Gas cylinder transportation scheduling system

Abstract

To solve the problems that scheduling system usually needs to dispatch personnel to make transportation plan according to experience and manual calculation, which is not only time-consuming but also prone to errors, the present application provides a gas cylinder transportation scheduling system, which comprises the following modules: a data integration and analysis module, which integrates customer addresses, product information, transportation restrictions and the like to generate analysis results; a route planning module, which designs an optimal route based on GIS and real-time traffic data; a vehicle and driver allocation module, which optimally allocates according to the route and driver conditions; a dynamic adjustment module, which monitors in real time and adjusts the route and resources when an exception occurs; a customer notification module, which provides automatic notification and query functions; a data recording and analysis module, which records and analyzes transportation data; and a system learning and optimization module, which uses machine learning to improve operation logic, so as to realize efficient scheduling and continuous optimization.

Description

Technical Field

[0001] This invention relates to the field of logistics transportation scheduling technology, specifically a gas cylinder transportation scheduling system. It optimizes the allocation and scheduling of transportation resources, addressing problems in traditional transportation processes such as inaccurate route planning, low efficiency in vehicle and driver allocation, insufficient capacity to handle traffic anomalies, delayed customer notifications, and a lack of data analysis and optimization capabilities. By integrating data from multiple sources and utilizing advanced geographic information technology and machine learning methods, this system effectively improves transportation efficiency, reduces operating costs, and enhances customer satisfaction. Background Technology

[0002] In the field of gas cylinder transportation scheduling, traditional techniques and methods mainly rely on manual scheduling and basic logistics management systems. These systems typically require dispatchers to develop transportation plans based on experience and manual calculations, which is not only time-consuming but also prone to errors.

[0003] Traditional gas cylinder transportation scheduling relies heavily on manual labor and experience. This approach is not only time-consuming but also prone to human error, especially when handling large orders or complex routes, making it difficult to guarantee efficiency and accuracy. Dispatchers are also difficult to train and develop. Furthermore, traditional systems often lack real-time data updates and dynamic adjustment capabilities, failing to quickly respond to sudden order changes or route adjustments. With increasing logistics demands and rising customer expectations, these systems are proving inadequate in terms of efficiency and accuracy. Existing solutions often fail to effectively integrate multiple data sources, such as inventory status, vehicle location, and traffic conditions, leading to improper resource allocation and increased transportation costs. These technological limitations present an opportunity for the development of new cloud-based SaaS platforms to optimize gas cylinder transportation scheduling in a smarter and more automated way. Summary of the Invention

[0004] This invention provides a gas cylinder transportation scheduling system. Through multi-module collaboration, the system achieves data integration and intelligent analysis, optimal route planning, efficient vehicle and driver allocation, and dynamic response to abnormal situations. Combining Geographic Information System (GIS), real-time traffic data, and machine learning technology, this system can generate optimal solutions based on customer needs, transportation constraints, and real-time conditions, and improve the customer's transportation experience through automatic notifications. The system also possesses data recording and analysis capabilities, continuously improving transportation efficiency and resource utilization through historical data mining and optimization, providing intelligent solutions for the logistics and transportation industry.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] This invention provides a gas cylinder transportation scheduling system, comprising: a data integration and analysis module, which integrates and analyzes the following data: customer delivery location, product information, delivery and receipt quantities, specified delivery time intervals, available vehicle carrying capacity limits, and driver-related information, generating and outputting analysis results; a route planning module, which interacts with the data integration and analysis module and receives the analysis results, generates optimal transportation routes based on Geographic Information System (GIS) and real-time traffic data, considering shortest distance, traffic conditions, fuel consumption, and vehicle carrying capacity limits, and outputs the planning results; a vehicle and driver allocation module, which allocates suitable transportation vehicles and drivers according to the planning results of the route planning module, and optimizes the allocation results based on driver license type, working hour limits, and workload information provided by the data integration and analysis module; and a dynamic adjustment module, which interacts with the route planning module and the vehicle and driver allocation module. The system receives the allocation results, continuously monitors vehicle location and traffic conditions, and updates transportation routes or reassigns vehicles and drivers in abnormal situations, outputting notification messages and notifying relevant personnel. The customer notification module interacts with the dynamic adjustment module, receives the notification messages, automatically generates delivery notifications, and sends estimated delivery times and delay information to customers via email or SMS, while also providing real-time order status queries. The data recording and analysis module receives data from the data integration and analysis module, the route planning module, the vehicle and driver allocation module, and the dynamic adjustment module, records relevant data during transportation, and outputs analysis results. The system learning and optimization module utilizes historical data and the analysis results from transportation records provided by the data recording and analysis module to perform machine learning, optimizing the computational logic of the data integration and analysis module, the route planning module, and the vehicle and driver allocation module to improve future scheduling efficiency.

[0007] In one embodiment, the data integration and analysis module can prioritize orders based on their characteristics and mark orders that require specific types of transport vehicles.

[0008] In one embodiment, the route planning module uses a geographic data-based algorithm to generate an optimal route by comprehensively considering multiple path factors, and ensures that each vehicle does not exceed the carrying limit.

[0009] In one embodiment, the vehicle and driver assignment module selects a suitable driver based on the driver's historical work data and safety record.

[0010] In one embodiment, the dynamic adjustment module can update route planning based on real-time traffic data and reallocate resources in abnormal situations.

[0011] In one embodiment, the customer notification module can provide real-time order tracking functionality based on a mobile application.

[0012] In one embodiment, the data recording and analysis module can generate a transportation efficiency report and automatically identify inefficient links in the transportation process.

[0013] In one embodiment, the system's learning and optimization module can predict future changes in order demand based on historical transportation data and proactively adjust scheduling strategies. Attached Figure Description

[0014] Figure 1 A schematic diagram illustrating the gas cylinder transportation scheduling system according to an embodiment of the present invention;

[0015] Figure 2 A schematic diagram illustrating the participants in the gas cylinder transportation scheduling system according to an embodiment of the present invention;

[0016] Figure 3 A schematic diagram illustrating the participants in a gas cylinder transportation scheduling system according to another embodiment of the present invention.

[0017] Explanation of symbols in the attached drawings:

[0018] 1: Gas cylinder transportation scheduling system;

[0019] 11: Data Integration and Analysis Module;

[0020] 12: Route planning module;

[0021] 13: Vehicle and driver assignment module;

[0022] 14: Dynamic adjustment module;

[0023] 15: Customer Notification Module;

[0024] 16: Data recording and analysis module;

[0025] 17: System Learning and Optimization Module. Detailed Implementation

[0026] The same reference numerals in different figures represent the same or similar components and therefore perform similar functions. Furthermore, for the sake of simplicity, descriptions and details of well-known steps and components have been omitted. In addition, numerous specific details are set forth in the following detailed description of the invention to provide a thorough understanding of the invention. However, it is to be understood that the invention can be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail to avoid unnecessarily obscuring various aspects of the invention. Examples of various embodiments will be further illustrated and described below. It is to be understood that the description herein is not intended to limit the claims to the specific embodiments described. Rather, it is intended to cover alternatives, modifications, and equivalents that may be included within the spirit and scope of the invention as defined by the appended claims.

[0027] The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of the invention. The singular form "a" as used herein may also include the plural form unless the context clearly indicates otherwise. It will be further understood that "comprising" and "including," when used in this specification, specify the presence of the stated feature, integral, operation, component, and / or component, but do not exclude the presence or addition of one or more other features, integrals, operations, components, and / or portions thereof.

[0028] The embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without making any innovative contributions are within the scope of protection of the present invention.

[0029] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0030] Secondly, the present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the structure of the device will be partially enlarged, not according to general proportions. The relative sizes between the device components do not represent the actual sizes, and the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. Furthermore, in actual manufacturing, the three-dimensional spatial dimensions of length, width, and height should be included.

[0031] like Figure 1 , Figure 2 and Figure 3As shown, the gas cylinder transportation scheduling system 1 of the present invention is set up on a cloud-based SaaS platform. Smartphones, tablets, server monitoring devices, etc., used by customers and drivers are connected to the gas cylinder transportation scheduling system 1 via a network. The system includes: a data integration and analysis module 11, which integrates and analyzes the following data: customer's delivery location, product information, delivery and receipt quantities, specified delivery time intervals, available vehicle carrying capacity limits, and driver-related information, generating and outputting analysis results; a route planning module 12, which interacts with the data integration and analysis module 11 and receives the analysis results, generating optimal transportation routes based on Geographic Information System (GIS) and real-time traffic data. The generated routes consider shortest distance, traffic conditions, fuel consumption, and vehicle carrying capacity limits, and outputting the planning results; a vehicle and driver allocation module 13, which allocates suitable transportation vehicles and drivers according to the planning results of the route planning module 12, and optimizes the allocation results based on driver license type, working hour limits, and workload information provided by the data integration and analysis module 11; and a dynamic adjustment module 14. The system interacts with the route planning module 12 and the vehicle and driver allocation module 13, receives the allocation results, continuously monitors the vehicle's location and traffic conditions, and updates the transportation route or reassigns vehicles and drivers in abnormal situations, outputting notification messages and notifying relevant personnel. The customer notification module 15 interacts with the dynamic adjustment module 14, receives the notification messages, automatically generates delivery notifications, and sends estimated delivery times and delay information to customers via email or SMS, and provides a real-time order status query function. The data recording and analysis module 16 receives data from the data integration and analysis module 11, the route planning module 12, the vehicle and driver allocation module 13, and the dynamic adjustment module 14, records relevant data during transportation, and outputs analysis results. The system learning and optimization module 17 uses the historical data and the analysis results of the transportation records provided by the data recording and analysis module 16 to perform machine learning, optimize the calculation logic of the data integration and analysis module 11, the route planning module 12, and the vehicle and driver allocation module 13, and improve future scheduling efficiency. By using machine learning models (such as reinforcement learning or deep learning) to optimize transportation routes, taking into account various factors such as road conditions, peak traffic periods, and vehicle carrying capacity limitations, and by continuously learning and adjusting strategies, route planning can be continuously optimized, ultimately improving transportation efficiency, reducing delays, and lowering fuel consumption.

[0032] In one embodiment of the present invention, the data integration and analysis module 11 can classify and prioritize orders based on their characteristics and mark orders requiring specific types of transport vehicles. It prioritizes urgent or high-value orders to ensure timely delivery of critical orders and avoid losses due to delays; it also rationally plans transportation resources to reduce the resource consumption of low-priority orders. Orders requiring specific vehicles (such as refrigerated cylinders, special chemical gases, etc.) are matched to avoid rescheduling or additional costs due to incorrect allocation; it reduces the transportation failure rate caused by mismatched vehicle types, lowering risks and costs. It accurately judges order demands to ensure that specific conditions (such as temperature control, overload restrictions) are met, improving customer satisfaction; by marking and prioritizing orders from high-demand customers, it establishes good cooperative trust, generates more reasonable route and resource allocation schemes based on order priorities, and optimizes scheduling plans. It provides data support to managers to quickly formulate response strategies and handle abnormal situations; it collects historical data on priority and specific demand orders to help the system learning module perform more accurate classification and prediction; and through data analysis, it improves transportation planning logic and resource allocation models to achieve continuous optimization. Variables to consider in cylinder transportation include: the full weight of the cylinder, the size of the cylinder's base, whether cage loading is required, and gas repulsion. For example, if a vehicle has a load capacity of 5000 kg and a single hydrogen cylinder weighs 68 kg, theoretically the vehicle could carry a maximum of 73 hydrogen cylinders. However, because these cylinders require cage loading (each cage weighs 120 kg and can hold 15 cylinders), the vehicle can actually only carry a maximum of 64 cylinders. Furthermore, in reality, the gas carried by the vehicle is not always the same, cylinder sizes may vary, and customers may require cylinder recycling. In actual transportation scheduling, these complex factors significantly impact vehicle carrying capacity and route planning. The following is a specific transportation scenario analysis, highlighting the need for transportation scheduling platforms to accurately handle complex load and route planning logic:

[0033] Assume this transportation task includes:

[0034] - New shipment of hydrogen cylinders: 40 cylinders (packed in 3 cages);

[0035] - Cylinder weight: 40 × 68 kg = 2,720 kg;

[0036] - Cage weight: 3 × 120 kg = 360 kg;

[0037] - Total outbound load capacity: 2,720 + 360 = 3,080 kg;

[0038] Outbound load characteristics:

[0039] - Only newly shipped cylinders are counted;

[0040] - Recycled cylinders were not included;

[0041] - Complies with the vehicle's 5000 kg load limit;

[0042] - Reserved space for return transport and recycling of steel cylinders;

[0043] Return transportation considerations:

[0044] -30 recycled empty bottles;

[0045] - 2 dedicated cages;

[0046] - It may be necessary to plan a different transportation route than the outbound journey.

[0047] Furthermore, drivers can use mobile mission devices to confirm their mission itineraries. In the event of temporary changes during transport, over-the-air (OTA) technology can be used to synchronize route updates and mission sequence adjustments to the mobile mission device in real time, allowing drivers to be proactively informed of the latest mission changes and improving maneuverability. For intelligent transport scheduling platforms, this complex load and classification calculation requires highly intelligent algorithms capable of adjusting loading plans in real time to ensure transport safety, efficiency, and economy.

[0048] In one embodiment of the present invention, the route planning module 12 uses a geographic data-based algorithm to generate an optimal route by comprehensively considering multiple path factors, ensuring that the carrying capacity of each vehicle is not exceeded. This improves transportation efficiency; based on real-time traffic data, it selects the route with the least traffic flow, reducing delays caused by congestion; automated route planning reduces human intervention and helps to quickly generate optimal transportation plans; it selects the shortest distance or the route with the lowest fuel consumption, reducing energy consumption during transportation. Route optimization reduces losses caused by frequent vehicle starts and stops and detours. Strict consideration of vehicle carrying capacity in route planning ensures compliance with transportation regulations and reduces the risks associated with overloading; it selects safe routes for specific goods (such as hazardous gas cylinders) and avoids high-risk areas (such as schools and hospitals); reliable route planning ensures on-time delivery and reduces customer dissatisfaction caused by delays. In the event of traffic anomalies or emergencies, routes can be quickly replanned to reduce impact; by combining load constraints and route optimization, overloading or empty transport of some vehicles can be avoided, thus achieving rational allocation of resources; through optimal route planning, the amount of transport that can be completed per unit time can be increased; historical transport routes and actual effects can be recorded to provide a basis for system improvement and optimize future route planning algorithms; algorithms based on geographic data can simulate multiple scenarios during the planning stage to help managers select the best solution.

[0049] In one embodiment of the invention, the vehicle and driver assignment module 13 selects a suitable driver based on the driver's historical work data and safety record. This reduces the risk of accidents, especially when transporting hazardous materials (such as gas cylinders), and improves the reliability and safety of the transportation process. Assigning tasks according to the driver's historical working hours and rest schedule ensures that the driver works in optimal condition, reducing accidents caused by fatigue. Matching Skills and Tasks: Selecting drivers with relevant experience for specific tasks (such as long-distance transport or hazardous materials transport) improves the accuracy and efficiency of task execution; drivers familiar with certain routes or vehicles can get up to speed faster, reducing unnecessary route deviations or vehicle operation errors; Reducing Wear and Tear: Selecting drivers with good driving behavior to operate vehicles can reduce fuel consumption and vehicle wear and tear, extending vehicle lifespan; Avoiding Economic Losses Due to Safety Issues: Reducing economic losses such as fines, increased insurance costs, or cargo damage caused by accidents or violations; Allocating work based on historical data avoids overloading or idleness of some drivers, maintaining a reasonable workload and rest schedule; A transparent allocation system avoids human intervention, increasing drivers' acceptance and trust in the scheduling arrangement; Accumulating historical driver data provides a basis for subsequent system optimization and driver management, such as training needs and performance evaluations; Recording driver task types and performance data helps identify training needs, further improving driver capabilities and overall system efficiency; Ensuring the allocation process complies with local labor regulations (such as driving time restrictions) and traffic regulations to avoid legal disputes; Assigning hazardous materials transport tasks based on driver license type and training records complies with relevant legal requirements.

[0050] In one embodiment of the present invention, the dynamic adjustment module 14 can update route planning based on real-time traffic data and reallocate resources in abnormal situations. Improve on-time delivery rates; select optimal alternative routes based on real-time traffic data to ensure timely delivery and reduce losses due to delays; quickly adjust plans to cope with unexpected events (such as road closures and accidents), improving plan flexibility and emergency response capabilities; reduce fuel consumption and additional waiting time costs by avoiding congested sections and selecting efficient routes; promptly reallocate resources to reduce duplicate transportation or delays caused by vehicle or route issues; quickly allocate other available vehicles or drivers in abnormal situations to maximize the use of available resources and avoid resource idleness; ensure reasonable resource allocation through dynamic adjustments to avoid overburdening certain routes or vehicles; immediately inform customers of updated information (such as estimated delivery time) through customer notification module 15 when routes or plans change, increasing transparency; ensure efficient order processing even in unexpected situations, building customer trust in the system; record data during the adjustment process for subsequent analysis to help improve algorithms and predictive capabilities; improve the system's ability to respond to similar situations in the future by analyzing historical anomalies, achieving long-term optimization; when updating routes based on traffic and environmental data, avoid areas with safety hazards due to accidents or severe weather. Real-time adjustments reduce the risk of transportation disruptions due to abnormal situations, ensuring the safe delivery of goods.

[0051] In one embodiment of the present invention, the customer notification module 15 can provide real-time order tracking functionality based on a mobile application. Customers can check their order status at any time (such as estimated delivery time and current location), reducing uncertainty about the delivery progress. If problems are found (such as delays), customers can contact customer service in real time to make requests or modify the plan. The real-time tracking function reduces the need for customers to check the order status, reducing the workload of customer service. The system automatically generates and pushes updated information, keeping customers informed without manual intervention. Transparent processes and accurate data display enhance customer trust and satisfaction with the company's transportation services. The high-tech mobile application tracking function demonstrates the company's professionalism and modern management capabilities. In abnormal situations, it automatically notifies customers of the reasons for delays and the latest progress, reducing customer dissatisfaction with delays. For example, it pushes relevant remedial measures (such as refunds and discounts), making customers feel the company's attention and responsibility. The convenient tracking function improves the customer experience and attracts more users to choose the service. It records customer behavior data (such as query frequency and feedback), helping the company optimize the application and operational strategies. With the popularization of mobile devices, the real-time tracking function meets customers' expectations for convenient services. The function is compatible with smartphones and tablets, expanding the scope of customer use and convenience.

[0052] In one embodiment of the present invention, the data recording and analysis module 16 can generate a transportation efficiency report and automatically identify inefficient links in the transportation process; through data analysis, it can identify bottlenecks in the transportation process (such as traffic delays, vehicle idleness, and excessive loading and unloading time) and provide improvement suggestions; combined with the efficiency report, it can adjust route strategies for common problems to improve the overall efficiency of the transportation process; by analyzing resource usage (such as fuel consumption and vehicle maintenance frequency), it can reduce unnecessary costs; based on efficiency data, it can optimize the operation process, improve the completion speed of a single transportation, and reduce time costs; through key data in the report, it can provide managers with evidence-based suggestions to help formulate more accurate transportation strategies; by comparing the performance of different vehicles, routes, or drivers, it can identify efficient and inefficient transportation modes, providing a basis for performance evaluation and improvement; and it can identify inefficient links. Data serves as input to the system's learning model, continuously optimizing route planning and resource allocation algorithms to improve intelligence. Historical data analysis predicts potential problems (such as high-traffic periods or frequently malfunctioning vehicles), allowing for proactive preventative measures. Optimizing inefficient processes leads to more accurate delivery time predictions, reducing customer dissatisfaction caused by delays. Improved transportation efficiency directly impacts customer experience and builds a reliable brand image. Analysis reports can detect issues like vehicle overloading, speeding, or driver overtime, mitigating legal risks. Improving inefficient processes, such as avoiding high-risk routes or prolonged driving, enhances transportation safety. The data provided in the reports helps systematically improve operational processes, achieving long-term, stable efficiency gains. Based on efficiency analysis results, vehicles, drivers, and other resources are rationally allocated, avoiding over- or under-allocation.

[0053] In one embodiment of the present invention, the system learning and optimization module 17 can predict future changes in order demand based on historical transportation data and proactively adjust scheduling strategies. Based on demand forecasts, it pre-allocates vehicles, drivers, and other resources to avoid resource shortages during peak periods or idle resources during periods of low demand; optimizes scheduling to reduce driver overtime or vehicle overload, improving the stability of the transportation process; automatically updates scheduling strategies based on demand fluctuations to quickly respond to sudden order peaks or temporary demand changes; and when high demand is predicted, it pre-optimizes route planning and resource allocation to reduce transportation delays caused by insufficient planning. Accurate demand forecasting avoids excessive resource investment, reducing vehicle idleness and operating costs; based on predicted order density distribution, it plans efficient routes and vehicle combinations to minimize fuel and time costs; precise demand forecasting allows the system to better meet customer delivery time requirements, increasing customer satisfaction; even during periods of abnormal demand fluctuations, the system maintains service stability, enhancing customer trust; historical data analysis predicts future market demand changes, helping businesses develop long-term strategies and expansion plans; sales or promotional strategies are suggested based on demand distribution to attract more customers; the system's learning model is optimized based on demand forecast results and actual implementation, improving the accuracy and practicality of future forecasts; considering multiple factors such as time, location, and order type, it generates more accurate forecast results, optimizing the decision-making process; proactively adjusting strategies maintains the system's competitive advantage in the market, attracting more users to choose its services; and it can provide flexible transportation solutions based on different demand forecasts to meet diverse customer needs. Figure 3 As shown, server monitoring equipment and / or computers can monitor system operating status and traffic load, and support monitoring and adjustment of backend data.

[0054] In summary, the gas cylinder transportation scheduling system provided by this invention achieves efficient and accurate transportation scheduling by integrating advanced data analysis, route planning, dynamic adjustment, and vehicle and driver allocation functions. This system can automatically process large amounts of complex data, reduce human error, and improve the utilization efficiency of transportation resources. Dynamic monitoring and real-time adjustment ensure rapid response and plan adjustment in abnormal situations, enhancing the overall service flexibility and reliability. Furthermore, the application of machine learning technology further optimizes the scheduling logic, achieving continuous improvement and efficiency enhancement. This not only improves transportation efficiency but also enhances customer experience and reduces operating costs, making it of significant practical value for the transportation management of special goods such as gas cylinders.

[0055] Furthermore, by automating various scheduling tasks, the need for manual intervention is significantly reduced, transportation plan development time is shortened, and the risks caused by human error are mitigated. The system can analyze and process various transportation conditions in real time and dynamically adjust transportation plans according to different demand changes, thereby achieving more flexible and efficient transportation management. In addition, the system's machine learning module can continuously optimize the computational logic and predict future demand based on historical data, further improving scheduling accuracy and resource allocation efficiency. Overall, this invention can significantly improve the overall efficiency of transportation operations, providing a more intelligent and reliable transportation solution for the gas cylinder industry.

[0056] The above describes the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also within the scope of protection of the present invention.

Claims

1. A gas cylinder transportation scheduling system, characterized in that, include: The data integration and analysis module integrates and analyzes the following data: customer delivery location, product information, delivery and receipt quantities, specified delivery time intervals, available transport vehicle carrying restrictions, and driver-related information, generating and outputting analysis results. The route planning module interacts with the data integration and analysis module and receives the analysis results. Based on the geographic information system and real-time traffic data, it generates the optimal transportation route. The generated route takes into account the shortest distance, traffic conditions, fuel consumption, and vehicle carrying capacity, and outputs the planning results. The vehicle and driver allocation module allocates suitable transport vehicles and drivers based on the planning results of the route planning module, and optimizes the allocation results based on the driver license type, working time restrictions and workload information provided by the data integration and analysis module. The dynamic adjustment module interacts with the route planning module and the vehicle and driver allocation module and receives the allocation results. It continuously monitors the vehicle's location and traffic conditions, and updates the transportation route or reassigns vehicles and drivers in abnormal situations, outputs notification messages and notifies relevant personnel. The customer notification module interacts with the dynamic adjustment module and receives the notification message. It automatically generates delivery notifications and sends the estimated delivery time and delay information to customers via email or SMS. It also provides a real-time order status query function. The data recording and analysis module receives data from the data integration and analysis module, the route planning module, the vehicle and driver allocation module, and the dynamic adjustment module, records relevant data during the transportation process, and outputs analysis results. The system learning and optimization module utilizes the historical data and transportation records provided by the data recording and analysis module to perform machine learning, optimize the computational logic of the data integration and analysis module, the route planning module, and the vehicle and driver allocation module, in order to improve future scheduling efficiency.

2. The gas cylinder transportation scheduling system according to claim 1, characterized in that, This data integration and analysis module can prioritize orders based on their characteristics and mark orders that require specific types of transport vehicles.

3. The gas cylinder transportation scheduling system according to claim 1, characterized in that, The route planning module uses a geographic data-based algorithm to generate the optimal route by taking into account multiple path factors, and ensures that each vehicle does not exceed the carrying capacity limit.

4. The gas cylinder transportation scheduling system according to claim 1, characterized in that, The vehicle and driver assignment module selects a suitable driver based on the driver's historical work data and safety record.

5. The gas cylinder transportation scheduling system according to claim 1, characterized in that, This dynamic adjustment module can update route planning based on real-time traffic data and reallocate resources in abnormal situations.

6. The gas cylinder transportation scheduling system according to claim 1, characterized in that, This customer notification module provides real-time order tracking via a mobile application.

7. The gas cylinder transportation scheduling system according to claim 1, characterized in that, This data recording and analysis module can generate transportation efficiency reports and automatically identify inefficient links in the transportation process.

8. The gas cylinder transportation scheduling system according to claim 1, characterized in that, The system's learning and optimization module can predict future changes in order demand based on historical transportation data and proactively adjust scheduling strategies.

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