Order Assignment Optimization Method, Device and Storage Medium Based on Automobile Service Supply Chain

Abstract

The present invention relates to the technical field of supply chain order dispatching optimization, and particularly to an order dispatching optimization method, device and storage medium based on an automotive service supply chain. The method includes the following steps: obtaining multi-source automotive service supply chain information and user real-time order data based on a supply chain platform; identifying supply chain service manufacturers and fitting the dynamic node spatial distribution for the multi-source automotive service supply chain information to construct a supply chain service node distribution map; analyzing the service requirements of user real-time order data and conducting real-time service requirement load analysis to obtain the real-time service requirement load characteristics of the supply chain; evolving the spatio-temporal demand distribution of the real-time service requirement load characteristics of the supply chain to generate dynamic service demand prediction data; and performing optimal demand supply matching on the supply chain service node distribution map based on the dynamic service demand prediction data to construct a dynamic service demand allocation strategy. The present invention realizes efficient and accurate supply chain order dispatching optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of order assignment optimization in the supply chain, and particularly to an order assignment optimization method, device, and storage medium based on the automotive service supply chain. Background Art

[0002] In the modern automotive service industry, with the diversification of consumers' automotive repair and maintenance needs and the improvement of service quality, the management of the automotive service supply chain has become increasingly complex. The automotive service supply chain usually includes multiple links, such as automotive parts supply, repair services, logistics distribution, customer appointments, etc. Each link may be affected by different factors, such as order volume fluctuations, service personnel's skill levels, changes in parts inventory, traffic conditions, etc. These factors have an important impact on the timeliness and quality of automotive services.

[0003] In the traditional automotive service supply chain, the order assignment system usually relies on manual scheduling and manual matching. The order assignment process is both cumbersome and error-prone, making it difficult to achieve efficient resource utilization and precise service matching. Especially when faced with a large number of service demands and a dynamically changing market environment, manual order assignment often struggles to quickly respond to changes in customer needs and service resource allocation, resulting in low service efficiency and a decline in customer satisfaction.

[0004] In addition, with the development of the automotive industry towards intelligence and digitization, more and more automotive service enterprises have started to use information management systems. However, there are still many problems with traditional order assignment systems, such as mismatches between service demands and actual resources, the inability to promptly reflect the timeliness of customer needs, and scheduling lags during the vehicle repair service process. These problems not only increase operating costs but also affect service quality and customer experience. Therefore, with the continuous development of technologies such as big data, artificial intelligence, and the Internet of Things, the automotive service supply chain requires a more intelligent and flexible order assignment optimization method to address various unexpected problems that may occur during the service process and achieve optimized management of each link in the supply chain. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes an order assignment optimization method, device, and storage medium based on the automotive service supply chain to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides an order assignment optimization method based on the automotive service supply chain, including the following steps:

[0007] Step S1: Obtain multi-source automotive service supply chain information and user real-time order data based on the supply chain platform; identify supply chain service manufacturers for the multi-source automotive service supply chain information and fit the dynamic node spatial distribution to construct a supply chain service node distribution map;

[0008] Step S2: Analyze the user's order service requirements for the real-time order data of the user, and conduct a real-time service demand load analysis to obtain the real-time service demand load characteristics of the supply chain;

[0009] Step S3: Conduct spatio-temporal demand distribution evolution on the real-time service demand load characteristics of the supply chain, and then perform dynamic service demand prediction to generate dynamic service demand prediction data;

[0010] Step S4: Based on the dynamic service demand prediction data, perform optimal demand-supply matching on the supply chain service node distribution map to construct a dynamic service demand allocation strategy;

[0011] Step S5: According to the optimal node matching data of service demands, perform adaptive order dispatching optimization on the supply chain platform and adjust the service delay of the supply chain nodes to obtain a dynamic delay adjustment strategy;

[0012] Step S6: Conduct multi-scenario transfer learning on the dynamic delay adjustment strategy based on the user's real-time order data, and perform intelligent order dispatching decision analysis to construct a supply chain intelligent order dispatching model.

[0013] The present invention also provides an order dispatching optimization device based on an automotive service supply chain, including:

[0014] A node distribution module, configured to obtain multi-source automotive service supply chain information and the user's real-time order data based on the supply chain platform; identify supply chain service manufacturers and perform dynamic node spatial distribution fitting on the multi-source automotive service supply chain information to construct a supply chain service node distribution map;

[0015] A demand load module, configured to analyze the user's order service requirements for the real-time order data of the user, and conduct a real-time service demand load analysis to obtain the real-time service demand load characteristics of the supply chain;

[0016] A demand prediction module, configured to conduct spatio-temporal demand distribution evolution on the real-time service demand load characteristics of the supply chain, and then perform dynamic service demand prediction to generate dynamic service demand prediction data;

[0017] An optimal demand matching module, configured to perform optimal demand-supply matching on the supply chain service node distribution map based on the dynamic service demand prediction data to construct a dynamic service demand allocation strategy;

[0018] A delay adjustment module, configured to perform adaptive order dispatching optimization on the supply chain platform according to the optimal node matching data of service demands, and adjust the service delay of the supply chain nodes to obtain a dynamic delay adjustment strategy;

[0019] An intelligent order assignment decision module, which is used to perform multi-scenario transfer learning on a dynamic delay adjustment strategy based on real-time user order data, perform intelligent order assignment decision analysis, and construct a supply chain intelligent order assignment model.

[0020] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the order assignment optimization method based on the automotive service supply chain described in any one of the above are realized.

[0021] The beneficial effects of the present invention are specifically as follows: By obtaining multi-source automotive service supply chain information and real-time user order data, a distribution map of supply chain service nodes can be established, which helps to comprehensively understand the supply chain structure and the locations of key nodes. Identifying the fitting of supply chain service providers and dynamic node spatial distributions can improve the transparency and operability of the supply chain, providing basic data support for subsequent order assignment optimization. Through user order service demand analysis and real-time service demand load analysis, the user demand characteristics and the current load situation of the supply chain can be deeply understood, providing a basis for subsequent service demand prediction and optimization. Obtaining the real-time service demand load characteristics of the supply chain helps to improve the service response speed and efficiency, ensuring the balance between service supply and demand. Through the evolution of spatio-temporal demand distribution and dynamic service demand prediction, accurate prediction of future service demands can be achieved, which helps to plan and adjust service supply in advance. Generating dynamic service demand prediction data can help improve the flexibility and adaptability of the supply chain, ensuring timely adjustments when demand changes. Based on the dynamic service demand prediction data, optimal demand-supply matching and construction of a dynamic service demand allocation strategy can be realized, achieving optimal resource allocation and efficient service provision. Constructing a dynamic service demand allocation strategy helps to improve the flexibility and efficiency of the supply chain, ensuring that services can meet user needs in a timely manner. Through adaptive order assignment optimization and supply chain node service delay adjustment, the intelligence of the order assignment process and the optimization of service response time can be realized. The establishment of a dynamic delay adjustment strategy helps to improve service quality and user satisfaction, ensuring that services can be completed on time and efficiently. Through multi-scenario transfer learning and intelligent order assignment decision analysis based on real-time user order data, an intelligent order assignment model can be constructed, improving the accuracy and efficiency of order assignment decisions. Constructing a supply chain intelligent order assignment model helps to optimize the order assignment process, improve the processing capacity of supply chain services, enhance the order assignment efficiency and accuracy of the supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic flow chart of the steps of an order assignment optimization method based on an automotive service supply chain according to the present invention;

[0023] Figure 2 It is a schematic detailed implementation step flow chart of step S1;

[0024] Figure 3It is a schematic diagram of the detailed implementation steps of step S2;

[0025] Figure 4 It is a schematic diagram of the detailed implementation steps of step S3. Specific implementation manners

[0026] 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.

[0027] The embodiments of the present application provide a dispatching optimization method, device and storage medium based on an automotive service supply chain. The execution subjects of the dispatching optimization method, device and storage medium based on the automotive service supply chain include but are not limited to: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that carry this system, which can be regarded as general computing nodes of the present application. 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.

[0028] Please refer to Figures 1 to 4 , the present invention provides a dispatching optimization method based on an automotive service supply chain. The dispatching optimization method based on the automotive service supply chain includes the following steps:

[0029] Step S1: Obtain multi-source automotive service supply chain information and user real-time order data based on the supply chain platform; identify supply chain service manufacturers and fit the dynamic node spatial distribution for the multi-source automotive service supply chain information to construct a supply chain service node distribution map;

[0030] Step S2: Analyze the user order service requirements for the user real-time order data and conduct real-time service demand load analysis to obtain the supply chain real-time service demand load characteristics;

[0031] Step S3: Evolve the spatio-temporal demand distribution of the supply chain real-time service demand load characteristics, and then conduct dynamic service demand prediction to generate dynamic service demand prediction data;

[0032] Step S4: Based on the dynamic service demand prediction data, perform optimal demand supply matching on the supply chain service node distribution map to construct a dynamic service demand allocation strategy;

[0033] Step S5: Perform adaptive dispatching optimization on the supply chain platform according to the optimal node matching data of service requirements, and adjust the service delay of the supply chain nodes to obtain a dynamic delay adjustment strategy;

[0034] Step S6: Perform multi-scenario transfer learning on the dynamic delay adjustment strategy based on the user real-time order data, and conduct intelligent dispatching decision analysis to construct a supply chain intelligent dispatching model.

[0035] By obtaining multi-source automotive service supply chain information and real-time user order data, a distribution map of supply chain service nodes can be established, which helps to comprehensively understand the supply chain structure and the locations of key nodes. Identifying supply chain service providers and fitting the spatial distribution of dynamic nodes can improve the transparency and operability of the supply chain, providing basic data support for subsequent dispatch optimization. Through the analysis of user order service requirements and real-time service demand load, the characteristics of user needs and the current load of the supply chain can be deeply understood, providing a basis for subsequent service demand prediction and optimization. Obtaining the characteristics of real-time service demand load in the supply chain helps to improve the service response speed and efficiency, ensuring the balance between service supply and demand. Through the evolution of spatio-temporal demand distribution and dynamic service demand prediction, accurate prediction of future service demand can be achieved, which helps to plan and adjust service supply in advance. Generating dynamic service demand prediction data can help improve the flexibility and adaptability of the supply chain, ensuring timely adjustment when demand changes. Based on the dynamic service demand prediction data, optimal demand-supply matching and the construction of a dynamic service demand allocation strategy can achieve the optimal allocation of resources and the efficient provision of services. Constructing a dynamic service demand allocation strategy helps to improve the flexibility and efficiency of the supply chain, ensuring that services can meet user needs in a timely manner. Through adaptive dispatch optimization and adjustment of service delays at supply chain nodes, the intelligence of the dispatch process and the optimization of service response time can be achieved. The establishment of a dynamic delay adjustment strategy helps to improve service quality and user satisfaction, ensuring that services can be completed on time and efficiently. Based on real-time user order data, multi-scenario transfer learning and intelligent dispatch decision analysis can be carried out to build an intelligent dispatch model, improving the accuracy and efficiency of dispatch decisions. Constructing an intelligent supply chain dispatch model helps to optimize the dispatch process, improve the processing capacity of supply chain services, enhance the dispatch efficiency and accuracy of the supply chain.

[0036] In the embodiment of the present invention, refer to Figure 1 , which is a schematic diagram of the step process of a dispatch optimization method based on an automotive service supply chain according to the present invention. In this example, the steps of the dispatch optimization method based on the automotive service supply chain include:

[0037] Step S1: Obtain multi-source automotive service supply chain information and real-time user order data based on the supply chain platform; identify supply chain service providers for the multi-source automotive service supply chain information and fit the spatial distribution of dynamic nodes to construct a distribution map of supply chain service nodes;

[0038] In this embodiment, ensure to establish a connection with the API interface of the supply chain platform, so as to be able to obtain multi-source automotive service supply chain information and user order data in real time. Send a request to the supply chain platform using an HTTP request to obtain the required data. The data includes service provider information (such as name, address, contact information, service type) and user real-time order data (such as order ID, user ID, service demand, order placement time). Format the obtained data into a structure suitable for subsequent processing (such as JSON, CSV, or DataFrame format). The requests library in Python can be used for API calls, and the pandas library can be used to process the data. Clean the obtained data, handle missing values, duplicate values, and outliers to ensure the accuracy and consistency of the data. The dropna() and drop_duplicates() methods in pandas can be used. Integrate the cleaned multi-source automotive service supply chain information with the user real-time order data to form a unified dataset for subsequent analysis and processing. Select a suitable identification method, such as string matching, classification algorithms, or clustering analysis, to identify the unique service provider nodes. Analyze the integrated data to extract the unique service provider information, including name, address, and contact information. Use the groupby() method in pandas to deduplicate the service providers. Store the identified service provider information in a new data table for subsequent analysis. Ensure that each service provider has corresponding geographical location information (such as address, city, longitude, and latitude). If the longitude and latitude are missing, a geocoding service (such as Google Maps API) can be used for conversion. Select a suitable spatial distribution fitting method, such as K-Means clustering, heatmap analysis, or other spatial statistical methods, to analyze the distribution of service nodes. Use a clustering algorithm to analyze the geographical coordinates of service providers to identify the distribution pattern of service nodes. KMeans in scikit-learn can be used for clustering analysis. Store the fitting results in a database and use a visualization tool (such as matplotlib or folium) to draw a service node distribution map to show the spatial distribution of service providers. Organize the geographical coordinates of service nodes and the identified service provider information to prepare the data for drawing the distribution map. Use the folium or matplotlib library in Python to draw a map, mark the locations of each service provider on the map, and highlight them. Save the constructed supply chain service node distribution map as an image file or display it in a web application for subsequent decision support and analysis.

[0039] Step S2: Analyze the user order service demand of the user real-time order data and conduct a real-time service demand load analysis to obtain the real-time service demand load characteristics of the supply chain;

[0040] In this embodiment, real-time order data of users is extracted from the database, ensuring that key fields such as order ID, user ID, service type, order placement time, service location, and order status are included. The extracted data is converted into a format suitable for analysis (such as a pandas DataFrame) for subsequent processing. The pandas library in Python is used for data reading and processing. Service types are defined according to business requirements (such as repair, maintenance, cleaning, etc.) to ensure that the service requirements of each order are accurately classified. Suitable demand analysis methods are selected. Common methods include frequency statistics, trend analysis, and clustering analysis. The groupby() method in pandas is used to group and count the order data by service type, calculating the demand frequency and total demand for each service type. Through time series analysis, the changing trend of service demand in different time periods is analyzed (for example, summarized by hour, day, or week). Key indicators for service demand load analysis are determined, such as real-time demand, response time, service completion rate, etc. By establishing a data flow pipeline (such as using Apache Kafka or Apache Spark Streaming), the user order placement situation is obtained in real time to ensure the real-time nature of the data. Statistical analysis methods (such as descriptive statistics) are used to calculate the service demand load in the current time period. Pandas can be used for real-time data analysis to generate load indicators and calculate the real-time service demand load characteristics. For example: the current service demand volume (such as the number of orders in the current hour), the demand distribution of each service type (such as the number of demands for each service type), the statistics of service response time (such as average response time, maximum response time, etc.). The results of real-time service demand load analysis are stored in the database, and visualization tools (such as Matplotlib or Tableau) are used to generate visualization charts of load characteristics. The visualization results can include: a time series chart of service demand volume, a pie chart or bar chart of the demands of each service type, and a distribution chart of response time.

[0041] Step S3: Evolve the spatio-temporal demand distribution of the real-time service demand load of the supply chain, and then perform dynamic service demand forecasting to generate dynamic service demand forecasting data;

[0042] In this embodiment, extract the real-time service demand load characteristic data of the supply chain from the database, ensuring that the information includes timestamps, service types, demand quantities, service nodes, etc. Use the pandas library in Python to organize the extracted data into a format suitable for analysis, ensuring the structuring and integrity of the data. Select a suitable spatio-temporal analysis model, such as spatio-temporal clustering, heatmap analysis, or time series analysis, to identify the spatio-temporal evolution characteristics of the demand. Use the folium or seaborn library to generate a heatmap of the service demand, showing the service demand distribution at different time periods and locations. Use the pivot_table() method in pandas to summarize the demand data by time and location, generating grid data of the demand. Conduct a time series analysis on the service demand quantity, and use the seasonal_decompose() method in the statsmodels library to analyze the demand for trends, seasonality, and residuals, identifying the periodic changes and trend characteristics of the demand. Store the results of the spatio-temporal demand distribution evolution (such as heatmaps, time series analysis diagrams) in the database for subsequent use and reference. Select a suitable dynamic service demand prediction model, such as the ARIMA model, LSTM (Long Short-Term Memory Network), or XGBoost regression model, and make predictions based on historical demand data. According to the analysis results of the spatio-temporal demand distribution, organize the historical demand data, extract relevant features (such as dates, times, service types, etc.), and preprocess the data, including normalization, missing value handling, and timestamp conversion. Use the training set and test set to train and validate the selected prediction model. You can use train_test_split() to divide the data into a training set and a test set. Use the statsmodels library in Python to fit the ARIMA model, or use Keras to build and train the LSTM model. Based on the trained model, predict the future service demand, generating dynamic service demand prediction data. The prediction results should include the demand quantities of each service type in different future time periods.

[0043] Step S4: Based on the dynamic service demand prediction data, perform optimal demand-supply matching on the supply chain service node distribution map, and construct a dynamic service demand allocation strategy;

[0044] In this embodiment, dynamic service demand prediction data is extracted from a database, including information such as service type, predicted demand quantity, time period, etc. Service node information in the supply chain service node distribution map is extracted, including node location, available service types, available resources, etc. A suitable optimal matching algorithm is selected. Common methods include linear programming, Hungarian algorithm, or minimum cost flow algorithm. A demand and supply matrix is created, where rows represent service demands (based on prediction data), columns represent service nodes, and the elements of the matrix represent the matching costs (such as distance, resource availability, etc.) between each demand and each node. This matrix is constructed using numpy or pandas. The matching costs between each demand and node are calculated, and the selected algorithm is used to solve the constructed matching matrix to find the optimal supply matching plan, identify which service nodes are most suitable for meeting which service demands, and record the matching results. The goal of the dynamic service demand allocation strategy is determined, such as minimizing response time, maximizing resource utilization, improving user satisfaction, etc. A suitable strategy model is selected, such as a rule-based allocation model, an optimization-based allocation model, or a machine learning-based prediction model. Allocation rules are set according to business requirements, such as giving priority to service nodes closest to users, preferentially allocating nodes with sufficient resources, etc. If an optimization model is used, the objective function (such as maximizing service response rate) is defined and optimization is implemented. Linear programming is performed using Python's scipy.optimize or PuLP library to determine the best service node allocation plan. Considering demand changes, a dynamic adjustment mechanism is designed to adjust the allocation strategy according to real-time data during actual operation. This can include regularly re-evaluating demand prediction and service resource status and updating the allocation strategy accordingly.

[0045] Step S5: Adaptive order dispatching optimization is performed on the supply chain platform according to the optimal node matching data of service demands, and service delays of supply chain nodes are adjusted to obtain a dynamic delay adjustment strategy;

[0046] In this embodiment, a suitable adaptive order assignment optimization model is selected. Common models include rule-based order assignment systems, optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.), or machine learning models. The objective function of the optimization model is designed, such as minimizing the average response time, maximizing resource utilization, etc. If an optimization algorithm is used, the constraints of the problem are set (such as service type consistency, resource availability, etc.). The scipy.optimize or PuLP library in Python is used to input the optimal node matching data, solve the optimization model, obtain the adaptive order assignment plan, generate the best service node allocation plan for each service demand, design a dynamic delay adjustment strategy based on the current service delay data, with the goal of reducing the load of high-delay service nodes and reasonably allocating service demands. Statistical analysis methods (such as descriptive statistics, box plot analysis) are used to analyze the service delay data to identify high-delay nodes. For the identified high-delay nodes, their order assignment strategies are adjusted. For example, the allocation ratio of new service demands to them is reduced, and new demands are preferentially assigned to nodes with lower delays. Rules are designed, such as threshold-based adjustment rules: if the average response time of a certain node exceeds the set threshold, the number of orders assigned to this node is reduced. According to the designed dynamic delay adjustment strategy, the actual order assignment behavior is adjusted, and the automated order assignment function is implemented in the system to ensure that new demands can be reasonably allocated according to the latest delay data. The response time and service quality of service nodes are monitored in real time to ensure that the dynamic adjustment strategy can operate effectively. The data after implementation is collected to analyze the actual effect of the adjustment strategy.

[0047] Step S6: Perform multi-scenario transfer learning on the dynamic delay adjustment strategy based on real-time user order data, and conduct intelligent order assignment decision analysis to construct a supply chain intelligent order assignment model.

[0048] In this embodiment, multiple scenarios are determined, such as holidays, weekdays, promotional activities, weather changes, etc. The impacts of these scenarios on user service requirements and response times are identified, and user order data under different scenarios are integrated together to ensure that each scenario has corresponding features (such as time, location, user characteristics, etc.). Data cleaning is performed using pandas in Python to handle missing values, standardize features, etc. A suitable transfer learning framework is selected, such as transfer learning based on deep learning (such as convolutional neural network CNN or recurrent neural network RNN). The pre-trained model is used for transfer learning to adapt to the service requirement characteristics under different scenarios. Deep learning frameworks such as Keras or PyTorch are adopted to load the pre-trained model and perform fine-tuning to ensure that the model can adapt to the new scenario data. When training the model, the user requirement characteristics under different scenarios are input, and the model parameters are optimized to improve the prediction ability of the model in the new scenario. The goals of designing the intelligent order assignment decision model are defined, such as minimizing the response time, maximizing the service quality, etc. A suitable decision analysis method is selected, such as reinforcement learning, decision tree, random forest, etc. Based on the user's real-time order data and the implementation effect of the dynamic delay adjustment strategy, relevant features are extracted (such as user historical behavior, service node performance, service demand volume, etc.). The feature selection method in the sklearn library (such as random forest feature importance) is used to identify the key features. The extracted features are used to train the intelligent order assignment decision model, such as using random forest for classification or regression analysis to predict the optimal order assignment plan. The training set and test set are divided, and data splitting is performed using train_test_split(). The model performance (such as accuracy, recall, etc.) is evaluated. The transfer learning model and the intelligent order assignment decision model are integrated to form a comprehensive intelligent order assignment system. The accuracy and stability of the model are improved through ensemble learning methods (such as voting method, weighted average method). A dynamic adjustment mechanism is designed to ensure that the intelligent order assignment model can adapt to the real-time changing user requirements and service environment. A real-time feedback mechanism is added to the model to regularly update the model parameters and decision rules to cope with the changes in new data.

[0049] In this embodiment, refer to Figure 2 , which is the schematic diagram of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of the said step S1 include:

[0050] Step S11: Obtain multi-source automotive service supply chain information and user real-time order data based on the supply chain platform;

[0051] Step S12: Identify supply chain service manufacturers for the multi-source automotive service supply chain information and extract multiple supply chain service nodes;

[0052] Step S13: Analyze the supply chain service types of the multiple supply chain service nodes to obtain the service type of each node;

[0053] Step S14: Calculate the geospatial positions of multiple supply chain service nodes to obtain the geospatial coordinates of each service node;

[0054] Step S15: Analyze the spatial distribution of each service node's geospatial coordinates to obtain service node distribution data;

[0055] Step S16: Based on the service type of each node, perform dynamic node spatial distribution fitting on the service node distribution data to construct a supply chain service node distribution map.

[0056] In this embodiment, after obtaining the authorization of the platform and the user, ensure that the interface (API) with the supply chain platform has been established and can obtain relevant automotive service supply chain information and user order data in real time. Use the API call to obtain multi-source automotive service supply chain information, including information such as service providers, service types, prices, availability, etc. At the same time, obtain the user's real-time order data, including order ID, customer information, service requirements, etc. Integrate the obtained data into a unified database, and use a data framework (such as Pandas) for data cleaning and preprocessing to ensure the consistency and integrity of the data. Select a suitable data analysis method (such as cluster analysis or classification algorithm) to identify supply chain service manufacturers, analyze the integrated supply chain information, and extract the unique supply chain service manufacturer nodes, including the name, type, and contact information of the service provider, etc. Use the pandas library of Python for data processing and deduplication, store the identified multiple supply chain service nodes in the database for subsequent analysis, formulate classification criteria for service types (such as repair, maintenance, cleaning, etc.) to ensure the accuracy and consistency of classification, conduct service type analysis on each supply chain service node, identify and mark the service type of each node, use pandas for data mapping to ensure that each node is assigned the correct service type, store the service type information of each node in the database to form a detailed record of the service type, ensure that each service node has relevant geographical information (such as address, city, or postal code), use a geocoding service (such as Google Maps API, Geocodio, or OpenStreetMap) to convert the address into geographical coordinates (latitude and longitude), geocode the addresses of all service nodes, obtain the geospatial coordinates of each service node, record the geographical coordinates of each node, and store them in the database. Select a suitable spatial distribution analysis method, such as K-Means clustering, heat map analysis, or spatial statistical method, to analyze the geospatial coordinates of each service node, generate the spatial distribution data of the service nodes, use the scikit-learn library of Python for cluster analysis, or use the folium library to generate a heat map, store the service node distribution data in the database for subsequent visualization and analysis, select a suitable fitting method (such as polynomial regression, locally weighted regression, or time series analysis) to adapt to the node distribution changes in different time periods, based on the service type and spatial distribution data of each node, conduct dynamic node spatial distribution fitting, construct a supply chain service node distribution map, use visualization tools such as matplotlib or seaborn to display the fitting results, store the generated service node distribution map in the database, and display the distribution map through a visualization platform (such as Tableau or PowerBI) for easy analysis and decision-making.

[0057] In this embodiment, refer toFigure 3 , which is a schematic diagram of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0058] Step S21: Analyze the user order service requirements for the user's real-time order data to obtain multiple order service requirement data;

[0059] Step S22: Calculate the demand type frequency for the multiple order service requirement data to obtain the demand frequency for each service type;

[0060] Step S23: Quantify the temporal demand changes for the demand frequency of each service type to generate user service type demand change data;

[0061] Step S24: Calculate the demand peak for the user service type demand change data to obtain the user service demand peak;

[0062] Step S25: Conduct a real-time service demand load analysis based on the user service demand peak to obtain the real-time service demand load characteristics of the supply chain.

[0063] In this embodiment, real-time order data of users is extracted from the database to ensure the integrity and consistency of the data, including fields such as order ID, user ID, service type, order placement time, etc. The user order data is analyzed to extract the service demand information for each order, forming multiple order service demand data sets. The pandas library in Python is used to filter and process the data to extract the service demand fields. The multiple order service demand data extracted is stored in a new data table or database for subsequent analysis. The type classification criteria for service demands are determined, such as repair, maintenance, cleaning, etc., to ensure the consistency of classification. Statistical analysis methods (such as counting, proportion calculation) are used to calculate the demand frequency for each service type. The demand type frequency calculation is performed on the multiple order service demand data, and the number of occurrences of each service type is counted. The value_counts() function in pandas is used to implement the frequency calculation. The demand frequency for each service type is stored in the database for subsequent analysis. The demand frequency data is converted into a time series format to facilitate the analysis of the change trend of the demand. Appropriate change quantification methods, such as the difference method, percentage change method, or moving average method, are selected to quantify the time series demand change for each service type, generating user service type demand change data. The diff() function in pandas is used to calculate the change amount, or pct_change() is used to calculate the percentage change. The generated user service type demand change data is stored in the database for subsequent analysis. Appropriate peak recognition methods, such as local maximum detection, sliding window method, or Z-score method, are selected to calculate the demand peaks for the user service type demand change data to identify the peak demand periods. The find_peaks() function in the scipy.signal library can be used for peak detection. The calculated user service demand peaks are stored in the database for subsequent analysis. Appropriate load analysis models, such as queuing theory models, load prediction models, or machine learning-based regression models, are selected to analyze the real-time service demand load based on the user service demand peaks, calculate the load characteristics (such as processing capacity, response time, etc.) of the service provider during the peak period, use statistical analysis or machine learning methods to predict the service load under different demand situations, store the analysis results in the database, and use data visualization tools (such as Matplotlib or Tableau) to display the service demand load characteristics for decision support.

[0064] In this embodiment, refer to Figure 4 , which is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0065] Step S31: Extract the service demand location for each user based on the user real-time order data;

[0066] Step S32: Divide the service demand locations of each user into demand location regions, so as to obtain multiple service demand regions;

[0067] Step S33: Based on the multiple service demand regions, conduct spatio-temporal demand distribution evolution on the real-time service demand load characteristics of the supply chain to obtain spatio-temporal demand distribution characteristics;

[0068] Step S34: Mine the dynamic demand fluctuation law of the spatio-temporal demand distribution characteristics to generate the dynamic demand fluctuation law of users;

[0069] Step S35: Based on the dynamic demand fluctuation law of users, conduct dynamic service demand prediction to generate dynamic service demand prediction data.

[0070] In this embodiment, relevant fields of service requirements are extracted from the real-time order data of users to ensure that information such as user ID, service type, and service location is included. The order data is screened and cleaned using data processing tools (such as pandas in Python), and the service requirement locations of each user are extracted. Ensure that the extracted location information is accurate, and handle missing or incorrect data. Store the service requirement locations of each user in a new data table for subsequent analysis. Determine the criteria for regional division of service requirement locations, such as by city, administrative region, or clustering analysis based on geographical coordinates. Select a suitable regional division method, such as K-Means clustering, DBSCAN, etc., to group the service requirement locations. Perform regional division on the extracted service requirement locations to generate multiple service requirement regions. Use the scikit-learn library in Python to implement clustering analysis and assign each location to the corresponding region. Combine the service requirement regions with time information to construct a spatio-temporal demand dataset, ensuring that fields such as timestamp, service region, and demand volume are included. Adopt statistical analysis methods, such as frequency statistics and average value calculation, to analyze the demand situation of each service requirement region at different time periods. Use time series analysis methods (such as ARIMA model) or spatio-temporal data visualization tools (such as heat maps) to display the evolution characteristics of demand distribution. Select a suitable analysis method, such as moving average method, Fourier transform, or wavelet transform, to identify the demand fluctuation pattern. Conduct dynamic demand fluctuation pattern analysis on the spatio-temporal demand distribution characteristics to identify the patterns and trends of demand fluctuations. Use the statsmodels library in Python for time series analysis to extract the fluctuation pattern. Store the identified dynamic demand fluctuation patterns of users in the database for subsequent demand forecasting. Select a suitable dynamic demand forecasting model, such as linear regression, decision tree, random forest, or LSTM (long short-term memory network), etc. Use historical demand data and the identified dynamic demand fluctuation patterns to train the forecasting model and verify it to ensure the accuracy of the model. Based on the dynamic demand fluctuation patterns of users, use the trained model to conduct dynamic service demand forecasting and generate forecasting data. Use scikit-learn or TensorFlow in Python for model training and forecasting. Store the generated dynamic service demand forecasting data in the database and use visualization tools (such as Matplotlib or Tableau) to display the forecasting results to support decision-making.

[0071] In this embodiment, step S4 includes the following steps:

[0072] Step S41: Analyze the currently idle service nodes in the supply chain service node distribution map and extract the idle service nodes;

[0073] Step S42: Analyze the node resource configuration of the idle service nodes and extract the resource configuration data of the idle nodes;

[0074] Step S43: Perform optimal demand supply matching on the resource configuration data of idle nodes based on the dynamic service demand prediction data to generate service demand optimal node matching data;

[0075] Step S44: Make a dynamic demand allocation decision on the service demand optimal node matching data to construct a dynamic service demand allocation strategy.

[0076] In this embodiment, extract the status information of all service nodes from the service node distribution map to ensure that it includes the current status of each node (such as busy or idle). Use a data processing tool (such as pandas in Python) to filter the service node status data and extract the currently idle service nodes. For example, a conditional filtering method can be used to obtain a list of idle nodes, such as: free_nodes = nodes[nodes['status'] == 'free']. Collect the resource configuration data of idle service nodes, including information such as available personnel, equipment, and time. Select a suitable analysis method and use descriptive statistics (such as mean, standard deviation) or clustering analysis to evaluate the resource configuration of idle nodes. Analyze the resource configuration data of idle service nodes, extract the resource configuration characteristics of each idle node, use pandas for data analysis, obtain the availability and configuration of resources, store the analyzed resource configuration data of idle nodes in a database for subsequent matching and decision-making. Obtain the dynamic service demand prediction data to ensure that the data includes the predicted service demand type, quantity, and time. Select a suitable matching algorithm, such as linear programming, the Hungarian algorithm, or an optimization algorithm based on machine learning, to ensure that the demand and resources can be effectively matched. Based on the dynamic service demand prediction data, perform optimal demand supply matching on the resource configuration data of idle nodes, create a matching model, associate the predicted service demand with the available service node resources, and generate service demand optimal node matching data. Use the scipy.optimize library or PuLP library in Python to implement the matching algorithm. Determine the decision criteria for dynamic service demand allocation, such as response time, service quality, resource utilization, etc. Select a suitable decision-making model, such as a multi-objective optimization model, the analytic hierarchy process (AHP), or a dynamic decision-making method based on reinforcement learning. Make a dynamic demand allocation decision according to the service demand optimal node matching data and construct a dynamic service demand allocation strategy. Evaluate the effect of the allocation strategy by simulating different scenarios to ensure that it can adapt to demand changes in real time.

[0077] In this embodiment, the specific steps of Step S43 are as follows:

[0078] Calculate the spatial positions of idle service nodes;

[0079] Highlight the nodes of the supply chain service node distribution map based on the spatial location to obtain the distribution map of idle service nodes;

[0080] Calculate the current location of the user for the dynamic service demand prediction data to obtain the current location coordinates of the user;

[0081] Perform the minimum distance matching calculation on the distribution map of idle service nodes based on the current location coordinates of the user to obtain the minimum distance of the user's service demand;

[0082] Perform service type resource matching on the resource configuration data of the idle nodes based on the dynamic service demand prediction data to obtain the resource matching data of the user demand type;

[0083] Perform the minimum service response analysis on the minimum distance of the user's service demand and the resource matching data of the user demand type to generate the minimum service response data of the user;

[0084] Perform the optimal demand supply matching on the distribution map of idle service nodes based on the minimum service response data of the user to generate the optimal node matching data of the service demand.

[0085] In this embodiment, the geographical coordinate information (such as latitude and longitude) of all idle service nodes is extracted from the database, and the extracted coordinate information is organized into a format suitable for visualization, usually a list of coordinates. The geopandas library or matplotlib in Python is used for visualizing the node positions. Based on the coordinates of the idle service nodes, highlighting is performed on the supply chain service node distribution map. Using the matplotlib library, the distribution map is drawn, and the scatter() method is called to mark the idle nodes with different colors or shapes, generating the idle service node distribution map and saving it as an image file or displaying it in the user interface. The current position coordinates (longitude, latitude) of the user are obtained using GPS or other location services (such as Google Maps API), ensuring that the obtained location information is real-time and accurate. The user's current position coordinates are stored in the database for subsequent use. The record format can be JSON or directly stored in the database table. A suitable distance calculation method is selected, usually the Euclidean distance or the Haversine distance formula (applicable to geographical coordinates), and the distance between the coordinates of each idle service node and the user's current position is calculated, generating a list containing the distance between each node and the user. The cdist() function in the scipy.spatial.distance module of Python is used for the calculation. The minimum distance and its corresponding idle service node are identified from the calculated distance list. The criteria for service type resource matching are determined, such as service type, available resource quantity, etc. The resource configuration data of the idle service nodes is extracted from the database, including the available service types and quantities of each node. Based on the user's dynamic service demand prediction data, the resource configuration of the idle nodes is matched. Using the method of conditional filtering, it is determined whether each idle node meets the user's service requirements. The factors affecting service response are determined, such as distance, service type matching degree, etc. The minimum distance and resource matching data of the user's service requirements are collected to form the basic data for response analysis. Using the multi-objective optimization method, the impact of the minimum distance and resource matching data on service response is analyzed, generating the minimized service response data. The numpy library in Python can be used for calculation and analysis. A suitable matching algorithm is selected, such as linear programming, multi-objective optimization, or genetic algorithm, to achieve the optimal demand-supply matching. Based on the user's minimized service response data and the resource configuration of the idle service nodes, the optimal demand-supply matching is performed. Using the scipy.optimize library in Python, a matching model is constructed to solve the optimal configuration. The optimal node matching data for service demand is stored in the database for subsequent decision support and dynamic scheduling. The optimal node matching result is visually displayed for easy viewing and analysis by decision-makers.

[0086] In this embodiment, the specific steps of step S5 are as follows:

[0087] Step S51: Optimize the adaptive order assignment of the supply chain platform according to the service demand optimal node matching data, so as to generate an adaptive order assignment optimization strategy;

[0088] Step S52: Conduct real-time order assignment tracking on the adaptive order assignment optimization strategy, and extract real-time order assignment tracking response data;

[0089] Step S53: Calculate the service delay of the supply chain node for the real-time order assignment tracking response data to obtain the supply chain node service delay data;

[0090] Step S54: Dynamically adjust the adaptive order assignment optimization strategy based on the supply chain node service delay data to obtain a dynamic delay adjustment strategy.

[0091] In this embodiment, the service demand optimal node matching data is extracted from the database, including user requirements, service node information and its resource configuration status. A suitable adaptive order assignment optimization algorithm is selected, such as genetic algorithm, particle swarm optimization or linear programming, to ensure the efficiency and flexibility of the order assignment strategy. Based on the service demand optimal node matching data, the selected optimization algorithm is used to generate an adaptive order assignment optimization strategy. The algorithm is implemented using the scipy.optimize or DEAP library in Python. The objective function (such as minimizing the response time, maximizing the resource utilization rate, etc.) is set, and a real-time order assignment tracking system is constructed to ensure that the order assignment status and service node response information can be obtained in real time. The real-time order assignment response data is obtained from the supply chain platform, including order assignment time, completion time, service node status, etc. The real-time data stream processing framework (such as Apache Kafka or Apache Spark) is used for real-time data stream processing to extract real-time order assignment tracking response data. The real-time data is stored in an in-memory database (such as Redis) for quick access. A suitable delay calculation method is selected, such as response time calculation, average delay calculation or percentile delay analysis, to analyze the real-time order assignment tracking response data, calculate the service delay of each supply chain node (i.e., the time difference from order assignment to response), use the pandas library in Python to calculate the response time, and generate a list of delay data. Determine the criteria and objectives for dynamic delay adjustment, such as the maximum allowable delay, service level agreement (SLA), etc. Based on the supply chain node service delay data, analyze the distribution characteristics and change trends of the delay, identify the delay peaks and anomalies, adjust the adaptive order assignment optimization strategy according to the delay data, determine the response strategy when the delay exceeds the preset value (such as reassigning tasks, increasing resources, etc.), use machine learning models (such as regression analysis, decision tree) to predict the delay and dynamically adjust the order assignment strategy. The generated dynamic delay adjustment strategy is stored in the database and fed back to the order assignment system to ensure the improvement of real-time response capabilities.

[0092] In this embodiment, the specific steps of step S6 are as follows:

[0093] Step S61: Analyze the evolution of users' personalized needs from real-time order data to generate users' personalized need habits.

[0094] Step S62: Conduct multi-scenario transfer learning on users' personalized need habits to obtain multi-scenario user need habit characteristics.

[0095] Step S63: Based on the multi-scenario user need habit characteristics, conduct intelligent dispatching decision analysis on the dynamic delay adjustment strategy to construct a supply chain intelligent dispatching model.

[0096] In this embodiment, extract the users' real-time order data from the database, including information such as user ID, order time, service type, service location, etc. Select suitable analysis methods, such as clustering analysis, time series analysis, or trend analysis, to identify the evolution patterns of users' needs, process the users' order data, and identify the users' personalized need habits. For example, classify the users' needs into different categories through clustering analysis, or identify the seasonal changes of needs through time series analysis. Use the scikit-learn library in Python for clustering analysis, or use statsmodels for time series analysis. Determine the multi-scenarios to be analyzed, such as different time periods, seasons, holidays, or special events (such as promotional activities). Select a suitable transfer learning framework and model, such as transfer learning methods based on deep learning (such as convolutional neural network CNN, recurrent neural network RNN). Integrate and preprocess the users' need habits in different scenarios to ensure the consistency and availability of the data. Use pandas for data cleaning and feature engineering to extract relevant features. Use the transfer learning model to train and analyze the users' need habits in different scenarios. After training the model in one scenario, apply it to other scenarios to identify the users' need characteristics. Use TensorFlow or PyTorch in Python for model construction and training. Determine the goals of the intelligent dispatching model, such as minimizing the response time, improving user satisfaction, or optimizing resource utilization. Select suitable intelligent decision analysis methods, such as reinforcement learning, decision tree, random forest, or hybrid model. Based on the multi-scenario user need habit characteristics, construct an intelligent dispatching decision model. Input the users' need characteristics and the current service node status, and output the best dispatching strategy. Use the scikit-learn library or deep learning framework in Python to train the model and evaluate it. Store the constructed supply chain intelligent dispatching model and the corresponding decision results in the database, and formulate a feedback mechanism to continuously optimize the performance of the model.

[0097] In this embodiment, a dispatching optimization device based on the automotive service supply chain is also provided, including:

[0098] A node distribution module, configured to obtain multi-source automotive service supply chain information and user real-time order data based on a supply chain platform; identify supply chain service manufacturers and perform dynamic node spatial distribution fitting on the multi-source automotive service supply chain information, and construct a supply chain service node distribution map;

[0099] A demand load module, configured to analyze the service demands of user real-time orders and perform real-time service demand load analysis on the user real-time order data to obtain the real-time service demand load characteristics of the supply chain;

[0100] A demand prediction module, configured to perform spatio-temporal demand distribution evolution on the real-time service demand load characteristics of the supply chain and then perform dynamic service demand prediction to generate dynamic service demand prediction data;

[0101] An optimal demand matching module, configured to perform optimal demand supply matching on the supply chain service node distribution map based on the dynamic service demand prediction data and construct a dynamic service demand allocation strategy;

[0102] A delay adjustment module, configured to perform adaptive order dispatching optimization on the supply chain platform according to the optimal node matching data of service demands and perform supply chain node service delay adjustment to obtain a dynamic delay adjustment strategy;

[0103] An intelligent order dispatching decision module, configured to perform multi-scenario transfer learning on the dynamic delay adjustment strategy based on the user real-time order data and perform intelligent order dispatching decision analysis to construct a supply chain intelligent order dispatching model.

[0104] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the order dispatching optimization method based on the automotive service supply chain described in any one of the above are implemented.

[0105] Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system, system and unit are referred to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0106] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it is stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application essentially, or the part that contributes to the prior art, or all or part of the technical solution is embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (such as a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media for storing program codes such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0107] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0108] As described above, these are only specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can 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 these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dispatch optimization method based on automobile service supply chain, characterized in that: The following steps are involved: Step S1: obtaining multi-source automobile service supply chain information and user real-time order data based on the supply chain platform; identifying supply chain service vendors and performing dynamic node spatial distribution fitting on the multi-source automobile service supply chain information, and constructing a supply chain service node distribution map; Step S2: Performing user order service demand analysis on user real-time order data and performing real-time service demand load analysis to obtain supply chain real-time service demand load characteristics; Step S3: Performing temporal and spatial demand distribution evolution on the real-time service demand load characteristics of the supply chain, and then performing dynamic service demand forecasting to generate dynamic service demand forecasting data; Step S4: Based on the dynamic service demand forecast data, optimal demand and supply matching is performed on the supply chain service node distribution map to construct a dynamic service demand allocation strategy; Step S5: Adaptively optimize the dispatch of orders on the supply chain platform according to the service demand optimal node matching data, and adjust the supply chain node service delay to obtain a dynamic delay adjustment strategy; Step S6: Based on the user's real-time order data, multi-scenario transfer learning is performed on the dynamic delay adjustment strategy, and intelligent dispatching decision analysis is performed to build a supply chain intelligent dispatching model.

2. The dispatch optimization method based on the automobile service supply chain according to claim 1 is characterized in that: The specific steps of step S1 are: Step S11: acquiring multi-source automobile service supply chain information and user real-time order data based on the supply chain platform; Step S12: Identify supply chain service vendors for multi-source automobile service supply chain information and extract multiple supply chain service nodes; Step S13: Analyze the supply chain service types of multiple supply chain service nodes to obtain the service type of each node; Step S14: Calculate the geographic spatial position of multiple supply chain service nodes to obtain the geographic spatial coordinates of each service node; Step S15: performing node space distribution analysis on the geographic space coordinates of each service node, thereby obtaining service node distribution data; Step S16: Perform dynamic node spatial distribution fitting on the service node distribution data based on the service type of each node to construct a supply chain service node distribution map.

3. The dispatch optimization method based on the automobile service supply chain according to claim 1 is characterized in that: The specific steps of step S2 are: Step S21: Performing user order service demand analysis on user real-time order data to obtain multiple order service demand data; Step S22: performing demand type frequency calculation on a plurality of order service demand data, thereby obtaining the demand frequency of each service type; Step S23: quantify the time-series demand change of the demand frequency of each service type, and generate user service type demand change data; Step S24: Calculate the demand peak value of the user service type demand change data to obtain the user service demand peak value; Step S25: Perform real-time service demand load analysis based on the user service demand peak to obtain the real-time service demand load characteristics of the supply chain.

4. The dispatch optimization method based on the automobile service supply chain according to claim 1 is characterized in that: The specific steps of step S3 are: Step S31: extracting the service demand location of each user based on the user's real-time order data; Step S32: dividing the service demand location of each user into demand location areas, thereby obtaining multiple service demand areas; Step S33: performing spatiotemporal demand distribution evolution on the real-time service demand load characteristics of the supply chain based on multiple service demand areas to obtain spatiotemporal demand distribution characteristics; Step S34: mining the dynamic demand fluctuation law based on the spatiotemporal demand distribution characteristics to generate the user dynamic demand fluctuation law; Step S35: Perform dynamic service demand prediction based on the user dynamic demand fluctuation pattern to generate dynamic service demand prediction data.

5. The dispatch optimization method based on the automobile service supply chain according to claim 1 is characterized in that: The specific steps of step S4 are: Step S41: Analyze the current idle service nodes on the supply chain service node distribution map and extract the idle service nodes; Step S42: performing node resource configuration analysis on the idle service nodes and extracting resource configuration data of the idle nodes; Step S43: performing optimal demand-supply matching on resource configuration data of idle nodes based on dynamic service demand prediction data to generate service demand optimal node matching data; Step S44: Make dynamic demand allocation decisions on the service demand optimal node matching data and construct a dynamic service demand allocation strategy.

6. The dispatch optimization method based on the automobile service supply chain according to claim 5 is characterized in that: The specific steps of step S43 are: Calculate the spatial location of idle service nodes; Highlighting nodes on the supply chain service node distribution map based on the spatial position to obtain an idle service node distribution map; Calculate the user's current location based on the dynamic service demand prediction data to obtain the user's current location coordinates; Based on the user's current location coordinates, the minimum distance matching calculation is performed on the idle service node distribution map to obtain the minimum distance of the user's service demand; Based on the dynamic service demand prediction data, the resource configuration data of the idle nodes is matched with service type resources to obtain resource matching data of the user demand type; Performing a minimum service response analysis on the resource matching data of the minimum distance of user service demand and the type of user demand to generate user minimum service response data; Based on the user's minimized service response data, the idle service node distribution map is optimally matched with the supply and demand to generate the service demand optimal node matching data.

7. The dispatch optimization method based on the automobile service supply chain according to claim 1 is characterized in that: The specific steps of step S5 are: Step S51: Adaptively optimize the dispatch of orders on the supply chain platform according to the service demand optimal node matching data, thereby generating an adaptive dispatch optimization strategy; Step S52: performing real-time dispatch tracking on the adaptive dispatch optimization strategy, and extracting real-time dispatch tracking response data; Step S53: Calculate the supply chain node service delay for the real-time dispatch tracking response data to obtain the supply chain node service delay data; Step S54: dynamically adjust the delay of the adaptive dispatch optimization strategy based on the supply chain node service delay data to obtain a dynamic delay adjustment strategy.

8. The dispatch optimization method based on the automobile service supply chain according to claim 1 is characterized in that: The specific steps of step S6 are: Step S61: Analyze the evolution of user personalized demand on user real-time order data to generate user personalized demand habits; Step S62: Perform multi-scenario transfer learning on the user's personalized demand habits, so as to obtain multi-scenario user demand habit characteristics; Step S63: Based on the characteristics of user demand habits in multiple scenarios, intelligent dispatching decision analysis is performed on the dynamic delay adjustment strategy to build a supply chain intelligent dispatching model.

9. A dispatch optimization device based on an automobile service supply chain, characterized in that: The method for optimizing dispatching orders based on the automobile service supply chain according to claim 1 comprises: The node distribution module is used to obtain multi-source automotive service supply chain information and user real-time order data based on the supply chain platform; identify supply chain service vendors and perform dynamic node spatial distribution fitting on multi-source automotive service supply chain information to build a supply chain service node distribution map; The demand load module is used to perform user order service demand analysis on user real-time order data and perform real-time service demand load analysis to obtain the real-time service demand load characteristics of the supply chain; The demand forecasting module is used to perform temporal and spatial demand distribution evolution on the real-time service demand load characteristics of the supply chain, and then perform dynamic service demand forecasting to generate dynamic service demand forecasting data; The optimal demand matching module is used to perform optimal demand-supply matching on the supply chain service node distribution map based on dynamic service demand forecast data and build a dynamic service demand allocation strategy; The delay adjustment module is used to optimize the supply chain platform's adaptive dispatching according to the optimal node matching data of service demand, and adjust the supply chain node service delay to obtain a dynamic delay adjustment strategy; The intelligent dispatch decision module is used to perform multi-scenario transfer learning of dynamic delay adjustment strategies based on users' real-time order data, conduct intelligent dispatch decision analysis, and build a supply chain intelligent dispatch model.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the dispatch optimization method based on the automobile service supply chain described in any one of claims 1 to 8 are implemented.

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