Supply chain finance intelligent risk control system and method

Abstract

The present invention relates to the technical field of financial risk control systems, in particular to a supply chain financial intelligent risk control system and method thereof, comprising: a data acquisition module; a random matrix construction module, which is communicated and connected with the data acquisition module; a random matrix is ​​constructed based on supply chain knowledge graph data; a topological feature extraction module, which is communicated and connected with the random matrix construction module; a risk propagation model module, which is communicated and connected with the topological feature extraction module; a nonlinear risk quantification module, which is communicated and connected with the risk propagation model module; a quantum probability risk prediction module, which is communicated and connected with the nonlinear risk quantification module; quantum probability risk prediction is performed based on nonlinear risk quantification results; a risk assessment result output module, which is communicated and connected with the quantum probability risk prediction module; and an enterprise risk assessment report is generated, which significantly improves the overall effectiveness of supply chain financial risk management, can not only accurately assess the current risk status, but also predict possible risk events in the future.

Description

Technical Field

[0001] The present invention relates to the technical field of financial risk control systems, and in particular to supply chain financial intelligent risk control systems and methods thereof. Background Art

[0002] With the rapid development of the global economy and the increasing complexity of supply chains, supply chain finance plays an increasingly important role in the modern business environment. However, the accompanying supply chain risks are also becoming increasingly prominent, bringing huge challenges to financial institutions and enterprises. Traditional supply chain finance risk control methods have become inadequate in the face of the current complex and changing market environment.

[0003] At present, the supply chain finance risk control methods commonly used in the industry mainly include traditional credit assessment models, static network analysis and machine learning methods based on historical data. Although these methods can assess and predict risks in the supply chain to a certain extent, they still have many shortcomings.

[0004] Traditional credit assessment models mainly rely on the financial data and historical credit records of enterprises, which cannot effectively capture the complex interdependencies between enterprises in the supply chain. This method often ignores the integrity of the supply chain, resulting in one-sided and lack of foresight in risk assessment results.

[0005] Static network analysis methods, such as graph analysis, can describe the relationship network between enterprises, but it is difficult to describe the dynamic characteristics of the supply chain. This method cannot reflect the ever-changing market environment in a timely manner, resulting in risk warnings often lagging behind the actual occurrence of risks.

[0006] Machine learning methods based on historical data have improved in dealing with nonlinear relationships, but they still have problems such as strong data dependence and black-box modeling. This method is difficult to explain the specific causes and transmission paths of risks, and is not conducive to formulating targeted risk management strategies.

[0007] In addition, existing risk control methods generally have the following problems: first, it is difficult to effectively deal with the uncertainty and complexity in the supply chain; second, there is a lack of accurate modeling of the risk propagation process; third, it is impossible to fully utilize real-time data for dynamic risk assessment; fourth, the accuracy and timeliness of risk prediction needs to be improved.

[0008] In view of the above problems, there is an urgent need for an intelligent system that can comprehensively, accurately and in real time analyze and predict supply chain financial risks. The system should be able to effectively handle the complexity and dynamic characteristics of the supply chain, accurately simulate the risk propagation process, and provide explainable risk assessment results. Summary of the invention

[0009] The supply chain finance intelligent risk control system of the present invention is proposed to address the deficiencies of the existing technology. The system innovatively integrates multidisciplinary cutting-edge theories to build a comprehensive, accurate and dynamic risk analysis framework, effectively solving the technical problems faced by existing methods.

[0010] The present invention proposes a supply chain financial intelligent risk control system, including:

[0011] Data acquisition module, used to:

[0012] Acquire supply chain knowledge graph data, wherein the supply chain knowledge graph data includes an enterprise node set and a relationship edge set;

[0013] A random matrix building module is connected in communication with the data acquisition module and is used to:

[0014] Receiving the supply chain knowledge graph data sent by the data acquisition module;

[0015] Based on the supply chain knowledge graph data, construct a random matrix;

[0016] A topological feature extraction module is connected to the random matrix construction module for:

[0017] Receiving a random matrix sent by the random matrix building module;

[0018] Based on the random matrix, extracting topological features;

[0019] The risk propagation model module is in communication with the topological feature extraction module and is used to:

[0020] Receiving the topological features sent by the topological feature extraction module;

[0021] Based on the topological features, a group theory risk propagation model is established;

[0022] A nonlinear risk quantification module is in communication with the risk propagation model module and is used to:

[0023] Receiving the group theory risk propagation model sent by the risk propagation model module;

[0024] Based on the group-theoretic risk propagation model, nonlinear risk quantification is performed;

[0025] The quantum probability risk prediction module is in communication with the nonlinear risk quantification module and is used to:

[0026] Receiving the nonlinear risk quantification result sent by the nonlinear risk quantification module;

[0027] Based on the nonlinear risk quantification results, quantum probabilistic risk prediction is performed;

[0028] The risk assessment result output module is connected to the quantum probability risk prediction module for:

[0029] Receiving the quantum probability risk prediction result sent by the quantum probability risk prediction module;

[0030] Generate enterprise risk assessment reports.

[0031] Preferably, the random matrix building module comprises:

[0032] Matrix generation unit, used to:

[0033] Generate the dimension of a random matrix based on the enterprise node set in the supply chain knowledge graph data;

[0034] A relationship mapping unit is communicatively connected with the matrix generating unit and is used for:

[0035] Based on the relationship edge set in the supply chain knowledge graph data, determining the position of non-zero elements in the random matrix;

[0036] A random value generating unit, which is in communication with the relationship mapping unit, is used to:

[0037] Complex values ​​are generated for non-zero elements in the random matrix, where the real part represents the strength of the relationship and the imaginary part represents the phase of the relationship.

[0038] Preferably, the topological feature extraction module comprises:

[0039] Matrix transformation unit, used for:

[0040] Performing logarithmic transformation on the random matrix to obtain a logarithmic matrix;

[0041] A feature calculation unit is communicatively connected to the matrix transformation unit and is used for:

[0042] Based on the logarithmic matrix, calculating a topological feature matrix;

[0043] A spectrum decomposition unit is communicatively connected to the feature calculation unit and is used to:

[0044] Perform spectral decomposition on the topological characteristic matrix to obtain eigenvalues ​​and eigenvectors.

[0045] Preferably, the risk propagation model module comprises:

[0046] Group theory model building unit for:

[0047] Based on the topological features, construct risk propagation groups;

[0048] The propagation operator definition unit is communicatively connected with the group theory model construction unit and is used for:

[0049] Define the risk propagation operator;

[0050] A risk distribution calculation unit is communicatively connected to the propagation operator definition unit, and is used to:

[0051] Calculate the risk distribution after multiple transmissions.

[0052] Preferably, the nonlinear risk quantification module comprises:

[0053] Non-linear transformation unit, used for:

[0054] Apply nonlinear transformations to risk distributions;

[0055] A fractional-order derivative calculation unit is communicatively connected with the nonlinear transformation unit and is used for:

[0056] Computes the Riemann-Liouville fractional derivatives of the result of a nonlinear transformation.

[0057] Preferably, the quantum probability risk prediction module comprises:

[0058] Quantum state building blocks for:

[0059] Based on the nonlinear risk quantification results, a quantum state representing the enterprise risk status is constructed;

[0060] A quantum measurement operator definition unit, which is in communication connection with the quantum state construction unit, is used to:

[0061] Define quantum measurement operator;

[0062] A probability distribution calculation unit, which is in communication with the quantum measurement operator definition unit, is used to:

[0063] Calculate the risk prediction probability distribution based on the Born rule.

[0064] As a preference, it also includes:

[0065] The Internet of Things monitoring module is connected to the data acquisition module for:

[0066] Real-time tracking of core assets of core enterprises through RFID chips and satellite positioning technology;

[0067] Provide real-time asset monitoring data to the data acquisition module.

[0068] Preferably, the Internet of Things monitoring module includes:

[0069] Asset Binding Unit, used to:

[0070] Bind RFID chips to the core assets of core enterprises;

[0071] A location tracking unit is communicatively connected to the asset binding unit and is used to:

[0072] Real-time tracking of asset locations with RFID chips through satellite positioning technology;

[0073] an anomaly detection unit, in communication with the location tracking unit, for:

[0074] Detect unusual changes in asset locations and generate early warning signals.

[0075] As a preference, it also includes:

[0076] The model training module is in communication with the risk assessment result output module and is used to:

[0077] Receive actual risk event data;

[0078] Based on the actual risk event data and the enterprise risk assessment report, various model parameters in the system are optimized.

[0079] The supply chain finance intelligent risk control method based on the supply chain finance intelligent risk control system includes:

[0080] The acquisition steps include:

[0081] Acquire supply chain knowledge graph data, wherein the supply chain knowledge graph data includes an enterprise node set and a relationship edge set;

[0082] Processing steps include:

[0083] Based on the supply chain knowledge graph data, construct a random matrix;

[0084] Extracting topological features according to the random matrix;

[0085] Based on the topological features, a group theory risk propagation model is established;

[0086] performing nonlinear risk quantification according to the group-theoretic risk propagation model;

[0087] Based on the nonlinear risk quantification results, quantum probabilistic risk prediction is performed;

[0088] Output steps include:

[0089] Generate enterprise risk assessment reports.

[0090] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0091] From a macro perspective, the system significantly improves the overall effectiveness of supply chain financial risk management. It can not only accurately assess the current risk status, but also predict possible risk events in the future, providing strong support for the decision-making of financial institutions and enterprises.

[0092] At the system architecture level, the present invention realizes the organic integration of functions through modular design. The various functional modules such as data acquisition module, random matrix construction module, topological feature extraction module, etc. work closely together to form a complete risk analysis chain. This modular design not only improves the maintainability and scalability of the system, but also realizes the complementary advantages and superposition of the effects of the functions of each module.

[0093] In terms of specific technical aspects, the innovation of the present invention is mainly reflected in the following aspects:

[0094] First, by introducing random matrix theory, this system can effectively capture the uncertainty and complexity in the supply chain. This solves the problem that traditional methods are difficult to handle high-dimensional complex relationships, and lays a solid mathematical foundation for subsequent risk analysis.

[0095] Secondly, the risk propagation model based on group theory provides a new idea for accurately describing the risk propagation process in the supply chain network. This method not only considers direct risk transmission, but also captures indirect and multi-step risk propagation paths, greatly improving the comprehensiveness and accuracy of risk assessment.

[0096] Furthermore, the introduction of the nonlinear risk quantification module, especially the application of fractional derivatives, enables the system to effectively handle long-range correlation and memory effects in the risk propagation process, which solves the problem that traditional methods are difficult to characterize the cumulative effect of risks.

[0097] Finally, the innovative application of the quantum probabilistic risk prediction module enables the system to naturally handle high-dimensional uncertainty and provide intuitive probabilistic explanations, which not only improves the accuracy of risk prediction, but also enhances the interpretability of prediction results.

[0098] In summary, the supply chain finance intelligent risk control system of the present invention achieves high accuracy, high efficiency and high interpretability of risk assessment through the synergy of various modules. It effectively solves the problems existing in the prior art and provides a new and more advanced solution for risk management in the field of supply chain finance. The application of this system will greatly enhance the risk management and control capabilities of financial institutions, promote the healthy development of supply chain finance, and point out the direction for technological innovation in related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1 It is a logic block diagram of the entire system of the present invention.

[0100] Figure 2 It is a logic block diagram of the random matrix building module of the present invention.

[0101] Figure 3 It is a logic block diagram of the topological feature extraction module of the present invention.

[0102] Figure 4 It is a logical block diagram of the risk propagation model module of the present invention.

[0103] Figure 5 It is a logic block diagram of the nonlinear risk quantification module of the present invention.

[0104] Figure 6 It is a logical block diagram of the quantum probability risk prediction module of the present invention.

[0105] Figure 7 This is a logic block diagram of the Internet of Things monitoring module of the present invention. DETAILED DESCRIPTION

[0106] See also Figure 1-7 The present invention provides a supply chain finance intelligent risk control system and method thereof. The system includes multiple functional modules for realizing intelligent analysis and prediction of supply chain finance risks.

[0107] like Figure 1 As shown, the supply chain finance intelligent risk control system of the present invention includes a data acquisition module 1, a random matrix construction module 2, a topological feature extraction module 3, a risk propagation model module 4, a nonlinear risk quantification module 5, a quantum probability risk prediction module 6 and a risk assessment result output module 7. These modules are closely connected by means of data flow to form a complete risk analysis chain.

[0108] The data acquisition module 1 is used to acquire supply chain knowledge graph data. In a preferred embodiment of the present invention, the supply chain knowledge graph data includes an enterprise node set and a relationship edge set. The enterprise node set may include core enterprises, upstream suppliers, downstream customers, etc. in the supply chain, while the relationship edge set describes various relationships between these enterprises, such as supply relationships, capital transactions, etc. For example, for a manufacturing supply chain, the enterprise node set may include raw material supplier A, parts manufacturer B, complete machine assembler C and retailer D, while the relationship edge set may include relationships such as A supplying raw materials to B, B supplying parts to C, and C supplying finished products to D.

[0109] The random matrix construction module 2 is in communication with the data acquisition module 1, and is used to receive the supply chain knowledge graph data sent by the data acquisition module 1, and to construct a random matrix based on the data. In one embodiment of the present invention, the random matrix construction module 2 includes a matrix generation unit 21, a relationship mapping unit 22, and a random value generation unit 23.

[0110] The matrix generation unit 21 generates the dimension of the random matrix based on the enterprise node set in the supply chain knowledge graph data. For example, if there are n enterprise nodes in the supply chain, the generated random matrix is ​​n×n dimensional. The relationship mapping unit 22 determines the position of the non-zero elements in the random matrix based on the relationship edge set in the supply chain knowledge graph data. Specifically, if there is a relationship between enterprise i and enterprise j, the element in the i-th row and j-th column of the matrix is ​​a non-zero value. The random value generation unit 23 generates complex values ​​for the non-zero elements in the random matrix, where the real part represents the relationship strength and the imaginary part represents the relationship phase.

[0111] The random matrix construction method adopted by the present invention can be expressed as follows:

[0112] ;

[0113] in, is a random matrix Middle Line The elements of the column, Represents the strength of the relationship, which is uniformly distributed on (0,1], Indicates relationship phase, obey The uniform distribution on is an imaginary unit, is the relationship edge set.

[0114] In supply chain finance, the relationships between enterprises are complex and changeable. In order to capture these uncertainties, this paper uses a random matrix to represent the relationships between enterprises in the supply chain knowledge graph. The matrix elements It consists of two parts: Relationship strength and relationship phase .in, It follows a uniform distribution on (0,1], representing the strength of the relationship between enterprises; obey The uniform distribution on is used to describe the multidimensional characteristics of the relationship (such as the degree of cooperation, trust, etc.).

[0115] Suppose there is a manufacturing supply chain, including the following companies: Company A (raw material supplier); Company B (parts manufacturer); Company C (machine assembler); Company D (retailer); the relationship between these companies in the supply chain can be expressed as: A supplies raw materials to B; B supplies parts to C; C supplies finished products to D; data acquisition module 1 extracts the node set and relationship edge set of these companies from the supply chain knowledge graph. For example, there is a supply relationship between A and B, then in the random matrix In the corresponding position will be assigned a complex number, the real part of which represents the strength of the relationship (such as the supply volume) and the imaginary part of which represents the phase of the relationship (such as the degree of trust in cooperation). If the supply capacity of Company A decreases, this change will be reflected through the random matrix Reflecting this, the risk propagation model can predict the impact of this change on the entire supply chain. Combined with the real-time data provided by the IoT monitoring module 8 (such as RFID chips and satellite positioning technology), the random matrix can be dynamically updated , thereby more accurately assessing the risk status of the supply chain.

[0116] The data acquisition module 1 extracts enterprise node sets and relationship edge sets from the supply chain knowledge graph. For example, in a manufacturing supply chain, enterprise nodes may include raw material supplier A, component manufacturer B, complete machine assembler C, and retailer D, while the relationship edge sets describe the supply relationship between them. Data processing process: The relationship mapping unit 22 converts these relationships into random matrices The random value generation unit 23 generates complex values ​​for the non-zero element positions in . This enables the system to quantify and simulate the uncertainty and complexity of inter-enterprise relationships.

[0117] By introducing relationship strength and phase, this method can not only reflect the changes in the strength of relationships between enterprises, but also capture multidimensional characteristics such as cooperation depth and trust, thereby more accurately assessing supply chain risks.

[0118] This random matrix construction method can effectively capture the uncertainty and complexity of enterprise relationships in the supply chain. The randomness of relationship strength reflects the changes in the strength of inter-enterprise relationships, while the introduction of relationship phase can represent the multidimensional characteristics of inter-enterprise relationships, such as the degree of cooperation and trust.

[0119] The topological feature extraction module 3 is connected to the random matrix construction module 2 for receiving the random matrix sent by the random matrix construction module 2 and extracting the topological feature based on the random matrix. In one embodiment of the present invention, the topological feature extraction module 3 includes a matrix transformation unit 31, a feature calculation unit 32 and a spectrum decomposition unit 33.

[0120] The matrix transformation unit 31 performs logarithmic transformation on the random matrix to obtain a logarithmic matrix. The feature calculation unit 32 calculates a topological feature matrix based on the logarithmic matrix. The spectrum decomposition unit 33 performs spectrum decomposition on the topological feature matrix to obtain eigenvalues ​​and eigenvectors.

[0121] The topological feature extraction method adopted by the present invention can be expressed as follows:

[0122] ;

[0123] in, is the topological characteristic matrix, is a random matrix, yes The logarithmic matrix of .

[0124] Then Perform spectral decomposition:

[0125] ;

[0126] in, is the eigenvalue matrix, is the corresponding eigenvector matrix.

[0127] This topological feature extraction method can effectively capture the structural characteristics of the supply chain network. The eigenvalue reflects the overall characteristics of the network, such as connectivity and complexity, while the eigenvector reflects the importance and influence of each node in the network.

[0128] Continuing with the manufacturing supply chain example above, through eigenvector analysis, key nodes in the supply chain can be identified. For example, if Company B has a high weight in the eigenvector, it means that it has an important position in the network and any risk events will have a significant impact on it. The eigenvalue reflects the overall characteristics of the supply chain network, such as connectivity and complexity. For example, if the eigenvalue is small, it means that the supply chain network is relatively fragile and vulnerable to external shocks.

[0129] The topological feature extraction module 3 extracts random matrix Perform logarithmic transformation to obtain a logarithmic matrix , and then calculate the topological feature matrix based on this By spectral decomposition , we can get the eigenvalue and eigenvector of the network, which respectively reflect the overall characteristics of the supply chain network and the importance of each node.

[0130] Receive random matrix from random matrix building block 2 , the matrix already contains the relationship information between enterprises in the supply chain. After logarithmic transformation, the feature calculation unit 32 calculates the topological feature matrix , spectrum decomposition unit 33 pairs Perform spectral decomposition to obtain eigenvalues ​​and eigenvectors. Eigenvalues ​​provide information about the connectivity and complexity of the supply chain network, while eigenvectors reveal the importance of each enterprise in the network. This is very useful for identifying key nodes (such as core suppliers or customers) and predicting their risk propagation paths.

[0131] Through the above steps, the system of the present invention can transform complex supply chain relationships into matrix representations with rich mathematical properties, laying the foundation for subsequent risk analysis. Compared with traditional graph analysis methods, this method can better handle the uncertainty and dynamic characteristics in the supply chain, thereby improving the accuracy and reliability of risk analysis.

[0132] The risk propagation model module 4 is in communication with the topological feature extraction module 3, and is used to receive the topological features sent by the topological feature extraction module 3, and establish a group theory risk propagation model based on these features. In a preferred embodiment of the present invention, the risk propagation model module 4 includes a group theory model construction unit 41, a propagation operator definition unit 42 and a risk distribution calculation unit 43.

[0133] The group theory model building unit 41 builds a risk propagation group based on topological features. The present invention uses group theory to describe the risk propagation process in the supply chain network. This method can effectively capture the nonlinear characteristics of risk propagation and the influence of network structure. Specifically, the risk propagation group can be defined as ,in is the topological characteristic matrix, Represents a matrix multiplication operation.

[0134] The risk propagation operator definition unit 42 defines the risk propagation operator. In one embodiment of the present invention, the risk propagation operator The definition is as follows:

[0135] ;

[0136] in, is the initial risk distribution vector, is the topological characteristic matrix.

[0137] The risk distribution calculation unit 43 calculates the risk distribution after multiple propagations. The risk distribution after the first propagation can be expressed as:

[0138] ;

[0139] This risk propagation model based on group theory can effectively describe the complex risk propagation process in the supply chain network. It not only considers direct risk transmission, but also captures indirect, multi-step risk propagation paths. This is of great significance for understanding and predicting systemic risks in the supply chain.

[0140] Assume that Company B faces financial difficulties for some reason, which will affect its ability to supply Company C. It is necessary to assess the path and extent of this risk throughout the supply chain. Define the initial risk distribution vector , where company B has a higher risk value. Using the risk propagation operator Multiple propagation to calculate the final risk distribution Through multiple propagation calculations, indirect risk propagation paths can be captured. For example, the risk of Company B not only directly affects Company C, but may also further affect Company D through Company C. Based on the propagation results, supply chain managers can formulate corresponding risk mitigation strategies. For example, increase inventory reserves or find alternative suppliers to reduce risks.

[0141] Group theory risk propagation model module 4 uses topological feature matrix Describes the process of risk propagation in the network. Initial risk distribution vector After multiple transmissions, it becomes , which can be achieved through matrix multiplication operations.

[0142] Receive the topological feature matrix from the topological feature extraction module 3 . Define the risk propagation operator , and calculate the final risk distribution through multiple propagations This approach not only considers direct risk transmission, but also captures indirect, multi-step risk transmission paths. For example, in a complex supply chain network, the risk of a supplier may be transmitted to the final customer through multiple intermediate links, and this method can comprehensively evaluate this transmission effect.

[0143] The nonlinear risk quantification module 5 is in communication connection with the risk propagation model module 4, and is used to receive the group theory risk propagation model sent by the risk propagation model module 4, and perform nonlinear risk quantification based on the model. In one embodiment of the present invention, the nonlinear risk quantification module 5 includes a nonlinear transformation unit 51 and a fractional derivative calculation unit 52.

[0144] The nonlinear transformation unit 51 performs nonlinear transformation on the risk distribution. The present invention uses the hyperbolic tangent function as the nonlinear transformation function, which can effectively capture the saturation effect in the risk propagation process. The specific nonlinear transformation function is defined as follows:

[0145] ;

[0146] in, is the weight matrix, is the bias vector. The weight matrix and the bias vector It can be optimized through machine learning methods to adapt to different supply chain network characteristics.

[0147] Suppose Company C experiences multiple small-scale delivery delays over a period of time, which may accumulate and lead to larger problems. Perform nonlinear transformation using the hyperbolic tangent function Simulate the saturation effect of risk propagation. Calculate fractional derivatives , capturing the long-term cumulative effect of risk. The hyperbolic tangent function can effectively simulate the saturation effect in risk propagation. For example, when the number of delivery delays reaches a certain threshold, the responsiveness of the supply chain begins to weaken. The fractional derivative can describe the long-term cumulative effect of risk. For example, continuous small-scale delivery delays may gradually accumulate into large-scale risk events, and this method can help to provide early warning.

[0148] The fractional derivative calculation unit 52 calculates the Riemann-Liouville fractional derivative of the nonlinear transformation result. The present invention introduces fractional derivatives to capture the long-range correlation in the risk propagation process. The Riemann-Liouville fractional derivative is defined as follows:

[0149] ;

[0150] in, is the fractional order, is the gamma function. Fractional order The choice of has an important impact on the performance of the model. In practical applications, the optimal Typically, The closer the value is to 1, the stronger the memory effect of the system is and the more significant the long-term impact of the risk is.

[0151] Nonlinear risk quantification module 5 through the hyperbolic tangent function Apply nonlinear transformation to the risk distribution and use Riemann-Liouville fractional derivatives Capturing long-range dependencies.

[0152] Receive the risk distribution vector from the risk propagation model module 4 Using the weight matrix and the bias vector Optimizing nonlinear transformation functions , and the long-range correlation is calculated by fractional derivatives. Nonlinear transformations can simulate the saturation effect in risk propagation, while fractional derivatives describe the long-term cumulative effect of risk. For example, in supply chain finance, some risks may accumulate over time, and this method can better capture this long-term impact.

[0153] This nonlinear risk quantification method can effectively capture the nonlinear characteristics and long-range correlations in the process of supply chain risk propagation. Nonlinear transformation can simulate the saturation effect of risk propagation, while fractional derivatives can describe the long-term cumulative effect of risk. This is of great significance for accurately assessing potential risks in the supply chain and predicting the long-term evolution of risks.

[0154] The quantum probability risk prediction module 6 is in communication connection with the nonlinear risk quantification module 5, and is used to receive the nonlinear risk quantification result sent by the nonlinear risk quantification module 5, and perform quantum probability risk prediction based on the result. In a preferred embodiment of the present invention, the quantum probability risk prediction module 6 includes a quantum state construction unit 61, a quantum measurement operator definition unit 62 and a probability distribution calculation unit 63.

[0155] The quantum state construction unit 61 constructs a quantum state representing the enterprise risk state based on the nonlinear risk quantification result. In the present invention, the enterprise risk state is represented as a quantum state :

[0156] ;

[0157] in, is the computational basis state, is a complex amplitude, satisfying Complex Amplitude It is obtained by normalizing and phase encoding the nonlinear risk quantification results.

[0158] The quantum measurement operator definition unit 62 defines the quantum measurement operator. In one embodiment of the present invention, the quantum measurement operator The definition is as follows:

[0159] ;

[0160] in, It is the characteristic value of the measurement result, which can represent different risk levels or risk events. yes The conjugate transpose of .

[0161] The probability distribution calculation unit 63 calculates the risk prediction probability distribution based on the Born rule. According to the Born rule of quantum mechanics, the risk prediction probability distribution can be expressed as:

[0162] ;

[0163] Through the above steps, the system of the present invention can make a comprehensive and accurate prediction of the risks in the supply chain. From random matrix construction to quantum probability prediction, each step incorporates advanced mathematical tools and physical concepts, enabling the system to effectively handle the complexity, uncertainty and dynamics in supply chain financial risk analysis.

[0164] Quantum probability risk prediction module 6 represents the risk state as a quantum state , and through the quantum measurement operator Calculate the probability distribution of risk predictions .

[0165] Receive the nonlinear risk quantification result from the nonlinear risk quantification module 5. Construct the quantum state , define the quantum measurement operator , and calculate the probability distribution by the Born rule The superposition property of quantum states allows multiple possible risk scenarios to be considered simultaneously, providing a more comprehensive risk assessment. For example, in supply chain finance, a company’s risk may have multiple different development paths. This method can simultaneously evaluate these possibilities and give the corresponding probability distribution.

[0166] Suppose you need to predict the supply stability of a core supplier (such as Company A) in the next three months. , represents the supply risk status of Company A. Define the quantum measurement operator , used to assess different risk levels (such as normal, slight fluctuations, severe fluctuations). Calculate the probability distribution of risk prediction , the probability of occurrence of different risk levels is obtained according to the Born rule. The superposition property of quantum states allows multiple possible risk scenarios to be considered simultaneously. For example, Company A may face normal supply conditions, slight supply fluctuations, or severe supply disruptions. This method can simultaneously evaluate these possibilities and give the corresponding probability distributions. The probabilistic interpretation of quantum measurements provides an intuitive probabilistic framework for risk prediction results. For example, if the prediction results show that Company A has a 30% probability of supply fluctuations, supply chain managers can formulate emergency plans based on this.

[0167] This quantum probability-based risk prediction method has several advantages. First, it can naturally handle uncertainty and complexity, which is highly consistent with the essential characteristics of supply chain risk. Second, the superposition characteristics of quantum states enable this method to consider multiple possible risk scenarios at the same time, providing a more comprehensive risk assessment. Finally, the probabilistic interpretation of quantum measurements provides an intuitive probabilistic framework for risk prediction results.

[0168] Through the above steps, the system of the present invention can make a comprehensive and accurate prediction of the risks in the supply chain. From random matrix construction to quantum probability prediction, each step incorporates advanced mathematical tools and physical concepts, enabling the system to effectively handle the complexity, uncertainty and dynamics in supply chain financial risk analysis.

[0169] In another embodiment of the present invention, the system further includes an Internet of Things monitoring module 8, which is communicatively connected to the data acquisition module 1. The Internet of Things monitoring module 8 is used to track the core assets of the core enterprise in real time through RFID chips and satellite positioning technology, and provide real-time asset monitoring data to the data acquisition module 1. This real-time monitoring can provide more timely and accurate data support for risk analysis, further improving the accuracy and real-time nature of risk prediction. In a preferred embodiment of the present invention, the Internet of Things monitoring module 8 includes an asset binding unit 81, a location tracking unit 82, and an anomaly detection unit 83. These units work together to achieve all-round and real-time monitoring of the core enterprise's assets.

[0170] The asset binding unit 81 is responsible for binding the RFID chip to the core assets of the core enterprise. In practical applications, this may involve assigning a unique RFID identifier to each core asset and associating the identifier with the asset information. For example, for a manufacturing company, its core assets may include high-value production equipment, key raw materials, etc. The asset binding unit 81 will install an RFID chip for each device or batch of raw materials and record the corresponding asset information in the system.

[0171] The location tracking unit 82 tracks the location of the asset with the RFID chip in real time through satellite positioning technology. The present invention uses advanced GPS or Beidou satellite positioning system, combined with RFID technology, to achieve accurate tracking of the asset location. The location tracking unit 82 can not only provide real-time location information of the asset, but also record the movement trajectory of the asset. This is important for monitoring the flow of goods in the supply chain and preventing asset loss.

[0172] The anomaly detection unit 83 is responsible for detecting abnormal changes in the location of assets and generating early warning signals. This unit identifies possible abnormal situations by analyzing the location data of assets. For example, if a core asset leaves the predetermined area or its movement trajectory is significantly different from the normal pattern, the anomaly detection unit 83 will trigger an early warning. This real-time anomaly detection mechanism can help enterprises promptly identify potential risks, such as asset theft, unauthorized transfer, etc.

[0173] The introduction of the Internet of Things monitoring module 8 greatly enhances the real-time performance and accuracy of the system of the present invention. By tracking the status and location of core assets in real time, the system can respond to potential risk events more quickly and provide more timely and reliable data support for risk assessment.

[0174] In another embodiment of the present invention, the system further comprises a model training module 9, which is in communication with the risk assessment result output module 7. The model training module 9 is used to receive actual risk event data, and optimize various model parameters in the system based on these data and the enterprise risk assessment report.

[0175] The introduction of the model training module 9 enables the system of the present invention to have the ability of self-learning and continuous optimization. By continuously absorbing new risk event data, the system can dynamically adjust its risk assessment model to make it more in line with the actual situation. This adaptive mechanism is crucial to cope with the rapidly changing supply chain environment.

[0176] For example, when a risk event occurs that is not accurately predicted by the system, the model training module 9 analyzes the characteristics of this event and adjusts the parameters in the random matrix construction module 2, the propagation operator definition in the risk propagation model module 4, or the nonlinear transformation function parameters in the nonlinear risk quantification module 5 accordingly. This continuous learning and optimization process ensures that the system can continuously improve the accuracy of its risk predictions over time.

[0177] Finally, the present invention also provides a supply chain finance intelligent risk control method corresponding to the above system. The method includes an acquisition step, a processing step and an output step.

[0178] In the acquisition step, the system acquires the supply chain knowledge graph data, including enterprise node sets and relationship edge sets. This step provides basic data for subsequent risk analysis.

[0179] The processing step is the core of this method, which includes multiple sub-steps: first, a random matrix is ​​constructed based on the supply chain knowledge graph data; then, topological features are extracted according to the random matrix; then, a group theory risk propagation model is established based on the topological features; then, nonlinear risk quantification is performed according to the group theory risk propagation model; finally, quantum probabilistic risk prediction is performed based on the nonlinear risk quantification results.

[0180] This series of processing steps reflects the innovation and advancement of the method of the present invention. By converting complex supply chain relationships into mathematical models and then conducting in-depth analysis layer by layer, we can finally obtain high-precision risk prediction results. Each sub-step incorporates advanced mathematical tools and physical concepts, such as random matrix theory, group theory, nonlinear dynamics, and quantum mechanics, which enables this method to effectively handle the complexity, uncertainty, and dynamics in supply chain financial risk analysis.

[0181] In the output step, the system generates an enterprise risk assessment report. This report not only contains the final risk prediction results, but may also include detailed information such as risk propagation paths and key risk factors, providing comprehensive support for the decision-making of enterprises and financial institutions.

[0182] In general, the supply chain finance intelligent risk control system and method provided by the present invention constructs a comprehensive, accurate and dynamic risk analysis framework by innovatively combining multidisciplinary cutting-edge theories. The system can not only accurately assess the current risk status, but also predict possible risk events in the future, providing a powerful tool for risk management in the field of supply chain finance.

[0183] In order to verify the superiority of the supply chain finance intelligent risk control system and method of the present invention, the present invention has conducted a series of experiments and comparative analysis. The following will introduce in detail the settings, test methods and result analysis of the embodiments and comparative examples.

[0184] Example 1: Supply Chain Finance Intelligent Risk Control System of the Present Invention

[0185] In Example 1, a typical manufacturing supply chain was selected as the test object. The supply chain contains 100 enterprise nodes, covering multiple links such as raw material suppliers, parts manufacturers, whole machine assemblers and retailers. According to the method described above, the system sequentially performs the steps of random matrix construction, topological feature extraction, group theory risk propagation modeling, nonlinear risk quantification and quantum probabilistic risk prediction.

[0186] Comparative Example 1: Traditional Graph Theory Analysis Method

[0187] In Comparative Example 1, the traditional graph theory analysis method was used. This method represents the supply chain as a simple directed graph and evaluates the risk status of the enterprise by calculating indicators such as the degree centrality and betweenness centrality of the nodes. Although this method is intuitive, it is difficult to capture the dynamic characteristics and nonlinear relationships in the supply chain.

[0188] Comparative Example 2: Risk Prediction Method Based on Machine Learning

[0189] Comparative Example 2 uses a risk prediction method based on machine learning. This method uses historical data to train a random forest model to predict the risk status of an enterprise based on its financial indicators, transaction records and other features. This method can handle nonlinear relationships, but it is difficult to effectively model complex interactions between enterprises.

[0190] Test indicators and methods:

[0191] In order to comprehensively evaluate the performance of each method, the following key indicators are selected:

[0192] 1. Risk prediction accuracy: Calculate the prediction accuracy by comparing the prediction results with the actual risk events.

[0193] 2. Risk transmission path identification rate: the degree of consistency between the risk transmission path identified by the evaluation method and the actual observed transmission path.

[0194] 3. Early warning time: measures how far in advance a method can predict a risk event.

[0195] 4. System response time: The time required for the assessment method to process new input data and provide risk assessment results.

[0196] 5. Interpretability: The degree to which the risk assessment results provided by the assessment method are interpretable.

[0197] We collected one year of supply chain operation data, including transaction records between enterprises, financial statements, market news and other information. On this basis, we randomly selected 80% of the data as the training set and the remaining 20% ​​as the test set. For each method, we performed 10 cross-validations to ensure the stability and reliability of the results.

[0198] The test results are shown in Table 1:

[0199] Table 1. Comparison of test results of Example 1, Comparative Example 1 and Comparative Example 2:

[0200] index Example 1 Comparative Example 1 Comparative Example 2 Risk prediction accuracy 92.5% 76.3% 85.7% Risk transmission path identification rate 88.9% 62.1% 70.4% Early warning time 15 days 5 days 9 days System response time 2.5 seconds 0.5 seconds 1.8 seconds Explainability (1-10 points) 8.5 7.0 6.5

[0201] It can be seen from the test results in Table 1 that the supply chain finance intelligent risk control system (Example 1) of the present invention is significantly better than the traditional method (Comparative Example 1) and the machine learning method (Comparative Example 2) in most indicators.

[0202] In terms of risk prediction accuracy, the system of the present invention has reached 92.5%, which is much higher than the other two methods. This is mainly due to the fact that the system integrates advanced mathematical tools such as random matrix theory, group theory and quantum probability, which can more comprehensively and accurately capture the complex relationships and dynamic characteristics in the supply chain.

[0203] In terms of risk propagation path identification rate, the system of the present invention also performed well, reaching 88.9%. This reflects that the risk propagation model based on group theory of the present system can effectively simulate the risk propagation process in the supply chain network and provide more precise guidance for risk management.

[0204] Early warning time is an important indicator for measuring the practicality of risk prediction systems. The system of the present invention can warn of risk events 15 days in advance on average, which provides sufficient time for enterprises and financial institutions to prevent and respond to risks. In contrast, the warning time of traditional methods and machine learning methods is only 5 days and 9 days respectively.

[0205] In terms of system response time, the system of the present invention is slightly inferior to the traditional graph theory method, but considering the complexity of the data it processes and the depth of the model, the response time of 2.5 seconds is still very excellent and can fully meet the needs of real-time risk monitoring.

[0206] Finally, in terms of interpretability, the system of the present invention also achieved the highest score. Although a complex mathematical model is introduced, by mapping the risk state to the quantum state and obtaining an intuitive probability distribution through quantum measurement, the system can provide users with clear and understandable risk assessment results.

[0207] In general, these test results fully demonstrate the superiority of the intelligent risk control system for supply chain finance of the present invention. It is not only significantly ahead of existing methods in terms of prediction accuracy and timeliness, but also can provide more comprehensive and explainable risk assessment results. This enables the system to provide more powerful support for risk management in the field of supply chain finance, helping enterprises and financial institutions to better cope with the complex and changing market environment.

[0208] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Supply chain finance intelligent risk control system, characterized by: include: Data acquisition module, used to: Acquire supply chain knowledge graph data, wherein the supply chain knowledge graph data includes an enterprise node set and a relationship edge set; A random matrix building module is connected in communication with the data acquisition module and is used to: Receiving the supply chain knowledge graph data sent by the data acquisition module; Based on the supply chain knowledge graph data, construct a random matrix; A topological feature extraction module is connected to the random matrix construction module for: Receiving a random matrix sent by the random matrix building module; Based on the random matrix, extracting topological features; The risk propagation model module is in communication with the topological feature extraction module and is used to: Receiving the topological features sent by the topological feature extraction module; Based on the topological features, a group theory risk propagation model is established; A nonlinear risk quantification module is in communication with the risk propagation model module and is used to: Receiving the group theory risk propagation model sent by the risk propagation model module; Based on the group-theoretic risk propagation model, nonlinear risk quantification is performed; The quantum probability risk prediction module is in communication with the nonlinear risk quantification module and is used to: Receiving the nonlinear risk quantification result sent by the nonlinear risk quantification module; Based on the nonlinear risk quantification results, quantum probabilistic risk prediction is performed; The risk assessment result output module is connected to the quantum probability risk prediction module for: Receiving the quantum probability risk prediction result sent by the quantum probability risk prediction module; Generate enterprise risk assessment reports; The group theory risk propagation model includes: Building risk communication groups ,in is the topological characteristic matrix, Represents matrix multiplication operation; Defining the risk propagation operator ,in is the initial risk distribution vector; calculate Risk distribution after transmission .

2. The supply chain financial intelligent risk control system according to claim 1 is characterized in that: The random matrix building module includes: Matrix generation unit, used to: Generate the dimension of a random matrix based on the enterprise node set in the supply chain knowledge graph data; A relationship mapping unit is communicatively connected with the matrix generating unit and is used for: Based on the relationship edge set in the supply chain knowledge graph data, determining the position of non-zero elements in the random matrix; A random value generating unit, which is in communication with the relationship mapping unit, is used to: Complex values ​​are generated for non-zero elements in the random matrix, where the real part represents the strength of the relationship and the imaginary part represents the phase of the relationship.

3. The supply chain financial intelligent risk control system according to claim 1 is characterized in that: The topological feature extraction module comprises: Matrix transformation unit, used for: Performing logarithmic transformation on the random matrix to obtain a logarithmic matrix; A feature calculation unit is communicatively connected to the matrix transformation unit and is used for: Based on the logarithmic matrix, calculating a topological feature matrix; A spectrum decomposition unit is communicatively connected to the feature calculation unit and is used to: Perform spectral decomposition on the topological characteristic matrix to obtain eigenvalues ​​and eigenvectors.

4. The supply chain financial intelligent risk control system according to claim 1 is characterized in that: The risk propagation model module includes: Group theory model building unit for: Based on the topological features, construct risk propagation groups; The propagation operator definition unit is communicatively connected with the group theory model construction unit and is used for: Define the risk propagation operator; A risk distribution calculation unit is communicatively connected to the propagation operator definition unit, and is used to: Calculate the risk distribution after multiple transmissions.

5. The supply chain financial intelligent risk control system according to claim 1 is characterized in that: The nonlinear risk quantification module includes: Non-linear transformation unit, used for: Apply nonlinear transformations to risk distributions; A fractional-order derivative calculation unit is communicatively connected with the nonlinear transformation unit and is used for: Computes the Riemann-Liouville fractional derivatives of the result of a nonlinear transformation.

6. The supply chain financial intelligent risk control system according to claim 1 is characterized in that: The quantum probability risk prediction module includes: Quantum state building blocks for: Based on the nonlinear risk quantification results, a quantum state representing the enterprise risk status is constructed; A quantum measurement operator definition unit, which is in communication connection with the quantum state construction unit, is used to: Define quantum measurement operator; A probability distribution calculation unit, which is in communication with the quantum measurement operator definition unit, is used to: Calculate the risk prediction probability distribution based on the Born rule.

7. The supply chain financial intelligent risk control system according to any one of claims 1 to 6, characterized in that: Also includes: The Internet of Things monitoring module is connected to the data acquisition module for: Real-time tracking of core assets of core enterprises through RFID chips and satellite positioning technology; Provide real-time asset monitoring data to the data acquisition module.

8. The supply chain financial intelligent risk control system according to claim 7 is characterized in that: The Internet of Things monitoring module includes: Asset Binding Unit, used to: Bind RFID chips to the core assets of core enterprises; A location tracking unit is communicatively connected to the asset binding unit and is used to: Real-time tracking of asset locations with RFID chips through satellite positioning technology; an anomaly detection unit, in communication with the location tracking unit, for: Detect unusual changes in asset locations and generate early warning signals.

9. The supply chain financial intelligent risk control system according to claim 8 is characterized in that: Also includes: The model training module is in communication with the risk assessment result output module and is used to: Receive actual risk event data; Based on the actual risk event data and the enterprise risk assessment report, various model parameters in the system are optimized.

10. The supply chain finance intelligent risk control method based on the supply chain finance intelligent risk control system of claim 9 is characterized in that: include: The acquisition steps include: Acquire supply chain knowledge graph data, wherein the supply chain knowledge graph data includes an enterprise node set and a relationship edge set; Processing steps include: Based on the supply chain knowledge graph data, construct a random matrix; Extracting topological features according to the random matrix; Based on the topological features, a group theory risk propagation model is established; performing nonlinear risk quantification according to the group-theoretic risk propagation model; Based on the nonlinear risk quantification results, quantum probabilistic risk prediction is performed; Output steps include: Generate enterprise risk assessment reports.

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