Transaction supervision method and device based on block chain, equipment and storage medium

Abstract

The invention discloses a block chain-based transaction supervision method, apparatus and device, and a storage medium, and relates to the technical field of block chains. The transaction supervision method based on the block chain comprises the following steps: through an intelligent contract in a supervision chain, obtaining transaction data uploaded to the supervision chain by at least one supervision participation mechanism; the smart contract is constructed and operated in a supervision chain according to a transaction supervision rule; extracting supervision items associated with risk early warning conditions in the smart contract from the transaction data; based on the supervision item, determining whether the transaction data triggers a risk early warning condition in the smart contract; and under the condition that the risk early warning condition in the smart contract is triggered, initiating a transaction risk alarm. According to the technical scheme of the embodiment of the invention, the transaction data transparency and the transaction supervision efficiency are improved.

Description

Technical Field

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

[0002] With the continuous development of the financial market, financial transactions often need to be supervised for safety in order to ensure that they proceed normally. At present, joint supervision by multiple regulatory agencies has become an important means of maintaining financial stability and security.

[0003] At present, in the process of supervision involving multiple regulatory agencies, regulators are required to manually review and judge each transaction data. The above traditional supervision methods have problems such as data opacity and low supervision efficiency, and are difficult to cope with the complex and changing financial market environment. Summary of the invention

[0004] The present invention provides a transaction supervision method, device, equipment and storage medium based on blockchain to solve the problems of data opacity and low supervision efficiency in traditional supervision methods.

[0005] According to one aspect of the present invention, a transaction supervision method based on blockchain is provided, which is applied to a node in a supervision chain, wherein the supervision chain is constructed by multiple supervision participating institutions as nodes, and comprises:

[0006] Obtaining transaction data uploaded to the supervision chain by at least one supervision participating institution through a smart contract in the supervision chain; the smart contract is constructed and runs in the supervision chain according to transaction supervision rules;

[0007] Extracting regulatory items associated with risk warning conditions in the smart contract from the transaction data;

[0008] Based on the supervision item, determining whether the transaction data triggers a risk warning condition in the smart contract;

[0009] When the risk warning conditions in the smart contract are triggered, a transaction risk alert is initiated.

[0010] According to another aspect of the present invention, there is provided a transaction supervision device based on blockchain, which is configured as a node in a supervision chain, wherein the supervision chain is constructed by multiple supervision participating institutions as nodes, and includes:

[0011] A transaction data acquisition module, used to acquire transaction data uploaded to the supervision chain by at least one supervision participating institution through a smart contract in the supervision chain; the smart contract is constructed according to transaction supervision rules and runs in the supervision chain;

[0012] A regulatory item extraction module, used to extract regulatory items associated with risk warning conditions in smart contracts from the transaction data;

[0013] A risk identification module, used to determine whether the transaction data triggers a risk warning condition in the smart contract based on the supervision item;

[0014] The risk alert initiation module is used to initiate a transaction risk alert when the risk warning conditions in the smart contract are triggered.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the blockchain-based transaction supervision method described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the blockchain-based transaction supervision method described in any embodiment of the present invention when executed.

[0020] According to another aspect of the present invention, a computer program product is provided, including a computer program, which, when executed by a processor, implements the blockchain-based transaction supervision method of any embodiment of the present disclosure.

[0021] According to the technical solution of the embodiment of the present invention, a node in the supervision chain obtains transaction data uploaded to the supervision chain by at least one supervision participating institution through a smart contract in the supervision chain, and extracts supervision items associated with risk warning conditions in the smart contract from the transaction data, and then determines whether the transaction data triggers the risk warning conditions in the smart contract based on the supervision items. When the risk warning conditions in the smart contract are triggered, a transaction risk alarm is initiated, which solves the problems of data opacity and low supervision efficiency in traditional supervision methods, improves the transparency of transaction data, and improves supervision efficiency.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 is a flowchart of a transaction supervision method based on blockchain provided according to Embodiment 1 of the present invention;

[0025] Figure 2 is a flowchart of a transaction supervision method based on blockchain provided according to Embodiment 2 of the present invention;

[0026] Figure 3 is a flowchart of a transaction supervision method based on blockchain provided according to Embodiment 3 of the present invention;

[0027] Figure 4 1 is a schematic diagram of the structure of a transaction supervision device based on blockchain provided according to Embodiment 4 of the present invention;

[0028] Figure 5 It is a structural diagram of an electronic device that implements the blockchain-based transaction supervision method of an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Embodiment 1

[0032] Figure 1 A flowchart of a transaction supervision method based on blockchain is provided for the first embodiment of the present invention. This embodiment is applicable to the situation of multi-party transaction supervision based on blockchain technology. The method can be executed by a transaction supervision device based on blockchain. The transaction supervision device based on blockchain can be implemented in the form of hardware and / or software. The transaction supervision device based on blockchain can be configured in various general computing devices, for example, nodes in the supervision chain. The supervision chain is constructed by multiple supervision participating institutions as nodes. Figure 1 As shown, the method includes:

[0033] S110. Obtain transaction data uploaded to the supervision chain by at least one participating supervision institution through a smart contract in the supervision chain; the smart contract is constructed and runs in the supervision chain according to transaction supervision rules.

[0034] The chain of supervision is constructed based on multiple regulatory participating institutions, that is, each regulatory participating institution is a node in the chain of supervision, and they jointly maintain the integrity and security of the chain of supervision. For example, the regulatory participating institution can be a bank, that is, multiple banks serve as blockchain nodes, together forming a chain of supervision.

[0035] Each regulatory participant uploads transaction data to the chain of custody in real time, and uses blockchain technology to achieve sharing and transparency of transaction data. For example, the regulatory participants are all banks, and each bank uploads transaction data to the chain of custody in real time. Through blockchain technology, transaction data can be shared and transparent in real time. Each bank can view and verify the transaction data of other banks in real time to determine the authenticity and consistency of the transaction data. External regulators can also access transaction data on the chain of custody through the set interface to understand the operating status and risk status of each bank.

[0036] In addition, when building a chain of supervision, one or more regulatory participating institutions can be selected as initial nodes to build a blockchain-based chain of supervision. Each regulatory participating institution needs to install and run the corresponding blockchain software to ensure that it can participate in the verification and maintenance of on-chain data. The authority principles of each node in the chain of supervision are as follows:

[0037] The regulatory participant institutional user of the initial node has the authority to access the corresponding channels of other nodes. For example, each node’s corresponding channel includes two channels, a and b. Channel a is used to view the data uploaded by the node to the chain of custody, and channel b is used to view and modify the data uploaded by the node to the chain of custody.

[0038] Regulatory participating institutional users who are not initial nodes only have the authority to the b channel corresponding to this node.

[0039] Smart contracts are used to monitor the risks of transaction data. Smart contracts are constructed based on the regulatory transaction rules uploaded by the regulatory agency or regulatory participating institutions, and run in the regulatory chain. Smart contracts include at least one risk warning condition. When the transaction data triggers one or more of the risk warning conditions, it is determined that the current transaction data is at risk.

[0040] In the embodiment of the present invention, the smart contract adopts a timer mechanism to periodically obtain transaction data uploaded to the supervision chain by at least one supervision participating institution by calling the application programming interface (API) provided by the supervision chain. Among them, transaction data refers to various data generated in the process of financial transactions, such as transaction amount, transaction time, transaction parties (transaction subject and transaction counterparty), and transaction frequency of the current transaction subject within a set time period.

[0041] In addition, for data access to some traditional financial systems, there may be problems with different data formats, and middleware is needed to convert the format of transaction data so that the transaction data can be understood and processed by smart contracts.

[0042] S120. Extract regulatory items associated with risk warning conditions in the smart contract from the transaction data.

[0043] The risk warning conditions in the smart contract may be related to one or more items of transaction data. For example, the risk warning condition is that the amount of a single transaction is greater than the amount threshold, and the regulatory item is the transaction amount. For another example, the risk warning condition is that the credit rating of the counterparty is lower than the set threshold, and the regulatory item is the counterparty.

[0044] In an embodiment of the present invention, before conducting a transaction risk assessment based on the risk warning conditions in the smart contract, it is necessary to extract the regulatory items associated with the risk warning conditions in the smart contract from the transaction data. For example, the smart contract includes two risk warning conditions, namely, the amount of a single transaction is greater than the amount threshold, and the transaction frequency of the same current transaction subject within a set time period is greater than the frequency threshold. It is necessary to extract the transaction amount and the transaction frequency of the current transaction subject within the set time period from the transaction data as regulatory items.

[0045] S130. Based on the supervision items, determine whether the transaction data triggers the risk warning conditions in the smart contract.

[0046] In an embodiment of the present invention, based on the supervision item, it is determined whether the transaction data triggers the risk warning condition in the smart contract. Specifically, the supervision item and the threshold associated with the supervision item in the risk warning condition can be compared to determine whether the risk warning condition is triggered. It is also possible to obtain historical transaction information associated with the supervision item from the outside based on the supervision item, and determine whether the risk warning condition is triggered based on the historical transaction information. In addition to triggering the risk warning condition, the smart contract can also take corresponding measures to restrict the continuation of the transaction to which the transaction data that triggers the risk warning condition belongs. For example, specific measures may include restricting the flow of funds, etc.

[0047] In a specific example, the risk warning condition is that the amount of a single transaction is greater than the amount threshold, and the supervision item is the transaction amount. The transaction amount is compared with the amount threshold in the risk warning condition. If it is greater than the amount threshold, the risk warning condition is triggered.

[0048] In another specific example, the risk warning condition is that the credit rating of the counterparty is lower than the set threshold, and the regulatory item is the counterparty. It is necessary to extract the counterparty from the transaction data and read the counterparty's historical transaction data from the counterparty association database. Further, the credit rating of the counterparty is determined based on the historical transaction data, and the credit rating is compared with the set threshold. If the credit rating is lower than the set threshold, the risk warning condition is triggered.

[0049] Optionally, the regulatory item associated with the risk warning condition is the transaction amount;

[0050] Based on the regulatory items, determine whether the transaction data triggers the risk warning conditions in the smart contract, including:

[0051] The transaction amount is compared with the amount threshold included in the risk warning conditions. If the transaction amount is greater than the risk threshold, it is determined whether the transaction data triggers the risk warning conditions in the smart contract.

[0052] In this optional embodiment, the regulatory item associated with the risk warning condition is limited to the transaction amount. Accordingly, based on the regulatory item, the specific method for determining whether the transaction data triggers the risk warning condition in the smart contract is: compare the transaction amount with the amount threshold included in the risk warning condition, and determine whether the transaction data triggers the risk warning condition in the smart contract when the transaction amount is greater than the risk threshold. By setting the amount threshold, transaction risk assessment can be performed in the transaction amount dimension, and risk warnings can be issued when the transaction amount exceeds the set threshold to prompt regulators to review transaction data and ensure transaction security.

[0053] S140. When the risk warning conditions in the smart contract are triggered, a transaction risk alert is initiated.

[0054] In an embodiment of the present invention, when a risk warning condition in a smart contract is triggered, a transaction risk alert is initiated. Specifically, the smart contract can indicate that the transaction data is at risk by sending a risk event associated with the transaction data on the custody chain. The risk event can be captured by a monitoring agency or other application connected to the custody chain.

[0055] Smart contracts can also be integrated with external notification services to send notification messages by calling the API provided by the external notification service. The notification messages can be diverse, for example, they can be notified to people related to transaction risks via email, SMS or other instant messaging tools.

[0056] In addition, smart contracts need to store risk assessment results and transaction data in the custody chain to ensure the immutability and traceability of the assessment results. Among them, transaction data can be stored in blocks as key-value pairs or characteristic data structures.

[0057] By storing risk assessment results and transaction data in the chain of custody, smart contracts can provide complete transaction data and risk monitoring history records to meet the regulatory and audit needs of regulatory agencies, allowing auditors to trace the process and causes of risky transactions.

[0058] According to the technical solution of the embodiment of the present invention, a node in the supervision chain obtains transaction data uploaded to the supervision chain by at least one supervision participating institution through a smart contract in the supervision chain, and extracts supervision items associated with risk warning conditions in the smart contract from the transaction data, and then determines whether the transaction data triggers the risk warning conditions in the smart contract based on the supervision items. When the risk warning conditions in the smart contract are triggered, a transaction risk alarm is initiated, which solves the problems of data opacity and low supervision efficiency in traditional supervision methods, improves the transparency of transaction data, and improves supervision efficiency.

[0059] Embodiment 2

[0060] Figure 2 This is a flowchart of a blockchain-based transaction supervision method provided in Embodiment 2 of the present invention. This embodiment further refines the above embodiment and provides specific steps for determining whether transaction data triggers risk warning conditions in the smart contract based on supervision items. Figure 2 As shown, the method includes:

[0061] S210. Obtain transaction data uploaded to the supervision chain by at least one participating supervision institution through a smart contract in the supervision chain; the smart contract is constructed and runs in the supervision chain according to transaction supervision rules.

[0062] S220. Extract the transaction counterparty from the transaction data.

[0063] In an embodiment of the present invention, after reading the transaction data uploaded by the regulatory participating institution, the counterparty corresponding to the transaction subject to which the current transaction data belongs is extracted from the transaction data.

[0064] S230. Obtain historical credit data of the counterparty through the oracle.

[0065] In an embodiment of the present invention, historical credit data of a counterparty is obtained from the outside through an oracle. Exemplarily, the historical credit data of a counterparty is read from an external database through an oracle according to a unique representation of the counterparty. The historical credit data may include historical credit ratings and historical transaction behaviors, etc., which can characterize the credit status of the counterparty.

[0066] S240. Determine the credit rating of the counterparty based on historical credit data, and determine whether the transaction data triggers risk warning conditions in the smart contract based on the credit rating.

[0067] In the embodiment of the present invention, the credit rating of the counterparty is calculated based on the historical credit data obtained above, and whether the transaction data triggers the risk warning condition in the smart contract is determined based on the credit rating. For example, it is determined whether the credit rating is lower than the level threshold in the risk warning condition. If so, the risk warning condition is confirmed to be triggered, which can remind relevant personnel that the current counterparty has risks and may cause economic losses, improve the timeliness and comprehensiveness of supervision, and reduce transaction risks.

[0068] S250. When the risk warning conditions in the smart contract are triggered, a transaction risk alert is initiated.

[0069] Optionally, in the embodiment of the present invention, the following further comprises:

[0070] Read the transaction data uploaded by the regulatory participating institutions to be assessed in the chain of custody during the unit assessment period;

[0071] Clustering the transaction data using a clustering algorithm to obtain at least one cluster;

[0072] Based on the amount of transaction data contained in each cluster, the transaction data is risk assessed.

[0073] In addition to using smart contracts to conduct risk assessments on transaction data uploaded by regulatory participating institutions, big data analysis and machine learning technologies are also used to assess transaction risks. Once potential transaction risks are discovered, early warning information can be immediately sent to regulatory agencies to help them take timely measures to deal with risks.

[0074] In this optional embodiment, the transaction data uploaded by the regulatory participating institution to be evaluated in the regulatory chain within the unit evaluation period is read, wherein the regulatory participating institution to be evaluated may be a bank, and the unit evaluation period may be adjusted according to the actual risk assessment requirements, for example, it may be 1 hour. Further, the acquired transaction data is clustered by a clustering algorithm to obtain at least one cluster. Finally, based on the number of transaction data contained in each cluster, abnormal patterns in the transaction data are identified, and risk assessment of the transaction data is performed. For example, if the number of transaction data contained in the cluster is less than a quantity threshold, for example, the quantity threshold is 2, then it is determined that the transaction data in the cluster is isolated and may be an abnormal transaction.

[0075] By clustering the transaction data of the regulatory institutions to be assessed over a period of time, abnormal transaction data can be quickly identified and risk assessment can be achieved with a smaller amount of calculation.

[0076] Optionally, in the embodiment of the present invention, the following further comprises:

[0077] Read the transaction data uploaded by the regulatory participating institutions to be assessed in the chain of custody during the unit assessment period;

[0078] The transaction data is subject to risk assessment using a pre-trained risk assessment model; the risk assessment model is trained using historical transaction data and corresponding risk labels.

[0079] In this optional embodiment, the transaction data uploaded by the regulatory participating institution to be evaluated in the chain of supervision within the unit evaluation period is read, and then the transaction data is risk evaluated by a pre-trained risk assessment model, wherein the risk assessment model is trained using historical transaction data and corresponding risk labels.

[0080] In a specific example, the risk assessment model is obtained by training an initial classification model such as a logistic regression model or a support vector machine using labeled historical transaction data. For example, transaction data that has defaulted in the past can be marked as high risk, and transaction data that has not defaulted can be marked as low risk. The initial classification model such as a logistic regression model or a support vector machine is trained to obtain a risk assessment model. Subsequently, the transaction data is input into the risk assessment model to obtain the risk category.

[0081] By training the initial classification model to obtain a risk assessment model, and then using the risk assessment model to conduct risk assessment on transaction data in the regulatory chain, early warnings can be sent to regulatory agencies in a timely manner to improve transaction security.

[0082] In another specific example, the risk assessment model is obtained by training the initial prediction model using labeled historical transaction data. Based on the transaction data in the chain of custody, the risk level in the future can be predicted. If the predicted risk level is high, the supervision can be strengthened to avoid abnormal transactions.

[0083] Optionally, in the embodiment of the present invention, the following further comprises:

[0084] Read the transaction data uploaded by the regulatory participating institutions to be assessed in the chain of custody during the unit assessment period;

[0085] Extract a set amount of risk factor data from transaction data through principal component analysis method;

[0086] Based on the risk factor data, risk assessment is performed on transaction data.

[0087] In the embodiment of the present invention, the transaction data uploaded by the regulatory participating institution to be evaluated in the regulatory chain within the unit evaluation period is read, and then a set number of risk factor data is extracted from the transaction data through the principal component analysis method, and finally the transaction data is risk assessed based on the risk factor data. For example, the transaction data includes 5 items of data, and 2 risk factor data are extracted from the transaction data through the principal component analysis method, and the transaction data can be risk assessed based on these two risk factor data. By simplifying a large number of transaction data into several main risk factor data, it is easy to intuitively understand and analyze the risk of transaction data.

[0088] The technical solution of the embodiment of the present invention obtains transaction data uploaded to the supervision chain by at least one supervision participating institution through the smart contract in the supervision chain, extracts the counterparty from the transaction data, obtains the historical credit data of the counterparty through the oracle, determines the credit rating of the counterparty based on the historical credit data, and determines whether the transaction data triggers the risk warning conditions in the smart contract based on the credit rating. When the risk warning conditions in the smart contract are triggered, a transaction risk alarm is initiated, which reduces the supervision cost and improves the data supervision efficiency.

[0089] Embodiment 3

[0090] Figure 3 This is a flowchart of a blockchain-based transaction supervision method provided in Embodiment 3 of the present invention. This embodiment further refines the above embodiment and provides specific steps for determining whether transaction data triggers risk warning conditions in the smart contract based on supervision items. Figure 3 As shown, the method includes:

[0091] S310. Obtain transaction data uploaded to the supervision chain by at least one participating supervision institution through a smart contract in the supervision chain; the smart contract is constructed and runs in the supervision chain according to transaction supervision rules.

[0092] S320: extract the transaction frequency of the current transaction subject within a set time period from the transaction data.

[0093] In an embodiment of the present invention, after reading the transaction data uploaded by the regulatory participating institution, the transaction frequency of the current transaction subject within a set time period is extracted from the transaction data.

[0094] S330: Compare the transaction frequency of the current transaction subject within the set time period with the frequency threshold included in the risk warning condition. When the transaction frequency is greater than the frequency threshold, determine whether the transaction data triggers the risk warning condition in the smart contract.

[0095] In an embodiment of the present invention, the transaction frequency of the current transaction subject within a set time period is compared with the frequency threshold included in the risk warning condition. When the transaction frequency is greater than the frequency threshold, it is determined whether the transaction data triggers the risk warning condition in the smart contract. For example, if the same transaction subject initiates more than 10 transactions within one minute, the risk warning condition in the smart contract is triggered. By setting the frequency threshold, transaction risk assessment can be performed in the transaction frequency dimension, and risk warnings can be issued when the transaction frequency exceeds the set threshold to prompt regulators to review transaction data and ensure transaction security.

[0096] S340. Initiate a transaction risk alert when the risk warning conditions in the smart contract are triggered.

[0097] The technical solution of the embodiment of the present invention obtains transaction data uploaded to the supervision chain by at least one supervision participating institution through the smart contract in the supervision chain, and extracts the transaction frequency of the current transaction subject within a set period of time from the transaction data, and compares the transaction frequency of the current transaction subject within the set period of time with the frequency threshold included in the risk warning condition. When the transaction frequency is greater than the frequency threshold, it is determined whether the transaction data triggers the risk warning condition in the smart contract. When the risk warning condition in the smart contract is triggered, a transaction risk alarm is initiated, which reduces the supervision cost and improves the data supervision efficiency.

[0098] Embodiment 4

[0099] Figure 4 This is a schematic diagram of the structure of a blockchain-based transaction supervision device provided in Embodiment 4 of the present invention. Figure 4 As shown, the device comprises:

[0100] The transaction data acquisition module 410 is used to acquire the transaction data uploaded to the custody chain by at least one regulatory participating institution through a smart contract in the custody chain; the smart contract is constructed according to the transaction supervision rules and runs in the custody chain;

[0101] A regulatory item extraction module 420, configured to extract regulatory items associated with risk warning conditions in the smart contract from the transaction data;

[0102] A risk identification module 430, configured to determine whether the transaction data triggers a risk warning condition in the smart contract based on the supervision item;

[0103] The risk alert initiation module 440 is used to initiate a transaction risk alert when the risk warning condition in the smart contract is triggered.

[0104] According to the technical solution of the embodiment of the present invention, a node in the supervision chain obtains transaction data uploaded to the supervision chain by at least one supervision participating institution through a smart contract in the supervision chain, and extracts supervision items associated with risk warning conditions in the smart contract from the transaction data, and then determines whether the transaction data triggers the risk warning conditions in the smart contract based on the supervision items. When the risk warning conditions in the smart contract are triggered, a transaction risk alarm is initiated, which solves the problems of data opacity and low supervision efficiency in traditional supervision methods, improves the transparency of transaction data, and improves supervision efficiency.

[0105] Optionally, the supervision item associated with the risk warning condition is the transaction amount;

[0106] The risk identification module 430 is specifically used for:

[0107] The transaction amount is compared with the amount threshold included in the risk warning condition. If the transaction amount is greater than the risk threshold, it is determined whether the transaction data triggers the risk warning condition in the smart contract.

[0108] Optionally, the regulatory item associated with the risk warning condition is a counterparty;

[0109] The risk identification module 430 is further specifically used for:

[0110] Obtain historical credit data of the counterparty through the oracle;

[0111] Based on the historical credit data, the credit rating of the counterparty is determined, and based on the credit rating, it is determined whether the transaction data triggers the risk warning condition in the smart contract.

[0112] Optionally, the supervision item associated with the risk warning condition is the transaction frequency of the current transaction subject within a set time period;

[0113] The risk identification module 430 is further specifically used for:

[0114] The transaction frequency of the current transaction subject within the set time period is compared with the frequency threshold included in the risk warning condition. When the transaction frequency is greater than the frequency threshold, it is determined whether the transaction data triggers the risk warning condition in the smart contract.

[0115] Optionally, the blockchain-based transaction supervision device also includes:

[0116] The first data reading module is used to read the transaction data uploaded by the supervision participating institution to be evaluated in the supervision chain during the unit evaluation period;

[0117] A clustering module, used for clustering the transaction data by a clustering algorithm to obtain at least one cluster;

[0118] The first risk assessment module is used to perform risk assessment on the transaction data based on the amount of transaction data included in each cluster.

[0119] Optionally, the blockchain-based transaction supervision device also includes:

[0120] The second data reading module is used to read the transaction data uploaded by the supervision participating institution to be evaluated in the supervision chain during the unit evaluation period;

[0121] The second risk assessment module is used to perform risk assessment on the transaction data using a pre-trained risk assessment model; the risk assessment model is trained using historical transaction data and corresponding risk labels.

[0122] Optionally, the blockchain-based transaction supervision device also includes:

[0123] The third data reading module is used to read the transaction data uploaded by the supervision participating institution to be evaluated in the supervision chain during the unit evaluation period;

[0124] A principal component analysis module, used to extract a set amount of risk factor data from the transaction data by using a principal component analysis method;

[0125] The third risk assessment module is used to perform risk assessment on the transaction data based on the risk factor data.

[0126] The blockchain-based transaction supervision device provided in the embodiment of the present invention can execute the blockchain-based transaction supervision method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0127] In the technical solution of the present invention, the collected information is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0128] Embodiment 5

[0129] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium and a computer program product.

[0130] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, applicators, blade applicators, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0131] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0132] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0133] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a blockchain-based transaction supervision method.

[0134] In some embodiments, the blockchain-based transaction supervision method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the blockchain-based transaction supervision method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the blockchain-based transaction supervision method in any other appropriate manner (e.g., by means of firmware).

[0135] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0136] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or application.

[0137] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0138] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0139] The systems and techniques described herein may be implemented in a computing system that includes a backend component (e.g., as a data applier), or a computing system that includes a middleware component (e.g., an application applier), or a computing system that includes a frontend component (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0140] A computing system may include a client and an application server. The client and the application server are generally remote from each other and usually interact through a communication network. The relationship between the client and the application server is generated by computer programs running on corresponding computers and having a client-application server relationship with each other. The application server may be a cloud application server, also known as a cloud computing application server or a cloud host, which is a host product in a cloud computing application system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS applications.

[0141] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0142] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A transaction supervision method based on blockchain, characterized in that: Applied to nodes in a chain of custody, where the chain of custody is constructed by multiple regulatory participating institutions as nodes, including: Obtaining transaction data uploaded to the supervision chain by at least one supervision participating institution through a smart contract in the supervision chain; the smart contract is constructed and runs in the supervision chain according to transaction supervision rules; Extracting regulatory items associated with risk warning conditions in the smart contract from the transaction data; Based on the supervision item, determining whether the transaction data triggers a risk warning condition in the smart contract; When the risk warning conditions in the smart contract are triggered, a transaction risk alert is initiated.

2. The method according to claim 1, characterized in that The regulatory item associated with the risk warning condition is the transaction amount; Based on the supervision item, determining whether the transaction data triggers a risk warning condition in the smart contract includes: The transaction amount is compared with the amount threshold included in the risk warning condition. If the transaction amount is greater than the risk threshold, it is determined whether the transaction data triggers the risk warning condition in the smart contract.

3. The method according to claim 1, characterized in that The regulatory item associated with the risk warning condition is the counterparty; Based on the supervision item, determining whether the transaction data triggers a risk warning condition in the smart contract includes: Obtain historical credit data of the counterparty through the oracle; Based on the historical credit data, the credit rating of the counterparty is determined, and based on the credit rating, it is determined whether the transaction data triggers the risk warning condition in the smart contract.

4. The method according to claim 1, characterized in that: The regulatory item associated with the risk warning condition is the transaction frequency of the current transaction subject within the set time period; Based on the supervision item, determining whether the transaction data triggers a risk warning condition in the smart contract includes: The transaction frequency of the current transaction subject within the set time period is compared with the frequency threshold included in the risk warning condition. When the transaction frequency is greater than the frequency threshold, it is determined whether the transaction data triggers the risk warning condition in the smart contract.

5. The method according to claim 1, characterized in that Also includes: Read the transaction data uploaded by the regulatory participating institutions to be assessed in the chain of custody during the unit assessment period; Clustering the transaction data using a clustering algorithm to obtain at least one cluster; Based on the amount of transaction data contained in each cluster, risk assessment is performed on the transaction data.

6. The method according to claim 1, characterized in that Also includes: Read the transaction data uploaded by the regulatory participating institutions to be assessed in the chain of custody during the unit assessment period; Performing risk assessment on the transaction data using a pre-trained risk assessment model; The risk assessment model is trained using historical transaction data and corresponding risk labels.

7. The method according to claim 1, characterized in that Also includes: Read the transaction data uploaded by the regulatory participating institutions to be assessed in the chain of custody during the unit assessment period; Extracting a set amount of risk factor data from the transaction data by using a principal component analysis method; Based on the risk factor data, a risk assessment is performed on the transaction data.

8. A transaction supervision device based on blockchain, characterized in that: A node configured in a chain of custody, wherein the chain of custody is constructed by multiple regulatory participating institutions as nodes, including: A transaction data acquisition module, used to acquire transaction data uploaded to the supervision chain by at least one supervision participating institution through a smart contract in the supervision chain; the smart contract is constructed according to transaction supervision rules and runs in the supervision chain; A regulatory item extraction module, used to extract regulatory items associated with risk warning conditions in smart contracts from the transaction data; A risk identification module, used to determine whether the transaction data triggers a risk warning condition in the smart contract based on the supervision item; The risk alert initiation module is used to initiate a transaction risk alert when the risk warning conditions in the smart contract are triggered.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the blockchain-based transaction supervision method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the blockchain-based transaction supervision method described in any one of claims 1 to 7 when executed.

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