Financing method and system for supply chain financial platform

Abstract

The present invention relates to the technical field of trade financing, and specifically to a financing method and system for a supply chain financial platform, comprising the following steps: collecting transaction data between upstream and downstream enterprises in the supply chain, extracting fund flow information between a target enterprise and its upstream and downstream enterprises from the transaction data, constructing a fund flow path set, analyzing the paths in the fund flow path set, marking risk paths according to the analysis results, and generating fund flow path distribution analysis results. In the present invention, accurate analysis of fund flow paths is achieved by extracting fund flow information from transaction data of upstream and downstream enterprises in the supply chain, constructing a fund flow path set, and marking and dynamically optimizing path risks. The diffusion simulation and probability calculation of risk paths can clarify potential weak links in the capital chain, accurately assess the scope and probability of risk diffusion, and dynamically optimize the allocation of capital flows.

Description

Technical Field

[0001] The present invention relates to the technical field of trade financing, and in particular to a financing method and system for a supply chain financial platform. Background Art

[0002] The field of trade finance technology includes financial services and technical support related to supply chain management, and mainly focuses on providing financing services to upstream and downstream enterprises in the supply chain. The core content of this technology field is to solve the problem of capital turnover in various links of the supply chain through financial means to achieve efficient flow of funds. The field of trade finance technology usually involves the analysis of capital demand, the assessment of transaction risks, and the optimization of capital allocation and circulation. It relies on digital technology, combines data processing, online trading platforms and financial tools to provide enterprises with full-process financing solutions.

[0003] Among them, the financing method used by the supply chain financial platform refers to the technical method of intelligently analyzing, classifying and managing the capital needs of supply chain participating enterprises and providing precise financing support by building an online platform dedicated to supply chain financial services. It includes the collection and verification of capital demand data, the construction and application of credit assessment models, the setting and execution of capital allocation strategies, and the dynamic management of real-time transaction data based on the platform.

[0004] When managing the capital needs of enterprises participating in the supply chain, the existing technology lacks in-depth mining and dynamic optimization of risk paths in transaction data, making it difficult to comprehensively assess the potential scope and impact of risk propagation in the path. This limitation results in the inability to take targeted measures in a timely manner when capital anomalies occur in high-risk paths, which may cause risk diffusion on a larger scale. At the same time, the existing technology does not conduct sufficient time matching analysis between capital demanders and suppliers, which is prone to delayed capital allocation or resource mismatch, weakening the ability to respond to emergency financing needs. In addition, the existing technology only relies on basic credit assessment models and fails to make full use of real-time transaction data for dynamic management. It is difficult to optimize complex capital flow paths, and the efficiency and stability of capital flow are insufficient. For example, in a scenario where multiple paths are parallel, the allocation strategy of the existing technology may prioritize allocation to potential high-risk paths due to the lack of risk diffusion simulation, resulting in a decrease in the efficiency of capital flow, further increasing the uncertainty and cost of overall supply chain finance. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a financing method and system for a supply chain financial platform.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a financing method for a supply chain financial platform, comprising the following steps:

[0007] S1: Collect transaction data between upstream and downstream enterprises in the supply chain, extract capital flow information between the target enterprise and its upstream and downstream enterprises from the transaction data, construct a capital flow path set, analyze the paths in the capital flow path set, mark risk paths according to the analysis results, and generate capital flow path distribution analysis results;

[0008] S2: Based on the fund flow path distribution analysis results, perform risk diffusion simulation on the marked risk path, calculate the risk diffusion probability according to the simulation process, record the nodes covered by the propagation path according to the risk diffusion probability, and generate the risk diffusion path simulation results;

[0009] S3: Based on the risk diffusion path simulation results, the screened risk paths are optimized, the capital flow allocation is dynamically adjusted, and the capital flow path optimization results are generated;

[0010] S4: Based on the optimization result of the capital flow path, analyze the time matching between the optimized capital flow path and the financing demander and the capital supplier, extract the capital demand time node of the financing demander and the capital release time node of the capital supplier, adjust the capital allocation of the time window, and generate the financing priority allocation result.

[0011] As a further solution of the present invention, the steps of obtaining the fund flow path set are specifically as follows:

[0012] S111: Based on the transaction data of the target enterprise and its upstream and downstream enterprises, the fund flow information between the target enterprise and the counterparty is extracted, including the transaction amount, transaction time, and transaction status. By analyzing the counterparty relationship, the target enterprise is regarded as a network node and the fund flow is regarded as a connection path. The data is sorted to generate fund flow information;

[0013] S112: Based on the fund flow information, the transaction amount, transaction frequency and performance records in the fund flow path are counted and analyzed, including obtaining the total transaction amount, transaction frequency and performance ratio of each path, and collating and integrating the analysis results to establish a fund flow path set.

[0014] As a further solution of the present invention, the steps for obtaining the capital flow path distribution analysis result are specifically as follows:

[0015] S121: Based on the fund flow path set, the formula is used:

[0016] and ;

[0017] Calculate the total time it takes for all transactions in the capital flow path set to be completed within the statistical period , and the cumulative transaction cost of the path during the statistical period ;

[0018] in, Indicates the total number of transactions of the target path within the statistical period. is the transaction frequency of the path, is the total transaction amount of the path, is the cost ratio corresponding to the performance record;

[0019] S122: Based on the total time for completing all transactions on the path within the statistical period, and the accumulated transaction cost of the path within the statistical period, the frequency of risk events is obtained by counting the number of risk events and the total number of transactions on each path, and the risk path is marked according to a preset risk threshold to generate a capital flow path distribution analysis result.

[0020] As a further solution of the present invention, the step of calculating the risk diffusion probability is specifically as follows:

[0021] S211: Based on the analysis result of the capital flow path distribution, the marked risk paths and associated nodes are loaded into the analysis environment, and the behavior of risk propagation along the path is simulated by processing the capital flow data and the associated information between the nodes one by one, and the propagation range of the nodes and paths is counted to generate a risk diffusion behavior record;

[0022] S212: Based on the risk diffusion behavior record, the formula is used:

[0023] ;

[0024] Calculate the probability of risk diffusion , represents the probability of a path being selected in the risk diffusion process;

[0025] in, is the capital flow intensity of the path, is the average number of transactions, is the stability factor, is the total number of paths, Indicates The intensity of capital flow through the path, Indicates The average number of transactions in the corresponding time for each path, Indicates The stability record of the path.

[0026] As a further solution of the present invention, the steps for obtaining the risk diffusion path simulation results are specifically as follows:

[0027] S221: Select the path with the highest risk diffusion probability, record the node set and diffusion range in the target path in sequence, mark various node information according to the frequency of node access and diffusion probability, and generate a node set covered by the path;

[0028] S222: Based on the set of nodes covered by the path, the node classification and diffusion probability information of each path are integrated, and the nodes are sorted from high to low according to the diffusion probability, and the risk diffusion path simulation result is generated according to the sorted list.

[0029] As a further solution of the present invention, the steps for obtaining the optimization result of the capital flow path are specifically as follows:

[0030] S311: Based on the risk diffusion path simulation results, the selected risk path is optimized using the formula:

[0031] ;

[0032] Calculate the optimized risk assessment index ;

[0033] in, is the total number of paths, Indicates The remaining capacity of the path, Represents the total remaining capacity of all paths, Indicates The time consumption of each path, Indicates the maximum time consumption value in all paths, Indicates The cost factor of each path, Indicates the maximum cost factor value among all paths;

[0034] S312: Based on the optimized risk assessment index, by reducing the weight of the risk path and dynamically adjusting the allocation of capital flow, the capital flow is directed to the normal path, the weight and flow of the path are reallocated, and the optimization result of the capital flow path is generated.

[0035] As a further solution of the present invention, the steps for obtaining the financing priority allocation result are specifically as follows:

[0036] S411: Based on the optimization result of the capital flow path, extract the capital demand time node of the demander and the capital release time node of the supplier, mark the urgency of the capital demand of the demander by analyzing the matching between the application time and the required capital in the financing data record, classify the demander into multiple types of demand in combination with the transaction frequency and performance record, analyze the release time and remaining capacity distribution in the capital release record of the supplier, and generate time node matching basic data;

[0037] S412: Matching basic data based on the time node, using the formula:

[0038] ;

[0039] Calculate the optimal matching efficiency of the intersection between the capital flow path and time ;

[0040] in, is the total number of paths, is the total number of time nodes, Is the path and time intersection Whether the status variable matches, Is the path and time intersection The matching strength;

[0041] S413: According to the optimal matching efficiency, the matching efficiency range is divided, and the fund allocation of the time window is adjusted to give priority to meeting the financing requests of the demand side whose urgency exceeds the emergency threshold, while balancing the fund release cycle of the supply side, and generating a financing priority allocation result.

[0042] A financing system for a supply chain finance platform, the financing system for the supply chain finance platform is used to execute the financing method for the supply chain finance platform, the system comprising:

[0043] The capital flow path analysis module collects transaction data between upstream and downstream enterprises in the supply chain, constructs a capital flow path set, analyzes the paths in the capital flow path set and marks risk paths, and generates capital flow path distribution analysis results;

[0044] The risk diffusion simulation module performs risk diffusion simulation on the marked risk paths based on the fund flow path distribution analysis results, analyzes the interaction between risk paths, records the impact nodes covered by the risk paths, and generates risk diffusion path simulation results;

[0045] The capital path optimization module optimizes the screened risk paths based on the risk diffusion path simulation results, and generates capital flow path optimization results by dynamically adjusting capital flow allocation;

[0046] The financing time matching module analyzes the optimized capital flow path and the time matching between the financing demander and the capital supplier based on the capital flow path optimization result, and generates the financing priority allocation result by adjusting the capital allocation within the time window.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by extracting the information on the flow of funds from the transaction data of upstream and downstream enterprises in the supply chain, constructing a set of fund circulation paths, and marking and dynamically optimizing the path risks, an accurate analysis of the fund flow path is achieved. The diffusion simulation and probability calculation of the risk path can clarify the potential weak links in the capital chain, accurately assess the scope and probability of risk diffusion, and dynamically optimize the allocation of capital flow. By further combining the time node matching situation of the fund demander and the supplier, the priority of fund allocation is adjusted, and the efficiency and accuracy of the financing demand response are effectively improved. The optimized fund allocation method combined with the precise adjustment of the time window not only improves the matching degree between fund allocation and actual demand, but also reduces the impact of risk diffusion on the stability of the supply chain. During the statistical period, through the comprehensive analysis of the total transaction amount, number of times, performance records and frequency of risk events of the path, the intelligence of the fund allocation process is significantly improved, and the identification and adjustment capabilities of high-risk paths are strengthened. Finally, while balancing the urgency of fund demand and the fund release cycle, the cost and risk of fund circulation are reduced, and the adaptability and stability of the supply chain financial platform to dynamic financing needs are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 A flow chart of obtaining a fund flow path set for the present invention;

[0051] Figure 3 A flow chart of obtaining the capital flow path distribution analysis results of the present invention;

[0052] Figure 4 A flow chart for calculating the risk diffusion probability of the present invention;

[0053] Figure 5 A flow chart for obtaining the simulation results of the risk diffusion path for the present invention;

[0054] Figure 6 A flow chart of obtaining the optimization result of the capital flow path for the present invention;

[0055] Figure 7 A flow chart for obtaining the financing priority allocation results for the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0058] See also Figure 1 The present invention provides a technical solution: a financing method for a supply chain financial platform, comprising the following steps:

[0059] S1: Collect transaction data between upstream and downstream enterprises in the supply chain, extract capital flow information between the target enterprise and its upstream and downstream enterprises from the transaction data, construct a capital flow path set, analyze the paths in the capital flow path set, mark risk paths according to the analysis results, and generate capital flow path distribution analysis results;

[0060] S2: Based on the results of the capital flow path distribution analysis, perform risk diffusion simulation on the marked risk path, calculate the risk diffusion probability according to the simulation process, record the nodes covered by the propagation path according to the risk diffusion probability, and generate the risk diffusion path simulation results;

[0061] S3: Based on the simulation results of the risk diffusion path, the selected risk path is optimized, and the allocation of capital flow is dynamically adjusted so that more funds flow to the normal path, generating the optimization results of the capital flow path;

[0062] S4: Based on the optimization results of the capital flow path, analyze the time matching between the optimized capital flow path and the financing demander and the capital supplier, extract the capital demand time node of the financing demander and the capital release time node of the capital supplier, adjust the capital allocation of the time window, and generate the financing priority allocation results;

[0063] The analysis results of the capital flow path distribution include transaction amount distribution, transaction frequency distribution, path time consumption, path cost consumption, and path risk marking; the risk diffusion path simulation results include risk diffusion probability, propagation path node range, and risk path impact range; the capital flow path optimization results are specifically path time optimization results, path cost optimization results, and path flow adjustment results; the financing priority allocation results include financing demand priority, capital supply release cycle matching results, and capital allocation adjustment plan.

[0064] See also Figure 2, the specific steps for obtaining the capital flow path set are:

[0065] S111: Based on the transaction data of the target enterprise and its upstream and downstream enterprises, the fund flow information between the target enterprise and the counterparty is extracted, including the transaction amount, transaction time, and transaction status. By analyzing the counterparty relationship, the target enterprise is regarded as a network node and the fund flow is regarded as a connection path. The data is sorted to generate fund flow information;

[0066] Through the built-in data management tools of the supply chain finance platform (such as SQL language scripts or database analysis tools), historical transaction data is queried to extract the transaction records of the target enterprise and its upstream and downstream enterprises. Taking target enterprise A as an example, the transaction records in the past 12 months are extracted from the database, and the filter fields include transaction amount, transaction time, transaction counterparty and transaction status. By checking the integrity of the data, invalid data is eliminated, such as missing transaction amount, data without clear transaction counterparty or repeated records; for the transaction amount field, ensure that all amount data are positive and the unit is unified as 10,000 yuan, and only retain "completed" records in the transaction status field. According to the transaction relationship between target enterprise A and the transaction counterparty (such as supplier B, customer C), the target enterprise is regarded as a network node, and the transaction amount and direction are regarded as the path connecting the nodes. For example, in the analysis, it is determined that target enterprise A has 5 transactions with supplier B in the past 12 months, with a total transaction amount of 2 million yuan, and 8 transactions with customer C, with a total transaction amount of 3.2 million yuan. Finally, the capital flow information of target enterprise A and its upstream and downstream enterprises is organized into a basic network relationship represented by nodes and paths.

[0067] S112: Based on the fund flow information, statistics and analysis are performed on the transaction amount, transaction number and performance records in the fund flow path, including obtaining the total transaction amount, transaction number and performance ratio of each path, and collating and integrating the analysis results to establish a fund flow path set;

[0068] Through the data analysis function of the supply chain finance platform, the capital flow data extracted in paragraph 1 are processed. First, the transaction amount of each capital flow path is counted. For example, in the capital flow information of target enterprise A, the total amount of transaction records with supplier B is 2 million yuan, with an average of 400,000 yuan. The transaction amount data is arranged in chronological order to analyze the trend of capital flow; secondly, the number of transactions for each path is counted. For example, the number of transactions between target enterprise A and customer C is 8 times. By comparing the interval time of transaction time records, the frequency and regularity of capital flow are analyzed; at the same time, the performance records are summarized, for example, the performance results shown in the records are classified and counted, the number of successful performances is compared with the total number of transactions, and the performance completion ratio is sorted out. After all the analysis is completed, the data results of transaction amount, transaction frequency and performance records are summarized to construct a capital flow path set containing detailed information of each path.

[0069] See also Figure 3 , the specific steps for obtaining the results of the capital flow path distribution analysis are as follows:

[0070] S121: Based on the set of capital flow paths, the formula is:

[0071] and ;

[0072] Calculate the total time it takes for all transactions in the capital flow path set to be completed within the statistical period (Unit: day), and the cumulative transaction cost of the path during the statistical period (Unit: Yuan);

[0073] in, Indicates the total number of transactions on the target path within the statistical period. It is obtained by counting the number of transaction records on each path from the supply chain transaction data. For example, if the transaction record shows that a path has 20 transactions in the past 30 days, the recorded transaction frequency data is directly used for subsequent calculations. is the transaction frequency of the path, which represents the average number of transactions per unit time, specifically the total number of transactions in the statistical period divided by the total number of days in the period For example, if we want to collect statistics on the data of the past 30 days, , the calculation formula is: , It is the total transaction amount of the path (unit: yuan), which indicates the total amount of funds flow of the path during the statistical period. It is obtained by summing up the transaction amount fields of each path. For example, you can directly add up the total transaction amounts of a path obtained from the transaction data. It is the cost ratio corresponding to the fulfillment record, which indicates the cost factor added to the path due to the fulfillment record. The lower the fulfillment ratio (the proportion of successful transactions), The higher the value, the lower the value. , It is the number of successful transactions, counting the number of transaction records in the path whose fulfillment status is "successful".

[0074] If the transaction record of a path shows that the total number of transactions in the past 30 days is 20 ( ), the statistical period is 30 days ( ), then the transaction frequency is calculated as: , substitute into the formula: The result shows that the total time consumed by the path during the statistical period is 30 days.

[0075] If the total transaction amount of a path is 500,000 yuan ( ), the total number of transactions is 20 times ( ), the number of successful transactions is 18 times ( ), the cost ratio corresponding to the performance record is calculated as follows: , substitute into the formula: This result shows that the cumulative transaction cost of the path is 50,000 yuan.

[0076] S122: Based on the total time for completing all transactions of the path within the statistical period and the accumulated transaction cost of the path within the statistical period, the frequency of risk events is obtained by counting the number of risk events and the total number of transactions of each path, and the risk path is marked according to a preset risk threshold, so as to generate a capital flow path distribution analysis result;

[0077] The time consumption and transaction cost results are associated with the risk event data of the path. The risk event data is extracted through the status marked as "failure" or "abnormal" in the transaction performance record. For example, if 18 out of 20 transactions in a path are marked as "successful" and 2 are marked as "failed", then the 2 failure records are extracted as the number of risk events for the path. Subsequently, the risk event frequency of each path is obtained by dividing the number of risk events in the statistical period by the total number of transactions. For example, the risk event frequency of the above path is 2 / 20, that is, 10%. For all paths, the risk event frequency is sorted by high and low, and a risk marking threshold is set. For example, when the risk event frequency is greater than or equal to 10%, the path is marked as a high-risk path, and when it is less than 10%, it is marked as a normal path. Assuming that in the analysis, the risk event frequency of another path is 15%, it is marked as a high-risk path and recorded as a key monitoring object. By counting and marking the risk event frequency of all paths, a complete capital flow path distribution analysis result is generated, which contains detailed classification information of path time consumption, transaction costs and risk paths.

[0078] See also Figure 4, the specific steps for calculating the risk diffusion probability are:

[0079] S211: Based on the analysis results of the capital flow path distribution, the marked risk paths and associated nodes are loaded into the analysis environment, and the behavior of risk propagation along the path is simulated by processing the capital flow data and the associated information between the nodes one by one, and the propagation range of the nodes and paths is counted to generate a record of risk diffusion behavior;

[0080] The capital flow path is modeled through data analysis software (such as the NetworkX library in Python). First, the marked high-risk paths and related nodes are loaded into the simulation environment. The path weights are assigned based on the transaction amount and transaction frequency. For example, high transaction amounts and high-frequency paths are assigned higher propagation weights. During the simulation, the random walk algorithm starts from the marked risk nodes, allocates selection probabilities based on the path weights, selects the next node at the current node and completes the jump, and repeatedly iterates to simulate the risk propagation behavior between nodes during the capital flow process. After each simulation is completed, the visited node information and propagation path are recorded, and multiple iterations are repeated to increase the randomness of the simulation and the stability of the results. Finally, all iterative results are summarized, the frequency of each node being visited is counted, and the set of nodes covered within the propagation range is calculated, thereby completing the entire process of risk diffusion simulation.

[0081] S212: Based on the risk diffusion behavior record, the formula is:

[0082] ;

[0083] Calculate the probability of risk diffusion , which indicates the probability of a certain path being selected in the risk diffusion process. The calculated value is used to determine the weight of the path in risk diffusion.

[0084] in, It is the intensity of capital flow of the path, indicating the total transaction amount of the path, reflecting the scale of capital flow. The total transaction amount field of a certain path is directly extracted from the transaction data of the supply chain platform and accumulated. For example, a total of 10 transactions occurred on a path, with amounts of 100,000 yuan, 150,000 yuan, etc. The total transaction amount can be obtained by summing up these amounts. is the average number of transactions, It is a stability factor, which indicates the long-term transaction reliability of the path. It is derived based on the historical transaction performance records and is used to measure the path risk. It is calculated by analyzing the number of successful transactions in the historical transaction records of the path. Total number of transactions The ratio is calculated as: For example, if there are 10 transactions in a path, 8 of which are marked as successful transactions, then the fulfillment rate is , is the sum of all paths and is used to standardize the risk diffusion probability of a single path. is the total number of paths, Indicates The capital flow intensity of a path is the sum of the transaction amounts in the path, which is used to measure the scale of capital flow on the path. Indicates The average number of transactions per unit time on a path is used to measure the frequency of capital flow on the path. Indicates The historical stability of a path is used to measure the reliability of the path in fulfilling its obligations in historical transactions. The higher the value, the more stable the historical transactions of the path and the lower the risk of default.

[0085] If the capital flow intensity of a high-risk path is 5 million yuan ( ), the transaction frequency is 0.67 times / day ( ), the historical stability factor is 0.9 ( ), the parameters of the remaining three paths in the path set are: Path 2: , Path 3: , Path 4: ;

[0086] Path 1: ;

[0087] Path 2: ;

[0088] Path 3: ;

[0089] Path 4: ;

[0090] Add the weight products of all paths to get the total: ;

[0091] Diffusion probability of path 1: ;

[0092] Diffusion probability of path 2: ;

[0093] Diffusion probability of path 3: ;

[0094] Diffusion probability of path 4: ;

[0095] The results show that the diffusion probability of path 1 is 27.9%, the diffusion probability of path 2 is 11.1%, the diffusion probability of path 3 is 18.9%, and the diffusion probability of path 4 is 42.2%.

[0096] See also Figure 5 , the specific steps for obtaining the risk diffusion path simulation results are:

[0097] S221: Select the path with the highest risk diffusion probability, record the node set and diffusion range in the target path in sequence, mark various node information according to the frequency of node access and diffusion probability, and generate a node set covered by the path;

[0098] During the simulation, we start with path 4, which has the highest risk, because its diffusion probability is 42.2% and its coverage range is likely to be the largest. Prioritization analysis can ensure that the spread range of high-risk paths is fully evaluated. The nodes in the path are obtained through the analysis results of the distribution of capital flow paths. For example, the node set of path 4 is node 1, node 2, node 3 and node 4, indicating that funds flow from node 1 to node 2, and then to node 3 and node 4 in turn. In the simulation, path 4 starts from node 1 and records the spread range of risks along the path. For example, the risk coverage probability from node 1 to node 2 is 42.2%, and the coverage probability of spreading to node 3 and node 4 gradually decreases to 35.0% and 30.5%. Similarly, the diffusion probability of path 1 is 27.9%, and the nodes contained in the path are node 5, node 6, node 7 and node 8. In the simulation, it starts from node 5 and connects to node 6, node 7 and node 8, among which the coverage probability of node 6 is 27.9%, and the coverage probabilities of node 7 and node 8 are 22.5% and 18.7% respectively. All covered nodes are classified according to the diffusion probability threshold. For example, when the diffusion probability is greater than 25%, it is marked as a high-risk node, when it is between 15% and 25%, it is marked as a second-highest-risk node, and when it is less than 15%, it is marked as a general-risk node. Finally, the path coverage nodes are recorded and the risk path impact range is marked.

[0099] S222: Based on the node set covered by the path, the node classification and diffusion probability information of each path are integrated, and the nodes are sorted from high to low according to the diffusion probability, and the risk diffusion path simulation result is generated according to the sorted list;

[0100] The marked node data and path risk diffusion probability information are integrated to further generate the risk diffusion path simulation results. Taking path 4 as an example, the marking results show that the nodes it covers include node 1 (starting point, high-risk node), node 2 (high-risk node), node 3 (secondary high-risk node) and node 4 (general risk node), and a detailed record of path risk diffusion is generated according to the coverage range and diffusion probability. Similarly, the marked nodes in path 1 include node 5 (starting point, high-risk node), node 6 (secondary high-risk node), node 7 (general risk node) and node 8 (general risk node). The simulation results of all paths are sorted from high to low according to the diffusion probability. The final simulation results include the coverage node classification information, node diffusion probability, coverage range and path priority data of each path.

[0101] See also Figure 6 , the specific steps for obtaining the optimization results of the capital flow path are:

[0102] S311: Based on the risk diffusion path simulation results, the selected risk paths are optimized using the formula:

[0103] ;

[0104] Calculate the optimized risk assessment index ;

[0105] in, Indicates the sum operation of all paths. is the total number of paths, Indicates The remaining capacity of the path, in units of 10,000 yuan. After recording the total capacity and used capacity of the path through the path real-time monitoring tool, calculate the remaining capacity. For example, the maximum capacity of a path is 20 million yuan, and 15 million yuan has been used, then the remaining capacity is 5 million yuan. Indicates the total remaining capacity of all paths. It is used to normalize the remaining capacity of the paths. The remaining capacity of all paths is summed up. For example, if the remaining capacity of path 1, path 2, and path 3 is 5 million yuan, 3 million yuan, and 2 million yuan respectively, then Ten thousand yuan, Indicates The time consumption of a path, in hours, is calculated by analyzing the delay time of each fund flow through the path's historical transaction data. For example, if the time of the last five transactions of a path is 2 hours, 3 hours, 2.5 hours, 3.5 hours and 3 hours respectively, the time consumption of the path is its average value. Hour, Indicates the maximum time consumption value among all paths. It is used to normalize the path time consumption. The time consumption of all paths is compared and the maximum value is taken. For example, the time consumption of path 1, path 2 and path 3 is 2.8 hours, 3.5 hours and 4 hours respectively. Hour, Indicates The cost factor of a path is in 10,000 yuan. It is extracted by analyzing the various fees involved in the historical transactions of the path (such as transaction service fees, fund maintenance fees, etc.). For example, if the historical transaction records of a path show that the service fee totals 300,000 yuan, then the cost factor is 300,000 yuan. Indicates the maximum cost factor value among all paths, which is used to normalize the path cost factor. The cost factors of all paths are compared and the maximum value is obtained. For example, the cost factors of path 1, path 2, and path 3 are 200,000 yuan, 300,000 yuan, and 500,000 yuan respectively. Ten thousand yuan.

[0106] If the parameters of path 1, path 2, and path 3 are as follows: Path 1: , , , Path 2: , , , Path 3: , , The other parameters are: , , .

[0107] Calculate the normalized value for each path:

[0108] Path 1: ;

[0109] Path 2: ;

[0110] Path 3: ;

[0111] Calculate the comprehensive risk value: .

[0112] Comprehensive risk value It indicates the overall risk level of the three paths under normalized conditions. The contribution value of path 2 is 0.3, which is the highest risk path, indicating that its risk factors account for the highest proportion under current conditions. During the optimization process, priority should be given to reducing the risk contribution value of path 2 (such as adjusting the remaining capacity or reducing the cost factor) to improve the overall capital flow efficiency.

[0113] S312: Based on the optimized risk assessment indicators, by reducing the weight of the risk path and dynamically adjusting the allocation of capital flow, the capital flow is directed to the normal path, the weight and flow of the path are reallocated, and the optimization result of the capital flow path is generated;

[0114] Based on comprehensive risk value The risk contribution value of path 2 is 0.3, the highest. Therefore, in the adjustment process, the weight of path 2 is reduced first. According to the demand for capital flow and the path optimization goal, the weight of path 2 is reduced by 20%, and the weights of paths 1 and 3 are reallocated. The specific adjustment steps are as follows: reduce the remaining capacity of path 2 by the corresponding flow share. For example, if the current remaining capacity of path 2 is 3 million yuan, its flow will be reduced by 600,000 yuan and allocated to other paths, of which 300,000 yuan will flow to path 1 and the other 300,000 yuan will flow to path 3; at the same time, recalculate the normalized parameters of path 1, path 2 and path 3 according to the remaining capacity, and record the new path remaining capacity as path 1: 5.3 million yuan, path 2: 2.4 million yuan, and path 3: 2.3 million yuan; then update the comprehensive weight contribution value of each path, and dynamically allocate weights to low-risk paths first. According to the lower contribution values ​​of path 1 and path 3, the capital allocation ratio will be updated to 50% for path 1, 30% for path 2, and 20% for path 3 after adjustment; after the adjustment, generate the optimization results of the capital flow path, including the adjusted path weight allocation, new remaining capacity, time consumption and cost factor, which will be used for subsequent path dynamic optimization analysis.

[0115] See also Figure 7 , the specific steps for obtaining the financing priority allocation results are:

[0116] S411: Based on the optimization results of the capital flow path, extract the capital demand time nodes of the demand side and the capital release time nodes of the supply side, mark the urgency of the capital demand of the demand side by analyzing the matching of the application time and the required capital in the historical financing data, and divide the demand side into multiple types of demand in combination with the transaction frequency and performance record, analyze the release time and remaining capacity distribution in the capital release record of the supplier, and generate the basic data for time node matching;

[0117] Extract the time matching between the capital flow after path optimization and the demand side and the supply side, including the capital demand time node of the demand side and the capital release time node of the supply side. The time node of the demand side is obtained by analyzing the time period of the financing application time and the required capital matching time in the historical financing data. Extract the data of the financing application data with a time range of the past 12 months, calculate the time distribution of the financing application submitted by the demand side on a monthly basis, divide the time period into 24 hours a day, count the number of financing applications in each time period, and select the time period with more than 80% of the total application volume as the capital demand time node; combine the historical transaction frequency and performance record of each demand side to mark the urgency of capital demand, calculate the ratio of the historical transaction number to the total transaction volume on a monthly basis, and set three intervals according to the ratio: the transaction number ratio ≥60% is high-frequency demand, 30%-60% is medium-frequency demand, and <30% is low-frequency demand; combined with the performance record, according to the non-performance rate ≤5%, 5%-10%, ≥10 % is divided into low, medium and high default risks, high-frequency demand and low default risk are marked as emergency demand, and others are marked as ordinary demand; the supply side's fund release time node is extracted by analyzing the distribution of fund release time and remaining capacity in the historical fund supply records, and the fund release time is recorded on a weekly basis. The fund release frequency of each day of the week in the past 12 months is calculated, and the average remaining capacity of fund release is calculated on a daily basis. The time period with the highest proportion of release frequency and remaining capacity average is selected as the fund release time node; the supplier is divided into high-frequency release (weekly release frequency ≥ 4 times), periodic release (weekly release frequency 2-3 times), and occasional release (weekly release frequency <2 times) according to the fund release cycle; the fund flow is associated with the supply and demand time nodes according to the optimized path weight, and the matching results are sorted according to the path weight. Emergency demand and high-frequency release supply are matched first, and the secondary matching results are matched with ordinary demand and periodic or occasional release supply in turn, and the preliminary allocation results of time node matching are generated.

[0118] S412: Match basic data based on time nodes, using the formula:

[0119] ;

[0120] Calculate the optimal matching efficiency of the intersection between the capital flow path and time ;

[0121] in, It is a maximization operator, which is used to select the best combination among all matching results. It evaluates all matching possibilities of path remaining capacity and time node requirements and selects the maximum value. is a double summation symbol, which indicates the path (index ) and time nodes (index ) are summed up for all possible combinations of is the total number of paths, is the total number of time nodes. Based on the path number and time node number, the combined calculation of all paths and nodes is realized through nested loops. Is the path and time intersection Status variable indicating whether the match occurs, with a value of 0 or 1: : indicates the path and time nodes No match; : indicates the path and time nodes Matching is obtained by analyzing the matching conditions of the remaining capacity of the path and the capital demand at the time node. Matching conditions: remaining capacity of the path Funds required at time points, Is the path and time intersection The matching strength indicates the degree to which the path meets the time node requirements and is calculated using the following formula: The urgency is extracted from the historical records of the demand side. For example, the urgency of fund use in the past is quantified and weighted from 1 to 10 (the larger the value, the higher the urgency). The remaining capacity is extracted from the path optimization results in 10,000 yuan. If the total path capacity is 10 million yuan and 8 million yuan has been used, the remaining capacity is 2 million yuan. Represents the path number and time node number respectively, which are generated by the number mark of the intersection of the path and time (the path is from 1 to , time nodes from 1 to serial number).

[0122] If the data of the following three paths and three time nodes: path remaining capacity: path 1: 3 million yuan; path 2: 2 million yuan; path 3: 1 million yuan; time node urgency: time node 1: 8; time node 2: 5; time node 3: 3.

[0123] Calculate the matching strength of each path and time node :

[0124] Path 1 matching strength: , , . Path 2 matching strength: , , . Path 3 matching strength: , , .

[0125] Maximizing matching results through dynamic programming :

[0126] Assume that the optimal matching solution is: path 1 matches time node 1; path 2 matches time node 2; path 3 matches time node 3, then: .

[0127] The results show that the calculated , indicating that the matching efficiency of the capital path and the time intersection is 8.12%.

[0128] S413: According to the optimal matching efficiency, the matching efficiency range is divided, and the fund allocation of the time window is adjusted to give priority to meeting the financing requests of the demand side whose urgency exceeds the emergency threshold, while balancing the fund release cycle of the supply side, and generating the financing priority allocation result;

[0129] According to the calculation results, the matching efficiency of the intersection of the capital path and time is 8.12%. First, the matching efficiency range is divided into three intervals: low matching efficiency (0%-30%), medium matching efficiency (31%-70%) and high matching efficiency (71%-100%). For the path in the low matching efficiency interval, the time window of both the capital supply and demand sides is readjusted first. The specific operation is to advance the capital release time node of the capital supplier and postpone the capital use time node of the capital demander to ensure that the time intersection of the two gradually increases and improve the matching efficiency; for the path in the medium matching efficiency interval, the time window of both the capital supply and demand sides is adjusted first. For paths in the high matching efficiency interval, the original time window is kept unchanged, and a dynamic fund allocation mechanism is adopted to give priority to the financing requests of demanders with higher urgency. The urgency is quantified by the historical performance records of the demanders and the priority of the financing request tags. The emergency threshold is set to 70 points, and financing requests exceeding this threshold are given priority in fund allocation. For paths in the high matching efficiency interval, the current fund flow arrangement is maintained, and at the same time, the fund release cycle of the supplier is optimized to ensure that the fund supplier releases funds periodically in the multi-path allocation without causing fund backlog or insufficient liquidity, and generate the financing priority allocation result.

[0130] A financing system for a supply chain finance platform, the financing system for a supply chain finance platform is used to execute the financing method for the supply chain finance platform, and the system includes:

[0131] The capital flow path analysis module collects transaction data between upstream and downstream enterprises in the supply chain, constructs a capital flow path set, analyzes the paths in the capital flow path set and marks risk paths, and generates capital flow path distribution analysis results;

[0132] The risk diffusion simulation module performs risk diffusion simulation on the marked risk paths based on the analysis results of the capital flow path distribution. By analyzing the interactive relationship between risk paths, the impact nodes covered by the risk paths are recorded, and the simulation results of the risk diffusion paths are generated.

[0133] The capital path optimization module optimizes the selected risk paths based on the risk diffusion path simulation results, and generates capital flow path optimization results by dynamically adjusting the capital flow allocation;

[0134] The financing time matching module is based on the optimization results of the capital flow path, analyzes the time matching between the optimized capital flow path and the financing demander and the capital supplier, and generates the financing priority allocation results by adjusting the capital allocation within the time window.

[0135] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A financing method for a supply chain finance platform, characterized in that: The following steps are involved: S1: Collect transaction data between upstream and downstream enterprises in the supply chain, extract capital flow information between the target enterprise and its upstream and downstream enterprises from the transaction data, construct a capital flow path set, analyze the paths in the capital flow path set, mark risk paths according to the analysis results, and generate capital flow path distribution analysis results; S2: Based on the fund flow path distribution analysis results, perform risk diffusion simulation on the marked risk path, calculate the risk diffusion probability according to the simulation process, record the nodes covered by the propagation path according to the risk diffusion probability, and generate the risk diffusion path simulation results; The steps of calculating the risk diffusion probability are specifically as follows: S211: Based on the analysis result of the capital flow path distribution, the marked risk paths and associated nodes are loaded into the analysis environment, and the behavior of risk propagation along the path is simulated by processing the capital flow data and the associated information between the nodes one by one, and the propagation range of the nodes and paths is counted to generate a risk diffusion behavior record; S212: Based on the risk diffusion behavior record, the formula is used: ； Calculate the probability of risk diffusion , represents the probability of a path being selected in the risk diffusion process; in, is the capital flow intensity of the path, is the average number of transactions, is the stability factor, is the total number of paths, Indicates The intensity of capital flow through the path, Indicates The average number of transactions in the corresponding time for each path, Indicates The stability record of each path; The steps for obtaining the risk diffusion path simulation results are specifically as follows: S221: Select the path with the highest risk diffusion probability, record the node set and diffusion range in the target path in sequence, mark various node information according to the frequency of node access and diffusion probability, and generate a node set covered by the path; S222: Based on the node set covered by the path, integrate the node classification and diffusion probability information of each path, sort them from high to low according to the diffusion probability, and generate a risk diffusion path simulation result according to the sorted list; S3: Based on the risk diffusion path simulation results, the screened risk paths are optimized, the capital flow allocation is dynamically adjusted, and the capital flow path optimization results are generated; S4: Based on the optimization result of the capital flow path, analyze the time matching between the optimized capital flow path and the financing demander and the capital supplier, extract the capital demand time node of the financing demander and the capital release time node of the capital supplier, adjust the capital allocation of the time window, and generate the financing priority allocation result.

2. The financing method for the supply chain financial platform according to claim 1, characterized in that: The steps for obtaining the fund flow path set are specifically as follows: S111: Based on the transaction data of the target enterprise and its upstream and downstream enterprises, the fund flow information between the target enterprise and the counterparty is extracted, including the transaction amount, transaction time, and transaction status. By analyzing the counterparty relationship, the target enterprise is regarded as a network node and the fund flow is regarded as a connection path. The data is sorted to generate fund flow information; S112: Based on the fund flow information, the transaction amount, transaction frequency and performance records in the fund flow path are counted and analyzed, including obtaining the total transaction amount, transaction frequency and performance ratio of each path, and collating and integrating the analysis results to establish a fund flow path set.

3. The financing method for the supply chain financial platform according to claim 2, characterized in that: The steps for obtaining the fund flow path distribution analysis results are specifically as follows: S121: Based on the fund flow path set, the formula is used: and ; Calculate the total time it takes for all transactions in the capital flow path set to be completed within the statistical period , and the cumulative transaction cost of the path during the statistical period ; in, Indicates the total number of transactions of the target path within the statistical period. is the transaction frequency of the path, is the total transaction amount of the path, is the cost ratio corresponding to the performance record; S122: Based on the total time for completing all transactions on the path within the statistical period, and the accumulated transaction cost of the path within the statistical period, the frequency of risk events is obtained by counting the number of risk events and the total number of transactions on each path, and the risk path is marked according to a preset risk threshold to generate a capital flow path distribution analysis result.

4. The financing method for the supply chain financial platform according to claim 1, characterized in that: The steps for obtaining the optimization result of the capital flow path are specifically as follows: S311: Based on the risk diffusion path simulation results, the selected risk path is optimized using the formula: ； Calculate the optimized risk assessment index ; in, is the total number of paths, Indicates The remaining capacity of the path, Represents the total remaining capacity of all paths, Indicates The time consumption of each path, Indicates the maximum time consumption value in all paths, Indicates The cost factor of each path, Indicates the maximum cost factor value among all paths; S312: Based on the optimized risk assessment index, by reducing the weight of the risk path and dynamically adjusting the allocation of capital flow, the capital flow is directed to the normal path, the weight and flow of the path are reallocated, and the optimization result of the capital flow path is generated.

5. The financing method for the supply chain financial platform according to claim 4, characterized in that: The specific steps for obtaining the financing priority allocation result are: S411: Based on the optimization result of the capital flow path, extract the capital demand time node of the demander and the capital release time node of the supplier, mark the urgency of the capital demand of the demander by analyzing the matching between the application time and the required capital in the financing data record, classify the demander into multiple types of demand in combination with the transaction frequency and performance record, analyze the release time and remaining capacity distribution in the capital release record of the supplier, and generate time node matching basic data; S412: Matching basic data based on the time node, using the formula: ； Calculate the optimal matching efficiency of the intersection between the capital flow path and time ; in, is the total number of paths, is the total number of time nodes, Is the path and time intersection Whether the status variable matches, Is the path and time intersection The matching strength; S413: According to the optimal matching efficiency, the matching efficiency range is divided, and the fund allocation of the time window is adjusted to give priority to meeting the financing requests of the demand side whose urgency exceeds the emergency threshold, while balancing the fund release cycle of the supply side, and generating a financing priority allocation result.

6. A financing system for a supply chain finance platform, characterized in that: According to the financing method for a supply chain financial platform according to any one of claims 1 to 5, the system comprises: The capital flow path analysis module collects transaction data between upstream and downstream enterprises in the supply chain, constructs a capital flow path set, analyzes the paths in the capital flow path set and marks risk paths, and generates capital flow path distribution analysis results; The risk diffusion simulation module performs risk diffusion simulation on the marked risk paths based on the fund flow path distribution analysis results, analyzes the interaction between risk paths, records the impact nodes covered by the risk paths, and generates risk diffusion path simulation results; The capital path optimization module optimizes the screened risk paths based on the risk diffusion path simulation results, and generates capital flow path optimization results by dynamically adjusting capital flow allocation; The financing time matching module analyzes the optimized capital flow path and the time matching between the financing demander and the capital supplier based on the capital flow path optimization result, and generates the financing priority allocation result by adjusting the capital allocation within the time window.

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