Order fulfillment process delay early warning method and system based on multi-node state acquisition

Abstract

The invention belongs to the technical field of order fulfillment process monitoring, and discloses an order fulfillment process delay early warning method and system based on multi-node state acquisition, and the method comprises the steps: collecting the dynamic state data of multiple nodes in a fulfillment process in real time; matching judgment is carried out through the node rule base, primary early warning is triggered, and an early warning context data set is constructed; inputting the data set into a pre-trained delay risk prediction model, and outputting an overall risk probability and a key influence node identifier; issuing a grading early warning signal according to the risk probability; carrying out interpretable root cause positioning through a root cause analysis rule chain based on directed acyclic graph representation; incremental learning is carried out on the prediction model based on early warning feedback data to form an optimized closed loop; the system correspondingly comprises a data acquisition module, a rule judgment module, a risk prediction module, an early warning release module, a root cause positioning module, a feedback learning module and the like. According to the invention, the early-stage, accurate and automatic early warning and root cause analysis of the full-process risk of order fulfillment are realized.

Description

Technical Field

[0001] This invention relates to the field of order fulfillment process monitoring technology, specifically to a method and system for early warning of order fulfillment process delays based on multi-node status collection. Background Technology

[0002] In complex business scenarios such as e-commerce, intelligent manufacturing, and modern supply chain management, order fulfillment processes typically exhibit characteristics of high distribution, multi-system collaboration, strong time-series dependence, and significant resource competition.

[0003] A typical fulfillment chain consists of multiple heterogeneous nodes, including inventory query, order review, production scheduling, picking and packing, logistics collection, transportation transfer and last-mile delivery. Each node belongs to a different business system (such as WMS, ERP, TMS, MES, etc.) and runs on a heterogeneous technology stack (microservices, monolithic applications, edge devices, third-party SaaS platforms). Its status data is scattered, has different formats, uneven collection frequency, and inconsistent timestamp benchmarks. It also generally suffers from high data noise, frequent abnormal changes, and lack of contextual semantics.

[0004] Traditional delay monitoring methods mainly rely on single-node threshold alarms (such as "alarm when order review timeout is 5 minutes") or end-result backtracking (such as T+1 statistics on on-time fulfillment rate). They lack the ability to perceive the real-time evolution of the status linkage of multiple nodes and cannot identify hidden bottlenecks across nodes (such as inventory node response delays causing subsequent scheduling node resource idleness, which then leads to backlog at picking nodes), time-series cascading effects (such as a slight increase in upstream node processing time coupled with a contraction in downstream node waiting tolerance leading to a non-linear increase in the overall delay probability), and resource contention conflicts (such as a shared database connection pool being exhausted by a sudden request from a node, causing concurrent delays in multiple dependent nodes).

[0005] Furthermore, existing early warning systems generally lack interpretability, with alerts only indicating "possible delay" but failing to pinpoint the root cause node and failure path; rule bases are statically fixed for a long time, making it difficult to adapt to changes in business rhythm, promotional peak impacts, and state distribution drift caused by system version iterations; model prediction capabilities are limited by crude feature engineering (often using the average or maximum value of isolated node indicators) and the lack of modeling of temporal dependencies and causal constraints between nodes, resulting in delayed risk identification and high false alarm and false negative rates; early warning responses are also fragmented, lacking standardized integration mechanisms with automated handling systems (such as dynamic rescheduling engines and backup logistics channel triggers) and visual decision-making terminals.

[0006] The aforementioned deficiencies make it difficult for companies to make precise interventions before delays occur, resulting in large fluctuations in SLA compliance rates, and the average time spent investigating the root causes of anomalies can reach several hours, while operational optimization lacks data-driven support.

[0007] Therefore, there is an urgent need for a full-process delay early warning technology system that integrates real-time perception of the status of multi-source heterogeneous nodes, collaborative judgment driven by rules and data, explainable risk reasoning, dynamic rule self-evolution, and end-to-end feedback optimization. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a method for early warning of order fulfillment process delays based on multi-node status acquisition. This method solves the problems of insufficient real-time performance, poor interpretability, rigid rules, and low integration with the handling system in existing methods and systems for early warning of order fulfillment process delays, particularly in multi-node status acquisition, linkage analysis, and early warning response. Its core objective is to construct a technical solution capable of real-time, accurate, and dynamic perception and analysis of the status of multiple nodes in a distributed, heterogeneous order fulfillment process, achieving interpretable delay early warning and effective linkage with automated handling systems. This improves the timeliness and reliability of order fulfillment, reduces the cost of root cause analysis of anomalies, and provides data-driven support for operational optimization.

[0009] This invention provides a method for early warning of order fulfillment process delays based on multi-node status collection, comprising the following steps: S1: Real-time data collection and rule judgment: Real-time collection of dynamic status data of at least two collaborative nodes in the order fulfillment process, and matching and judgment of the dynamic status data with the preset node rule base; S2: Primary warning and context construction: When the dynamic state data of any current node violates the rules of its corresponding node, a primary delayed warning is triggered, and the state data of the current node and its directly related nodes are extracted to form a warning context dataset that includes cross-node time sequence and dependency relationships. S3: Intelligent Risk Prediction: Input the early warning context dataset into the pre-trained delay risk prediction model to obtain early warning prediction results including the overall delay risk probability and the identification of key impact nodes; the delay risk prediction model models the temporal dependence and resource competition relationship between the states of multiple nodes based on machine learning algorithms; S4: Tiered Early Warning Issuance: Based on the overall delay risk probability, compare it with the preset first threshold and second threshold to generate and issue delay early warning signals of low, medium and high levels, where the first threshold is higher than the second threshold; S5: Explainable Root Cause Localization: In response to the issuance of a high-level delay warning signal, based on the key impact node identifiers and warning context dataset, reasoning is performed through a pre-configured root cause analysis rule chain to locate the root cause node leading to the delay risk; wherein, the root cause analysis rule chain is represented by a directed acyclic graph. S6: Feedback optimization closed loop: Based on the comparison results between the actual performance data after the issuance of the graded early warning and the early warning prediction results, the delay risk prediction model is optimized.

[0010] Furthermore, real-time collection of dynamic status data from at least two collaborative nodes in the order fulfillment process includes the following steps: Raw state data is obtained at a fixed frequency or in an event-driven manner by using agent programs deployed on each node or by calling application programming interfaces provided by the node system. The original state data is standardized and cleaned to unify the time base and data format, and abnormal noise data is filtered out to obtain dynamic state data.

[0011] Furthermore, the pre-training process of the delay risk prediction model includes: Acquire status data covering all nodes in the historical order fulfillment process and corresponding actual fulfillment result tags; Construct a sample set with multi-node complete state sequence data as input features and whether delay occurs and the degree of delay as training objectives; The gradient boosting decision tree algorithm is used for supervised training on the sample set, and the model parameters are optimized through cross-validation.

[0012] Furthermore, generating and issuing corresponding levels of delay warning signals includes: Three mutually exclusive and continuous risk probability threshold ranges are preset. Each risk probability threshold is determined by statistical modeling of historical performance data and calibration of business tolerance, and supports dynamic updates. Based on the overall probability of delay risk, a step-by-step comparison method is used to determine the threshold range to which it belongs and to determine the corresponding warning level. The constructed structured early warning message sequence is asynchronously published via message queue middleware or pushed to the preset monitoring terminal system or automated response system through standardized RESTful API interface calls.

[0013] Furthermore, it also includes step S7: visualization, specifically including: Construct a multi-node topology diagram that strictly corresponds to the logical structure of the order fulfillment process, and render the status data of each node in real time; The nodes that trigger delay warnings and / or the key impact nodes that are located are visually highlighted.

[0014] Furthermore, it also includes step S8: dynamic optimization of the rule base; specifically including: Based on the model feedback data and root cause nodes generated in the feedback optimization closed loop, a quantitative analysis is conducted on the statistical correlation between the triggering event of each rule in the node rule base and the final order fulfillment delay, and the effectiveness of the correlation between rule triggering and final delay is analyzed. If the association effectiveness of any rule is lower than a preset threshold, or if its effectiveness shows a continuous decay trend in the most recent N scrolling windows, then the adaptive adjustment mechanism will be activated. The adaptive adjustment mechanism is as follows: For parameter range rules, the upper and lower bounds of the threshold are dynamically scaled based on the actual distribution quantiles and abnormal offset directions of the corresponding indicators in the historical delay samples; for time constraint rules, the time window width and relaxation tolerance are reset based on the historical drift curve of node processing time, seasonal fluctuation cycle, and the waiting delay propagation coefficient of adjacent nodes; for rule logic rules, based on the high-frequency composite failure modes in the root cause nodes, logic gate combinations are introduced, context dependency conditions are embedded, and after A / B testing verification, the updated rule version is entered into the node rule library to complete the closed-loop iteration.

[0015] Furthermore, the collaborative nodes include at least two of the following: inventory query node, order review node, production scheduling node, picking and packing node, logistics collection node, transportation transfer node, and last-mile delivery node, and there are deterministic business dependencies and data flow paths between any selected nodes.

[0016] A system for early warning of order fulfillment process delays based on multi-node status acquisition is provided to implement a method for early warning of order fulfillment process delays based on multi-node status acquisition, comprising: The data acquisition and rule judgment module is configured to execute step S1. The primary warning and context building module is configured to execute step S2. The risk prediction module has a built-in pre-trained delay risk prediction model and is configured to execute step S3. The early warning generation and release module is configured to execute step S4. Root cause localization module, configured to perform step S5; The feedback learning module is configured to execute step S6.

[0017] Furthermore, it also includes a model training management module, configured to manage historical data, perform initial training, optimization, and incremental learning or retraining tasks based on model feedback data provided by the feedback learning module for the delay risk prediction model.

[0018] Furthermore, it also includes a visualization monitoring module, configured to provide a graphical interface and perform visualization display steps.

[0019] Compared with the prior art, the present invention has the following advantages and beneficial effects: First, by collecting dynamic status data from at least two collaborative nodes in real time and performing rule matching and judgment, it breaks through the limitations of traditional single-node threshold early warning, and can capture potential delay risks from the perspective of the entire order fulfillment chain, thus achieving early identification of process anomalies.

[0020] Second, a delay risk prediction model based on machine learning algorithms is introduced, which can deeply explore the temporal dependencies and resource competition relationships between the states of multiple nodes, and output accurate overall delay risk probability and key influencing node identifiers. Combined with the root cause analysis rule chain based on directed acyclic graph representation, the root cause node causing delay risk can be quickly located, providing clear guidance for subsequent intervention measures.

[0021] Third, it presets three mutually exclusive and continuous risk probability threshold ranges. Each threshold is determined through historical data statistics and business tolerance calibration and supports dynamic updates. It can generate and issue warning signals of corresponding levels according to the actual risk level, meet the warning needs of different business scenarios, and avoid excessive or insufficient warnings.

[0022] Fourth, a complete optimization closed loop from early warning and prediction to actual result feedback has been constructed. By comparing the actual performance results with the early warning and prediction results, the delay risk prediction model is continuously optimized. At the same time, the node rule base is dynamically adjusted in combination with the root cause analysis results of high-level early warnings, so as to continuously improve the accuracy and adaptability of the early warning system.

[0023] Fifth, it supports obtaining raw status data at a fixed frequency or in an event-driven manner through agent programs or API interfaces, and performing standardization, cleaning and other processing to ensure data quality; the release of early warning messages supports asynchronous release through message queues or RESTful API interface calls, which facilitates integration with various monitoring terminal systems or automated response systems.

[0024] Sixth, it provides a multi-node topology diagram that strictly corresponds to the logical structure of the order fulfillment process, renders node status data in real time, and visually highlights nodes that trigger warnings and key impact nodes, enabling managers to intuitively and comprehensively grasp the operation status and risk points of the order fulfillment process.

[0025] Seventh, the node rule base and root cause analysis rule chain support flexible configuration and dynamic optimization. The delay risk prediction model adopts mature algorithms such as gradient boosting decision tree and supports incremental learning or retraining, enabling the system to adapt to constantly changing business processes, node characteristics and external environment, and maintain long-term effective early warning capabilities.

[0026] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0027] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0028] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram illustrating the steps of an order fulfillment process delay early warning method based on multi-node status collection. Figure 2 A schematic diagram illustrating the steps for generating and issuing delay warning signals of corresponding levels; Figure 3 This is a schematic diagram of the structure of an order fulfillment process delay early warning system based on multi-node status collection. Detailed Implementation

[0029] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0030] This invention provides a method for early warning of order fulfillment process delays based on multi-node status acquisition, such as... Figure 1 As shown, it includes the following steps: S1: Real-time data collection and rule-based judgment: Real-time collection of dynamic status data of at least two collaborative nodes in the order fulfillment process, and matching and judging the dynamic status data with the preset node rule base; the dynamic status data includes at least the node completion timestamp and real-time resource utilization rate, and the node rule base includes the parameter range and time constraints for the normal operation of each node defined based on historical data. S2: Primary warning and context construction: When the dynamic state data of any current node violates the rules of its corresponding node, a primary delayed warning is triggered, and the state data of the current node and its directly related nodes are extracted to form a warning context dataset that includes cross-node time sequence and dependency relationships. S3: Intelligent Risk Prediction: Input the early warning context dataset into the pre-trained delay risk prediction model to obtain early warning prediction results including the overall delay risk probability and the identification of key impact nodes; the delay risk prediction model models the temporal dependence and resource competition relationship between the states of multiple nodes based on machine learning algorithms; S4: Tiered Early Warning Issuance: Based on the overall delay risk probability, compare it with the preset first threshold and second threshold to generate and issue delay early warning signals of low, medium and high levels, where the first threshold is higher than the second threshold; S5: Explainable Root Cause Localization: In response to the issuance of a high-level delay warning signal, based on the key impact node identifier and the warning context dataset, reasoning is performed through a pre-configured root cause analysis rule chain to locate the root cause node leading to the delay risk. The root cause analysis rule chain is represented by a directed acyclic graph, where nodes represent analysis rules and edges represent the triggering logic between rules. The reasoning process includes: starting from the initial rule node associated with the key impact node identifier, traversing along the edges to activate subsequent rule nodes based on the state data in the warning context dataset that meets the rule conditions, until the leaf rule node identifying the root cause is reached. S6: Feedback Optimization Closed Loop: Collect actual performance data for issued early warnings, compare the actual performance data with the early warning prediction results, and generate model feedback data; use the model feedback data to perform incremental learning or retraining on the delay risk prediction model; In practical application, a power battery manufacturer implemented an order fulfillment delay early warning method in its OMS (Order Management System). This method involves full-process monitoring and coordination around key nodes in the order status flow, covering order receipt, order generation, order commitment (delivery date confirmation), inventory reservation, production warehousing, logistics delivery, and customer receipt. Because information transmission delays or resource conflicts are prone to occur during collaboration among various functional departments (such as sales, planning, warehousing, and logistics), the system uses a multi-stage mechanism to accurately identify, predict, and optimize fulfillment delays. The specific implementation steps are as follows: S1: Real-time data collection and rule-based judgment. The OMS system collects order status change events at key nodes at a minute-level frequency (updated every minute) to ensure data timeliness and process transparency. Specific data collection includes: Order approval node (recording the timestamp of customer order access and determining whether the system automatically approves or suspends the order to assess front-end response efficiency), Production scheduling node (collecting the timestamp of promised delivery date confirmation and calculating the deviation between the promised delivery date and the company's standard delivery cycle to identify any excessive compression of delivery time), Inventory query node (tracking the completion time of locking the required battery cell model and quantity, calculating inventory availability based on current available inventory, reflecting material support capabilities), and Picking and packing node (obtaining the delivery note creation time, carrier order acceptance status, and outbound scan timestamp, used for...). (To monitor whether shipment execution is progressing as planned); the system has a built-in node rule library, which sets processing time limits for each stage based on historical fulfillment data and SLA service level agreements. For example: order receipt to order generation should be completed within ≤15 minutes, relying on automated review processes; after order generation to delivery date commitment, the planner should complete the response within ≤48 hours; inventory reservation must be locked within 72 hours before the committed delivery date, and the inventory availability rate must not be less than 90%, otherwise an alert will be triggered; outbound scanning must be completed within 1 hour after the logistics appointment time to prevent shipment from being delayed; when an order is not locked on time in the "inventory reservation" stage, and the system detects that the real-time inventory availability rate of its corresponding cell model is 88% (below the 90% warning line), the node status is determined to be abnormal, the preset rule conditions are met, and the process proceeds to the next stage. S2: Primary Early Warning and Context Construction. Once the rule judgment triggers an abnormal signal, the system immediately activates the primary early warning mechanism and automatically extracts data from the current problem node and its preceding and following related nodes to construct a complete early warning context, supporting subsequent analysis and decision-making. The extracted information includes: the current node (inventory reservation), which includes the order number, the model and quantity of battery cells to be reserved, the cumulative waiting time since the reservation request was initiated, and the current inventory availability rate; the direct preceding node (order commitment), which is configured to: extract the committed delivery date, the person responsible for executing the commitment operation (planner), and the specific time when the commitment operation occurred, for tracing the upstream decision-making basis; and the direct succeeding node, which is configured as follows: if the reserved object is a finished battery, the subsequent node is "logistics delivery," extracting the original scheduled delivery time; if it involves work-in-process, it connects to the "production warehousing" node to obtain the estimated warehousing time. The time sequence relationship between all nodes is clearly marked, such as the transition interval of "commitment completed → reservation started," and logical dependencies are defined, such as "successful inventory reservation is a prerequisite for generating a delivery instruction." This structured data constitutes a complete snapshot of the business chain, providing the input basis for the intelligent model. Next, S3: Intelligent Risk Prediction is implemented. The aforementioned early warning context data is input into the deployed delay risk prediction model. This model uses Gradient Boosting Decision Tree (GBDT) as the basic modeling framework and designs a multi-task supervised learning objective function to improve prediction accuracy. The model output results are as follows: Overall delay risk probability: 72%, exceeding the preset first-level early warning threshold of 65%; Key impact node identification: It clearly points out that the "inventory reservation node" is the current bottleneck point, and at the same time identifies that the "order commitment node" has become an indirect driving factor because the committed delivery date is set too tightly, which has aggravated the pressure on subsequent resource scheduling; The model not only judges whether a delay may occur, but also quantifies the contribution of each node to the overall risk, supporting priority ranking and focused intervention. Next, S4 is implemented: tiered early warning issuance. Given that the overall probability of delay risk is higher than the first threshold, the system automatically generates a high-level delay warning signal and pushes it to relevant responsible persons through multiple channels to ensure rapid response. The recipients include: inventory planners (responsible for handling inventory lock-up anomalies), sales operations managers (who need to assess the reasonableness of sales delivery commitments), and logistics coordinators (who prepare in advance to deal with delivery delays). Notification methods cover OMS system workbench pop-up reminders, SMS alerts, and messaging channels integrated into enterprise IM platforms (such as DingTalk / WeChat Work). The structured presentation of the warning information includes the order number, customer name, predicted delay stage (in this example, "delivery delay approximately 12-24 hours"), suggested key points to focus on (inventory reservation), and relevant pre-emptive triggers. This mechanism achieves a shift from passive response to proactive intervention, improving cross-departmental collaboration efficiency. Next, S5 is implemented: Explainable Root Cause Localization. To further reveal the causes of delays, the system initiates a pre-configured root cause analysis rule chain, deducing from a reasoning path organized by a directed acyclic graph (DAG), drilling down layer by layer to locate the root cause. The reasoning process is as follows: First, based on rule R1 (initial judgment): determine whether the current order inventory reservation has timed out; if so, continue investigating downwards; then, based on rule R2: determine whether the inventory availability rate of the corresponding cell model is less than 90%; if so, it indicates a supply shortage; then, based on rule R3: determine whether there is a general shortage of specific high-demand models (such as the "high energy density 21700 model"); if so... Yes, this indicates that the issue is not limited to individual orders, but rather a category-level bottleneck. Further reasoning based on Rule R4: tracing back to the "order commitment node," we examine whether recent committed delivery dates for this model are highly concentrated and generally shorter than the standard production cycle. If so, it reflects a concentrated ordering by the sales department and a request for expedited delivery. Further reasoning based on Rule R5 (leaf node conclusion): the root cause is ultimately identified as: "The sales department's commitment delivery dates for popular battery cell models are set too concentrated and aggressively, leading to a large number of pre-orders in the short term, exceeding actual supply capacity and causing congestion in the reserved queue." This reasoning chain possesses complete causal logic, and the output is highly interpretable, facilitating management in developing targeted improvement measures. Next, S6: Feedback Optimization Closed Loop was implemented. The order was ultimately shipped with a 20-hour delay. The system collected real result data based on this, forming a closed-loop feedback for continuous optimization of the early warning system: First, the effectiveness of the prediction was verified: the risk probability output by the model was 72%, and the actual delay verified the accuracy of the prediction; then, the contribution of key nodes was confirmed: after review and analysis, the "inventory reservation node" was identified as the main bottleneck, with its attribution contribution to the total delay reaching 70%; next, the authenticity of the root cause was verified: after cross-departmental verification, it was confirmed that the sales strategy led to a concentrated surge in demand for high-energy-density models in the short term, causing inventory shortages. Feedback data is fed back into two core modules: In incremental model learning, the feature association weights between "concentration of order commitments for specific models" and "inventory reservation timeout" are strengthened to improve the predictive sensitivity in similar scenarios in the future; In dynamic adjustment of the rule base, based on the latest supply and demand patterns, the inventory availability warning threshold for "high-demand model cells" is adjusted from a fixed 90% to a dynamic 85% to enhance the adaptability and practicality of the rules.

[0031] The working principle of the above technical solution is as follows: by constructing a multi-node collaborative status perception and dynamic risk prediction mechanism covering the entire order fulfillment process, intelligent early warning of process delay risks in a phased, explainable, and closed-loop optimization manner can be achieved; specifically, In step S1, the system relies on a distributed data acquisition agent to acquire dynamic status data of at least two collaborative nodes in real time. The dynamic status data includes, but is not limited to, node task completion timestamps, current resource utilization, queue backlog, upstream dependent task completion status, and network communication latency. The node rule base is stored in the form of a structured knowledge graph, where each node corresponds to a set of weighted time constraint rules (such as "the time between the completion of the warehouse sorting node and the order creation shall not exceed 90 minutes ± 5% tolerance") and resource constraint rules (such as "if the real-time CPU utilization of the sorting equipment group is continuously higher than 85% for 30 seconds, it is considered overloaded"). The rule parameter range and time constraints are all derived from historical fulfillment data through statistical distribution fitting and anomaly detection algorithm calibration, and support dynamic loading of versioned rule sets according to business scenarios. In step S2, when the dynamic state data of any node violates the hard threshold or soft trend threshold of its association rule at any sampling time, the system immediately triggers a primary delay warning event and simultaneously extracts the full-dimensional state snapshot of the violating node and the synchronous state data of its direct predecessor node, direct successor node, and shared resource node to construct a warning context dataset with spatiotemporal alignment characteristics. This dataset contains a timestamp-aligned multidimensional time series tensor, with dimensions covering node identifier, state variable type, value, confidence label, and cross-node relationship label. In step S3, the warning context dataset is standardized, normalized and time-series sliced ​​by the feature engineering module and then input into the pre-trained delay risk prediction model. The model uses gradient boosting decision tree (GBDT) as the basic modeling framework, designs a multi-task supervised learning objective function, and outputs a scalarized overall delay risk probability value and a key impact node identification sequence. In step S4, the system compares the overall latency risk probability with a preset first threshold. When the risk probability is greater than or equal to the first threshold (80%), a first-level warning signal is generated. When the risk probability is between the second threshold (60%) and the first threshold (80%), a second-level warning signal is generated. When the risk probability is less than the second threshold (60%), no warning is issued but monitoring continues. The first and second thresholds can be dynamically configured according to the business scenario. In step S5, in response to the issuance of the Level 1 warning signal, the root cause analysis engine is activated. This engine loads a pre-configured root cause analysis rule chain, which is organized in the form of a directed acyclic graph (DAG). Each node in the graph encapsulates a formally expressed diagnostic rule (e.g., "If the resource occupancy rate of the sorting node is >90% and the completion delay of the upstream inbound node is >15 minutes, then the 'resource bottleneck propagation' rule is triggered"). Edges represent the logical implications between rules. The reasoning process adopts a forward chain reasoning mechanism: starting from the key impact node identifier, the corresponding initial rule node in the DAG is located, and the corresponding state variables in the warning context dataset are checked sequentially to see if they meet the requirements. If the preconditions of this rule are met, the rule is activated and jumps to the next rule node along the outgoing edge, repeating until a leaf rule node with no outgoing edges is reached. The semantics carried by this leaf rule node are the root cause confirmed through multi-hop logic deduction, such as "the delay in issuing instructions by the AGV scheduling system leads to an abnormally high idle rate at the sorting station". The root cause analysis rule chain is represented by a directed acyclic graph, which can clearly define the triggering order and dependency relationship between rules through directed edges, avoiding confusion in reasoning logic. Its acyclic nature ensures that the reasoning process does not fall into loops, improving the efficiency of root cause location. The structured graph structure facilitates the dynamic addition, deletion and adjustment of rules, adapting to changes in business processes. For example, when adding a "correlation rule between abnormal logistics pickup node and congestion in transportation transfer node", only the corresponding node and triggering edge need to be added in the DAG, without reconstructing the entire rule chain. In step S6, the final order fulfillment result data is continuously collected, including the actual fulfillment completion time, the actual time consumed at each node, the actual peak resource usage, records of manual intervention, and customer feedback events. This actual fulfillment result data is then compared in a structured manner with the early warning prediction results to generate a model feedback data tuple that includes prediction deviation items, false alarm / missed alarm indicators, and root cause location accuracy. The model feedback data is then injected into the loss function of the delay risk prediction model through online gradient updates or batch retraining, achieving dynamic calibration of model parameters and knowledge feedback from the rule base. This forms a complete closed-loop optimization mechanism of "state perception, risk inference, early warning decision-making, root cause attribution, effect verification, and model evolution".

[0032] The beneficial effects of the above technical solution are as follows: The solution provided in this embodiment enables early identification of delay risks through real-time collection and rule matching of dynamic status data from multiple nodes, overcoming the limitations of traditional single-node threshold alarms. By constructing an early warning context dataset and combining it with a pre-trained delay risk prediction model, it can not only predict the overall probability of delay risks but also accurately locate key influencing node identifiers, improving the foresight and accuracy of early warnings. The introduction of an interpretable root cause localization mechanism, using root cause analysis rule chains to deduce the root cause of delays, provides clear direction for subsequent intervention measures, and solves the pain point of traditional early warnings where "only an alarm is triggered, but the reason for the alarm is unknown." Through feedback optimization loops, the model is continuously optimized using actual performance data, continuously improving the model's predictive capabilities, enhancing the system's adaptability and robustness, and effectively supporting refined operation and risk management in complex order fulfillment scenarios. Especially in high-concurrency, high-dynamic environments such as e-commerce promotions, it can significantly reduce order fulfillment delay rates and improve customer satisfaction and overall supply chain efficiency.

[0033] In one embodiment, real-time collection of dynamic status data from at least two collaborative nodes in the order fulfillment process includes the following steps: Raw state data is obtained at a fixed frequency or in an event-driven manner by using agent programs deployed on each node or by calling application programming interfaces provided by the node system. The original state data is standardized and cleaned to unify the time base and data format, and abnormal noise data is filtered out to obtain dynamic state data.

[0034] The working principle of the above technical solution is as follows: Lightweight agent programs are deployed at various collaborative nodes in the order fulfillment process (such as order receiving, inventory verification, picking, packing, and shipping), or standard API interfaces opened by node systems (such as ERP, WMS, and TMS) are directly called to establish a two-way data channel. The data collection method supports dual-mode triggering: periodic polling at preset time intervals (such as every second / every 5 seconds) or proactive push notifications from nodes when key business events occur (such as order status changes, successful inventory deductions, and logistics label generation), ensuring the timeliness and low redundancy of status capture. The acquired raw data covers multi-source heterogeneous fields (such as varying timestamp precision, different status code systems, and different unit expressions). (Due to the presence of null values ​​or logically contradictory values), a unified processing procedure is required: First, all timestamps are converted to the UTC+8 standard time zone and normalized to millisecond precision. Then, semantic mapping is performed on the status fields based on a predefined data dictionary (for example, "SHIPPED", "Outbound", and "Shipping Completed" are uniformly mapped to the standard status code "S04"). Next, a three-pronged mechanism of sliding window statistics, Z-score outlier detection, and business rule verification (such as "Shipping time earlier than picking completion time" is considered a logical anomaly) is used to identify and remove noisy data. Finally, a structured, spatiotemporally aligned, and semantically consistent dynamic status data stream is output, providing a high-fidelity data foundation for subsequent real-time fulfillment dashboards, anomaly warnings, root cause tracing, and intelligent scheduling.

[0035] The beneficial effects of the above technical solution are as follows: The solution provided in this embodiment can break through the bottleneck of "information silos" in the order fulfillment process and realize the real-time connection and standardized integration of status data across systems and links; the data acquisition method combining agent programs and API interfaces takes into account both the universality of data acquisition and intrusive control; the dual-mode triggering mechanism ensures the second-level response of key node status while avoiding excessive occupation of system resources by high-frequency polling and reducing interference with the normal operation of business systems; the data standardization and cleaning process effectively eliminates the heterogeneity and noise interference of multi-source data through strict time benchmark unification, semantic mapping and anomaly filtering, ensuring the accuracy, consistency and availability of dynamic status data.

[0036] In one embodiment, the pre-training process of the delay risk prediction model includes: Acquire status data covering all nodes in the historical order fulfillment process. The complete status sequence data for multiple nodes includes timestamps, status codes, processing times, anomaly markers, operator IDs, equipment response delays, and network delays for key nodes. Simultaneously collect the actual fulfillment result tags for the corresponding orders. Key nodes include order creation, inventory verification, payment confirmation, warehouse sorting, packaging verification, outbound handover, trunk transportation, regional distribution, last-mile delivery, and receipt completion. The actual fulfillment result tags are in triplet form, which includes whether there is a delay, the delay level, and the primary cause node. Construct a sample set with multi-node complete state sequence data as input features; the sample set is divided in chronological order, wherein the training set and the validation set are both extracted from continuous time periods, and the validation set is the complete order sequence collected in an independent time period immediately following the training set; The gradient boosting decision tree algorithm is used for supervised training on the sample set. The objective function is to jointly optimize the multi-task loss of delay occurrence probability prediction and delay level classification. The main task is: whether there is a delay, and the auxiliary task is: delay level. The model hyperparameters were optimized using five-fold time series cross-validation, with the validation strategy following the chronological order. The optimization objective was the weighted sum of the macro-average F1 score and the prediction accuracy of the delay level. Finally, the parameter combination that made the validation set meet the objective condition was selected as the optimal hyperparameter configuration. The objective condition was: the highest percentage of samples where the predicted primary cause node was completely consistent with the actual primary cause node.

[0037] The working principle of the above technical solution is as follows: By collecting multi-dimensional status sequence data generated by historical orders at key nodes throughout the entire chain (order creation, inventory verification, payment confirmation, warehouse sorting, packaging verification, outbound handover, trunk transportation, regional distribution, last-mile delivery, and receipt completion), the data includes timestamps, status codes, processing times, anomaly markers, operator IDs, equipment response delays, and network delays for each node. Simultaneously, the data is bound to the final actual fulfillment result label of the order (a triplet: whether there was a delay, delay level, and the primary cause node). This constructs a high-quality training sample set with strong temporal sequence and causal traceability. The sample set is strictly divided according to the real timeline, with the training set and validation set taken from continuous and non-overlapping time periods to ensure that the model learning process conforms to the business evolution pattern and avoids future information leakage and time travel deviations. Based on this, Gradient Boosting Decision Tree (GBDT) is adopted as the basic modeling framework, and a multi-task supervised learning objective function is designed. The main task predicts "whether there is a delay" in a binary classification manner (outputting 0 / 1 probability), and the auxiliary task predicts "delay level" (such as mild / moderate / severe level) in a multi-class classification manner. The two share the underlying feature representation and are jointly optimized, so that the model can accurately characterize the severity of the delay while judging the possibility of the delay occurring. Next, a five-fold time series cross-validation mechanism is introduced, where each fold maintains the time constraint that the training window precedes the validation window. The hyperparameter search space is then optimized by weighting the macro-average F1 score (which balances the prediction accuracy for different classes of latency) and the latency level prediction accuracy. Ultimately, the "perfect match rate of the predicted primary cause node" is used as the final screening criterion. That is, only when the fundamental starting node that the model infers as causing the delay (such as "in-warehouse sorting timeout" rather than "last-mile delivery delay") is strictly consistent with the real primary cause node labeled by humans is it considered a valid attribution. This ensures that the model not only has a high-precision delay identification capability, but also has the ability to locate the root cause in an interpretable, interventionable, and traceable manner, providing a direct, reliable, and implementable decision-making basis for subsequent risk prevention intervention, dynamic resource scheduling, and process bottleneck optimization.

[0038] The beneficial effects of the above technical solution are as follows: By constructing a multi-dimensional state sequence data sample set covering key nodes of the entire order fulfillment chain and strictly following the time order to divide the training set and validation set, the potential future information leakage problem caused by traditional random sample division is effectively avoided. This makes the model training process more in line with the temporal evolution of actual business, thereby improving the model's adaptability to real scenarios. At the same time, the gradient boosting decision tree algorithm is used for multi-task learning, jointly optimizing the main task of "whether there is a delay" and the auxiliary task of "delay level", sharing the underlying feature representation. This not only improves the accuracy of the probability prediction of delay occurrence, but also accurately characterizes the severity of delay, realizing a multi-dimensional assessment of delay risk. The introduced five-fold time series cross-validation mechanism follows the time order in the hyperparameter optimization process, using the weighted sum of the macro average F1 score and the prediction accuracy of the delay level as the optimization target, and finally using the complete matching rate of the first cause node prediction as the core screening criterion, ensuring that the model not only has high-precision delay identification capabilities.

[0039] In one embodiment, such as Figure 2 As shown, generating and issuing corresponding level delay warning signals includes: Three mutually exclusive and continuous risk probability threshold intervals are preset: the first threshold interval [0, α], corresponding to the warning level of "attention"; the second threshold interval [α, β], corresponding to the warning level of "alert"; and the third threshold interval [β, 1], corresponding to the warning level of "severe". Where 0 < α < β ≤ 1, each threshold is determined by statistical modeling of historical performance data and calibration of business tolerance, and supports dynamic updates. Based on the overall delay risk probability P∈[0,1], a step-by-step comparison method is used to determine the threshold interval to which it belongs and to determine the corresponding warning level; Construct a structured early warning message, which includes: an early warning level identifier field, a key impact node identifier field, and a predicted delay time range field; wherein, the key impact node identifier field is an ordered string composed of the IDs of the top N nodes that contribute the most to the overall delay, where N≥1; The structured early warning messages are serialized into JSON format and asynchronously published via message queue middleware or pushed to the preset monitoring terminal system or automated response system through standardized RESTful API interface calls. The monitoring terminal or response system is pre-registered and bound to permissions. After receiving the early warning message, the receiving end triggers the corresponding level of alarm display, work order creation, resource scheduling or manual intervention process.

[0040] The working principle of the above technical solution is as follows: taking the overall delay risk probability P as the input variable, a deterministic mapping of the warning level is achieved through a preset three-segment mutually exclusive continuous threshold interval ([0,α], [α,β], [β,1]). When P falls into a certain interval, its semantic level (attention / warning / serious) is uniquely locked. This division ensures both logical completeness (covering the entire range of [0,1] without overlap) and reflects the gradient response of business risk sensitivity. The thresholds α and β are not static constants, but are based on historical performance data to build a probability distribution model (such as Logistic regression, survival analysis, or time series risk prediction model). They are jointly calibrated in conjunction with the quantitative constraints of the business side on delay tolerance (such as SLA default cost threshold, customer complaint rate inflection point, and upper limit of resource elastic scheduling window). The solution also supports continuous iteration and updating with new performance samples through an online learning mechanism to ensure that the thresholds always reflect the true business risk boundary. In the level determination stage, a sequential comparison strategy is adopted (first determine whether P is less than α, then determine whether P is less than β) to avoid floating-point precision errors and boundary condition ambiguities, ensuring that the determination process is unambiguous, low-overhead, and highly real-time. The construction of structured early warning messages follows the principle of minimum necessary information: the early warning level identifier field directly carries the decision result, and the key impact node identifier field identifies the top N upstream nodes with the highest contribution to the overall delay P through attribution analysis algorithm, and concatenates them into a unique and resolvable ID string in descending order of contribution to ensure that the root cause can be traced. The predicted delay time range field is inferred based on the joint delay distribution of the node combination (such as Monte Carlo simulation sampling or quantile regression output), giving a time interval with confidence guarantee (such as P50-P90); the serialization stage strictly follows the JSON Schema specification, embedding metadata fields such as version number, timestamp, and traceability ID to ensure message auditability; The deployment process employs a dual-channel redundancy design. Message queues (such as Kafka / RocketMQ) provide high throughput, persistence, and decoupled asynchronous broadcast capabilities, while RESTful API calls (compliant with the OpenAPI 3.0 standard, including JWT authentication and idempotent control) support strong consistency synchronous push. The receiving system must complete service discovery registration, role and permission binding, and event subscription configuration in advance at the central registration center. After the warning message arrives, the unified event bus triggers a predefined response contract based on the level identifier: Attention level, only visual highlighting and trend dashboard refresh are activated; Warning level, maintenance work order with priority label is automatically created and the secondary response group is notified; Critical level, the relevant link traffic is immediately frozen, the cross-departmental emergency command process is initiated, the pre-set elastic resource pool is called to perform dynamic scaling, and SMS / telephone alarms are pushed to the third-level responsible person at the same time. The whole process forms a closed-loop control loop of "risk quantification → level determination → root cause location → structure coding → reliable distribution → graded handling".

[0041] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment, the early warning level can be accurately mapped through the refined risk probability interval division and dynamic threshold calibration mechanism. This avoids the problem of insufficient early warning sensitivity or frequent false alarms caused by a single threshold, and ensures the scientific nature and adaptability of the threshold setting through historical data modeling and business tolerance calibration.

[0042] In one embodiment, step S7 is further included: visualization, specifically including: Construct a multi-node topology graph that strictly corresponds to the logical structure of the order fulfillment process. Each node in the topology graph maps to a discrete processing unit with time constraints and state measurability in the fulfillment process, and the directed edges between nodes represent the direction of data flow, task flow or dependency relationship. Real-time access and rendering of status data for each node. The status data includes at least: the current processing status of the node, the amount of task backlog, the average response latency, the frequency of timeouts, the resource utilization rate, and the upstream input ready timestamp and the downstream output confirmation timestamp. For nodes that trigger delay warnings, a first visual identifier is used to highlight them. The first visual identifier is a dynamically flashing red solid icon, and its flashing frequency is positively correlated with the delay deviation of the node. For key influencing nodes located by the causal reasoning module, a second visual identifier is used to highlight them. The second visual identifier is an orange semi-transparent layer with an arrow-shaped radiating halo, and its radiation radius is linearly proportional to the weight of the node's contribution to the overall delay. The global quantitative indicators are displayed synchronously in the side or floating panel of the topology diagram, including: the overall latency risk probability value derived from Bayesian network inference and the root cause description text. The overall latency risk probability value is presented as a continuous value of 0-100% and a corresponding five-level color code bar. The root cause description text is generated by the attribution analysis model and is accurate to the specific anomaly type, anomaly occurrence time window, associated configuration parameters and executable repair suggestions.

[0043] The working principle of the above technical solution is as follows: Based on the discretization, observability, and causal interpretability of the order fulfillment process, a multi-node directed topology graph that is strictly isomorphic to the business logic is constructed to transform the abstract end-to-end fulfillment link into a graph structure model with clear spatiotemporal semantics. Each node, as an atomic processing unit with time constraints (such as SLA thresholds) and state measurability (supporting millisecond-level acquisition) (such as "inventory verification", "waybill generation", "waybill printing"), has its state data collected in real time by a lightweight probe and injected into the rendering engine through a unified timing bus to ensure that the state update latency is ≤200ms. The directed edges not only represent explicit data dependencies or task triggering relationships, but also implicitly encode control flow priority and fault-tolerant jump paths, thereby supporting dynamic path recalculation. The delay warning mechanism employs a two-layer judgment: the bottom layer calculates the current delay deviation in real time based on sliding window statistics (window length = 3 × P95 response delay) and maps it to the flicker frequency, achieving a strong coupling between visual urgency and quantitative deviation; the identification of key influencing nodes relies on a trained lightweight Bayesian causal graph model (the prior probability of nodes is learned from historical fault root cause annotation data, and the conditional probability table is incrementally updated every 15 minutes through the online EM algorithm) to perform reverse probability tracing of the current abnormal propagation path, outputting the posterior contribution weight of each node to the overall delay, driving the radius of the orange radiation halo; the global indicator rendering module synchronously calls two inference services: the Bayesian network inference engine infers the overall delay risk based on the joint probability distribution of the current full graph node states. The rate is mapped to a five-level color code (0–20% gray → 20–40% blue → 40–60% yellow → 60–80% orange → 80–100% red); the attribution analysis model integrates node weights, time-series offset features (difference Δt between upstream ready and downstream confirmation timestamps), and configuration parameter variation detection results (such as the inventory verification timeout threshold being mistakenly set to 500ms) to generate a structured root cause description, in the format of "[Abnormal type]: {Inventory verification response timeout}; [Time window]: 2024-06-12T08:23:17–08:23:22; [Associated parameters]: Check timeout = 500ms (should be ≥1200ms); [Remediation suggestion]: Increase the inventory service check timeout configuration to 1500ms and restart the instance."

[0044] The beneficial effects of the above technical solution are as follows: by adopting the solution provided in this embodiment, the complex and abstract order fulfillment process can be transformed into an intuitive and interactive visual decision-making interface. Through the real-time linkage of topology map and status data, the dual perception of spatial positioning and temporal dynamics for delay warning is realized.

[0045] In one embodiment, step S8 is further included: dynamic optimization of the rule base; specifically including: Based on the model feedback data and root cause nodes generated in the feedback optimization closed loop, a quantitative analysis is conducted on the statistical correlation between the triggering event of each rule in the node rule base and the final order fulfillment delay, and the effectiveness of the correlation between rule triggering and final delay is analyzed. If the association effectiveness of any rule is lower than a preset threshold, or if its effectiveness shows a continuous decay trend in the most recent N scrolling windows, then the adaptive adjustment mechanism will be activated. The adaptive adjustment mechanism is as follows: For parameter range rules, the upper and lower bounds of the threshold are dynamically scaled based on the actual distribution quantiles and abnormal offset directions of the corresponding indicators in the historical delay samples; for time constraint rules, the time window width and relaxation tolerance are reset based on the historical drift curve of node processing time, seasonal fluctuation cycle, and the waiting delay propagation coefficient of adjacent nodes; for rule logic rules, based on the high-frequency composite failure modes in the root cause nodes, logic gate combinations are introduced, context dependency conditions are embedded, and after A / B testing verification, the updated rule version is entered into the node rule library to complete the closed-loop iteration.

[0046] The working principle of the above technical solution is as follows: by constructing a dynamic closed-loop mechanism encompassing "feedback, analysis, optimization, verification, and data entry," the continuous adaptive evolution of the node rule base is achieved. Its core lies in mapping the terminal business outcome of order fulfillment delay back to the rule-triggered behavior, quantifying the actual contribution of each rule to the delay in a causal traceability manner. Specifically, First, model feedback data (such as prediction bias and misjudged samples) from real performance scenarios are aggregated with root cause node outputs (such as structured attribution labels for inventory shortages, logistics interruptions, and approval timeouts) to form a rule performance observation set with time-series and causal annotations. Next, statistical significance tests (such as chi-square test, mutual information entropy, or dual robust estimation in causal effect estimation) are used to calculate the conditional association strength between each rule-triggered event and the final order delay. A sliding window mechanism is introduced to dynamically track the time series change trend of this strength, thereby identifying rule items with initial failure, performance degradation, or environmental drift. When the association effectiveness of a rule is detected to be lower than a preset threshold (e.g., mutual information value < 0.08), or shows a continuous monotonically decreasing trend in the most recent N rolling windows (e.g., N=5, each window covering a 7-day fulfillment period) (p < 0.05 according to Mann-Kendall trend test), it is determined that the rule has deviated from the current business state distribution, triggering a three-level differentiated adaptive adjustment mechanism: For parameter range rules (such as "Inventory level < 5 items triggers warning"), the system retrieves the full distribution of the corresponding indicator (such as inventory level) in the historical delay samples, and scales the upper and lower bounds of the original threshold proportionally based on the P10–P90 quantile range and the offset direction (left skew / right skew) of the indicator in the abnormal delay samples (for example, if the delay occurs frequently in the scenario where the inventory level is > 15 items, the lower bound is moved up to the P30 quantile value). For time-constrained rules (such as "the review node must be completed within 2 hours"), the system fits the historical drift curve of the node's processing time (using Holt-Winters exponential smoothing to model trends and seasonality), combines the transmission coefficient of the waiting delay of adjacent upstream and downstream nodes (identifying the delay transmission path and estimating the impact weight through Granger causality test), and dynamically resets the time window width (e.g., expanding it from 2 hours to 2.3 hours) and relaxation tolerance (e.g., allowing ±12% elastic fluctuation). For rule logic types (such as "block only when supplier rating 50,000 yuan"), the system identifies frequently occurring composite failure modes in the root cause nodes (such as the triple "supplier rating < B + logistics carrier switching + weekend time" has a support rate of 67% in the delay sample). Based on this, the rule logic structure is reconstructed, introducing AND / OR nested logic gates and adding context dependency conditions (such as "and the current period is the peak settlement period at the end of the quarter"). The old and new rule versions are simultaneously deployed to the A / B test traffic pool. Under the premise of ensuring that the delay interception accuracy is improved by ≥3 percentage points and the false blocking rate is reduced by ≤1 percentage point, the verified new rule version is atomically written into the node rule library to replace the old version, completing a complete closed-loop iteration. The entire process relies on real-time data pipelines, lightweight online learning engines, and rule version control systems to ensure that rule optimization has low-latency response capabilities, rollbackability, and consistency with business semantics.

[0047] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment, the node rule base can get rid of the limitations of static preset and continuously optimize the accuracy and adaptability of the rules by dynamically capturing changes in the business environment and the characteristics of the fulfillment scenario. On the one hand, by quantitatively analyzing the correlation between rule triggering and order delay, inefficient or invalid rules can be eliminated in a timely manner, avoiding early warning delays or misjudgments caused by rule solidification, and improving the explanatory power and predictive reliability of the early warning model for complex fulfillment links. On the other hand, the differentiated adaptive adjustment mechanism designed for different types of rules can accurately match the distribution drift of business parameters (such as the expansion of inventory fluctuation range), the periodic changes of time characteristics (such as the extension of processing time during holidays), and the compound failure mode of multi-factor coupling (such as the linkage risk between suppliers and logistics links), so that the rule system has a closed-loop learning capability of "perception-decision-evolution".

[0048] In one embodiment, the collaborative nodes include at least two of the following: inventory query node, order approval node, production scheduling node, picking and packing node, logistics collection node, transportation transfer node, and last-mile delivery node, and there are deterministic business dependencies and data flow paths between any of the selected nodes.

[0049] The working principle of the above technical solution is as follows: the collaborative nodes form a closed-loop business execution network for the entire supply chain. The technical principle is that: with event-driven architecture (EDA) as the underlying support, each node is modeled as a lightweight service unit with state awareness, rule judgment and task triggering capabilities; the nodes clarify the input data structure, output semantic constraints, processing time thresholds and exception rollback protocols through predefined business contracts. The inventory query node first responds to the order request, outputs a real-time available inventory snapshot, and triggers the order review node as a prerequisite. The order review node performs atomic approval based on credit policy, compliance rules and inventory lock results. Upon successful approval, a production work order with a unique tracking code is generated and pushed to the production scheduling node. The production scheduling node generates an executable work plan based on BOM expansion, equipment load, delivery date constraints and dynamic priority algorithm, and synchronizes the process instructions and material availability status to the picking and packing node. The picking and packing node calls the WMS interface to complete wave splitting, route optimization and packaging verification, generate parcel units with barcode / RFID tags, and push logistics waybill data and loading information to the logistics collection node; The logistics pickup node connects with a third-party transportation platform to complete pickup reservations, generate electronic waybills, and report the status of the first mile. It also injects the waybill number and the carrier's routing strategy into the transportation transfer node. The transportation transfer node relies on TMS real-time trajectory analysis, transfer site operation logs and anomaly early warning models to dynamically update the package location and estimated arrival time. When entering the last-mile delivery area, the delivery address, time requirements, customer preferences and signature verification methods are packaged into standardized delivery instructions and issued to the last-mile delivery node. Last-mile delivery nodes receive instructions via mobile terminals, execute route replanning, contactless delivery, electronic signature, and reverse feedback loop, and finally write the complete fulfillment status (including timestamp, geofence coordinates, image voucher, and customer evaluation) back to the central business bus, triggering order state machine transitions and automatic inventory ledger verification. Throughout the process, the data flow between nodes strictly follows the deterministic directed graph (DAG) topology. Each edge corresponds to a unique data contract and transaction boundary. All state changes are guaranteed by a distributed transaction coordinator (such as the Seata AT mode) to ensure the eventual consistency of cross-node operations, thereby achieving a highly reliable collaborative execution mechanism with orchestratable business logic, traceable data flow, delimitable exception handling, and measurable performance bottlenecks.

[0050] The beneficial effects of the above technical solution are as follows: by adopting the solution provided in this embodiment, it is possible to achieve refined management of the entire supply chain order fulfillment process by constructing a collaborative node network based on an event-driven architecture.

[0051] An order fulfillment process delay early warning system based on multi-node status acquisition is provided to implement a method for early warning of order fulfillment process delays based on multi-node status acquisition, such as... Figure 3 As shown, it includes: The data acquisition and rule judgment module is configured to execute step S1; it includes a rule engine submodule and a context graph construction submodule; the rule engine submodule loads the node rule library, matches the rule conditions one by one with the current node status data, and outputs a set of rule trigger identifiers. The primary early warning and context building module is configured to execute step S2. It includes a context graph building submodule, which aggregates the status snapshots of the same order across various collaborative nodes based on the unique key of the order ID, and builds a dynamic context graph with the order as the root node, the node status as the attribute edge, and the business flow between nodes as the directed edge, forming a complete early warning context dataset and pushing it to the risk prediction module. The risk prediction module is used to receive the early warning context dataset; extract cross-node time series features, state transition features and path cumulative delay features; and has a built-in pre-trained delay risk prediction model. The delay risk prediction model uses Gradient Boosting Decision Tree (GBDT) as the basic modeling framework, designs a multi-task supervised learning objective function, and configures it to execute step S3. The early warning generation and release module is configured to execute step S4. It compares the probability value output by the risk prediction module with the preset classification threshold, generates a three-level early warning event, and binds the order ID, predicted delay duration, high-risk node identifier and suggested handling action. It then pushes the event to the corresponding responsible role through multiple channels, including WeChat, DingTalk, SMS and internal work order system. The root cause localization module is configured to execute step S5. It adopts a root cause search algorithm based on counterfactual reasoning. Taking the predicted delayed orders as input, it traverses the state anomaly score of each node in the context graph in reverse along the business flow. Combining SHAP value decomposition and Bayesian network posterior probability calculation, it outputs the Top-K root cause node set, the attribution contribution of each root cause node, and the joint explanatory confidence of the joint root cause combination. The feedback learning module is configured to execute step S6. It receives feedback data of early warning handling results, constructs feedback sample triples, organizes them into batches of feedback data according to a time sliding window, and outputs them to the rule base dynamic optimization and model incremental learning stage.

[0052] The working principle of the above technical solution is as follows: Taking the entire order fulfillment chain as the object, the system uses distributed status acquisition probes deployed at various business nodes (such as order placement, payment, inventory verification, sorting, packaging, outbound, logistics transportation, and receipt) to acquire the real-time running status, timestamps, processing time, anomaly markers, and contextual metadata of the order at each node, forming a fine-grained and highly timely multi-source heterogeneous status stream. The data acquisition and rule judgment module performs streaming aggregation and spatiotemporal alignment of the original status events according to the order ID, generates a structured node status snapshot sequence, and synchronously writes it to the time-series database and graph database. The rule engine submodule loads the pre-set node-level static rule library (such as "payment timeout > 5 minutes triggers warning" and "inventory verification failure times ≥ 2 times mark high risk") and dynamic threshold rules (based on the P95 processing time adaptive baseline of sliding window statistics), matches each status snapshot condition by condition, and outputs a Boolean rule trigger identifier set as an explicit risk signal. The context graph construction submodule in the primary early warning and context construction module uses the order ID as a globally unique key to map the status snapshots of the same order at different nodes to graph node attributes (e.g., "sorting node: processing time = 142s, status code = 200, exception label = none"). It models the deterministic business flow between nodes (such as order placement → payment → inventory verification) as directed edges and injects timestamp weights and delay decay factors to construct a dynamically evolving directed attribute graph. Based on this graph structure, three types of key features are extracted: cross-node temporal features (e.g., "standard deviation of the time interval from payment completion to inventory verification start"), state transition features (e.g., "ratio of the frequency of inventory verification transition from 'to be verified' to 'verification failed' to the historical mean"), and path cumulative delay features (e.g., "number of delayed nodes on the current path / total number of nodes, and weighted sum of delay durations"), forming a joint representation that combines topological structure and temporal dynamics. The risk prediction module receives the early warning context dataset; extracts cross-node time-series features, state transition features, and path cumulative delay features; it has a built-in pre-trained delay risk prediction model, which uses Gradient Boosting Decision Tree (GBDT) as the basic modeling framework and designs a multi-task supervised learning objective function, which includes the main task and auxiliary tasks; it achieves feature sharing and knowledge transfer through the multi-task learning framework to improve the prediction accuracy of the main task. The early warning generation and release module determines the early warning level based on the p-value and three-level thresholds (e.g., p<0.3 for low risk, 0.3≤p<0.7 for medium risk, and p≥0.7 for high risk). It automatically binds the predicted delay time of the order (derived from the expected delay time and confidence interval output by the model), high-risk node identifiers (weighted sorting based on node embedding attention weight and rule triggering strength), and suggested actions (optimal intervention strategies for similar historical cases retrieved from the knowledge graph, such as "contact the supplier for expedited allocation" or "switch to a backup carrier"). It also connects to WeChat, DingTalk, SMS platform, and internal work order system through the API gateway, and achieves multi-channel accurate reach according to preset role routing strategies (e.g., pushing inventory anomalies to the warehouse manager and logistics anomalies to the transportation dispatcher). After receiving high-risk order input, the root cause localization module initiates counterfactual reasoning in its dedicated context graph: First, it traverses each node in reverse along the business flow, calculating the state anomaly score of each node (combining rule trigger strength, time sequence deviation, and local sensitivity of SHAP value); then, it uses SHAP value decomposition technology to quantify the marginal contribution of each node's state variables to the final delay probability prediction, identifying significant attribution dimensions; next, it constructs a Bayesian network with node state as random variables and delay occurrence as observational evidence, uses variational inference to solve the posterior probability of each node's state anomaly, and searches for Top-K node combinations based on maximizing joint probability, outputting the attribution contribution of each root cause node (normalized SHAP value) and the joint explanatory confidence of the joint root cause combination (posterior joint probability). The feedback learning module continuously receives feedback data from the early warning and response closed loop, including whether the early warning was a false alarm, whether there was an actual delay, whether the response action was effective, and the correction label for the true cause of the delay. This data is structured into a quintuple (order ID, predicted label, actual label, response action, root cause correction), and after cleaning, it is constructed into a feedback sample triple (input graph features, predicted risk level, actual result label). Feedback data is aggregated into batches by a sliding window of fixed duration (e.g., 1 hour). On the one hand, this drives the rule base dynamic optimization submodule: through association rule mining and causal discovery algorithms, it identifies high-frequency false alarm / missed alarm scenarios and automatically generates new rules or adjusts the thresholds of existing rules. On the other hand, it feeds into the model incremental learning submodule: using elastic weight solidification (EWC) constraints to prevent catastrophic forgetting, combined with an online gradient update mechanism, it performs lightweight parameter fine-tuning of the delay risk prediction model, realizing the closed-loop evolution of the system's risk identification capability.

[0053] The beneficial effects of the above technical solution are as follows: By constructing a multi-node status acquisition mechanism covering the entire order fulfillment chain, the system achieves fine-grained and timely perception of the operational status of each stage from order placement to receipt; through the deep integration of dynamic context graph and hybrid architecture prediction model, it captures the topological association features of orders among collaborative nodes and mines the cross-node time-series evolution patterns, thus improving the accuracy of delay risk prediction compared to a single time series model; through a three-level early warning grading mechanism and a multi-channel targeted push strategy, it achieves accurate reach and rapid response to risk events, shortening the average response time of early warnings; by combining counterfactual reasoning and SHAP value attribution root cause localization methods, it can quantitatively identify key risk nodes and their contribution, improving the accuracy of root cause localization; relying on feedback learning closed loop, the system can dynamically optimize the rule base and prediction model.

[0054] In one embodiment, it further includes a model training management module, configured to manage historical data, perform initial training, optimization, and incremental learning or retraining tasks based on model feedback data provided by the feedback learning module; the model training management module includes a historical data warehouse submodule, an offline training submodule, an online learning submodule, and a model version control submodule; The historical data warehouse submodule is used to store the full historical order node status data after anonymization, corresponding fulfillment result tags, manually annotated root cause data, and historical early warning handling logs, and is organized by time partition, order dimension index, and feature version tagging; The offline training submodule is used to perform initial model training and hyperparameter tuning based on data slices of a specified period in the historical data warehouse, evaluate AUC, F1-score and calibration error using cross-validation strategy, and store the best model version in the model registry center. The online learning submodule receives batches of feedback data output from the feedback learning module, performs lightweight gradient updates or parameter fine-tuning, and supports selective retraining based on importance sampling to ensure that the model adapts to the latest business distribution while maintaining the stability of the backbone structure. The model version control submodule maintains the model version lineage graph, records the input data version, hyperparameter configuration, evaluation metric snapshot and deployment timestamp for each training session, and supports canary release, A / B testing verification and one-click rollback.

[0055] The working principle of the above technical solution is as follows: the model training management module serves as the intelligent evolution hub of the latency risk prediction system, realizing automated, traceable and highly adaptive management of the entire model lifecycle. Its core lies in building a collaborative learning architecture that integrates offline deep modeling and online dynamic optimization, and ensuring model reliability and business consistency through refined data organization and strict version governance. The historical data warehouse submodule first performs structured governance on the original order fulfillment data: all data undergoes compliant anonymization processing to eliminate personal identity and sensitive business information; all node status data (such as timestamps and status codes for each stage, including order creation, payment success, warehouse sorting, outbound handover, in-transit transportation, and final receipt) are strongly correlated with corresponding fulfillment result tags (such as "on-time delivery", "delay ≥ 24h", "abnormal cancellation", etc.); manually annotated root cause data (such as "supplier out of stock", "logistics transit congestion", "system interface timeout") provide interpretable supervisory signals; historical early warning and handling logs (including early warning time, responders, handling actions, and closed-loop results) constitute the original basis for real business feedback loops. This dataset is partitioned by natural day time to support efficient range queries; order ID is used as the primary index to ensure document-level full-link traceability capabilities; and feature version tags (such as v3.2.1_finance_enhanced) are bound to ensure the consistency and reproducibility of feature engineering in subsequent training; The offline training submodule extracts data slices from the historical data warehouse for a specified period (e.g., the past 180 days), loads them uniformly into the feature engineering pipeline, and generates standardized input tensors. It then uses a distributed training framework to perform initial model training. Simultaneously, it performs automatic hyperparameter search (e.g., Bayesian optimization of learning rate, Dropout rate, and embedding dimension combination) and rigorously implements k-fold cross-validation (k=5). In each fold, it independently calculates AUC (measuring ranking and discrimination ability), F1-score (balancing precision and recall, with particular attention to delayed positive sample recognition), and calibration error (e.g., ECE, evaluating the consistency between predicted probability and true frequency to ensure the credibility of threshold decisions on the business side). Finally, it packages the model with the best overall performance along with its complete metadata (data slice range, feature version, hyperparameter configuration, evaluation curve) and registers it to the model registry center, forming the first production-ready baseline version. The online learning submodule receives batches of feedback data pushed by the feedback learning module in real time (including newly occurring delayed events, manually corrected labels, false alarm / missed alarm markers, and confidence weights fed back from the handling results). Instead of triggering full retraining, it performs lightweight incremental updates, implementing small-step gradient updates for the parameters of the classification head or key attention layers, or only fine-tuning the last two layers of the network. Furthermore, an importance sampling mechanism is introduced, which dynamically weights training samples based on sample uncertainty (such as prediction entropy), business impact weights (such as high-value customer orders), and time decay factors (the weight of samples in the last 7 days is increased by 1.5 times). This allows the model to prioritize adapting to the latest and most critical data distribution shifts under limited computing power, while keeping the backbone encoder structure unchanged to avoid catastrophic forgetting and maintain the stability of historical knowledge and the consistency of inference. The model version control submodule is integrated throughout the process, constructing an end-to-end auditable model lineage system: each training task node is recorded in the form of a directed acyclic graph (DAG), and each node fully stores the input data version hash value, hyperparameter JSON snapshot, evaluation metric numerical matrix (including AUC / F1 / ECE three dimensions), training start and end timestamps, and operator identification; during the deployment phase, it supports a canary release strategy (e.g., first route the new model to the order flow of the 5% low-risk area, and automatically expand the flow when the monitoring latency warning accuracy fluctuation is ≤0.3pp); the A / B testing and verification module can run the old and new versions of the model in parallel and compare the differences in business metrics on the same test set; once the online SLO is detected to exceed the threshold (e.g., the F1-score drops by more than 0.05 in the 24-hour sliding window), the system automatically triggers a one-click rollback command, switches to the previous stable version in seconds, and simultaneously archives all inference logs during the abnormal period for root cause analysis.

[0056] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment, and by constructing a collaborative learning architecture that integrates offline deep modeling and online dynamic optimization, intelligent management of the entire lifecycle of the delay risk prediction model is realized.

[0057] In one embodiment, a visualization monitoring module is also included, configured to provide a graphical interface and perform visualization display steps; the visualization monitoring module includes a multi-dimensional dashboard sub-module, a root cause heatmap sub-module, an early warning tracking sub-module, and a prediction confidence dashboard module; The multi-dimensional dashboard submodule is used to support dynamic slicing and drill-down by time, order, node, and risk dimensions. The root cause heatmap submodule is used to map the real-time anomaly density, root cause attribution strength, and historical recurrence frequency of each collaborative node on the order fulfillment process topology map using color depth and flashing frequency. It also supports clicking on a node to expand the node's status trend curve for the past 24 hours, the TOP3 root cause distribution pie chart, and the list of related early warning events. The early warning tracking submodule is used to provide a full lifecycle view of early warning events, displaying a complete timeline from early warning generation, push notification, manual response, execution of handling actions to final performance results, and marking the time spent at each stage, responsible persons, operation logs, and automatically supplemented analysis annotations by the system; The prediction confidence dashboard module is used to present the overall confidence distribution of the current model prediction results in a combination of histograms and box plots, overlay the conditional confidence offset under different node combinations, and identify the context graph anomaly patterns corresponding to low confidence prediction samples.

[0058] The working principle of the above technical solution is as follows: The visualization monitoring module gathers multi-source heterogeneous data (including time-series status logs, node execution indicators, early warning event streams, model prediction outputs and contextual graph features) in real time through a unified data access layer. After standardized cleaning, spatiotemporal alignment and semantic annotation, the data is distributed to four collaborative sub-modules for professional visualization modeling and interactive presentation. The multidimensional Kanban submodule is built on an OLAP engine to construct a dynamic cube model. It uses timestamps, order IDs, fulfillment node IDs, and risk level tags as core dimension keys. Users can trigger multidimensional slice, dicing, and drill-down / up calculations in real time on the front-end interface through drag-and-drop, drop-down, or click operations. For example, after selecting the "high-risk order" slice, users can drill down to specific nodes (such as "warehousing and sorting → trunk transportation → last-mile delivery") and observe the linkage changes of indicators such as on-time rate, anomaly rate, and processing time of each node at the hourly granularity. All aggregation calculations are completed in milliseconds in the in-memory columnar database to ensure real-time interaction. The root cause heatmap submodule relies on the order fulfillment process topology to map three types of root cause intensity signals—real-time anomaly density (normalized value of the number of anomaly alarms at the node within a unit time window), root cause attribution strength (counterfactual contribution score of the node to downstream fulfillment failure output by the causal inference model), and historical recurrence frequency (weighted attenuation statistics of the number of times the same anomaly occurred at the node in the past 7 days)—to the three channels of hue (H), saturation (S), and brightness (V) in the HSV color space, and overlays a pulsed flashing animation (frequency proportional to the real-time anomaly density change rate). When a user clicks on any node, the front end immediately calls the microservice interface to load the time series curves of the node's key indicators for the past 24 hours (such as task backlog, average response latency, and failure retry rate), simultaneously renders a pie chart of the top 3 root cause distributions (sorted according to attribution strength, with each root cause accompanied by semantic labels and confidence intervals), and displays a list of all warning events triggered by it (including warning type, trigger time, associated orders, and current handling status). The early warning tracking submodule uses the unique identifier of the early warning event (AlertID) as the root node to construct a directed event chain with timestamps and role permission tags. It fully reproduces the entire lifecycle trajectory from early warning generation (triggered by the rule engine or AI model), automatic system push (channel, time, recipient), manual response (first response time, response content, work order number), execution of handling actions (API call / manual intervention record, execution result code), to the final fulfillment result closed loop (whether the order is delivered on time, SLA achievement status). The timeline accurately marks the absolute time and relative time consumption of each link (e.g., "manual response time: 23 minutes and 17 seconds"). The responsible person field is bound to the organizational structure directory service to achieve automatic identification and de-identified display. The operation log uses structured JSON format to embed audit fields (operator, IP, terminal device, values ​​before and after change). The system automatically supplements the analysis annotations, which are generated in real time by the NLP summary model, extracting the key decision basis, common pattern matching results and cross-linking clues for this link (e.g., "the timeout of this link is strongly correlated with the upstream node 'trunk line transportation delay', with similar cases accounting for 86%"). The prediction confidence dashboard module first extracts a confidence score from the current batch of prediction results (such as the predicted order fulfillment timeliness) (based on an ensemble model uncertainty quantification method, such as Monte Carlo). Dropout outputs variance, quantile regression interval width, or Bayesian posterior entropy, generating an overall confidence distribution histogram. A box plot characterizes the five-digit summary of this distribution (minimum, first quartile Q1, median, third quartile Q3, and maximum), identifying outlier low-confidence samples. Furthermore, the system constructs a conditional confidence offset matrix based on node combination features (e.g., the triple "supplier A + logistics provider B + region C"), displaying the confidence gain / loss percentage of each combination relative to the global mean in a superimposed heatmap. For predicted samples with confidence levels below a threshold (e.g., 0.65), a context graph backtracking mechanism is automatically triggered to extract associated order attributes, historical fulfillment patterns, real-time resource load, and external events (e.g., weather, traffic control) and other multi-dimensional graph edge relationships, identifying abnormal patterns (e.g., "This sample simultaneously satisfies the triple graph constraints of 'high-concurrency ordering + insufficient cold chain warehouse inventory + daily rainstorm warning,' belonging to a known vulnerable pattern"), and marks them with a highlighted border and icon in the dashboard, providing an entry point for expanding the graph subgraph. The four sub-modules share the underlying unified metadata management service and access control engine. All visualization components follow the WebGL accelerated rendering specification, support smooth scaling and interaction of topology graphs with tens of millions of nodes, and all operation behaviors are broadcast to the audit center via the event bus, forming a traceable, reproducible, and measurable intelligent monitoring closed loop.

[0059] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment, a visual monitoring system including a multi-dimensional dashboard sub-module, a root cause heatmap sub-module, an early warning tracking sub-module, and a prediction reliability dashboard module is constructed, realizing a comprehensive, refined, and intelligent presentation of order fulfillment process delay early warning.

[0060] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for early warning of order fulfillment process delays based on multi-node status acquisition, characterized in that, Includes the following steps: S1: Real-time data collection and rule judgment: Real-time collection of dynamic status data of at least two collaborative nodes in the order fulfillment process, and matching and judgment of the dynamic status data with the preset node rule base; S2: Primary warning and context construction: When the dynamic state data of any current node violates the rules of its corresponding node, a primary delayed warning is triggered, and the state data of the current node and its directly related nodes are extracted to form a warning context dataset that includes cross-node time sequence and dependency relationships. S3: Intelligent Risk Prediction: Input the early warning context dataset into the pre-trained delay risk prediction model to obtain early warning prediction results including the overall delay risk probability and the identification of key impact nodes; the delay risk prediction model models the temporal dependence and resource competition relationship between the states of multiple nodes based on machine learning algorithms; S4: Tiered Early Warning Issuance: Based on the overall delay risk probability, compare it with the preset first threshold and second threshold to generate and issue delay early warning signals of low, medium and high levels, where the first threshold is higher than the second threshold; S5: Explainable Root Cause Localization: In response to the issuance of a high-level delay warning signal, based on the key impact node identifiers and warning context dataset, reasoning is performed through a pre-configured root cause analysis rule chain to locate the root cause node leading to the delay risk; wherein, the root cause analysis rule chain is represented by a directed acyclic graph. S6: Feedback optimization closed loop: Based on the comparison results between the actual performance data after the issuance of the graded early warning and the early warning prediction results, the delay risk prediction model is optimized.

2. The order fulfillment process delay early warning method based on multi-node status acquisition according to claim 1, characterized in that, Real-time collection of dynamic status data from at least two collaborative nodes in the order fulfillment process, including the following steps: Raw state data is obtained at a fixed frequency or in an event-driven manner by using agent programs deployed on each node or by calling application programming interfaces provided by the node system. The original state data is standardized and cleaned to unify the time base and data format, and abnormal noise data is filtered out to obtain dynamic state data.

3. The order fulfillment process delay early warning method based on multi-node status acquisition according to claim 1, characterized in that, The pre-training process of the delay risk prediction model includes: Acquire status data covering all nodes in the historical order fulfillment process and corresponding actual fulfillment result tags; Construct a sample set with multi-node complete state sequence data as input features and whether delay occurs and the degree of delay as training objectives; The gradient boosting decision tree algorithm is used for supervised training on the sample set, and the model parameters are optimized through cross-validation.

4. The order fulfillment process delay early warning method based on multi-node status acquisition according to claim 1, characterized in that, Generating and issuing corresponding level of delay warning signals includes: Three mutually exclusive and continuous risk probability threshold ranges are preset. Each risk probability threshold is determined by statistical modeling of historical performance data and calibration of business tolerance, and supports dynamic updates. Based on the overall probability of delay risk, a step-by-step comparison method is used to determine the threshold range to which it belongs and to determine the corresponding warning level. The constructed structured early warning message sequence is asynchronously published via message queue middleware or pushed to the preset monitoring terminal system or automated response system through standardized RESTful API interface calls.

5. The order fulfillment process delay early warning method based on multi-node status acquisition according to claim 1, characterized in that, It also includes step S7: visualization, which specifically includes: Construct a multi-node topology diagram that strictly corresponds to the logical structure of the order fulfillment process, and render the status data of each node in real time; The nodes that trigger delay warnings and / or the key impact nodes that are located are visually highlighted.

6. The order fulfillment process delay early warning method based on multi-node status acquisition according to claim 1, characterized in that, It also includes step S8: dynamic optimization of the rule base; specifically including: Based on the model feedback data and root cause nodes generated in the feedback optimization closed loop, a quantitative analysis is conducted on the statistical correlation between the triggering event of each rule in the node rule base and the final order fulfillment delay, and the effectiveness of the correlation between rule triggering and final delay is analyzed. If the association effectiveness of any rule is lower than a preset threshold, or if its effectiveness shows a continuous decay trend in the most recent N scrolling windows, then the adaptive adjustment mechanism will be activated. The adaptive adjustment mechanism is as follows: For parameter range rules, the upper and lower bounds of the threshold are dynamically scaled based on the actual distribution quantiles and abnormal offset directions of the corresponding indicators in the historical delay samples; for time constraint rules, the time window width and relaxation tolerance are reset based on the historical drift curve of node processing time, seasonal fluctuation cycle, and the waiting delay propagation coefficient of adjacent nodes; for rule logic rules, based on the high-frequency composite failure modes in the root cause nodes, logic gate combinations are introduced, context dependency conditions are embedded, and after A / B testing verification, the updated rule version is entered into the node rule library to complete the closed-loop iteration.

7. The order fulfillment process delay early warning method based on multi-node status acquisition according to claim 1, characterized in that, Collaboration nodes include at least two of the following: inventory query node, order approval node, production scheduling node, picking and packing node, logistics collection node, transportation transfer node, and last-mile delivery node, and there are deterministic business dependencies and data flow paths between any selected nodes.

8. A delay early warning system for order fulfillment process based on multi-node status acquisition, characterized in that, A method for implementing an order fulfillment process delay early warning system based on multi-node status acquisition as described in any one of claims 1 to 7, comprising: The data acquisition and rule judgment module is configured to execute step S1. The primary warning and context building module is configured to execute step S2. The risk prediction module has a built-in pre-trained delay risk prediction model and is configured to execute step S3. The early warning generation and release module is configured to execute step S4. Root cause localization module, configured to perform step S5; The feedback learning module is configured to execute step S6.

9. The order fulfillment process delay early warning system based on multi-node status acquisition according to claim 8, characterized in that, It also includes a model training management module, configured to manage historical data, perform initial training, optimization, and incremental learning or retraining tasks based on model feedback data provided by the feedback learning module.

10. The order fulfillment process delay early warning system based on multi-node status acquisition according to claim 8, characterized in that, It also includes a visualization monitoring module, configured to provide a graphical interface and perform the visualization display steps as described in claim 5.

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