction counterparties.

[0036] Step 102: Classify customer relationships to obtain multiple customer relationship categories;

[0037] Step 103: For each customer relationship category, determine the risk probability of each dimension corresponding to the customer relationship category based on the historical transaction data of the customer relationship category;

[0038] Step 104: Determine the security relationship category based on the risk probability corresponding to each dimension;

[0039] Step 105: For each customer of the bank, determine the security transaction counterparty corresponding to the customer based on the security relationship category;

[0040] Step 106: Push the information of the customer's corresponding secure counterparty to the customer. After the customer confirms, upload the customer's confirmation information and the secure counterparty information to the blockchain.

[0041] Step 107: Before the customer's network signal strength is less than a predetermined value, the information of multiple secure transaction counterparties corresponding to the customer is sent to the customer's mobile terminal; when the network signal strength of the customer's mobile terminal is less than the predetermined value, the mobile terminal confirms whether the transaction counterparty is a secure transaction counterparty based on the customer's current transaction data. If so, the mobile terminal supports the customer's current transaction.

[0042] Specifically, transaction data refers to the data recorded by the bank regarding a customer's transaction. This data is generally multi-dimensional, including transaction time, location, amount, channel (mobile banking, smart terminal, WeChat banking), counterparty information, and indicators indicating whether the transaction involves risk, as well as the type of risk.

[0043] A customer relationship is a transaction between two customers, one being the transaction party and the other being the transaction counterparty.

[0044] Dimensions are used to categorize transaction data, such as transaction scenario, business category, transaction time, and transaction channel. Because customer transactions may pose security risks for both transactions and funds, it's necessary to determine the risk probability of transactions. This can be determined using different dimensions. Risk varies across different dimensions, and by calculating risk probabilities across these dimensions, we can determine the security of each customer relationship category.

[0045] In the embodiment of the present invention, Figure 2 As shown, step 102 classifies customer relationships to obtain multiple customer relationship categories, including:

[0046] Step 201: For each customer relationship, obtain historical transaction data of the transaction party of the customer relationship, historical transaction data of the transaction counterparty of the customer relationship, and transaction data between the transaction party and the transaction counterparty;

[0047] Step 202: For each customer relationship, determine a first transaction vector for the customer relationship based on the historical transaction data of the transaction party of the customer relationship, wherein each component of the first transaction vector corresponds one-to-one to a transaction category of the bank, and the value of each component is equal to the number of transactions of the transaction category corresponding to the component in the historical transaction data of the transaction party of the customer relationship;

[0048] Step 203: For each customer relationship, determine a second transaction vector for the customer relationship based on the historical transaction data of the transaction counterparty of the customer relationship, wherein each component of the second transaction vector corresponds one-to-one to a transaction category of the bank, and the value of each component is equal to the number of transactions of the transaction category corresponding to the component in the historical transaction data of the transaction counterparty of the customer relationship;

[0049] Step 204: For each customer relationship, determine the primary transaction scenario corresponding to the customer relationship (i.e., the transaction scenario with the most transaction data) based on the transaction data between the transaction party of the customer relationship and the transaction counterparty of the customer relationship;

[0050] Step 205: Determine a distance function corresponding to the customer relationship based on the first transaction vector and the second transaction vector of the customer relationship, wherein the distance function can determine the distance between any two customer relationships;

[0051] Step 206: Classify the customer relationships based on the distance functions corresponding to the customer relationships and the corresponding main transaction scenarios to obtain multiple customer relationship categories.

[0052] In one embodiment, customer relationships are classified based on the distance function corresponding to the customer relationship and the corresponding main transaction scenarios to obtain multiple customer relationship categories, including:

[0053] Based on the distance function corresponding to the customer relationship, all customer relationships are clustered to obtain multiple customer relationship categories; (for example, K-means is selected)

[0054] For each customer relationship category obtained above, determine the quantitative proportion of customer relationships corresponding to each major transaction scenario in the customer relationship category, and determine the maximum value of the quantitative proportion as the consistency index of the customer relationship category; determine whether the consistency index of the customer relationship category is greater than the set value; if so, continue to cluster all customer categories of the customer relationship category until the obtained consistency index of each customer relationship category is greater than the set value.

[0055] In the embodiment of the present invention, Figure 3 As shown, step 103 determines, for each customer relationship category, the risk probability of each dimension corresponding to the customer relationship category based on the historical transaction data of the customer relationship category, including:

[0056] Step 301: Divide the historical transaction data of the customer relationship category into multiple historical transaction sub-data in chronological order, so that the number of transactions contained in each historical transaction sub-data is greater than a transaction volume threshold;

[0057] Step 302: For each historical transaction sub-data, determine the risk probability of each dimension corresponding to the historical transaction sub-data;

[0058] Step 303: Determine the risk probability of each dimension corresponding to the customer relationship category as the average value of the risk probability of each dimension corresponding to the plurality of historical transaction sub-data.

[0059] In one embodiment, for each historical transaction sub-data, determining the risk probability of each dimension corresponding to the historical transaction sub-data includes:

[0060] Select the historical transaction data corresponding to each dimension from the historical transaction sub-data;

[0061] The proportion of risky transaction data in the historical transaction data corresponding to each dimension is determined as the risk probability of each dimension corresponding to the historical transaction sub-data.

[0062] In one embodiment, the method further comprises:

[0063] Setting a first threshold;

[0064] Using the risk probabilities corresponding to the various dimensions of the multiple historical transaction sub-data (obtained in steps 301 and 302) as risk probability samples corresponding to the various dimensions, and determining the variance and sample size corresponding to the various dimensions based on the risk probability samples corresponding to the various dimensions;

[0065] For each dimension, calculate the quotient of the square of the variance corresponding to the dimension and the number of samples corresponding to the dimension, and compare the quotient with the first threshold; if the quotient is greater than the first threshold, loop through the following steps until the quotient is less than or equal to the first threshold:

[0066] Obtain new transaction data for the customer relationship category; divide the new transaction data into multiple new transaction sub-data in chronological order, such that the number of transactions contained in each new transaction sub-data is greater than a transaction volume threshold; for each new transaction sub-data, determine the risk probability of the new transaction sub-data corresponding to the dimension, and use the risk probability of the new transaction sub-data corresponding to the dimension as a risk probability sample corresponding to the dimension; and based on all the obtained risk probability samples corresponding to the dimension, update the variance and the number of samples corresponding to the dimension;

[0067] The risk probability of each dimension corresponding to the customer relationship category is updated to the mean of all risk probability samples corresponding to each dimension obtained.

[0068] According to the weak law of large numbers, the first threshold can be determined as follows:

[0069] Set a tolerable probability error threshold and the probability that the tolerable probability error is greater than the probability error threshold;

[0070] The first threshold is determined as the product of the square of the tolerable probability error threshold and the probability that the tolerable probability error is greater than the probability error threshold.

[0071] In the embodiment of the present invention, Figure 4 As shown, step 104 determines the security relationship category based on the risk probability corresponding to each dimension, including:

[0072] Step 401: Determine a partial order of customer relationship categories based on the risk probabilities corresponding to each dimension. For any two customer relationship categories, the partial order can be used to determine whether the first of the two customer relationship categories is safer than the second.

[0073] Step 402: Determine, based on the partial order of the customer relationship categories, a plurality of maximal customer relationship categories of the partial order, wherein the maximal customer relationship categories are maximal elements of the partial order;

[0074] Step 403: Determine the multiple maximum customer relationship categories in the partial order as security relationship categories.

[0075] In one embodiment, based on the partial order of customer relationship categories, a plurality of maximum customer relationship categories of the partial order are determined, including:

[0076] 1. Initialize the pending customer relationship category set and the comparison customer relationship category set to all customer relationship categories, and initialize the maximum customer relationship category set to empty;

[0077] 2. Loop through the following steps until the set of pending customer relationship categories is empty:

[0078] 2.1 Select a customer relationship category A from the set of pending customer relationship categories, and compare the customer relationship category A with each customer relationship category B in the set of comparison customer relationship categories except the customer relationship category A;

[0079] 2.2 If customer relationship category B is safer than customer relationship category A, then delete customer relationship category A from the set of pending customer relationship categories; if customer relationship category A is safer than customer relationship category B, then delete customer relationship category B from the set of pending customer relationship categories and determine customer relationship category B as the secondary customer relationship category of customer relationship category A;

[0080] 2.3 If it is confirmed that every customer relationship category B in the comparison customer relationship categories except the customer relationship category is not safer than the customer relationship category A, then the customer relationship category A is added to the maximum customer relationship category set, and all sub-customer relationship categories of the customer relationship category A are deleted from the comparison customer relationship category set;

[0081] 3. Take the customer relationship category in the maximum customer relationship category set as the maximum customer relationship category of the partial order.

[0082] In the embodiment of the present invention, Figure 5 As shown, step 401 determines the partial order of customer relationship categories based on the risk probability corresponding to each dimension, including:

[0083] Step 501: For any two customer relationship categories, if, for any dimension, the risk probability of the first customer relationship category of the two customer relationship categories corresponding to the dimension is less than or equal to the risk probability of the second customer relationship category of the two customer relationship categories corresponding to the dimension, and the risk probability of the first customer relationship category corresponding to each dimension is less than or equal to a set threshold, then the first customer relationship category is determined to be safer than the second customer relationship category.

[0084] In the embodiment of the present invention, Figure 6 As shown, the method further includes:

[0085] Step 601: For each dimension, the maximum value of the risk probabilities of multiple maximum customer relationship categories corresponding to the dimension is determined as the threshold value corresponding to the dimension;

[0086] Step 602: For each other customer relationship category except the maximum customer relationship category, if, for each dimension, the risk probability of the other customer relationship category corresponding to the dimension is less than the threshold corresponding to the dimension, then the other customer relationship category is determined to be a less secure relationship category;

[0087] Step 603: Determine a transaction amount threshold corresponding to the secondary security relationship category based on the historical transaction data of the secondary security relationship category;

[0088] Step 604: For each customer of the bank, determine the customer's corresponding sub-safe transaction counterparty and the corresponding transaction amount threshold according to the sub-safe relationship category;

[0089] Step 605: Push the customer's corresponding second-safest counterparty information and the corresponding transaction amount threshold to the customer. After the customer confirms, upload the customer's confirmation information, the second-safest counterparty information and the corresponding transaction amount threshold to the blockchain;

[0090] Step 606: Before the customer's network signal strength falls below a predetermined value, information of multiple less secure transaction counterparties corresponding to the customer and corresponding transaction amount thresholds are sent to the customer's mobile terminal;

[0091] Step 607: When the network signal strength of the customer's mobile terminal is less than a predetermined value, the mobile terminal confirms whether to support the customer's current transaction based on the customer's current transaction data, the information of the less secure counterparty and the corresponding transaction amount threshold.

[0092] In the embodiment of the present invention, Figure 7 As shown, step 603 determines the transaction amount threshold corresponding to the secondary security relationship category based on the historical transaction data of the secondary security relationship category, including:

[0093] Step 701: Determine multiple discrete values ​​of transaction amounts;

[0094] Step 702: For each discrete value of transaction amount, determine the proportion of risk data in the historical transaction data when the transaction threshold is the discrete value of transaction amount;

[0095] Step 703: Select the maximum ratio that is smaller than the risk probability threshold from the multiple ratios, and determine the transaction amount discrete value corresponding to the selected ratio as the transaction amount threshold corresponding to the secondary security relationship category.

[0096] The acquisition, storage, use, and processing of data in this application's technical solution comply with relevant national laws and regulations.

[0097] The present invention also provides a blockchain-based secure transaction device, as described in the following embodiments. Since the principles of the device are similar to those of the blockchain-based secure transaction method, the implementation of the device can refer to the implementation of the blockchain-based secure transaction method, and the repeated parts will not be repeated here.

[0098] Figure 8 This is a structural diagram of a secure transaction device based on blockchain in an embodiment of the present invention. Figure 8 As shown, the blockchain-based secure transaction device includes:

[0099] A customer relationship determination module 02 is configured to determine a plurality of customer relationships based on the bank's historical transaction data, wherein the customer relationships include transaction parties and transaction counterparties;

[0100] Classification module 04, used to classify customer relationships and obtain multiple customer relationship categories;

[0101] Risk probability determination module 06, for determining, for each customer relationship category, the risk probability of each dimension corresponding to the customer relationship category based on the historical transaction data of the customer relationship category;

[0102] The security relationship category determination module 08 is used to determine the security relationship category according to the risk probability corresponding to each dimension;

[0103] A secure transaction counterparty determination module 10 is configured to determine, for each customer of the bank, the corresponding secure transaction counterparty according to the security relationship category;

[0104] The information push module 12 is used to push the information of the customer's corresponding security counterparty to the customer. After the customer confirms, the customer's confirmation information and the information of the security counterparty are uploaded to the blockchain;

[0105] The information sending module 14 is used to send the multiple secure transaction counterparty information corresponding to the customer to the customer's mobile terminal before the customer's network signal strength is less than a predetermined value; when the network signal strength of the customer's mobile terminal is less than the predetermined value, the mobile terminal confirms whether the transaction counterparty is a secure transaction counterparty based on the customer's current transaction data. If so, the mobile terminal supports the customer's current transaction.

[0106] In the embodiment of the present invention, the classification module 04 is specifically used to:

[0107] For each customer relationship, obtain historical transaction data of the transaction party of the customer relationship, historical transaction data of the transaction counterparty of the customer relationship, and transaction data between the transaction party and the transaction counterparty;

[0108] For each customer relationship, a first transaction vector for the customer relationship is determined based on the historical transaction data of the transaction parties of the customer relationship, wherein each component of the first transaction vector corresponds one-to-one to a transaction category of the bank, and the value of each component is equal to the number of transactions of the transaction category corresponding to the component in the historical transaction data of the transaction parties of the customer relationship;

[0109] For each customer relationship, a second transaction vector for the customer relationship is determined based on the historical transaction data of the counterparty of the customer relationship, wherein each component of the second transaction vector corresponds one-to-one to a transaction category of the bank, and the value of each component is equal to the number of transactions of the transaction category corresponding to the component in the historical transaction data of the counterparty of the customer relationship;

[0110] For each customer relationship, determine the primary transaction scenario corresponding to the customer relationship based on the transaction data between the transaction party and the transaction counterparty of the customer relationship;

[0111] Determining a distance function corresponding to the customer relationship based on the first transaction vector and the second transaction vector of the customer relationship, wherein the distance function can determine the distance between any two customer relationships;

[0112] According to the distance function corresponding to the customer relationship and the corresponding main transaction scenario, the customer relationship is classified to obtain multiple customer relationship categories.

[0113] In the embodiment of the present invention, the risk probability determination module 06 is specifically configured to:

[0114] Divide the historical transaction data of the customer relationship category into multiple historical transaction sub-data in chronological order, so that the number of transactions contained in each historical transaction sub-data is greater than the transaction volume threshold;

[0115] For each historical transaction sub-data, determine the risk probability of each dimension corresponding to the historical transaction sub-data;

[0116] The risk probability of each dimension corresponding to the customer relationship category is determined as the average of the risk probabilities of each dimension corresponding to the multiple historical transaction sub-data.

[0117] In the embodiment of the present invention, the security relationship category determination module 08 is specifically configured to:

[0118] Determine a partial order of customer relationship categories based on the risk probabilities corresponding to each dimension, wherein, for any two customer relationship categories, the partial order can be used to determine whether the first of the two customer relationship categories is safer than the second;

[0119] Determining, based on the partial order of the customer relationship categories, a plurality of maximal customer relationship categories of the partial order, wherein the maximal customer relationship categories are maximal elements of the partial order;

[0120] A plurality of maximal customer relationship categories in the partial order are determined as safe relationship categories.

[0121] In the embodiment of the present invention, the security relationship category determination module 08 is specifically configured to:

[0122] For any two customer relationship categories, if for any dimension, the risk probability of the first customer relationship category of the two customer relationship categories corresponding to the dimension is less than or equal to the risk probability of the second customer relationship category of the two customer relationship categories corresponding to the dimension, and the risk probability of the first customer relationship category corresponding to each dimension is less than or equal to the set threshold, then the first customer relationship category is determined to be safer than the second customer relationship category.

[0123] In an embodiment of the present invention, the following further comprises:

[0124] For each dimension, the maximum value of the risk probability of multiple extremely large customer relationship categories corresponding to the dimension is determined as the threshold corresponding to the dimension;

[0125] For each other customer relationship category except the extremely large customer relationship category, if, for each dimension, the risk probability of the other customer relationship category corresponding to the dimension is less than the threshold corresponding to the dimension, then the other customer relationship category is determined to be a sub-safe relationship category;

[0126] Determine the transaction amount threshold corresponding to the secondary security relationship category based on the historical transaction data of the secondary security relationship category;

[0127] For each customer of the bank, determine the customer's corresponding sub-safe counterparty and the corresponding transaction amount threshold based on the sub-safe relationship category;

[0128] Push the customer's corresponding second-safest counterparty information and the corresponding transaction amount threshold to the customer. After the customer confirms, upload the customer's confirmation information, the second-safest counterparty information and the corresponding transaction amount threshold to the blockchain;

[0129] Before the customer's network signal strength falls below a predetermined value, information of multiple less secure counterparties corresponding to the customer and corresponding transaction amount thresholds are sent to the customer's mobile terminal;

[0130] When the network signal strength of the customer's mobile terminal is less than a predetermined value, the mobile terminal confirms whether to support the customer's current transaction based on the customer's current transaction data, the information of the less secure counterparty and the corresponding transaction amount threshold.

[0131] In an embodiment of the present invention, determining a transaction amount threshold corresponding to a secondary security relationship category based on historical transaction data of the secondary security relationship category includes:

[0132] Determine multiple discrete values ​​of transaction amounts;

[0133] For each discrete value of transaction amount, determine the proportion of risk data in the historical transaction data when the transaction threshold is the discrete value of transaction amount;

[0134] A maximum ratio smaller than the risk probability threshold is selected from the multiple ratios, and a discrete value of the transaction amount corresponding to the selected ratio is determined as the transaction amount threshold corresponding to the secondary security relationship category.

[0135] An embodiment of the present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned blockchain-based secure transaction method when executing the computer program.

[0136] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned blockchain-based secure transaction method.

[0137] An embodiment of the present invention also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the above-mentioned blockchain-based secure transaction method.

[0138] In the embodiment of the present invention, compared with the technical solution in the prior art in which when the network signal is weak, the customer's mobile terminal may not be able to authenticate due to the weak network signal, and thus cannot conduct transactions, the present invention determines multiple customer relationships based on the bank's historical transaction data, wherein the customer relationships include transaction parties and transaction counterparties; classifies the customer relationships to obtain multiple customer relationship categories; for each customer relationship category, determines the risk probability of each dimension corresponding to the customer relationship category based on the historical transaction data of the customer relationship category; determines the security relationship category based on the risk probability corresponding to each dimension; for each customer of the bank, determines the customer's corresponding security transaction counterparty based on the security relationship category; and The information of the corresponding secure transaction counterparty is pushed to the customer, and after the customer confirms, the customer's confirmation information and the information of the secure transaction counterparty are uploaded to the blockchain; before the customer's network signal strength is less than a predetermined value, the multiple secure transaction counterparty information corresponding to the customer is sent to the customer's mobile terminal; when the network signal strength of the customer's mobile terminal is less than a predetermined value, the mobile terminal confirms whether the transaction counterparty is a secure transaction counterparty based on the customer's current transaction data. If so, the mobile terminal supports the customer's current transaction. Through the above series of operations, the present invention provides the secure transaction counterparty information to the mobile terminal. When the signal is weak, transactions can be directly performed based on the secure transaction counterparty without waiting for the network signal to become stronger.

[0139] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0140] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0143] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A secure transaction method based on blockchain, characterized in that: include: determining a plurality of customer relationships based on the bank's historical transaction data, wherein the customer relationships include transaction parties and transaction counterparties; Classify customer relationships and obtain multiple customer relationship categories; For each customer relationship category, determine the risk probability of each dimension corresponding to that customer relationship category based on its historical transaction data; Determine the security relationship category based on the risk probability corresponding to each dimension; For each customer of the bank, determine the corresponding security counterparty based on the security relationship category; Push the customer's corresponding security counterparty information to the customer. After the customer confirms, upload the customer's confirmation information and the security counterparty information to the blockchain; Before the customer's network signal strength falls below a predetermined value, the information of multiple secure trading counterparties corresponding to the customer is sent to the customer's mobile terminal. When the network signal strength of the customer's mobile terminal falls below the predetermined value, the mobile terminal verifies whether the trading counterparty is a secure trading counterparty based on the customer's current transaction data. If so, the mobile terminal supports the customer's current transaction. Among them, the security relationship category is determined according to the risk probability corresponding to each dimension, including: Determine a partial order of customer relationship categories based on the risk probabilities corresponding to each dimension, wherein, for any two customer relationship categories, the partial order can be used to determine whether the first of the two customer relationship categories is safer than the second; Determining, based on the partial order of the customer relationship categories, a plurality of maximal customer relationship categories of the partial order, wherein the maximal customer relationship categories are maximal elements of the partial order; determining a plurality of maximal customer relationship categories of the partial order as safe relationship categories; Also includes: For each dimension, the maximum value of the risk probability of multiple extremely large customer relationship categories corresponding to the dimension is determined as the threshold corresponding to the dimension; For each other customer relationship category except the extremely large customer relationship category, if, for each dimension, the risk probability of the other customer relationship category corresponding to the dimension is less than the threshold corresponding to the dimension, then the other customer relationship category is determined to be a sub-safe relationship category; Determine the transaction amount threshold corresponding to the secondary security relationship category based on the historical transaction data of the secondary security relationship category; For each customer of the bank, determine the customer's corresponding sub-safe counterparty and the corresponding transaction amount threshold based on the sub-safe relationship category; Push the customer's corresponding second-safest counterparty information and the corresponding transaction amount threshold to the customer. After the customer confirms, upload the customer's confirmation information, the second-safest counterparty information and the corresponding transaction amount threshold to the blockchain; Before the customer's network signal strength falls below a predetermined value, information of multiple less secure counterparties corresponding to the customer and corresponding transaction amount thresholds are sent to the customer's mobile terminal; When the network signal strength of the customer's mobile terminal is less than a predetermined value, the mobile terminal confirms whether to support the customer's current transaction based on the customer's current transaction data, the information of the less secure counterparty and the corresponding transaction amount threshold.

2. The method according to claim 1, wherein Categorize customer relationships and obtain multiple customer relationship categories, including: For each customer relationship, obtain historical transaction data of the transaction party of the customer relationship, historical transaction data of the transaction counterparty of the customer relationship, and transaction data between the transaction party and the transaction counterparty; For each customer relationship, a first transaction vector for the customer relationship is determined based on the historical transaction data of the transaction parties of the customer relationship, wherein each component of the first transaction vector corresponds one-to-one to a transaction category of the bank, and the value of each component is equal to the number of transactions of the transaction category corresponding to the component in the historical transaction data of the transaction parties of the customer relationship; For each customer relationship, a second transaction vector for the customer relationship is determined based on the historical transaction data of the counterparty of the customer relationship, wherein each component of the second transaction vector corresponds one-to-one to a transaction category of the bank, and the value of each component is equal to the number of transactions of the transaction category corresponding to the component in the historical transaction data of the counterparty of the customer relationship; For each customer relationship, determine the primary transaction scenario corresponding to the customer relationship based on the transaction data between the transaction party and the transaction counterparty of the customer relationship; Determining a distance function corresponding to the customer relationship based on the first transaction vector and the second transaction vector of the customer relationship, wherein the distance function can determine the distance between any two customer relationships; According to the distance function corresponding to the customer relationship and the corresponding main transaction scenario, the customer relationship is classified to obtain multiple customer relationship categories.

3. The method according to claim 1, wherein For each customer relationship category, determine the risk probability of each dimension corresponding to that customer relationship category based on its historical transaction data, including: Divide the historical transaction data of the customer relationship category into multiple historical transaction sub-data in chronological order, so that the number of transactions contained in each historical transaction sub-data is greater than the transaction volume threshold; For each historical transaction sub-data, determine the risk probability of each dimension corresponding to the historical transaction sub-data; The risk probability of each dimension corresponding to the customer relationship category is determined as the average of the risk probabilities of each dimension corresponding to the multiple historical transaction sub-data.

4. The method according to claim 1, wherein Determine the partial order of customer relationship categories based on the risk probability corresponding to each dimension, including: For any two customer relationship categories, if for any dimension, the risk probability of the first customer relationship category of the two customer relationship categories corresponding to the dimension is less than or equal to the risk probability of the second customer relationship category of the two customer relationship categories corresponding to the dimension, and the risk probability of the first customer relationship category corresponding to each dimension is less than or equal to the set threshold, then the first customer relationship category is determined to be safer than the second customer relationship category.

5. The method according to claim 1, wherein Based on the historical transaction data of the secondary security relationship category, the transaction amount threshold corresponding to the secondary security relationship category is determined, including: Determine multiple discrete values ​​of transaction amounts; For each discrete value of transaction amount, determine the proportion of risk data in the historical transaction data when the transaction threshold is the discrete value of transaction amount; A maximum ratio smaller than the risk probability threshold is selected from the multiple ratios, and a discrete value of the transaction amount corresponding to the selected ratio is determined as the transaction amount threshold corresponding to the secondary security relationship category.

6. A secure transaction device based on blockchain, characterized in that: include: A customer relationship determination module, configured to determine a plurality of customer relationships based on the bank's historical transaction data, wherein the customer relationships include transaction parties and transaction counterparties; A classification module is used to classify customer relationships and obtain multiple customer relationship categories; A risk probability determination module is used to determine, for each customer relationship category, the risk probability of each dimension corresponding to the customer relationship category based on the historical transaction data of the customer relationship category; A security relationship category determination module is used to determine the security relationship category based on the risk probability corresponding to each dimension; A security counterparty determination module is used to determine, for each customer of the bank, the security counterparty corresponding to the customer based on the security relationship category; An information push module is used to push the information of the customer's corresponding security counterparty to the customer. After the customer confirms, the customer's confirmation information and the information of the security counterparty are uploaded to the blockchain; An information delivery module is configured to deliver information on multiple secure trading counterparties corresponding to a customer to the customer's mobile terminal before the customer's network signal strength falls below a predetermined value. When the network signal strength of the customer's mobile terminal falls below the predetermined value, the mobile terminal determines whether the trading counterparty is a secure trading counterparty based on the customer's current transaction data. If so, the mobile terminal supports the customer's current transaction. The security relationship category determination module is specifically used to: Determine a partial order of customer relationship categories based on the risk probabilities corresponding to each dimension, wherein, for any two customer relationship categories, the partial order can be used to determine whether the first of the two customer relationship categories is safer than the second; Determining, based on the partial order of the customer relationship categories, a plurality of maximal customer relationship categories of the partial order, wherein the maximal customer relationship categories are maximal elements of the partial order; determining a plurality of maximal customer relationship categories of the partial order as safe relationship categories; Also includes: For each dimension, the maximum value of the risk probability of multiple extremely large customer relationship categories corresponding to the dimension is determined as the threshold corresponding to the dimension; For each other customer relationship category except the extremely large customer relationship category, if, for each dimension, the risk probability of the other customer relationship category corresponding to the dimension is less than the threshold corresponding to the dimension, then the other customer relationship category is determined to be a sub-safe relationship category; Determine the transaction amount threshold corresponding to the secondary security relationship category based on the historical transaction data of the secondary security relationship category; For each customer of the bank, determine the customer's corresponding sub-safe counterparty and the corresponding transaction amount threshold based on the sub-safe relationship category; Push the customer's corresponding second-safest counterparty information and the corresponding transaction amount threshold to the customer. After the customer confirms, upload the customer's confirmation information, the second-safest counterparty information and the corresponding transaction amount threshold to the blockchain; Before the customer's network signal strength falls below a predetermined value, information of multiple less secure counterparties corresponding to the customer and corresponding transaction amount thresholds are sent to the customer's mobile terminal; When the network signal strength of the customer's mobile terminal is less than a predetermined value, the mobile terminal confirms whether to support the customer's current transaction based on the customer's current transaction data, the information of the less secure counterparty and the corresponding transaction amount threshold.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

9. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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