Intelligent logistics distribution platform and working method thereof

Abstract

The invention discloses an intelligent logistics distribution platform and a working method thereof, and relates to the technical field of intelligent logistics. The intelligent logistics distribution platform comprises a logistics order processing module, a logistics resource monitoring module, a logistics environment sensing module, a distribution scheme planning module and a logistics communication control module, and seamless cooperation and dynamic switching of a rider, an unmanned aerial vehicle and an unmanned vehicle can be realized through the communication connection relation and mutual cooperation of the modules. And continuously optimizing a global optimal distribution relay scheme comprising at least one distribution mode in the whole distribution process according to internal and external data such as order attributes, real-time resource positions, real-time traffic conditions and real-time weather conditions, so that the distribution relay can be continuously optimized on the premise of ensuring the reliability. The maximum end-to-end distribution time efficiency and the minimum overall cost are achieved, the core competitiveness of the overall logistics distribution system is improved, and practical application and popularization are facilitated.

Description

Technical Field

[0001] This invention belongs to the field of smart logistics technology, specifically relating to a smart logistics distribution platform and its working method. Background Technology

[0002] With the rapid development of e-commerce and on-demand delivery services, the timeliness and cost control of logistics and delivery have become core competitive advantages. Currently, the mainstream delivery method relies on riders, but its efficiency is greatly affected by factors such as traffic conditions and weather, and labor costs are increasing daily. Drones and unmanned vehicles, as emerging delivery tools, each have their own advantages and disadvantages: drones fly in straight lines and are not affected by ground traffic, but are greatly limited by weather, airspace, and load capacity; while unmanned vehicles can carry heavy loads and adapt to various weather conditions, but their speed is constrained by road conditions.

[0003] Existing technologies have attempted to combine various delivery tools, but most remain at the level of a simple "relay" model. For example, drones are used to transport goods from warehouses to transit stations, where riders then complete the final delivery. This model's scheduling is static or semi-static, unable to cope with sudden changes in the external environment during delivery (such as temporary traffic congestion or severe weather), leading to unreliable delivery times and even delivery failures. Furthermore, existing solutions fail to fully utilize riders as flexible mobile transit nodes, exhibiting insufficient coordination capabilities and leaving significant room for improvement in overall system efficiency.

[0004] Therefore, there is an urgent need for a smart logistics and delivery solution that can deeply integrate the three types of delivery resources and has real-time perception and dynamic decision-making capabilities. Summary of the Invention

[0005] The purpose of this invention is to provide a smart logistics distribution platform and its working method to solve the problems that existing logistics distribution solutions cannot cope with sudden changes in the external environment during delivery and the lack of coordination capabilities of multiple distribution resources.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a smart logistics and distribution platform is provided, including a logistics order processing module, a logistics resource monitoring module, a logistics environment perception module, a distribution plan planning module, and a logistics communication control module; The logistics order processing module is used to receive and parse the logistics orders to be delivered from the customer terminal to obtain the corresponding order data. The order data package contains delivery origin and destination information, delivery time window information and item attribute information to be delivered. The logistics resource monitoring module is used to monitor the status data of all logistics resources in real time. The logistics resources include drones, unmanned vehicles and riders. The status data package contains the current location information, current idle carrying capacity information and current delivery task information of the corresponding logistics resources. The logistics environment perception module is used to access external data sources to obtain environmental data in the logistics distribution area in real time. The environmental data package contains traffic information, weather information, and airspace control information. The delivery plan planning module is communicatively connected to the logistics order processing module, the logistics resource monitoring module, and the logistics environment perception module. It is used to generate and adjust a globally optimal delivery relay plan that includes at least one delivery method in real time for the logistics order to be delivered, based on the order data, the status data, and the environmental data, with the goal of minimizing delivery costs and maximizing delivery timeliness. The at least one delivery method includes a delivery method based on the rider, or the at least one delivery method includes a delivery method based on the rider, a delivery method based on the drone, and / or a delivery method based on the unmanned vehicle. The logistics communication control module is communicatively connected to the delivery plan planning module and the logistics resource monitoring module, respectively. It is used to send instructions to the riders, drones or unmanned vehicles participating in the delivery relay to execute the delivery task of the logistics order to be delivered, according to the globally optimal delivery relay plan, and to receive the execution results and transmit the execution results to the logistics resource monitoring module to update the status data.

[0007] Based on the above-mentioned invention, a smart logistics delivery solution is provided that deeply integrates riders, drones, and unmanned vehicles, and possesses real-time perception and dynamic decision-making capabilities. This solution includes a logistics order processing module, a logistics resource monitoring module, a logistics environment perception module, a delivery plan planning module, and a logistics communication control module. Through their communication connections and mutual cooperation, seamless collaboration and dynamic switching between riders, drones, and unmanned vehicles can be achieved. Throughout the delivery process, the globally optimal delivery relay plan, incorporating at least one delivery method, is continuously optimized based on internal and external data such as order attributes, real-time resource locations, real-time traffic conditions, and real-time weather conditions. This ensures maximum end-to-end delivery efficiency and minimizes overall costs while maintaining reliability, enhancing the core competitiveness of the overall logistics delivery system and facilitating practical application and promotion.

[0008] In one possible design, based on the order data, the status data, and the environmental data, and with the objectives of minimizing delivery costs and maximizing delivery timeliness, a globally optimal delivery relay plan containing at least one delivery method is initially generated for the logistics order to be delivered, including: Based on the delivery origin and destination information in the order data, a route planning algorithm is applied to generate multiple delivery relay routes within the logistics delivery area. Each delivery relay route includes sequentially arranged delivery start nodes, ... Each delivery transit node and delivery termination node. Represent natural numbers; Based on the item attribute information to be delivered in the order data, as well as the status data and the environmental data, corresponding delivery constraints and fitness functions with the objectives of minimizing delivery costs and maximizing delivery time are constructed for each of the multiple delivery relay paths. For each delivery relay path, based on the corresponding delivery constraints and fitness function, an optimization algorithm is used to obtain the delivery relay logistics resource allocation result with the best fitness under the delivery constraints. The delivery relay logistics resource allocation result includes the drones, unmanned vehicles or riders allocated to each pair of adjacent nodes in the corresponding path. Select the delivery relay logistics resource allocation result with the best adaptability from the delivery relay logistics resource allocation results of the multiple delivery relay paths, and select a delivery relay path corresponding to the delivery relay logistics resource allocation result from the multiple delivery relay paths. The delivery relay route and the logistics resource allocation result of the delivery relay are integrated to generate a globally optimal delivery relay plan that includes at least one delivery method for the logistics order to be delivered. The at least one delivery method includes a delivery method based on the rider, or the at least one delivery method includes a delivery method based on the rider, a delivery method based on the drone, and / or a delivery method based on the unmanned vehicle.

[0009] In one possible design, based on the item attribute information to be delivered in the order data, as well as the status data and the environmental data, corresponding delivery constraints are constructed for each of the multiple delivery relay paths, including: For each of the multiple delivery relay paths, the corresponding delivery flow balance constraints are constructed according to the following formula:

[0010] In the formula, This refers to all the logistics resources mentioned above. Indicates logistics resources, , and These represent three consecutive nodes arranged sequentially in the corresponding path, and each node has a corresponding node. It is a distribution transit node. Indicates logistics resources Whether to participate from the node To the node Single-segment delivery sub-task, Indicates the logistics resources Whether to participate from the node To the node The single-segment delivery subtasks are represented by the value "1" to indicate participation and "0" to indicate non-participation, respectively. For each of the aforementioned delivery relay routes, the corresponding delivery time window constraints are constructed according to the following formula:

[0011] In the formula, and These represent two consecutive nodes arranged sequentially in the corresponding path. Indicates the delivery arrival node of the item. At that moment, Indicates the delivery arrival point of the item. At that moment, Indicates the logistics resources From the node To the node Delivery time required; For each of the aforementioned delivery relay routes, the corresponding delivery capacity constraints are constructed according to the following formula:

[0012] In the formula, This indicates the required quantity of goods to be transported, determined based on the item attribute information in the order data. Indicates the logistics resources Whether to participate from the node To the node The single-segment delivery subtask is represented by the value "1" to indicate participation and "0" to indicate non-participation. Indicates based on the logistics resources The current idle carrying capacity information and current delivery task information in the status data are determined, and the logistics resources During the period Available carrying capacity.

[0013] In one possible design, based on the item attribute information to be delivered in the order data, as well as the status data and the environmental data, a corresponding fitness function is constructed for each of the multiple delivery relay paths, aiming to minimize delivery costs and maximize delivery time, including: For each of the multiple delivery relay paths, based on the state data and the environmental data, a corresponding fitness function is constructed according to the following formula, with the objectives of minimizing delivery costs and maximizing delivery time. :

[0014] In the formula, This represents the normalized value of the total delivery time. This represents the normalized value of the total delivery cost. and These represent the preset first weight coefficients and have , Represents the normalization function. This refers to all the logistics resources mentioned above. Indicates logistics resources, and These represent two consecutive nodes arranged sequentially in the corresponding path. Represents the set of nodes for the corresponding path. Indicates logistics resources Whether to participate from the node To the node The single-segment delivery subtask is represented by the value "1" to indicate participation and "0" to indicate non-participation. Indicates from the node To the node And based on the environmental data, the logistics resources The shortest planned route Indicates the logistics resources The nominal speed, This represents the traffic impact coefficient determined based on traffic condition information from the environmental data. This represents the weather impact coefficient determined based on weather condition information from the environmental data. This indicates the delivery transfer node in the corresponding route. This represents the set of delivery transfer nodes for the corresponding route. Indicates the location at the delivery transfer node. This indicates whether an item transfer has occurred, with a value of "1" indicating that it has occurred and a value of "0" indicating that it has not occurred. Indicates from prior logistics resources Post-logistics resources The average time required for goods to be transferred. This represents the function that takes the maximum value. This indicates the post-logistics resources Arrival at the delivery transfer node At that moment, Indicates the prior logistics resources Arrival at the delivery transfer node At that moment, Indicates variable costs. Indicates the logistics resources Energy consumption cost per unit distance Indicates the logistics resources unit time cost Indicates the logistics resources The fixed cost per call Indicates the logistics resources Failure rate, This represents the average cost of loss for a single failure.

[0015] In one possible design, the optimization algorithm may employ particle swarm optimization, Newton's optimization, genetic optimization, gray wolf optimization, whale optimization, or tuna swarm optimization.

[0016] In one possible design, based on the order data, the status data, and the environmental data, with the objectives of minimizing delivery costs and maximizing delivery timeliness, a globally optimal delivery relay scheme, incorporating at least one delivery method, is adjusted in real time for the logistics orders to be delivered, including: Based on the currently executing globally optimal delivery relay plan, the first moment when the goods arrive at the destination is estimated. During the execution of the globally optimal delivery relay plan, based on the status data and the environmental data, if it is found that the logistics resource currently participating in the delivery relay is malfunctioning, the logistics resource is an unmanned vehicle and the traffic conditions on the current route are deteriorating / improving, the logistics resource is an unmanned vehicle and the weather conditions in the current airspace are deteriorating / improving, and / or a new logistics resource is added, then the current location of the goods is taken as the new delivery starting point. Based on the order data, the status data, and the environmental data, with the goal of minimizing delivery costs and maximizing delivery time, a new globally optimal delivery relay plan containing at least one delivery method is regenerated for the logistics order to be delivered. Based on the new globally optimal delivery relay scheme, the second moment when the goods arrive at the destination is estimated. Based on the delivery time window information in the order data, the absolute value of the first time difference from the first moment to the center moment of the delivery time window is calculated, and the absolute value of the second time difference from the second moment to the center moment is also calculated. Determine whether the difference between the absolute value of the second time difference and the absolute value of the first time difference is greater than a preset time difference threshold; If so, the currently executing globally optimal delivery relay plan will be adjusted and updated to the new globally optimal delivery relay plan.

[0017] In one possible design, the instructions sent to riders participating in the delivery relay include: a first instruction to pick up the goods at the delivery start node and carry them to the delivery end node for delivery to the customer; a second instruction to pick up the goods at the delivery start node and carry them to the first delivery transfer node for transfer to a driverless vehicle / drone; a third instruction to pick up the goods from a drone at a non-final delivery transfer node and transfer them to a driverless vehicle or another drone; a fourth instruction to pick up the goods from a driverless vehicle at a non-final delivery transfer node and transfer them to a drone or another driverless vehicle; a fifth instruction to pick up the goods from a driverless vehicle / drone at a non-final delivery transfer node and carry them to the next non-final delivery transfer node for transfer to another driverless vehicle / drone; and / or a sixth instruction to pick up the goods from a drone / driverless vehicle at the final delivery transfer node and carry them to the delivery end node for delivery to the customer.

[0018] In one possible design, the instructions sent to the unmanned vehicles or drones participating in the delivery relay include a seventh instruction to instruct them to proceed to a non-last delivery transfer node to await pickup and / or an eighth instruction to instruct them to proceed to the next delivery transfer node after pickup.

[0019] In one possible design, the logistics communication control module is also used to update the logistics information corresponding to the logistics order to be delivered in real time based on the execution result, and to feed the logistics information back to the customer terminal in real time.

[0020] Secondly, a method for operating the smart logistics and distribution platform as described in the first aspect or any possible design within the first aspect is provided, comprising: The logistics order processing module receives and parses the logistics orders to be delivered from the customer terminal to obtain the corresponding order data. The order data package contains delivery origin and destination information, delivery time window information, and item attribute information to be delivered. The status data of all logistics resources are obtained in real time by the logistics resource monitoring module. The logistics resources include drones, unmanned vehicles and riders. The status data package contains the current location information, current idle carrying capacity information and current delivery task information of the corresponding logistics resources. The logistics environment sensing module connects to an external data source to obtain environmental data in the logistics distribution area in real time. The environmental data package contains traffic information, weather information, and airspace control information. Based on the order data, status data, and environmental data, the delivery plan planning module generates and adjusts a globally optimal delivery relay plan that includes at least one delivery method in real time for the logistics order to be delivered, with the goal of minimizing delivery costs and maximizing delivery timeliness. The at least one delivery method includes a delivery method based on the rider, or a delivery method based on the rider, a delivery method based on the drone, and / or a delivery method based on the unmanned vehicle. The logistics communication control module sends instructions to the riders, drones, or unmanned vehicles participating in the delivery relay according to the globally optimal delivery relay plan to execute the delivery task of the logistics order to be delivered, receives the execution results, and transmits the execution results to the logistics resource monitoring module to update the status data.

[0021] The beneficial effects of the above scheme are: (1) This invention provides a smart logistics delivery solution that can deeply integrate riders, drones and unmanned vehicles and has real-time perception and dynamic decision-making capabilities. It includes a logistics order processing module, a logistics resource monitoring module, a logistics environment perception module, a delivery plan planning module and a logistics communication control module. Through their communication connection and mutual cooperation, it can realize seamless cooperation and dynamic switching between riders, drones and unmanned vehicles. During the entire delivery process, it continuously optimizes the global optimal delivery relay plan containing at least one delivery method based on internal and external data such as order attributes, real-time resource location, real-time traffic conditions and real-time weather conditions. Thus, under the premise of ensuring reliability, it can achieve the highest end-to-end delivery timeliness and the lowest overall cost, thereby enhancing the core competitiveness of the overall logistics delivery system. (2) It can significantly improve delivery efficiency, that is, by selecting the optimal means of transportation (i.e. air logistics resources or ground logistics resources) to bypass bottlenecks and make a second-level response to emergencies, greatly reducing the average delivery time; (3) It can effectively control the overall cost, that is, systematically balance the high cost of drones (which are used in key acceleration sections) with the low cost of unmanned vehicles / riders, avoid the waste of single resources, and achieve the minimum overall cost; (4) It can have extremely high robustness and reliability. That is, by dynamically adjusting the delivery relay plan, it can have a strong fault tolerance for uncertain factors such as traffic and weather, ensuring the final completion of the delivery task. (5) It can maximize the utilization of resources, that is, the rider role can be expanded from a simple "deliveryman" to a "mobile transfer hub", deeply integrated into the automated delivery network, give full play to the maximum value of human flexibility, realize the "1+1>2" effect of human-machine collaboration, and facilitate practical application and promotion. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the structure of the intelligent logistics and distribution platform provided in the embodiments of this application.

[0024] Figure 2 This is a flowchart illustrating the working method of the intelligent logistics and distribution platform provided in the embodiments of this application. Detailed Implementation

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these embodiments without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0026] It should be understood that although the terms "first" and "second", etc., may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, the first object may be referred to as the second object, and similarly, the second object may be referred to as the first object, without departing from the scope of the exemplary embodiments of the invention.

[0027] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, or A and B exist simultaneously. Another example is A, B and / or C, which can mean that any one of A, B, and C or any combination thereof exists. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone or A and B exist simultaneously. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0028] Example 1 like Figure 1As shown, the intelligent logistics and distribution platform provided in this embodiment includes, but is not limited to, a logistics order processing module, a logistics resource monitoring module, a logistics environment perception module, a distribution plan planning module, and a logistics communication control module.

[0029] The logistics order processing module is used to receive and parse logistics orders to be delivered from customer terminals to obtain corresponding order data. The order data includes, but is not limited to, delivery origin and destination information, delivery time window information, and item attribute information. The logistics orders to be delivered can be generated and uploaded by the customer on the customer terminal (i.e., by the customer's electronic device, such as a smartphone, tablet, or personal computer) through a regular order placement operation, and the order data can then be obtained through regular parsing. Specifically, the delivery origin and destination information includes, but is not limited to, the delivery start point and delivery destination; the delivery time window information includes, but is not limited to, the earliest delivery time and the latest delivery time, for example, 15:00 is the earliest delivery time, 19:00 is the latest delivery time, and the delivery time window is [15:00, 19:00]; the item attribute information includes, but is not limited to, category, packaging volume, and weight.

[0030] The logistics resource monitoring module is used to monitor the status data of all logistics resources in real time. These resources include, but are not limited to, drones, unmanned vehicles, and delivery riders. The status data includes, but is not limited to, the current location information, current idle carrying capacity information, and current delivery task information of the corresponding logistics resources. The aforementioned drones, unmanned vehicles, and delivery riders are all existing logistics and delivery resources. Drones can be conventionally implemented by equipping them with autopilots, cargo compartments, and a first communication unit, and are suitable for delivery scenarios characterized by long straight-line distances, traffic congestion, and small, lightweight goods. Unmanned vehicles can be conventionally implemented by equipping them with an autopilot system, cargo box, and a second communication unit, and are suitable for ground delivery scenarios characterized by high load capacity, insensitivity to weather, and standardized routes. Delivery riders can act as independent delivery personnel or as flexible mobile transit nodes, primarily responsible for: receiving goods from drones / unmanned vehicles and delivering them to customers (i.e., solving the "last ten meters" problem), and transferring goods between drones and unmanned vehicles (e.g., in parks or no-parking zones where direct handover is not possible), etc. The status data can be routinely uploaded by the drone, the unmanned vehicle, and the smart mobile terminal equipped for the rider (such as a mobile phone or dedicated device with a dedicated APP installed). The current location information can be obtained in real-time by the satellite locator within the drone, unmanned vehicle, or smart mobile terminal. The current idle carrying capacity information can be routinely measured by the current remaining load capacity, the current remaining available space, the current remaining available storage lockers, or the rider's remaining load capacity, and can be routinely updated after receiving or delivering goods. The current delivery task information specifically includes at least one unfinished single-segment delivery sub-task, each with its own start and end point for a single delivery segment, as well as the attribute information of the items to be delivered. Furthermore, the status data may also include, but is not limited to, the corresponding logistics resource number, the remaining battery / fuel level of the drone / unmanned vehicle, and the rider's skill level.

[0031] The logistics environment sensing module is used to access external data sources to acquire environmental data within the logistics delivery area in real time. This environmental data includes, but is not limited to, traffic conditions, weather conditions, and airspace control information. The external data sources specifically include, but are not limited to, traffic data platforms and meteorological bureaus. The logistics delivery area encompasses both the delivery start point and the delivery destination.

[0032] The delivery plan planning module is communicatively connected to the logistics order processing module, the logistics resource monitoring module, and the logistics environment perception module. Based on the order data, status data, and environmental data, and with the goals of minimizing delivery costs and maximizing delivery timeliness, it initially generates and adjusts a globally optimal delivery relay plan that includes at least one delivery method for the logistics orders to be delivered. This plan includes, in real-time, a delivery method based on the rider, or a delivery method based on the rider, a drone, and / or an unmanned vehicle. Specifically, the globally optimal delivery relay plan includes a globally optimal delivery relay path and at least one delivery method corresponding to at least one one-way delivery segment within that path, where each delivery method represents the invocation of a logistics resource (i.e., drone, unmanned vehicle, or rider). In order to obtain the globally optimal delivery relay plan initially, preferably, based on the order data, the status data and the environmental data, with the goal of minimizing delivery costs and maximizing delivery timeliness, a globally optimal delivery relay plan containing at least one delivery method is initially generated for the logistics order to be delivered, including but not limited to the following steps S41 to S45.

[0033] S41. Based on the delivery origin and destination information in the order data, a route planning algorithm is applied to generate multiple delivery relay routes within the logistics delivery area, wherein the delivery relay routes include, but are not limited to, sequentially arranged delivery start nodes, Delivery transit nodes and delivery termination nodes, etc. Represents natural numbers.

[0034] In step S41, the path planning algorithm can be implemented conventionally, but is not limited to, using existing algorithms such as the A* algorithm (commonly known as the A* algorithm, a type of heuristic search algorithm that finds the lowest cost path across multiple nodes on a graph plane). Furthermore, since... Representing a natural number, which can be zero, the delivery relay path can only include a delivery start node and a delivery end node (in which case riders can be assigned to perform direct delivery).

[0035] S42. Based on the item attribute information to be delivered in the order data, as well as the status data and the environment data, construct corresponding delivery constraints and fitness functions with the objectives of minimizing delivery costs and maximizing delivery timeliness for each of the multiple delivery relay paths.

[0036] In step S42, the delivery constraints are used to ensure the correctness of subsequent delivery relay logistics resource allocation. Specifically, constraints can be imposed from the perspectives of delivery flow balance, delivery time window, and delivery capacity. In other words, based on the item attribute information to be delivered in the order data, as well as the status data and the environmental data, corresponding delivery constraints are constructed for each of the multiple delivery relay paths, including but not limited to the following steps S421 to S423.

[0037] S421. For each delivery relay path among the multiple delivery relay paths, construct the corresponding delivery flow balance constraint conditions according to the following formula:

[0038] In the formula, This refers to all the logistics resources mentioned above. Indicates logistics resources, , and These represent three consecutive nodes arranged sequentially in the corresponding path, and each node has a corresponding node. It is a distribution transit node. Indicates logistics resources Whether to participate from the node To the node Single-segment delivery sub-task, Indicates the logistics resources Whether to participate from the node To the node The single-segment delivery subtask is represented by the value "1" to indicate participation and "0" to indicate non-participation.

[0039] S422. For each of the aforementioned delivery relay routes, construct the corresponding delivery time window constraints according to the following formula:

[0040] In the formula, and These represent two consecutive nodes arranged sequentially in the corresponding path. Indicates the delivery arrival point of the item. At that moment, Indicates the delivery arrival point of the item. At that moment, Indicates the logistics resources From the node To the node Delivery time required.

[0041] In step S422, when the node When it is the starting point of delivery, This indicates the current moment, and takes into account the existence of waiting logistics resources. Reach the node Therefore, constraints are needed. . The calculation can be performed using, but is not limited to, the following formula:

[0042] In the formula, Indicates from the node To the node And based on the environmental data, the logistics resources The shortest planned route Indicates the logistics resources The nominal speed, This represents the traffic impact coefficient (specifically between 0.3 and 1.0) determined based on traffic condition information in the environmental data. This represents the weather impact coefficient (specifically between 0.5 and 1.0) determined based on weather condition information in the environmental data. Furthermore, the specific planning method for the shortest route, the specific determination method for the traffic impact coefficient, and the specific determination method for the weather impact coefficient can all be derived conventionally using existing technical means.

[0043] S423. For each of the aforementioned delivery relay routes, construct the corresponding delivery capacity constraints according to the following formula:

[0044] In the formula, This indicates the required quantity of goods to be transported, determined based on the item attribute information in the order data. Indicates the logistics resources Whether to participate from the node To the node The single-segment delivery subtask is represented by the value "1" to indicate participation and "0" to indicate non-participation. Indicates based on the logistics resources The current idle carrying capacity information and current delivery task information in the status data are determined, and the logistics resources During the period Available carrying capacity.

[0045] In step S423, the unit of the item transport demand is the same as that of the available transport capacity, which can be expressed by load capacity, space size, number of storage cabinets or rider load, etc. Therefore, the item transport demand can be conventionally determined based on the attribute information of the item to be delivered, and the available transport capacity can be conventionally determined based on the current idle transport capacity information and the current delivery task information.

[0046] In step S42, the fitness function is used to calculate the fitness in the subsequent optimization process for optimization based on the fitness. The delivery cost can be considered from the perspectives of variable cost, fixed cost, and risk cost, while the delivery time can be represented by time cost (i.e., the lower the time cost, the greater the delivery time) and considered from the perspectives of transportation time, transit time, and waiting time. Specifically, based on the item attribute information to be delivered in the order data, as well as the status data and the environmental data, a corresponding fitness function with the objectives of minimizing delivery cost and maximizing delivery time is constructed for each of the multiple delivery relay paths, including the following step S424.

[0047] S424. For each delivery relay path among the multiple delivery relay paths, based on the state data and the environmental data, construct a corresponding fitness function with the objectives of minimizing delivery costs and maximizing delivery timeliness according to the following formula. :

[0048] In the formula, This represents the normalized value of the total delivery time. This represents the normalized value of the total delivery cost. and These represent the preset first weight coefficients and have , Represents the normalization function. This refers to all the logistics resources mentioned above. Indicates logistics resources, and These represent two consecutive nodes arranged sequentially in the corresponding path. Represents the set of nodes for the corresponding path. Indicates logistics resources Whether to participate from the node To the node The single-segment delivery subtask is represented by the value "1" to indicate participation and "0" to indicate non-participation. Indicates from the node To the node And based on the environmental data, the logistics resources The shortest planned route Indicates the logistics resources The nominal speed, This represents the traffic impact coefficient determined based on traffic condition information from the environmental data. This represents the weather impact coefficient determined based on weather condition information from the environmental data. This indicates the delivery transfer node in the corresponding route. This represents the set of delivery transfer nodes for the corresponding route. Indicates the location at the delivery transfer node. This indicates whether an item transfer has occurred, with a value of "1" indicating that it has occurred and a value of "0" indicating that it has not occurred. Indicates from prior logistics resources Post-logistics resources The average time required for goods to be transferred. This represents the function that takes the maximum value. This indicates the post-logistics resources Arrival at the delivery transfer node At that moment, Indicates the prior logistics resources Arrival at the delivery transfer node At that moment, Indicates variable costs. Indicates the logistics resources Energy consumption cost per unit distance Indicates the logistics resources unit time cost Indicates the logistics resources The fixed cost per call Indicates the logistics resources Failure rate, This represents the average cost of loss for a single failure.

[0049] In step S424, This represents the total transportation time. This indicates the total transfer time. This represents the total waiting time. This represents the total fixed cost. This represents the total risk cost. Specifically, the average time required for goods transfer between different logistics resources can be exemplified as follows: drone to rider: 2 minutes; unmanned vehicle to rider: 1 minute; etc. Specifically, the unit distance energy consumption cost for different logistics resources can be exemplified as follows: drone: 0.15-0.30 yuan per kilometer (electricity); unmanned vehicle: 0.08-0.20 yuan per kilometer (electricity); rider: 0.02-0.05 yuan per kilometer (human labor + vehicle). Specifically, the unit time cost for different logistics resources can be exemplified as follows: rider: 0.3-0.8 yuan per minute (wage); drone / unmanned vehicle: 0.05-0.15 yuan per minute (depreciation + maintenance). Furthermore, the fixed cost and failure rate of a single call to different logistics resources, as well as the average loss cost per failure, can be obtained through routine statistics based on historical data.

[0050] S43. For each delivery relay path, based on the corresponding delivery constraints and fitness function, an optimization algorithm is used to optimize and obtain the delivery relay logistics resource allocation result with the best fitness under the delivery constraints, wherein the delivery relay logistics resource allocation result includes the drones, unmanned vehicles or riders allocated to each pair of adjacent nodes in the corresponding path.

[0051] In step S43, each pair of adjacent nodes represents a single-trip delivery segment. Therefore, it is necessary to allocate corresponding logistics resources to each single-trip delivery segment: drones, unmanned vehicles, or riders. The optimization algorithm may specifically employ, but is not limited to, particle swarm optimization, Newton's algorithm, genetic optimization, Grey Wolf algorithm, whale optimization, or tuna swarm optimization. The technical principles of these optimization algorithms are all existing technologies; therefore, the specific optimization process can be conventionally derived based on existing technical means, and will not be elaborated upon here.

[0052] S44. Select a delivery relay logistics resource allocation result with the best adaptability from the delivery relay logistics resource allocation results of the multiple delivery relay paths, and select a delivery relay path corresponding to the delivery relay logistics resource allocation result from the multiple delivery relay paths.

[0053] S45. Integrate the delivery relay route and the delivery relay logistics resource allocation result to initially generate a globally optimal delivery relay plan for the logistics order to be delivered, which includes at least one delivery method. The at least one delivery method includes a delivery method based on the rider, or the at least one delivery method includes a delivery method based on the rider, a delivery method based on the drone, and / or a delivery method based on the unmanned vehicle.

[0054] In addition, in order to adjust the globally optimal delivery relay plan in real time, preferably, based on the order data, the status data and the environmental data, with the goal of minimizing delivery costs and maximizing delivery timeliness, the globally optimal delivery relay plan containing at least one delivery method is adjusted in real time for the logistics order to be delivered, including but not limited to the following steps S461 to S465.

[0055] S461. Based on the currently executing globally optimal delivery relay plan, estimate the first moment when the goods arrive at the destination. During the execution of the globally optimal delivery relay plan, based on the status data and the environmental data, if it is found that the logistics resource currently participating in the delivery relay is malfunctioning, the logistics resource is an unmanned vehicle and the traffic conditions on the current route are deteriorating / improving, the logistics resource is a drone and the weather conditions in the current airspace are deteriorating / improving, and / or a new logistics resource is added, then the current location of the goods is taken as the new delivery starting point. Based on the order data, the status data, and the environmental data, with the goal of minimizing delivery costs and maximizing delivery timeliness, a new globally optimal delivery relay plan containing at least one delivery method is regenerated for the logistics order to be delivered.

[0056] In step S461, the specific execution process of the globally optimal delivery relay plan is as follows: according to the globally optimal delivery relay plan, instructions are sent to the riders, drones, or unmanned vehicles participating in the delivery relay to execute the delivery task of the logistics order to be delivered, and the execution results are received. Since the calculation of the total time cost is involved in generating the globally optimal delivery relay plan, the estimation process of the first moment when the goods arrive at the destination can be conventionally modified with reference to the calculation method of the total time cost, and the first moment can be updated in real time. The failure of the logistics resources currently participating in the delivery relay can be exemplified by: drone / unmanned vehicle malfunction, rider being involved in a traffic accident, etc. The deterioration / improvement of traffic conditions can be conventionally judged based on the traffic color status of the current travel segment; for example, if the traffic color of the current travel segment changes from green to red, it indicates that the traffic conditions are deteriorating, and vice versa. The method for judging the deterioration / improvement of weather conditions can also be similar. Furthermore, the process of regenerating the new globally optimal delivery relay plan can be derived conventionally by referring to the aforementioned steps S41 to S45, and will not be repeated here.

[0057] S462. Based on the new globally optimal delivery relay scheme, estimate the second moment when the goods arrive at the destination.

[0058] In step S462, since the calculation of total time cost is also involved in generating the new globally optimal delivery relay plan, the estimation process of the second moment when the goods arrive at the destination can be achieved by conventionally modifying the calculation method of total time cost.

[0059] S463. Based on the delivery time window information in the order data, calculate the absolute value of the first time difference from the first time to the center time of the delivery time window, and also calculate the absolute value of the second time difference from the second time to the center time.

[0060] In step S463, the center time of the delivery time window is the center value of the earliest delivery time and the latest delivery time.

[0061] S464. Determine whether the difference between the absolute value of the second time difference and the absolute value of the first time difference is greater than a preset time difference threshold.

[0062] In step S464, the time difference threshold can preferably be designed to be proportional to the time width of the delivery time window (i.e., the time difference between the latest delivery time and the earliest delivery time), for example, 25% of the time width.

[0063] S465. If so, the currently executing globally optimal delivery relay plan will be adjusted and updated to the new globally optimal delivery relay plan.

[0064] The logistics communication control module is communicatively connected to the delivery plan planning module and the logistics resource monitoring module, respectively. It is used to send instructions to the riders, drones or unmanned vehicles participating in the delivery relay to execute the delivery task of the logistics order to be delivered, according to the globally optimal delivery relay plan, and to receive the execution results and transmit the execution results to the logistics resource monitoring module to update the status data. Specifically, the instructions sent to riders participating in the delivery relay include, but are not limited to, a first instruction to pick up the goods at the delivery start point and carry them to the delivery end point for delivery to the customer; a second instruction to pick up the goods at the delivery start point and carry them to the first delivery transfer point for transfer to an unmanned vehicle / drone; a third instruction to pick up the goods from a drone at a non-final delivery transfer point and transfer them to an unmanned vehicle or another drone; a fourth instruction to pick up the goods from an unmanned vehicle at a non-final delivery transfer point and transfer them to a drone or another unmanned vehicle; a fifth instruction to pick up the goods from an unmanned vehicle / drone at a non-final delivery transfer point and carry them to the next non-final delivery transfer point for transfer to another unmanned vehicle / drone; and / or a sixth instruction to pick up the goods from a drone / unmanned vehicle at the final delivery transfer point and carry them to the delivery end point for delivery to the customer. Instructions, etc.; instructions sent to unmanned vehicles or drones participating in the delivery relay include, but are not limited to, a seventh instruction to instruct them to proceed to a non-final delivery transfer node to wait for pickup and / or an eighth instruction to instruct them to carry the goods to the next delivery transfer node after pickup; through these instructions, various collaborative methods among riders, drones, and unmanned vehicles can be realized, ensuring enhanced collaborative capabilities of multiple delivery resources; for example, when a severe congestion occurs ahead of an unmanned vehicle, it can be instructed to drive to the nearest transfer node and transfer the goods to a waiting drone or rider for subsequent delivery; when a sudden weather change occurs on the drone's flight path (such as a sudden rainstorm), it can be instructed to immediately land at a transfer node and transfer the goods to an unmanned vehicle or rider heading to that point; when a rider encounters a bridge blockage or other situation, but the weather is good, a nearby drone can be dispatched to the transfer node to pick up the goods, performing a "leapfrog" delivery; and so on. In addition, in order to promptly inform customers of logistics information, preferably, the logistics communication control module is also used to update the logistics information corresponding to the logistics order to be delivered in real time based on the execution result, and to feed the logistics information back to the customer terminal in real time.

[0065] In summary, the intelligent logistics and distribution platform provided in this embodiment has the following technical effects: (1) This embodiment provides a smart logistics delivery solution that can deeply integrate riders, drones and unmanned vehicles and has real-time perception and dynamic decision-making capabilities. It includes a logistics order processing module, a logistics resource monitoring module, a logistics environment perception module, a delivery plan planning module and a logistics communication control module. Through their communication connection and mutual cooperation, the seamless cooperation and dynamic switching of riders, drones and unmanned vehicles can be realized. During the entire delivery process, the global optimal delivery relay plan containing at least one delivery method is continuously optimized based on internal and external data such as order attributes, real-time resource location, real-time traffic conditions and real-time weather conditions. Thus, under the premise of ensuring reliability, the end-to-end delivery timeliness is maximized and the overall cost is minimized, thereby enhancing the core competitiveness of the overall logistics delivery system. (2) It can significantly improve delivery efficiency, that is, by selecting the optimal means of transportation (i.e. air logistics resources or ground logistics resources) to bypass bottlenecks and make a second-level response to emergencies, greatly reducing the average delivery time; (3) It can effectively control the overall cost, that is, systematically balance the high cost of drones (which are used in key acceleration sections) with the low cost of unmanned vehicles / riders, avoid the waste of single resources, and achieve the minimum overall cost; (4) It can have extremely high robustness and reliability. That is, by dynamically adjusting the delivery relay plan, it can have a strong fault tolerance for uncertain factors such as traffic and weather, ensuring the final completion of the delivery task. (5) It can maximize the utilization of resources, that is, the rider role can be expanded from a simple "deliveryman" to a "mobile transfer hub", deeply integrated into the automated delivery network, give full play to the maximum value of human flexibility, realize the "1+1>2" effect of human-machine collaboration, and facilitate practical application and promotion.

[0066] Example 2 Based on the technical solution of Embodiment 1, this embodiment also provides a working method for the intelligent logistics distribution platform as described in Embodiment 1. Figure 2 As shown, it includes, but is not limited to, the following steps S1 to S5.

[0067] S1. The logistics order processing module receives and parses the logistics orders to be delivered from the customer terminal to obtain the corresponding order data, wherein the order data package contains delivery origin and destination information, delivery time window information and item attribute information to be delivered.

[0068] S2. The status data of all logistics resources are obtained in real time by the logistics resource monitoring module. The logistics resources include drones, unmanned vehicles and riders. The status data package contains the current location information, current idle carrying capacity information and current delivery task information of the corresponding logistics resources.

[0069] S3. The logistics environment sensing module accesses an external data source to obtain environmental data within the logistics distribution area in real time. The environmental data package contains traffic information, weather information, and airspace control information.

[0070] S4. Based on the order data, the status data, and the environmental data, the delivery plan planning module generates an initial global optimal delivery relay plan for the logistics order to be delivered, with the goal of minimizing delivery costs and maximizing delivery timeliness. The plan plan includes at least one delivery method, wherein the at least one delivery method includes a delivery method based on the rider, or the at least one delivery method includes a delivery method based on the rider, a delivery method based on the drone, and / or a delivery method based on the unmanned vehicle.

[0071] S5. The logistics communication control module sends instructions to the riders, drones, or unmanned vehicles participating in the delivery relay according to the globally optimal delivery relay plan to execute the delivery task of the logistics order to be delivered, receives the feedback execution results, and transmits the execution results to the logistics resource monitoring module to update the status data.

[0072] The technical effects of this embodiment can be derived conventionally by referring to the technical effects of Embodiment 1, and will not be repeated here.

[0073] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A smart logistics distribution platform, characterized in that, The logistics order processing module, the logistics resource monitoring module, the logistics environment perception module, the delivery scheme planning module and the logistics communication control module are included. The logistics order processing module is configured to receive and analyze a to-be-delivered logistics order from a client terminal to obtain corresponding order data, wherein the order data includes delivery start and end point information, delivery time window information and to-be-delivered item attribute information. The logistics resource monitoring module is configured to monitor the state data of all logistics resources in real time, wherein the all logistics resources include drones, unmanned vehicles and riders, and the state data includes current location information, current idle carrying capacity information and current delivery task information of the corresponding logistics resources. The logistics environment perception module is configured to access external data sources to obtain environmental data in the logistics delivery area in real time, wherein the environmental data includes traffic condition information, weather condition information and airspace control information. The delivery scheme planning module is communicatively connected to the logistics order processing module, the logistics resource monitoring module and the logistics environment perception module, and is configured to generate and adjust a globally optimal delivery relay scheme including at least one delivery method for the to-be-delivered logistics order based on the order data, the state data and the environmental data, with the goal of minimizing delivery cost and maximizing delivery timeliness, wherein the at least one delivery method includes a delivery method based on the rider, or the at least one delivery method includes a delivery method based on the rider, a delivery method based on the drone and / or a delivery method based on the unmanned vehicle. The logistics communication control module is communicatively connected to the delivery scheme planning module and the logistics resource monitoring module, and is configured to send instructions to riders, drones or unmanned vehicles participating in delivery relay to perform delivery tasks of the to-be-delivered logistics order based on the globally optimal delivery relay scheme, receive feedback execution results, and transmit the execution results to the logistics resource monitoring module to update the state data. 2.The smart logistics distribution platform of claim 1, wherein, Based on the order data, the state data and the environmental data, a globally optimal delivery relay scheme including at least one delivery method is initially generated for the to-be-delivered logistics order with the goal of minimizing delivery cost and maximizing delivery timeliness, including: According to the delivery start and end point information in the order data, a path planning algorithm is applied to generate a plurality of delivery relay paths in the logistics delivery area, wherein the delivery relay path contains a delivery starting node, a delivery transfer node and a delivery ending node arranged in sequence, denotes a natural number; Based on the to-be-delivered item attribute information in the order data and the state data and the environmental data, a corresponding delivery constraint condition and a fitness function with the goal of minimizing delivery cost and maximizing delivery timeliness are constructed for each delivery relay path in the plurality of delivery relay paths. For each delivery relay path, based on the corresponding delivery constraint condition and the fitness function, an optimization algorithm is used to obtain a delivery relay logistics resource allocation result with the best fitness under the delivery constraint condition, wherein the delivery relay logistics resource allocation result includes drones, unmanned vehicles or riders allocated to each pair of adjacent nodes in the corresponding path. selecting a certain distribution relay logistics resource allocation result with the best fitness from the distribution relay logistics resource allocation results of the multiple distribution relay paths, and selecting a certain distribution relay path corresponding to the certain distribution relay logistics resource allocation result from the multiple distribution relay paths; integrating the certain distribution relay path and the certain distribution relay logistics resource allocation result to initially generate a globally optimal distribution relay scheme containing at least one distribution mode for the to-be-distributed logistics order, wherein the at least one distribution mode includes a distribution mode based on the rider, or the at least one distribution mode includes a distribution mode based on the rider, a distribution mode based on the unmanned aerial vehicle, and / or a distribution mode based on the unmanned vehicle. 3.The smart logistics distribution platform of claim 2, wherein, constructing a corresponding distribution constraint condition for each distribution relay path in the multiple distribution relay paths according to the to-be-sent item attribute information in the order data and the state data and the environment data, including: constructing a corresponding distribution flow balance constraint condition for each distribution relay path in the multiple distribution relay paths according to the following formula: In the formula, This refers to all the logistics resources mentioned above. Indicates logistics resources, , and These represent three consecutive nodes arranged sequentially in the corresponding path, and each node has a corresponding node. It is a distribution transit node. Indicates logistics resources Whether to participate from the node To the node Single-segment delivery sub-task, Indicates the logistics resources Whether to participate from the node To the node The single-segment delivery subtasks are represented by the value "1" to indicate participation and "0" to indicate non-participation. constructing a corresponding distribution time window constraint condition for each distribution relay path according to the following formula: wherein, and respectively represent two consecutive nodes arranged in sequence in the corresponding path, represents the time at which the item delivery arrives at the node , represents the time at which the item delivery arrives at the node , represents the time duration required for the delivery of the logistic resource from the node to the node ; constructing a corresponding distribution capacity constraint condition for each distribution relay path according to the following formula: In the formula, represents the amount of the item carrying demand determined according to the item attribute information in the order data, represents the logistics resource whether to participate in the single-stage delivery subtask from the node to the node , and the value "1" represents participation and the value "0" represents non-participation, represents the available carrying capacity of the logistics resource in the time period determined according to the current idle carrying capacity information and the current delivery task information in the state data of the logistics resource . 4.The smart logistics distribution platform of claim 2, wherein, constructing a corresponding fitness function for each distribution relay path in the multiple distribution relay paths according to the to-be-sent item attribute information in the order data and the state data and the environment data, with the objective of minimizing distribution cost and maximizing distribution timeliness, including: For each of the plurality of distribution relay paths, according to the state data and the environment data, a corresponding fitness function aiming at minimizing the distribution cost and maximizing the distribution timeliness is constructed in accordance with the following formula : In the formula, represents a normalized value of total delivery timeliness, represents a normalized value of total delivery cost, and respectively represent preset first weight coefficients and have , represents a normalization function, represents all logistics resources, represents a logistics resource, and respectively represent two continuous nodes arranged in sequence in a corresponding path, represents a node set of a corresponding path, represents a logistics resource whether participates in a single-segment delivery subtask from a node to a node , and uses a numerical value "1" to represent participation and a numerical value "0" to represent non-participation, represents a shortest path from the node to the node and planned for the logistics resource according to the environmental data, represents a nominal speed of the logistics resource , represents a traffic influence coefficient determined according to traffic condition information in the environmental data, represents a weather influence coefficient determined according to weather condition information in the environmental data, represents a delivery transfer node in a corresponding path, represents a delivery transfer node set of a corresponding path, represents whether an article transfer occurs at a delivery transfer node , and uses a numerical value "1" to represent occurrence and a numerical value "0" to represent non-occurrence, represents an average length of time required for article transfer from a preceding logistics resource to a subsequent logistics resource , represents a max function, represents a time at which the subsequent logistics resource arrives at the delivery transfer node , represents a time at which the preceding logistics resource arrives at the delivery transfer node , represents a variable cost, represents a unit distance energy consumption cost of the logistics resource , represents the logistics resource the cost per unit of time, denotes the logistic resource the fixed cost per single call of the logistic resource, denotes the logistic resource the failure rate of the logistic resource, denotes the average loss cost per single failure. 5.The smart logistics distribution platform of claim 2, wherein, The optimization algorithm adopts a particle swarm optimization algorithm, a Newton optimization algorithm, a genetic optimization algorithm, a grey wolf optimization algorithm, a whale optimization algorithm, or a tuna swarm optimization algorithm. 6.The smart logistics distribution platform of claim 1, wherein, According to the order data, the state data, and the environment data, a globally optimal distribution relay scheme containing at least one distribution mode is adjusted in real time for the to-be-distributed logistics order with the objective of minimizing distribution cost and maximizing distribution timeliness, including: According to the currently executing globally optimal distribution relay scheme, a first time at which the item is delivered to the destination is estimated, and during the execution of the globally optimal distribution relay scheme, according to the state data and the environment data, if it is found that the logistics resource currently participating in the distribution relay fails, the logistics resource is an unmanned vehicle and the current travel section has a traffic condition that is deteriorating or improving, the logistics resource is an unmanned aerial vehicle and the current travel airspace has a weather condition that is deteriorating or improving, and / or a new logistics resource is online, a new distribution starting point is set as the current item location, and a globally optimal distribution relay new scheme containing at least one distribution mode is regenerated for the to-be-distributed logistics order according to the order data, the state data, and the environment data, with the objective of minimizing distribution cost and maximizing distribution timeliness; According to the globally optimal distribution relay new scheme, a second time at which the item is delivered to the destination is estimated. According to the delivery time window information in the order data, a first time difference absolute value from the first time to a center time of the delivery time window is calculated, and a second time difference absolute value from the second time to the center time is also calculated; It is judged whether the difference between the second time difference absolute value and the first time difference absolute value is greater than a preset time difference threshold value; If yes, the currently executing global optimal distribution relay scheme is adjusted and updated to the global optimal distribution relay new scheme. 7.The smart logistics distribution platform of claim 1, wherein, The instructions sent to the rider participating in the distribution relay include a first instruction for indicating picking up goods at the distribution starting node and carrying to the distribution termination node for delivery to the customer, a second instruction for indicating picking up goods at the distribution starting node and carrying to the first distribution transfer node for transfer to the unmanned vehicle / unmanned aerial vehicle, a third instruction for indicating picking up goods at the non-last distribution transfer node from the unmanned aerial vehicle and transferring to the unmanned vehicle or another unmanned aerial vehicle, a fourth instruction for indicating picking up goods at the non-last distribution transfer node from the unmanned vehicle and transferring to the unmanned aerial vehicle or another unmanned aerial vehicle, a fifth instruction for indicating picking up goods at the non-last distribution transfer node from the unmanned vehicle / unmanned aerial vehicle and carrying to the next non-last distribution transfer node for transfer to another unmanned vehicle / another unmanned aerial vehicle, and / or a sixth instruction for indicating picking up goods at the last distribution transfer node from the unmanned aerial vehicle / unmanned vehicle and carrying to the distribution termination node for delivery to the customer. 8.The smart logistics distribution platform of claim 1, wherein, The instructions sent to the unmanned vehicle or unmanned aerial vehicle participating in the distribution relay include a seventh instruction for indicating going to the non-last distribution transfer node to wait for picking up goods and / or an eighth instruction for indicating carrying to the next distribution transfer node after picking up goods. 9.The smart logistics distribution platform of claim 1, wherein, The logistics communication control module is also used for updating logistics information corresponding to the to-be-delivered logistics order in real time according to the execution result, and feeding back the logistics information to the customer terminal in real time.

10. A working method of the intelligent logistics distribution platform according to any one of claims 1-9, comprising: receiving and analyzing, by a logistics order processing module, a to-be-delivered logistics order from a customer terminal to obtain corresponding order data, wherein the order data contains distribution starting and ending point information, delivery time window information and to-be-sent goods attribute information; monitoring, by a logistics resource monitoring module, to obtain state data of all logistics resources in real time, wherein the all logistics resources include unmanned aerial vehicles, unmanned vehicles and riders, and the state data contains current position information, current idle carrying capacity information and current distribution task information of the corresponding logistics resources; accessing, by a logistics environment sensing module, an external data source to obtain environmental data in a logistics distribution area in real time, wherein the environmental data contains traffic road condition information, weather condition information and airspace control information; The delivery scheme planning module generates and adjusts a globally optimal delivery relay scheme for the to-be-delivered logistics order according to the order data, the state data and the environment data, with the goal of minimizing delivery cost and maximizing delivery timeliness, wherein the globally optimal delivery relay scheme contains at least one delivery mode, and the at least one delivery mode includes a delivery mode based on the rider or the at least one delivery mode includes a delivery mode based on the rider, a delivery mode based on the UAV and / or a delivery mode based on the unmanned vehicle; The logistics communication control module sends instructions to the rider, the UAV or the unmanned vehicle participating in the delivery relay to perform the delivery task of the to-be-delivered logistics order according to the globally optimal delivery relay scheme, receives feedback of the execution result, and transmits the execution result to the logistics resource monitoring module to update the state data.

Images