Automobile market logistics distribution scheduling system based on AI optimization

Abstract

The invention relates to the technical field of intelligent logistics distribution, in particular to an automobile market logistics distribution scheduling system based on AI optimization, which comprises a data acquisition module, an order management module, a traffic flow analysis module and a scheduling decision module. Wherein the data acquisition module is used for acquiring current position data and traffic flow data of a delivery vehicle and order information of a customer in real time; the order management module is used for carrying out priority ranking on the orders; the traffic flow analysis module is used for predicting traffic conditions in a period of time in the future and identifying a road section and a time period which are about to be congested; and the scheduling decision module is used for optimizing the scheduling and distribution paths of the vehicles to generate an optimal scheduling scheme. According to the invention, by combining real-time traffic prediction, order priority ranking and a path optimization algorithm, the logistics distribution efficiency is effectively improved, distribution delay and resource waste are reduced, and timely distribution of high-priority orders is ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent logistics and distribution technology, and in particular to an automobile market logistics and distribution scheduling system based on AI optimization. Background Art

[0002] With the rapid development of e-commerce and logistics industries, the distribution demand in the automobile market has gradually increased, and the optimization of logistics distribution systems has become the key to improving efficiency and reducing costs. However, traditional logistics distribution systems mostly rely on manual planning and static scheduling, ignoring factors such as real-time traffic conditions, order priority and distribution route optimization. This approach not only leads to low distribution efficiency, but may also cause delivery delays due to traffic congestion, improper route selection and other reasons, thereby affecting customer experience and corporate operating costs. Therefore, it has become a top priority to improve the intelligence and automation level of the logistics distribution system in the automobile market, especially to optimize scheduling and route planning.

[0003] Although the existing logistics distribution system has adopted a rule-based scheduling method to a certain extent, it often shows limitations when dealing with complex real-time traffic flows, dynamic order demands and different delivery priorities. Such systems are usually unable to quickly adapt to complex traffic environments and changes in order demands, resulting in the inability to adjust the delivery plan in a timely manner, affecting the overall delivery efficiency and resource utilization. Especially in large-scale orders and high-density traffic areas, traditional scheduling and path planning algorithms cannot fully consider the actual traffic flow, vehicle load restrictions and real-time order priorities, and often result in unreasonable delivery routes and serious delays. Summary of the invention

[0004] Based on the above objectives, the present invention provides an automobile market logistics distribution scheduling system based on AI optimization.

[0005] The AI-optimized automobile market logistics distribution dispatching system includes data collection module, order management module, traffic flow analysis module and dispatching decision module; among which:

[0006] Data collection module: used to collect the current location data of the delivery vehicle, traffic flow data and customer order information in real time;

[0007] Order management module: used to receive customer order information, prioritize orders according to order urgency, delivery time requirements and destination location, and pass the sorted order information to the scheduling decision module;

[0008] Traffic flow analysis module: used to combine historical traffic flow data and real-time traffic flow data to build a traffic flow prediction model, predict traffic conditions in the future, identify road sections and time periods that are about to be congested, and provide the prediction results to the scheduling decision module;

[0009] Scheduling decision module: Based on order priority, delivery time requirements and predicted traffic conditions, an optimization algorithm is used to optimize vehicle scheduling and delivery routes to generate the optimal scheduling plan.

[0010] Optionally, the data collection module includes a vehicle location collection unit, a traffic flow collection unit and an order information collection unit, wherein:

[0011] Vehicle location acquisition unit: obtains the geographic location coordinates of the delivery vehicle in real time through GPS positioning equipment;

[0012] Traffic flow collection unit: collects traffic flow data of each road section in real time through traffic monitoring sensors or third-party traffic data interfaces, including vehicle flow, speed and congestion conditions;

[0013] Order information collection unit: Receives customer order information through the order management interface, including order number, customer address, order creation time, order urgency, delivery time requirements and destination location.

[0014] Optionally, the order management module includes an order receiving unit, a priority calculation unit and an order sorting unit, wherein:

[0015] Order receiving unit: used to receive and store order information from customers;

[0016] Priority calculation unit: used to comprehensively evaluate the urgency, delivery time requirements and destination location of each order, and assign priority scores to the orders;

[0017] Order sorting unit: sorts all orders according to the priority score provided by the priority calculation unit and generates a priority-sorted order list; arranges the order list from high to low according to the priority score, and passes the sorted order information to the scheduling decision module.

[0018] Optionally, the priority calculation unit includes:

[0019] Urgency evaluation subunit: used to score orders according to their urgency. The formula is: Among them, E is the urgency score of the order, T current is the current time, T order is the earliest delivery time required by the order, T max and T min They are the maximum and minimum delivery time requirements for all orders respectively;

[0020] Delivery time efficiency evaluation subunit: used to score orders according to their delivery time efficiency requirements. The formula is: Among them, S is the delivery time efficiency score, T deadline T is the final delivery time limit of the order. current is the current time, T order Create the time for the order;

[0021] Destination location evaluation subunit: used to score orders according to their destination location. The formula is: Where D is the destination location score, D order is the distance between the order destination and the distribution center, D base is the reference distance, k is the distance weight coefficient;

[0022] Priority score calculation subunit: used to calculate the comprehensive priority score of the order by weighted average method according to the urgency score, delivery time efficiency score and destination location score. The calculation formula is: P = w1·E+w2·S+w3·D, where P is the comprehensive priority score of the order, E is the urgency score, S is the delivery time efficiency score, D is the destination location score, and w1, w2 and w3 are the weight coefficients of urgency, time efficiency and destination location respectively.

[0023] Optionally, the traffic flow analysis module includes a historical traffic data processing unit, a real-time traffic data processing unit, a machine learning prediction unit and a congestion identification unit, wherein:

[0024] Historical traffic data processing unit: used to store historical traffic flow data of the past six months; and pre-process the historical traffic flow data, including removing noise data, filling missing values ​​and standardization;

[0025] Real-time traffic data receiving unit: used to receive real-time traffic flow data provided by the data acquisition module;

[0026] Machine learning prediction unit: Based on historical traffic flow data and real-time traffic flow data, it uses machine learning algorithms to predict traffic conditions in the future;

[0027] Congestion identification unit: Based on the traffic condition prediction results provided by the machine learning prediction unit, it identifies the road sections and time periods where congestion is about to occur. Specifically, it sets congestion thresholds, analyzes the prediction results, and marks the road sections and time periods where congestion is about to occur.

[0028] Optionally, the machine learning prediction unit includes:

[0029] Feature extraction subunit: used to extract representative features from historical traffic data and real-time traffic data, including time features, weather features and road section features;

[0030] Model training subunit: Based on the feature data extracted by the feature extraction subunit, a machine learning algorithm based on long short-term memory network is used to train the traffic flow prediction model;

[0031] Prediction generation subunit: After the training is completed, the trained traffic flow prediction model is used to predict the traffic flow in the future and generate the prediction results of future traffic conditions.

[0032] Optionally, the model training subunit includes:

[0033] Input layer: takes the feature data extracted from the feature extraction subunit as the input of the model;

[0034] Hidden layer: The long short-term memory network processes the input data through its memory units and gating mechanism, and continuously adjusts the internal state through iterative operations of multiple time steps to capture the long-term and short-term dependencies of traffic flow, understand and remember the changing patterns of traffic flow;

[0035] Output layer: The output of the hidden layer is passed through the fully connected layer to generate the traffic flow prediction result at the current time point.

[0036] Optionally, the scheduling decision module includes a scheduling optimization unit, a path planning unit and a scheduling execution unit, wherein:

[0037] Dispatch optimization unit: used to receive the order information after priority sorting and delivery time requirements transmitted by the order management module, as well as the traffic condition prediction results provided by the traffic flow analysis module, and use the optimization algorithm to calculate the vehicle dispatch to generate the optimal dispatch plan;

[0038] Path planning unit: Based on the optimal dispatching plan generated by the dispatching optimization unit, the path optimization algorithm is used to calculate the optimal delivery path for each vehicle to ensure the efficiency and feasibility of the delivery path;

[0039] Scheduling execution unit: used to command delivery vehicles to perform delivery tasks based on the optimal delivery path calculated by the path planning unit.

[0040] Optionally, the scheduling optimization unit includes:

[0041] Constraint setting subunit: Set constraints based on actual delivery needs and resource constraints, including vehicle capacity, transportation time, traffic flow, and road restrictions;

[0042] Optimization calculation subunit: Use heuristic optimization algorithm to solve the scheduling problem and generate the optimal scheduling solution; optimize the scheduling order and delivery path of vehicles based on the comprehensive priority score P of the order and the constraints provided by the constraint setting subunit; the optimization goal is to minimize the total delivery time and ensure that high-priority orders are delivered on time; the specific optimization objective function is expressed as: Among them, P i is the comprehensive priority score of order i, T i is the delivery time requirement for order i, d ij is the distance of the vehicle from order i to order j, x ij is the path selected from order i to order j in the scheduling scheme, and λ is the balance parameter of the scheduling scheme.

[0043] Optionally, the path planning unit includes:

[0044] Path initialization: The delivery vehicles and the delivery targets of the orders are regarded as nodes in the graph. Each node in the graph represents a delivery point, i.e. the starting or ending location of the order, and the edges represent the paths of the vehicles.

[0045] Calculate the distance matrix: Calculate the distance between all delivery points and construct the distance matrix D, where D ij represents the distance from node i to node j; for any two nodes i and j, the distance D ij Calculated according to the following formula: Among them, x i ,y i is the coordinate of node i, x j ,y j is the coordinate of node j;

[0046] Path optimization: The shortest delivery path for each vehicle is calculated based on the distance matrix D through an iterative algorithm to ensure that each vehicle has the shortest journey. For each vehicle, the objective function is to minimize the total distance of the delivery path: min Among them, Z ij is the selection variable for the vehicle to go from node i to node j, D ij is the distance from node i to node j, n1 is the total number of distribution points;

[0047] Feasibility verification: Based on the optimal path calculated by the path calculation unit, check whether the generated path meets the actual delivery constraints; if the path violates the constraints, recalculate and adjust the path until all constraints are met.

[0048] Beneficial effects of the present invention:

[0049] The present invention uses an intelligent path optimization algorithm, and the system can quickly calculate the shortest and optimal delivery path under multiple constraints, avoiding the lengthy and unreasonable paths in traditional scheduling methods, reducing empty vehicle driving and unnecessary energy consumption; in addition, the system can intelligently adjust the priority according to factors such as the urgency of the order and the delivery time requirements, ensuring that high-priority orders can be delivered first, thereby improving the overall delivery efficiency.

[0050] The present invention uses a machine learning algorithm to predict traffic flow data in real time. The system can identify possible traffic congestion areas in advance and adjust the delivery routes in time, avoiding delivery delays caused by changes in traffic conditions and improving customer satisfaction. At the same time, the scalability and flexibility of the system enable it to adapt to logistics and delivery needs of different scales, and has a strong market application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 A schematic diagram of a logistics distribution scheduling system according to an embodiment of the present invention;

[0053] Figure 2 Schematic diagram of a scheduling decision module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0055] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0056] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0057] like Figure 1-Figure 2 As shown in the figure, the AI-optimized automobile market logistics distribution scheduling system includes a data collection module, an order management module, a traffic flow analysis module, and a scheduling decision module; among which:

[0058] Data collection module: used to collect the current location data of the delivery vehicle, traffic flow data and customer order information in real time;

[0059] Order management module: used to receive customer order information, prioritize orders according to order urgency, delivery time requirements and destination location, and pass the sorted order information to the scheduling decision module;

[0060] Traffic flow analysis module: used to combine historical traffic flow data and real-time traffic flow data to build a traffic flow prediction model, predict traffic conditions in the future, identify road sections and time periods that are about to be congested, and provide the prediction results to the scheduling decision module;

[0061] Scheduling decision module: Based on order priority, delivery time requirements and predicted traffic conditions, an optimization algorithm is used to optimize vehicle scheduling and delivery routes, generate an optimal scheduling plan, and perform actual delivery operations based on the optimal scheduling plan.

[0062] The data collection module includes a vehicle location collection unit, a traffic flow collection unit and an order information collection unit, wherein:

[0063] Vehicle location acquisition unit: obtains the geographic location coordinates of the delivery vehicle in real time through GPS positioning equipment;

[0064] Traffic flow collection unit: collects traffic flow data of each road section in real time through traffic monitoring sensors or third-party traffic data interfaces, including vehicle flow, speed and congestion conditions;

[0065] Order information collection unit: Receives customer order information through the order management interface, including order number, customer address, order creation time, order urgency, delivery time requirements and destination location; by setting up a vehicle location collection unit, a traffic flow collection unit and an order information collection unit in the data collection module, the system can obtain the location of the delivery vehicle, the current traffic conditions and detailed order information in real time and accurately; these precise data support the subsequent order management, traffic flow analysis and optimization of the scheduling decision module.

[0066] The order management module includes an order receiving unit, a priority calculation unit and an order sorting unit, wherein:

[0067] Order receiving unit: used to receive and store order information from customers, including order number, customer address, order creation time, order urgency, delivery time requirements and destination location and other detailed data;

[0068] Priority calculation unit: used to comprehensively evaluate the urgency, delivery time requirements and destination location of each order, and assign priority scores to the orders;

[0069] Order sorting unit: sorts all orders according to the priority score provided by the priority calculation unit and generates a prioritized order list; arranges the order list from high to low according to the priority score, and passes the sorted order information to the scheduling decision module; by setting up the order receiving unit, priority calculation unit and order sorting unit in the order management module, the system can accurately and effectively prioritize orders according to the urgency of the order, delivery time requirements and destination location.

[0070] The priority calculation unit includes:

[0071] Urgency evaluation subunit: used to score orders according to their urgency. The formula is: Among them, E is the urgency score of the order, T current is the current time, T order is the earliest delivery time required by the order, T max and T min are the maximum and minimum delivery time requirements of all orders respectively; the formula maps the urgency to a value between 0 and 1, with a larger value indicating more urgency;

[0072] Delivery time efficiency evaluation subunit: used to score orders according to their delivery time efficiency requirements. The formula is: Among them, S is the delivery time efficiency score, T deadline T is the final delivery time limit of the order. current is the current time, T orderThe time when the order was created. This formula calculates the ratio of the remaining time from the order’s final deadline to the total remaining time. The stronger the timeliness, the higher the score.

[0073] Destination location evaluation subunit: used to score orders according to their destination location. The formula is: Where D is the destination location score, D order is the distance between the order destination and the distribution center, D base is the reference benchmark distance, and k is the distance weight coefficient. This formula uses the sigmoid function to standardize the order distance. The farther the distance, the lower the score.

[0074] Priority score calculation subunit: used to calculate the comprehensive priority score of the order by weighted average method according to the urgency score, delivery time score and destination location score. The calculation formula is: P = w1·E+w2·S+w3·D, where P is the comprehensive priority score of the order, E is the urgency score, S is the delivery time score, D is the destination location score, w1, w2 and w3 are the weight coefficients of urgency, timeliness and destination location respectively. By adjusting the weight coefficients, the system can adjust the impact of each indicator according to actual needs to obtain the best priority ranking; by introducing the scoring mechanism of urgency, delivery time and destination location in the priority calculation unit, and using the weighted average method to comprehensively calculate the priority score of the order, the system can accurately evaluate the delivery priority of each order.

[0075] The traffic flow analysis module includes a historical traffic data processing unit, a real-time traffic data processing unit, a machine learning prediction unit, and a congestion identification unit, among which:

[0076] Historical traffic data processing unit: used to store historical traffic flow data of the past six months; and pre-process historical traffic flow data, including removing noise data, filling missing values ​​and standardizing, to ensure data accuracy and consistency;

[0077] Real-time traffic data receiving unit: used to receive real-time traffic flow data provided by the data acquisition module;

[0078] Machine learning prediction unit: Based on historical traffic flow data and real-time traffic flow data, it uses machine learning algorithms to predict traffic conditions in the future;

[0079] Congestion identification unit: Based on the traffic condition prediction results provided by the machine learning prediction unit, the road sections and time periods where congestion is about to occur are identified. Specifically, by setting congestion thresholds (such as vehicle speed below a certain value or traffic volume exceeding a certain level), the prediction results are analyzed, the road sections and time periods where congestion is about to occur are marked, and the identification results are provided to the scheduling decision module; by setting up a historical traffic data processing unit, a real-time traffic data processing unit, a machine learning prediction unit and a congestion identification unit in the traffic flow analysis module, the system can comprehensively utilize historical and real-time traffic data, use advanced machine learning algorithms to accurately predict future traffic conditions, and promptly identify potential traffic congestion areas and time periods; this precise traffic prediction and congestion identification mechanism ensures that the scheduling decision module can perform optimal vehicle scheduling and route planning based on reliable data, significantly improving the efficiency and reliability of logistics distribution, and reducing distribution delays and operating costs caused by traffic congestion.

[0080] The machine learning prediction unit includes:

[0081] Feature extraction subunit: used to extract representative features from historical traffic data and real-time traffic data, including time features, weather features and road section features; among them, time features include time periods of the day, days of the week, holidays, etc., which are used to reflect the impact of different times on traffic flow; weather features include temperature, precipitation and humidity, etc., which are used to reflect the impact of weather conditions on traffic flow; road section features include road type, number of lanes and traffic light density, etc., which are used to reflect the impact of different road sections and infrastructure on traffic flow;

[0082] Model training subunit: Based on the feature data extracted by the feature extraction subunit, a machine learning algorithm based on long short-term memory network is used to train the traffic flow prediction model;

[0083] Prediction generation subunit: After the training is completed, the trained traffic flow prediction model is used to predict the traffic flow in the future period of time and generate the prediction results of future traffic conditions; through the organic combination of the above subunits, the long short-term memory network (LSTM) algorithm can be used to train a high-precision traffic flow prediction model and generate accurate future traffic condition prediction results. This design ensures the accuracy and timeliness of traffic flow prediction and provides reliable data support for the scheduling decision module.

[0084] The model training subunit includes:

[0085] Input layer: takes the feature data extracted from the feature extraction subunit as the input of the model;

[0086] Hidden layer: The long short-term memory network processes the input data through its unique memory unit and gating mechanism. This processing process continuously adjusts the internal state through iterative operations of multiple time steps to capture the long-term and short-term dependencies of traffic flow, understand and remember the changing patterns of traffic flow;

[0087] Output layer: The output of the hidden layer is passed through the fully connected layer to generate the traffic flow prediction result at the current time point; the prediction result is an estimate of the traffic flow in the future period of time.

[0088] The calculation steps for traffic flow prediction model training are as follows:

[0089] First: The time features, weather features and road section features extracted by the feature extraction subunit are used as the input vector X of the model t , where t represents the current time step;

[0090] Then: Through the long short-term memory network through its memory unit and gating mechanism, the input data X t Process and calculate the forget gate f t , input gate i t and output gate o t And cell state C t and the hidden state h t , and its calculation formula is as follows:

[0091] f t =σ(W f ·[h t-1 ,X t ]+b f );

[0092] i t =σ(W i ·[h t-1 ,X t ]+b i );

[0093]

[0094] o t =σ(W o ·[h t-1 ,X t ]+b o );

[0095] h t =o t *tanh(C t ), where σ represents the Sigmoid activation function, tanh represents the hyperbolic tangent activation function, and W f ,W i ,W C,W o is the weight matrix, b f ,b i ,b C ,b o is the bias vector, * means element-by-element multiplication;

[0096] Next: The output h of the hidden layer t Mapped to the prediction result through the fully connected layer The calculation formula is as follows: Among them, W y is the weight matrix of the output layer, b y is the bias vector of the output layer;

[0097] Finally, the constructed traffic flow prediction model expression is: in, is the traffic flow predicted in the next k time steps, X t is the input feature vector of the current time step t, and θ is the model parameter, including the weight matrix and the bias vector.

[0098] The scheduling decision module includes a scheduling optimization unit, a path planning unit, and a scheduling execution unit, among which:

[0099] Dispatch optimization unit: used to receive the order information after priority sorting and delivery time requirements transmitted by the order management module, as well as the traffic condition prediction results provided by the traffic flow analysis module, and use the optimization algorithm to calculate the vehicle dispatch to generate the optimal dispatch plan;

[0100] Path planning unit: Based on the optimal dispatching plan generated by the dispatching optimization unit, the path optimization algorithm is used to calculate the optimal delivery path for each vehicle to ensure the efficiency and feasibility of the delivery path;

[0101] Scheduling execution unit: used to command the delivery vehicles to perform delivery tasks according to the optimal delivery path calculated by the path planning unit, ensuring the accurate implementation of the delivery operation; through the coordinated work of the scheduling optimization unit, the path planning unit and the scheduling execution unit, the system can efficiently generate and execute the optimal vehicle scheduling and delivery path plan to ensure the efficient completion of the delivery task.

[0102] The scheduling optimization unit includes:

[0103] Constraint setting subunit: According to the actual distribution demand and resource constraints, set constraints to ensure that the optimization plan meets the actual implementation requirements. Constraints include vehicle capacity, transportation time, traffic flow, and road restrictions. For example, the maximum cargo capacity of a vehicle is set to: Among them, x i To select order i, w iis the weight of order i, C is the maximum cargo capacity of the vehicle, ensuring that the delivery load of each vehicle does not exceed its carrying capacity; n is the total number of orders, ensuring that the delivery load of each vehicle does not exceed its carrying capacity;

[0104] Optimization calculation subunit: Use heuristic optimization algorithms (such as genetic algorithms or particle swarm optimization algorithms) to solve the scheduling problem and generate the optimal scheduling solution; optimize the scheduling order and delivery path of vehicles based on the comprehensive priority score P of the order and the constraints provided by the constraint setting subunit; the optimization goal is to minimize the total delivery time and ensure that high-priority orders are delivered on time; the specific optimization objective function is expressed as: Among them, P i is the comprehensive priority score of order i, T i is the delivery time requirement for order i, d ij is the distance of the vehicle from order i to order j, x ij is the path selected from order i to order j in the scheduling plan, and λ is the balance parameter of the scheduling plan; the objective function aims to minimize the total delivery time and the total delivery path to achieve global optimal scheduling; through the coordination of constraint setting and optimization calculation subunits, the optimal scheduling plan can be efficiently generated, and the delivery path and scheduling order can be optimized while ensuring that the constraints are met, thereby improving the delivery efficiency and resource utilization.

[0105] The path planning unit includes:

[0106] Path initialization: The delivery vehicles and the delivery targets of the orders are regarded as nodes in the graph. Each node in the graph represents a delivery point, i.e. the starting or ending location of the order, and the edges represent the paths of the vehicles.

[0107] Calculate the distance matrix: Calculate the distance between all delivery points and construct the distance matrix D, where D ij represents the distance from node i to node j; for any two nodes i and j, the distance D ij Calculated according to the following formula: Among them, x i ,y i is the coordinate of node i, x j ,y j is the coordinate of node j;

[0108] Path optimization: The shortest delivery path for each vehicle is calculated based on the distance matrix D through an iterative algorithm to ensure that each vehicle has the shortest journey. For each vehicle, the objective function is to minimize the total distance of the delivery path: Among them, Z ij is the selection variable for the vehicle to go from node i to node j, D ijis the distance from node i to node j, n1 is the total number of distribution points;

[0109] Feasibility verification: Based on the optimal path calculated by the path calculation unit, check whether the generated path meets the actual delivery constraints, such as traffic flow, road restrictions, vehicle capacity, etc.; if the path violates the constraints, recalculate and adjust the path until all constraints are met; the path optimization algorithm used in the above steps can efficiently calculate the shortest path for each delivery vehicle, greatly reducing delivery time and fuel consumption, and improving delivery efficiency; and through feasibility verification constraint checks, it can be ensured that the path is not only optimal in theory, but also meets all traffic and vehicle constraints in actual execution, ensuring the smooth progress of the delivery process.

[0110] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0111] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. The AI-optimized automobile market logistics distribution scheduling system is characterized by: It includes data collection module, order management module, traffic flow analysis module and dispatch decision module; among which: Data collection module: used to collect the current location data of the delivery vehicle, traffic flow data and customer order information in real time; Order management module: used to receive customer order information, prioritize orders according to order urgency, delivery time requirements and destination location, and pass the sorted order information to the scheduling decision module; Traffic flow analysis module: used to combine historical traffic flow data and real-time traffic flow data to build a traffic flow prediction model, predict traffic conditions in the future, identify road sections and time periods that are about to be congested, and provide the prediction results to the scheduling decision module; Scheduling decision module: Based on order priority, delivery time requirements and predicted traffic conditions, an optimization algorithm is used to optimize vehicle scheduling and delivery routes to generate the optimal scheduling plan.

2. The AI-optimized automobile market logistics distribution scheduling system according to claim 1 is characterized in that: The data collection module includes a vehicle location collection unit, a traffic flow collection unit and an order information collection unit, wherein: Vehicle location acquisition unit: obtains the geographic location coordinates of the delivery vehicle in real time through GPS positioning equipment; Traffic flow collection unit: collects traffic flow data of each road section in real time through traffic monitoring sensors or third-party traffic data interfaces, including vehicle flow, speed and congestion conditions; Order information collection unit: Receives customer order information through the order management interface, including order number, customer address, order creation time, order urgency, delivery time requirements and destination location.

3. The AI-optimized automobile market logistics distribution scheduling system according to claim 1 is characterized in that: The order management module includes an order receiving unit, a priority calculation unit and an order sorting unit, wherein: Order receiving unit: used to receive and store order information from customers; Priority calculation unit: used to comprehensively evaluate the urgency, delivery time requirements and destination location of each order, and assign priority scores to the orders; Order sorting unit: sorts all orders according to the priority score provided by the priority calculation unit and generates a priority-sorted order list; arranges the order list from high to low according to the priority score, and passes the sorted order information to the scheduling decision module.

4. The AI-optimized automobile market logistics distribution scheduling system according to claim 3 is characterized in that: The priority calculation unit comprises: Urgency evaluation subunit: used to score orders according to their urgency. The formula is: Among them, E is the urgency score of the order, T current is the current time, T order is the earliest delivery time required by the order, T max and T min They are the maximum and minimum delivery time requirements for all orders respectively; Delivery time efficiency evaluation subunit: used to score orders according to their delivery time efficiency requirements. The formula is: Among them, S is the delivery time efficiency score, T deadline T is the final delivery time limit of the order. current is the current time, T order Create the time for the order; Destination location evaluation subunit: used to score orders according to their destination location. The formula is: Where D is the destination location score, D order is the distance between the order destination and the distribution center, D base is the reference distance, k is the distance weight coefficient; Priority score calculation subunit: used to calculate the comprehensive priority score of the order by weighted average method according to the urgency score, delivery time efficiency score and destination location score. The calculation formula is: P = w1·E+w2·S+w3·D, where P is the comprehensive priority score of the order, E is the urgency score, S is the delivery time efficiency score, D is the destination location score, and w1, w2 and w3 are the weight coefficients of urgency, time efficiency and destination location respectively.

5. The AI-optimized automobile market logistics distribution scheduling system according to claim 1 is characterized in that: The traffic flow analysis module includes a historical traffic data processing unit, a real-time traffic data processing unit, a machine learning prediction unit and a congestion identification unit, wherein: Historical traffic data processing unit: used to store historical traffic flow data of the past six months; and pre-process the historical traffic flow data, including removing noise data, filling missing values ​​and standardization; Real-time traffic data receiving unit: used to receive real-time traffic flow data provided by the data acquisition module; Machine learning prediction unit: Based on historical traffic flow data and real-time traffic flow data, it uses machine learning algorithms to predict traffic conditions in the future; Congestion identification unit: Based on the traffic condition prediction results provided by the machine learning prediction unit, it identifies the road sections and time periods where congestion is about to occur. Specifically, it sets congestion thresholds, analyzes the prediction results, and marks the road sections and time periods where congestion is about to occur.

6. The AI-optimized automobile market logistics distribution scheduling system according to claim 5 is characterized in that: The machine learning prediction unit includes: Feature extraction subunit: used to extract representative features from historical traffic data and real-time traffic data, including time features, weather features and road section features; Model training subunit: Based on the feature data extracted by the feature extraction subunit, a machine learning algorithm based on long short-term memory network is used to train the traffic flow prediction model; Prediction generation subunit: After the training is completed, the trained traffic flow prediction model is used to predict the traffic flow in the future and generate the prediction results of future traffic conditions.

7. The AI-optimized automobile market logistics distribution scheduling system according to claim 6 is characterized in that: The model training subunit comprises: Input layer: takes the feature data extracted from the feature extraction subunit as the input of the model; Hidden layer: The long short-term memory network processes the input data through its memory units and gating mechanism, and continuously adjusts the internal state through iterative operations of multiple time steps to capture the long-term and short-term dependencies of traffic flow, understand and remember the changing patterns of traffic flow; Output layer: The output of the hidden layer is passed through the fully connected layer to generate the traffic flow prediction result at the current time point.

8. The AI-optimized automobile market logistics distribution scheduling system according to claim 1 is characterized in that: The scheduling decision module includes a scheduling optimization unit, a path planning unit and a scheduling execution unit, wherein: Dispatch optimization unit: used to receive the order information after priority sorting and delivery time requirements transmitted by the order management module, as well as the traffic condition prediction results provided by the traffic flow analysis module, and use the optimization algorithm to calculate the vehicle dispatch to generate the optimal dispatch plan; Path planning unit: Based on the optimal dispatching plan generated by the dispatching optimization unit, the path optimization algorithm is used to calculate the optimal delivery path for each vehicle to ensure the efficiency and feasibility of the delivery path; Scheduling execution unit: used to command delivery vehicles to perform delivery tasks based on the optimal delivery path calculated by the path planning unit.

9. The AI-optimized automobile market logistics distribution scheduling system according to claim 8 is characterized in that: The scheduling optimization unit comprises: Constraint setting subunit: Set constraints based on actual delivery needs and resource constraints, including vehicle capacity, transportation time, traffic flow, and road restrictions; Optimization calculation subunit: Use heuristic optimization algorithm to solve the scheduling problem and generate the optimal scheduling solution; optimize the scheduling order and delivery path of vehicles based on the comprehensive priority score P of the order and the constraints provided by the constraint setting subunit; the optimization goal is to minimize the total delivery time and ensure that high-priority orders are delivered on time; the specific optimization objective function is expressed as: Among them, P i is the comprehensive priority score of order i, T i is the delivery time requirement for order i, d ij is the distance of the vehicle from order i to order j, x ij is the path selected from order i to order j in the scheduling scheme, and λ is the balance parameter of the scheduling scheme.

10. The AI-optimized automobile market logistics distribution scheduling system according to claim 9 is characterized in that: The path planning unit comprises: Path initialization: The delivery vehicles and the delivery targets of the orders are regarded as nodes in the graph. Each node in the graph represents a delivery point, i.e. the starting or ending location of the order, and the edges represent the paths of the vehicles. Calculate the distance matrix: Calculate the distance between all delivery points and construct the distance matrix D, where D ij represents the distance from node i to node j; for any two nodes i and j, the distance D ij Calculated according to the following formula: Among them, x i ,y i is the coordinate of node i, x j ,y j is the coordinate of node j; Path optimization: The shortest delivery path for each vehicle is calculated based on the distance matrix D through an iterative algorithm to ensure that each vehicle has the shortest journey. For each vehicle, the objective function is to minimize the total distance of the delivery path: min Among them, Z ij is the selection variable for the vehicle to go from node i to node j, D ij is the distance from node i to node j, n1 is the total number of distribution points; Feasibility verification: Based on the optimal path calculated by the path calculation unit, check whether the generated path meets the actual delivery constraints; if the path violates the constraints, recalculate and adjust the path until all constraints are met.

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