Warehouse logistics financial risk dynamic assessment system fusing multi-dimensional customer data

Abstract

The invention relates to the technical field of financial science and logistics management, and discloses a warehouse logistics financial risk dynamic assessment system fusing multi-dimensional customer data, which comprises a multi-source heterogeneous event flow access and semantic standardization module, a federated event flow reasoning and dynamic causal atlas construction module and a risk dynamic assessment and uncertainty quantification module. And an adaptive risk toughness optimization and decision support module. The system collects an original data stream, processes the original data stream into a risk atomic event stream, and constructs and dynamically updates a warehouse logistics financial risk causal map in real time; and risk aggregation and uncertainty quantification are carried out based on a causal path in the map, a comprehensive financial risk index is calculated, an uncertainty quantification result is obtained, and a toughness optimization strategy of the warehouse logistics financial system is generated. According to the method, closed-loop management from risk identification and quantification to optimization treatment is realized, and the accuracy of risk assessment and the adaptability of decision making are improved.

Description

Technical Field

[0001] This invention relates to the fields of financial technology and logistics management technology, specifically a dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data. Background Technology

[0002] In modern warehousing and logistics management, customer data is characterized by its multidimensionality, heterogeneity, and real-time concurrency, making the transmission mechanism of financial risks increasingly complex for enterprises. Accurately assessing and managing these risks is crucial for maintaining supply chain stability and healthy business operations.

[0003] Existing risk assessment technologies are inadequate for handling such scenarios. Many systems struggle to achieve deep semantic understanding and standardized processing when dealing with multi-source heterogeneous data streams, often relying on pre-set static rules or traditional statistical models for correlation analysis. This makes it difficult for systems to automatically discover and construct deep, dynamically evolving causal relationships between risk events, resulting in only superficial correlation monitoring that fails to reveal the actual transmission path of risks.

[0004] In the risk quantification phase, traditional methods often employ a simple weighted summation of risk indicators to calculate overall risk. This approach ignores the path-dependent aggregation of risks within complex networks, resulting in limited accuracy. Furthermore, these systems rarely consider the integration of external macroeconomic data with internal causal graphs when assessing risk, lacking the quantification of the probability of risk paths occurring and their uncertainties, leading to insufficient reliability of the assessment results and making it difficult to support high-risk decision-making.

[0005] Most existing risk management systems are limited to risk monitoring and alerting. When high-risk signals are identified, the systems lack the ability to automatically generate adaptive resilience optimization strategies. There is a gap between risk assessment and decision support, requiring significant human intervention to formulate countermeasures. This results in a lag in response in a rapidly changing logistics environment, making it difficult to form a closed-loop management system from risk identification and quantification to proactive decision-making. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data. The system aims to solve the problems in existing technologies, such as the difficulty in automatically discovering and constructing deep dynamic causal relationships between risk events, the insufficient accuracy and reliability of financial risk assessment, and the lack of closed-loop management due to the gap between risk assessment and decision support.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data, the system comprising: The multi-source heterogeneous event stream access and semantic standardization module is configured to collect raw data streams from multiple heterogeneous data sources in real time and process the raw data streams into risk atomic event streams with unified business semantics. The Federal Event Flow Reasoning and Dynamic Causal Graph Construction Module is configured to receive the risk atomic event flow and perform causal reasoning processing on the risk atomic event flow to construct and dynamically update the causal graph of warehousing and logistics financial risks in real time. The risk dynamic assessment and uncertainty quantification module is configured to obtain the causal graph of the financial risk of the warehousing and logistics, perform risk aggregation based on the causal paths in the causal graph to calculate the comprehensive financial risk index of the entity, and perform uncertainty quantification on the risk paths to obtain the uncertainty quantification result. The adaptive risk resilience optimization and decision support module is configured to receive the comprehensive financial risk index and the uncertainty quantification result, and generate a resilience optimization strategy for the warehousing and logistics financial system based on the comprehensive financial risk index and the uncertainty quantification result.

[0008] In one specific embodiment, the multi-source heterogeneous event stream access and semantic standardization module uses domain-adaptive event pattern extraction technology to process the original data stream. This domain-adaptive event pattern extraction technology is implemented through a domain-adaptive event pattern extraction unit, which includes: The time-series pattern mining unit is configured to identify event sequence patterns with a specific time order in the data stream; And a semantic association and extraction unit, configured to perform semantic-level analysis, extraction and association on the event sequence patterns and unstructured data.

[0009] Preferably, the federated event flow reasoning and dynamic causal graph construction module performs causal reasoning processing on the risk atomic event flow through a federated event flow reasoning engine. This federated event flow reasoning engine includes: Multiple local inference units are configured to run in their respective local environments to generate local causal patterns; And a global fusion unit, configured to receive all the local causal patterns and perform structured union and parameter aggregation operations to construct and update the global warehousing and logistics financial risk causal graph.

[0010] In one specific embodiment, the causal reasoning process is implemented through an embedded distributed incremental causal discovery algorithm. The distributed incremental causal discovery algorithm includes: Adopt the time priority principle; Perform conditional independence test; And an incremental update mechanism that performs the conditional independence test only between a new risky atomic event and its local neighborhood node set.

[0011] Preferably, the causal map of financial risks in warehousing and logistics includes: The node set includes: atomic event nodes and aggregated risk nodes; A set of directed edges, used to represent the causal relationships between the nodes; And an edge weight set associated with the directed edge set, used to quantify the causal relationship, wherein the edge weight includes at least: causal strength, confidence level and timestamp.

[0012] In one specific embodiment, the risk dynamic assessment and uncertainty quantification module employs a path-dependent risk aggregation algorithm for risk aggregation. This algorithm calculates the comprehensive financial risk index in the following manner: Identify the set of all valid causal paths from the source risk node to the target financial risk node, and sum the individual risk contribution values ​​of all paths in the set; wherein, the individual risk contribution value of each path is calculated based on the cumulative path causal strength, source event frequency weighting, and recent event time weighting.

[0013] Preferably, the risk dynamic assessment and uncertainty quantification module further includes a causal path backtracking unit, which is configured to: when the comprehensive financial risk index exceeds a preset risk threshold, sort the individual risk contribution values ​​of each path constituting the comprehensive financial risk index in descending order, and extract the N causal paths with the highest contribution.

[0014] In one specific embodiment, the risk dynamic assessment and uncertainty quantification module employs multimodal uncertainty quantification technology to quantify the uncertainty. This technology is implemented through a multimodal uncertainty quantification unit, which is configured as follows: Construct a hybrid belief network, using the causal topology and nodes of the causal graph of financial risk in warehousing and logistics as its prior structural skeleton; Multimodal external data is integrated into the hybrid belief network as new nodes or evidence; Furthermore, a Bayesian inference algorithm is used to calculate the posterior probability distribution of the target risk event under given evidence and external data conditions.

[0015] Preferably, the adaptive risk resilience optimization and decision support module generates the resilience optimization strategy through an adaptive risk resilience optimization engine, which combines multi-objective dynamic programming and reinforcement learning strategies. The adaptive risk resilience optimization engine includes: The strategy evaluation unit is configured to use multi-objective dynamic programming techniques to generate a set of Pareto optimal strategies; It also includes a policy learning unit configured to use reinforcement learning techniques to adaptively learn and adjust the optimal policy in an online environment.

[0016] In one specific embodiment, the federated event flow reasoning and dynamic causal graph construction module is further configured with a dynamic graph maintenance mechanism. This mechanism includes: Concept drift adaptation automatically adjusts edge weights or adds / removes causal edges; Information decay processing involves introducing a time decay strategy when updating edge weights; And graph pruning, periodically removing edges whose causal strength or confidence is below a preset threshold.

[0017] This invention provides a dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data. It offers the following advantages: 1. This invention transforms data into a unified risk atomic event stream by employing domain-adaptive event pattern extraction technology, and uses a federated event stream inference engine and a distributed incremental causal discovery algorithm to perform causal inference processing on the event stream; it overcomes the limitations of traditional methods that rely on static rules or only perform surface correlation analysis, and can automatically construct and dynamically update the causal graph of financial risks in warehousing and logistics, reflecting the deep transmission mechanism between risk events.

[0018] 2. This invention utilizes a path-dependent risk aggregation algorithm to traverse the actual transmission paths in the causal graph of financial risks in warehousing and logistics to calculate a comprehensive financial risk index, which is more accurate than simple indicator weighting. At the same time, the system adopts multimodal uncertainty quantification technology, combining the internal structure of the graph and external macroeconomic environment data to quantify the probability of occurrence of risk paths and their impact, providing a more reliable basis for decision-making.

[0019] 3. Based on the calculated comprehensive financial risk index and uncertainty quantification results, this invention utilizes an adaptive risk resilience optimization engine and combines multi-objective dynamic programming and reinforcement learning strategies to automatically generate resilience optimization strategies. This enables the system to not only passively monitor risks but also proactively provide dynamic response solutions, realizing a complete closed loop from risk identification and quantification to optimized handling, thereby enhancing the overall resilience of the warehousing and logistics financial system. Attached Figure Description

[0020] Figure 1 This is a system framework diagram of the present invention.

[0021] Among them, 10 is the module for multi-source heterogeneous event stream access and semantic standardization; 20 is the module for federated event stream reasoning and dynamic causal graph construction; 30 is the module for dynamic risk assessment and uncertainty quantification; and 40 is the module for adaptive risk resilience optimization and decision support. Detailed Implementation

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see the appendix Figure 1 This invention provides a dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data. The system includes: The multi-source heterogeneous event stream access and semantic standardization module 10 is configured to collect raw data streams in real time from multiple heterogeneous data sources (such as warehouse management system WMS, transportation management system TMS, enterprise resource planning system ERP, customer relationship management system CRM, Internet of Things (IoT) devices, and external data sources); and adopts domain adaptive event pattern extraction (DAEPE) technology to process the raw data streams into risk atomic event (RAE) streams with unified business semantics.

[0024] The Federated Event Flow Inference and Dynamic Causal Graph Construction Module 20 is used to receive risk atomic event flows. Through the Federated Event Flow Inference Engine (FESIE) and its embedded Distributed Incremental Causal Discovery (DICD) algorithm, the Federated Event Flow Inference and Dynamic Causal Graph Construction Module 20 processes the RAE flow to construct and dynamically update the Warehouse Logistics Financial Risk Causal Graph (LFRCG) in real time.

[0025] The risk dynamic assessment and uncertainty quantification module 30 is connected to the federated event flow reasoning and dynamic causal graph construction module 20 to obtain a causal graph of financial risks in warehousing and logistics. The risk dynamic assessment and uncertainty quantification module 30 is configured to first employ the path-dependent risk aggregation (PDRA) algorithm to traverse the causal graph of financial risks in warehousing and logistics. Causal paths in computational entities Comprehensive financial risk index Employing multimodal uncertainty quantification (MMUQ) technology, combined with... And external macroeconomic environment data, for The probability of occurrence of the identified risk paths and the uncertainty of their impact are quantified.

[0026] The adaptive risk resilience optimization and decision support module 40, connected to the risk dynamic assessment and uncertainty quantification module 30, is used to receive the comprehensive financial risk index. And the uncertainty quantification results. The Adaptive Risk Resilience Optimization and Decision Support Module 40, through the Adaptive Risk Resilience Optimization Engine (AMM-RARE) and combined with Multi-Objective Dynamic Programming (MODP) and Reinforcement Learning (RL) strategies, is based on Based on the quantification results of uncertainty, a resilience optimization strategy for the warehousing and logistics financial system is generated.

[0027] The following is a detailed description of each module of the system.

[0028] The multi-source heterogeneous event stream access and semantic standardization module 10 is the data entry point of the system of the present invention. Its function is to realize real-time and reliable data access to heterogeneous data sources distributed in different business systems and external environments, and to transform these data into a unified and standardized data stream for subsequent modules to analyze and process.

[0029] In one embodiment, the multi-source heterogeneous event stream access and semantic standardization module 10 may include multiple data access units to adapt to different types of data sources. These data access units may include: Database Access Unit: Configured to access the backend databases of internal enterprise systems such as Warehouse Management System (WMS), Enterprise Resource Planning System (ERP), and Transportation Management System (TMS) via standard interfaces such as Open Database Connectivity (ODBC) or Java Database Connectivity (JDBC) through periodic polling or trigger-based access. The database access unit is used to acquire structured existing data and incremental change data generated during business processes, such as order status changes, inventory level fluctuations, and financial accounting vouchers.

[0030] Message Queue Subscription Unit: Configured to subscribe to relevant topics in the Enterprise Message Bus or specific message queues (e.g., Kafka, RabbitMQ, MQTT). Message Queue Subscription Units are suitable for receiving high-throughput real-time event streams, such as sensor data from Internet of Things (IoT) devices (e.g., warehouse temperature and humidity, logistics vehicle GPS tracks) or real-time logistics node updates from TMS systems (e.g., loading, dispatch, arrival, receipt).

[0031] API Call Unit: This unit calls the application programming interface (API) provided by an external system or service via network protocols (such as HTTP / HTTPS). API call units are used to retrieve, for example, customer interaction records and customer complaint data from a customer relationship management system (CRM), or to obtain data such as macroeconomic indices, industry prosperity indices, and currency exchange rates provided by external data service providers.

[0032] Web crawling unit (d): Configured to collect business-related unstructured or semi-structured data from public Internet channels (such as industry websites, news portals, and policy release platforms) according to preset rules and scheduling strategies. These data include industry news, policy and regulatory changes, supply chain disruption warnings, and public opinion related to specific customers.

[0033] The multi-source heterogeneous event stream access and semantic standardization module 10 further includes a data preprocessing unit, which performs a series of standardization operations on the raw data streams obtained through the above access units, providing data input with uniform format and reliable quality for subsequent Domain Adaptive Event Pattern Extraction (DAEPE).

[0034] In one embodiment, the operations performed by the data preprocessing unit include: Data cleaning: Identify and remove duplicate data records; correct obvious errors or inconsistencies in the data (e.g., malformed IDs, abnormal numerical ranges); handle invalid entries (e.g., records containing null strings or empty values).

[0035] Format conversion: Parses various data formats (such as XML, JSON, CSV, database row sets, unstructured text) from different data sources and converts them into a unified internal data structure (such as a collection of key-value pairs or a standardized data frame).

[0036] Timestamp Alignment: The data preprocessing unit is configured to parse various non-standard time format strings and convert the timestamps of all events to Coordinated Universal Time (UTC) or a specified standard time zone. For data lacking precise timestamps, this unit is configured to perform link latency-based estimation logic: obtain the arrival time of the data packet at the system and subtract the pre-determined average network transmission latency and average processing latency for this data source type to calculate the estimated timestamp of the event; or, mark the generation time of the data to be consistent with the average time of its upstream and downstream related data processed in the same batch.

[0037] Following the data preprocessing unit, the multi-source heterogeneous event stream access and semantic standardization module 10 further includes a Domain Adaptive Event Pattern Extraction (DAEPE) unit. The function of this DAEPE unit is to further process the data stream output by the data preprocessing unit, which has a unified format but heterogeneous semantics, into a standardized risk atomic event (RAE) stream with unified business semantics.

[0038] In one embodiment, a DAEPE unit may include the following units: The time-series pattern mining unit is configured to process structured or semi-structured data streams with time-series characteristics, such as inventory change logs from a WMS, logistics node update logs from a TMS, or status reports from IoT devices. The time-series pattern mining unit employs a stream-based sequence pattern mining algorithm to identify frequently occurring event sequence patterns with a specific temporal order in the data stream.

[0039] For example, the time-series pattern mining unit can automatically identify patterns representing standard operating procedures from TMS data streams, such as "order creation - warehouse order acceptance - picking - outbound scanning - loading - transportation - receipt"; at the same time, the time-series pattern mining unit can also identify non-standard or abnormal event sequence patterns, such as "outbound scanning - long period of inactivity (exceeding preset threshold) - transportation", or "order creation - multiple modifications - order cancellation".

[0040] For the implementation of sequence pattern mining algorithms, those skilled in the art can adopt a variety of mature technical solutions known in the art, such as streaming variants based on the Prefix Span or SPADE algorithm ideas. Their specific implementations are well-known technologies in the art and will not be elaborated here.

[0041] The semantic association and extraction unit is configured to perform semantic-level analysis, extraction, and association on event sequence patterns identified by the temporal pattern mining unit, as well as on preprocessed unstructured data (such as customer complaint texts, industry news, and policy announcements).

[0042] Missing value handling: Identify missing values ​​in data fields. Based on preset business rules, fill missing values ​​using specific methods (e.g., using default values, using the previous valid value, or interpolating based on statistical methods), or explicitly mark them as unknown or inapplicable so that subsequent modules can distinguish them during analysis.

[0043] For the specific algorithm implementation involved in data cleaning, format conversion and missing value handling, those skilled in the art can adopt a variety of mature technical solutions known in the art. The specific implementation methods are well-known technologies in the art and will not be elaborated here.

[0044] In one embodiment, the semantic association and extraction unit employs a weakly supervised learning mechanism. This mechanism is based on a predefined domain knowledge base or a set of labeling rules. This knowledge base or rule set defines how specific keywords, phrases, numerical ranges, or specific event sequence patterns are mapped to specific risk atomic event types. The logic of ). For example, a rule set can be defined as: When keywords such as "delay," "damage," or "lost goods" are detected in unstructured text (such as customer complaints), the event is associated with... .

[0045] When the time sequence pattern mining unit identifies the "outbound scan - long period of inactivity" pattern, and the "inactivity" time exceeds a certain threshold, the pattern is associated with... .

[0046] When the overdue days of accounts payable are detected to be greater than 0 in the ERP data, this event is associated with... .

[0047] To handle unstructured data, the semantic association and extraction unit is further configured to employ natural language processing (NLP) techniques. In one embodiment, the semantic association and extraction unit may utilize word embedding models (e.g., Word2Vec, BERT) and named entity recognition (NER) models pre-trained on corpora in the warehousing, logistics, and finance domains.

[0048] NLP technology is used to accurately extract key information from unstructured text and treat it as event attributes. ) or related entities ( For example, for the complaint text "Customer C001 reports that the goods for order O002 were severely damaged in warehouse A", the semantic association and extraction unit can extract: , , .

[0049] The RAE formatted output unit is configured to summarize the processing results of the temporal pattern mining unit and the semantic association and extraction unit. The RAE formatted output unit is based on the event type obtained from the analysis ( ), event properties Event timestamp ( ), data source ( ), related entities ( ) and aggregation level ( This information is then assembled into a standardized Risk Atomic Event (RAE) tuple structure.

[0050] For example, an identified and associated ERP payment event will be formatted as follows: , , , , , ).

[0051] The processing results of the Domain Adaptive Event Pattern Extraction (DAEPE) unit are uniformly assembled into a standardized Risk Atomic Event (RAE) data structure by a RAE formatted output unit.

[0052] In one embodiment, each risk atomic event in the system All are represented using a unified tuple structure, which is defined as follows: ; The components of a tuple are defined as follows: Indicates the semantic type of the event. This field is a string or enumeration value used to uniquely identify a business or risk meaning; This field represents a collection of attributes for an event. It is a collection of key-value pairs used to store contextual details related to the event. A precise timestamp indicating when the event occurred.

[0053] This field indicates the original data source identifier for the event and is used for data tracing and credibility assessment. This field represents one or more business entity identifiers associated with the event. It is a collection used to anchor the event to a specific business object for subsequent entity-based risk assessment. This indicates the aggregation level of the event; this field is used to distinguish between the original event and derived events.

[0054] The multi-source heterogeneous event stream access and semantic standardization module 10 publishes all risk atomic event (RAE) streams formatted according to the above unified tuple structure through a data output interface (e.g., an internal message queue or data stream bus).

[0055] The Federation Event Stream Inference and Dynamic Causal Graph Construction Module 20 is configured to subscribe to or connect to the data output interface to receive standardized RAE streams in real time as data input for its subsequent causal discovery and graph construction.

[0056] For the implementation of data stream transmission, those skilled in the art can use a variety of mature technical solutions known in the art (such as Apache Kafka, Pulsar and other stream processing platforms). Their specific implementations are well-known technologies in the art and will not be described in detail here.

[0057] The Federation Event Flow Reasoning and Dynamic Causal Graph Construction Module 20, whose input is connected to the output of the Multi-Source Heterogeneous Event Flow Access and Semantic Standardization Module 10, receives the standardized Risk Atom Event (RAE) stream output by the Multi-Source Heterogeneous Event Flow Access and Semantic Standardization Module 10 in real time, processes the event stream, and constructs and continuously updates a warehouse logistics financial risk causal graph (LFRCG) in real time and dynamically. Used to represent different risk atomic events (nodes) Causal relationship between (directed edges) ) and its strength (weight) ).

[0058] In one embodiment, the Federated Event Flow Inference and Dynamic Causal Graph Construction Module 20 includes a Federated Event Flow Inference Engine (FESIE). The FESIE adopts a distributed architecture to construct a global causal graph of financial risks in warehousing and logistics while ensuring data privacy across different business domains.

[0059] FESIE can include multiple local inference units and one global fusion unit. For example, FESIE can be deployed as follows: The warehouse inference unit is configured specifically for receiving and processing. RAE streams labeled as WMS or IoT-Sensor; The logistics inference unit is configured specifically for receiving and processing. RAE streams labeled TMS; The financial reasoning unit is configured specifically for receiving and processing. RAE streams tagged as ERP or CRM.

[0060] The federal learning mechanism works as follows: The warehousing inference unit, logistics inference unit, and financial inference unit all operate in their respective local environments. They directly process the RAE streams they receive without sending the raw RAE data to other units. These three units are local inference units.

[0061] Each local inference unit is configured to execute a local causal discovery algorithm on its local data stream to identify and quantify local causal relationships within its domain (e.g., discovering a relationship between inventory backlog and outbound delays, or a relationship between overdue payments and order cancellations).

[0062] The local inference unit will then analyze the local causal patterns it discovers (e.g., newly discovered causal edges, updated edge weights). Instead of the original RAE data, the conditional probability parameter is encrypted and sent to the global fusion unit.

[0063] Local causal patterns include: a list of locally identified causal edges. and the weight parameters corresponding to each edge. (such as the causal strength coefficient).

[0064] The global fusion unit is configured to receive updates from all local inference units and perform structured union and parameter aggregation operations to construct and update the global warehousing and logistics financial risk causal graph (LFRCG). Specifically: For graph structure fusion, the global fusion unit takes the union of the edge lists submitted by all local units, i.e. .

[0065] For edge weight fusion, if the same edge If an edge is identified by multiple local units (e.g., both the logistics unit and the finance unit have detected the financial impact of a logistics event), the global fusion unit updates the global weight of that edge using a weighted average method. The coefficient of the weight is proportional to the data sample size of each local unit or the preset neighborhood confidence level; if an edge is identified by only one local unit, then the local weight is directly retained.

[0066] Each local inference unit and the global fusion unit is configured to run a distributed incremental causal discovery (DICD) algorithm. This DICD algorithm is used to efficiently discover causal structures from continuously arriving RAE streams. The implementation of the DICD algorithm follows these principles: Time-first principle: This algorithm utilizes timestamps in the RAE. In determining two event nodes and Is there a causal relationship between them? At that time, the algorithm requires timestamp Must be earlier than timestamp This principle is used to significantly reduce the search space of causal graphs.

[0067] Conditional Independence (CI) Test: The core of this algorithm lies in performing a conditional independence test to distinguish between direct causality, indirect causality, and spurious correlations (caused by confounding factors). To determine... and The algorithm will test whether there is a direct causal relationship between them. and Given one or more other sets of variables Whether the conditions are independent under the given conditions, i.e. Is it zero?

[0068] In one embodiment, the implementation method of CI testing is selected based on the data type: For discrete event types ( It can be computed using conditional mutual information (CMI) based on information theory.

[0069] ; in, and Corresponding to the event and variables, Corresponding to the set of condition variables, This represents the joint or conditional probability distribution obtained statistically from the RAE stream. If... If it is close to zero (below a preset threshold), then it is considered... and In the given Time conditions are independent.

[0070] For continuous event properties ( For numerical or time-series data, Granger causality tests or their nonlinear variants can be used.

[0071] For the calculation of probability distribution in conditional independence tests, those skilled in the art can use a variety of mature technical solutions known in the art. Their specific implementations are well-known technologies in the art and will not be elaborated here.

[0072] Incremental update mechanism: To adapt to the high-speed characteristics of RAE streams, the DICD algorithm uses an incremental approach to update the causal graph of financial risk in warehousing and logistics. Instead of rebuilding at every time step, when a new risky atomic event occurs... Upon arrival, the mechanism is triggered: Local search: The algorithm first searches within the existing local search area. In China, based on of and Identify an affected local neighborhood node set. .

[0073] Local testing: The algorithm is only tested locally. With sets CI tests are performed between internal nodes to determine... Whether it appears as a new cause, result, or intermediate variable (mediator).

[0074] Graph structure correction: Based on the CI test results, the algorithm... Perform local structural operations, including: adding new causal edges (if found). or ); Delete causal edges (if The emergence of the original edge To become conditionally independent, that is (This is a more direct reason); or keep the structure unchanged.

[0075] Weight update: For all affected edges, their edge weights are updated. (e.g., representing conditional probability) (or causal strength coefficient) will be based on the newly arrived The provided evidence is updated. This update can be achieved using a weighted average or Bayesian method, and a time decay factor can be introduced to ensure that the graph weights dynamically reflect the latest risk transmission relationships.

[0076] The output of the Federal Event Flow Reasoning and Dynamic Causal Graph Construction Module 20 is the causal graph of financial risk in warehousing and logistics. Cause-and-effect diagram of financial risks in warehousing and logistics It is a directed, weighted graph data structure, defined as: ; In one embodiment, Graph data structures are used for storage and representation, such as adjacency lists, adjacency matrices, or configuration in a dedicated graph database. This storage method aims to support efficient path traversal, neighborhood queries, and pattern matching for the risk dynamic assessment and uncertainty quantification module 30.

[0077] The components are defined as follows: Node set , Each node in This represents a risk event or an aggregated risk state. In one embodiment, a set of nodes... It may include: Atomic Event Node: This type of node directly corresponds to the event type of Risk Atomic Event (RAE) defined in the Multi-Source Heterogeneous Event Stream Access and Semantic Standardization Module 10. (For example, overdue payments, inventory backlogs, and shipping delays). Each atomic event node can further store dynamic statistics related to that type of event, such as the frequency of occurrence, average intensity, or the timestamp of the most recent occurrence.

[0078] Aggregated Risk Node: This type of node is a high-level risk indicator aggregated from one or more atomic event nodes based on business logic. For example, the system can define a customer C001-financial risk node, the state of which is determined by... The identifier C001 is an aggregation of the states of multiple atomic event nodes, such as overdue payments, order cancellations, and customer complaints.

[0079] Directed edge set , Each edge in Represents a node and nodes There is a causal relationship between them, in which yes The reason is that these directed edges are determined by the Distributed Incremental Causal Discovery (DICD) algorithm through the time-first principle and conditional independence test.

[0080] Edge weight set , With the set of directed edges Related. Each element in Corresponding to a directed edge This is used to quantify the causal relationship. In one embodiment, each edge... Each is associated with a set of attributes, which must include at least: Causal strength This is the core weight of the edge, used for quantization. right The degree of causal influence. Based on the implementation of the DICD algorithm, this... It can be represented as: Conditional probability ; probability lift or correlation coefficient; regression coefficient calculated in a time series model (such as the Granger causality test).

[0081] Confidence: Indicates the causal relationship. The confidence level for the test to be valid, which can be derived from the statistical significance of the CI test (e.g., p-value).

[0082] Timestamp: Represents the causal relationship. The time of the most recent update or verification by the DICD algorithm.

[0083] The Federation Event Flow Reasoning and Dynamic Causal Graph Construction Module 20 is further configured with a dynamic graph maintenance mechanism to ensure... It is capable of adapting to changes in the business environment and reflecting the latest risk transmission patterns. This dynamic maintenance mechanism includes: Concept Drift Adaptation: The incremental update mechanism of the DIC algorithm (as described above) is itself a concept drift adaptation mechanism. When changes in business logic lead to changes in the statistical characteristics of the RAE flow (e.g., new regulatory policies increase the impact of payment delinquencies on customer churn), this incremental update mechanism automatically adjusts through continuous CI testing and weight updates. Or add or delete edges In order to capture this change.

[0084] Information Decay Processing: To ensure Prioritizing causal relationships revealed by recent data, this maintenance mechanism updates weights. A time decay strategy is introduced. In one embodiment, exponential decay or a sliding window approach can be used. For example, in updating... At that time, newly observed evidence The weight given to it is higher than that of historical evidence. This can be achieved through a time decay factor. ( (as the update rate) to achieve: ; Alternatively, an exponential decay function can be used. To adjust the contribution of historical evidence, among which This is the time interval since the last update. It is the preset decay rate constant.

[0085] Graph Pruning: This maintenance mechanism can be configured to run a graph pruning process periodically. This process iterates through... and remove those causal strengths Or the confidence level is lower than the preset threshold edge This operation is used to remove weak causal relationships that are no longer statistically significant or business-meaningful, in order to maintain the sparsity and interpretability of the graph and reduce the computational complexity of subsequent risk assessment modules.

[0086] The risk dynamic assessment and uncertainty quantification module 30 has its input connected to the output of the federated event flow reasoning and dynamic causal graph construction module 20, and is used to receive the causal graph of financial risk in warehousing and logistics. The core function of the risk dynamic assessment and uncertainty quantification module 30 is to utilize... The revealed causal relationships are used to calculate the comprehensive financial risk index of a specific entity (such as a customer), and further combine external multimodal data to quantify the uncertainty of the probability of occurrence and the scope of impact of the risk, while providing an explainable risk source.

[0087] In one embodiment, the risk dynamic assessment and uncertainty quantification module 30 includes a path-dependent risk aggregation (PDRA) unit. The PDRA unit is configured to aggregate risk based on a causal graph of warehousing and logistics financial risks. Calculation with specific entities Related comprehensive financial risk index .

[0088] The PDRA unit is initially configured to execute a graph traversal algorithm (e.g., a depth-first search-based algorithm) to perform causal graph analysis of financial risks in warehouse logistics. Identify all entities A relevant and valid causal path. A valid causal path. This refers to a risk node at the source. (For example, 'rising raw material prices') begins, goes through a series of intermediate nodes, and finally reaches a point related to the entity. Strongly correlated target financial risk nodes (For example, a customer's k-payment overdue) sequence of nodes. All these paths constitute a set. .

[0089] The PDRA unit is further configured to calculate the comprehensive financial risk index using the following formula. : ; The implementation of each term in the formula is as follows: Path causal strength accumulation It is by obtaining the path Each directed edge on Causal strength weight (this weight) The Federal Event Flow Reasoning and Dynamic Causal Graph Construction Module 20 calculates and stores the causal graph of financial risk in warehousing and logistics. (in the middle), and these weight values ​​are along the path Perform cumulative multiplication to obtain the path. The overall conductivity.

[0090] Source event frequency weighting It is configured as a path-based The source of risk A weighting function for attributes. In one embodiment, this function... It can be ,in The source of the incident The frequency of occurrence within a preset time window, The source of the incident The average magnitude of the impact (e.g., the average increase in raw material prices), and These are preset weighting coefficients.

[0091] Recent Events Time Weighting This is configured as a time decay function to give higher weight to recently occurring risk paths. In one embodiment, recent event time weighting... Exponential decay can be used: ,in This is the current system time. It is a path The timestamp of the most recent event node being activated. It is the preset time decay rate constant.

[0092] The Risk Dynamic Assessment and Uncertainty Quantification Module 30 further includes a Causal Path Backtracking (CPT) unit. The CPT unit is configured to, when the comprehensive financial risk index... Exceeding a preset risk threshold It is activated at certain times to provide interpretability of the causes of risk.

[0093] In one embodiment, the PDRA unit performs calculations. During the process, the CPT unit is configured to synchronously store and record pairs. The paths that contributed the most Individual risk contribution value Individual risk contribution value That is Summation in the calculation formula Items within the symbol: .

[0094] when When activated, this CPT unit applies to all ( Sort the results in descending order and extract the results with the highest contribution. causal path (e.g., The PDRA unit will... Path (e.g., inventory backlog) Outbound delay Customer complaints and their corresponding risk contribution values Or percentage of contribution The data is then output to the decision support interface to enable visual backtracking of risk causes.

[0095] The risk dynamic assessment and uncertainty quantification module 30 further includes a multimodal uncertainty quantification (MMUQ) unit. The MMUQ unit is configured to perform causal mapping of financial risks in warehousing and logistics. Based on this, a Hybrid Belief Network (HBN) (or Hybrid Bayesian Network) is constructed to integrate external data. And quantify the uncertainty of risk.

[0096] The HBN construction process includes: Structural skeleton import: This MMUQ unit is configured to import the structural skeleton. causal topology and nodes As the prior structural framework of HBN.

[0097] Multimodal data integration: The MMUQ is further configured to receive multimodal external data from the multi-source heterogeneous event stream access and semantic normalization module 10 or an external data interface. And integrate it into HBN as a new node or evidence.

[0098] For continuous external data (e.g., macroeconomic indices, industry PPI), treat them as continuous variable nodes in HBN (e.g., model using a Gaussian distribution).

[0099] For discrete external data (e.g., new policy release: yes / no), treat it as a discrete variable node in HBN.

[0100] For fuzzy external data (e.g., market sentiment from news reports: negative), the unit can use natural language processing techniques (as before) to convert it into a sentiment score (e.g., range [-1, 1]), and then use a pre-defined fuzzy membership function or piecewise mapping table to convert the score into a prior probability distribution of HBN nodes. For example, the system pre-defined mapping rule is: if the sentiment score... This is then mapped to the probability of high risk in discrete states. The probability of medium risk ;like Then it is mapped to , .

[0101] After the HBN constructs and integrates the evidence, the MMUQ unit is configured to employ a Bayesian inference algorithm (e.g., Markov chain Monte Carlo (MCMC) sampling or variational inference) to compute the target risk event. (For example, 'customer k-payment default') within a given internal causal path Evidence and external data Posterior probability distribution under given conditions .

[0102] The output of this MMUQ unit is the posterior probability distribution. or statistics derived from that distribution, such as the risk event. The mean probability of occurrence and its uncertainty interval (e.g., 95% confidence interval). This uncertainty quantification result will be transmitted together to the adaptive risk resilience optimization and decision support module 40.

[0103] The adaptive risk resilience optimization and decision support module 40 is configured to generate resilience optimization strategies for the warehousing and logistics financial system based on the received risk assessment data. Its input is connected to the output of the risk dynamic assessment and uncertainty quantification module 30. The adaptive risk resilience optimization and decision support module 40 receives a comprehensive financial risk index. A traceable causal path and the results of uncertainty quantification .

[0104] The adaptive risk resilience optimization and decision support module 40 is the top-level decision-making unit of the system. Its core function is to transform the risk situation perception results output by the risk dynamic assessment and uncertainty quantification module 30 into proactive and forward-looking risk resilience optimization strategies.

[0105] In one embodiment, the optimization objective of the adaptive risk resilience optimization and decision support module 40 is defined as a multi-dimensional objective vector. The vector The aim is to balance multiple conflicting business metrics, such as: Minimize expected financial losses (e.g., bad debts and losses due to customer defaults and inventory write-offs).

[0106] Optimize or stabilize operating cash flow (e.g., shorten accounts receivable cycles and reduce the capital tied up in safety stock).

[0107] Control operating costs (e.g., logistics costs, warehousing costs, labor costs).

[0108] Maintain or improve customer satisfaction (e.g., reduce order delay rates, reduce complaint rates).

[0109] To achieve the aforementioned multidimensional objectives, the Adaptive Risk Resilience Optimization and Decision Support Module 40 includes an Adaptive Risk Resilience Optimization Engine (AMM-RARE). This engine (AMM-RARE) is a hybrid decision engine that integrates multi-objective dynamic programming (MODP) and reinforcement learning (RL).

[0110] The AMM-RARE engine is configured to receive risk situation awareness input from the risk dynamic assessment and uncertainty quantification module 30 and output a set of optimal resilience optimization strategies. The architecture of this engine (AMM-RARE) includes a strategy evaluation unit and a strategy learning unit.

[0111] The policy evaluation unit is implemented using multi-objective dynamic programming (MODP). This unit is used to evaluate different policy sequences for multi-dimensional objective vectors in offline or quasi-offline states. The long-term impact is determined, and a set of Pareto optimal policies is generated.

[0112] In one embodiment, the MODP model of the policy evaluation unit is defined as follows: state space :state Defined as a snapshot of the system at a specific point in time, it includes at least: a causal graph of financial risks in warehousing and logistics. The current activation state and the risk index vector of key entities And the status of the company's available resources (e.g., total available credit line, spare capacity in each warehouse, and dispatchable logistics capacity).

[0113] Action space :action It is a discrete set of strategy combinations, representing the risk intervention measures that the system can execute. For example, actions. This could be done by {adjusting customer C001's credit limit to X, launching a Y-level promotion for product S002, and switching route R003 to the backup carrier B}.

[0114] Evaluation and Output: The policy evaluation unit traverses the state-action space using dynamic programming algorithms (such as value iteration or policy iteration) and calculates the output for each state. Perform different actions and its subsequent sequences, for multidimensional target vectors The long-term expected returns of each objective are considered. The output of the strategy evaluation unit is a set of Pareto optimal strategies, which is a combination of strategies that cannot further optimize other objectives without sacrificing any one objective.

[0115] The policy learning unit is implemented using reinforcement learning (RL) technology and is used in online environments to adaptively learn and adjust the optimal policy based on real-time feedback. This is to address unforeseen circumstances or dynamic environmental changes not covered by the MODP model.

[0116] In one embodiment, the RL model of the policy learning unit is defined as follows: state Defined as in At any given moment, the RL agent observes the state of the environment. This state... This includes: real-time risk indices received from the risk dynamic assessment and uncertainty quantification module 30. Uncertainty range and real-time available resources obtained from internal enterprise systems. .

[0117] action Defined as in At any given moment, the RL agent selects the specific intervention strategy to execute. This action... The policy set can be selected from the Pareto optimal policy set generated by the policy evaluation unit, or explored by the RL agent itself.

[0118] reward function Defined as in Perform actions at all times Afterwards, the environment was The scalar reward returned at each time step. This reward function. Designed as a multidimensional target vector A weighted combination to reflect the improvement in system resilience. For example: ; in, This represents the change in financial loss (the objective is to minimize it, hence the negative value). It refers to changes in cash flow (with the objective of maximizing it). This represents the change in operating costs (the objective is to minimize these costs, hence the negative value). It refers to changes in customer satisfaction metrics (with the goal of maximizing them). These are preset weighting coefficients used to balance different objectives.

[0119] The policy learning unit is configured to utilize tuples of observed states, actions, rewards, and the next state. Continuously update its strategy In one embodiment, when the state space When the dimensionality is high, this unit can employ the Deep Q-Network (DQN) algorithm, using a neural network to approximate the optimal action value function. And continuously optimize network parameters based on environmental feedback.

[0120] For the specific implementation of Q-learning or DQN algorithms, those skilled in the art can adopt a variety of mature technical solutions known in the field. Their specific implementations are well-known technologies in the field and will not be elaborated here.

[0121] The Adaptive Risk Resilience Optimization and Decision Support Module 40 further includes a decision support interface unit configured to output and present the resilience optimization strategy generated by the AMM-RARE engine.

[0122] In one embodiment, the unit presents the optimal strategy calculated by the AMM-RARE engine (e.g., recommending a 15% reduction in customer A's credit limit, recommending the launch of promotional program P1 for stockpiled goods B, and recommending the activation of alternative transportation route C to avoid logistics risk nodes X along the way) along with the risk causes provided by the CPT unit (e.g., the increase in customer A's risk index is mainly due to its downstream customers' overdue payments) on the decision dashboard of business personnel (e.g., risk managers, operations managers).

[0123] This system supports two policy execution modes: Semi-automatic decision-making (human-machine collaboration): The strategy is presented as a suggestion to business personnel. After final confirmation by the business personnel based on their business experience, the strategy is sent to the corresponding business system (such as ERP, WMS) for execution via an interface.

[0124] Automated decision-making: For specific scenarios with pre-defined risk thresholds (e.g., a certain indicator exceeds a high-risk threshold and the intervention action is singular and clear), the system can be configured to send the policy instructions generated by the AMM-RARE engine to the corresponding business system for execution without human confirmation.

Claims

1. A dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data, characterized in that: include: The multi-source heterogeneous event stream access and semantic standardization module collects raw data streams from multiple heterogeneous data sources in real time and processes the raw data streams into risk atomic event streams with unified business semantics. The Federal Event Flow Reasoning and Dynamic Causal Graph Construction Module receives the risk atomic event flow and performs causal reasoning processing on the risk atomic event flow to construct and dynamically update the causal graph of warehousing and logistics financial risks in real time. The risk dynamic assessment and uncertainty quantification module obtains the causal graph of the financial risks of warehousing and logistics. Risk aggregation is performed based on the causal paths in the aforementioned warehousing and logistics financial risk causal graph to calculate the entity's comprehensive financial risk index, and the uncertainty of the causal paths is quantified to obtain the uncertainty quantification result. The adaptive risk resilience optimization and decision support module receives the comprehensive financial risk index and uncertainty quantification results. Based on the comprehensive financial risk index and the uncertainty quantification results, a resilience optimization strategy for the warehousing and logistics financial system is generated.

2. The dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data as described in claim 1, characterized in that, The multi-source heterogeneous event stream access and semantic standardization module uses domain-adaptive event pattern extraction technology to process the original data stream. This domain-adaptive event pattern extraction technology is implemented through a domain-adaptive event pattern extraction unit, which includes: The time-series pattern mining unit identifies event sequence patterns with a specific time order in the original data stream; The semantic association and extraction unit performs semantic-level analysis, extraction, and association on the event sequence patterns and unstructured data.

3. The dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data as described in claim 1, characterized in that, The federated event flow reasoning and dynamic causal graph construction module performs causal reasoning processing on the risk atomic event flow through the federated event flow reasoning engine, which includes: Multiple local inference units run in their respective local environments to generate local causal patterns; The global fusion unit receives all the local causal patterns and performs structural union and parameter aggregation operations to construct and update the global causal graph of the warehousing and logistics financial risks.

4. The dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data according to claim 3, characterized in that, The causal reasoning process is implemented through an embedded distributed incremental causal discovery algorithm, the implementation of which includes: The time priority principle is adopted; Perform conditional independence test; An incremental update mechanism is adopted, wherein the conditional independence test is performed only between the new risky atomic event and the local neighborhood node set.

5. The dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data as described in claim 1, characterized in that, The causal diagram of financial risks in warehousing and logistics includes: A set of nodes, comprising atomic event nodes and aggregated risk nodes; A set of directed edges represents the causal relationships between the nodes; An edge weight set, associated with the directed edge set, is used to quantify the causal relationship; the edge weights include at least: causal strength, confidence level, and timestamp.

6. The dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data as described in claim 1, characterized in that, The risk dynamic assessment and uncertainty quantification module uses a path-dependent risk aggregation algorithm to aggregate the risks. The path-dependent risk aggregation algorithm calculates the comprehensive financial risk index in the following way: Identify the set of all valid causal paths from the source risk node to the target financial risk node, and accumulate the individual risk contribution value of all paths in the set of valid causal paths; wherein, the individual risk contribution value of each path is calculated based on the cumulative path causal strength of the valid causal path, the frequency weighting of the source event, and the time weighting of the most recent event.

7. The dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data as described in claim 1, characterized in that, The dynamic risk assessment and uncertainty quantification module also includes: The causal path backtracking unit sorts the individual risk contribution values ​​of each path constituting the comprehensive financial risk index in descending order when the comprehensive financial risk index exceeds a preset risk threshold, and extracts the N causal paths with the highest contribution.

8. The dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data as described in claim 1, characterized in that, The risk dynamic assessment and uncertainty quantification module employs multimodal uncertainty quantification technology to quantify the uncertainty. This multimodal uncertainty quantification technology is implemented through a multimodal uncertainty quantification unit, configured as follows: Construct a hybrid belief network, using the causal topology and nodes of the causal graph of financial risk in warehousing and logistics as the prior structural skeleton; Multimodal external data is integrated into the hybrid belief network as new nodes or evidence; A Bayesian inference algorithm is used to calculate the posterior probability distribution of the target risk event under given evidence and external data conditions.

9. The dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data as described in claim 1, characterized in that, The adaptive risk resilience optimization and decision support module generates the resilience optimization strategy through an adaptive risk resilience optimization engine, which includes: The strategy evaluation unit employs multi-objective dynamic programming techniques to generate a set of Pareto-optimal strategies; and The policy learning unit employs reinforcement learning techniques to adaptively learn and adjust the optimal policy in an online environment.

10. The dynamic assessment system for financial risks in warehousing and logistics that integrates multi-dimensional customer data according to claim 1, characterized in that, The federated event flow reasoning and dynamic causal graph construction module is configured with a dynamic graph maintenance mechanism, which includes: Concept drift adaptation automatically adjusts edge weights or adds / removes causal edges; Information decay processing involves introducing a time decay strategy when updating edge weights; Graph pruning involves periodically removing edges whose causal strength or confidence level is below a preset threshold.

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