CN117273935A - Supply chain financial wind control system and method based on blockchain technology - Google Patents
Supply chain financial wind control system and method based on blockchain technology Download PDFInfo
- Publication number
- CN117273935A CN117273935A CN202311237867.4A CN202311237867A CN117273935A CN 117273935 A CN117273935 A CN 117273935A CN 202311237867 A CN202311237867 A CN 202311237867A CN 117273935 A CN117273935 A CN 117273935A
- Authority
- CN
- China
- Prior art keywords
- risk
- node
- supply chain
- nodes
- transaction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000005516 engineering process Methods 0.000 title claims abstract description 20
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 22
- 238000012954 risk control Methods 0.000 claims abstract description 8
- 238000013528 artificial neural network Methods 0.000 claims abstract description 7
- 238000012502 risk assessment Methods 0.000 claims description 39
- 238000012544 monitoring process Methods 0.000 claims description 10
- 230000001276 controlling effect Effects 0.000 claims description 7
- 238000011156 evaluation Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 230000002708 enhancing effect Effects 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000001960 triggered effect Effects 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000011144 upstream manufacturing Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Technology Law (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention discloses a supply chain financial wind control method based on a block chain technology, which relates to the technical field of supply chain financial wind control, and comprises the following steps: establishing a blockchain network among nodes involved in the supply chain financial business; calculating a closeness coefficient between two nodes; constructing a supply chain financial risk model by using a neural network algorithm, and calculating risk coefficients of all nodes; and presetting a risk threshold, automatically triggering an early warning signal when the risk model detects that the node risk coefficient reaches the risk threshold, and taking corresponding risk control measures. The supply chain financial wind control system based on the blockchain technology comprises a blockchain module, a data processing module, a risk model module and a wind control rule module. By adopting corresponding measures to the nodes associated with the risk nodes according to the difference of the node closeness, the stability of the whole supply chain network is ensured, and the expansion of risks is avoided.
Description
Technical Field
The invention relates to the technical field of supply chain financial wind control, in particular to a supply chain financial wind control system and method based on a blockchain technology.
Background
The traditional supply chain financial wind control method mainly depends on a centralized data system and a trust mechanism, and has the problems of low efficiency, insufficient data safety, insufficient transparency and the like when processing large-scale and high-complexity risk control tasks. Therefore, a new wind control method capable of improving the financial wind control efficiency and data security of the supply chain is urgently needed.
In the invention of China with the application publication number of CN110992170A, a supply chain financial wind control system based on a blockchain is disclosed, the system comprises a blockchain platform and a wind control platform which are established based on the current supply chain finance, the blockchain platform comprises a plurality of blocks, the wind control platform acquires four-stream information, a alliance chain is formed between the blocks, the wind control platform sets up a reward and punishment mechanism, a blockchain structure is integrated into the supply chain financial structure, real four-stream information is acquired, the four-stream information is analyzed and approved by the wind control platform, the financial risk of the supply chain is greatly reduced, chain blocks which are highly self-checked, highly responsible, highly autonomous and self-made are formed through chain distribution and combination among the blocks, the credit range is enlarged, the financing difficulty is reduced, the high reality and high transparency of the four-stream information are ensured, financial risks are reduced, the self-reduction risk degree is ensured, the financial operation of the supply chain is ensured, and meanwhile, the self-checking and benign suspicious behaviors are developed through the punishment mechanism, and the originality of the four-stream information is verified.
In the technical solution described in the above invention, the relevance between the supply chain direct transaction nodes and the relevance between the indirect transaction nodes are not considered, and when one node is at risk, the nodes directly and indirectly related to the node are not processed.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a supply chain financial wind control system and a supply chain financial wind control method based on a block chain technology, which aim to ensure the stability of the whole supply chain network and avoid the expansion of risks by taking corresponding measures for nodes associated with risk nodes according to the difference of the compactness among the nodes.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: a supply chain financial wind control method based on a blockchain technique, comprising the steps of:
establishing a blockchain network among nodes related to the supply chain financial business, wherein the nodes comprise core enterprises, suppliers and financial institutions in the supply chain, and uplink transaction data, logistics information and fund flow data in the supply chain financial business and carry out encryption processing through an encryption algorithm;
transaction data among the provider, producer and seller nodes on the blockchain are obtained, the transaction frequency Trf, the transaction amount Trv and the indirect transaction amount Itv are used as weights between the two nodes, and the compactness coefficient Tig between the two nodes is calculated;
acquiring supply chain node data, calculating a current node credit risk assessment index, a logistics risk assessment index and a market demand risk assessment index, constructing a supply chain financial risk model by using a neural network algorithm, and calculating risk coefficients of all nodes through the credit risk assessment index, the logistics risk assessment index and the market demand risk assessment index;
the method comprises the steps that a risk threshold value is preset, when a risk model detects that a node risk coefficient reaches the risk threshold value, an early warning signal is automatically triggered, corresponding risk control measures are adopted, a wind control rule is deployed in an intelligent contract on a blockchain platform, wind control operation is automatically executed by the intelligent contract according to the preset rule, a first threshold value and a second threshold value are preset, the first threshold value and the second threshold value are compared with compactness coefficients of a risk node and an associated node, and corresponding measures are made according to comparison results;
and acquiring historical transaction data of the risk node, calculating a transaction period of the node enterprise, carrying out risk coefficient evaluation on the node again after one transaction period, removing the node from the supply chain network if the risk coefficient of the node is still higher than a risk threshold value, and prohibiting the node from participating in the operation of the blockchain network.
Further, the blockchain network construction process includes:
establishing a blockchain network, wherein the blockchain network is established among all nodes related to a supply chain financial business, and each node comprises key roles of a core enterprise, a supplier and a financial institution in the supply chain;
collecting data and processing, performing identity verification on each node, collecting related data, including supply chain financial transaction data, logistics information and fund flow data, and preprocessing the collected data, including data cleaning, duplication removal and missing value filling;
and (3) data uplink, namely uplink transaction data, logistics information and fund flow data information in the supply chain financial business, and carrying out encryption processing through an encryption algorithm, wherein the encryption algorithm comprises a hash algorithm, a symmetric encryption algorithm and an asymmetric encryption algorithm.
Further, the computation process of the affinity coefficient Tig between two nodes is as follows:
acquiring transaction data between suppliers, manufacturers and seller nodes on the blockchain, wherein the transaction data comprises transaction frequency, direct transaction amount and indirect transaction amount;
the nodes in the supply chain are regarded as nodes in the network, the connection between the nodes, namely the transaction, is regarded as an edge in the network, and a supply chain network is constructed;
and taking the transaction frequency Trf, the transaction amount Trv and the indirect transaction amount Itv as weights between the two nodes, performing dimensionless processing, and calculating a compactness coefficient Tig between the two nodes.
Further, the calculation formula of the compactness coefficient Tig is as follows:
wherein Trf ij For the transaction frequency between node i and node j, trv ij For the transaction amount between node i and node j, itv ij For indirect transaction amount, trf, between node i and node j i For transaction frequency of node i with other associated nodes, trv i For transaction amount of node i and other associated nodes, trv i Indirect transaction amount for node i and other associated nodes, trf j For transaction frequency of node i with other associated nodes, trv j For transaction amount of node i and other associated nodes, trv j Indirect transaction amount for node i and other associated nodes.
Further, the construction process of the financial risk model of the supply chain comprises the following steps:
taking the current node enterprise historical performance rate, liability rate and profit rate as credit risk assessment indexes, taking the current node logistics historical delivery cycle, transportation timing rate and transportation cost as logistics risk assessment indexes, and taking historical sales data and market demand data as market demand risk assessment indexes;
acquiring supply chain node data, constructing a supply chain financial risk model by using a neural network algorithm, training the model by using historical data, predicting credit risk, logistics risk and market demand risk, and carrying out parameter adjustment and optimization on the model;
for the constructed risk model, evaluating the model by using test data, checking the accuracy and stability of the model, and adjusting and optimizing the model when the performance of the model is poor, including adjusting model parameters and improving model structure;
and monitoring each node in real time by using a supply chain financial risk model, and automatically calculating the risk coefficient of each node by integrating a credit risk assessment index, a logistics risk assessment index and a market demand risk assessment index and distributing different weights to each index.
Further, the wind control rule control process is as follows:
presetting a risk threshold, and when the risk model detects that the node risk coefficient reaches the risk threshold, automatically triggering an early warning signal by the system and taking corresponding risk control measures;
presetting a first threshold and a second threshold, and screening out all associated nodes with the closeness coefficient of the current node being greater than the first threshold when the risk coefficient of the node reaches the risk threshold;
broadcasting the associated nodes with the compactness coefficient larger than the first threshold value and smaller than the second threshold value and sending out early warning;
reducing credit limit for the associated node with the closeness coefficient larger than or equal to the second threshold, adjusting the strategy of the supply chain, and enhancing the monitoring of the associated node so as to ensure the stability of the whole supply chain;
intercepting or early warning all transactions of the risk node, deploying the wind control rule in an intelligent contract on a blockchain platform, and automatically executing wind control operation according to a preset rule by the intelligent contract.
Further, the controlling of the risk node includes:
acquiring historical transaction data of the risk nodes, and calculating a transaction period of an enterprise through the historical transaction records of the enterprise;
carrying out risk coefficient evaluation on the node again after a transaction period, and if the risk coefficient of the node is lower than a risk threshold value, releasing the related control of the node before; if the node risk coefficient is still higher than the risk threshold, removing the node from the supply chain network and prohibiting the node from participating in the operation of the blockchain network;
and when the enterprise resumes normal operation and needs to join the blockchain network, re-auditing the blockchain network, and re-starting the node where the enterprise is located after the auditing.
A supply chain financial pneumatic control system based on blockchain technology, comprising: the system comprises a block chain module, a data processing module, a risk model module and a wind control rule module; wherein,
the block chain module is used for constructing a block chain network of a supply chain, carrying out identity verification and related data acquisition on each node, preprocessing acquired data and uploading the preprocessed data;
the data processing module is used for processing and analyzing transaction data among the nodes of suppliers, manufacturers and sellers on the blockchain and calculating the closeness coefficient among the nodes;
the risk model module is used for constructing a supply chain financial risk model, calculating the risk coefficient of each node through a credit risk assessment index, a logistics risk assessment index and a market demand risk assessment index, monitoring each node in real time, and automatically triggering an early warning signal when the risk coefficient of each node reaches a risk threshold;
the wind control rule module is used for controlling the nodes with risk coefficients reaching the risk threshold, intercepting or early warning all transactions of the risk nodes, and regulating and controlling the nodes associated with the risk nodes.
(III) beneficial effects
The invention provides a supply chain financial wind control system and method based on a block chain technology, which have the following beneficial effects:
(1) By utilizing the characteristics of distributed, decentralised, data non-tamperable and transparent properties of the blockchain technology, the risk in the supply chain financial business is comprehensively, effectively controlled and managed in real time, and the robustness and the safety of the supply chain financial business are improved.
(2) Transaction data among the supply chain network nodes are analyzed to obtain a closeness coefficient among the nodes, and when a certain node has a problem, an upstream enterprise and a downstream enterprise can be influenced, so that the enterprise can be helped to take measures in advance to manage and control risks by calculating the closeness coefficient among the supplier and other nodes.
(3) By analyzing the enterprise historical data, a supply chain financial risk model is constructed, the supply chain financial risk is automatically estimated through data analysis and a machine learning algorithm, the time and cost of manual estimation are saved, the estimation efficiency is improved, and a financial institution can better know the operation condition of the supply chain through the supply chain financial risk model, so that the accuracy of risk identification and estimation is improved.
(4) Through presetting the risk threshold, when the risk model detects that the node risk coefficient reaches the risk threshold, corresponding measures are executed, so that the financial risk of the supply chain can be effectively controlled, further expansion of the risk is avoided, and through broadcasting and early warning of the associated nodes, the associated nodes are facilitated to make countermeasures in advance, and the risk expansion caused by information lag is avoided.
(5) By removing nodes whose risk coefficients remain above the risk threshold after one transaction period from the supply chain network, the impact on the supply chain network can be reduced, the security and stability of the network can be improved, the high risk nodes can be infected with risks to other nodes, and the risk diffusion can be avoided by removing the high risk nodes from the supply chain network, so that other nodes in the supply chain network can be protected.
Drawings
FIG. 1 is a schematic flow chart of a supply chain financial wind control method based on a blockchain technique according to the present invention;
FIG. 2 is a schematic diagram of a supply chain network structure according to the present invention;
FIG. 3 is a schematic diagram of a supply chain financial pneumatic control system based on blockchain technology.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 to 2, the present invention provides a supply chain financial wind control method based on a blockchain technology, which includes the following steps:
step one: establishing a blockchain network among nodes related to the supply chain financial business, wherein the nodes comprise core enterprises, suppliers and financial institutions in the supply chain, and uplink transaction data, logistics information and fund flow data in the supply chain financial business and carry out encryption processing through an encryption algorithm;
step 101: establishing a blockchain network, wherein the blockchain network is established among all nodes related to a supply chain financial business, and each node comprises key roles of a core enterprise, a supplier, a financial institution and the like in the supply chain;
step 102: collecting data and processing, performing identity verification on each node, collecting related data, including supply chain financial transaction data, logistics information and fund flow data, and preprocessing the collected data, including data cleaning, duplication removal, missing value filling and the like;
step 103: and (3) data uplink, namely uplink transaction data, logistics information, fund flow data and other key information in the supply chain financial business, and carrying out encryption processing through an encryption algorithm, wherein the encryption algorithm comprises a hash algorithm, a symmetric encryption algorithm and an asymmetric encryption algorithm.
It should be noted that, the collected data is real and credible, and the subsequent analysis of the data is established on the real and credible data, so that strict management and monitoring of transaction data are required to prevent risk problems such as data tampering, repeated transaction, privacy disclosure and the like.
Combining the contents of steps 101 to 103:
by utilizing the characteristics of distributed, decentralised, data non-tamperable and transparent properties of the blockchain technology, the risk in the supply chain financial business is comprehensively, effectively controlled and managed in real time, and the robustness and the safety of the supply chain financial business are improved.
Step two: transaction data among nodes such as suppliers, manufacturers, sellers and the like on the blockchain are obtained, the transaction frequency Trf, the transaction amount Trv and the indirect transaction amount Itv are used as weights between the two nodes, and the compactness coefficient Tig between the two nodes is calculated;
step 201: acquiring transaction data between nodes such as suppliers, manufacturers, sellers and the like on the blockchain, wherein the transaction data comprises transaction frequency, direct transaction amount and indirect transaction amount;
step 202: the nodes in the supply chain are regarded as nodes in the network, the connection between the nodes, namely the transaction, is regarded as an edge in the network, and a supply chain network is constructed;
step 203: taking the transaction frequency Trf, the transaction amount Trv and the indirect transaction amount Itv as weights between two nodes, performing dimensionless processing, and calculating a compactness coefficient Tig between the two nodes, wherein the calculation formula is as follows:
wherein Trf ij For the transaction frequency between node i and node j, trv ij For the transaction amount between node i and node j, itv ij For indirect transaction amount, trf, between node i and node j i For transaction frequency of node i with other associated nodes, trv i For transaction amount of node i and other associated nodes, trv i Indirect transaction amount for node i and other associated nodes, trf j For transaction frequency of node i with other associated nodes, trv j For transaction amount of node i and other associated nodes, trv j Indirect transaction amount for node i and other associated nodes.
It should be noted that, the greater the transaction amount between two nodes, the higher the transaction frequency, and the higher the closeness coefficient between the two nodes, and vice versa, the lower the closeness coefficient between the two nodes, and the single node in the supply chain may be connected to multiple nodes, so when a problem occurs in one node in the supply chain network, the connected nodes including indirectly related nodes may be affected.
Combining the contents of steps 201 to 203:
transaction data among the supply chain network nodes are analyzed to obtain a closeness coefficient among the nodes, and when a certain node has a problem, an upstream enterprise and a downstream enterprise can be influenced, so that the enterprise can be helped to take measures in advance to manage and control risks by calculating the closeness coefficient among the supplier and other nodes.
Step three: acquiring supply chain node data, constructing a supply chain financial risk model by using a neural network algorithm, and calculating risk coefficients of all nodes through credit risk assessment indexes, logistics risk assessment indexes and market demand risk assessment indexes;
step 301: taking the current node enterprise historical performance rate, liability rate and profit rate as credit risk assessment indexes, taking the current node logistics historical delivery cycle, transportation timing rate and transportation cost as logistics risk assessment indexes, and taking historical sales data and market demand data as market demand risk assessment indexes;
it should be noted that, the credit risk of the enterprise may be estimated by indexes such as historical performance records, debt conditions, profitability, etc., the market demand risk may be estimated by indexes such as market demand, market price fluctuation, competition conditions, etc., the logistic risk may be estimated by indexes such as delivery cycle, time rate of delivery, cost of delivery, safety of goods, etc., and the estimated indexes are not uniform and may be selected according to specific situations.
Step 302: acquiring supply chain node data, constructing a supply chain financial risk model by using a neural network algorithm, training the model by using historical data, predicting credit risk, logistics risk and market demand risk, and carrying out parameter adjustment and optimization on the model so as to improve the accuracy and stability of the model;
step 303: for the constructed risk model, test data are required to be used for evaluating the model, the accuracy and stability of the model are checked, and if the performance of the model is poor, the model is required to be adjusted and optimized, such as adjusting model parameters, improving model structures and the like;
step 304: monitoring each node in real time by using a supply chain financial risk model, and automatically calculating risk coefficients of each node by integrating credit risk assessment indexes, logistics risk assessment indexes and market demand risk assessment indexes and distributing different weights to each index:
risk coefficient = credit risk weight coefficient × credit risk + logistics risk × logistics risk +
Market demand risk weighting factor market demand risk, wherein,
credit risk= (historical performance rate + liability rate)/(profit margin);
logistic risk = historical delivery cycle × cost of transportation/time rate of transportation;
market demand risk = historical sales data/market demand data.
It should be noted that, for different risk types and service scenarios, the weight coefficients of the various indexes may be different, so that adjustment is required according to actual situations, and the determination of the weight coefficients needs to be verified by using historical data and a model to ensure the stability and accuracy of the weight coefficients.
Combining the contents of steps 301 to 304:
by analyzing the enterprise historical data, a supply chain financial risk model is constructed, the supply chain financial risk is automatically estimated through data analysis and a machine learning algorithm, the time and cost of manual estimation are saved, the estimation efficiency is improved, and a financial institution can better know the operation condition of the supply chain through the supply chain financial risk model, so that the accuracy of risk identification and estimation is improved.
Step four: the method comprises the steps that a risk threshold value is preset, when a risk model detects that a node risk coefficient reaches the risk threshold value, an early warning signal is automatically triggered, corresponding risk control measures are adopted, a wind control rule is deployed in an intelligent contract on a blockchain platform, wind control operation is automatically executed by the intelligent contract according to the preset rule, a first threshold value and a second threshold value are preset, the first threshold value and the second threshold value are compared with compactness coefficients of a risk node and an associated node, and corresponding measures are made according to comparison results;
step 401: presetting a risk threshold, when the risk model detects that the node risk coefficient reaches the risk threshold, automatically triggering an early warning signal by the system, and taking corresponding risk control measures, such as suspending financing, recovering loan in advance, requiring additional guarantee and the like;
step 402: presetting a first threshold and a second threshold, and screening out all associated nodes with the closeness coefficient of the current node being greater than the first threshold when the risk coefficient of the node reaches the risk threshold;
step 403: broadcasting and early warning is sent to the associated nodes with the closeness coefficient larger than the first threshold and smaller than the second threshold, so that the associated nodes do countermeasures in advance;
reducing credit limit for the associated node with the closeness coefficient larger than or equal to the second threshold, adjusting the strategy of the supply chain, and enhancing the monitoring of the associated node so as to ensure the stability of the whole supply chain;
step 404: intercepting or early warning all transactions of the risk node, deploying the wind control rule in an intelligent contract on a blockchain platform, and automatically executing wind control operation according to a preset rule by the intelligent contract.
It should be noted that, when setting the financial risk threshold of the supply chain, the characteristics and risk conditions of different supply chains need to be considered to ensure that the set threshold is consistent with the actual service requirement, and meanwhile, the set risk threshold needs to be evaluated and adjusted regularly to adapt to the changes of markets and services.
Combining the contents of steps 401 to 404:
through presetting the risk threshold, when the risk model detects that the node risk coefficient reaches the risk threshold, corresponding measures are executed, so that the financial risk of the supply chain can be effectively controlled, further expansion of the risk is avoided, and through broadcasting and early warning of the associated nodes, the associated nodes are facilitated to make countermeasures in advance, and the risk expansion caused by information lag is avoided.
Step five: and acquiring historical transaction data of the risk node, calculating a transaction period of the node enterprise, carrying out risk coefficient evaluation on the node again after one transaction period, removing the node from the supply chain network if the risk coefficient of the node is still higher than a risk threshold value, and prohibiting the node from participating in the operation of the blockchain network.
Step 501: acquiring historical transaction data of the risk nodes, and calculating a transaction period of an enterprise through the historical transaction records of the enterprise;
step 502: carrying out risk coefficient evaluation on the node again after a transaction period, and if the risk coefficient of the node is lower than a risk threshold value, releasing the related control of the node before; if the node risk coefficient is still higher than the risk threshold, removing the node from the supply chain network and prohibiting the node from participating in the operation of the blockchain network;
step 503: and when the enterprise resumes normal operation and needs to join the blockchain network, re-auditing the blockchain network, and re-starting the node where the enterprise is located after the auditing.
It should be noted that, the calculation of the transaction period may be performed according to the following steps:
collecting historical data, and collecting transaction data within a certain time range, wherein the transaction data comprises the starting time and the ending time of a transaction;
calculating the trading period of all the trades, and for each trade, calculating the trading period of the trade, namely, subtracting the starting time from the ending time; calculating an average trading period, adding the trading periods of all the transactions, and dividing the sum by the total number of the transactions to obtain the average trading period.
Combining the contents of steps 501 to 503:
by removing nodes whose risk coefficients remain above the risk threshold after a transaction period from the supply chain network, the impact on the supply chain network can be reduced, the security and stability of the network can be improved, the high risk nodes can be infected with risks to other nodes, and after the high risk nodes are removed from the supply chain network, the risk spread can be avoided, and other nodes in the supply chain network can be protected.
Referring to fig. 3, the present invention further provides a supply chain financial wind control system based on a blockchain technology, including: the system comprises a block chain module, a data processing module, a risk model module and a wind control rule module; wherein,
the block chain module is used for constructing a block chain network of a supply chain, carrying out identity verification and related data acquisition on each node, preprocessing acquired data and uploading the preprocessed data;
the data processing module is used for processing and analyzing transaction data among nodes such as suppliers, manufacturers, sellers and the like on the blockchain and calculating the closeness coefficient among the nodes;
the risk model module is used for constructing a supply chain financial risk model, calculating the risk coefficient of each node through a credit risk assessment index, a logistics risk assessment index and a market demand risk assessment index, monitoring each node in real time, and automatically triggering an early warning signal when the risk coefficient of each node reaches a risk threshold;
the wind control rule module is used for controlling the nodes with risk coefficients reaching the risk threshold, intercepting or early warning all transactions of the risk nodes, and regulating and controlling the nodes associated with the risk nodes.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application.
Claims (8)
1. A supply chain financial wind control method based on a blockchain technology, comprising the following steps:
establishing a blockchain network among nodes related to the supply chain financial business, wherein the nodes comprise core enterprises, suppliers and financial institutions in the supply chain, and uplink transaction data, logistics information and fund flow data in the supply chain financial business and carry out encryption processing through an encryption algorithm;
transaction data among the provider, producer and seller nodes on the blockchain are obtained, the transaction frequency Trf, the transaction amount Trv and the indirect transaction amount Itv are used as weights between the two nodes, and the compactness coefficient Tig between the two nodes is calculated;
acquiring supply chain node data, constructing a supply chain financial risk model by using a neural network algorithm, and calculating risk coefficients of all nodes through credit risk assessment indexes, logistics risk assessment indexes and market demand risk assessment indexes;
the method comprises the steps that a risk threshold value is preset, when a risk model detects that a node risk coefficient reaches the risk threshold value, an early warning signal is automatically triggered, corresponding risk control measures are adopted, a wind control rule is deployed in an intelligent contract on a blockchain platform, wind control operation is automatically executed by the intelligent contract according to the preset rule, a first threshold value and a second threshold value are preset, the first threshold value and the second threshold value are compared with compactness coefficients of a risk node and an associated node, and corresponding measures are made according to comparison results;
and acquiring historical transaction data of the risk node, calculating a transaction period of the node enterprise, carrying out risk coefficient evaluation on the node again after one transaction period, removing the node from the supply chain network if the risk coefficient of the node is still higher than a risk threshold value, and prohibiting the node from participating in the operation of the blockchain network.
2. The blockchain technology-based supply chain financial wind control method of claim 1, wherein the blockchain network construction process includes:
establishing a blockchain network among nodes involved in a supply chain financial business, wherein each node comprises a core enterprise, a supplier and a financial institution in the supply chain; each node is subjected to identity verification, relevant data of the node are collected, the data comprise supply chain financial transaction data, logistics information and fund flow data, and the collected data are preprocessed; and (3) uploading transaction data, logistics information and fund flow data information in the supply chain financial business, and carrying out encryption processing through an encryption algorithm, wherein the encryption algorithm comprises a hash algorithm, a symmetric encryption algorithm and an asymmetric encryption algorithm.
3. The supply chain financial wind control method based on the blockchain technology according to claim 1, wherein the computation process of the compactness coefficient Tig between two nodes is as follows:
acquiring transaction data between suppliers, manufacturers and seller nodes on the blockchain, wherein the transaction data comprises transaction frequency, direct transaction amount and indirect transaction amount; the nodes in the supply chain are regarded as nodes in the network, the connection between the nodes, namely the transaction, is regarded as an edge in the network, and a supply chain network is constructed;
and taking the transaction frequency Trf, the transaction amount Trv and the indirect transaction amount Itv as weights between the two nodes, performing dimensionless processing, and calculating a compactness coefficient Tig between the two nodes.
4. A supply chain financial wind control method based on blockchain technology according to claim 3, wherein the calculation formula of the compactness coefficient Tig is as follows:
wherein Trf ij For the transaction frequency between node i and node j, trv ij For the transaction amount between node i and node j, itv ij For indirect transaction amount, trf, between node i and node j i For transaction frequency of node i with other associated nodes, trv i For transaction amount of node i and other associated nodes, trv i Indirect transaction amount for node i and other associated nodes, trf j For transaction frequency of node i with other associated nodes, trv j For transaction amount of node i and other associated nodes, trv j Indirect transaction amount for node i and other associated nodes.
5. The method of claim 1, wherein the process of constructing the supply chain financial risk model comprises:
taking the current node enterprise historical performance rate, liability rate and profit rate as credit risk assessment indexes, taking the current node logistics historical delivery cycle, transportation timing rate and transportation cost as logistics risk assessment indexes, and taking historical sales data and market demand data as market demand risk assessment indexes;
acquiring supply chain node data, constructing a supply chain financial risk model by using a neural network algorithm, training the model by using historical data, predicting credit risk, logistics risk and market demand risk, and carrying out parameter adjustment and optimization on the model; and monitoring each node in real time by using a supply chain financial risk model, and automatically calculating the risk coefficient of each node by integrating a credit risk assessment index, a logistics risk assessment index and a market demand risk assessment index and distributing different weights to each index.
6. The supply chain financial wind control method based on the blockchain technology as in claim 1, wherein the wind control rule control process is as follows:
presetting a risk threshold, and when the risk model detects that the node risk coefficient reaches the risk threshold, automatically triggering an early warning signal by the system and taking corresponding risk control measures; presetting a first threshold and a second threshold, and screening out all associated nodes with the closeness coefficient of the current node being greater than the first threshold when the risk coefficient of the node reaches the risk threshold;
broadcasting the associated nodes with the compactness coefficient larger than the first threshold value and smaller than the second threshold value and sending out early warning; reducing credit limit for the associated node with the closeness coefficient larger than or equal to the second threshold, adjusting the strategy of the supply chain, and enhancing the monitoring of the associated node so as to ensure the stability of the whole supply chain;
intercepting or early warning all transactions of the risk node, deploying the wind control rule in an intelligent contract on a blockchain platform, and automatically executing wind control operation according to a preset rule by the intelligent contract.
7. The blockchain technology-based supply chain financial wind control method of claim 1, wherein the control of the risk node comprises:
acquiring historical transaction data of the risk nodes, and calculating a transaction period of an enterprise through the historical transaction records of the enterprise;
carrying out risk coefficient evaluation on the node again after a transaction period, and if the risk coefficient of the node is lower than a risk threshold value, releasing the related control of the node before; if the node risk coefficient is still higher than the risk threshold, removing the node from the supply chain network and prohibiting the node from participating in the operation of the blockchain network; and when the enterprise resumes normal operation and needs to join the blockchain network, re-auditing the blockchain network, and re-starting the node where the enterprise is located after the auditing.
8. A supply chain financial wind control system based on blockchain technology for implementing the method of any of claims 1 to 7, comprising: the system comprises a block chain module, a data processing module, a risk model module and a wind control rule module; wherein,
the block chain module is used for constructing a block chain network of a supply chain, carrying out identity verification and related data acquisition on each node, preprocessing acquired data and uploading the preprocessed data;
the data processing module is used for processing and analyzing transaction data among the nodes of suppliers, manufacturers and sellers on the blockchain and calculating the closeness coefficient among the nodes;
the risk model module is used for constructing a supply chain financial risk model, calculating the risk coefficient of each node through a credit risk assessment index, a logistics risk assessment index and a market demand risk assessment index, monitoring each node in real time, and automatically triggering an early warning signal when the risk coefficient of each node reaches a risk threshold;
the wind control rule module is used for controlling the nodes with risk coefficients reaching the risk threshold, intercepting or early warning all transactions of the risk nodes, and regulating and controlling the nodes associated with the risk nodes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311237867.4A CN117273935A (en) | 2023-09-25 | 2023-09-25 | Supply chain financial wind control system and method based on blockchain technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311237867.4A CN117273935A (en) | 2023-09-25 | 2023-09-25 | Supply chain financial wind control system and method based on blockchain technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117273935A true CN117273935A (en) | 2023-12-22 |
Family
ID=89219174
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311237867.4A Pending CN117273935A (en) | 2023-09-25 | 2023-09-25 | Supply chain financial wind control system and method based on blockchain technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117273935A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109949142A (en) * | 2019-02-01 | 2019-06-28 | 深圳尚融供应链科技有限公司 | A kind of dominant supply chain finance implementation method, system and terminal device |
| CN110992170A (en) * | 2019-11-29 | 2020-04-10 | 上海玉薇科技有限公司 | Supply chain finance wind control system based on block chain |
| CN112132441A (en) * | 2020-09-16 | 2020-12-25 | 西安科技大学 | Risk propagation information evaluation method, risk propagation information evaluation system, storage medium and computer equipment |
| CN114491583A (en) * | 2021-12-31 | 2022-05-13 | 山西金蝉电子商务有限公司 | Supply chain financial optimization system based on block chain |
| CN116051272A (en) * | 2023-02-15 | 2023-05-02 | 深圳微众信用科技股份有限公司 | Enterprise risk analysis method and related equipment |
| CN116757820A (en) * | 2023-03-31 | 2023-09-15 | 中国工商银行股份有限公司 | Enterprise risk determination method and device, storage medium and electronic equipment |
-
2023
- 2023-09-25 CN CN202311237867.4A patent/CN117273935A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109949142A (en) * | 2019-02-01 | 2019-06-28 | 深圳尚融供应链科技有限公司 | A kind of dominant supply chain finance implementation method, system and terminal device |
| CN110992170A (en) * | 2019-11-29 | 2020-04-10 | 上海玉薇科技有限公司 | Supply chain finance wind control system based on block chain |
| CN112132441A (en) * | 2020-09-16 | 2020-12-25 | 西安科技大学 | Risk propagation information evaluation method, risk propagation information evaluation system, storage medium and computer equipment |
| CN114491583A (en) * | 2021-12-31 | 2022-05-13 | 山西金蝉电子商务有限公司 | Supply chain financial optimization system based on block chain |
| CN116051272A (en) * | 2023-02-15 | 2023-05-02 | 深圳微众信用科技股份有限公司 | Enterprise risk analysis method and related equipment |
| CN116757820A (en) * | 2023-03-31 | 2023-09-15 | 中国工商银行股份有限公司 | Enterprise risk determination method and device, storage medium and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yang et al. | Research on financial risk management model of internet supply chain based on data science | |
| Kogan et al. | Design and evaluation of a continuous data level auditing system | |
| Lensberg et al. | Bankruptcy theory development and classification via genetic programming | |
| Jerman-Blažič et al. | Managing the investment in information security technology by use of a quantitative modeling | |
| US20080262981A1 (en) | Method, System and Computer Program for Operational-Risk Modeling | |
| JP2012150788A (en) | System and method for prediction of frequency related to loss in future and related automatic processing of loss determination unit | |
| KR102011689B1 (en) | Method for monitoring time-series data, System for monitoring time-series data and Computer program for the same | |
| CN112817939A (en) | Construction method of data wind control model and data wind control model | |
| CN114022273A (en) | Financial risk management system and method for financing supply chain | |
| CN117151882B (en) | Risk assessment method and system based on multi-variety power transaction | |
| CN112766814A (en) | Training method, device and equipment for credit risk pressure test model | |
| O'Halloran et al. | Big data and graph theoretic models: simulating the impact of collateralization on a financial system | |
| Jacobs Jr | The quantification and aggregation of model risk: perspectives on potential approaches | |
| CN117273935A (en) | Supply chain financial wind control system and method based on blockchain technology | |
| Roy et al. | Corporate governance compliance, governance structures, and firm performance | |
| Maggi et al. | IFRS 9: A silent revolution in banks’ business models | |
| US20210158357A1 (en) | Computer implemented system, method and program for processing data in order to identify one or more anomalies | |
| CN111179077B (en) | Stock abnormal transaction identification method and system | |
| Kul’ba et al. | Scenario Research of Complex Manufacturing Systems’ Vulnerability | |
| CN114066616A (en) | Engineering field supply chain financial risk assessment system based on block chain | |
| US20130091072A1 (en) | Algorithm for post-trade analysis and formulation of optimized strategy for subsequent trades | |
| KR20100058101A (en) | Operating foreign exchange risk management system supporting enterprise's decision-making and method of the same | |
| KR20140029994A (en) | Method for verifying sutability of loss given default/exposure at default | |
| Hirano et al. | Comparison of behaviors of actual and simulated hft traders for agent design | |
| Pandit et al. | Remanufactured Product Warranty Oversight Fraud |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |