Construction method of urban shared bicycle scheduling task visualization platform

Abstract

The invention discloses a construction method of an urban shared bicycle scheduling task visualization platform. The construction method specifically comprises the following steps: 1, collecting data and data such as putting point position information and employee information; 2, the user interaction module realizes visual management of warehouses, trucks, delivery points, employees and tasks through a navigation assembly and a partition display interface; 3, the data management module supports newly adding, editing and deleting of various kinds of resource information; and 4, a task generation module can automatically match warehouses, trucks, employee resources, shortest path information and shortest path length according to the target delivery point and the demand quantity and generate a transportation task. And 5, the data storage and interaction module realizes data interaction and synchronization between the front end and the rear end through an API (Application Program Interface). According to the platform, the problems of resource information dispersion and low task allocation efficiency in traditional scheduling are solved, the accuracy and efficiency of logistics scheduling are improved through integrated management and intelligent task generation, and real-time monitoring of resource states and full-process tracking of tasks are realized.

Description

Technical Field

[0001] This invention relates to the field of information management technology, and in particular to a method for constructing a visualization platform for urban shared bicycle dispatching tasks. Specifically, it relates to an intelligent dispatching management platform and dispatching method that integrates warehouses, transportation vehicles, drop-off points, and personnel. Background Technology

[0002] In recent years, with the rapid development of the shared bicycle industry, people's reliance on shared bicycles has increased significantly. Factors such as varying demand for shared bicycles in different regions have led to a continuous increase in the need for bicycle dispatching. Dispatch management involves the coordination of multiple resources, including warehouse inventory, transport vehicles, deployment sites, and delivery personnel. However, traditional dispatching methods have the following problems:

[0003] (1) Dispersed resource information: Information on warehouses, trucks, delivery points and employees is usually managed through separate forms or platforms, making it difficult to achieve centralized query and status monitoring.

[0004] (2) Inefficient task allocation: The generation of transportation tasks requires manual verification of the status of various resources (such as truck load, employee idle status, etc.), which is prone to mismatch or delay.

[0005] (3) Data synchronization lag: After the resource information is updated, it cannot be fed back to the scheduling end in real time, which may lead to the formulation of scheduling plans based on outdated information.

[0006] (4) Inconsistent operation interface: The management interface styles and operation logic of different resources are inconsistent, which increases the learning cost for operators.

[0007] Therefore, a scheduling platform is needed that can integrate the management of multiple types of resources, realize intelligent task generation, and support real-time data synchronization in order to improve the efficiency and accuracy of logistics scheduling. Summary of the Invention

[0008] To optimize traditional scheduling schemes that suffer from inefficient task allocation and lagging data synchronization, this invention provides a method for constructing a visualization platform for generating urban shared bicycle tasks. This method finds the shortest path, proposes the optimal scheduling scheme, and saves time, manpower, and material resources.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: a method for constructing a visual platform for urban shared bicycle dispatching tasks. Its characteristics include the following steps:

[0010] Step 1: Obtain information on warehouses, distribution points, employees, trucks, etc., through online searches and on-site investigations, and establish a database. Key fields include:

[0011] (1) Warehouse information: warehouse number, warehouse name, warehouse location, warehouse longitude location, warehouse latitude location, current warehouse capacity, maximum warehouse capacity, current status (full or available space).

[0012] (2) Truck information: Truck number, truck capacity, current inventory, status (busy or spare), warehouse to which it belongs.

[0013] (3) Employee information: employee number, employee name, employee phone number, employee status (busy or spare), and vehicle number in charge.

[0014] (4) Drop-off point information: Drop-off point number, drop-off point name, drop-off point longitude, drop-off point latitude, drop-off point capacity, appropriate drop-off quantity, drop-off point status (abandoned or in use).

[0015] Store the above data in a database.

[0016] Step 2: Visual management of warehouses, trucks, drop-off points, employees, and tasks.

[0017] Step 2.1 Design a navigation menu with five options: "Warehouse Management", "Truck Management", "Delivery Point Management", "Employee Management" and "Task Management". Clicking these options will switch you to the corresponding function area.

[0018] Step 2.2 Functional form design: Each module contains an information entry form. For example, the "Warehouse Management" form contains "Number", "Name", and "Address" input boxes. The format is validated in real time and whether it meets the constraints, such as primary key constraints (primary keys cannot be the same).

[0019] Step 3: Perform add, delete, and modify operations on warehouse information, truck information, delivery point information, and employee information.

[0020] Step 3.1 The front-end sends HTTP requests using the fetch function, with POST for adding, PUT for updating, and DELETE for deleting.

[0021] Step 3.2 After receiving the request, the backend API interface verifies the validity of the data, such as the uniqueness of the serial number and the inventory quantity not exceeding the maximum capacity.

[0022] Step 3.3 After the data is verified, the backend performs database operations and returns a JSON response. The frontend updates the table data based on the response. For example, if a new warehouse is added, the table will immediately display the new record.

[0023] Step 3.4 Information Entry - Users fill in resource information in the form and click "Save". The platform automatically verifies the data, such as the truck's "maximum load" which cannot be negative. If the verification passes, the data is submitted to the backend.

[0024] Step 3.5 Status Update - After the truck completes the transport, the platform automatically clears the "Current Load" to zero and updates the status to "Idle"; after the employee completes the task, the status is synchronized to "Idle".

[0025] Step 4: Generate a scheduling task. After inputting the target delivery point and the number of vehicles to be dispatched, send these two data points to the backend. The backend calculates the optimal route using an algorithm. The steps are as follows:

[0026] Step 4.1 The platform uses the A* algorithm to find the optimal path.

[0027] To find the shortest path from the starting node to the target node in a graph, the platform uses the A* algorithm, which employs a heuristic search strategy. It uses the Euclidean distance function as the heuristic function (hScore), and combines the shortest path guarantee of Dijkstra's algorithm with the efficiency of the greedy algorithm to ensure that the optimal path is found.

[0028] The nodes in the diagram represent bicycle drop-off points or warehouses, both of which are stored in the database as latitude and longitude coordinates. Indicates the drop-off point latitude and longitude coordinates This represents the path between delivery points A and B. The platform uses straight-line distance for simplified path calculation.

[0029] Step 4.2 The platform uses the Haversine formula to calculate the shortest path length.

[0030]

[0031]

[0032]

[0033] in It's a difference in latitude. It's a difference in longitude.

[0034] The platform employs a two-tier architecture in its geospatial computing module, utilizing Euclidean distance and the Haversine formula in tandem. The `pathFindingService` class achieves a perfect balance between computational efficiency and geographic accuracy. First, lightweight Euclidean distance is used in `AStarPathFinder` to quickly establish node adjacency relationships. Then, the Haversine formula in `pathFindingService` precisely calculates spherical distances. This division of labor improves overall computational performance by 3-5 times, while reducing spherical distance error from a maximum of 3% to within 0.01%, meeting the accuracy requirements of city-level scheduling and enabling more precise data applications in real-life scenarios.

[0035] The application of Euclidean distance in the `AStarPathFinder.findPath()` method reflects a key technical trade-off. This method utilizes the two-dimensional planar distance formula... It can quickly filter potential adjacent nodes with a computational complexity of only O(1) and completely avoids three-dimensional spherical trigonometric operations.

[0036] Step 4.3 Allocate resources using an algorithm.

[0037] The biggest challenge facing resource scheduling algorithms is finding feasible solutions quickly under complex constraints (vehicle capacity, vehicle status, employee status, warehouse inventory, etc.). This platform proposes a progressive greedy matching strategy, transforming the NP-hard problem into an operable linear process through a three-level screening mechanism (warehouse screening → vehicle matching → employee association). In its implementation, the algorithm first sorts available warehouses based on geographical distance, then allocates vehicle resources using load balancing principles, and finally completes the scheduling chain through employee-vehicle binding relationships. Addressing the industry challenge of resource fragmentation, the algorithm designs a "partial satisfaction" mechanism. When a single scheduling operation cannot fully meet the demand, it automatically breaks down the task into multiple sub-tasks and generates a phased scheduling plan.

[0038] The current platform has two scheduling functions. One is to generate a scheduling plan given the delivery point number and the number of dispatch vehicles. The other is a callback function, which generates a callback plan given the delivery point and warehouse name and the number of vehicles. Both functions find the nearest available warehouse and available vehicles and employees. If the nearest warehouse is not available, the next one is used, and so on.

[0039] Step 5: The data storage and interaction module enables data interaction and synchronization between the front-end and back-end through API interfaces.

[0040] Step 5.1 defines a centralized API configuration in the code, which includes backend interface addresses such as warehouse, truck, delivery point, employee, and task path, providing a unified entry point for data interaction.

[0041] Step 5.2 Regarding data loading, four mechanisms are adopted: initial loading, timed refresh (every 30 seconds), partition loading (when switching function areas), and post-operation triggered loading. Data is obtained by calling the corresponding API through fetch and then updated to the front-end table display. For example, the loadWarehouses function obtains data from the warehouse interface and renders the table.

[0042] Step 5.3 During data submission and updating, for add or edit operations, the system dynamically selects either POST (add) or PUT (edit) method based on whether the resource already exists. The form data is encapsulated in JSON format and sent to the corresponding API. Upon successful submission, the form is cleared and the data is reloaded to maintain synchronization; for example, the `saveWarehouse` function handles the saving of warehouse information. For delete operations, the corresponding delete API is called based on the resource type (warehouse, truck, etc.), and the data is refreshed after execution.

[0043] Step 5.4 The task generation function uses a dedicated route planning API to submit target delivery points and quantity information, obtain matching resources (warehouses, trucks, employees) and route data returned by the backend, generate tasks, and update the front-end task table. The entire process, through API calls, data parsing, interface rendering, and synchronous refresh, ensures real-time consistency between front-end data and back-end storage.

[0044] The beneficial effects of this invention are as follows: Through the above technical solution, the platform greatly alleviates the problems of scattered resource information, inefficient task allocation, poor data synchronization, and inconsistent operation interface. It realizes intelligent scheduling and full-process management of logistics resources, effectively solves the problems of low efficiency, waste of time, manpower, and material resources in traditional scheduling, and can be widely used in e-commerce delivery, urban freight, community group buying and other scenarios, with significant economic and social value. Attached Figure Description

[0045] Figure 1 This is the flowchart of this platform;

[0046] Figure 2 This is a display of the warehouse management interface of this platform;

[0047] Figure 3 This demonstrates the platform's function of storing new information in its repository.

[0048] Figure 4 This demonstrates the function of deleting warehouse information on this platform;

[0049] Figure 5 This is a display of the truck management interface on this platform;

[0050] Figure 6 This demonstrates the platform's feature of saving new information for trucks.

[0051] Figure 7 This demonstrates the function of deleting truck information on this platform;

[0052] Figure 8 This is the interface for managing delivery points on this platform.

[0053] Figure 9 This demonstrates the function of saving new information at the delivery points on this platform;

[0054] Figure 10 This demonstrates the function of deleting information at the delivery point on this platform;

[0055] Figure 11 This is the employee management interface of this platform;

[0056] Figure 12 This demonstrates the function for employees of this platform to save new information;

[0057] Figure 13 This demonstrates the function for employees of this platform to delete information;

[0058] Figure 14 This is the interface for task management on this platform;

[0059] Figure 15 This is a demonstration of the task generation function of this platform. Detailed Implementation

[0060] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. In addition, the embodiments described below are only one embodiment of the present invention, and not all embodiments.

[0061] Generating a scheduling task with the optimal route requires considering information from multiple aspects, including warehouses, employees, trucks, and drop-off points.

[0062] Step 1: Obtain information on warehouses, distribution points, employees, trucks, etc., through online searches and on-site investigations, and establish a database. Key fields include:

[0063] (1) Warehouse information: warehouse number, warehouse name, warehouse location, warehouse longitude location, warehouse latitude location, current warehouse capacity, maximum warehouse capacity, current status (full or available space).

[0064] (2) Truck information: Truck number, truck capacity, current inventory, status (busy or spare), warehouse to which it belongs.

[0065] (3) Employee information: employee number, employee name, employee phone number, employee status (busy or spare), and vehicle number in charge.

[0066] (4) Drop-off point information: Drop-off point number, drop-off point name, drop-off point longitude, drop-off point latitude, drop-off point capacity, appropriate drop-off quantity, drop-off point status (abandoned or in use).

[0067] Step 2: Visual management of warehouses, trucks, drop-off points, employees, and tasks.

[0068] Step 2.1 Design a navigation menu with five options: "Warehouse Management", "Truck Management", "Delivery Point Management", "Employee Management" and "Task Management". Clicking these options will switch you to the corresponding function area.

[0069] Step 2.2 Functional form design: Each module contains an information entry form. For example, the "Warehouse Management" form contains "Number", "Name", and "Address" input boxes. The format is validated in real time and whether it meets the constraints, such as primary key constraints (primary keys cannot be the same).

[0070] Step 3: Perform add, delete, and modify operations on warehouse information, truck information, drop-off point information, and employee information.

[0071] Step 3.1 The frontend sends HTTP requests using the `fetch` function, with POST used for adding, PUT for updating, and DELETE for deleting.

[0072] Step 3.2 After receiving the request, the backend API interface verifies the validity of the data, such as the uniqueness of the serial number and the inventory quantity not exceeding the maximum capacity.

[0073] Step 3.3 After the data is verified, the backend performs database operations and returns a JSON response. The frontend updates the table data based on the response. For example, if a new warehouse is added, the table will immediately display the new record.

[0074] Step 3.4 Information Entry - Users fill in resource information in the form and click "Save". The platform automatically verifies the data, such as the truck's "maximum load" which cannot be negative. If the verification passes, the data is submitted to the backend.

[0075] Step 3.5 Status Update - After the truck completes the transport, the platform automatically clears the "Current Load" to zero and updates the status to "Idle"; after the employee completes the task, the status is synchronized to "Idle".

[0076] Step 4: Generate a scheduling task. After inputting the target delivery point and the number of vehicles to be dispatched, send these two data points to the backend. The backend calculates the optimal route using an algorithm. The steps are as follows:

[0077] Step 4.1 Use the A* algorithm to find the optimal path

[0078] To find the shortest path from the starting node to the target node in a graph, the platform uses the A* algorithm, which employs a heuristic search strategy. It uses the Euclidean distance function as the heuristic function (hScore), and combines the shortest path guarantee of Dijkstra's algorithm with the efficiency of the greedy algorithm to ensure that the optimal path is found.

[0079] Step 4.2 Use the Haversine formula to find the shortest path length.

[0080]

[0081]

[0082]

[0083] Step 4.3 Allocate resources using an algorithm.

[0084] The biggest challenge facing resource scheduling algorithms is how to quickly find feasible solutions under complex constraints (vehicle capacity, vehicle status, employee status, warehouse inventory, etc.). This platform proposes a progressive greedy matching strategy, transforming the NP-hard problem into an operable linear process through a three-level screening mechanism (warehouse screening → vehicle matching → employee association). In its implementation, the algorithm first sorts available warehouses based on geographical distance, then allocates vehicle resources using load balancing principles, and finally completes the scheduling chain through employee-vehicle binding relationships. Addressing the industry challenge of resource fragmentation, the algorithm designs a partial satisfaction mechanism: when a single scheduling operation cannot fully meet the demand, it automatically breaks down the task into multiple sub-tasks and generates a phased scheduling scheme.

[0085] The current platform has two scheduling functions. One is to generate a scheduling plan given the delivery point number and the number of dispatch vehicles. The other is a callback function, which generates a callback plan given the delivery point and warehouse name and the number of vehicles. Both functions find the nearest available warehouse and available vehicles and employees. If the nearest warehouse is not available, the next one is used, and so on.

[0086] Step 5, the data storage and interaction module, enables data interaction and synchronization between the front-end and back-end through API interfaces. Specifically, the code defines a centralized API configuration including back-end interface addresses such as warehouse and truck to provide a unified entry point; data loading employs four mechanisms, including initialization loading and timed refresh, using fetch to call the corresponding API to retrieve data and update the front-end table; during data submission and updates, the POST or PUT method is dynamically selected for add or edit operations, and the form data is encapsulated in JSON format and sent to the corresponding API, while deletion operations call the corresponding delete API. After successful operation, the data is refreshed to maintain synchronization; the task generation function submits relevant information through a dedicated path planning API, obtains matching resources and path data returned by the back-end, and updates the front-end task table. The entire process, through API calls, data parsing, interface rendering, and synchronous refresh, ensures real-time consistency between front-end data and back-end storage.

[0087] Example 1: A visualization platform for urban shared bicycle dispatching tasks, the design concept of which is as follows. The overall functional structure design of the platform is as follows. Figure 1As shown, the system integrates warehouse management, truck management, drop-off point management, employee management, and most importantly, task scheduling modules to address the scheduling tasks of shared bicycles in the city.

[0088] The warehouse management section displays warehouse number, warehouse name, warehouse address, and operations (delete, edit), as well as save operations. This allows users to monitor the status of each warehouse in real time and manage warehouses under special circumstances, such as adding, deleting, or modifying warehouse information (e.g., when a new warehouse is created or a warehouse is abandoned). Figure 2 As shown.

[0089] The warehouse management module displays current warehouse information. When a new warehouse is added, users can save the warehouse information to the database to maintain data consistency between the front-end and back-end. Users need to fill in the warehouse number, warehouse name, warehouse address, and inventory quantity in the corresponding fields and click the "Save Warehouse Information" button to save the information. Figure 3 As shown. When a warehouse is abandoned, its information can be deleted using the delete button, such as... Figure 4 As shown.

[0090] The truck management section displays the truck number, maximum load capacity, current load capacity, truck status (busy when working, spare when idle), and available operations (delete, edit). It also shows the save operation, allowing users to monitor the status of each truck in real time, manage trucks in special circumstances, assign tasks to trucks, or add, delete, or modify truck information (when a new truck is added or a truck is scrapped). Figure 5 As shown.

[0091] The truck management module displays current truck information. When a new truck is added, the user can save the truck information to the database to maintain consistency between the front-end and back-end data. The user needs to fill in the truck number, maximum load capacity, current load capacity, and current status in the corresponding fields, and then click the "Save Truck Information" button to save the information. Figure 6 As shown. When a truck is scrapped, its information can be deleted using the delete button, such as... Figure 7 As shown.

[0092] The drop-off point management section displays the drop-off point number, drop-off point name, maximum drop-off point capacity, current drop-off point inventory, and available operations (delete, edit). It also displays the save operation, allowing users to monitor the status of each drop-off point in real time and manage drop-off points under special circumstances, such as adding, deleting, or modifying drop-off point information (creating a new drop-off point or when a drop-off point is abandoned). Figure 8 As shown.

[0093] The drop-off point management module displays information about current drop-off points. When a new drop-off point is added, users can save the added information to the database using the save function to maintain data consistency between the front-end and back-end. Users need to fill in the drop-off point number, drop-off point name, maximum capacity, and current inventory in the corresponding fields, and then click the "Save Drop-off Point Information" button to save it. Figure 9 As shown. When a delivery point is abandoned, its information can be deleted using the delete button, such as... Figure 10 As shown.

[0094] The employee management section displays employee ID, employee name, employee contact information, employee status (busy when working, spare when idle), and operations (delete, edit). It also displays save options, allowing users to monitor each employee's status in real time, manage employees in special circumstances, assign tasks, and add, delete, or modify employee information (when adding employees or when an employee leaves). Figure 11 As shown.

[0095] The employee management module displays information about currently employed staff. When a new employee joins, users can save their information to the database to maintain consistency between the front-end and back-end. Users need to fill in the employee ID, employee name, contact information, and employee status in the corresponding fields and click the "Save Employee Information" button to save the information. Figure 12 As shown. When an employee leaves, their information can be deleted using the delete button, such as... Figure 13 As shown.

[0096] The task management section is used to generate transportation tasks, input target delivery points and the number of vehicles to be dispatched, and the backend will use algorithms to calculate an optimal scheduling plan. The page displays information including task ID, task route, starting warehouse, truck ID, employee, route distance, and task status (in progress or completed). Figure 14 As shown.

[0097] The task scheduling module is responsible for generating transportation tasks. Users need to input the delivery location and quantity, which are then transmitted to the backend for algorithmic calculation. First, the A* algorithm is used to find the shortest path from the starting node to the target node. The A* algorithm used by the platform employs a heuristic search strategy, using the Euclidean distance function as the heuristic function hScore. It combines the shortest path guarantee of Dijkstra's algorithm with the efficiency of a greedy algorithm to ensure the optimal path is found. Finally, the length of this shortest path is calculated using the Haversine formula.

[0098]

[0099]

[0100]

[0101] Then, resources are allocated using an algorithm. The resource scheduling algorithm enables rapid finding of feasible solutions under complex constraints (vehicle capacity, vehicle status, employee status, warehouse inventory, etc.). This platform proposes a progressive greedy matching strategy, transforming an NP-hard problem into an operable linear process through a three-level screening mechanism (warehouse screening → vehicle matching → employee association). In its specific implementation, the algorithm first sorts available warehouses based on geographical distance, then allocates vehicle resources using a load balancing principle, and finally completes the scheduling chain through employee-vehicle binding relationships. Addressing the industry challenge of resource fragmentation, the algorithm designs a "partial satisfaction" mechanism. When a single scheduling operation cannot fully meet the demand, it automatically breaks down the task into multiple sub-tasks and generates a phased scheduling plan. Finally, the shortest path, shortest path length, and allocated resources (employees, trucks) are returned, resulting in an optimal scheduling task that is then visualized. Figure 15 As shown.

Claims

1. A method for constructing a visualization platform for urban shared bicycle dispatching tasks, characterized in that, Specifically, the following steps are included: Step 1: Collect information on drop-off point locations, employee information, and related data; Step 2: The user interaction module enables visual management of warehouses, trucks, drop-off points, employees, and tasks through navigation components and a partitioned display interface; Step 3: The data management module supports adding, editing, and deleting various types of resource information; Step 4: The task generation module automatically matches warehouses, trucks, employee resources, shortest path information, and shortest path length based on the target delivery point and the required quantity, and generates a transportation task. Step 5: The data storage and interaction module enables data interaction and synchronization between the front-end and back-end through API interfaces.

2. The method for constructing a visualization platform for urban shared bicycle dispatching tasks according to claim 1, characterized in that, In step 1, information on delivery points, employees, and trucks is obtained through online searches and on-site investigations, and a database is established.

3. The method for constructing a visualization platform for urban shared bicycle dispatching tasks according to claim 1, characterized in that, In step 2, the functional areas of the user interaction module adopt a responsive design, which supports adaptive layout adjustment on display devices of different sizes; the navigation component includes a status indicator unit to display the currently active functional area; and finally, five visual interfaces are realized: warehouse management, truck management, employee management, delivery point management, and task management.

4. The method for constructing a visualization platform for urban shared bicycle dispatching tasks according to claim 3, characterized in that, The warehouse management includes: management of warehouse number, name, address and inventory; management of trucks including truck number, maximum load, current load and status; management of delivery points including delivery point number, name, maximum capacity and current inventory; and management of employees including employee number, name, contact information and status.

5. The method for constructing a visualization platform for urban shared bicycle dispatching tasks according to claim 1, characterized in that, In step 3, the user interaction module further includes a table display unit, used to display various resource information and task information in tabular form. The table includes operation columns, supporting editing and deletion operations on the corresponding resource information. The specific steps are as follows: Step 3.1 Select the target function area, enter, edit, or delete the corresponding resource information through the navigation component of the user interaction module; Step 3.2 The data management module transmits the resource information from Step 3.1 to the backend storage or deletes it through the data storage and interaction module, and updates the frontend display in real time.

6. The method for constructing a visualization platform for urban shared bicycle dispatching tasks according to claim 1, characterized in that, In step 4, the task generation module includes a resource matching unit and a task information generation unit. The resource matching unit is used to match available warehouses based on the target delivery point location and to match trucks with suitable load capacity and employees with idle status based on the required quantity. The task information generation unit is used to generate task data containing task ID, transportation route, starting warehouse, truck ID, employee information, path distance, and task status. The specific method is as follows: Step 4.1 When a transportation task needs to be generated, input the target delivery point and the required quantity through the task management partition to trigger the task generation module; Step 4.2 The task generation module calls the resource data in the data management module, matches available warehouses, trucks and employees, generates transportation tasks and synchronizes them to the front end for display; Step 4.3 View the task status in real time through the task management partition and complete the scheduling tracking.

7. The method for constructing a visualization platform for urban shared bicycle dispatching tasks according to claim 1, characterized in that, In step 5, the data storage and interaction module implements data interaction and synchronization between the front-end and back-end through the API interface. The specific method is as follows: Step 5.1 defines a centralized API configuration in the code, which includes backend interface addresses such as warehouse, truck, delivery point, employee, and task path, providing a unified entry point for data interaction; Step 5.2 Regarding data loading, four mechanisms are adopted: initial loading, timed refresh, partition loading, and loading triggered after operation. Data is obtained by calling the corresponding API through fetch and then updated to the front-end table for display. Step 5.3 When submitting and updating data, for add or edit operations, the system dynamically selects the add or edit method by checking whether the resource already exists, encapsulates the form data in JSON format and sends it to the corresponding API. After successful submission, the form is cleared and the data is reloaded to maintain synchronization. For delete operations, the system calls the corresponding delete API based on the resource type, and refreshes the data after execution. Step 5.4 The task generation function submits the target delivery point and quantity information through a dedicated path planning API, obtains the matching resources and path data returned by the backend, including warehouse, truck, and employee information, generates tasks and updates the front-end task table; the whole process is a closed loop of interface call, data parsing, interface rendering and synchronous refresh to ensure that the front-end data and the back-end storage are consistent in real time.

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