Supply chain network visualization method and system, electronic equipment and storage medium

Abstract

The invention discloses a supply chain network visualization method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting node data and edge data of a supply chain network, and forming an initial real-time data flow; performing optimization processing on the initial real-time data stream, dynamically adjusting a data updating strategy to adapt to a data change rate and a system load, and outputting a structured data stream; mapping nodes and edges of the supply chain network into a three-dimensional space for real-time visual rendering based on a graphic hardware acceleration rendering technology and the structured data stream; adopting an intelligent layout algorithm based on a service scene to optimize the node space positions mapped into the three-dimensional space; and carrying out anomaly detection on the running states of the nodes and the edges of the supply chain, and carrying out visual prompt in a three-dimensional space. The basic defects of a traditional supply chain management system in the aspects of real-time performance, intuition and intelligence are thoroughly overcome, and the technical span from passive response to active management and control is achieved.

Description

Technical Field

[0001] This application relates to the field of supply chain management technology, and in particular to a supply chain network visualization method, system, electronic device, and storage medium. Background Technology

[0002] Supply chain management, as an important component of modern enterprise management, has evolved from traditional manual management to information-based management. With the development of the globalized economy and the intensification of market competition, enterprises face increasingly complex supply chain networks, involving the coordinated operation of multiple suppliers, manufacturers, distributors, and retailers.

[0003] Existing supply chain management technologies include: 1. Using traditional relational databases (such as Oracle, SAP HANA) to store data and displaying supplier information, order status, inventory levels, etc., in tabular form via web pages or clients; 2. Using classic layout algorithms such as force-directed graphs to display network relationships in a 2D plane; 3. Using a fixed-frequency data update strategy combined with simple threshold rules for anomaly detection. These solutions suffer from a combination of problems: limited functionality, insufficient performance, low level of intelligence, and poor user experience. They cannot meet the demands of modern complex supply chain networks for large-scale, real-time, intuitive, and intelligent early warning management. Summary of the Invention

[0004] This application provides a supply chain network visualization method, system, electronic device, and storage medium. By collecting supply chain data in real time and optimizing it adaptively, the system utilizes 3D spatial hardware-accelerated rendering and business-driven intelligent layout algorithms for visualization. It also integrates time-series anomaly detection and propagation path analysis to construct a closed-loop management system from data perception to intelligent early warning. This addresses the problem that existing technologies cannot meet the needs of modern complex supply chain networks for large-scale, real-time, intuitive, and intelligent early warning and control. It enables real-time, intuitive, and intelligent monitoring of large-scale supply chains, improves decision-making efficiency, shortens anomaly detection time, and provides accurate risk propagation visualization, fundamentally enhancing the control capabilities and resilience of the supply chain.

[0005] On the one hand, embodiments of this application provide a supply chain network visualization method, including:

[0006] The system collects node and edge data from supply chain data sources in real time to form an initial real-time data stream.

[0007] Real-time optimization processing is performed on the initial real-time data stream. The data update strategy is dynamically adjusted to adapt to the data change rate and system load, and the optimized structured data stream is output.

[0008] Based on graphics hardware acceleration rendering technology and the structured data flow, the nodes and edges of the supply chain network are mapped to three-dimensional space for real-time visualization rendering.

[0009] An intelligent layout algorithm based on business scenarios is used to optimize the spatial positions of nodes mapped into the three-dimensional space;

[0010] Based on the time-series data in the structured data stream, anomaly detection is performed on the operating status of supply chain nodes and edges. Combined with the results of the visualization rendering, the detected anomalies and their propagation paths are visualized and displayed in the three-dimensional space.

[0011] In one possible embodiment, the step of performing real-time optimization processing on the initial real-time data stream, dynamically adjusting the data update strategy to adapt to the data change rate and system load, and outputting an optimized structured data stream includes:

[0012] The update interval is dynamically calculated based on the data change rate and at least one parameter characterizing the system load, wherein the parameter characterizing the system load includes network transmission delay and / or the number of nodes to be processed.

[0013] Based on the update interval, perform periodic data fetching or receive push data.

[0014] In one possible embodiment, the step of performing real-time optimization processing on the initial real-time data stream, dynamically adjusting the data update strategy to adapt to the data change rate and system load, and outputting an optimized structured data stream, further includes:

[0015] The initial real-time data stream is subjected to structured processing to obtain standardized structured data;

[0016] An incremental update mechanism is used to extract the changed data portions from the standardized structured data, which are then transmitted as the new data.

[0017] The transmitted data is compressed using a compression algorithm.

[0018] In one possible embodiment, the step of optimizing the spatial positions of nodes mapped into the three-dimensional space using a business scenario-based intelligent layout algorithm includes:

[0019] The interaction forces between nodes are simulated based on a physical mechanics model, and the business importance of each node is mapped to physical attributes.

[0020] Calculate the business importance of each node in the three-dimensional space;

[0021] The nodes are strategically positioned in the three-dimensional space according to their importance to the business objectives.

[0022] In one possible embodiment, the step of optimizing the spatial positions of nodes mapped into the three-dimensional space using a business scenario-based intelligent layout algorithm includes:

[0023] Based on the type of each node or the business level of each node, the three-dimensional space is initially divided into multiple different spatial regions;

[0024] A force-oriented layout algorithm based on a physical mechanics model is used to optimize the node positions within or between the aforementioned spatial regions.

[0025] In one possible embodiment, the force-guided layout algorithm includes:

[0026] The physical and mechanical model is used to map the importance of node services in the three-dimensional space to the mass or charge of the node, and the repulsive force between nodes is calculated iteratively.

[0027] The physical and mechanical model is used to map the connection strength of the edges in the three-dimensional space to the elastic stiffness coefficient, and the edge attraction force is calculated iteratively.

[0028] In one possible embodiment, the step of detecting anomalies in the operational status of supply chain nodes and edges based on time-series data in the structured data stream, and visually indicating the detected anomalies and their propagation paths in the three-dimensional space in conjunction with the results of the visualization rendering, includes:

[0029] Continuously monitor the runtime metrics of nodes and edges in the supply chain network;

[0030] When the runtime metrics deviate from the normal behavior pattern established by the node and / or edge based on historical data, it is determined to be abnormal;

[0031] Based on the results of the visualization rendering, the potential propagation path of the anomaly in the supply chain network is analyzed and determined, and a visual prompt is provided in the three-dimensional space.

[0032] On one hand, embodiments of this application provide a supply chain network visualization system, including:

[0033] The data acquisition module is used to collect node and edge data of the supply chain network from supply chain data sources in real time to form an initial real-time data stream;

[0034] The processing module is used to perform real-time optimization processing on the initial real-time data stream, dynamically adjust the data update strategy to adapt to the data change rate and system load, and output the optimized structured data stream.

[0035] The rendering module is used to map the nodes and edges of the supply chain network to three-dimensional space for real-time visualization rendering based on graphics hardware accelerated rendering technology and the structured data stream.

[0036] The layout module is used to optimize the spatial positions of nodes mapped into the three-dimensional space by employing an intelligent layout algorithm based on business scenarios.

[0037] The detection module is used to detect anomalies in the operating status of supply chain nodes and edges based on the time-series data in the structured data stream, and to visualize the detected anomalies and their propagation paths in the three-dimensional space in conjunction with the results of the visualization rendering.

[0038] On one hand, embodiments of this application provide an electronic device including a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes any of the above-described supply chain network visualization methods.

[0039] On the one hand, this application provides a computer-readable storage medium including program code, which, when the storage medium is run on an electronic device, causes the electronic device to execute any of the above-described supply chain network visualization methods.

[0040] On one hand, an embodiment of this application provides a computer program product, which includes computer instructions stored in a computer-readable storage medium; when the processor of an electronic device reads the computer instructions from the computer-readable storage medium, the processor executes the computer instructions, causing the electronic device to perform any of the above-described supply chain network visualization methods.

[0041] The beneficial effects of this application are as follows:

[0042] This application provides a supply chain network visualization method, system, electronic device, and storage medium. Through adaptive data updates and intelligent compression, it reduces network transmission volume by 90% and memory usage by 60%, solving the system load bottleneck under massive data and achieving real-time and smooth visualization of large-scale supply chain data. Through a hybrid layout algorithm with adaptive business weights, it automatically generates a clear topology that conforms to the supply chain business logic, overcoming the rigidity of traditional single layouts. Through the deep integration of high-performance rendering, intelligent algorithms, and intuitive visualization, it completely solves the fundamental defects of traditional supply chain management systems in terms of real-time performance, intuitiveness, and intelligence, achieving a technological leap from "passive response" to "proactive control."

[0043] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of an application scenario in the embodiments of this application;

[0046] Figure 2 This is a flowchart illustrating the implementation of a supply chain network visualization method in this application.

[0047] Figure 3 This is a schematic diagram of the structure of a supply chain network visualization system according to an embodiment of this application;

[0048] Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0050] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein.

[0051] The design concept of the embodiments of this application is briefly introduced below:

[0052] A supply chain network is a complex mesh system composed of entities such as raw material suppliers, manufacturers, distributors, retailers, and end users. These entities act as nodes, connected by logistics, information flow, and capital flow as edges, forming a multi-level, multi-directional dynamic network structure. The node states and edge connections in a supply chain network are constantly changing, exhibiting dynamic complexity. The supply chain network presents a multi-layered structure, encompassing several levels including raw material supply, manufacturing, warehousing, and distribution. Edge connections are strongly correlated; any local anomaly can trigger a chain reaction through the corresponding connecting edges. In a supply chain network, nodes are geographically dispersed, and business activities exhibit temporal characteristics, resulting in an overall spatiotemporal distribution. By leveraging supply chain networks to achieve global, visualized management of the supply chain system, managers can intuitively view the entire chain's business relationships, promptly identify bottlenecks and abnormal nodes, provide data-driven support for supply chain optimization, and promote efficient collaboration across all links of the supply chain.

[0053] Existing technologies in supply chain management use traditional tables or two-dimensional charts to display the supply chain network, which cannot support complex network relationships and is not conducive to viewing key information; fixed data update mechanisms cannot adapt to real-time dynamic changes in the supply chain, resulting in data processing delays and inability to efficiently and promptly handle anomalies after decision-making; low level of intelligence leads to inaccurate anomaly detection and a lack of risk propagation analysis capabilities.

[0054] In view of this, embodiments of this application provide a supply chain network visualization method, system, electronic device, and storage medium, aiming to build an intelligent perception and decision-making system that automatically extracts business insights from real-time data and presents them intuitively. First, an adaptive data stream processing engine transforms real-time data from various links in the supply chain into a stable and efficient structured data stream. The core lies in dynamically adjusting and updating strategies, laying the foundation for subsequent intelligent layout and anomaly detection. Then, using graphics hardware acceleration technology, this data stream is mapped to a three-dimensional space. Specifically, a business scenario-based intelligent layout algorithm transforms the business importance and type attributes of nodes into physical parameters, driving the entire network to iteratively optimize in three-dimensional space, forming a three-dimensional supply chain network diagram that can both display the topology and reflect the business logic. Based on this, continuous monitoring of time-series data is used to proactively capture operational anomalies of nodes and edges, and innovatively, combined with the generated visualized network topology, the propagation path of anomalies is dynamically deduced and highlighted. Ultimately, this achieves a fundamental leap in supply chain management from a passive, abstract, and lagging traditional model to a proactive, intuitive, and real-time intelligent model.

[0055] The preferred embodiments of this application are 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 this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0056] like Figure 1 The diagram shown illustrates an application scenario according to an embodiment of this application. The application scenario diagram includes a terminal device 101 and a server 102. The terminal device 101 and the server 102 communicate via a communication network.

[0057] Terminal device 101 is an electronic device used by the target object. This electronic device can be a personal computer, mobile phone, tablet computer, laptop computer, e-book reader, vehicle terminal, etc. Furthermore, terminal device 101 can have a client application related to the supply chain management system installed. This client application can be software (e.g., an app, browser), a webpage, a mini-program, etc. The target object can use the aforementioned client application related to the supply chain management system through terminal device 101 to interact with the supply chain network visualization system, such as inputting data, setting parameters, viewing the supply chain network diagram, and anomaly detection results.

[0058] Server 102 can be a standalone physical server, an edge device in the cloud computing field, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, cloud functions, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0059] There is no limit to the number of the aforementioned terminal devices 101 and / or servers 102.

[0060] It should be noted that the supply chain network visualization method in this embodiment is jointly executed by terminal device 101 and server 102. Server 102 can receive a supply chain business scenario created by the target object; then, it collects data from the supply chain data source in real time according to the business scenario to form an initial real-time data stream; then, it performs real-time optimization processing on the initial real-time data stream, performs three-dimensional visualization rendering of the supply chain network, and optimizes the rendering result; next, it performs anomaly detection based on the three-dimensional visualization rendering result, outputs the detection result and propagation path, and terminal device 102 can receive the anomaly prompt and propagation path and present them to the target object so that the target object can view the supply chain network topology in real time.

[0061] The following describes the supply chain network visualization method provided by the exemplary embodiments of this application in conjunction with the above application scenarios and with reference to the accompanying drawings. It should be noted that the above application scenarios are only shown to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited in any way.

[0062] refer to Figure 2 This is a flowchart illustrating the implementation of a supply chain network visualization method provided in this application embodiment. The method is described here with the server as the execution entity, and the specific implementation process is as follows:

[0063] S201 collects node and edge data of the supply chain network in real time from supply chain data sources to form an initial real-time data stream.

[0064] In this embodiment, the system can establish real-time connections with different supply chain data sources by deploying various data adapters or API connectors. These data sources include, but are not limited to: master data such as orders, inventory, and suppliers from enterprise information systems (e.g., ERP (SAP, Oracle), SCM (Supply Chain Management System), WMS (Warehouse Management System), etc.), primarily obtained through direct database connections or APIs; real-time logistics data from IoT platforms (e.g., cargo location, warehouse environment, equipment status, etc.), primarily obtained through sensors or RFID tags; and external data (e.g., traffic conditions, weather information, customs clearance status, etc.), primarily obtained from third-party services via APIs. In practice, specific parsing and mapping rules can be pre-configured for each data source within the system to ensure the system can understand data formats from different sources.

[0065] This embodiment forms an initial real-time data stream by processing supply chain network data from different data sources in a streaming manner. The purpose is to unify and continuously integrate scattered and heterogeneous supply chain data into the system, breaking down data silos within enterprises and providing the possibility for visualization of a global perspective.

[0066] S202, perform real-time optimization processing on the initial real-time data stream, dynamically adjust the data update strategy to adapt to the data change rate and system load, and output the optimized structured data stream.

[0067] This embodiment balances data real-time performance with system resource consumption by dynamically adjusting the update frequency, avoiding problems such as data lag or resource waste caused by a fixed frequency.

[0068] In some implementations, step S202 includes:

[0069] The update interval is dynamically calculated based on the data change rate and at least one parameter characterizing the system load, wherein the parameter characterizing the system load includes network transmission delay and / or the number of nodes to be processed.

[0070] Based on the update interval, perform periodic data fetching or receive push data.

[0071] This embodiment constructs a multi-factor model to dynamically calculate the update interval, completing a closed-loop feedback process. This allows the system to continuously collect key indicators from the operating environment: data change rate and parameters characterizing system load. This enables a dynamic update strategy, recursively calling and executing periodic data retrieval or data push reception. The multi-factor model includes a data change rate factor, a network transmission latency factor, and a node count factor.

[0072] The data change rate refers to the frequency or variance of changes in key business metrics (such as order quantity, inventory level, etc.) within a fixed time window. For example, it monitors how many nodes in the supply chain network have updated their inventory status within a certain time period (1 minute or 3 minutes), and calculates the change rate of different business data based on these node status changes. A higher data change rate results in a larger data change rate factor value and a smaller calculation result (update interval), thus increasing the update frequency and ensuring users see the latest data.

[0073] Network latency is measured in real-time between the client and server using the round-trip time (RTT) of heartbeat packets or data requests. Higher latency results in a larger network latency factor value and a smaller calculated update interval, but this leads to a lower update frequency. The logic is that when network conditions are poor, the number of requests should be reduced to avoid exacerbating network congestion.

[0074] The scale of nodes to be processed refers to the total number of supply chain nodes that need to be rendered and processed in real time. In the multi-factor model, this is reflected as a node quantity factor. The more nodes there are, the larger the node quantity factor value, and the smaller the calculation result (update interval), which also leads to a decrease in the update frequency. The logic is that when processing large-scale data, the computation and rendering pressure of each update is high, and the update interval needs to be appropriately widened to maintain the front-end frame rate.

[0075] Based on the calculated update interval, a timer can be set to periodically perform data exchange: data retrieval or receiving pushed data. For example, after the timer is triggered, the client actively sends a request to the server to obtain the latest data and performs data retrieval; when receiving pushed data, the client prepares to receive data pushed by the server according to the update interval. After each data exchange is completed, the latest data change rate and parameters characterizing the system load are immediately collected again to calculate the update interval for the next round.

[0076] This embodiment abandons the rigid pattern of fixed-frequency updates, avoiding useless queries during data calm periods or untimely updates during data storms, maximizing resource utilization efficiency while ensuring data freshness. By introducing network transmission latency and the size of nodes to be processed as control parameters, the system no longer merely responds to the data itself, but can also sense and adapt to the environmental pressures of its operation. Regardless of network fluctuations or data volume growth, the system can maintain stable operation through self-adjustment, preventing lag or crashes caused by overload, demonstrating strong robustness. Dynamically calculating the update interval and executing data exchange accordingly ensures that the speed of data inflow matches the front-end rendering and computing capabilities, making it possible to process massive supply chain data in ordinary browsers and avoiding performance collapse caused by data congestion.

[0077] In some implementations, step S202 further includes:

[0078] The initial real-time data stream is subjected to structured processing to obtain standardized structured data;

[0079] An incremental update mechanism is used to extract the changed data portions from the standardized structured data, which are then transmitted as the new data.

[0080] The transmitted data is compressed using a compression algorithm.

[0081] In this embodiment, structured processing prepares data for the incremental update mechanism and compression algorithm by transforming data from different data sources into a unified data structure using predefined parsing and mapping rules. Specifically, for example, a purchase order record from an ERP system can be mapped to an order node and an edge connecting to a supplier node using predefined parsing and mapping rules; location information from an IoT device can be mapped to the geographic coordinates (or latitude and longitude) of a transport vehicle node using predefined parsing and mapping rules. By structuring the initial real-time data stream, all data from different data sources possess unified fields such as ID (unique identifier), type (node ​​type: e.g., supplier / manufacturer), status (running status), and position (initial spatial coordinates), laying the data foundation for subsequent incremental update mechanisms and data compression.

[0082] In this embodiment, the incremental update mechanism refers to comparing the current structured data with the historical data from the last transmission, and extracting only the changed data parts (such as nodes with changed states and edges with adjusted weights) through ID matching, row-by-row field comparison, or hash value verification (such as MD5 hashing). Specifically, when new standardized structured data is received, node comparison involves traversing all nodes in the new standardized structured data and comparing them with the nodes in the history (last update). If a node does not exist in the history, or its key attributes (such as inventory or state) have changed, the node is marked as a changed part. Edge comparison involves traversing all edges in the new standardized structured data and identifying newly added, disappeared, or changed edges. Finally, all the changed nodes and edges obtained from the comparison are packaged into an incremental data packet as the transmitted data.

[0083] In this embodiment, the LZ4 compression algorithm is preferred. Its advantages include extremely fast compression / decompression speed. While its compression ratio may not be the highest, it achieves an optimal balance between speed and efficiency, making it very suitable for real-time applications. In practice, the compression algorithm can be automatically selected based on the data size. Other usable compression algorithms include: Gzip compression (a general-purpose compression algorithm with higher compression ratio but slower speed), Brotli compression (an efficient compression algorithm suitable for web applications), Snappy compression (a high-speed compression algorithm with speed comparable to LZ4), and Zstandard compression (a modern compression algorithm that balances speed and compression ratio). Using the LZ4 compression algorithm, the data compression process includes: serializing the transmitted data (incremental data packets) into a string, and then using the LZ4 compression algorithm as a high-speed compressor to compress this string. The compressed data is transmitted as a binary data stream, which is the smallest data format ultimately transmitted over the network.

[0084] This embodiment employs a fast compression algorithm, which offers high compression speed and is suitable for real-time transmission, solving the problem of transmission volume and further reducing the size of each byte. Through difference calculation, only changed data is transmitted, reducing the transmission volume by 90%, solving the problem of transmitted content and avoiding the repeated transmission of unchanged data. By combining these two approaches, the network transmission load is greatly reduced, and the system response speed and real-time performance are significantly improved.

[0085] S203, based on graphics hardware acceleration rendering technology and the structured data flow, maps the nodes and edges of the supply chain network to three-dimensional space for real-time visualization rendering.

[0086] Hardware-accelerated graphics rendering (HARF) is a technique that utilizes the GPU (GPU) to replace the CPU in performing primary graphics computation tasks. Its core principle is that the CPU excels at handling complex, sequential tasks, while the GPU (Graphics Processing Unit) consists of thousands of small, efficient cores designed for parallel processing of massive amounts of simple computations (such as vertex transformations and pixel shading). In a browser environment, rendering is primarily implemented through the WebGL API. WebGL allows JavaScript to directly call the native system's graphics driver, thereby leveraging the GPU for high-performance 2D and 3D graphics rendering without requiring any plugins.

[0087] This embodiment achieves data-to-graphics conversion by mapping the nodes and edges of the supply chain network into three-dimensional space (3D space). Specifically, node rendering involves traversing each node in the data stream and creating a corresponding 3D geometry based on its business attributes (such as type and status). Then, dynamic materials are assigned to this 3D geometry; for example, color is used to distinguish node types, and glowing effects are used to represent active states. Finally, the business coordinates of the nodes are mapped to 3D space coordinates to generate a 3D mesh object, which is then added to the scene. Edge rendering involves traversing each edge to find the positions of its source and target nodes in 3D space. Then, a line segment connecting the two points is created. Finally, the attributes of the edge (such as flow rate and relationship strength) are expressed through the color, thickness, and transparency of the line segment.

[0088] S204 employs an intelligent layout algorithm based on business scenarios to optimize the spatial positions of nodes mapped into the three-dimensional space.

[0089] Simply mapping data to three-dimensional space is only an initial step. Unoptimized spatial layouts are usually random, chaotic, and lack business logic, making them unsuitable for effective analysis and decision-making. This embodiment optimizes the spatial location of nodes, expressing abstract business attributes (such as node type, importance, and risk level) through intuitive visual variables (such as spatial location, level, and region), forming a reinforced supply chain network topology that greatly improves visual clarity and user interaction experience.

[0090] In some implementations, step S204 includes: simulating the interaction forces between nodes based on a physical mechanics model, and mapping the business importance of each node to physical attributes;

[0091] Calculate the business importance of each node in the three-dimensional space;

[0092] The nodes are strategically positioned in the three-dimensional space according to their importance to the business objectives.

[0093] This embodiment transforms the business rules of the supply chain into the mechanical rules of the physical world. Through computational simulation, nodes automatically find the spatial location that best reflects their business status, transforming complex business management problems into a calculable and optimizable physical problem. Ultimately, it generates an intelligent layout that reflects both business depth and visual clarity. Specifically, the calculation of business importance can be achieved through a weighted formula that combines different dimensions of nodes into a comprehensive importance score. In practice, different dimensions can be set according to specific business scenarios. Selectable dimensions include, but are not limited to: business weight (e.g., node type, core manufacturer, edge supplier), pre-set strategic level; data activity (e.g., order processing frequency, data update rate, reflecting the node's activity level); and risk coefficient (e.g., whether inventory levels are below a safety threshold, whether there is a history of delivery delays).

[0094] In some implementations, step S204 includes:

[0095] Based on the type of each node or the business level of each node, the three-dimensional space is initially divided into multiple different spatial regions;

[0096] A force-oriented layout algorithm based on a physical mechanics model is used to optimize the node positions within or between the aforementioned spatial regions.

[0097] This embodiment uses a physical mechanics model to simulate the physical interactions between nodes. Through multiple iterations, the node positions are gradually optimized, and the advantages of hierarchical layout and force-directed layout are combined to form a hybrid layout strategy.

[0098] The process involves initially dividing the three-dimensional space into multiple distinct spatial regions based on node type or business level. This macro-level spatial region division, based on business logic, aims to establish a global framework that maps business levels to spatial levels. First, a set of spatial division rules is defined according to node type (e.g., raw material supplier, manufacturer, distribution center, retailer) or business level (e.g., strategic, tactical, execution; or first-tier supplier, second-tier supplier). Then, based on these rules, different logical regions are created, divided vertically by level and horizontally by type. Finally, all nodes are traversed, and based on their type or business level, they are initially placed into their corresponding preset spatial regions. At this point, the node positions within the regions may be random or arranged according to simple rules. Further optimization of the node micro-positions is required using a force-directed layout algorithm based on a physics model. This includes calculating the Coulomb repulsion between nodes within each independent spatial region to ensure even distribution and avoid overlap, and simultaneously calculating the Hooke attraction between nodes to bring directly related nodes closer together, forming compact sub-clusters. It also includes calculating the Coulomb repulsion and Hooke attraction for nodes between individual spatial regions.

[0099] The force-guided layout algorithm includes:

[0100] The physical and mechanical model is used to map the importance of node services in the three-dimensional space to the mass or charge of the node, and the repulsive force between nodes is calculated iteratively.

[0101] The physical and mechanical model is used to map the connection strength of the edges in the three-dimensional space to the elastic stiffness coefficient, and the edge attraction force is calculated iteratively.

[0102] S205, based on the time-series data in the structured data stream, anomaly detection is performed on the operating status of supply chain nodes and edges, and combined with the results of the visualization rendering, the detected anomalies and their propagation paths are visualized and displayed in the three-dimensional space.

[0103] This embodiment enables clear anomaly recording and propagation analysis, providing data-driven decision-making support for reviewing and optimizing the supply chain structure. It can dynamically deduce and visualize the propagation path of anomalies along the supply chain network, enabling managers to predict the overall impact and shift from dealing with problems that have already occurred to preventing the spread of problems.

[0104] In some implementations, step S205 includes:

[0105] Continuously monitor the runtime metrics of nodes and edges in the supply chain network;

[0106] When the runtime metrics deviate from the normal behavior pattern established by the node and / or edge based on historical data, it is determined to be abnormal;

[0107] Based on the results of the visualization rendering, the potential propagation path of the anomaly in the supply chain network is analyzed and determined, and a visual prompt is provided in the three-dimensional space.

[0108] In this embodiment, for each monitored indicator (such as inventory level), the system dynamically calculates its moving average (μ) and standard deviation (σ) using a sliding window technique (e.g., set to a 24-hour window). This range of "moving average ± 3σ" represents the baseline of the dynamic normal behavior pattern of that node or edge in the current period. This baseline adaptively adjusts over time, thereby achieving continuous monitoring.

[0109] The analysis process of the potential propagation path can be carried out by taking the abnormal node as the starting point, starting the graph traversal algorithm, and automatically analyzing and finding all potential nodes affected by the abnormality according to the connection direction of the edge (such as upstream and downstream relationship), forming one or more propagation paths.

[0110] In practical applications, this embodiment utilizes a detection method based on Z-Score and dynamic baselines. Compared to the fixed threshold method, this reduces the false alarm rate by 80% and significantly improves detection accuracy. Combined with graph traversal propagation analysis, users no longer obtain an isolated alarm point, but a clear risk impact map, enabling them to instantly understand the potential impact of local failures on overall business operations. This makes risk management in complex supply chains intuitive and easy to understand.

[0111] Based on the same inventive concept, embodiments of this application also provide a supply chain network visualization system. For example... Figure 3 As shown, this is a structural diagram of a supply chain network visualization system 300, which may include:

[0112] The acquisition module 301 is used to collect node data and edge data of the supply chain network from the supply chain data source in real time to form an initial real-time data stream;

[0113] Processing module 302 is used to perform real-time optimization processing on the initial real-time data stream, dynamically adjust the data update strategy to adapt to the data change rate and system load, and output the optimized structured data stream;

[0114] The rendering module 303 is used to map the nodes and edges of the supply chain network to three-dimensional space for real-time visualization rendering based on graphics hardware accelerated rendering technology and the structured data stream.

[0115] The layout module 304 is used to optimize the spatial positions of nodes mapped to the three-dimensional space by adopting an intelligent layout algorithm based on business scenarios.

[0116] The detection module 305 is used to perform anomaly detection on the operating status of supply chain nodes and edges based on the time-series data in the structured data stream, and to provide visual prompts on the detected anomalies and their propagation paths in the three-dimensional space in combination with the results of the visualization rendering.

[0117] In some possible implementations, the supply chain network visualization system according to this application may include at least a processor and a memory. The memory stores program code that, when executed by the processor, causes the processor to perform the steps in the supply chain network visualization methods according to various exemplary embodiments of this application described in this specification. For example, the processor may perform actions such as... Figure 2 The steps are shown in the figure.

[0118] Based on the same inventive concept, this application also provides an electronic device that can realize the functions of the aforementioned supply chain network visualization method system. (Refer to...) Figure 4 The electronic device includes:

[0119] At least one processor 401 and a memory 402 connected to at least one processor 401. In this embodiment, the specific connection medium between the processor 401 and the memory 402 is not limited. Figure 4 The example shown is the connection between processor 401 and memory 402 via bus 400. Bus 400 is... Figure 4 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. The 400 bus can be divided into address bus, data bus, control bus, etc., for ease of representation. Figure 4 The term is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, processor 401 can also be called a controller; there is no restriction on the name.

[0120] In this embodiment, memory 402 stores instructions executable by at least one processor 401. By executing the instructions stored in memory 402, at least one processor 401 can execute the supply chain network visualization method discussed above. Processor 401 can implement... Figure 3 The system shown illustrates the functions of each module.

[0121] The processor 401 is the control center of the system. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory 402 and calling data stored in memory 402, the system can perform various functions and process data, thereby monitoring the system as a whole.

[0122] In one possible design, processor 401 may include one or more processing units. Processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into processor 401. In some embodiments, processor 401 and memory 402 may be implemented on the same chip; in some embodiments, they may also be implemented separately on separate chips.

[0123] Processor 401 can be a general-purpose processor, such as a central processing unit (CPU), digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the supply chain network visualization method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0124] Memory 402, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory 402 may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic storage, magnetic disk, optical disk, etc. Memory 402 can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. In the embodiments of this application, memory 402 can also be a circuit or any other system capable of implementing storage functions for storing program instructions and / or data.

[0125] By designing and programming the processor 401, the code corresponding to the supply chain network visualization method described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute it during runtime. Figure 2The steps of the supply chain network visualization method shown in the embodiment are described below. How to design and program the processor 401 is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0126] Based on the same inventive concept, embodiments of this application also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the supply chain network visualization method described above.

[0127] In some possible implementations, various aspects of the supply chain network visualization method provided in this application can also be implemented in the form of a program product, which includes program code that, when the program product is run on a system, causes the control device to perform the steps in the supply chain network visualization method according to the various exemplary embodiments of this application described above.

[0128] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0129] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0130] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0131] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0132] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A supply chain network visualization method, characterized by, The method comprises the following steps: Collecting node data and edge data of a supply chain network from a supply chain data source in real time to form an initial real-time data stream; Performing real-time optimization processing on the initial real-time data stream, dynamically adjusting a data update strategy to adapt to a data change rate and a system load, and outputting an optimized structured data stream; Mapping nodes and edges of the supply chain network to a three-dimensional space for real-time visual rendering based on a graphics hardware acceleration rendering technology and the structured data stream; Optimizing the spatial positions of the nodes mapped to the three-dimensional space by using an intelligent layout algorithm based on a business scenario; Detecting abnormalities in the running states of the supply chain nodes and edges based on time-series data in the structured data stream, and combining the results of the visual rendering to visually prompt the detected abnormalities and their propagation paths in the three-dimensional space.

2. The method of claim 1, wherein, The step of performing real-time optimization processing on the initial real-time data stream, dynamically adjusting a data update strategy to adapt to a data change rate and a system load, and outputting an optimized structured data stream comprises the following steps: Dynamically calculating an update interval based on a data change rate and at least one parameter representing a system load, wherein the parameter representing the system load includes network transmission delay and / or the scale of nodes to be processed; Performing periodic data pulling or receiving pushed data according to the update interval.

3. The method of claim 2, wherein, The step of performing real-time optimization processing on the initial real-time data stream, dynamically adjusting a data update strategy to adapt to a data change rate and a system load, and outputting an optimized structured data stream further comprises the following steps: Structuring the initial real-time data stream to obtain standardized structured data; Extracting the changed data part in the standardized structured data as transmission data by using an incremental update mechanism; Compressing the transmission data by using a compression algorithm.

4. The method of claim 1, wherein, The step of optimizing the spatial positions of the nodes mapped to the three-dimensional space by using an intelligent layout algorithm based on a business scenario comprises the following steps: Simulating the interaction forces between nodes based on a physical mechanics model, and mapping the business importance of each node to a physical attribute; Calculating the business importance of each node in the three-dimensional space; Differentially arranging the positions of the nodes in the three-dimensional space according to the business importance.

5. The method of claim 1, wherein, The step of optimizing the spatial positions of the nodes mapped to the three-dimensional space by using an intelligent layout algorithm based on a business scenario comprises the following steps: Preliminarily dividing the three-dimensional space into multiple different spatial regions according to the types of the nodes or the business levels of the nodes; Optimizing the positions of the nodes in or between the spatial regions by using a force-directed layout algorithm of the physical mechanics model.

6. The method of claim 5, wherein, The force-directed layout algorithm comprises the following steps: Mapping the business importance of the nodes in the three-dimensional space to the mass or charge amount of the nodes by using the physical mechanics model, and iteratively calculating the repulsive forces between the nodes; Mapping the connection relationship strength of the edges in the three-dimensional space to the elastic stiffness coefficient by using the physical mechanics model, and iteratively calculating the attractive forces of the edges.

7. The method of claim 1, wherein, The step of performing anomaly detection on the running state of the nodes and edges of the supply chain based on the time series data in the structured data stream, and combining the results of the visual rendering, visualizing the detected anomalies and their propagation paths in the three-dimensional space, includes: continuously monitoring the runtime indicators of the nodes and edges of the supply chain network; determining that an anomaly has occurred when the runtime indicators deviate from the normal behavior patterns established based on historical data for the nodes and / or edges; analyzing and determining the potential propagation path of the anomaly in the supply chain network based on the results of the visual rendering, and visualizing the propagation path in the three-dimensional space.

8. A supply chain network visualization system, characterized by, It includes: a collection module for collecting node data and edge data of the supply chain network from supply chain data sources in real time to form an initial real-time data stream; a processing module for performing real-time optimization processing on the initial real-time data stream, adjusting the data update strategy dynamically to adapt to the data change rate and system load, and outputting an optimized structured data stream; a rendering module for mapping the nodes and edges of the supply chain network to a three-dimensional space for real-time visual rendering based on graphics hardware acceleration rendering technology and the structured data stream; a layout module for optimizing the spatial position of the nodes mapped to the three-dimensional space using a business scenario-based intelligent layout algorithm; a detection module for performing anomaly detection on the running state of the nodes and edges of the supply chain based on the time series data in the structured data stream, and combining the results of the visual rendering to visualize the detected anomalies and their propagation paths in the three-dimensional space.

9. An electronic device, comprising: It includes a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It includes program code, and when the storage medium is running on an electronic device, the program code is used to make the electronic device execute the method of any one of claims 1-7.

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