Multi-mode fusion take-out delivery scheduling system and method

Abstract

The invention relates to the technical field of distribution scheduling, in particular to a multi-mode fusion take-out distribution scheduling system and method, and the system comprises a real-time data collection module, a scheduling algorithm optimization module, an autonomy enhancement module, an adaptation module, a rider behavior analysis module, a distribution dynamic division module, an emergency response module, and a performance optimization module. The real-time data acquisition module is used for data acquisition and data verification; according to the multi-mode fusion take-out delivery scheduling system and method, multi-channel data acquisition, algorithm optimization, autonomy enhancement, environment adaptation, behavior analysis, regional management, customer service support and performance optimization are fused; according to the system and the method, the problems of data singleness, algorithm stiffness, lack of flexibility and poor environmental adaptability in a traditional intelligent delivery process are effectively solved, the intelligent level of take-out delivery is improved, the delivery efficiency and the customer experience are improved, and powerful support is provided for sustainable development of the take-out industry.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution scheduling, and specifically provides an off - premise food delivery scheduling system and method integrating multiple modes. Background Art

[0002] In recent years, the off - premise food delivery industry has maintained a rapid growth trend. Consumers have higher and higher requirements for delivery speed, accuracy, and service experience. As a key link in the off - premise food delivery service, the intelligent delivery process usually includes multiple steps such as order reception, rider assignment, route planning, delivery execution, and customer feedback. However, the traditional intelligent delivery process often relies on a single data source and a fixed algorithm model, making it difficult to cope with complex factors such as real - time road conditions, weather, and rider status.

[0003] Therefore, generally, traditional delivery systems often only rely on GPS positioning data or data reported by rider apps, lacking the integration of multi - channel data, resulting in insufficient comprehensiveness and accuracy of data. Using fixed rules or models, it is difficult to dynamically adjust and optimize strategies based on real - time data, leading to low delivery efficiency. There is also a lack of a mechanism for riders to independently select tasks or routes, making it difficult to meet the personalized needs of riders. In addition, it is unable to effectively respond to emergencies such as bad weather or traffic control, resulting in delivery disruptions or delays.

[0004] In summary, there is a need to propose an off - premise food delivery scheduling system and method integrating multiple modes to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide an off - premise food delivery scheduling system and method integrating multiple modes to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An off - premise food delivery scheduling system integrating multiple modes, including a real - time data acquisition module, a scheduling algorithm optimization module, an autonomy enhancement module, an adaptation module, a rider behavior analysis module, a delivery dynamic division module, an emergency response module, and a performance optimization module;

[0008] The real - time data acquisition module is used for data acquisition and data verification;

[0009] The scheduling algorithm optimization module is used for algorithm optimization and complexity management;

[0010] The autonomy enhancement module is used for task recommendation and route planning;

[0011] The adaptation module is used for coping with bad weather and traffic control;

[0012] The rider behavior analysis module is used for behavior monitoring and behavior incentive;

[0013] The delivery dynamic partitioning module is used for area partitioning and balancing the area load;

[0014] The emergency response module is used to provide online customer service and an emergency response mechanism;

[0015] The performance optimization module is used for performance evaluation and policy optimization.

[0016] Preferably, the real-time data acquisition module further includes a data acquisition unit and a data verification unit;

[0017] The data acquisition unit collects real-time data on the rider's location, order status, and road conditions through multiple channels such as the GPS positioning system, Internet of Things sensors, and rider APP;

[0018] Use data fusion technology to integrate data from different sources and improve the comprehensiveness and accuracy of the data;

[0019] The data verification unit denoises and verifies the collected data through the Kalman filter algorithm to ensure the real-time and reliability of the data;

[0020] Establish a data anomaly detection mechanism to mark or eliminate data with large deviations or untimely updates to avoid negative impacts on scheduling decisions.

[0021] Preferably, the scheduling algorithm optimization module further includes an algorithm optimization unit and a complexity management unit;

[0022] The algorithm optimization unit optimizes the intelligent scheduling algorithm using the genetic algorithm to improve the efficiency and accuracy of the algorithm when processing a large amount of real-time data;

[0023] Introduce machine learning technology, train a model based on historical data, predict future order volumes and delivery requirements, and provide a basis for scheduling decisions;

[0024] The complexity management unit parallelizes the algorithm through a distributed computing framework to reduce the algorithm complexity and improve the response speed of the scheduling system;

[0025] Establish an algorithm performance monitoring mechanism to evaluate the running efficiency of the algorithm in real time and adjust the optimization strategy in a timely manner.

[0026] Preferably, the autonomy enhancement module further includes a task recommendation unit and a route planning unit;

[0027] The task recommendation unit uses the collaborative filtering algorithm to recommend the optimal delivery tasks for the rider based on the rider's historical delivery records and preferences;

[0028] Establish a rider self-selection mechanism that allows riders to select tasks within a certain range based on their own experience and judgment;

[0029] The route planning unit adopts the A* algorithm or the Dijkstra algorithm to provide multiple optional delivery routes for riders, and riders can select the optimal route according to the actual situation;

[0030] Introduce real-time traffic conditions information, dynamically adjust route planning, avoid congested sections, and improve delivery efficiency.

[0031] Preferably, the adaptation module further includes a bad weather response unit and a traffic control response unit;

[0032] The bad weather response unit obtains real-time weather information through the weather forecast API to give early warnings of bad weather;

[0033] Adopt the reinforcement learning algorithm to train the model to make scheduling decisions under bad weather conditions and improve the adaptability of the system;

[0034] The traffic control response unit obtains real-time traffic control information through the traffic control information API;

[0035] Use the dynamic programming algorithm to re-plan the delivery route, avoid the controlled area, and ensure the smooth progress of the delivery.

[0036] Preferably, the rider behavior analysis module further includes a behavior monitoring unit and a behavior incentive unit;

[0037] The behavior monitoring unit collects the delivery behavior data of riders through the rider APP, including the riding speed and the staying time;

[0038] Use data analysis technology to monitor and analyze the rider behavior in real time;

[0039] The behavior incentive unit sets up a reward mechanism according to the rider behavior analysis results and rewards the riders with excellent performance;

[0040] Through gamification design, link the rider behavior with points and rankings to stimulate the enthusiasm and competitive awareness of riders.

[0041] Preferably, the delivery dynamic division module further includes a region division unit and a region load balancing unit;

[0042] The region division unit adopts the K-means clustering algorithm to dynamically divide the delivery region into multiple sub-regions according to factors such as order density and rider distribution;

[0043] Establish a region boundary adjustment mechanism to dynamically adjust the region boundary according to the real-time order volume and rider location information;

[0044] The regional load balancing unit ensures relative balance of the order volume and the number of riders in each sub-region through a load balancing algorithm;

[0045] An inter-regional dispatching mechanism is established. When the order volume in a certain region is too large, some orders can be dispatched to adjacent regions for delivery.

[0046] Preferably, the emergency response module further includes a customer service unit and an emergency response unit;

[0047] The customer service unit introduces natural language processing technology to develop an intelligent customer service system, which can automatically answer common questions of riders and customers;

[0048] A problem feedback mechanism is established to record and analyze problems that cannot be solved by the intelligent customer service, and continuously optimize the customer service system;

[0049] The emergency response unit establishes an emergency response mechanism, which can quickly provide assistance and support when an emergency occurs or a rider encounters difficulties;

[0050] Through the intelligent dispatching system, nearby riders or resources are quickly allocated to assist in handling emergency situations.

[0051] Preferably, the performance optimization module further includes a performance evaluation unit and an optimization strategy unit;

[0052] The performance evaluation unit establishes system performance evaluation indicators, including dispatching accuracy rate, response speed, and rider satisfaction;

[0053] Through data analysis technology, the system performance is monitored and evaluated in real time;

[0054] The optimization strategy unit proposes optimization strategies and suggestions according to the performance evaluation results;

[0055] Through the A / B test method, the effectiveness of the optimization strategy is verified, and the system performance is continuously optimized.

[0056] Based on the above system, the present invention also proposes a multi-mode fusion takeaway delivery scheduling method, including the following steps:

[0057] S1. Real-time collect data on rider locations, order status, and road conditions through multiple channels, and use data fusion technology to integrate the data to improve the comprehensiveness and accuracy of the data;

[0058] Denoise and verify the collected data to ensure the timeliness and reliability of the data, and establish a data anomaly detection mechanism;

[0059] S2. Optimize the intelligent dispatching algorithm to improve the efficiency and accuracy of the algorithm in processing a large amount of real-time data;

[0060] Introduce machine learning techniques to train a model based on historical data to predict future order volumes and delivery requirements;

[0061] Parallelize the algorithm through a distributed computing framework to reduce algorithm complexity, improve response speed, and establish an algorithm performance monitoring mechanism to evaluate the running efficiency of the algorithm in real time;

[0062] S3. Recommend the optimal delivery tasks to riders based on their historical delivery records and preferences, and establish a rider self-selection mechanism to provide multiple optional delivery routes for riders. Riders can select the optimal route according to the actual situation. Introduce real-time traffic condition information to dynamically adjust route planning and avoid congested sections;

[0063] S4. Obtain real-time weather information, give early warnings for bad weather, and train the model to make scheduling decisions under bad weather conditions. Obtain real-time traffic control information, re-plan the delivery route, and avoid controlled areas;

[0064] S5. Collect riders' delivery behavior data through the rider APP for real-time monitoring and analysis. According to the results of rider behavior analysis, establish an incentive mechanism to stimulate riders' enthusiasm and sense of competition;

[0065] S6. Dynamically divide the delivery area into multiple sub-areas according to factors such as order density and rider distribution, and establish a regional boundary adjustment mechanism;

[0066] Through a load balancing algorithm, ensure that the order volume and the number of riders in each sub-area are relatively balanced, and establish an inter-regional scheduling mechanism;

[0067] S7. Introduce natural language processing techniques to develop an intelligent customer service system to automatically answer common questions, and establish a problem feedback mechanism;

[0068] Establish an emergency response mechanism to quickly provide assistance and support when emergencies occur or riders encounter difficulties;

[0069] S8. Establish system performance evaluation indicators, and use data analysis techniques to monitor and evaluate system performance in real time;

[0070] According to the performance evaluation results, propose optimization strategies and suggestions, and verify the effectiveness of the optimization strategies through the A / B test method.

[0071] Compared with the prior art, the beneficial effects of the present invention are:

[0072] Through multi-channel real-time data collection and verification, the present invention ensures the comprehensiveness, accuracy, and real-time nature of data. By using the Kalman filtering algorithm for denoising and anomaly detection mechanisms, the data quality is effectively improved. Through genetic algorithm optimization and machine learning technology prediction, the efficiency and accuracy of the algorithm in processing a large amount of real-time data are enhanced. At the same time, a distributed computing framework is used for parallel processing to reduce the algorithm complexity and speed up the response speed. Combining collaborative filtering algorithm and A* / Dijkstra algorithm, the optimal tasks and multiple alternative routes are provided for the riders and adjusted in real time to adapt to environmental changes such as weather and traffic control. Rider behavior analysis and area management monitor rider behavior in real time through data analysis technology, establish an incentive mechanism to stimulate enthusiasm, and at the same time use the K-means clustering algorithm to dynamically divide areas to ensure load balancing. The natural language processing technology is introduced to develop intelligent customer service, and a feedback and emergency response mechanism is established to improve service efficiency. Finally, by setting system performance evaluation indicators and A / B testing methods, the system performance is continuously optimized to ensure the efficient, intelligent, and reliable operation of the entire distribution system, bringing better experiences and benefits to riders, users, and the platform.

[0073] Therefore, the multi-mode fusion takeaway delivery scheduling system and method of the present invention effectively solve the problems of data singularity, algorithm rigidity, lack of flexibility, and poor environmental adaptability in traditional intelligent delivery processes through the integration of multi-channel data collection, algorithm optimization, enhanced autonomy, environmental adaptation, behavior analysis, area management, customer service support, and performance optimization. The system and method improve the intelligent level of takeaway delivery, enhance the delivery efficiency and customer experience, and provide strong support for the sustainable development of the takeaway industry. Brief Description of the Drawings

[0074] Figure 1 Shows the topology diagram of the multi-mode fusion takeaway delivery scheduling system of the present invention;

[0075] Figure 2 Shows the flowchart of the multi-mode fusion takeaway delivery scheduling method of the present invention. Detailed Embodiments

[0076] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0077] Embodiment 1

[0078] Please refer to Figure 1, the present invention proposes a multi - mode fusion take - out delivery scheduling system, including a real - time data acquisition module, a scheduling algorithm optimization module, an autonomy enhancement module, an adaptation module, a rider behavior analysis module, a delivery dynamic division module, an emergency response module, and a performance optimization module;

[0079] Among them, it should be noted that the real - time data acquisition module of this system is used for data acquisition and data verification, the scheduling algorithm optimization module of this system is used for algorithm optimization and complexity management, the autonomy enhancement module of this system is used for task recommendation and route planning, the adaptation module of this system is used for coping with bad weather and traffic control, the rider behavior analysis module of this system is used for behavior monitoring and behavior incentive, the delivery dynamic division module of this system is used for area division and balancing regional load, the emergency response module of this system is used to provide online customer service and an emergency response mechanism, and the performance optimization module of this system is used for performance evaluation and strategy optimization.

[0080] In this embodiment, it should also be noted that the real - time data acquisition module further includes a data acquisition unit and a data verification unit;

[0081] Furthermore, the data acquisition unit real - time collects rider location, order status, and road condition information data through multiple channels such as the GPS positioning system, Internet of Things sensors, and the rider APP;

[0082] Utilize data fusion technology to integrate data from different sources, improving the comprehensiveness and accuracy of the data;

[0083] Furthermore, the data verification unit denoises and verifies the collected data through the Kalman filter algorithm to ensure the real - time and reliability of the data;

[0084] Set up a data anomaly detection mechanism to mark or eliminate data with large deviations or untimely updates, avoiding negative impacts on scheduling decisions.

[0085] In this embodiment, it should also be noted that the scheduling algorithm optimization module further includes an algorithm optimization unit and a complexity management unit;

[0086] Furthermore, the algorithm optimization unit optimizes the intelligent scheduling algorithm using the genetic algorithm, improving the efficiency and accuracy of the algorithm when processing a large amount of real - time data;

[0087] Introduce machine learning technology, train a model based on historical data, predict future order volumes and delivery requirements, and provide a basis for scheduling decisions;

[0088] Furthermore, the complexity management unit parallelizes the algorithm through a distributed computing framework, reducing the algorithm complexity and improving the response speed of the scheduling system;

[0089] Establish an algorithm performance monitoring mechanism to evaluate the running efficiency of the algorithm in real time and adjust the optimization strategy in a timely manner.

[0090] In this embodiment, it should also be noted that the autonomy enhancement module further includes a task recommendation unit and a route planning unit;

[0091] Furthermore, the task recommendation unit uses a collaborative filtering algorithm to recommend the optimal delivery tasks for the rider based on the rider's historical delivery records and preferences;

[0092] Establish a rider autonomous selection mechanism that allows riders to select tasks within a certain range according to their own experience and judgment;

[0093] Furthermore, the route planning unit uses the A* algorithm or the Dijkstra algorithm to provide multiple optional delivery routes for the rider, and the rider can select the optimal route according to the actual situation;

[0094] Introduce real-time traffic conditions information to dynamically adjust route planning, avoid congested sections, and improve delivery efficiency.

[0095] In this embodiment, it should also be noted that the adaptation module further includes a bad weather response unit and a traffic control response unit;

[0096] Furthermore, the bad weather response unit obtains real-time weather information through the weather forecast API to give early warnings of bad weather;

[0097] Adopt a reinforcement learning algorithm to train the model to make scheduling decisions under bad weather conditions and improve the adaptability of the system;

[0098] Furthermore, the traffic control response unit obtains real-time traffic control information through the traffic control information API;

[0099] Use the dynamic programming algorithm to re-plan the delivery route, avoid the controlled area, and ensure the smooth progress of the delivery.

[0100] In this embodiment, it should also be noted that the rider behavior analysis module further includes a behavior monitoring unit and a behavior incentive unit;

[0101] Furthermore, the behavior monitoring unit collects the rider's delivery behavior data through the rider APP, including the riding speed and the staying time;

[0102] Use data analysis techniques to monitor and analyze the rider's behavior in real time;

[0103] Furthermore, the behavior incentive unit establishes a reward mechanism according to the results of the rider behavior analysis and rewards the riders with excellent performance;

[0104] Through gamification design, the rider's behavior is linked to points and rankings, stimulating the rider's enthusiasm and sense of competition.

[0105] In this embodiment, it should also be noted that the delivery dynamic division module further includes a region division unit and a region load balancing unit;

[0106] Furthermore, the region division unit adopts the K-means clustering algorithm to dynamically divide the delivery area into multiple sub-regions according to factors such as order density and rider distribution;

[0107] A region boundary adjustment mechanism is established to dynamically adjust the region boundary according to the real-time order volume and rider location information;

[0108] Furthermore, the region load balancing unit ensures the relative balance of the order volume and the number of riders in each sub-region through a load balancing algorithm;

[0109] A cross-region dispatching mechanism is established. When the order volume in a certain region is too large, some orders can be dispatched to adjacent regions for delivery.

[0110] In this embodiment, it should also be noted that the emergency response module further includes a customer service unit and an emergency response unit;

[0111] Furthermore, the customer service unit introduces natural language processing technology to develop an intelligent customer service system, which can automatically answer common questions from riders and customers;

[0112] A problem feedback mechanism is established to record and analyze problems that cannot be solved by the intelligent customer service, and continuously optimize the customer service system;

[0113] Furthermore, the emergency response unit establishes an emergency response mechanism to quickly provide assistance and support when encountering emergencies or when riders encounter difficulties;

[0114] Through the intelligent dispatching system, quickly allocate nearby riders or resources to assist in handling emergency situations.

[0115] In this embodiment, it should also be noted that the performance optimization module further includes a performance evaluation unit and an optimization strategy unit;

[0116] Furthermore, the performance evaluation unit establishes system performance evaluation indicators, including dispatching accuracy rate, response speed, and rider satisfaction;

[0117] Through data analysis technology, the system performance is monitored and evaluated in real time;

[0118] Furthermore, the optimization strategy unit proposes optimization strategies and suggestions according to the performance evaluation results;

[0119] Through the A / B test method, the effectiveness of the optimization strategy is verified, and the system performance is continuously optimized.

[0120] Example 2

[0121] Please refer to Figure 2 , in the actual application process, based on the above system, the present invention also proposes a multi-mode fusion method for takeaway delivery scheduling. Specifically, it includes the following steps:

[0122] (1) Data collection and verification

[0123] (1.1) Real-time data collection:

[0124] Obtain the real-time location information of the rider through the GPS positioning system;

[0125] Collect road condition information using Internet of Things sensors, including traffic flow and accident situations;

[0126] Collect order status information through the rider APP, including order received, meal picked up, and delivered;

[0127] (1.2) Data fusion and integration:

[0128] Adopt data fusion technology to integrate data from different channels and improve the comprehensiveness and accuracy of the data;

[0129] Implement the data cleaning process to remove duplicate, invalid or incorrect data;

[0130] (1.3) Data verification and processing:

[0131] Apply the Kalman filter algorithm to denoise and verify the collected data to ensure the real-time and reliability of the data;

[0132] Set up a data anomaly detection mechanism to mark or eliminate data with large deviations or untimely updates;

[0133] (2) Optimization of scheduling algorithm and parallel processing

[0134] (2.1) Algorithm optimization:

[0135] Adopt the genetic algorithm to optimize the intelligent scheduling algorithm and improve the efficiency and accuracy of the algorithm when processing a large amount of real-time data;

[0136] Introduce machine learning technology, including using regression models or time series analysis, train the model according to historical data, and predict future order volumes and delivery demands;

[0137] (2.2) Parallel processing:

[0138] Utilize the Hadoop and Spark distributed computing frameworks to perform parallel processing on the scheduling algorithm, reduce the algorithm complexity, and improve the response speed;

[0139] Establish an algorithm performance monitoring mechanism to evaluate the running efficiency of the algorithm in real time by monitoring the running time and memory occupancy metrics of the algorithm in real time;

[0140] (3) Task recommendation, route planning and adapting to the environment

[0141] (3.1) Task recommendation:

[0142] Based on the rider's historical delivery records and preferences, use collaborative filtering algorithm to recommend the optimal delivery tasks for them;

[0143] Establish a rider independent selection mechanism to allow riders to select tasks according to their own experience and judgment within a certain range;

[0144] (3.2) Route planning:

[0145] Adopt A* algorithm or Dijkstra algorithm to provide multiple optional delivery routes for riders;

[0146] Introduce real-time road condition information, including traffic congestion and accidents, and dynamically adjust route planning to avoid congested sections;

[0147] (3.3) Adapting to bad weather and traffic control:

[0148] Obtain real-time weather information through weather forecast API to give early warnings for bad weather;

[0149] Use reinforcement learning algorithm to train the model to make scheduling decisions under bad weather conditions and improve the adaptability of the system;

[0150] Obtain real-time traffic control information through traffic control information API, and use dynamic programming algorithm to re-plan the delivery route to avoid the controlled area;

[0151] (4) Rider behavior analysis and area management

[0152] (4.1) Rider behavior analysis:

[0153] Collect the delivery behavior data of riders through the rider APP, including riding speed and staying time;

[0154] Use data analysis techniques, including clustering analysis and time series analysis, to monitor and analyze rider behavior in real time;

[0155] (4.2) Behavior incentive:

[0156] According to the results of rider behavior analysis, establish a reward mechanism, including point rewards and ranking rewards, to stimulate the enthusiasm and competitive awareness of riders;

[0157] (4.3) Dynamic area division:

[0158] Using the K-means clustering algorithm, the delivery area is dynamically divided into multiple sub-areas according to factors such as order density and rider distribution;

[0159] A regional boundary adjustment mechanism is established to dynamically adjust the regional boundary according to real-time order volume and rider location information;

[0160] (4.4) Regional load balancing:

[0161] Through load balancing algorithms, including round-robin algorithm and least-connection algorithm, ensure that the order volume and the number of riders in each sub-region are relatively balanced;

[0162] A regional scheduling mechanism is established. When the order volume in a certain region is too large, some orders can be scheduled to adjacent regions for delivery;

[0163] (5) Customer service support and performance optimization

[0164] (5.1) Customer service system development:

[0165] Introduce natural language processing technology to develop an intelligent customer service system that can automatically answer common questions from riders and customers;

[0166] A problem feedback mechanism is established to record and analyze problems that cannot be solved by the intelligent customer service, and continuously optimize the customer service system;

[0167] (5.2) Emergency response mechanism:

[0168] An emergency response mechanism is established to quickly provide assistance and support when encountering emergencies or when riders encounter difficulties;

[0169] Through the intelligent dispatching system, quickly allocate nearby riders or resources to assist in handling emergency situations;

[0170] (5.3) Performance evaluation and optimization:

[0171] Establish system performance evaluation indicators, including dispatching accuracy rate, response speed, and rider satisfaction;

[0172] Through data analysis techniques, including statistical analysis and machine learning model evaluation, monitor and evaluate the system performance in real time;

[0173] According to the performance evaluation results, propose optimization strategies and suggestions, including algorithm adjustment and system architecture optimization;

[0174] Verify the effectiveness of the optimization strategy through the A / B test method and continuously optimize the system performance.

[0175] In summary, through the above steps, the present invention collects and verifies real-time data through multiple channels to ensure the comprehensiveness, accuracy, and real-time nature of the data. It uses the Kalman filter algorithm for denoising and anomaly detection mechanisms to effectively improve data quality. Through genetic algorithm optimization and machine learning technology prediction, it improves the efficiency and accuracy of the algorithm in processing a large amount of real-time data. At the same time, it uses a distributed computing framework for parallel processing to reduce algorithm complexity and speed up the response speed. By combining the collaborative filtering algorithm and the A* / Dijkstra algorithm, it provides the rider with the optimal task and multiple alternative routes and adjusts them in real time to adapt to environmental changes such as weather and traffic control. Rider behavior analysis and area management monitor rider behavior in real time through data analysis technology, establish an incentive mechanism to stimulate enthusiasm, and at the same time use the K-means clustering algorithm to dynamically divide areas to ensure load balancing. It introduces natural language processing technology to develop intelligent customer service, establishes a feedback and emergency response mechanism to improve service efficiency. Finally, by setting system performance evaluation indicators and A / B testing methods, it continuously optimizes system performance to ensure the efficient, intelligent, and reliable operation of the entire distribution system, bringing a better experience and benefits to riders, users, and the platform.

[0176] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-mode integrated takeaway delivery dispatching system, characterized in that: It includes real-time data collection module, scheduling algorithm optimization module, autonomy enhancement module, adaptation module, rider behavior analysis module, distribution dynamic division module, emergency response module, and performance optimization module; The real-time data acquisition module is used for data acquisition and data verification; The scheduling algorithm optimization module is used for algorithm optimization and complexity management; The autonomy enhancement module is used for task recommendation and route planning; The adaptation module is used for severe weather and traffic control response; The rider behavior analysis module is used for behavior monitoring and behavior motivation; The distribution dynamic division module is used for regional division and regional load balancing; The emergency response module is used to provide online customer service and emergency response mechanism; The performance optimization module is used for performance evaluation and strategy optimization.

2. According to the multi-mode integrated takeaway delivery scheduling system of claim 1, it is characterized by: The real-time data acquisition module also includes a data acquisition unit and a data verification unit; The data collection unit collects rider location, order status, and road condition information data in real time through multiple channels such as GPS positioning system, IoT sensor, and rider APP; The data verification unit performs denoising and verification on the collected data through a Kalman filter algorithm.

3. According to the multi-mode integrated takeaway delivery scheduling system of claim 2, it is characterized by: The scheduling algorithm optimization module also includes an algorithm optimization unit and a complexity management unit; The algorithm optimization unit uses a genetic algorithm to optimize the intelligent scheduling algorithm; The complexity management unit performs parallel processing on the algorithm through a distributed computing framework to reduce the complexity of the algorithm.

4. According to the multi-mode integrated takeaway delivery scheduling system of claim 3, it is characterized by: The autonomy enhancement module also includes a task recommendation unit and a route planning unit; The task recommendation unit uses a collaborative filtering algorithm to recommend the optimal delivery task based on the rider's historical delivery records and preferences; The route planning unit uses the A* algorithm or the Dijkstra algorithm to provide riders with multiple optional delivery routes.

5. According to the multi-mode integrated takeaway delivery scheduling system of claim 4, it is characterized by: The adaptation module also includes a severe weather response unit and a traffic control response unit; The severe weather response unit obtains real-time weather information through a weather forecast API; The traffic control response unit obtains real-time traffic control information through a traffic control information API.

6. The multi-mode integrated takeaway delivery dispatching system according to claim 5 is characterized by: The rider behavior analysis module also includes a behavior monitoring unit and a behavior incentive unit; The behavior monitoring unit collects the delivery behavior data of the rider through the rider APP; The behavior incentive unit establishes a reward mechanism according to the rider behavior analysis results.

7. The multi-mode integrated takeaway delivery dispatching system according to claim 6 is characterized by: The distribution dynamic division module also includes a regional division unit and a regional load balancing unit; The area division unit uses a K-means clustering algorithm to dynamically divide the delivery area into multiple sub-areas according to order density and rider distribution factors; The regional load balancing unit is used to balance the order volume and the number of riders in each sub-region through a load balancing algorithm.

8. The multi-mode integrated takeaway delivery dispatching system according to claim 7 is characterized by: The emergency response module also includes a customer service unit and an emergency response unit; The customer service unit develops an intelligent customer service system through natural language processing technology to automatically answer common questions from riders and customers; The emergency response unit is used to establish an emergency response mechanism.

9. The multi-mode integrated takeaway delivery dispatching system according to claim 8 is characterized by: The performance optimization module also includes a performance evaluation unit and an optimization strategy unit; The performance evaluation unit is used to establish system performance evaluation indicators; The optimization strategy unit is used to propose optimization strategies and suggestions according to the performance evaluation results.

10. A multi-mode integrated takeaway delivery scheduling method, according to a multi-mode integrated takeaway delivery scheduling system according to claim 9, characterized in that: The following steps are involved: S1. Collect rider location, order status, and road condition information data in real time through multiple channels, integrate data using data fusion technology to improve the comprehensiveness and accuracy of data, denoise and verify the collected data to ensure the real-time and reliability of data, and establish a data anomaly detection mechanism; S2. Optimize the intelligent scheduling algorithm to improve the efficiency and accuracy of the algorithm when processing large amounts of real-time data. Introduce machine learning technology to train models based on historical data to predict future order volumes and delivery needs. Parallelize the algorithm through a distributed computing framework to reduce algorithm complexity and improve response speed. Establish an algorithm performance monitoring mechanism to evaluate the algorithm's operating efficiency in real time. S3. Recommend the best delivery task to the rider based on his / her delivery history and preferences, and establish a rider self-selection mechanism to provide riders with multiple optional delivery routes. Riders can choose the best route based on actual conditions, introduce real-time traffic information, dynamically adjust route planning, and avoid congested sections; S4. Obtain real-time weather information, issue warnings for severe weather, and train models to make dispatch decisions under severe weather conditions, obtain real-time traffic control information, and re-plan delivery routes to avoid controlled areas; S5. Collect the delivery behavior data of riders through the rider APP, conduct real-time monitoring and analysis, and establish a reward mechanism based on the results of the rider behavior analysis to stimulate the riders' enthusiasm and competitive awareness; S6. Dynamically divide the delivery area into multiple sub-areas based on order density and rider distribution factors, and establish a regional boundary adjustment mechanism. Through the load balancing algorithm, ensure that the order volume and number of riders in each sub-area are relatively balanced, and establish an inter-regional scheduling mechanism; S7. Introduce natural language processing technology, develop an intelligent customer service system, automatically answer common questions, and establish a problem feedback mechanism and an emergency response mechanism to quickly provide assistance and support when encountering emergencies or riders encounter difficulties; S8. Establish system performance evaluation indicators, monitor and evaluate system performance in real time through data analysis technology, propose optimization strategies and suggestions based on performance evaluation results, and verify the effectiveness of optimization strategies through A / B testing methods.

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