Visualization method and system based on vehicle service supply chain

Abstract

The present invention relates to the field of data visualization, and in particular to a visualization method and system based on a vehicle service supply chain. The method includes the following steps: obtaining the full process information of the vehicle service supply chain; identifying the supply chain subject of the full process information of the vehicle service supply chain, and performing spatial topological distribution analysis to construct a supply chain subject topological distribution map; obtaining supply chain subject interaction logic data based on multiple supply chain subjects; using the supply chain subject interaction logic data to perform topological interaction logic connection on the supply chain subject topological distribution map to construct a supply chain topological interaction network; calculating the path transportation frequency within the cycle of the full process information of the vehicle service supply chain to generate logistics demand trend characteristics; analyzing the operation behavior of each link of the full process information of the vehicle service supply chain to obtain the production capacity characteristics of each link. The present invention improves the resource scheduling efficiency and flexibility of the supply chain by visualizing the vehicle service supply chain.

Description

Technical Field

[0001] The present invention relates to the field of data visualization, and in particular to a visualization method and system based on a vehicle service supply chain. Background Art

[0002] With the development of the automotive aftermarket industry, the automotive service supply chain is becoming increasingly complex, with many participants and complicated operation links. Traditional supply chain management methods cannot meet the needs of modern automotive services, and there is an urgent need for an intelligent supply chain management method based on visualization. The automotive service supply chain involves multiple links such as maintenance, spare parts distribution, and roadside assistance. There are complex interactions between information flows, logistics, and capital flows between each link. How to accurately perceive the operating status of each participant in the supply chain and promptly discover and diagnose supply chain problems has always been a difficulty in automotive service supply chain management.

[0003] Traditional supply chain analysis methods usually rely on static topological diagrams and discrete business data, which makes it difficult to fully and dynamically reflect the actual operation of the supply chain. The lack of in-depth analysis of the interaction logic between various links in the supply chain, logistics demand characteristics, and production capacity bottlenecks has led to poor supply chain optimization results and difficulty in meeting differentiated service needs. Therefore, there is an urgent need for a visualization-based intelligent analysis method for the vehicle service supply chain that can dynamically display the overall picture of the supply chain and deeply explore the key issues in the operation of the supply chain. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a visualization method and system based on the vehicle service supply chain to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a visualization method based on a vehicle service supply chain, comprising the following steps:

[0006] Step S1: Obtain the whole process information of the vehicle service supply chain; identify the supply chain entities in the whole process information of the vehicle service supply chain, and perform spatial topological distribution analysis to construct a topological distribution map of the supply chain entities;

[0007] Step S2: obtaining supply chain subject interaction logic data based on multiple supply chain subjects; using the supply chain subject interaction logic data to perform topological interaction logic connection on the supply chain subject topological distribution map to construct a supply chain topological interaction network;

[0008] Step S3: Calculate the frequency of path transportation within the cycle for the entire process information of the vehicle service supply chain, and perform logistics demand trend evolution to generate logistics demand trend characteristics;

[0009] Step S4: Analyze the operation behavior of each link of the whole process information of the vehicle service supply chain, and evaluate the capacity of each link to obtain the capacity characteristics of each link;

[0010] Step S5: dynamically render the supply chain topology interaction network based on the logistics demand trend characteristics and the capacity characteristics of each link, and build a dynamic supply chain twin model;

[0011] Step S6: Iterate the supply scheduling decision of the dynamic supply chain twin model, perform scheduling simulation visualization, and build a supply chain scheduling evolution visualization model.

[0012] The present invention identifies each supply chain subject in the entire process of the vehicle service supply chain, and constructs a supply chain subject topological distribution map through spatial topological distribution analysis, which can clearly show the position relationship and topological structure between the various supply chain subjects. The topological distribution map helps to fully understand the spatial position distribution of various supply chain subjects, and provides a visualization basis for subsequent interactive logic data analysis and dynamic rendering. Based on the interactive logic data of multiple supply chain subjects, the supply chain subject topological distribution map is topologically interactively connected to construct a supply chain topological interaction network, which can reveal the association and interaction mode between the various supply chain subjects. The construction of the topological interaction network helps to deeply understand the information flow and cooperative relationship between the supply chain subjects, and lays the foundation for the establishment of a dynamic supply chain twin model. By calculating the path transportation frequency within the cycle and analyzing the evolution of logistics demand trends, the logistics demand trend characteristics are generated, which can help predict the development trend and change law of logistics demand. The generation of logistics demand trend characteristics helps to optimize supply chain scheduling and resource allocation, and provides a basis for formulating The supply chain strategy provides data support, analyzes the operating behavior of each link in the entire process of the vehicle service supply chain, and evaluates the link capacity to obtain the capacity characteristics of each link, which helps to evaluate the efficiency and bottlenecks of each link in the supply chain. The evaluation of capacity characteristics can guide supply chain optimization and resource allocation, improve overall operational efficiency and responsiveness, and dynamically render the supply chain topology interaction network based on logistics demand trend characteristics and link capacity characteristics. A dynamic supply chain twin model is constructed to reflect the operating status and changes of the supply chain in real time. The establishment of a dynamic supply chain twin model helps to monitor the operation of the supply chain in real time, improve decision-making efficiency and the ability to respond to changes. Iterates supply scheduling decisions on the dynamic supply chain twin model, performs scheduling simulation visualization, and constructs a supply chain scheduling evolution visualization model, which can intuitively display the effects and evolution of supply chain scheduling decisions. The construction of a visualization model helps to optimize the supply chain scheduling plan, improve the efficiency and flexibility of supply chain operations, and provide intuitive reference and decision support for decision makers.

[0013] Preferably, step S1 comprises the following steps:

[0014] Step S11: Obtaining the whole process information of the vehicle service supply chain;

[0015] Step S12: identifying the supply chain entities of the entire process information of the vehicle service supply chain to obtain multiple supply chain entities;

[0016] Step S13: locating the physical space positions of multiple supply chain entities to obtain the physical space position coordinates of each supply chain entity;

[0017] Step S14: performing spatial topological distribution analysis on the physical space location coordinates of each supply chain entity to generate supply chain entity topological distribution data;

[0018] Step S15: Perform topological structure fitting on the supply chain entity topological structure data to construct a supply chain entity topological distribution map.

[0019] The present invention helps to fully understand the operation mechanism and links of the entire supply chain by acquiring the full process information of the vehicle service supply chain. By identifying the supply chain subject of the full process information, the main participants involved in the vehicle service supply chain can be determined. Identifying the supply chain subject helps to accurately locate the responsible parties of each key link, and provides key information for subsequent spatial position positioning and topological distribution analysis. Physical spatial position positioning of the supply chain subject can determine the position coordinates of each supply chain subject in the actual physical space. Physical spatial position positioning helps to accurately represent the position relationship of each subject in the geographic information system, and provides basic support for subsequent topological distribution analysis. By performing spatial topological distribution analysis on the physical spatial position coordinates of each supply chain subject, the spatial relationship and layout characteristics between the supply chain subjects can be revealed. Spatial topological distribution analysis helps to understand the spatial distance and connection relationship between the supply chain subjects, and provides a data basis for subsequent topological structure fitting. Topological structure fitting is performed according to the topological structure data of the supply chain subject to construct a topological distribution map of the supply chain subject. Topological structure fitting helps to display the spatial relationship between each supply chain subject in a visual way, and provides an intuitive supply chain structure map to help decision makers better understand the operation mode and correlation of the supply chain.

[0020] Preferably, step S2 specifically comprises the following steps:

[0021] Step S21: Analyze the business types of multiple supply chain entities to obtain the business type of each supply chain entity;

[0022] Step S22: performing type label mapping on multiple supply chain entities based on the business type of each supply chain entity to obtain entity label nodes;

[0023] Step S23: Analyze the upstream and downstream business flow of the subject tag node to obtain node business flow data;

[0024] Step S24: Performing an inter-subject interaction logic analysis on the node business flow data to obtain supply chain subject interaction logic data;

[0025] Step S25: Use the supply chain subject interaction logic data to perform topological interaction logic connections on the supply chain subject topological distribution map to construct a supply chain topological interaction network.

[0026] The present invention can understand the specific business type and characteristics involved in each subject by performing business type analysis on multiple supply chain subjects. Business type analysis helps to distinguish the functions and responsibilities of different subjects in the supply chain, and provides a basis for subsequent label mapping and flow analysis. Based on the business type of each supply chain subject, type label mapping is performed, and the business type information can be converted into a node label to form a subject label node. Type label mapping helps to associate the business type information with the supply chain subject, and provides node association information for subsequent flow analysis and interactive logic analysis. By performing upstream and downstream business flow analysis on the subject label node, the business flow relationship between the subjects can be revealed, and the transmission path of the business activities can be understood. Upstream and downstream business flow analysis helps to understand Understand the transmission and interaction of business activities in the supply chain, provide data support for subsequent interaction logic analysis, and conduct inter-subject interaction logic analysis on node business flow data to discover the business interaction logic and patterns between different supply chain entities. Interaction logic analysis helps to reveal the cooperative relationship and information flow between entities in the supply chain, and provides important clues for building a topological interaction network. Use the supply chain entity interaction logic data to perform topological interaction logic connection on the topological distribution map of the supply chain entity to build a supply chain topological interaction network. The topological interaction logic connection can visualize the interaction relationship between different supply chain entities, help understand the connection and cooperation mode between the various entities in the supply chain, and provide decision makers with a clearer supply chain interaction logic map.

[0027] Preferably, step S3 specifically comprises the following steps:

[0028] Step S31: Identify the logistics transportation paths for the entire process information of the vehicle service supply chain and extract all the logistics transportation paths;

[0029] Step S32: calculating the path transportation frequency within the cycle for all logistics transportation paths, and generating the path transportation frequency within the cycle;

[0030] Step S33: performing time-series frequency change analysis on the path transportation frequency within the cycle to generate time-series change data of the logistics transportation path;

[0031] Step S34: performing periodic logistics demand analysis on the time series change data of the logistics transportation path, thereby obtaining logistics demand data;

[0032] Step S35: Calculate the path operation efficiency of all logistics transportation paths to generate logistics transportation path utilization;

[0033] Step S36: Evolving the logistics demand trend of the logistics demand data according to the logistics transportation path utilization rate to generate logistics demand trend characteristics.

[0034] The present invention can determine the transmission path of goods or information in the supply chain by identifying the logistics transportation paths in the whole process of the vehicle service supply chain. Extracting all the logistics transportation paths is helpful to understand the logistics transmission mode and key paths in the supply chain. Calculating the path transportation frequency within the cycle for all logistics transportation paths is helpful to understand the transportation frequency and importance of different paths. Generating the path transportation frequency within the cycle can help identify the busyness and key paths of transportation activities in the supply chain. By performing time-series frequency change analysis on the path transportation frequency within the cycle, the changing trend of logistics transportation paths in different time periods can be revealed. Generating the time-series change data of logistics transportation paths is helpful to understand the seasonal or periodic changes of transportation activities in the supply chain, which is helpful for the subsequent To provide data support for demand analysis, periodic logistics demand analysis is performed on the time-series change data of logistics transportation paths, and periodic logistics demand data can be obtained. Logistics demand data helps to understand the logistics demand volume and change trends in the supply chain in different time periods. By calculating the operational efficiency of logistics transportation paths, the utilization and efficiency levels of different paths can be evaluated. Generating logistics transportation path utilization rates helps to optimize transportation path planning and resource allocation in the supply chain. According to the logistics transportation path utilization rates, logistics demand trend evolution analysis is performed on logistics demand data, which can reveal the development trends and characteristics of logistics demand. The logistics demand trend characteristics help to predict future changes in logistics demand in the supply chain and provide reference basis and planning direction for decision makers.

[0035] Preferably, the specific steps of step S35 are:

[0036] Based on multiple supply chain entities, all logistics transportation paths are identified through the entities passing through them, and the node sequences passing through the paths are marked;

[0037] Extract all the logistics flow nodes in the path flow node sequence;

[0038] Calculating the logistics resource load rate for all the nodes through which the logistics flows, and obtaining the logistics resource load rate of each node through which the logistics flows;

[0039] Calculate the residence time of each node for all the logistics passing through, and generate the residence time of each node passing through;

[0040] The path operation efficiency is calculated for the logistics resource load rate of each node flowing through and the length of stay of each node flowing through to generate the logistics transportation path utilization rate.

[0041] The present invention identifies the supply chain entities flowing through the logistics transportation path, marks the node sequence through which the path flows, and marks the node sequence through which the path flows. The marked node sequence through which the path flows helps to understand the role and position of different entities in the logistics transportation path, extracts all logistics flow nodes in the node sequence through which the path flows, and extracts the logistics flow nodes to help determine the specific nodes and location information involved in the supply chain, calculates the logistics resource load rate of each node through which the path flows, and understands the resource utilization of the node. The logistics resource load rate calculation helps to evaluate the resource utilization efficiency of the node and provide data support for the path operation efficiency. The residence time of each node through which the path flows is calculated to reveal the circulation speed and busyness of the node. The residence time calculation helps to understand the operation efficiency and circulation efficiency of the node. According to the logistics resource load rate and residence time of each node through which the path flows, the path operation efficiency is calculated to generate the logistics transportation path utilization rate. The path operation efficiency calculation helps to evaluate the utilization and efficiency level of the entire logistics transportation path and provides a basis for optimizing path planning and resource allocation.

[0042] Preferably, the specific steps of step S4 are:

[0043] Step S41: Obtain supply chain historical operation log;

[0044] Step S42: Calculate the production order turnaround time for the supply chain historical operation log, and extract multiple production order turnaround times;

[0045] Step S43: Analyze the operation behavior of each link of the whole process information of the vehicle service supply chain to obtain the operation behavior characteristics of multiple links;

[0046] Step S44: Calculate the full-link operation cycle of the multiple link operation behavior characteristics according to the multiple production order turnover times, so as to obtain the link operation cycle of each production order;

[0047] Step S45: Evaluate the link capacity of each link operation cycle of each production order to obtain the capacity characteristics of each link.

[0048] The present invention helps to understand past operating conditions and event records by obtaining historical operation logs of the supply chain. The historical operation logs provide a valuable source of data and can be used to analyze and optimize supply chain operations. Calculating the production order turnaround time can evaluate the time required to complete production orders in the supply chain. Extracting multiple production order turnaround times helps to understand the execution efficiency and time distribution of different orders in the supply chain. Analyzing the operation behavior of each link in the entire process information of the vehicle service supply chain can reveal the operation characteristics and behavior laws of different links. Extracting the operation behavior characteristics of multiple links helps to understand the functions and performance of each link in the supply chain. Calculating the operation cycle of all links based on the production order turnaround time and the operation behavior characteristics of the links helps to evaluate the efficiency and process of the entire production process. Calculating the operation cycle of each link of each production order can help identify potential bottlenecks and optimization points. Evaluating the link capacity of each link operation cycle of each production order can obtain the capacity characteristics of each link. The link capacity evaluation helps to determine the production capacity and bottleneck of each link, and provides guidance and optimization suggestions for improving the production efficiency of the supply chain.

[0049] Preferably, the specific steps of step S5 are:

[0050] Step S51: performing logistics demand situation forecasting on the logistics demand trend characteristics to generate logistics demand situation forecasting data;

[0051] Step S52: performing logistics peak calculation on the logistics demand situation forecast data to obtain the logistics transportation peak situation;

[0052] Step S53: predicting the resource production bottleneck of each node based on the capacity characteristics of each link, thereby obtaining resource production bottleneck prediction data;

[0053] Step S54: Dynamically render the supply chain topology interaction network based on the logistics transportation peak situation and resource production bottleneck prediction data to build a dynamic supply chain twin model.

[0054] By predicting the trend characteristics of logistics demand, the present invention can generate logistics demand situation prediction data to help the supply chain plan future logistics demand. The logistics demand situation prediction data is helpful to adjust resource allocation and transportation arrangements in advance to cope with future demand fluctuations. Calculating the logistics peak can determine the transportation peak period and demand peak in the supply chain, which is helpful to reasonably plan logistics resources and transportation capacity. Obtaining the logistics transportation peak situation is helpful to optimize transportation scheduling, improve transportation efficiency and meet sudden demand. Predicting resource production bottlenecks based on the production capacity characteristics of each link can help identify possible production bottlenecks in the supply chain. Resource production bottleneck prediction data is helpful to timely adjust resource allocation and process flow to improve production efficiency and avoid production interruptions. Based on the logistics transportation peak situation and resource production bottleneck prediction data, the supply chain topology interactive network is dynamically rendered to construct a dynamic supply chain twin model. The dynamic supply chain twin model can simulate the real-time status of supply chain operation, help decision makers understand the operation of the supply chain, optimize decisions and respond to potential risks.

[0055] Preferably, the specific steps of step S6 are:

[0056] Step S61: performing peak turnover simulation on the dynamic supply chain twin model to obtain supply chain peak operation simulation data;

[0057] Step S62: iteratively making supply scheduling decisions on the supply chain peak operation simulation data, thereby generating multiple supply scheduling strategies;

[0058] Step S63: Evaluate the scheduling efficiency of multiple supply scheduling strategies to generate a scheduling efficiency evaluation value for each supply scheduling strategy;

[0059] Step S64: extracting the optimal supply scheduling strategy based on the scheduling efficiency evaluation value of each supply scheduling strategy;

[0060] Step S65: Perform scheduling simulation visualization on the optimal supply scheduling strategy to generate supply scheduling visualization effect;

[0061] Step S66: Perform dynamic evolution mapping on the dynamic supply chain twin model according to the supply scheduling visualization effect, and construct a supply chain scheduling evolution visualization model.

[0062] The present invention can simulate the operation of the supply chain under the highest load by performing peak turnover simulation on the dynamic supply chain twin model, and obtain the supply chain peak operation simulation data. Iterative supply scheduling decision-making on the supply chain peak operation simulation data can generate multiple supply scheduling strategies for responding to different operating scenarios and demand changes. The generation of multiple supply scheduling strategies helps to improve the flexibility and adaptability of the supply chain. Scheduling efficiency evaluation of multiple supply scheduling strategies can quantify the operating effects of different strategies and generate a scheduling efficiency evaluation value for each supply scheduling strategy. Scheduling efficiency evaluation helps to determine the optimal supply chain scheduling strategy, improve the operating efficiency and resource utilization of the supply chain, and extracting the optimal supply scheduling strategy based on the scheduling efficiency evaluation value of each supply scheduling strategy can help It helps optimize the operating efficiency and cost control of the supply chain. The extraction of the optimal supply scheduling strategy helps to improve the overall performance of the supply chain and customer satisfaction. The scheduling simulation visualization of the optimal supply scheduling strategy can generate a visualization effect of the supply scheduling, which helps decision makers to intuitively understand the operation of the scheduling plan. The supply scheduling visualization effect is conducive to communication and decision-making, and improves the transparency and decision-making efficiency of supply chain management. According to the supply scheduling visualization effect, the dynamic supply chain twin model is dynamically evolved and mapped, and a supply chain scheduling evolution visualization model is constructed. The supply chain scheduling evolution visualization model helps to continuously monitor and optimize the supply chain scheduling strategy, improve the resilience and adaptability of the supply chain, and the supply chain peak operation simulation data helps to evaluate the operating performance and bottlenecks of the supply chain under extreme conditions.

[0063] In this specification, a visualization system based on a vehicle service supply chain is provided, which is used to execute the visualization method based on a vehicle service supply chain as described above, including:

[0064] The spatial topological distribution module is used to obtain the full process information of the vehicle service supply chain; identify the supply chain entities of the full process information of the vehicle service supply chain, and perform spatial topological distribution analysis to construct a topological distribution map of the supply chain entities;

[0065] A topological interaction module is used to obtain supply chain subject interaction logic data based on multiple supply chain subjects; use the supply chain subject interaction logic data to perform topological interaction logic connection on the supply chain subject topological distribution map to build a supply chain topological interaction network;

[0066] The logistics and transportation module is used to calculate the frequency of path transportation within the cycle of the entire process information of the vehicle service supply chain, and to perform the evolution of logistics demand trends to generate logistics demand trend characteristics;

[0067] The link operation module is used to analyze the operation behavior of each link in the whole process information of the vehicle service supply chain, and to evaluate the link capacity, thereby obtaining the capacity characteristics of each link;

[0068] Dynamic rendering module, which is used to dynamically render the supply chain topology interaction network based on the logistics demand trend characteristics and the capacity characteristics of each link, and build a dynamic supply chain twin model;

[0069] The scheduling simulation visualization module is used to make iterative supply scheduling decisions for the dynamic supply chain twin model, perform scheduling simulation visualization, and build a supply chain scheduling evolution visualization model.

[0070] The present invention acquires information on the entire process of the vehicle service supply chain, realizes data collection and integration of the entire supply chain, and provides a comprehensive information basis for subsequent analysis and decision-making. By identifying the supply chain subjects and analyzing the spatial topological distribution, the present invention can clearly identify the various subjects in the supply chain, reveal the association and connectivity of the various links in the supply chain, and provide a visual reference for supply chain optimization. Based on the interactive logic data of multiple supply chain subjects, a supply chain subject topological distribution map is constructed to show the association relationship and information transmission path between the various supply chain subjects, which is helpful for understanding the supply chain operation mechanism. Through topological interactive logical connections, a supply chain topological interactive network is constructed to promote collaboration and information sharing between different subjects in the supply chain, improve overall operation efficiency, calculate the path transportation frequency within the cycle and analyze the evolution of logistics demand trends, which is helpful for grasping the laws and trends of supply chain logistics transportation, providing a basis for transportation planning and resource allocation, generating logistics demand trend characteristics, and providing a reference for predicting future logistics demand and adjusting supply chain transportation plans, thereby improving logistics Transportation efficiency and responsiveness. Through the analysis of the operating behavior of each link and the assessment of the link capacity, we can gain an in-depth understanding of the operating conditions and bottlenecks of each link in the supply chain, provide a basis for optimizing production processes and resource allocation, obtain the capacity characteristics of each link, help identify production bottlenecks and optimize supply chain production efficiency, so as to improve the overall supply chain operation efficiency and quality. Based on the logistics demand trend characteristics and link capacity characteristics, the supply chain topology interaction network is dynamically rendered, and a dynamic supply chain twin model is constructed to achieve real-time simulation and analysis of the supply chain operation status. The dynamic supply chain twin model helps to predict the future operation of the supply chain, optimize resource allocation and transportation scheduling, and improve the resilience and operation efficiency of the supply chain. Through iterative supply scheduling decisions and scheduling simulation visualization, a supply chain scheduling evolution visualization model is constructed to help decision makers intuitively understand the effects and impacts of different scheduling strategies. The supply chain scheduling evolution visualization model helps to optimize the supply chain scheduling plan, improve scheduling efficiency and response speed, and strengthen visual management and decision support of the supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic diagram of the steps of a visualization method based on a vehicle service supply chain of the present invention;

[0072] Figure 2Detailed implementation flow chart of step S1;

[0073] Figure 3 Detailed implementation flow chart of step S2;

[0074] Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0075] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0076] The present application example provides a visualization method and system based on the vehicle service supply chain. The execution subject of the visualization method and system based on the vehicle service supply chain includes but is not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0077] See also Figures 1 to 4 The present invention provides a visualization method based on a vehicle service supply chain, and the visualization method based on a vehicle service supply chain comprises the following steps:

[0078] Step S1: Obtain the whole process information of the vehicle service supply chain; identify the supply chain entities in the whole process information of the vehicle service supply chain, and perform spatial topological distribution analysis to construct a topological distribution map of the supply chain entities;

[0079] Step S2: obtaining supply chain subject interaction logic data based on multiple supply chain subjects; using the supply chain subject interaction logic data to perform topological interaction logic connection on the supply chain subject topological distribution map to construct a supply chain topological interaction network;

[0080] Step S3: Calculate the frequency of path transportation within the cycle for the entire process information of the vehicle service supply chain, and perform logistics demand trend evolution to generate logistics demand trend characteristics;

[0081] Step S4: Analyze the operation behavior of each link of the whole process information of the vehicle service supply chain, and evaluate the capacity of each link to obtain the capacity characteristics of each link;

[0082] Step S5: dynamically render the supply chain topology interaction network based on the logistics demand trend characteristics and the capacity characteristics of each link, and build a dynamic supply chain twin model;

[0083] Step S6: Iterate the supply scheduling decision of the dynamic supply chain twin model, perform scheduling simulation visualization, and build a supply chain scheduling evolution visualization model.

[0084] The present invention identifies each supply chain subject in the entire process of the vehicle service supply chain, and constructs a supply chain subject topological distribution map through spatial topological distribution analysis, which can clearly show the position relationship and topological structure between the various supply chain subjects. The topological distribution map helps to fully understand the spatial position distribution of various supply chain subjects, and provides a visualization basis for subsequent interactive logic data analysis and dynamic rendering. Based on the interactive logic data of multiple supply chain subjects, the supply chain subject topological distribution map is topologically interactively connected to construct a supply chain topological interaction network, which can reveal the association and interaction mode between the various supply chain subjects. The construction of the topological interaction network helps to deeply understand the information flow and cooperative relationship between the supply chain subjects, and lays the foundation for the establishment of a dynamic supply chain twin model. By calculating the path transportation frequency within the cycle and analyzing the evolution of logistics demand trends, the logistics demand trend characteristics are generated, which can help predict the development trend and change law of logistics demand. The generation of logistics demand trend characteristics helps to optimize supply chain scheduling and resource allocation, and provides a basis for formulating The supply chain strategy provides data support, analyzes the operating behavior of each link in the entire process of the vehicle service supply chain, and evaluates the link capacity to obtain the capacity characteristics of each link, which helps to evaluate the efficiency and bottlenecks of each link in the supply chain. The evaluation of capacity characteristics can guide supply chain optimization and resource allocation, improve overall operational efficiency and responsiveness, and dynamically render the supply chain topology interaction network based on logistics demand trend characteristics and link capacity characteristics. A dynamic supply chain twin model is constructed to reflect the operating status and changes of the supply chain in real time. The establishment of a dynamic supply chain twin model helps to monitor the operation of the supply chain in real time, improve decision-making efficiency and the ability to respond to changes. Iterates supply scheduling decisions on the dynamic supply chain twin model, performs scheduling simulation visualization, and constructs a supply chain scheduling evolution visualization model, which can intuitively display the effects and evolution of supply chain scheduling decisions. The construction of a visualization model helps to optimize the supply chain scheduling plan, improve the efficiency and flexibility of supply chain operations, and provide intuitive reference and decision support for decision makers.

[0085] In the embodiment of the present invention, refer to Figure 1 , is a flow chart of steps of a visualization method based on a vehicle service supply chain of the present invention. In this example, the steps of the visualization method based on a vehicle service supply chain include:

[0086] Step S1: Obtain the whole process information of the vehicle service supply chain; identify the supply chain entities in the whole process information of the vehicle service supply chain, and perform spatial topological distribution analysis to construct a topological distribution map of the supply chain entities;

[0087] In this embodiment, the Internet of Things technology is used to deploy various types of sensor equipment, such as RFID tags, GPS locators, mechanical vibration sensors, etc., covering the production, manufacturing, logistics, transportation, maintenance services and other processes of each link of the supply chain, collecting real-time status data of various assets such as products, parts, transportation tools, maintenance equipment, etc. involved in the supply chain, as well as the identity authentication information of the participating entities. Through the big data collection platform, multi-source heterogeneous data are centrally stored and pre-processed to provide basic data support for subsequent analysis. Natural language processing technology is used to analyze the collected various text information, such as contract agreements, order records, maintenance work orders, etc., to identify the participating entities in the supply chain, including manufacturers, suppliers, logistics providers, maintenance service providers, etc., and combined with the identity authentication data, determine the role attributes and contact relationships of each entity, and build a relationship network of supply chain participants. Using methods such as cluster analysis, participants with similar attributes are classified to form an organizational structure view of the supply chain. Using geographic information system (GIS) technology, the supply chain participants identified in step 2 and their connections are mapped to geographic space. Based on GPS positioning, RFID tags and other data, the distribution of each entity in geographic space is analyzed to identify the regional layout characteristics of the supply chain. Using social network analysis methods, the connection strength and network centrality between supply chain participants are quantified to characterize the topological structure of the supply chain. Combining the analysis results of steps 2 and 3, an intuitive supply chain topological distribution map is constructed. In the map, nodes of different shapes and colors are used to represent supply chain participants, and lines are used to represent their connections. Using visualization techniques such as hierarchical layout and regional clustering, the organizational structure and regional distribution of the supply chain in geographic space are intuitively presented.

[0088] Step S2: obtaining supply chain subject interaction logic data based on multiple supply chain subjects; using the supply chain subject interaction logic data to perform topological interaction logic connection on the supply chain subject topological distribution map to construct a supply chain topological interaction network;

[0089] In this embodiment, the Internet of Things sensors and business system interfaces are used to continuously collect the interaction behavior data between the various participants in the supply chain, including various business interaction processes such as order placement, cargo transportation, and maintenance services, as well as the communication and collaboration between the participants. The collected interaction data is preprocessed, such as time alignment, outlier detection, etc., to ensure the integrity and accuracy of the data. The social network analysis method is used to conduct an in-depth analysis of the supply chain interaction data collected in step 1, identify the key interaction links and frequent contact patterns between the participants in the supply chain, and discover the collaboration mechanism between the participants. Combined with the supply chain topology distribution map constructed in step S1, the role positioning and influence of each participant in the entire supply chain network are analyzed. Based on the supply chain subject interaction logic analyzed in step 2, it is mapped to the supply chain topology distribution map constructed in step S1. The supply chain is constructed using the graph network modeling method. The participating entities are regarded as network nodes, and the interaction relationships between them are modeled as network edges. The importance and interaction strength of each entity in the network are represented by visual encodings such as node size and line thickness. An interactive supply chain topology interactive network visualization application is constructed using WebGL-based visualization technology. The overall picture of the supply chain network is presented in 3D space, and interactive functions such as zooming, panning, and rotating are provided for users to explore in depth. Combined with visualization methods such as heat maps and pop-up prompts, the attribute information of each node and the interaction strength between edges are intuitively displayed. Network analysis methods such as centrality analysis and community detection are used to deeply explore the key nodes and key links in the supply chain network. Combined with the geographic spatial distribution information in step S1, optimization opportunities for the regional layout and resource allocation of the supply chain are discovered, providing visualization insights for supply chain optimization decisions, such as node importance ranking, critical path analysis, and weakness diagnosis.

[0090] Step S3: Calculate the frequency of path transportation within the cycle for the entire process information of the vehicle service supply chain, and perform logistics demand trend evolution to generate logistics demand trend characteristics;

[0091] In this embodiment, IoT sensor devices are used to continuously collect logistics and transportation data from all links of the supply chain, including vehicle location tracking, cargo loading and unloading time, transportation routes, etc., from which key indicators such as transportation frequency, transportation volume, and transportation time of each logistics route are obtained. These indicator data are serialized according to the time dimension to lay the foundation for subsequent trend analysis. Time series analysis methods, such as ARIMA model and Fourier transform, are used to conduct in-depth analysis of the logistics indicator data collected in step 1 to identify the periodic change rules of each logistics route, such as the fluctuation characteristics of transportation volume during holidays. By decomposing factors such as trends, seasonality, and randomness, the dynamic change trend of logistics demand is accurately portrayed. Based on the time series characteristics of logistics demand analyzed in step 2, machine learning algorithms are used to predict demand, and linear regression, Neural network and other methods are used to establish prediction models, train and verify the models, and feed back the prediction results to the supply chain management system to support logistics optimization decisions such as dynamic path planning and reasonable allocation of vehicles. Visualization libraries such as Echarts and D3.js are used to intuitively display the results of logistics demand analysis in the form of charts, including time series trend charts of logistics paths, heat maps of transportation volume between nodes, comparative analysis of prediction results, etc., providing an interactive visualization interface for supply chain managers to deeply observe and analyze changes in logistics demand. The logistics demand prediction results in step 3 are associated with the supply chain topology interaction network constructed in step S2. Using simulation and other methods, the impact of different supply chain reconstruction schemes on logistics demand is evaluated, providing a basis for supply chain optimization, such as increasing transportation capacity, adjusting paths, and restructuring network structure.

[0092] Step S4: Analyze the operation behavior of each link of the whole process information of the vehicle service supply chain, and evaluate the capacity of each link to obtain the capacity characteristics of each link;

[0093] In this embodiment, IoT sensor devices are continuously used to collect real-time operation data of each link of the supply chain, including product output and equipment operation status in the manufacturing link, work order processing volume and personnel utilization rate in the maintenance service link, etc. These operation index data are recorded and stored according to the time dimension to provide a basis for subsequent capacity analysis. Time series analysis, anomaly detection and other methods are applied to deeply analyze the operation data of each link collected in step 1, identify the typical operation mode and periodic change law of each link, and find out the key factors affecting the operation of the link. According to the operation characteristics of the link, a corresponding simulation model is constructed to quantify the actual operation capacity of the link. On the basis of the simulation model constructed in step 2, Monte Carlo simulation and other methods are used to dynamically evaluate the capacity of each link. Based on the equipment capacity, human resources and process flow of the link, the production capacity of each link is dynamically evaluated. The system takes into account factors such as production process, simulates the product output under different working conditions, outputs the capacity limit, average capacity, fluctuation range and other indicators of each link to reflect its capacity characteristics, uses dashboards, pie charts and other visual charts to intuitively display the capacity indicators of each link obtained in step 3, provides functions such as capacity trend analysis and bottleneck diagnosis, and facilitates supply chain managers to quickly understand the capacity status. Combined with the geographic information system, the capacity level of each link is marked on the supply chain topology distribution map to intuitively display the overall situation. The capacity characteristics evaluated in step 3 are correlated with the logistics demand trend predicted in step S3 to identify the capacity bottleneck link in the supply chain, and simulates the plan to adjust the capacity, such as increasing equipment investment, optimizing process flow, etc., to provide a basis for supply chain collaborative optimization, improve the overall product supply capacity, and meet the differentiated needs of different regions.

[0094] Step S5: dynamically render the supply chain topology interaction network based on the logistics demand trend characteristics and the capacity characteristics of each link, and build a dynamic supply chain twin model;

[0095] In this embodiment, combined with the analysis results obtained in the aforementioned steps S2, S3, and S4, the topological distribution, interaction logic, logistics demand trends, and production capacity characteristics of the supply chain participants are integrated to establish a comprehensive supply chain operation data model to provide basic support for subsequent dynamic simulation. Based on the supply chain data integrated in step 1, a dynamic supply chain simulation model is built using the discrete event simulation method. In the model, the supply chain participants and their interaction relationships are abstracted as network nodes and edges, and characteristic attributes such as production capacity and logistics are assigned to each node. According to the predicted logistics demand trend, the dynamic operation process of the supply chain in the time dimension is simulated. The supply chain dynamic simulation model constructed in step 2 is associated with the visualized supply chain topology interaction network established in step S2, and a 3D display such as WebGL is used. Graphics rendering technology visualizes the dynamic changes of the simulation model, and displays the capacity status and logistics dynamics of each link in the supply chain through visual coding such as node size, color, and line thickness. It provides an interactive simulation analysis interface for supply chain managers and supports dynamic adjustment of model parameters and solution verification. For example, it can simulate the adjustment of the capacity of a certain link, optimize the logistics path, etc., and observe the impact on the overall supply chain. Through comparative analysis of different optimization solutions, it provides decision support for supply chain reconstruction. Using the Internet of Things technology, the real-time monitoring data collected during the actual operation of the supply chain is synchronized with the dynamic twin model constructed in step 3. Based on the simulation results of the twin model, it gives supply chain optimization suggestions, such as capacity allocation, logistics path adjustment, etc., and feeds the optimization solution back to the actual supply chain management system to achieve closed-loop intelligent optimization.

[0096] Step S6: Iterate the supply scheduling decision of the dynamic supply chain twin model, perform scheduling simulation visualization, and build a supply chain scheduling evolution visualization model.

[0097] In this embodiment, in combination with the dynamic supply chain twin model constructed in step S5, a supply scheduling optimization algorithm is further introduced. According to the logistics demand trend, production capacity characteristics, etc., a dynamic supply scheduling strategy is formulated, such as order allocation, inventory management, and transportation capacity allocation. Intelligent optimization methods such as genetic algorithms and particle swarm optimization are used to solve the optimal decision-making plan for supply chain scheduling. The supply scheduling strategy optimized in step 1 is fed back to the dynamic twin model in step S5 for simulation. The supply chain operation process of different scheduling schemes in the time dimension is simulated, and the changes in key performance indicators such as average delivery time, total transportation cost, and service level are observed to quantify the advantages and disadvantages of the supply scheduling scheme. Based on the supply chain simulation results in step 2, an interactive supply scheduling evolution model is constructed using visualization technologies such as WebGL to display the various supply chain participants and their The logistics operation status intuitively presents the scheduling process through animation effects, and provides interactive functions such as zooming and perspective switching, which is convenient for managers to deeply observe and analyze the scheduling plan. The supply scheduling evolution visualization model constructed in step 3 is embedded in the decision support interface of the supply chain management system, providing scheduling strategy comparison analysis, bottleneck diagnosis and other functions, supporting managers to make dynamic adjustments according to actual conditions, and synchronizing the optimized and adjusted supply scheduling plan to the dynamic twin model in step S5 in real time to form a closed-loop optimization. Using the Internet of Things technology, the key indicator data of the supply chain in actual operation, such as inventory levels, cargo turnover time, etc., are continuously monitored, and the monitoring data is fed back to the dynamic twin model constructed in step S5 in real time, which is continuously calibrated and updated. Based on the latest twin model status, optimization suggestions for supply scheduling are given to guide the dynamic management of the actual supply chain.

[0098] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0099] Step S11: Obtaining the whole process information of the vehicle service supply chain;

[0100] Step S12: identifying the supply chain entities of the entire process information of the vehicle service supply chain to obtain multiple supply chain entities;

[0101] Step S13: locating the physical space positions of multiple supply chain entities to obtain the physical space position coordinates of each supply chain entity;

[0102] Step S14: performing spatial topological distribution analysis on the physical space location coordinates of each supply chain entity to generate supply chain entity topological distribution data;

[0103] Step S15: Perform topological structure fitting on the supply chain entity topological structure data to construct a supply chain entity topological distribution map.

[0104] In this embodiment, after obtaining authorization from the user and the platform, relevant information about vehicle services, including supplier information, transportation routes, vehicle status, customer needs, etc., is collected from multiple channels (such as databases, APIs, sensors, manual input, etc.), and the collected information is organized into a unified format, such as using JSON, CSV or database tables, for subsequent processing. The collected information is cleaned to remove redundant data and outliers to ensure data quality, and the concepts of supply chain entities are determined, including suppliers, transporters, customers, etc., and identification standards are formulated. The cleaned data is analyzed using data mining algorithms (such as cluster analysis or classification algorithms) to identify different supply chain entities, assign unique identifiers to the identified entities, and record relevant attributes (such as name, contact information, etc.) to obtain each supply chain entity. The physical location data of the supply chain entities may be obtained through GPS data, address information or sensor data. If the address information is obtained, use the geocoding API (such as Google Maps API) to convert the address into geographic coordinates (longitude and latitude). The physical spatial location coordinates of each entity are recorded in the database. The physical spatial location coordinates of all supply chain entities are extracted from the database to prepare for topological analysis. Use geographic information system (GIS) software or open source tools (such as QGIS, ArcGIS) to perform spatial topological distribution analysis. Perform spatial topological analysis to identify the relative position relationship and distribution pattern between supply chain entities, and obtain the topological distribution data of the supply chain entities, including distance, connectivity and other information. Extract the topological structure data of the supply chain entities from the database to prepare for fitting analysis.

[0105] Select a suitable topological structure fitting method, such as the least squares method, Markov chain model or graph theory model, apply the selected method to fit the topological structure data, and construct a topological distribution map of the supply chain entities.

[0106] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0107] Step S21: Analyze the business types of multiple supply chain entities to obtain the business type of each supply chain entity;

[0108] Step S22: performing type label mapping on multiple supply chain entities based on the business type of each supply chain entity to obtain entity label nodes;

[0109] Step S23: Analyze the upstream and downstream business flow of the subject tag node to obtain node business flow data;

[0110] Step S24: Performing an inter-subject interaction logic analysis on the node business flow data to obtain supply chain subject interaction logic data;

[0111] Step S25: Use the supply chain subject interaction logic data to perform topological interaction logic connections on the supply chain subject topological distribution map to construct a supply chain topological interaction network.

[0112] In this embodiment, the business activity information of each supply chain entity is collected, including product classification, service type, market positioning, etc., the classification standard of the business type is determined, such as manufacturing, logistics, retail, service, etc., and corresponding classification rules are formulated. The business data of each entity is analyzed using natural language processing (NLP) technology or machine learning classification algorithms (such as decision trees, support vector machines) to identify its business type, and label mapping rules are defined according to the business type to map each business type to a specific label (such as "manufacturer", "logistics company", etc.). The business type data of each supply chain entity is traversed and the corresponding label is assigned to it according to the set mapping rules. The standard of upstream and downstream business flow is determined, such as supplier to manufacturer, manufacturer to customer, etc., and the data of transactions, cooperation or information flow between the entities is collected to identify their upstream and downstream relationships. The flow analysis of the entity label nodes is performed using network analysis tools (such as graph theory, social network analysis), the upstream and downstream business flow is determined, the analysis results are recorded, and the node Business flow data, including flow relationship and flow intensity, extract node business flow data from the database, prepare for interactive logic analysis, select appropriate logic analysis methods, such as causal analysis, state transition model or complex network analysis, apply the selected method to analyze the node business flow data, identify the interactive logic relationship between subjects, record the interactive logic data between subjects, including interaction mode, frequency and impact, etc., integrate the topological distribution data of supply chain subjects with the interactive logic data between subjects to form a comprehensive data set, select appropriate topological connection strategies (such as weighted connections, undirected graphs, directed graphs) to construct an interactive logic network, logically connect the supply chain subjects in the topological map according to the interactive logic data to form a supply chain topological interactive network, use network visualization tools (such as Gephi, Cytoscape) to display the supply chain topological interactive network for easy analysis and understanding, and store the constructed supply chain topological interactive network data in a database or file for subsequent analysis and decision support.

[0113] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0114] Step S31: Identify the logistics transportation paths for the entire process information of the vehicle service supply chain and extract all the logistics transportation paths;

[0115] Step S32: calculating the path transportation frequency within the cycle for all logistics transportation paths, and generating the path transportation frequency within the cycle;

[0116] Step S33: performing time-series frequency change analysis on the path transportation frequency within the cycle to generate time-series change data of the logistics transportation path;

[0117] Step S34: performing periodic logistics demand analysis on the time series change data of the logistics transportation path, thereby obtaining logistics demand data;

[0118] Step S35: Calculate the path operation efficiency of all logistics transportation paths to generate logistics transportation path utilization;

[0119] Step S36: Evolving the logistics demand trend of the logistics demand data according to the logistics transportation path utilization rate to generate logistics demand trend characteristics.

[0120] In this embodiment, the whole process information of the vehicle service supply chain is collected, including the place of shipment, destination, mode of transportation, transportation time, etc., and the identification standard of the logistics transportation path is determined, such as the complete route from the place of shipment to the destination. Data mining technology (such as trajectory analysis or clustering algorithm) is used to extract all valid logistics transportation paths from the collected information, and the time period of analysis (such as daily, weekly or monthly) is determined. A time window is set for the calculation of the path transportation frequency, and a suitable calculation method is selected, such as counting method or time series analysis, to calculate the transportation frequency of each path within the set period. All extracted logistics transportation paths are traversed, and the number of transportations of each path within the set period is counted. The path transportation frequency data is extracted from the database, and time series change analysis is prepared. A suitable time series analysis method is selected, such as moving average, exponential smoothing or frequency change rate calculation, and the selected method is applied to analyze the path transportation frequency data to identify the frequency. The changing trend and periodicity of the rate, select a suitable logistics demand analysis method, such as regression analysis, time series prediction model or seasonal analysis, apply the selected method to analyze the time series change data, identify the periodic logistics demand characteristics, and determine the calculation indicators of the path operation efficiency, such as transportation time, cost, load factor, etc., collect the operation data related to each logistics transportation path, including the actual transportation time, cost and load capacity, use the set efficiency indicators to calculate the operation efficiency of each logistics transportation path, and obtain the utilization rate of the logistics transportation path, integrate the logistics demand data with the path utilization rate data to form a comprehensive data set, select a suitable trend analysis model, such as linear regression analysis, time series prediction model or machine learning algorithm, apply the selected method to analyze the integrated data, identify the evolution trend characteristics of logistics demand, record the analysis results, and form logistics demand trend characteristic data, including predicted demand and changing trends.

[0121] In this embodiment, the specific steps of step S35 are:

[0122] Based on multiple supply chain entities, all logistics transportation paths are identified through the entities passing through them, and the node sequences passing through the paths are marked;

[0123] Extract all the logistics flow nodes in the path flow node sequence;

[0124] Calculating the logistics resource load rate for all the nodes through which the logistics flows, and obtaining the logistics resource load rate of each node through which the logistics flows;

[0125] Calculate the residence time of each node for all the logistics passing through, and generate the residence time of each node passing through;

[0126] The path operation efficiency is calculated for the logistics resource load rate of each node flowing through and the length of stay of each node flowing through to generate the logistics transportation path utilization rate.

[0127] In this embodiment, information on all logistics transportation paths is collected, including the starting point, the end point, and each node along the way (such as a warehouse, a sorting center, a customer, etc.). The criteria for determining the subject (node) through which the path flows usually include all supply chain entities involved in the transportation. Using graph analysis or network analysis methods, all node sequences flowing through are extracted from the path data. The identified path flow nodes are recorded in sequence to form a path flow node sequence. Traverse the path flow node sequence to extract all unique logistics flow nodes. The extracted nodes are deduplicated to form a unique set of logistics flow nodes. All identified logistics flow nodes are stored in a database or data structure for subsequent analysis and calculation. Relevant data on logistics resources (such as goods, vehicles, etc.) flowing through each node are collected, including maximum load and actual load. Load rate = (actual load / maximum load) × 100% All nodes flowing through are calculated one by one to obtain the logistics resource load rate of each node. The residence time data for each node flowing through is collected, which can usually be obtained through a monitoring system or historical data. The dwell time can be the actual dwell time of each node, and the calculation method may include the average or maximum value of the total dwell time. Calculate all nodes that flow through one by one to obtain the dwell time of each node. Determine the calculation index of the path operation efficiency, which may include a comprehensive consideration of load factor and dwell time. Operation efficiency = (load factor / dwell time) or other comprehensive indicators. Calculate all nodes that flow through one by one to obtain the path operation efficiency of each node. Store the calculation results in the database to form a utilization data set of the logistics transportation path.

[0128] In this embodiment, step S4 includes the following steps:

[0129] Step S41: Obtain supply chain historical operation log;

[0130] Step S42: Calculate the production order turnaround time for the supply chain historical operation log, and extract multiple production order turnaround times;

[0131] Step S43: Analyze the operation behavior of each link of the whole process information of the vehicle service supply chain to obtain the operation behavior characteristics of multiple links;

[0132] Step S44: Calculate the full-link operation cycle of the multiple link operation behavior characteristics according to the multiple production order turnover times, so as to obtain the link operation cycle of each production order;

[0133] Step S45: Evaluate the link capacity of each link operation cycle of each production order to obtain the capacity characteristics of each link.

[0134] In this embodiment, the source of log data generated by each link in the supply chain is confirmed, such as the production system, transportation management system, warehouse management system, etc., and historical operation logs are extracted from the relevant systems to ensure that the acquired data includes timestamps, order information, link status, operation records, etc. Time data related to production orders are extracted from the historical operation logs, including order time, production start time, completion time, etc. Turnaround time = completion time - order time, and each production order is calculated to obtain its turnaround time. Operation data of each link is extracted from the entire process information of the supply chain, including operation records of production, transportation, warehousing, etc., and analysis standards for the operation behavior characteristics of each link are determined, such as average processing time, number of delays, failure rate, etc. Statistical analysis methods (such as descriptive statistics and cluster analysis) are used to analyze the operation characteristics of each link. Analyze the operation behavior, extract the operation behavior characteristics of multiple links, integrate the turnaround time of multiple production orders with the operation behavior characteristics of each link to form a comprehensive data set, determine the calculation method of the link operation cycle according to the turnaround time and the processing characteristics of each link, which may include weighted average or sum calculation, calculate all production orders one by one, and obtain the link operation cycle of each production order, extract the link operation cycle data of each production order from the database, prepare for capacity evaluation, determine the indicators of link capacity evaluation, such as the output capacity, maximum processing capacity, bottleneck analysis, etc. of each link, use the capacity evaluation model (such as linear programming, efficiency analysis) to evaluate the operation cycle of each link, obtain the capacity characteristics of each link, store the evaluation results in the database, and form a link capacity characteristic data set.

[0135] In this embodiment, the specific steps of step S5 are:

[0136] Step S51: performing logistics demand situation forecasting on the logistics demand trend characteristics to generate logistics demand situation forecasting data;

[0137] Step S52: performing logistics peak calculation on the logistics demand situation forecast data to obtain the logistics transportation peak situation;

[0138] Step S53: predicting the resource production bottleneck of each node based on the capacity characteristics of each link, thereby obtaining resource production bottleneck prediction data;

[0139] Step S54: Dynamically render the supply chain topology interaction network based on the logistics transportation peak situation and resource production bottleneck prediction data to build a dynamic supply chain twin model.

[0140] In this embodiment, previously generated logistics demand trend characteristic data are sorted out, including historical demand data, seasonal changes, promotional activities and other factors, and appropriate forecasting models are selected, such as time series analysis (ARIMA, seasonal decomposition), machine learning models (random forest, LSTM), etc. The selected model is used to analyze the logistics demand trend characteristics, generate logistics demand situation forecast data for a period of time in the future, extract the logistics demand situation forecast data from the database, sort it into a format that is easy to analyze, determine the calculation standard of the peak, such as based on the standard deviation of the demand or set a threshold, analyze the forecast data, identify the peak demand period and peak intensity, store the calculated logistics transportation peak situation in the database for subsequent use, and extract the logistics demand situation forecast data from the database. The capacity characteristic data of each link is prepared for bottleneck prediction and the identification criteria of resource production bottlenecks are determined, such as capacity utilization exceeding a certain threshold, insufficient output, delay frequency, etc. Data analysis methods (such as sensitivity analysis and linear programming) are used to analyze each node to identify potential resource production bottlenecks. The logistics and transportation peak situation data and resource production bottleneck prediction data are integrated to form a comprehensive data set. Appropriate visualization and dynamic rendering tools (such as D3.js, Cytoscape, Unity3D) are selected to build a dynamic model. The integrated data is used to dynamically render the supply chain topology interactive network to show the impact of logistics and transportation peaks and potential bottlenecks. The operation status of the supply chain is displayed through a dynamic model to facilitate analysis and decision support.

[0141] In this embodiment, the specific steps of step S6 are:

[0142] Step S61: performing peak turnover simulation on the dynamic supply chain twin model to obtain supply chain peak operation simulation data;

[0143] Step S62: iteratively making supply scheduling decisions on the supply chain peak operation simulation data, thereby generating multiple supply scheduling strategies;

[0144] Step S63: Evaluate the scheduling efficiency of multiple supply scheduling strategies to generate a scheduling efficiency evaluation value for each supply scheduling strategy;

[0145] Step S64: extracting the optimal supply scheduling strategy based on the scheduling efficiency evaluation value of each supply scheduling strategy;

[0146] Step S65: Perform scheduling simulation visualization on the optimal supply scheduling strategy to generate supply scheduling visualization effect;

[0147] Step S66: Perform dynamic evolution mapping on the dynamic supply chain twin model according to the supply scheduling visualization effect, and construct a supply chain scheduling evolution visualization model.

[0148] In this embodiment, it is ensured that the dynamic supply chain twin model has been constructed and all relevant data (such as demand forecasts, resource bottlenecks, etc.) have been integrated. The conditions and parameters for peak operation are determined, including demand during peak hours, available resources, production and transportation capacity, etc. Simulation tools (such as AnyLogic, Simul8) are used to simulate the peak turnover of the supply chain model, observe the operation performance under high demand conditions, collect and record data during the simulation process, including order processing time, resource utilization, transportation efficiency, etc., to form supply chain peak operation simulation data, organize the supply chain peak operation simulation data, prepare for scheduling decision analysis, adopt appropriate scheduling decision models, such as heuristic algorithms, linear programming, genetic algorithms, etc., generate multiple supply scheduling strategies based on the simulation data through an iterative process, evaluate resource allocation and scheduling effects under different strategies, record the generated multiple supply scheduling strategies and their related parameters in a database, determine indicators for scheduling efficiency evaluation, such as delivery punctuality, resource utilization, cost-effectiveness, etc., extract all generated supply scheduling strategies and their related data from the database, prepare for efficiency evaluation, and use statistical analysis methods (such as adding The efficiency of each scheduling strategy is evaluated by weight scoring method, fuzzy comprehensive evaluation method, and scheduling efficiency evaluation value of each supply scheduling strategy is generated. The scheduling efficiency evaluation values ​​of all scheduling strategies are extracted from the database, and the optimal strategy is extracted. The identification criteria of the optimal strategy are determined, which is usually the strategy with the highest scheduling efficiency evaluation value. The supply scheduling strategy with the highest scheduling efficiency is identified through sorting or screening methods, marked as the optimal strategy and recorded. The relevant data of the optimal supply scheduling strategy, including resource allocation, scheduling schedule, etc., are sorted out. Suitable visualization tools (such as Tableau, PowerBI, D3.js) are selected for scheduling simulation visualization. The optimal scheduling strategy data is input into the visualization tool to generate a visualization effect of supply scheduling, showing the scheduling process, resource utilization and efficiency. The supply scheduling visualization effect is integrated with the data of the dynamic supply chain twin model, and dynamic evolution mapping is prepared. Suitable mapping tools or algorithms (such as dynamic system modeling and simulation visualization) are selected for dynamic evolution mapping. According to the visualization effect and related data, the dynamic supply chain twin model is adjusted and improved to build a visualization model of supply chain scheduling evolution.

[0149] In this embodiment, a visualization system based on a vehicle service supply chain is provided, which is used to execute the visualization method based on a vehicle service supply chain as described above, including:

[0150] The spatial topological distribution module is used to obtain the full process information of the vehicle service supply chain; identify the supply chain entities of the full process information of the vehicle service supply chain, and perform spatial topological distribution analysis to construct a topological distribution map of the supply chain entities;

[0151] A topological interaction module is used to obtain supply chain subject interaction logic data based on multiple supply chain subjects; use the supply chain subject interaction logic data to perform topological interaction logic connection on the supply chain subject topological distribution map to build a supply chain topological interaction network;

[0152] The logistics and transportation module is used to calculate the frequency of path transportation within the cycle of the entire process information of the vehicle service supply chain, and to perform the evolution of logistics demand trends to generate logistics demand trend characteristics;

[0153] The link operation module is used to analyze the operation behavior of each link in the whole process information of the vehicle service supply chain, and to evaluate the link capacity, thereby obtaining the capacity characteristics of each link;

[0154] Dynamic rendering module, which is used to dynamically render the supply chain topology interaction network based on the logistics demand trend characteristics and the capacity characteristics of each link, and build a dynamic supply chain twin model;

[0155] The scheduling simulation visualization module is used to make iterative supply scheduling decisions for the dynamic supply chain twin model, perform scheduling simulation visualization, and build a supply chain scheduling evolution visualization model.

[0156] The present invention acquires information on the entire process of the vehicle service supply chain, realizes data collection and integration of the entire supply chain, and provides a comprehensive information basis for subsequent analysis and decision-making. By identifying the supply chain subjects and analyzing the spatial topological distribution, the present invention can clearly identify the various subjects in the supply chain, reveal the association and connectivity of the various links in the supply chain, and provide a visual reference for supply chain optimization. Based on the interactive logic data of multiple supply chain subjects, a supply chain subject topological distribution map is constructed to show the association relationship and information transmission path between the various supply chain subjects, which is helpful for understanding the supply chain operation mechanism. Through topological interactive logical connections, a supply chain topological interactive network is constructed to promote collaboration and information sharing between different subjects in the supply chain, improve overall operation efficiency, calculate the path transportation frequency within the cycle and analyze the evolution of logistics demand trends, which is helpful for grasping the laws and trends of supply chain logistics transportation, providing a basis for transportation planning and resource allocation, generating logistics demand trend characteristics, and providing a reference for predicting future logistics demand and adjusting supply chain transportation plans, thereby improving logistics Transportation efficiency and responsiveness. Through the analysis of the operating behavior of each link and the assessment of the link capacity, we can gain an in-depth understanding of the operating conditions and bottlenecks of each link in the supply chain, provide a basis for optimizing production processes and resource allocation, obtain the capacity characteristics of each link, help identify production bottlenecks and optimize supply chain production efficiency, so as to improve the overall supply chain operation efficiency and quality. Based on the logistics demand trend characteristics and link capacity characteristics, the supply chain topology interaction network is dynamically rendered, and a dynamic supply chain twin model is constructed to achieve real-time simulation and analysis of the supply chain operation status. The dynamic supply chain twin model helps to predict the future operation of the supply chain, optimize resource allocation and transportation scheduling, and improve the resilience and operation efficiency of the supply chain. Through iterative supply scheduling decisions and scheduling simulation visualization, a supply chain scheduling evolution visualization model is constructed to help decision makers intuitively understand the effects and impacts of different scheduling strategies. The supply chain scheduling evolution visualization model helps to optimize the supply chain scheduling plan, improve scheduling efficiency and response speed, and strengthen visual management and decision support of the supply chain.

[0157] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0158] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A visualization method based on the vehicle service supply chain, characterized in that: The following steps are involved: Step S1: Obtain the whole process information of the vehicle service supply chain; identify the supply chain entities in the whole process information of the vehicle service supply chain, and perform spatial topological distribution analysis to construct a topological distribution map of the supply chain entities; Step S2: obtaining supply chain subject interaction logic data based on multiple supply chain subjects; using the supply chain subject interaction logic data to perform topological interaction logic connection on the supply chain subject topological distribution map to construct a supply chain topological interaction network; Step S3: Calculate the frequency of path transportation within the cycle for the entire process information of the vehicle service supply chain, and perform the evolution of the logistics demand trend to generate the logistics demand trend characteristics; the specific steps of step S3 are: Step S31: Identify the logistics transportation paths for the entire process information of the vehicle service supply chain and extract all the logistics transportation paths; Step S32: calculating the path transportation frequency within the cycle for all logistics transportation paths, and generating the path transportation frequency within the cycle; Step S33: performing time-series frequency change analysis on the path transportation frequency within the cycle to generate time-series change data of the logistics transportation path; Step S34: performing periodic logistics demand analysis on the time series change data of the logistics transportation path, thereby obtaining logistics demand data; Step S35: Calculate the path operation efficiency of all logistics transportation paths to generate logistics transportation path utilization; Step S36: performing logistics demand trend evolution on the logistics demand data according to the logistics transportation path utilization rate to generate logistics demand trend characteristics; Step S4: Analyze the operation behavior of each link of the whole process information of the vehicle service supply chain, and evaluate the capacity of each link to obtain the capacity characteristics of each link; Step S5: Based on the logistics demand trend characteristics and the production capacity characteristics of each link, the supply chain topology interaction network is dynamically rendered to build a dynamic supply chain twin model; the specific steps of step S5 are: Step S51: performing logistics demand situation forecasting on the logistics demand trend characteristics to generate logistics demand situation forecasting data; Step S52: performing logistics peak calculation on the logistics demand situation forecast data to obtain the logistics transportation peak situation; Step S53: predicting the resource production bottleneck of each node based on the capacity characteristics of each link, thereby obtaining resource production bottleneck prediction data; Step S54: dynamically rendering the supply chain topology interaction network based on the logistics transportation peak situation and resource production bottleneck prediction data to build a dynamic supply chain twin model; Step S6: Iterate the supply scheduling decision of the dynamic supply chain twin model, perform scheduling simulation visualization, and build a supply chain scheduling evolution visualization model; the specific steps of step S6 are: Step S61: performing peak turnover simulation on the dynamic supply chain twin model to obtain supply chain peak operation simulation data; Step S62: iteratively making supply scheduling decisions on the supply chain peak operation simulation data, thereby generating multiple supply scheduling strategies; Step S63: Evaluate the scheduling efficiency of multiple supply scheduling strategies to generate a scheduling efficiency evaluation value for each supply scheduling strategy; Step S64: extracting the optimal supply scheduling strategy based on the scheduling efficiency evaluation value of each supply scheduling strategy; Step S65: Perform scheduling simulation visualization on the optimal supply scheduling strategy to generate supply scheduling visualization effect; Step S66: Perform dynamic evolution mapping on the dynamic supply chain twin model according to the supply scheduling visualization effect, and construct a supply chain scheduling evolution visualization model.

2. The visualization method based on the vehicle service supply chain according to claim 1 is characterized in that: The specific steps of step S1 are: Step S11: Obtaining the whole process information of the vehicle service supply chain; Step S12: identifying the supply chain entities of the entire process information of the vehicle service supply chain to obtain multiple supply chain entities; Step S13: locating the physical space positions of multiple supply chain entities to obtain the physical space position coordinates of each supply chain entity; Step S14: performing spatial topological distribution analysis on the physical space location coordinates of each supply chain entity to generate supply chain entity topological distribution data; Step S15: Perform topological structure fitting on the supply chain entity topological structure data to construct a supply chain entity topological distribution map.

3. The visualization method based on the vehicle service supply chain according to claim 1 is characterized in that: The specific steps of step S2 are: Step S21: Analyze the business types of multiple supply chain entities to obtain the business type of each supply chain entity; Step S22: performing type label mapping on multiple supply chain entities based on the business type of each supply chain entity to obtain entity label nodes; Step S23: Analyze the upstream and downstream business flow of the subject tag node to obtain node business flow data; Step S24: Performing an inter-subject interaction logic analysis on the node business flow data to obtain supply chain subject interaction logic data; Step S25: Use the supply chain subject interaction logic data to perform topological interaction logic connections on the supply chain subject topological distribution map to construct a supply chain topological interaction network.

4. The visualization method based on the vehicle service supply chain according to claim 1 is characterized in that: The specific steps of step S35 are: Based on multiple supply chain entities, all logistics transportation paths are identified through the entities passing through them, and the node sequences passing through the paths are marked; Extract all the logistics flow nodes in the path flow node sequence; Calculating the logistics resource load rate for all the nodes through which the logistics flows, and obtaining the logistics resource load rate of each node through which the logistics flows; Calculate the residence time of each node for all the logistics passing through, and generate the residence time of each node passing through; The path operation efficiency is calculated for the logistics resource load rate of each node flowing through and the length of stay of each node flowing through to generate the logistics transportation path utilization rate.

5. The visualization method based on the vehicle service supply chain according to claim 1 is characterized in that: The specific steps of step S4 are: Step S41: Obtain supply chain historical operation log; Step S42: Calculate the production order turnaround time for the supply chain historical operation log, and extract multiple production order turnaround times; Step S43: Analyze the operation behavior of each link of the whole process information of the vehicle service supply chain to obtain the operation behavior characteristics of multiple links; Step S44: Calculate the full-link operation cycle of the multiple link operation behavior characteristics according to the multiple production order turnover times, so as to obtain the link operation cycle of each production order; Step S45: Evaluate the link capacity of each link operation cycle of each production order to obtain the capacity characteristics of each link.

6. A visualization system based on the vehicle service supply chain, characterized in that: The method for executing the visualization method based on the vehicle service supply chain as claimed in claim 1 comprises: The spatial topological distribution module is used to obtain the full process information of the vehicle service supply chain; identify the supply chain entities of the full process information of the vehicle service supply chain, and perform spatial topological distribution analysis to construct a topological distribution map of the supply chain entities; A topological interaction module is used to obtain supply chain subject interaction logic data based on multiple supply chain subjects; use the supply chain subject interaction logic data to perform topological interaction logic connection on the supply chain subject topological distribution map to build a supply chain topological interaction network; The logistics and transportation module is used to calculate the frequency of path transportation within the cycle of the entire process information of the vehicle service supply chain, and to perform the evolution of logistics demand trends to generate logistics demand trend characteristics; The link operation module is used to analyze the operation behavior of each link in the whole process information of the vehicle service supply chain, and to evaluate the link capacity, thereby obtaining the capacity characteristics of each link; Dynamic rendering module, which is used to dynamically render the supply chain topology interaction network based on the logistics demand trend characteristics and the capacity characteristics of each link, and build a dynamic supply chain twin model; The scheduling simulation visualization module is used to make iterative supply scheduling decisions for the dynamic supply chain twin model, perform scheduling simulation visualization, and build a supply chain scheduling evolution visualization model.

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