A logistics rapid delivery analysis system based on artificial intelligence

Abstract

The present invention discloses an artificial intelligence-based logistics rapid delivery analysis system, which relates to the technical field of logistics delivery analysis. The present invention collects order, vehicle location and traffic flow data in real time to construct the delivery environment status. The intelligent agent, as an independent decision-making unit deployed on the delivery vehicle, uses a local planning module to generate path and resource allocation plans, and shares local plans through a communication module to achieve collaborative optimization, which solves the disadvantage that traditional static planning is difficult to cope with order changes and emergencies. During the delivery process, the intelligent agent dynamically adjusts the path and task priority according to the real-time updated traffic and order data, significantly reducing the losses caused by traffic congestion or order delays, and realizing continuous improvement of path planning through feedback adjustment. The analysis center combines the NSGA-II algorithm to incorporate delivery time, cost and task balance into the objective function, optimizes the MADDPG strategy based on actual execution data, and improves the overall efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics distribution analysis, and in particular to an artificial intelligence-based logistics rapid distribution analysis system. Background Art

[0002] With the rapid development of e-commerce and instant logistics, logistics and delivery tasks are becoming increasingly complex. Current logistics and delivery methods mainly rely on path planning algorithms and resource scheduling models. They perform well in single static tasks, but are less effective when faced with real-time, changeable and complex delivery scenarios.

[0003] The orders in actual delivery tasks are highly random, such as new orders, canceled orders or priority adjustments. The existing path planning algorithms lack dynamic adjustment capabilities. At the same time, most optimization strategies only optimize the efficiency of a single delivery vehicle or a single delivery node, ignoring the importance of multi-node collaboration in the delivery network, resulting in the overall delivery efficiency failing to meet expectations; in addition, in delivery tasks, multiple objectives such as delivery time, operating costs and energy consumption need to be optimized simultaneously. Current delivery analysis mostly uses fixed weights for single optimization, which cannot weigh the priorities between different objectives in real time, resulting in the optimization results being difficult to meet dynamic actual needs; therefore, there is an urgent need for an artificial intelligence-based logistics rapid delivery analysis system to solve such problems. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] The present invention provides an artificial intelligence-based logistics rapid delivery analysis system to solve the problem that existing path planning algorithms lack dynamic adjustment capabilities. At the same time, most optimization strategies only optimize the efficiency of a single delivery vehicle or a single delivery node, ignoring the importance of multi-node collaboration in the distribution network; and are unable to balance the priorities of different goals in real time.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The embodiment of the present invention provides a logistics rapid delivery analysis system based on artificial intelligence, which includes:

[0008] The delivery network data collection unit collects delivery data in real time through IoT devices, including order type, order address, vehicle location, and traffic flow, and extracts features to construct the delivery environment state as input to the intelligent agent;

[0009] The multi-agent unit is responsible for receiving real-time environmental status data during the delivery process, dynamically updating the delivery route and priority, and feeding back the adjusted results to the analysis center; the multi-agent unit contains multiple agents;

[0010] Intelligent agents are independent decision-making units in the delivery network, deployed on delivery vehicles. They are responsible for autonomously planning routes and allocating resources based on environmental conditions, and for interaction and collaborative optimization among agents. Intelligent agents are devices with autonomous decision-making capabilities, deployed on delivery vehicles, and capable of completing delivery tasks through perception, computation, and execution within the delivery environment.

[0011] The intelligent agent includes a local planning module and a communication module.

[0012] In the local planning module, each agent generates path planning and vehicle allocation solutions based on the received delivery environment status using the multi-agent deep deterministic policy gradient MADDPG algorithm.

[0013] A communication module, through which agents share local solutions and dynamically adjust paths and task allocations based on a collaborative interaction mechanism;

[0014] The analysis center collects feedback data from multi-agent units and uses the NSGA-II algorithm to iteratively adjust the MADDPG strategy.

[0015] As a preferred solution of the artificial intelligence-based logistics rapid delivery analysis system of the present invention, the analysis method of the logistics rapid delivery analysis system includes:

[0016] Step S1: Collect delivery data from the delivery network through IoT devices, including order type, order address, vehicle location, and traffic flow data; extract features from the delivery data, construct a representation of the delivery environment state based on the extracted features, and distribute it to each agent as input;

[0017] In step S2, each delivery agent generates a local plan based on the received delivery environment status using the MADDPG algorithm strategy, including the path planning and vehicle allocation plan for each agent;

[0018] Step S3: Agents share local solutions through the communication module and perform collaborative optimization based on the environment state.

[0019] Step S4: During the delivery process, each agent obtains traffic flow data and order urgency data collected by the delivery network data collection unit in real time, dynamically updates the delivery route and delivery priority, and feeds back the adjusted execution results to the analysis center.

[0020] In step S5, the analysis center collects the execution result data of each agent. Based on the execution results of the overall delivery, the multi-objective optimization NSGA-II algorithm is used to re-evaluate the MADDPG algorithm strategy in step S2, and delivery time, cost, and task balance are incorporated into the objective function for iterative adjustment.

[0021] As a preferred solution of the artificial intelligence-based logistics rapid delivery analysis system described in the present invention, the order type is used to mark the urgency of the order.

[0022] As a preferred solution of the artificial intelligence-based logistics rapid delivery analysis system of the present invention, the step of extracting features from the delivery data and constructing a representation of the delivery environment state based on the extracted features is as follows:

[0023] The distribution data is converted into feature vectors, and the feature vector formula is defined as:

[0024] F=[f1,f2,f3,…,f n ],

[0025] Among them, F represents the characteristic vector of the distribution environment, f1 represents the order distribution density, and the calculation formula is: f1=N o / A r , where N o is the total number of orders in the region, A r is the area of the region, f2 represents the traffic flow characteristics, which is defined as the flow density of a certain road section. The calculation formula is: f2 = N v / L r , where N v is the number of vehicles passing through the road section per unit time, L r is the length of the road section, f3 represents the vehicle state characteristics, defined as the current load rate, and the calculation formula is: f3 = W c / W m , where W c is the current load, W m is the maximum load of the vehicle, f4,f5,…,f n Indicates other extended features, defined according to actual delivery requirements;

[0026] The delivery environment state is constructed based on the extracted feature vectors. The expression formula of the environment state is:

[0027] S e =g(f1,f2,…,f n ),

[0028] Among them, S e is the global state of the delivery environment, g is the feature mapping function, which is normalized and defined as:

[0029]

[0030] Among them, min(f i ) and max(f i ) are the features f i The minimum and maximum values of

[0031] The method of marking the urgency of an order is:

[0032] For each order, the urgency weight is calculated based on the order type. The urgency weight formula is:

[0033] u j =h(d j ,t j ),

[0034] Among them, u j represents the urgency weight of order j, d j represents the delivery distance of order j, t j represents the deadline of order j, h(d j ,t j ) is the urgency calculation function, defined as:

[0035]

[0036] Among them, τ represents the current time, and k represents the weight coefficient of the delivery distance;

[0037] Combine the feature vector and the urgency mark to form the delivery status vector. The combination formula is:

[0038] S c =[S e ,u1,u2,…,u m ],

[0039] Among them, S c is the comprehensive state vector, u1,u2,…,u m Represents the set of urgency weights for all orders.

[0040] As a preferred solution of the artificial intelligence-based logistics rapid delivery analysis system described in the present invention, the steps of generating a local solution based on the received delivery environment state using the MADDPG algorithm strategy, including path planning and vehicle allocation for each agent, are as follows:

[0041] The global state S of the distribution environment e Divided into multiple local states, the local state formula is:

[0042] o i,t =f i (S e ),

[0043] Among them, i,t is the local observation of agent i at time t, f i It is a local state extraction function that is segmented according to the agent's geographical location or task assignment.

[0044] Each agent generates actions based on local state input through the policy network. The policy function is defined as:

[0045] a i,t =π i (o i,t θ i ),

[0046] Among them, a i,t is the action performed by agent i at time t, including path planning and vehicle allocation plan, π i is the strategy function of agent i, with parameter θ i ,θ i are the trainable parameters of the policy network;

[0047] The local delivery plan is formed by the action set of all agents, which is expressed as:

[0048] A t =[a 1,t ,a 2,t ,…,a m,t ],

[0049] Among them, A t is the set of actions of all agents at time t, m is the total number of agents,

[0050] Define the reward function r i,t , the formula is:

[0051]

[0052] Among them, T i The time it takes for agent i to complete the current task, u i is the urgency weight of the order currently being processed by the agent, α, β, γ are the weight coefficients of the reward function, R g is the global efficiency reward, defined as:

[0053]

[0054] Using the centralized training and distributed execution mechanism of the MADDPG algorithm, the policy parameters are updated by gradient descent. The update formula is:

[0055]

[0056] in,

[0057] J(θ i ) is the objective function of agent i, defined as:

[0058]

[0059] Where η is the learning rate. Through multiple rounds of iterative optimization, each agent gradually adjusts the path planning and vehicle allocation plan to improve the overall efficiency.

[0060] As a preferred solution of the artificial intelligence-based logistics rapid delivery analysis system described in the present invention, the collaborative optimization method is:

[0061] Utilize the interaction mechanism in multi-agent reinforcement learning to adjust their respective delivery paths.

[0062] As a preferred solution of the artificial intelligence-based logistics rapid delivery analysis system described in the present invention, the steps of sharing local solutions between the intelligent agents through the communication module and performing collaborative optimization in combination with the environmental status are as follows:

[0063] After the agent's action is executed, the global state of the delivery environment is updated. The update formula is:

[0064] S e,t+1 =f(S e,t ,A t ),

[0065] Among them, S e,t+1 is the global state at time t+1, S e,t is the global state at time t, A t is the set of actions performed by all agents at time t, f is the state transition function, and the state is dynamically updated based on traffic flow and order completion.

[0066] Each agent shares part of the global reward and exchanges local states through a communication mechanism. The collaboration formula is:

[0067]

[0068] in, is the collaboration reward of agent i, r j,t is the individual reward of agent j, γ is the collaboration coefficient, which adjusts the degree of reward sharing;

[0069] The objective function is designed by integrating individual and collaborative rewards. The objective function formula is:

[0070]

[0071] By optimizing the policy parameters through gradient descent, the update formula is:

[0072]

[0073] in,

[0074] η is the learning rate, is the gradient of the objective function,

[0075] Combining local states and collaborative rewards, the agent generates optimized path planning and vehicle allocation solutions. The path planning formula is:

[0076]

[0077] Among them, P i 新 is the new path of agent i, C(P i ,S e ) is the path P i Cost function, integrating time and urgency;

[0078] The vehicle allocation formula is:

[0079]

[0080] Among them, V i 新 is the vehicle allocation plan of agent i, U(V i ,S e ) is the benefit function of vehicle allocation, combining task balance and transportation capacity,

[0081] Define the overall benefit function to comprehensively evaluate the effect of collaborative optimization of intelligent agents. The overall benefit function is:

[0082]

[0083] Among them, E g is the global benefit, α, β are weight coefficients.

[0084] As a preferred solution of the artificial intelligence-based logistics rapid delivery analysis system of the present invention, the adjusted execution results include:

[0085] The agent's adjusted path nodes and estimated arrival time each time,

[0086] Priority changes based on order urgency,

[0087] As well as the number of delivery tasks and mileage delivered for each vehicle.

[0088] As a preferred solution of the artificial intelligence-based logistics rapid delivery analysis system of the present invention, the step of dynamically updating the delivery path and delivery priority is as follows:

[0089] The agent calculates the priority of each order based on the remaining delivery time of the order and the urgency of the current task.

[0090] Each agent replans its path based on the current traffic conditions and order priority, using dynamic programming and heuristic algorithms for planning.

[0091] After completing the route adjustment, each agent generates an updated delivery action based on the new priority information and route plan, and sends it to the delivery vehicle in real time.

[0092] As a preferred solution of the artificial intelligence-based logistics rapid delivery analysis system of the present invention, the step of re-evaluating the MADDPG algorithm strategy in step S2 using the multi-objective optimization NSGA-II algorithm based on the execution result of the overall delivery is as follows:

[0093] Collect the execution data of all agents after completing the delivery task, including actual delivery time, path cost and task allocation.

[0094] Based on the execution data, define the objective function, which includes:

[0095] Minimize total delivery time,

[0096] Minimize delivery costs,

[0097] and maximize the balance of task allocation,

[0098] The NSGA-II algorithm is used to generate the initial population based on the objective function, and the Pareto frontier solution set is selected through crossover, mutation and non-dominated sorting.

[0099] During the optimization process, each solution balances multiple objectives, and a solution is selected from the frontier solution set to readjust the MADDPG strategy.

[0100] The beneficial effects of the present invention are as follows: the present invention collects order, vehicle location and traffic flow data in real time to construct the distribution environment status. The intelligent agent, as an independent decision-making unit deployed on the distribution vehicle, uses the local planning module to generate path and resource allocation plans, and shares the local plans through the communication module to achieve collaborative optimization, which solves the disadvantage that traditional static planning is difficult to cope with order changes and emergencies; during the distribution process, the intelligent agent dynamically adjusts the path and task priority according to the real-time updated traffic and order data, significantly reducing the losses caused by traffic congestion or order delays, and realizing continuous improvement of path planning through feedback adjustment; the analysis center combines the NSGA-II algorithm to incorporate delivery time, cost and task balance into the objective function, optimizes the MADDPG strategy based on actual execution data, and improves the overall benefit.

[0101] The present invention introduces a collaborative reward mechanism and a global benefit function among multiple agents to avoid resource waste. At the same time, through comprehensive optimization combining time and task priority, it ensures dynamics and real-time responsiveness, while improving overall delivery efficiency, reducing operating costs and effectively solving the problem of imbalance in multi-objective optimization.

[0102] In summary, the present invention effectively improves delivery efficiency and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0104] Figure 1 This is a schematic diagram of the framework of the artificial intelligence-based logistics rapid delivery analysis system of the present invention. DETAILED DESCRIPTION

[0105] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0106] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0107] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0108] Example 1, reference Figure 1 This embodiment provides an artificial intelligence-based logistics rapid delivery analysis system, including:

[0109] The delivery network data collection unit collects delivery data in real time through IoT devices, including order type, order address, vehicle location, and traffic flow, and extracts features to construct the delivery environment state as input to the intelligent agent;

[0110] The multi-agent unit is responsible for receiving real-time environmental status data during the delivery process, dynamically updating the delivery route and priority, and feeding back the adjusted results to the analysis center; the multi-agent unit contains multiple agents;

[0111] Intelligent agents are independent decision-making units in the delivery network, deployed on delivery vehicles. They are responsible for autonomously planning routes and allocating resources based on environmental conditions, and for interaction and collaborative optimization among agents. Intelligent agents are devices with autonomous decision-making capabilities, deployed on delivery vehicles, and capable of completing delivery tasks through perception, computation, and execution within the delivery environment.

[0112] The intelligent agent includes a local planning module and a communication module.

[0113] In the local planning module, each agent generates path planning and vehicle allocation solutions based on the received delivery environment status using the multi-agent deep deterministic policy gradient MADDPG algorithm.

[0114] A communication module, through which agents share local solutions and dynamically adjust paths and task allocations based on a collaborative interaction mechanism;

[0115] The analysis center collects feedback data from multi-agent units and uses the NSGA-II algorithm to iteratively adjust the MADDPG strategy;

[0116] This embodiment also provides an analysis method for the above-mentioned artificial intelligence-based logistics rapid delivery analysis system, comprising the following steps:

[0117] Step S1: Collect delivery data from the delivery network through IoT devices, including order type, order address, vehicle location, and traffic flow data; extract features from the delivery data, construct a representation of the delivery environment state based on the extracted features, and distribute it to each agent as input;

[0118] Order type is used to mark the urgency of the order;

[0119] The steps of extracting features from delivery data and constructing a representation of the delivery environment state based on the extracted features are as follows:

[0120] The distribution data is converted into feature vectors, and the feature vector formula is defined as:

[0121] F=[f1,f2,f3,…,f n ],

[0122] Among them, F represents the characteristic vector of the distribution environment, f1 represents the order distribution density, and the calculation formula is: f1=N o / A r , where N o is the total number of orders in the region, Ar is the area of the region, f2 represents the traffic flow characteristics, which is defined as the flow density of a certain road section. The calculation formula is: f2 = N v / L r , where N v is the number of vehicles passing through the road section per unit time, L r is the length of the road section, f3 represents the vehicle state characteristics, defined as the current load rate, and the calculation formula is: f3 = W c / W m , where W c is the current load, W m is the maximum load of the vehicle, f4,f5,…,f n Indicates other extended features, defined according to actual delivery requirements;

[0123] The delivery environment state is constructed based on the extracted feature vectors. The expression formula of the environment state is:

[0124] S e =g(f1,f2,…,f n ),

[0125] Among them, S e is the global state of the delivery environment, g is the feature mapping function, which is normalized and defined as:

[0126]

[0127] Among them, min(f i ) and max(f i ) are the features f i The minimum and maximum values of

[0128] The way to mark the urgency of an order is:

[0129] For each order, the urgency weight is calculated based on the order type. The urgency weight formula is:

[0130] u j =h(d j ,t j ),

[0131] Among them, u j represents the urgency weight of order j, d j represents the delivery distance of order j, t j represents the deadline of order j, h(d j ,t j ) is the urgency calculation function, defined as:

[0132]

[0133] Among them, τ represents the current time, and k represents the weight coefficient of the delivery distance;

[0134] Combine the feature vector and the urgency mark to form the delivery status vector. The combination formula is:

[0135] S c =[S e ,u1,u2,…,u m ],

[0136] Among them, S c is the comprehensive state vector, u1,u2,…,u m represents the set of urgency weights of all orders;

[0137] Specifically, by g(f i ) is normalized to map the features to a uniform scale so that the state vector S e More suitable for the agent policy network input; In addition, the urgency u j Considering time and distance factors, orders are given reasonable priority, taking into account both expressiveness and computational efficiency. In actual deployment, the weight k or the number of features n can be adjusted according to data characteristics.

[0138] In step S2, each delivery agent generates a local plan based on the received delivery environment status using the MADDPG algorithm strategy, including the path planning and vehicle allocation plan for each agent;

[0139] Based on the received delivery environment state, the MADDPG algorithm strategy is used to generate a local solution, including the path planning and vehicle allocation steps of each agent.

[0140] The global state S of the distribution environment e Divided into multiple local states, the local state formula is:

[0141] o i,t =f i (S e ),

[0142] Among them, i,t is the local observation of agent i at time t, f i It is a local state extraction function that is segmented according to the agent's geographical location or task assignment.

[0143] Each agent generates actions based on local state input through the policy network. The policy function is defined as:

[0144] a i,t =π i (o i,t θ i ),

[0145] Among them, a i,t is the action performed by agent i at time t, including path planning and vehicle allocation plan, π i is the strategy function of agent i, with parameter θ i ,θ i are the trainable parameters of the policy network;

[0146] The local delivery plan is formed by the action set of all agents, which is expressed as:

[0147] A t =[a 1,t ,a 2,t ,…,a m,t ],

[0148] Among them, A t is the set of actions of all agents at time t, m is the total number of agents,

[0149] Define the reward function r i,t , the formula is:

[0150]

[0151] Among them, T i The time it takes for agent i to complete the current task, u i is the urgency weight of the order currently being processed by the agent, α, β, γ are the weight coefficients of the reward function, R g is the global efficiency reward, defined as:

[0152]

[0153] Using the centralized training and distributed execution mechanism of the MADDPG algorithm, the policy parameters are updated by gradient descent. The update formula is:

[0154]

[0155] in,

[0156] J(θ i ) is the objective function of agent i, defined as:

[0157]

[0158] Where η is the learning rate. Through multiple rounds of iterative optimization, each agent gradually adjusts the path planning and vehicle allocation scheme to improve the overall efficiency.

[0159] Specifically, step S2 generates a local delivery plan based on the MADDPG algorithm. During this process, each agent generates actions through the policy network based on the local state, and combines the multi-dimensional reward function to guide the agent to maintain a balance between local and global goals. Through centralized training and distributed execution, MADDPG effectively solves the collaboration problem between multiple agents, making path planning and vehicle allocation more dynamic and efficient. In practice, the weights α, β, and γ of the reward function are adjusted on demand.

[0160] Step S3: Agents share local solutions through the communication module and perform collaborative optimization based on the environment state.

[0161] The collaborative optimization method is:

[0162] Utilize the interaction mechanism in multi-agent reinforcement learning to adjust the delivery paths of each agent;

[0163] The agents share local solutions through the communication module and perform collaborative optimization based on the environment state.

[0164] After the agent's action is executed, the global state of the delivery environment is updated. The update formula is:

[0165] S e,t+1 =f(S e,t ,A t ),

[0166] Among them, S e,t+1 is the global state at time t+1, S e,t is the global state at time t, A t is the set of actions performed by all agents at time t, f is the state transition function, and the state is dynamically updated based on traffic flow and order completion.

[0167] Each agent shares part of the global reward and exchanges local states through a communication mechanism. The collaboration formula is:

[0168]

[0169] in, is the collaboration reward of agent i, r j,t is the individual reward of agent j, γ is the collaboration coefficient, which adjusts the degree of reward sharing;

[0170] The objective function is designed by integrating individual and collaborative rewards. The objective function formula is:

[0171]

[0172] By optimizing the policy parameters through gradient descent, the update formula is:

[0173]

[0174] in,

[0175] η is the learning rate, is the gradient of the objective function,

[0176] Combining local states and collaborative rewards, the agent generates optimized path planning and vehicle allocation solutions. The path planning formula is:

[0177]

[0178] Among them, P i 新 is the new path of agent i, C(P i ,S e ) is the path P i Cost function, integrating time and urgency;

[0179] The vehicle allocation formula is:

[0180]

[0181] Among them, V i 新 is the vehicle allocation plan of agent i, U(V i ,S e ) is the benefit function of vehicle allocation, combining task balance and transportation capacity,

[0182] Define the overall benefit function to comprehensively evaluate the effect of collaborative optimization of intelligent agents. The overall benefit function is:

[0183]

[0184] Among them, E g is the global benefit, α, β are weight coefficients,

[0185] Specifically, step S3 sets collaborative rewards and performs global state updates to enhance the overall optimization capability of the multi-agent system. While maintaining local benefits, each agent improves global benefits through a collaborative mechanism, avoiding resource waste or path conflicts caused by independent optimization. In addition, path planning and vehicle allocation are used to ensure the system's responsiveness in dynamic environments.

[0186] Step S4: During the delivery process, each agent obtains traffic flow data and order urgency data collected by the delivery network data collection unit in real time, dynamically updates the delivery route and delivery priority, and feeds back the adjusted execution results to the analysis center.

[0187] The adjusted execution results include:

[0188] The agent's adjusted path nodes and estimated arrival time each time,

[0189] Priority changes based on order urgency,

[0190] As well as the number of delivery tasks and mileage delivered for each vehicle;

[0191] The steps to dynamically update the delivery route and delivery priority are:

[0192] The agent calculates the priority of each order based on the remaining delivery time of the order and the urgency of the current task.

[0193] Each agent replans its path based on the current traffic conditions and order priority, using dynamic programming and heuristic algorithms for planning.

[0194] After completing the route adjustment, each agent generates an updated delivery action based on the new priority information and route plan, and sends it to the delivery vehicle in real time.

[0195] Specifically, the dynamic adjustment mechanism can significantly improve delivery efficiency and reduce losses caused by traffic jams or order delays.

[0196] In step S5, the analysis center collects the execution result data of each agent and, based on the overall delivery execution results, uses the multi-objective optimization NSGA-II algorithm to re-evaluate the MADDPG algorithm strategy in step S2, incorporating delivery time, cost, and task balance into the objective function and performing iterative adjustments.

[0197] Based on the execution results of the overall distribution, the steps of re-evaluating the MADDPG algorithm strategy in step S2 using the multi-objective optimization NSGA-II algorithm are as follows:

[0198] Collect the execution data of all agents after completing the delivery task, including actual delivery time, path cost and task allocation.

[0199] Based on the execution data, define the objective function, which includes:

[0200] Minimize total delivery time,

[0201] Minimize delivery costs,

[0202] and maximize the balance of task allocation,

[0203] The NSGA-II algorithm is used to generate the initial population based on the objective function, and the Pareto frontier solution set is selected through crossover, mutation and non-dominated sorting.

[0204] During the optimization process, each solution balances multiple objectives, and a solution is selected from the frontier solution set to readjust the MADDPG strategy.

[0205] Specifically, this feedback mechanism enables continuous improvement of the strategy and achieves a global optimization effect between time, cost, and balance. Combined with the NSGA-II optimization results, the distribution system can dynamically respond to demand changes and improve overall distribution efficiency and resource utilization.

[0206] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An artificial intelligence-based logistics rapid delivery analysis system, characterized by: include, The delivery network data collection unit collects delivery data in real time through IoT devices, including order type, order address, vehicle location, and traffic flow, and extracts features to construct the delivery environment state as input to the intelligent agent; The multi-agent unit is responsible for receiving real-time environmental status data during the delivery process, dynamically updating the delivery route and priority, and feeding back the adjusted results to the analysis center; the multi-agent unit contains multiple agents; Intelligent agents are independent decision-making units in the distribution network, deployed on delivery vehicles. They are responsible for autonomously planning routes and allocating resources based on environmental conditions, and for interaction and collaborative optimization among agents. The intelligent agent includes a local planning module and a communication module. In the local planning module, each agent generates path planning and vehicle allocation solutions based on the received delivery environment status using the multi-agent deep deterministic policy gradient MADDPG algorithm. A communication module, through which agents share local solutions and dynamically adjust paths and task allocations based on a collaborative interaction mechanism; The analysis center collects feedback data from multi-agent units and uses the NSGA-II algorithm to iteratively adjust the MADDPG strategy; The analysis method of the logistics rapid delivery analysis system includes: Step S1: Collect delivery data from the delivery network through IoT devices, including order type, order address, vehicle location, and traffic flow data; extract features from the delivery data, construct a representation of the delivery environment state based on the extracted features, and distribute it to each agent as input; In step S2, each delivery agent generates a local plan based on the received delivery environment status using the MADDPG algorithm strategy, including the path planning and vehicle allocation plan for each agent; Step S3: Agents share local solutions through the communication module and perform collaborative optimization based on the environment state. Step S4: During the delivery process, each agent obtains traffic flow data and order urgency data collected by the delivery network data collection unit in real time, dynamically updates the delivery route and delivery priority, and feeds back the adjusted execution results to the analysis center. In step S5, the analysis center collects the execution result data of each agent and, based on the overall delivery execution results, uses the multi-objective optimization NSGA-II algorithm to re-evaluate the MADDPG algorithm strategy in step S2, incorporating delivery time, cost, and task balance into the objective function and performing iterative adjustments. The steps of extracting features from the delivery data and constructing a representation of the delivery environment state based on the extracted features are: The distribution data is converted into a feature vector, and the feature vector formula is defined as: <h2 style=";text-align:left;direction:ltr">F=[f1,f2,f3,…,f<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ], Among them, F represents the characteristic vector of the distribution environment, f1 represents the order distribution density, and the calculation formula is: f1=N o / A r , where N o is the total number of orders in the region, A r is the area of the region, f2 represents the traffic flow characteristics, which is defined as the flow density of a certain road section. The calculation formula is: f2=N v / L r , where N v is the number of vehicles passing through the road section per unit time, L r is the length of the road section, f3 represents the vehicle state characteristics, defined as the current load rate, and the calculation formula is: f3=W c / W m , where W c is the current load, W m is the maximum load of the vehicle, f4,f5,…,f n Indicates other extended features, defined according to actual delivery requirements; The delivery environment state is constructed based on the extracted feature vectors. The expression formula of the environment state is: S e =g(f1,f2,…,f n ), Among them, S e is the global state of the delivery environment, g is the feature mapping function, which is normalized and defined as: Among them, min(f i ) and max(f i ) are the features f i The minimum and maximum values of The way to mark the urgency of an order is: For each order, the urgency weight is calculated based on the order type. The urgency weight formula is: u j =h(d j ,t j ), Among them, u j represents the urgency weight of order j, d j represents the delivery distance of order j, t j represents the deadline of order j, h(d j ,t j ) is the urgency calculation function, defined as: Among them, τ represents the current time, and k represents the weight coefficient of the delivery distance; Combine the feature vector and the urgency mark to form the delivery status vector. The combination formula is: S c =[S e ,u1,u2,…,u m ], Among them, S c is the comprehensive state vector, u1,u2,…,u m represents the set of urgency weights of all orders; The agents share local solutions through the communication module and perform collaborative optimization based on the environment state. After the agent's action is executed, the global state of the delivery environment is updated. The update formula is: S e,t+1 =f(S e,t ,A t ), Among them, S e,t+1 is the global state at time t+1, S e,t is the global state at time t, A t is the set of actions performed by all agents at time t, f is the state transition function, and the state is dynamically updated based on traffic flow and order completion. Each agent shares part of the global reward and exchanges local states through a communication mechanism. The collaboration formula is: in, is the collaboration reward of agent i, r j,t is the individual reward of agent j, γ is the collaboration coefficient, which adjusts the degree of reward sharing; The objective function is designed by integrating individual and collaborative rewards. The objective function formula is: By optimizing the policy parameters through gradient descent, the update formula is: in, η is the learning rate, is the gradient of the objective function, Combining local states and collaborative rewards, the agent generates optimized path planning and vehicle allocation solutions. The path planning formula is: in, is the new path of agent i, C(P i ,S e ) is the path P i Cost function, integrating time and urgency; The vehicle allocation formula is: in, is the vehicle allocation plan of agent i, U(V i ,S e ) is the benefit function of vehicle allocation, combining task balance and transportation capacity, Define the overall benefit function to comprehensively evaluate the effect of collaborative optimization of intelligent agents. The overall benefit function is: Among them, E g is the global benefit, α, β are weight coefficients.

2. The artificial intelligence-based logistics rapid delivery analysis system according to claim 1, characterized in that: The order type is used to mark the urgency of the order.

3. The artificial intelligence-based logistics rapid delivery analysis system according to claim 2, characterized in that: The steps of generating a local solution based on the received delivery environment state using the MADDPG algorithm strategy, including path planning and vehicle allocation for each agent, are as follows: The global state S of the distribution environment e Divided into multiple local states, the local state formula is: o i,t =f i (S e ), Among them, i,t is the local observation of agent i at time t, f i It is a local state extraction function that is segmented according to the agent's geographical location or task assignment. Each agent generates actions based on local state input through the policy network. The policy function is defined as: a i,t =π i (the i,t ;θ i ), Among them, a i,t is the action performed by agent i at time t, including path planning and vehicle allocation plan, π i is the strategy function of agent i, with parameter θ i ,θ i are the trainable parameters of the policy network; The local delivery plan is formed by the action set of all agents, which is expressed as: A t =[a 1,t ,a2, t ,…,a m,t ], Among them, A t is the set of actions of all agents at time t, m is the total number of agents, Define the reward function r i,t , the formula is: Among them, T i The time it takes for agent i to complete the current task, u i is the urgency weight of the order currently being processed by the agent, α, β, γ are the weight coefficients of the reward function, R g is the global efficiency reward, defined as: Using the centralized training and distributed execution mechanism of the MADDPG algorithm, the policy parameters are updated by gradient descent. The update formula is: in, J(θ i ) is the objective function of agent i, defined as: J(θ i )E[r i,t ·log(π i (you are i,t θ i ))], Where η is the learning rate.

4. The artificial intelligence-based logistics rapid delivery analysis system according to claim 3, characterized in that: The collaborative optimization method is: Utilize the interaction mechanism in multi-agent reinforcement learning to adjust their respective delivery paths.

5. The artificial intelligence-based logistics rapid delivery analysis system according to claim 4, characterized in that: The adjusted execution results include: The agent's adjusted path nodes and estimated arrival time each time, Priority changes based on order urgency, As well as the number of delivery tasks and mileage delivered for each vehicle.

6. The artificial intelligence-based logistics rapid delivery analysis system according to claim 5, characterized in that: The steps of dynamically updating the delivery path and delivery priority are: The agent calculates the priority of each order based on the remaining delivery time of the order and the urgency of the current task. Each agent replans its path based on the current traffic conditions and order priority, using dynamic programming and heuristic algorithms for planning. After completing the route adjustment, each agent generates an updated delivery action based on the new priority information and route plan, and sends it to the delivery vehicle in real time.

7. The artificial intelligence-based logistics rapid delivery analysis system according to claim 6, characterized in that: The step of re-evaluating the MADDPG algorithm strategy in step S2 using the multi-objective optimization NSGA-II algorithm based on the execution result of the overall distribution is as follows: Collect the execution data of all agents after completing the delivery task, including actual delivery time, path cost and task allocation. Based on the execution data, define the objective function, which includes: Minimize total delivery time, Minimize delivery costs, and maximize the balance of task allocation, The NSGA-II algorithm is used to generate the initial population based on the objective function, and the Pareto frontier solution set is selected through crossover, mutation and non-dominated sorting. During the optimization process, each solution balances multiple objectives, and a solution is selected from the frontier solution set to readjust the MADDPG strategy.

Images