Artificial intelligence driven supply chain risk early warning system

Abstract

The invention discloses an artificial intelligence-driven supply chain risk early warning system, which relates to the technical field of supply chain risk early warning, and performs closed-loop operation according to five steps of cross-level data acquisition, semantic alignment, graph expansion causal prediction and scene synthesis. The method comprises the following steps: firstly, converging heterogeneous data in milliseconds by using an adapter and constructing a named initial graph; calling an industry ontology to complete node and edge standardization so as to generate a semantic unified graph; inferring implicit dependency by using a multi-scale threshold and revising an edge weight to obtain an implicit dependency enhanced graph; then, a causal mask and time sequence attention are applied to the enhanced graph, and a risk vector combining the node influence degree and the propagation probability is output; and finally, according to the service context and the resource constraint optimization matching strategy template, pushing a signature slow-release instruction and returning the signature slow-release instruction. The method has the advantages of data real-time consistency, explainable risk quantification and auditable instruction execution, and improves the toughness and compliance level of the supply chain.

Description

Technical Field

[0001] This invention relates to the field of supply chain risk early warning technology, specifically to an artificial intelligence-driven supply chain risk early warning system. Background Technology

[0002] As global supply networks become increasingly high-density, multi-node, cross-industry, and cross-time zone, raw materials, work-in-process, and finished goods flow synchronously across multiple links in the procurement-production-logistics-finance chain. Any local disturbance can rapidly amplify along hidden paths and impact the entire value chain. Existing enterprise information systems are mostly composed of vertical platforms such as ERP, MES, WMS, and TMS, each using different communication protocols, data formats, and time bases. To meet cross-organizational collaboration needs, additional tools such as blockchain traceability, IoT tracking, and BI dashboards have been deployed. In actual operation, heterogeneous event flows are constantly generated from supply-side production equipment, logistics-IoT terminals, and third-party market data interfaces, while decision-making still relies on batch-processed static reports or manually pieced-together risk alerts. Faced with sudden geopolitical events, extreme weather, or upstream factory fires, supply chain management teams often need to manually collect data, verify names, and eliminate duplicates before leveraging experience to develop buffer inventory, alternative procurement, or financial hedging solutions.

[0003] Due to the lack of a unified semantic layer, real-time dependency reasoning, and closed-loop execution channels, this process usually takes several hours or even days, leading to a chain of consequences such as inventory disruptions, delivery defaults, frozen funds, and regulatory fines.

[0004] Existing technologies cannot access and integrate heterogeneous data across layers at the millisecond level, nor can they instantly deduce multi-hop implicit dependencies and the true propagation strength in a unified semantic view. Furthermore, they lack a mechanism to automatically translate quantified risks into compliant, auditable, and resource-feasible mitigation instructions.

[0005] Specifically: When multi-source event streams enter the enterprise boundary, protocol differences, clock drift, and missing metadata cause soft packet loss or pseudo-concurrency, making it difficult for subsequent algorithms to guarantee time consistency; even after initial cleaning through offline ETL, naming conflicts for the same supplier or material still exist between different systems, hindering cross-domain graph reasoning; traditional graph algorithms often only process explicit transaction edges, ignoring weak interactive dependencies indirectly transmitted through second- or third-level nodes, leading to a systematic underestimation of risk propagation paths; and current BI or ERP workflows cannot automatically match multi-dimensional resource constraints such as inventory, production lines, and cash flow based on real-time scoring, requiring manual confirmation across multiple departments, delaying the optimal mitigation window. These deficiencies are particularly prominent in scenarios involving global pandemic lockdowns, shipping congestion, or drastic fluctuations in key mineral prices. Once risk identification lags, it will directly trigger production stoppages, defaults, and brand losses, and even raise regulatory compliance risks.

[0006] Therefore, this invention provides an artificial intelligence-driven supply chain risk early warning system. Summary of the Invention

[0007] (a) Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides an AI-driven supply chain risk early warning system. It utilizes an adapter to aggregate heterogeneous data in milliseconds and construct a named initial graph. Subsequently, it calls industry ontology to standardize nodes and edges, generating a semantically unified graph. Then, it uses multi-scale threshold reasoning to infer implicit dependencies and revise edge weights, obtaining an enhanced implicit dependency graph. Next, it applies causal masks and temporal attention to the enhanced graph, outputting a risk vector combining node influence and propagation probability. Finally, it optimizes matching strategy templates based on business context and resource constraints, pushes signed mitigation instructions, and provides receipts. This system combines real-time data consistency, interpretable risk quantification, and auditable instruction execution, solving the technical problems described in the background art.

[0009] (II) Technical Solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solution: an artificial intelligence-driven supply chain risk early warning system, comprising: a cross-level heterogeneous data adapter that collects multi-protocol event streams and metadata in parallel, and after time calibration and unified identifier mapping, constructs an initial graph model of the supply network and writes it to the incremental log in real time;

[0011] The semantic alignment engine calls industry ontology libraries and lexical mapping rules to incrementally standardize node attributes and edge types, hierarchically label conflicting versions and write them back, and generate a semantically unified graph.

[0012] The progressive graph unfolding algorithm is based on multi-scale reachability thresholds and entropy suppression path sampling to deduce implicit supply dependencies, revises edge weights in real time and merges and submits them in parallel, and outputs an implicit dependency enhanced graph.

[0013] The causal attention predictor applies a causal mask and multi-head temporal attention to the latent dependency enhancement graph, generates node representations, calculates dependency scores by combining influence and propagation probability, and outputs a risk vector set.

[0014] The scenario synthesis module parses the risk vector and context, sorts the templates by tightness, selects a subset of templates through resource constraint optimization, packages the transactions into a chain of signatures, pushes instructions, and tracks receipts.

[0015] Furthermore, the cross-level heterogeneous data adapter is equipped with a protocol fingerprint classifier, a zero-copy circular buffer, and a distributed Kalman clock calibration unit. It first identifies the message protocol, then calibrates the clock in real time and writes it with a concurrent buffer to ensure the original order of the event stream and a unified time base. Incremental logs are generated during writing to support subsequent backtracking.

[0016] Furthermore, when generating the initial graph of the supply network, a hierarchical autoencoder is used to extract the static and dynamic features of the nodes, a globally unique identifier is generated using a namespace hash tree, and the initial weight of the edges is calculated using a sliding window time decay model. At the same time, a traceable incremental log mechanism is used to store the hash and timestamp of each node or edge change.

[0017] Furthermore, unified node attribute tags are generated jointly through industry ontology anchoring and upstream and downstream lexical gains. The conflict hierarchical parser constructs a tag stack based on time freshness and data credibility level and writes it into the semantic version table. The incremental standardization pipeline writes back to the graph database in real time under the write-time merging strategy.

[0018] Furthermore, the verb phrase semantic landing device identifies action roles based on extended FrameNet, the hierarchical relation mapper outputs three-level relation labels, the semantic correction edge weight generator revises the time decay edge weights according to relation importance, and generates relation version blocks that are attached to the semantic unified graph.

[0019] Furthermore, high-potential nodes are screened using multi-scale reachability thresholds, and low-entropy, high-weight paths are sampled preferentially by an entropy-suppressed path sampler. An implicit dependency candidate set constructor aggregates node pairs according to path probabilities and marks them with fluctuation flags. Subsequently, a parallel incremental expansion scheduler inserts candidate edges in local replicas and submits them to the main graph through a conflict merging table.

[0020] Furthermore, a temporal semantic coupling function is used to fuse revised edge weights, implicit dependency probabilities, and window time intervals to calculate new edge weights. A heuristic conflict mediator performs multi-view aggregation or version overriding based on edge type and weight difference.

[0021] The enhanced graph write-back pipeline sets a weighted priority lock and writes it to the enhanced graph log. At the same time, the early stop criterion is continuously monitored to control the termination of the process based on the density increment and revision magnitude.

[0022] Furthermore, after input tensor alignment and batch normalization, a directional causal mask is constructed to restrict illegal attention. The multi-head temporal attention module independently calculates attention weights on different time decay kernels and outputs a spliced ​​representation.

[0023] The compression residual regularization is characterized by using Fourier high-frequency masking to suppress noise and retain impulse information, ultimately forming a node causal characterization matrix.

[0024] Furthermore, the influence degree is first calculated by combining the node representation L2 norm with the in-degree and edge weights, and then the edge propagation probability is estimated by using the learnable Hawkes kernel.

[0025] The key relies on a scoring function that takes the product of the node's average influence and the propagation probability. After filtering by high and low thresholds, it is expanded into a risk vector group labeled with explicit, implicit, and multi-view sources.

[0026] Furthermore, the business context tensor is used to calculate the three-dimensional scarcity of inventory, production line and cash flow. The strategy template candidate ordering engine calculates the template priority score according to historical success rate, resource matching degree and industry matching weight and outputs the ranking list. The dynamic threshold gating references similar node migration estimation and regulatory policies to adjust the weighting coefficient in real time.

[0027] Furthermore, the subset of priority templates is input into the resource constraint optimizer for integer programming and marginal utility curve filtering, and the instruction transaction packer topology sorts the order to generate execution blocks and inserts cross-regional coordination placeholders.

[0028] After being signed by a chain of multiple sub-private keys, the data is streamed to the collaborative execution channel via a push gateway, and the automatic receipt tracking unit collects receipts to generate a drift event summary feedback model.

[0029] (III) Beneficial Effects

[0030] This invention provides an artificial intelligence-driven supply chain risk early warning system, which has the following beneficial effects:

[0031] The cross-level heterogeneous data adapter aggregates multi-protocol event streams and scattered metadata into a unified graph model within millisecond granularity. It works with namespace hash trees to eliminate the problem of homonyms and homonyms, maintaining time consistency and unique identification throughout the entire process from physical link to semantic view. This provides an uninterrupted, traceable, and zero-frame-loss data foundation for subsequent inference links, reducing the time loss and caliber deviation caused by traditional offline cleaning.

[0032] The semantic alignment engine uses industry ontology anchoring, lexical gain, and conflict layering to compress node attributes and edge types into the same namespace. The progressive graph unfolding algorithm further utilizes multi-scale reachability thresholds and entropy-suppressed path sampling to extract latent dependencies on the unified graph, enabling explicit and implicit relationships to be presented simultaneously. This achieves a dual improvement in structural integrity and semantic consistency, breaking through the inherent limitation of traditional methods that still have missing links after semantic cleaning.

[0033] By generating key dependency scores through a coupling function of node influence and propagation probability, the model retains high-dimensional temporal-semantic features while outputting transparent attention heatmaps and path family labels. The model captures short-term shocks and long-term vulnerabilities while providing causal factor sources, offering a credible explanation for operations, auditing, and model iteration.

[0034] The contextual semantic parser aligns the risk vector with business tensors such as inventory, production line, and funds. The strategy template candidate ordering combines historical success rate and resource matching for two-dimensional sorting. The resource constraint optimizer then uses integer programming to screen the cost-risk optimal solution. Finally, cross-departmental collaboration is achieved through instruction transaction packaging, multi-sub-private key chain signing, and push gateway.

[0035] Automatic feedback tracking decomposes and attributes execution deviations, writing resource costs and template fit back to the model in real time, driving weights, thresholds, and cost coefficients to adaptively iterate according to business realities. This continuous evolution maintains an interpretable and auditable cycle, establishing a long-term risk control ecosystem for enterprises. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the structure of the AI-driven supply chain risk early warning system of the present invention. Detailed Implementation

[0037] The technical solutions of 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.

[0038] Please see Figure 1 This invention provides an artificial intelligence-driven supply chain risk early warning system, including: In a highly uncertain global supply environment, supply chain risks have shifted from being controllable at a single point to spreading in a networked, coupled, and real-time manner. If the event stream and metadata cannot be captured and mapped synchronously and properly at the first moment when an anomaly occurs at a supply node, subsequent semantic alignment, dependency reasoning, and causal prediction will lose timeliness and accuracy due to input delays or distortions.

[0039] In a highly uncertain global supply environment, supply chain risks have shifted from being controllable at a single point to spreading in a networked, coupled, and real-time manner. If the event stream and metadata cannot be captured and mapped synchronously and properly at the first moment when an anomaly occurs at a supply node, subsequent semantic alignment, dependency reasoning, and causal prediction will lose timeliness and accuracy due to input delays or distortions.

[0040] Step 1: In a cross-level heterogeneous environment, synchronously collect event stream-metadata pairs in a zero-information-loss manner and map them into an initial graph model of the supply network that conforms to a unified namespace.

[0041] Step one includes the following:

[0042] Step 101: Synchronous Acquisition and Fine-Time Calibration of Heterogeneous Event Streams

[0043] With the surge of IoT sensing nodes, business middleware, and third-party data sources in the supply network, event streams exhibit three heterogeneous characteristics: diverse protocols, clock drift, and scattered metadata. If unified calibration is not completed at the entry point, data will experience soft packet loss and false concurrency, directly weakening the perceptibility of key supply indicators.

[0044] Therefore, this step aims to construct a closed-loop processing chain of multi-protocol event ingestion, clock drift estimation, and real-time metadata fusion, thereby achieving accurate synchronous characterization of the supply node state.

[0045] To ensure compatibility with mainstream protocols such as MQTT, AMQP, OPC-UA, and REST-Push, the cross-layer heterogeneous data adapter internally implements an event response pool based on asynchronous non-blocking I / O, and introduces a protocol fingerprint classifier based on k-nearest neighbor hash matching at the entry layer. Therefore, the adapter can identify data packet protocols and dynamically match unpacking paths at the microsecond level, thus avoiding the throughput bottleneck caused by traditional single-threaded blocking. This ensures that multiple events from the supply side, logistics side, and manufacturing side are written to the same timing buffer in their original order of occurrence, ultimately forming the first cross-protocol timing flat stream, providing a unified interface for subsequent calibration. Protocol fingerprinting eliminates the acquisition latency caused by interface differences, achieving a unified entry point.

[0046] In scenarios where multiple timezone nodes coexist, relying solely on NTP is insufficient to offset millisecond-level jitter. The adapter employs a hybrid clock model based on dual-end delay estimation, using the node's local timestamp t. i Projected to a unified reference clock c i Furthermore, an entropy regularization term is introduced to suppress the impact of jitter, and a synchronization loss function L is constructed. sync :

[0047]

[0048] Where: Synchronization loss function L sync : A scalar that measures the time alignment error of each node, with a value range of [0, +∞), used to dynamically adjust drift compensation; local timestamp t i The original time recorded when the i-th node collects an event;

[0049] Reference clock time c i The observation value of the i-th node under a unified reference clock;

[0050] Total number of nodes N: The number of discrete nodes participating in synchronization;

[0051] Transformation mapping function The local timestamp is converted into an injective function in the intermediate time domain, with a value range of (-∞, +∞), to unify the basis for subsequent calculations.

[0052] Reference clock mapping ψ clk (·): An injective function that maps the reference clock to the intermediate time domain, with a value range of (-∞, +∞), used to calculate the difference;

[0053] Event Stream Sequence S e : A time series set aggregated by nodes, with a range of values. Used for entropy regularization

[0054] Entropy regularity coefficient λ s : Positive real number, with a value range of (0,1], used to balance the time drift term and the entropy regularization term;

[0055] Therefore, each iteration will be based on the synchronization loss function L sync The calibration vector is updated to gradually converge the drift difference across multiple nodes, thereby sorting all events using the same reference clock; ultimately forming a globally ordered event stream. Entropy regularization is used to maintain the integrity of the sequence information, ensuring the temporal consistency of subsequent graph construction.

[0056] When an event arrives, most metadata (such as batch number and quality assurance code) is stored in a heterogeneous header or in an extended field included with the package. The adapter immediately calls the keyed dummy pool to place the unarrived fields after parsing and marks the timestamp t. miss Therefore, when the actual metadata arrives, a zero-copy update can be performed based on the placeholder key-value pairs, thus avoiding GC thrashing caused by frequent object reconstruction; this maintains the temporal coupling of the event stream and metadata pairs; and the final output data row contains the complete key-value pairs and carries the source unique identifier UID. By using dummy placeholders, the stable addressability of the data object structure is ensured throughout its lifecycle.

[0057] By employing protocol fingerprinting for rapid identification, hierarchical clock drift compensation, and placeholder metadata fusion, a low-latency, highly complete, and strongly consistent core data link is constructed. The system can obtain complete "event-metadata" pairs without sacrificing millisecond-level real-time performance, significantly reducing the misjudgment rate of subsequent algorithms due to information gaps. The distributed Kalman filter explicitly separates hardware drift from network noise, making the timeline a truly reliable global coordinate system. The placeholder timeout compensation mechanism explicitly manages missing information, avoiding the hidden dangers of "dirty data being stored in the database" in traditional ETL processes.

[0058] Step 102: Initial Graph Model Construction and Unified Naming Mapping of the Supply Network

[0059] High-fidelity event streams alone are insufficient to support cross-layer dependency reasoning; nodes, edges, and attributes must be uniformly encoded so that subsequent semantic alignment and graph reasoning can be carried out based on the same index space.

[0060] The event-metadata pairs output by the adapter first enter the feature extraction pipeline to generate the node static attribute matrix E. n With dynamic event tensor E t Subsequently, a hierarchical autoencoder was used to [E] n ||E t Projecting onto a low-dimensional continuous space yields the embedding matrix D. t :

[0061]

[0062] Where: Embedding matrix D t The representation of a node in d-dimensional vector space, and its range of values. Used for downstream graph learning;

[0063] Static attribute matrix E n : Set of static features of nodes, range of values Provides static descriptions such as supply capacity and geographical location; dynamic event tensor E t Event characteristics of nodes that change over time, and their value range. Record dynamic indicators such as anomaly frequency; autoencoder mapping Multilayer perceptron chain, value range Responsible for dimensionality reduction and noise reduction;

[0064] Therefore, node vectors capture cross-temporal information, providing a more comprehensive semantic context during graph construction; this improves the prior quality of subsequent causal attention prediction; and ultimately achieves a unified behavior-attribute node view. Dimensionality reduction, denoising, and compression enhance graph storage efficiency while preserving key anomaly features.

[0065] To address the issue of inconsistent identification of the same supplier across different subsystems, a Namespace Hash Tree (NS-Tree) is introduced. This structure stores the global namespace URI in the root node and local aliases in the leaf nodes. The mapping process is defined as follows:

[0066] f ns (x) = hash(NS_root||path(x))

[0067] Named mapping function f ns (x): Maps a local identifier to a global ID, with a value range of {0,1}. 256 ;

[0068] The hash algorithm hash(·) is a function that performs a one-way hash calculation on the input byte sequence, using the Blake-3 algorithm;

[0069] The root namespace NS_root: a system-wide global URI, with a value of string, used to construct a collision-free prefix;

[0070] Path description path(x): The hierarchical path of the identifier in the local system, with a value of string; used to preserve hierarchical information;

[0071] Therefore, all nodes and edges obtain a unique and traceable 256-bit hash ID, thus completely eliminating conflicts between identical items with different names and items with the same name but different names; thereby enabling incremental merging downstream; and ultimately ensuring the stability of graph vertices and edges. NS-Tree breaks down the isolated identifiers across systems.

[0072] A series of explicit interactions (shipping, receiving, quality inspection) can be inferred from the event flow sequence, which maps hidden dependencies. A sliding window causal measure is performed on the interaction sequence during the initial graph construction to calculate the initial edge weights.

[0073]

[0074] In the formula: initial value of edge weight w ij : The time-decayed interaction strength between node i and node j, with a value range of [0,1];

[0075] Interactive Indicators Whether node i in the k-th window points to j and whether an interaction occurs; the value is {0,1}.

[0076] Time interval Δt k : Distance from the center of the window to the current time; value range [0, +∞);

[0077] Attenuation factor β: a positive real number, ranging from (0,1], which controls the rate of decay over time;

[0078] Window count L: Sliding window count, value range

[0079] Therefore, recent interactions contribute more to the weights, highlighting the potential for risk propagation from short-term fluctuations; this then spreads preferentially in the progressive unfolding of step three; ultimately forming an initial weight distribution that combines long-term dependence with short-term sensitivity. Intended meaning: The time decay model inherently gives the graph a recency-first characteristic.

[0080] After the initial graph is generated, a traceable incremental log mechanism is used for storage: every change to a node or edge is accompanied by a triple of namespace hash + timestamp + change block, and written to the traceable log. Therefore, the system can revert to any version view at any time, supporting emergency simulation and root cause analysis; thereby achieving complementary online real-time and offline playback modes; and ultimately strengthening the auditability of the digital supply network. Intent: Incremental logging ensures transparent and reproducible graph evolution, reducing compliance risks.

[0081] When used, it upgrades scattered and messy data into a structured, evolvable, and auditable initial graph of the supply network. Deep embedding ensures the fusion of static and dynamic features of each node, truly reflecting its supply resilience; NS-Tree thoroughly eliminates identifier conflicts, achieving cross-system ID-primary key alignment; time-decay edge weights, combined with an intent classifier, allow dependency strength and semantic category to be structurally presented on a single edge; incremental logs and Merkle chains make all changes to the graph traceable, significantly reducing compliance auditing and error recovery costs.

[0082] Through the two-stage chain of cross-protocol capture-clock calibration-metadata placeholder in step 101 and embedding encoding-naming mapping-weight initialization-incremental logging in step 102, heterogeneous raw data from various ends of the supply chain have been transformed into an initial graph model of the supply network that is event-complete, time-consistent, and semantically unified. On the one hand, the nodes and edges of this model have unique hash identifiers, providing conflict-free standard input for the semantic alignment engine in step two; on the other hand, time-decayed edge weights and deep node embedding inject quantifiable dependency priors into the progressive graph unfolding algorithm in step three, thereby ensuring the mathematical foundation and real-time performance of the subsequent risk inference chain. More importantly, the incremental logging mechanism binds graph evolution and data governance within the same auditable framework, providing a traceable spatiotemporal index for the causal attention predictor in step four to perform advance verification in replay mode, and for the adaptive rollback of the deferred instructions in step five.

[0083] After undergoing digital transformation, the semantic heterogeneity between supply nodes in the global supply network has become a major bottleneck hindering the insight into risk linkage: the same cobalt ore raw material is named Co-Ore-Bulk in the upstream mining system, but is recorded as cobalt powder 3N5 in the downstream battery manufacturing system. If the two are not unified in the layer, it will lead to the truncation of the dependency path. To make matters more complicated, node attributes and edge types are also mixed with regional dialect abbreviations, industry jargon and enterprise-defined fields.

[0084] Step 2: Using industry ontology as anchor and lexical mapping as a bridge, the initial graph model of the supply network is converted into a semantically unified graph in real time through incremental semantic standardization.

[0085] Step two includes the following:

[0086] Step 201: Node attribute semantic alignment and conflict hierarchical resolution

[0087] In the initial graph model, node attributes originate from ERP, MES, IoT, and third-party market data. If the same entity carries multiple sets of heteronymous labels, the progressive graph unfolding algorithm in step three will miss potential dependencies due to misjudgment of reachability thresholds, thus amplifying the risk blind spot. This step is based on a three-element closed loop of industry ontology anchoring + lexical mapping gain + conflict hierarchical parsing, aiming to map all node attributes to a unique concept vector and ensure semantic consistency.

[0088] The semantic alignment engine first sets the embedding matrix D t The attribute fields of each node are fed into the ontology anchor. The anchor uses multi-hop semantic similarity retrieval to locate candidate concepts in the industry ontology, and then calculates the matching score.

[0089]

[0090] Semantic matching score The normalized similarity between the k-th attribute of node i and ontology concept k, with a value in the range (0,1), is used for candidate ranking; node attribute vector. From the embedding matrix D t The k-th dimension slice, with values ​​ranging from Used to provide semantic context; ontology concept vector c j Industry ontology library concept embedding, value range Used to provide authoritative industry semantics;

[0091] Lexical mapping function f lex (·,·): Weighted score of multilingual edit distance for comparing two strings, ranging from [0,+∞), used to compensate for naming differences; Non-linear activation σ(·): Sigmoid function; mapped to (0,1); Similarity weighting coefficient γ: Positive real number; value (0,1]; used to adjust the weights of vector similarity and lexical similarity; Lexical gain coefficient η: Positive real number, value (0,1], used to amplify synonym compensation; Local field terms The text string representing the k-th attribute of node i in the original data source;

[0092] Ontology concept term w j , refers to the specific term in the standardized name or alias set of concept j in the industry ontology library;

[0093] Therefore, high-scoring candidate concepts are labeled as semantic anchors to ensure that subsequent mappings do not cross industry boundaries, thereby reducing cross-domain mismatches and ultimately providing a consistent starting point for downstream conflict resolution. Intent: Locking the context at the ontology level avoids mistakenly mapping lithium ore to the glass silica sand category.

[0094] Ontology anchoring alone is insufficient to bridge industry jargon and regional colloquialisms. Therefore, an upstream and downstream lexical mapping rule base is introduced, and a gain network is used to perform zipper alignment after anchoring. The gain network consists of bidirectional LSTM encoding pairs, with the downstream field sequence and ontology label sequence cross-referenced. The output residual vector is then added to the node attribute vector. This residual preserves colloquial information without compromising anchoring results. Therefore, it retains authoritative labels while remembering unique modifiers from upstream and downstream partners; allowing for the identification of supply preferences using local details during step three reasoning; ultimately achieving the dual effect of industry consistency and traceability of regional differences. Intended purpose: The residual preserves local dialects, ensuring semantic consistency without losing business details.

[0095] When a node holds multiple anchor tags due to multi-source mapping, the system triggers a conflict layering resolver: first, it determines the dominant tag based on freshness; then, it constructs a stack of alternative tags based on data trustworthiness; and finally, it assigns a version number to each layer of tags and writes it into the semantic version table. The resolution output simultaneously updates the conflict bitmap, recording which fields are inconsistent for operational review. Therefore, contradictory information is explicitly layered and no longer overlaps; thus, the graph database can maintain multiple semantic views for the same node; this allows for future modifications to ontology rules to be traceable; and ultimately reduces the cost of manual intervention. Layered conflict isolation transforms unavoidable multilingual perspectives into controllable versions.

[0096] The dominant semantic tags and version information output by the parser are written back to the node attribute table in real time via an incremental standardization pipeline, and the source mapping path is registered in the write-back log. The pipeline adopts a write-time merging strategy: when the flow rate exceeds a threshold, similar operations are merged to reduce graph database write amplification. Therefore, the standardization operation maintains the same update rhythm as step one; thus, new nodes can achieve real-time semantic alignment without batch processing; ultimately ensuring that graph semantic consistency does not drift over time. The incremental pipeline allows semantic unification to run continuously, rather than as an offline batch job.

[0097] When in use, node attributes are bound to authoritative concepts in real time; the gain network residual mechanism preserves regional colloquialisms without disrupting anchoring, enabling the system to maintain the semantic awareness of the local operations team during deployment; the conflict layered resolver transforms unavoidable multi-source conflicts into an auditable version table, allowing enterprises to roll back semantic states along the timeline in audit or accountability scenarios, avoiding post-transfer tampering disputes; the incremental pipeline prevents second-level data storms from overwhelming the graph database through write-time merging, ensuring that the risk warning link always receives the latest and consistent node view.

[0098] Step 202: Edge type semantic mapping and weight context revision

[0099] Edge types implicitly contain multiple relationships such as transactions, logistics, quality inspection, and even financial guarantees. If the semantics are incomplete, the progressive graph unfolding algorithm will be unable to accurately measure the propagation threshold. Meanwhile, the upstream time-decaying edge weight w... ij To truly measure dependency strength, it is necessary to integrate relational context. This step relies on four technical points: semantic implementation of verb phrases, hierarchical relation mapping, weighted context revision, and semantically unified graph output, focusing on assigning a consistent and computable relational meaning to each edge.

[0100] First, the edge interaction logs are scanned to extract verb phrases, and then the FrameNet-Industry Extended Semantic Framework is called to generate action-role pairs. If the action lacks context, the node embedding matrix D is used. t The neighborhood features are completed. Therefore, the edges can be accurately located as industry verbs such as delivery, quality inspection, and prepayment; thus forming mutual verification with the semantic anchoring of node attributes; ultimately establishing the first line of defense for edge semantics. Intended purpose: To use a semantic framework to clarify the role of verbs and avoid mixing "trade" with "transport".

[0101] Verb phrases are fed into a hierarchical relation mapper, which performs longest common prefix matching between the semantic framework and the relation hierarchy in the industry ontology, outputting three-level labels <first-level relation r1, second-level relation r2, third-level relation r3>. Hierarchical relation paths are created in the graph, allowing step three to select different precision views according to algorithmic needs. Therefore, edge relationships receive multi-granular interpretation; this enables rapid diffusion at a macro level and also allows for locating specific lines of responsibility at a micro level; ultimately achieving flexible multi-scenario support, with hierarchical relationships improving the algorithm's adjustability.

[0102] To inject semantic weights into the original time-decayed edge weights w ij Calculate semantically corrected edge weights

[0103]

[0104] In the formula: semantically modified edge weight The weights of combined time decay and semantic strength, ranging from [0, 1+α], are used for diffusion in step three; the original edge weight w ij : As given in step one, the value range is [0,1];

[0105] Semantic importance function δ rel (r ij ): A scalar value representing the semantic strength of edge relationships, taking values ​​[0,1], with transaction-related relationships having higher strength than logistics-related relationships;

[0106] Semantic adjustment coefficient α: a positive real number, taking values ​​(0,1], used to adjust the semantic correction magnitude;

[0107] Therefore, important relationships such as quality inspection failure-return will have significantly increased weight; thus, high-risk paths will be prioritized during step three; ultimately improving the sensitivity of critical dependency detection. Intent: Semantic weights make graph propagation more aligned with business risks.

[0108] After weight revision, edge records are written to the semantic unified graph, and relation version blocks are generated and attached to the graph's global version tree via a Merkle chain, synchronized with the node semantic version table. The system also publishes semantic graph change events to upstream subscribers, with an index containing <edge ID, old label, new label, timestamp>, facilitating real-time consumption by downstream users. Therefore, the semantic unified graph is always incrementally aligned with the initial graph, ensuring consistent input for the risk predictor and ultimately completing the semantic layer loop. Intent: Version blocks ensure that any semantic adjustments are traceable.

[0109] When in use, time-decay edge weights are integrated with semantic importance, giving higher priority to key transactions and quality inspection links in risk reasoning, thereby shortening the time to identify potential chain breaks. The hierarchical relationship mapper generates grandparent-grandchild links, allowing different granular views of the same graph for procurement strategy simulation or warehouse deployment, without the need for additional subgraph extraction. The version control mechanism achieves fine-grained rollback through differential encoding and a Merkle chain, saving storage and ensuring a complete chain of evidence for future compliance audits.

[0110] Step two, through a two-level process—industry ontology anchoring, lexical gain, conflict layering, and incremental write-back in step 201, and verb placement, hierarchical relationship mapping, semantically corrected edge weights, and unified graph output in step 202—refines the heterogeneous node attributes and edge types of the initial graph model into a semantically unified graph. On the one hand, versioned node attribute annotation and multi-granularity edge relationship labels enable the progressive graph unfolding algorithm to select the most suitable semantic resolution at any level; on the other hand, the fusion of semantically corrected edge weights and time decay weights provides the causal attention predictor with high-quality input that balances timeliness and business importance.

[0111] More importantly, the semantic version blocks of nodes and edges are jointly mounted on the Merkle chain, making the entire chain traceable and rollbackable, thus establishing a trust foundation for the generation of subsequent mitigation instructions. Step two therefore not only achieves language layer cleaning, but also pre-embeds adjustability and auditability in the graph semantic dimension, laying a unified knowledge foundation compatible with multiple scenarios and temporalities for hidden dependency reasoning in step three and risk vector generation in step four.

[0112] The complexity of global supply networks is growing exponentially, and relying solely on explicit transaction data is no longer sufficient to fully reflect the true dependence structure between upstream and downstream sectors. Most key materials are not purchased directly from upstream nodes, but rather flow implicitly through second- and third-tier supply chain links. If these implicit supply dependencies cannot be deduced in a timely manner at the semantic unified graph level, the risk spillover path will be severely underestimated, and mitigation instructions will fail due to a lack of foresight.

[0113] Step 3: Using the progressive graph unfolding algorithm as the core, implicit supply dependencies are deduced and strengthened on the semantically unified graph, and an enhanced implicit dependency graph is output in real time.

[0114] Step three includes the following:

[0115] Step 301: Multi-scale reachability reasoning and implicit dependency mining

[0116] While semantically unified graphs have eliminated heterogeneity, they still primarily rely on explicit interactions. However, the supply effects between nodes are often propagated through multi-hop weak connections. If high-order traversal is applied directly to the entire graph, the computational cost will increase exponentially with the number of edges and introduce noise. Therefore, step 301 proposes a four-segment link approach: multi-scale reachability thresholding, entropy-suppressed path sampling, implicit dependency candidate construction, and parallel incremental scheduling. This approach aims to control computational complexity while ensuring interpretability.

[0117] First, adjust the edge weights based on the semantics in the semantic unification graph. Construct a hierarchical adjacency tensor and compute the nodal diffusion potential of node i at each scale s. Measuring the potential influence of nodes at different scales:

[0118]

[0119] Where: nodal diffusion potential The influence strength of node i on its neighborhood at scale s, with a value range of [0, +∞). Used for threshold determination; neighborhood set. The reachable set of node i within s hops is used to limit the diffusion radius; the attenuation coefficient μ is a positive real number (0,1]; it controls the attenuation of scale weights and prevents large-scale terms from dominating.

[0120] When the node diffusion potential Above the dynamic threshold:

[0121]

[0122] In the formula: node i is marked as a high-potential diffusion source and enters the next stage of path sampling.

[0123] Threshold τ (s) The global threshold at scale s guides sampling to focus on high-potential regions;

[0124] Scale factor κ: a positive real number, (0,1], used to adjust the screening granularity; median: the statistical median of the diffusion potential of all M nodes at scale s, used to measure the global typicality in a noise-resistant manner;

[0125] Therefore, multi-scale thresholding filters out a limited number of high-potential nodes, thereby compressing the search space and saving computational resources for subsequent path sampling. Ultimately, it ensures that the network can still be executed in real time in complex networks. Potential sparsity can be used to remove irrelevant nodes and reduce explosive combinations.

[0126] For each high-potential node i, the path sampler performs the following operations with probability:

[0127]

[0128] In the formula: the path of the recursive sampling length l

[0129] Path probability The normalized probability of a candidate path of length l from node i to node j being sampled guides the Monte Carlo sampling distribution;

[0130] Normalization factor Z: A constant obtained by summing the probabilities of all sampleable paths from the same starting point i; a constant that preserves the validity of the probabilities.

[0131] Local path entropy The path distribution entropy to the h-th hop is used to measure the path dispersion.

[0132] Entropy suppression coefficient λ: a positive real number, (0,1], used to control randomness and avoid excessive randomization;

[0133] Semantic modified edge weight The edge weights generated in step 202 by combining time decay and relational semantics are used to characterize the propagation strength of each hop;

[0134] Therefore, high-weighted and low-entropy paths are sampled first, thereby improving the quality of latent dependency candidates, reducing random walk noise, and ultimately improving inference accuracy. Among these, entropy suppression makes sampling more focused on possible true dependency paths.

[0135] The path set output by the sampler is filtered by a semantic consistency filter: if the hierarchical relationship labels on either side of a path have mutually exclusive levels, they will be discarded; the remaining paths are processed according to the implicit dependency probability Π. ij :

[0136]

[0137] The nodes are aggregated into a node pair probability matrix Π, and node pairs with probabilities higher than the threshold θ are included in the implicit dependency candidate set.

[0138] Where: Implicit dependency probability Π ij : Potential dependency probability from node i to node j, quantifying dependency confidence; Path length upper limit L: Positive integer used to control search depth; Threshold θ: Candidate selection threshold used to ensure candidate sparsity.

[0139] Therefore, the candidate set only contains highly reliable hidden chains, thus providing a clear target for edge weight revision, thereby avoiding large-scale edge insertion across the entire graph, ultimately maintaining the manageability of the enhanced graph, and achieving a balance between reliability and interpretability through probabilistic aggregation and path length penalty.

[0140] After the candidate set is generated, the scheduler divides it into several parallel tasks based on node partitioning and path overlap. The task executor adopts a write-temporary graph-merge strategy: each task inserts candidate edges into its local replica and calculates the local connectivity increment; after all replicas are completed, they are committed all at once in the global graph through a conflict merging table. Therefore, write lock contention is limited to a short merging window, thus supporting sub-second incremental edge insertion and enabling the system to continuously expand in high-speed data flow scenarios. Finally, an intermediate enhanced snapshot is output for step 302 to call. Among these, partitioned parallelism and temporary graph merging improve throughput and ensure consistency.

[0141] In use, through a quadruple mechanism of sliding quantile correction, dual caching of hot and cold paths, path family label graph, and two-phase commit, it maintains inference stability and result interpretability even under large-scale concurrency and structural mutation environments. Threshold self-stabilization avoids false propagation caused by node storms, cold and hot hierarchical sampling buffer improves coverage while suppressing meaningless random walks, path family labels encapsulate complex path semantics for downstream use, and two-phase commit of replicas significantly reduces write conflicts. Figure 1 The inherent risk of dependency mining gives it the quality of grid resilience, enabling it to operate for extended periods without human intervention amidst high-frequency fluctuations in the supply network.

[0142] Step 302: Real-time edge weight revision and implicit dependency enhancement graph solidification

[0143] The candidate edge set alone is insufficient to quantify the intensity of risk propagation. It is necessary to integrate the implicit dependency probability with the existing edge weights and continuously track temporal changes so as to adjust the propagation model at the beginning of risk trend formation. Therefore, step 302 introduces a four-stage processing chain: temporal-semantic coupled edge weight update function, heuristic conflict reconciler, enhanced graph write-back pipeline, and continuous monitoring of early stopping criteria.

[0144] For any candidate node pair (i,j), according to the implicit dependency probability Π ij The time interval Δt between the most recent synchronization window win Calculate the revised edge weight w′ ij :

[0145]

[0146] In the formula: revised edge weight w′ ij : A new weight that integrates time and probability, [0,1], used for implicit dependency enhancement graphs;

[0147] Sigmoidσ(·): smoothing function; adjustment coefficients β1, β2, β3: positive real numbers; β1+β2+β3=1, used to balance the contributions of each item;

[0148] Therefore, high-probability hidden chains have been significantly improved recently; thus, the risk propagation model is closer to the real time; in turn, dynamic weights are provided for the causal attention predictor; and finally, the graph weight distribution is updated in a timely manner, coupling time decays, and expired hidden chains are prevented from occupying high weight positions.

[0149] If the edge weight w′ is revised ij The original graph already has explicit edge weights. When a conflict occurs between the same pair of nodes, the mediator makes a decision according to the following rules:

[0150] If the two sides are of different types, retain their respective labels and generate multi-view edges in the graph;

[0151] If the types are the same and the edge weight w′ is revised ij With explicit edge weights If the difference is below the threshold ε, a weighted average is applied.

[0152] In other cases, the old value is overwritten with the latest revised edge weight, and conflicting version blocks are recorded.

[0153] Therefore, the insertion of hidden chains does not disrupt the original semantic consistency, thus ensuring the stability of the graph structure and facilitating rollback in subsequent versions; ultimately, it maintains data integrity, enables rule-driven conflict resolution, and reduces human intervention.

[0154] The revised edge weights are locally sorted and cached before being written back to the main graph in batches. A weight-priority lock is applied to the pipeline, locking only the range of edge keys to be written. Simultaneously, a quadruple of <node pair, old weight, new weight, timestamp> is written to the augmented graph log. Therefore, the write-back process is almost non-blocking for query threads, thus maintaining stable online prediction latency and ensuring alignment between the augmented graph and the real-time data stream. Ultimately, this provides a unified view for the subsequent step four. In use, fine-grained locking combined with log persistence balances performance and traceability.

[0155] To prevent excessive expansion from causing uncontrolled graph density, continuously monitor the following metrics:

[0156]

[0157] Where: density increment Δρ t Unit node edge increase rate; average revision magnitude Average weight change; lower bound δ ρ : Positive real number threshold; Window number m: Positive integer;

[0158] When density increment Δρ t For m consecutive rounds, the value is below the lower bound δ ρ And the average revision range Below the lower bound δ w If the early stop is triggered, proceed to the next batch of windows.

[0159] Therefore, automatic convergence determination avoids unproductive computations, saves resources, and ultimately maintains the sparsity and real-time performance of the enhanced graph. Specifically, dual-threshold early stopping ensures convergence and avoids overfitting.

[0160] In use, the active index-driven edge weight self-adjustment, semantic microscopic view aggregation, cold / hot write strategy switching, and prediction-reality closed-loop monitoring make the implicit dependency enhancement graph a truly real-time living document rather than a static database. Edge weight updates are no longer simple overwrites, but multi-factor collaboration across three dimensions: industry trends, semantic context, and time windows; the harmonizer performs hierarchical governance of multi-view edges, preserving information while maintaining graph readability; the write-back pipeline intelligently shifts between throughput and consistency, and reverse incremental logs make fault recovery highly deterministic; the early stop criterion is tightly coupled with step four, incorporating prediction accuracy into deployment decisions to ensure that system resources are only spent on directions that truly enhance risk control value.

[0161] Step 3, through a two-tiered mechanism—multi-scale threshold screening, entropy suppression path sampling, probability aggregation, and parallel submission in step 301, and temporal semantic coupling weight update, heuristic conflict reconciliation, enhanced graph write-back, and dual-threshold convergence monitoring in step 302—transforms the semantically unified graph into an implicit dependency enhanced graph. On one hand, multi-scale diffusion potential and path entropy suppression jointly ensure accurate and interpretable implicit chain mining; on the other hand, the edge weight update function, which integrates time decay, semantic strength, and implicit probability, enables the enhanced graph to dynamically map the real-world risk situation. The conflict reconciler and enhanced graph log, combined with Merkle chain version management, provide a complete basis for future compliance audits and policy replays; the dual-threshold early stopping criterion allows the system to achieve a balance between real-time performance and graph sparsity. Thus, step 3 not only fills the structural gaps in the explicit network but also, through refined edge weight revision, inputs high-dimensional features integrating structure, semantics, and temporal sequence into the causal attention predictor, completely opening up the inference link from data flow to risk quantification, laying a solid and reliable computational foundation for the key dependency scoring in step 4.

[0162] After the implicit dependency augmentation graph of the supply network is formed, the system still faces three challenges: complex structure, dynamic weights, and sudden events. First, the augmentation graph integrates explicit interactions and implicit dependencies, resulting in a heavy-tailed edge weight distribution. Second, the speed of risk propagation is not linearly related to the resilience of nodes, making it impossible to accurately measure with single-point indicators. Third, different business scenarios have significantly different sensitivities to risk thresholds; without causal interpretability, mitigation instructions will be difficult to gain the trust of operators. Therefore, step four introduces a causal attention predictor. While retaining the high-dimensional expressiveness of self-attention networks, it embeds a directed causal mask, a time decay kernel, and a multi-head semantic clustering mechanism to extract the features that contribute most to risk propagation from the implicit dependency augmentation graph and output a risk vector set. Each risk vector element includes both node influence and structural weights modulated by multi-hop propagation probability, ensuring that the subsequent scenario synthesis module can map to the policy template with one click. Simultaneously, the predictor records attention weights and causal factors throughout the process, providing a complete and auditable link, laying the foundation for subsequent regulatory evidence collection and model iteration.

[0163] Step 4: Learn the joint node-edge representation on the latent dependency enhancement graph through the causal attention predictor, calculate the key dependency score and generate a risk vector set to provide interpretable and traceable quantitative input for the scenario synthesis module.

[0164] Step four includes the following:

[0165] Step 401: Learning the characteristics of multi-headed causal attention

[0166] Implicit dependency enhancement revision edge weight w′ ij With both temporal decay and semantic amplification characteristics, if directly fed into the traditional GAT (Graph Attention Network), the attention weights will be highly concentrated, causing the risk of weak chains to be submerged. Therefore, step 401 constructs a four-stage pipeline of input tensor alignment, causal mask construction, multi-head temporal attention, and representation compression residual regularization to balance high-weight chain focusing and long-tail chain detection.

[0167] First, embed the enhanced graph nodes into matrix D. t Revise the border rights w′ ij With implicit dependency probability Π ij spliced ​​into a third-order tensor Where d′ = 2d + 1. Batch normalization is then performed, using the moving mean-square root method to counteract impulse surges within the window and avoid gradient explosion during training. By aligning the three sources of information and unifying the scale, stable input is provided for the subsequent attention module.

[0168] To ensure that information flows only along the time-bound and causally valid paths, the generator uses the timestamp sequence {t} iConstructing a Boolean matrix from the path family label graph Future moments or semantically conflicting edges are prohibited from participating in attention calculations; if a feedback loop path exists, the mask retains the shortest path with the highest weight, while the rest are set to zero to prevent self-reinforcing loops. By explicitly constraining the causal direction with a mask, the attention weights become interpretable.

[0169] The module uses H attention heads, each with a decay kernel κ at different times. h Independent learning on (Δt), the core attention weight calculation formula is:

[0170]

[0171] Where: query vector The query representation of node i on head h; range of values Used to measure attentional similarity;

[0172] key vector Node j key representation; Δt ij The duration from the start of the current window to the most recent interaction between nodes i and j;

[0173] Time decay kernel κ h (Δt ij ): Parameterized by a family of Gaussian kernels, taking values ​​(0,1], used to highlight near-time interactions; κ h Let represent the time decay kernel function specific to the h-th attention head;

[0174] Normalization factor Used to prevent the inner product from increasing with dimension;

[0175] Causal mask Boolean value used to restrict unauthorized access;

[0176] Therefore, each node learns local causal dependencies at different time scales, thus avoiding single-scale bias and allowing long-term and short-term risk patterns to coexist, ultimately outputting a multi-head weighted node representation z. i The time-core multi-head model allows it to capture both short-term shocks and long-term vulnerabilities.

[0177] The multi-head splicing result is reduced to d dimensionality via a linear layer, while residual Fourier regularization is added—to penalize excessively fluctuating high-frequency components in the frequency domain, maintaining a smooth representation. Regularization term

[0178]

[0179] Where: Fourier transform Node representation spectrum; high-frequency mask h high Elements above the cutoff threshold are set to 1; the regularization coefficient λ f : Positive real number, used to adjust the smoothing intensity;

[0180] Therefore, the model suppresses noise without sacrificing details, thereby enhancing generalization, and finally outputs the node causal representation matrix Z, where frequency domain regularization solves the oscillation caused by multiple superposition.

[0181] In use, by using the cross-aqueduct threshold balancing and cooling period masking, the node-edge three-source information completes noise suppression and causal consistency verification before entering the attention network, significantly reducing the risk of gradient explosion caused by extreme probability fluctuations; the multi-head kernel width conservation allows the model to simultaneously detect intraday disturbances and seasonal structural vulnerabilities in the same inference; the residual regularization linkage pulse compensation mechanism ensures that instantaneous anomalies are not masked by overall smoothing.

[0182] Step 402: Key Dependency Score Generation and Risk Vector Output

[0183] Node representation Z provides the potential for risk propagation, but operators are more concerned with prioritizing interventions on which node-edge combinations. Therefore, a pipeline is used to compress the high-dimensional representation into actionable quantitative indicators through node influence calculation, propagation probability estimator, key dependency score synthesis, and risk vector release.

[0184] The influence quantifier calculates the node influence ζ using the node representation Z and its in-degree and out-degree weights. i By combining characterization energy and structural weights, the potential impact force of nodes can be described:

[0185]

[0186] Where: ζ represents the influence degree of a node. i : The contribution of node i to the overall network risk; L2 norm ||z i ||2: Representation strength; Revise edge weight w′ ij Implicit dependency augmentation graph weights;

[0187] The estimator uses a learnable Hawkes kernel to calculate the propagation probability p for each edge (i,j). ij :

[0188] p ij =1-exp(-γw′) ij Δt ij )

[0189] Where: propagation probability p ij : The probability of risk propagating from node i to node j; Attenuation-amplification factor γ: a positive real number; Weight-time coupling strength; Time interval Δt ij : Distance from the most recent interaction to the current window; where the Hawkes kernel models edge weights synchronously with time, providing a probability factor for the scoring function.

[0190] Combined node influence and propagation probability pij The critical dependency score S is defined as follows: ij :

[0191]

[0192] Where: Key dependency score S ij : Quantify the risk priority of nodes in the portfolio; ζ i ζ j The node influence degree measures the single-point contribution of node i and node i to network risk, respectively.

[0193] The scoring matrix S is truncated by the upper quantile threshold φ to retain high-risk pairs. The product of the average impact of two nodes and the propagation probability reflects the risk intensity × the ease of path passage.

[0194] Expand the rating matrix S into a vector group r using nodes as indices. i ={S ij} j The risk vectors are tagged with explicit, implicit, and multi-view sources based on view coverage. The generated risk vector groups are written to the prediction publishing bus, pushed to the scenario synthesis module, and simultaneously written to the model interpretation repository, along with attention weight heatmaps and path family labels, supporting backend visualization and traceability of results. In use, structured publishing facilitates direct matching of policy templates by downstream modules while preserving interpretive evidence.

[0195] Step four uses the causal masking of step 401 to drive multi-head temporal attention and the influence degree-probability dual-factor scoring dual-core architecture of step 402 to complete the high-fidelity mapping from the implicit dependency enhancement graph to the risk vector group.

[0196] Multi-head time attention shields causal conflicts at the input layer and suppresses jitter through frequency domain residual regularization, resulting in node representations that are both profound and smooth. The coupling of influence and propagation probability allows the scoring function to simultaneously measure node intrinsic risk and link susceptibility. The publishing bus carries explanatory metadata, pushing attention heatmaps and path family tags together to ensure that the policy template parser can locate specific risk factors rather than black-box values.

[0197] Risk vector groups have compressed the multidimensional risks in implicit dependency enhancement graphs into node-level metrics. However, in actual operations, simple numerical values ​​are insufficient to trigger specific actions. Different companies may adopt drastically different strategies for the same risk threshold: a semiconductor wafer fab with rigid production tends to immediately lock in inventory and replenish stock, while a flexible foundry may first mitigate sudden demand through production scheduling adjustments. Further complicating matters, mitigation actions often involve multiple departments such as procurement, logistics, finance, and regulations. Without consistent semantic mapping and prioritization across roles, even the most intelligent risk scoring will be fragmented at the execution level.

[0198] One-way breach labeling allows the operations team to quickly distinguish between volatile sources and risk receivers, enabling precise deployment of slow-release solutions; the homogeneity discount factor makes the propagation probability more aligned with the realities of cross-industry friction, reducing false alarms caused by short-term noise; dual-threshold collaborative screening stratifies and outputs two types of risks: instantaneous supply chain disruptions and chronic vulnerabilities, helping strategy templates balance emergency replenishment with structural rebalancing; and the delayed visibility strategy ensures that real-time decisions are not hampered by the size of explanatory documents, while still allowing for full auditing in the background.

[0199] Step 5: Driven by the risk vector group, automatically match the optimal strategy template, generate a set of mitigation instructions under resource constraints, and push it to the collaborative execution channel with high reliability, thereby closing the data-decision-action loop.

[0200] Step five includes the following:

[0201] Step 501: Contextual Semantic Analysis and Policy Template Adaptation

[0202] Risk vector group r i It only describes the risk intensity of nodes, lacking a mapping to business context and resource availability; if a fixed threshold trigger template is used directly, the current inventory level or contract terms may be ignored.

[0203] The parser first combines the risk vector set with the business context tensor. Alignment, the latter including real-time inventory, in-transit logistics, and cash flow windows. The risk vector group r is then aligned using the semantic mapping matrix U. i Projected onto the business semantic space, a risk-resource mapping vector s is generated. i =Ur i Transform purely numerical risks into semantic signals that are perceptible to business entities.

[0204] In the risk-resource mapping vectors i Based on this, the system calculates the scarcity level across three dimensions: inventory, production lines, and cash flow.

[0205]

[0206] Where: scarcity level The relative urgency of node i in resource dimension d; risk-resource mapping vector components. The risk vector, calculated by the context semantic parser, represents the resource consumption pressure on node i in dimension d.

[0207] Business Context Tensor Components Dimension d: available quantity; micro-positive number ε: to prevent division by zero; business context tensor C includes at least five dimensions: inventory, in-transit quantity, available production line hours, cash flow balance, and compliance red line. The values ​​of each field are derived from the enterprise's ERP, WMS, APS, and financial middle platform interfaces.

[0208] Therefore, nodes with high scarcity are placed in the priority processing queue, allowing template matching to consider resource pressure, thereby improving command usability and ultimately reducing false triggers. In practice, the necessity of mitigation is measured by both risk magnitude and resource availability.

[0209] The engine maintains a template index triple (applicable industry, resource allocation threshold, historical success rate) for each risk scenario, and performs weighted sorting for nodes with high scarcity.

[0210] v k =ω1v k +ω2σ k +ω3η k

[0211] In the formula: template priority score v k : Ranking metric for template k in the current scenario; historical success rate v k Past execution success rate; resource matching degree σ k Template parameters and Matching degree; Industry matching weight η k : Template and node industry consistency; weighting coefficients ω1, ω2, ω3: non-negative and sum to 1;

[0212] The engine outputs a ranking list for further optimization, in which multi-metric weighted sorting takes into account both historical performance and current resource status.

[0213] If a node appears for the first time and has no historical success rate, then a similar node migration estimate is introduced to fill in the historical success rate v. k If resources are extremely scarce, the resource matching degree σ will be automatically increased. k The weighting and gating mechanism can also temporarily adjust the weighting coefficient ω3 based on regulatory policies on a quarterly or event-driven basis to ensure that the strategy complies with regulatory boundaries; thus giving the template selection the ability to adapt to new nodes and policy fluctuations. The strategy template refers to the parameterized mitigation scheme predefined in the knowledge base, and its structure includes applicable material categories, trigger threshold ranges, resource consumption vectors, expected execution time and historical success rate;

[0214] When in use, the parser's resampling and orthogonal projection make the risk-resource mapping interpretable and reversible, directly reducing the operations team's concerns about black-box projection; the scarcity adjustment injects enterprise safety stock and financing costs, so that template screening no longer only looks at the risk intensity, but also considers the overall operational margin; the time decay of the ordering engine allows the strategy library to continuously self-clean, preventing outdated best practices from taking over; the regulatory critical point predictor and embedded neighborhood migration improve cold start accuracy and compliance agility.

[0215] Step 502: Generating and Cooperatively Pushing Delayed Release Instructions

[0216] The strategy template queue remains a semi-structured list of entries, requiring the generation of actionable deferred instructions by considering resource constraints and execution concurrency capabilities. Simultaneously, to ensure that parallel collaboration across multiple departments does not overlap or conflict, the instruction set must be pushed in transactional form with a traceable signature. The optimizer performs integer programming on the priority templates: using the resource requirements of the template as variable coefficients, the objective is to minimize the total execution cost.

[0217]

[0218] Where: Execution cost ρ k : Unit cost of template k; Resource upper bound vector b, business context availability, which is the upper limit of resource capacity corresponding to resource matrix A; Resource matrix A: Template resource consumption coefficient matrix; Decision vector x, a Boolean column vector with a length equal to the total number of candidate strategy templates; Decision variable x k : Should a template be selected?

[0219] Therefore, the optimizer outputs a cost-constrained optimal template subset, thereby avoiding resource contention and improving overall buffering efficiency. Finally, it generates an instruction blueprint, where integer programming ensures reasonable resource allocation at the template layer.

[0220] The scheduler maps a subset of templates into instruction transaction blocks, each containing an execution entity, action parameters, timeout thresholds, and pre- and post-dependencies. Under the same supply chain entity, cross-departmental instructions are executed using topological ordering to prevent conflicting execution sequences. If cross-regional conflicts exist, the scheduler inserts a coordination placeholder, waiting for feedback from the other organization before releasing subsequent instructions. In practice, transaction blockization and topological ordering ensure deadlock-free multi-department collaboration.

[0221] The instruction transaction block is hashed and signed using the enterprise's private key. The signature and message are written together into the deferred instruction chain. The hash at the top of the chain is refreshed with each instruction. The push gateway uses AMQP streaming push and retains a retry count until a downstream ACK (acknowledgment signal or acknowledgment response) is received. If the ACK times out, the system automatically escalates the priority and triggers an operation and maintenance alarm. In use, the chained signature provides non-repudiation, and streaming push ensures timely delivery.

[0222] After the downstream system executes the instruction, it generates a receipt containing a four-tuple of <status, actual resource consumption, start time, and end time>, which is then written back through the receipt channel. The system compares the resource consumption in the receipt with the planned amount; if the deviation exceeds the tolerance rate δ... c Record planning drift events for updating execution cost ρ in the next training cycle. k Resource fit σ k Thus, the acknowledgment loop allows instructions and the model to form a self-supervised data flow.

[0223] The dual mechanisms of step 501 risk semantics-resource coupling template sorting and step 502 resource constraint optimization-chain instruction closed loop successfully transform the purely numerical risk vector into an executable mitigation action.

[0224] In use, the resource constraint optimizer uses marginal utility curves to filter and reinforce resource conflict points, ensuring that every unit of cost expenditure brings the highest possible risk reduction and that execution is not aborted due to conflicts; the scheduling simulator enables seamless switching of cross-shift collaboration and improves the success rate of emergency response at night; multi-department sub-private key joint signing eliminates single-point approval bottlenecks, and exponential backoff prevents avalanche and ensures stable delivery; drift event decomposition and attribution accurately focuses model iteration on the real bottlenecks that slow down the closed loop.

[0225] By leveraging business context tensors and scarcity metrics, the template selection process both inherits successful historical experiences and dynamically responds to resource pressures. Integer programming and transaction packaging ensure consistency across multiple template collaborations in both resource and process dimensions. Chained signatures and automatic receipt tracking enable instruction execution to enter a reliable and auditable pipeline, and accumulate accurate comparative data for subsequent model iterations. Thus, the AI-driven supply chain risk early warning system completes a five-level closed loop: data capture, semantic unification, implicit dependency reinforcement, risk quantification, and scenario mitigation. Each level's symbol mapping and parameter system maintain consistency, providing enterprises with a highly reliable, low-latency, and traceable end-to-end risk control solution.

[0226] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0227] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0228] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0229] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0230] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An AI-driven supply chain risk early warning system, characterized by: include, A cross-level heterogeneous data adapter collects multi-protocol event streams and metadata in parallel, and after time calibration and unified identifier mapping, constructs an initial graph model of the supply network and writes it to the incremental log in real time. The semantic alignment engine calls industry ontology libraries and lexical mapping rules to incrementally standardize node attributes and edge types, hierarchically label conflicting versions and write them back, and generate a semantically unified graph. The progressive graph unfolding algorithm is based on multi-scale reachability thresholds and entropy suppression path sampling to deduce implicit supply dependencies, revises edge weights in real time and merges and submits them in parallel, and outputs an implicit dependency enhanced graph. The causal attention predictor applies a causal mask and multi-head temporal attention to the latent dependency enhancement graph, generates node representations, calculates dependency scores by combining influence and propagation probability, and outputs a risk vector set. The scenario synthesis module parses the risk vector and context, sorts the templates by tightness, selects a subset of templates through resource constraint optimization, packages the transactions into a chain of signatures, pushes instructions, and tracks receipts.

2. The AI-driven supply chain risk early warning system according to claim 1, characterized in that: The cross-level heterogeneous data adapter sets up a protocol fingerprint classifier, a zero-copy circular buffer, and a distributed Kalman clock calibration unit. It first identifies the message protocol, then calibrates the clock in real time and writes it with a concurrent buffer to ensure the original order of the event stream and a unified time base. Incremental logs are generated during writing to support subsequent backtracking.

3. The AI-driven supply chain risk early warning system according to claim 2, characterized in that: When generating the initial graph of the supply network, a hierarchical autoencoder is used to extract the static and dynamic features of nodes, a namespace hash tree is used to generate a globally unique identifier, and the initial weight of the edge is calculated through a sliding window time decay model. At the same time, a traceable incremental log mechanism is used to store the hash and timestamp of each node or edge change.

4. The AI-driven supply chain risk early warning system according to claim 3, characterized in that: By jointly generating unified node attribute tags through industry ontology anchoring and upstream and downstream lexical gains, the conflict hierarchical parser constructs a tag stack based on time freshness and data credibility level and writes it into the semantic version table. The incremental standardization pipeline writes back to the graph database in real time under the write-time merging strategy.

5. The AI-driven supply chain risk early warning system according to claim 4, characterized in that: The verb phrase semantic landing device identifies action roles based on extended FrameNet, the hierarchical relation mapper outputs three-level relation labels, and the semantic correction edge weight generator revises the time decay edge weights according to relation importance and generates relation version blocks that are attached to the semantic unified graph.

6. The AI-driven supply chain risk early warning system according to claim 5, characterized in that: High-potential nodes are screened using multi-scale reachability thresholds, and low-entropy, high-weight paths are preferentially sampled by an entropy-suppressed path sampler. The implicit dependency candidate set constructor aggregates node pairs according to path probability and marks them with fluctuation flags. Then, the parallel incremental unfold scheduler inserts candidate edges in local replicas and submits them to the main graph through the conflict merging table.

7. The AI-driven supply chain risk early warning system according to claim 6, characterized in that: A temporal semantic coupling function is used to fuse revised edge weights, implicit dependency probabilities, and window time intervals to calculate new edge weights. A heuristic conflict mediator performs multi-view aggregation or version overriding based on edge type and weight difference. The enhanced graph write-back pipeline sets a weighted priority lock and writes it to the enhanced graph log. At the same time, the early stop criterion is continuously monitored to control the termination of the process based on the density increment and revision magnitude.

8. The AI-driven supply chain risk early warning system according to claim 7, characterized in that: After input tensor alignment and batch normalization, a directional causal mask is constructed to restrict illegal attention. The multi-head temporal attention module independently calculates attention weights on different time decay kernels and outputs a spliced ​​representation. The compression residual regularization is characterized by using Fourier high-frequency masking to suppress noise and retain impulse information, ultimately forming a node causal characterization matrix.

9. The AI-driven supply chain risk early warning system according to claim 8, characterized in that: First, the influence degree is calculated by combining the node representation L2 norm with the in-degree and edge weights. Then, the edge propagation probability is estimated by using a learnable Hawkes kernel. The key relies on a scoring function that takes the product of the node's average influence and the propagation probability. After filtering by high and low thresholds, it is expanded into a risk vector group labeled with explicit, implicit, and multi-view sources.

10. The AI-driven supply chain risk early warning system according to claim 9, characterized in that: The system utilizes business context tensors to calculate the three-dimensional scarcity of inventory, production lines, and cash flow. The strategy template candidate ordering engine calculates the template priority score based on historical success rate, resource fit, and industry matching weights, and outputs a ranking list. Dynamic threshold gating references similar node migration estimation and regulatory policies to adjust the weighting coefficients in real time.

11. The AI-driven supply chain risk early warning system according to claim 10, characterized in that: The subset of priority templates is input into the resource constraint optimizer for integer programming and marginal utility curve filtering. The instruction transaction packer topology sorts the instructions to generate execution blocks and inserts cross-regional coordination placeholders. After being signed by a chain of multiple sub-private keys, the data is streamed to the collaborative execution channel via a push gateway, and the automatic receipt tracking unit collects receipts to generate a drift event summary feedback model.

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