Intelligent logistics scheduling method and system based on distribution price optimization

Abstract

The invention discloses an intelligent logistics scheduling method and system based on distribution price optimization, and belongs to the technical field of intelligent logistics scheduling. Through a distribution price minimization optimization model, the accurate distribution price of each order is determined, and then historical logistics distribution data is utilized to carry out optimization training on the distribution price minimization model, so that the distribution price minimization optimization model is optimized; therefore, the delivery price of each order meets the real-time requirement and is more flexible, and the accuracy of real-time delivery price pricing is improved. Through the real-time optimal delivery price of each real-time order, the ant colony algorithm is updated, and a global optimal real-time logistics scheduling scheme based on comprehensive delivery distance and price guidance is obtained, so that the commercial demand of modern logistics is more accurately matched.

Description

Technical Field

[0001] This invention belongs to the field of intelligent logistics scheduling technology, specifically relating to an intelligent logistics scheduling method and system based on delivery price optimization. Background Technology

[0002] Existing intelligent logistics scheduling technologies have significant limitations. First, while optimization objectives are becoming more diverse, the core perspective remains limited to improving physical efficiency on the operational side, such as optimizing routes, reducing empty runs, and increasing load rates. They fail to incorporate "delivery price," a core market factor directly impacting enterprise revenue and customer behavior, as an active optimization variable into the model. Second, existing models are static and passive. They typically treat delivery prices as fixed costs or post-settlement parameters, failing to guide demand distribution and balance network load through flexible and dynamic pricing strategies. This results in missing a significant opportunity to further improve overall system efficiency (including economic and carbon efficiency) through price leverage. Finally, the "intelligence" dimension of the algorithms is incomplete. Traditional algorithms like ant colony optimization typically rely solely on physical information such as distance and time for their heuristic functions, lacking an understanding of the economic value of orders. This leads to a singular optimization direction, making it difficult to maximize economic benefits in complex business realities.

[0003] As mentioned above, how to provide an intelligent logistics scheduling method and system based on delivery price optimization that can optimize intelligent logistics scheduling by utilizing delivery prices and obtain delivery solutions that better meet the needs of modern logistics commerce through the synergistic optimization of distance and price benefits has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent logistics scheduling method based on delivery price optimization to solve the above-mentioned problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides an intelligent logistics scheduling method based on delivery price optimization, comprising: Obtain logistics and delivery data, wherein the logistics and delivery data includes historical logistics and delivery data and real-time logistics and delivery data; Obtain a preset delivery price minimization model, and optimize and train the delivery price minimization model based on historical logistics delivery data in the logistics delivery data to obtain a delivery price minimization optimization model; The real-time logistics and delivery data in the logistics and delivery data is input into the delivery price minimization optimization model to calculate the real-time optimal delivery price for each real-time order in the real-time logistics and delivery data. The ant colony algorithm is used as the original delivery scheme optimization algorithm. A preset delivery price influence factor is introduced to update the original delivery scheme optimization algorithm to obtain the delivery scheme optimization algorithm. Based on the real-time optimal delivery price of each real-time order in the real-time logistics delivery data, the globally optimal real-time logistics scheduling scheme is calculated using the delivery scheme optimization algorithm, and then the globally optimal real-time logistics scheduling scheme is distributed to each delivery vehicle for execution to complete the logistics scheduling.

[0006] In one possible design, the logistics and delivery data includes order information, delivery vehicle information, delivery traffic information, and customer price receipt information, and the historical logistics and delivery data is obtained by accessing the historical delivery record system of the logistics and delivery platform, while the real-time logistics and delivery data is obtained by accessing the real-time order system of the logistics and delivery platform. The order information includes at least one item, and each order information includes a unique corresponding customer information. The delivery vehicle information includes vehicle type information, vehicle energy consumption information, vehicle location information, and vehicle carrying capacity information. The delivery traffic information includes traffic conditions information and road network structure information. The customer price reception information includes customer satisfaction information for each order.

[0007] In one possible design, the preset method of the delivery price minimization model includes, Define path decision variables and price decision variables, wherein the path decision variable is a binary variable used to represent the driving path of the delivery vehicle, and the price decision variable is a continuous variable used to represent the price corresponding to the delivery; Obtain preset initial weight conditions, and based on the initial weight conditions, select delivery cost, delivery energy consumption and order delivery time as objectives, and establish a delivery price minimization objective function through weighted fusion; Obtain the preset delivery constraints, and establish the delivery price minimization model based on the delivery price minimization objective function and the delivery constraints.

[0008] In one possible design, the delivery price minimization model is optimized and trained based on historical logistics delivery data in the logistics delivery data to obtain a delivery price minimization optimization model, including: An optimization function is constructed using order delivery cost, order delivery time, and order price as objective features, and order delivery cost coefficient, order delivery time coefficient, and order price coefficient as variable parameters. Using the objective function for minimizing the delivery price as a fixed objective and the optimization function as an adjustable objective, the minimization objective function and the optimization function are concatenated to obtain the delivery price minimization optimization function. Feature extraction is performed on the historical logistics and delivery data in the logistics and delivery data to obtain historical order delivery cost, historical order delivery time, historical order price and historical order customer satisfaction, and the historical order delivery cost, historical order delivery time and historical order price are integrated as a training set; Using the training set as input and the historical order customer satisfaction as output, the adjustable objective in the delivery price minimization optimization function is optimized and trained to obtain the delivery price minimization optimization model.

[0009] In one possible design, real-time logistics delivery data from the logistics delivery data is input into the delivery price minimization optimization model to calculate the optimal real-time delivery price for each real-time order in the real-time logistics delivery data, including: Feature extraction is performed on the real-time logistics and delivery data in the logistics and delivery data to obtain real-time order information, real-time delivery vehicle information, real-time delivery traffic information, and real-time customer price receipt information; The real-time order information, real-time delivery vehicle information, real-time delivery traffic information, and real-time customer price reception information are input into the delivery price minimization optimization model to calculate the real-time optimal delivery price for each real-time order in the real-time logistics delivery data.

[0010] In one possible design, the ant colony optimization algorithm is used as the original delivery scheme optimization algorithm. A delivery price influencing factor is introduced to update the original delivery scheme optimization algorithm, resulting in a delivery scheme optimization algorithm that includes: Ant colony optimization algorithm was used as the original delivery scheme optimization algorithm, and the population size, maximum number of optimization iterations and pheromone of the ant colony were preset. Extract the delivery location and the distance between each real-time order from the real-time logistics delivery data in the logistics delivery data. Use the delivery location of each real-time order as a node and the distance between the delivery locations of each real-time order as the distance to establish a node distance matrix. Based on the preset pheromone and the node distance matrix, an original pheromone matrix is ​​established, wherein each element in the original pheromone matrix is ​​used to represent the influence of each node path on the ant colony. For each element in the original pheromone matrix, a corresponding delivery price influence factor is calculated. The delivery price influence factor is calculated based on the real-time optimal delivery price of each real-time order. The delivery price influence factor serves as a pheromone heuristic factor to represent the degree of influence of pheromones on the ant colony when the ant colony selects paths for each node. The original pheromone matrix is ​​updated using the delivery price influencing factors to obtain the pheromone matrix, and a delivery scheme optimization algorithm is formed based on the pheromone matrix.

[0011] In one possible design, based on the real-time optimal delivery price of each real-time order in the real-time logistics delivery data, a globally optimal real-time logistics scheduling scheme is calculated using the delivery scheme optimization algorithm, and the globally optimal real-time logistics scheduling scheme is distributed to each delivery vehicle for execution to complete the logistics scheduling, including: The global real-time logistics scheduling scheme is iteratively calculated using the delivery scheme optimization algorithm. When the number of iterations reaches the maximum number of optimization iterations, the global real-time logistics scheduling scheme obtained from the last iteration is output and used as the global optimal real-time logistics scheduling scheme. Based on the real-time logistics delivery data in the logistics delivery data, the delivery vehicles participating in the delivery are determined, and the global real-time logistics scheduling plan is distributed to each of the delivery vehicles participating in the delivery to complete the logistics scheduling.

[0012] Secondly, the present invention provides an intelligent logistics scheduling system based on delivery price optimization, applied to the intelligent logistics scheduling method based on delivery price optimization as described in the first aspect or any possible design of the first aspect, comprising: The data acquisition unit acquires logistics and distribution data, which includes historical logistics and distribution data and real-time logistics and distribution data. The model optimization unit is used to obtain a preset delivery price minimization model and optimize and train the delivery price minimization model based on historical logistics delivery data in the logistics delivery data to obtain a delivery price minimization optimization model. The price calculation unit is used to input the real-time logistics delivery data in the logistics delivery data into the delivery price minimization optimization model, so as to calculate the real-time optimal delivery price for each real-time order in the real-time logistics delivery data. The algorithm update unit is used to update the original delivery scheme optimization algorithm by introducing a preset delivery price influence factor, using the ant colony algorithm as the original delivery scheme optimization algorithm, to obtain the delivery scheme optimization algorithm. The scheme optimization unit is used to calculate the globally optimal real-time logistics scheduling scheme based on the real-time optimal delivery price of each real-time order in the real-time logistics delivery data, using the delivery scheme optimization algorithm, and then distribute the globally optimal real-time logistics scheduling scheme to each delivery vehicle for execution to complete the logistics scheduling.

[0013] Thirdly, the present invention provides an electronic device comprising a memory, a processor, and a transceiver connected in sequence and communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the intelligent logistics scheduling method based on delivery price optimization as described in the first aspect or any possible design of the first aspect.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the XXXXXXX method described in the first aspect or any possible design of the first aspect.

[0015] Fifthly, the present invention provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the intelligent logistics scheduling method based on delivery price optimization as described in the first aspect or any possible design of the first aspect.

[0016] Beneficial Effects: This invention provides an intelligent logistics scheduling method and system based on delivery price optimization, comprising: firstly, acquiring logistics delivery data, wherein the logistics delivery data includes historical logistics delivery data and real-time logistics delivery data; secondly, acquiring a preset delivery price minimization model, and optimizing and training the delivery price minimization model based on the historical logistics delivery data in the logistics delivery data to obtain a delivery price minimization optimization model; then, inputting the real-time logistics delivery data in the logistics delivery data into the delivery price minimization optimization model to calculate the real-time optimal delivery price for each real-time order in the real-time logistics delivery data; furthermore, using the ant colony algorithm as the original delivery scheme optimization algorithm, introducing a preset delivery price influence factor to update the original delivery scheme optimization algorithm to obtain a delivery scheme optimization algorithm; finally, based on the real-time optimal delivery price of each real-time order in the real-time logistics delivery data, using the delivery scheme optimization algorithm to calculate the globally optimal real-time logistics scheduling scheme, and distributing the globally optimal real-time logistics scheduling scheme to each delivery vehicle for execution to complete the logistics scheduling. By using a delivery price minimization optimization model, the accurate delivery price for each order is determined. Then, historical logistics delivery data is used to optimize and train the delivery price minimization model, making the delivery price of each order more flexible and in line with real-time requirements, thus improving the accuracy of real-time delivery price pricing. By using the real-time optimal delivery price of each real-time order, the ant colony algorithm is updated to obtain a globally optimal real-time logistics scheduling scheme that integrates delivery distance and price orientation, so as to more accurately match the business needs of modern logistics. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the intelligent logistics scheduling method based on delivery price optimization provided in an embodiment of the present invention; Figure 2 A functional structure diagram of an intelligent logistics scheduling system based on delivery price optimization provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] 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 drawings can be obtained based on these drawings 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.

[0019] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0020] 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, and A and B exist simultaneously. 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, and A and B exist alone. 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.

[0021] Example: like Figure 1 As shown, the first aspect of this embodiment provides an intelligent logistics scheduling method based on delivery price optimization, which may include, but is not limited to, the following steps: S1. Obtain logistics and delivery data, wherein the logistics and delivery data includes historical logistics and delivery data and real-time logistics and delivery data; S2. Obtain a preset delivery price minimization model, and optimize and train the delivery price minimization model based on historical logistics delivery data in the logistics delivery data to obtain a delivery price minimization optimization model; S3. Input the real-time logistics delivery data from the logistics delivery data into the delivery price minimization optimization model to calculate the real-time optimal delivery price for each real-time order in the real-time logistics delivery data. S4. Using the ant colony algorithm as the original delivery scheme optimization algorithm, the original delivery scheme optimization algorithm is updated by introducing a preset delivery price influence factor to obtain the delivery scheme optimization algorithm. S5. Based on the real-time optimal delivery price of each real-time order in the real-time logistics delivery data, calculate the globally optimal real-time logistics scheduling scheme using the delivery scheme optimization algorithm, and distribute the globally optimal real-time logistics scheduling scheme to each delivery vehicle for execution, thereby completing the logistics scheduling.

[0022] It should be noted that this embodiment introduces a delivery price minimization optimization model to confirm the delivery price of each order, thus obtaining the price updated by the subsequent optimization algorithm. However, since the accuracy of the result calculated by this fixed price is low, this embodiment further optimizes and trains the delivery price minimization model using historical logistics delivery data. While retaining the basic parameters of the price calculation model, additional parameter weights are calculated, ensuring that its original computing power is not affected and introducing multi-objective parameters, making the calculated price more realistic and reliable, and more in line with the complex logistics delivery environment. Applying it to real-time price calculation makes the calculated delivery prices of each order more in line with real-time requirements, the calculation more flexible, and improves the accuracy of real-time delivery price pricing, ensuring that the price updated by the subsequent optimization algorithm is more accurate and more in line with reality. Furthermore, by updating the ant colony algorithm with the real-time optimal delivery price of each real-time order, a globally optimal real-time logistics scheduling scheme that integrates delivery distance and price orientation is obtained. This makes the logistics scheduling scheme calculated in this embodiment take into account both logistics delivery distance (distance) and delivery revenue (price), not only more accurately matching the commercial needs of modern logistics but also meeting customer delivery requirements.

[0023] In one possible design, in step S1, the logistics and delivery data includes order information, delivery vehicle information, delivery traffic information, and customer price receipt information, and the historical logistics and delivery data is obtained by accessing the historical delivery record system of the logistics and delivery platform, while the real-time logistics and delivery data is obtained by accessing the real-time order system of the logistics and delivery platform. The order information includes at least one item, and each order information includes a unique corresponding customer information. The delivery vehicle information includes vehicle type information, vehicle energy consumption information, vehicle location information, and vehicle carrying capacity information. The delivery traffic information includes traffic conditions information and road network structure information. The customer price reception information includes customer satisfaction information for each order.

[0024] In one possible design, the preset method for the delivery price minimization model in step S2 may include, but is not limited to, the following steps S201-S203: S201. Define the path decision variable and the price decision variable, wherein the path decision variable is a binary variable used to represent the driving path of the delivery vehicle, and the price decision variable is a continuous variable used to represent the price corresponding to the delivery; S202. Obtain preset initial weight conditions, and based on the initial weight conditions, select delivery cost, delivery energy consumption and order delivery time as objectives, and establish a delivery price minimization objective function through weighted fusion; S203. Obtain the preset delivery constraints, and establish the delivery price minimization model based on the delivery price minimization objective function and the delivery constraints.

[0025] In one possible design, step S2, optimizing and training the delivery price minimization model based on historical logistics delivery data in the logistics delivery data to obtain a delivery price minimization optimization model, can be decomposed into, but is not limited to, the following steps S21-S24, specifically including: S21. Using order delivery cost, order delivery time, and order price as objective features, and order delivery cost coefficient, order delivery time coefficient, and order price coefficient as variable parameters, construct an optimization function; S22. Using the delivery price minimization objective function as a fixed objective and the optimization function as an adjustable objective, concatenate the minimization objective function and the optimization function to obtain the delivery price minimization optimization function. S23. Extract features from the historical logistics delivery data in the logistics delivery data to obtain historical order delivery cost, historical order delivery time, historical order price and historical order customer satisfaction, and integrate the historical order delivery cost, the historical order delivery time and the historical order price as a training set; S24. Using the training set as input and the historical order customer satisfaction as output, optimize the adjustable objective in the delivery price minimization function to obtain the delivery price minimization optimization model.

[0026] In one possible design, step S3 involves inputting the real-time logistics delivery data from the logistics delivery data into the delivery price minimization optimization model to calculate the optimal real-time delivery price for each real-time order in the real-time logistics delivery data. This can be decomposed, but is not limited to, the following steps S31-S32, specifically including: S31. Extract features from the real-time logistics delivery data in the logistics delivery data to obtain real-time order information, real-time delivery vehicle information, real-time delivery traffic information, and real-time customer price receipt information; S32. Input the real-time order information, the real-time delivery vehicle information, the real-time delivery traffic information, and the real-time customer price reception information into the delivery price minimization optimization model to calculate the real-time optimal delivery price for each real-time order in the real-time logistics delivery data.

[0027] In one possible design, step S4 uses the ant colony algorithm as the original delivery scheme optimization algorithm, and introduces the delivery price influence factor to update the original delivery scheme optimization algorithm to obtain the delivery scheme optimization algorithm. This can be decomposed into, but is not limited to, the following steps S41-S45, specifically including: S41. Use the ant colony algorithm as the optimization algorithm for the original delivery scheme, and pre-determine the ant colony population size, maximum number of optimization iterations, and pheromone. S42. Extract the delivery location corresponding to each real-time order and the distance between the delivery locations corresponding to each real-time order from the real-time logistics delivery data in the logistics delivery data. Establish a node distance matrix with the delivery location corresponding to each real-time order as the node and the distance between the delivery locations corresponding to each real-time order as the distance. S43. Based on the preset pheromone and the node distance matrix, a corresponding original pheromone matrix is ​​established, wherein each element in the original pheromone matrix is ​​used to represent the influence of each node path on the ant colony; S44. Calculate the corresponding delivery price influence factor for each element in the original pheromone matrix, wherein the delivery price influence factor is calculated based on the real-time optimal delivery price of each real-time order, and the delivery price influence factor serves as a pheromone heuristic factor to represent the degree of influence of pheromones on the ant colony when the ant colony selects paths for each node; S45. Update the original pheromone matrix using the delivery price influencing factors to obtain the pheromone matrix, and form a delivery scheme optimization algorithm based on the pheromone matrix.

[0028] In one possible design, in step S5, based on the real-time optimal delivery price of each real-time order in the real-time logistics delivery data, the globally optimal real-time logistics scheduling scheme is calculated using the delivery scheme optimization algorithm, and the globally optimal real-time logistics scheduling scheme is distributed to each delivery vehicle for execution to complete the logistics scheduling. This can be decomposed into, but is not limited to, the following steps S51-S52, specifically including: S51. The global real-time logistics scheduling scheme is iteratively calculated using the delivery scheme optimization algorithm. When the number of iterations reaches the maximum number of optimization iterations, the global real-time logistics scheduling scheme obtained by the last iteration calculation is output and used as the global optimal real-time logistics scheduling scheme. S52. Based on the real-time logistics delivery data in the logistics delivery data, determine the delivery vehicles participating in the delivery, and distribute the global real-time logistics scheduling plan to each delivery vehicle participating in the delivery to complete the logistics scheduling.

[0029] like Figure 2As shown, the second aspect of this embodiment provides a hardware system for implementing the intelligent logistics scheduling method based on delivery price optimization described in the first aspect of the embodiment, including: The data acquisition unit acquires logistics and distribution data, which includes historical logistics and distribution data and real-time logistics and distribution data. The model optimization unit is used to obtain a preset delivery price minimization model and optimize and train the delivery price minimization model based on historical logistics delivery data in the logistics delivery data to obtain a delivery price minimization optimization model. The price calculation unit is used to input the real-time logistics delivery data in the logistics delivery data into the delivery price minimization optimization model, so as to calculate the real-time optimal delivery price for each real-time order in the real-time logistics delivery data. The algorithm update unit is used to update the original delivery scheme optimization algorithm by introducing a preset delivery price influence factor, using the ant colony algorithm as the original delivery scheme optimization algorithm, to obtain the delivery scheme optimization algorithm. The scheme optimization unit is used to calculate the globally optimal real-time logistics scheduling scheme based on the real-time optimal delivery price of each real-time order in the real-time logistics delivery data, using the delivery scheme optimization algorithm, and then distribute the globally optimal real-time logistics scheduling scheme to each delivery vehicle for execution to complete the logistics scheduling.

[0030] The working process, working details and technical effects of the system provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0031] like Figure 3 As shown, the third aspect of this embodiment provides an electronic device, including: a memory, a processor, and a transceiver that are sequentially and communicatively connected, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the intelligent logistics scheduling method based on delivery price optimization as described in the first aspect of the embodiment.

[0032] For specific examples, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, the processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0033] In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. For example, the processor may not be limited to microprocessors of the STM32F105 series, reduced instruction set computer (RISC) microprocessors, x86 architecture processors, or processors with integrated neural network processing units (NPUs). The transceiver may be, but is not limited to, a Wi-Fi transceiver, a Bluetooth transceiver, a General Packet Radio Service (GPRS) transceiver, a ZigBee (a low-power LAN protocol based on the IEEE 802.15.4 standard) transceiver, a 3G transceiver, a 4G transceiver, and / or a 5G transceiver. Furthermore, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0034] The working process, working details and technical effects of the electronic device provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0035] The fourth aspect of this embodiment provides a storage medium for storing instructions containing the intelligent logistics scheduling method based on delivery price optimization as described in the first aspect of the embodiment. That is, the storage medium stores instructions, and when the instructions are run on a computer, the intelligent logistics scheduling method based on delivery price optimization as described in the first aspect of the embodiment is executed.

[0036] The storage medium refers to a carrier for storing data, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0037] The working process, working details and technical effects of the storage medium provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0038] The fifth aspect of this embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the intelligent logistics scheduling method based on delivery price optimization as described in the first aspect of this embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0039] 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 scheduling method based on delivery price optimization, characterized in that, include: Obtain logistics and delivery data, wherein the logistics and delivery data includes historical logistics and delivery data and real-time logistics and delivery data; Obtain a preset delivery price minimization model, and optimize and train the delivery price minimization model based on historical logistics delivery data in the logistics delivery data to obtain a delivery price minimization optimization model; The real-time logistics and delivery data in the logistics and delivery data is input into the delivery price minimization optimization model to calculate the real-time optimal delivery price for each real-time order in the real-time logistics and delivery data. The ant colony algorithm is used as the original delivery scheme optimization algorithm. A preset delivery price influence factor is introduced to update the original delivery scheme optimization algorithm to obtain the delivery scheme optimization algorithm. Based on the real-time optimal delivery price of each real-time order in the real-time logistics delivery data, the globally optimal real-time logistics scheduling scheme is calculated using the delivery scheme optimization algorithm, and then the globally optimal real-time logistics scheduling scheme is distributed to each delivery vehicle for execution to complete the logistics scheduling.

2. The intelligent logistics scheduling method based on delivery price optimization according to claim 1, characterized in that, The logistics and delivery data includes order information, delivery vehicle information, delivery traffic information, and customer price receipt information. The historical logistics and delivery data is obtained by accessing the historical delivery record system of the logistics and delivery platform, and the real-time logistics and delivery data is obtained by accessing the real-time order system of the logistics and delivery platform. The order information includes at least one item, and each order information includes a unique corresponding customer information. The delivery vehicle information includes vehicle type information, vehicle energy consumption information, vehicle location information, and vehicle carrying capacity information. The delivery traffic information includes traffic conditions information and road network structure information. The customer price reception information includes customer satisfaction information for each order.

3. The intelligent logistics scheduling method based on delivery price optimization according to claim 1, characterized in that, The preset method for the delivery price minimization model includes, Define path decision variables and price decision variables, wherein the path decision variable is a binary variable used to represent the driving path of the delivery vehicle, and the price decision variable is a continuous variable used to represent the price corresponding to the delivery; Obtain preset initial weight conditions, and based on the initial weight conditions, select delivery cost, delivery energy consumption and order delivery time as objectives, and establish a delivery price minimization objective function through weighted fusion; Obtain the preset delivery constraints, and establish the delivery price minimization model based on the delivery price minimization objective function and the delivery constraints.

4. The intelligent logistics scheduling method based on delivery price optimization according to claim 3, characterized in that, The delivery price minimization model is optimized and trained based on historical logistics delivery data in the logistics delivery data to obtain an optimized delivery price minimization model, including: An optimization function is constructed using order delivery cost, order delivery time, and order price as objective features, and order delivery cost coefficient, order delivery time coefficient, and order price coefficient as variable parameters. Using the objective function for minimizing the delivery price as a fixed objective and the optimization function as an adjustable objective, the minimization objective function and the optimization function are concatenated to obtain the delivery price minimization optimization function. Feature extraction is performed on the historical logistics and delivery data in the logistics and delivery data to obtain historical order delivery cost, historical order delivery time, historical order price and historical order customer satisfaction, and the historical order delivery cost, historical order delivery time and historical order price are integrated as a training set; Using the training set as input and the historical order customer satisfaction as output, the adjustable objective in the delivery price minimization optimization function is optimized and trained to obtain the delivery price minimization optimization model.

5. The intelligent logistics scheduling method based on delivery price optimization according to claim 1, characterized in that, The real-time logistics delivery data from the logistics delivery data is input into the delivery price minimization optimization model to calculate the real-time optimal delivery price for each real-time order in the real-time logistics delivery data, including: Feature extraction is performed on the real-time logistics and delivery data in the logistics and delivery data to obtain real-time order information, real-time delivery vehicle information, real-time delivery traffic information, and real-time customer price receipt information; The real-time order information, real-time delivery vehicle information, real-time delivery traffic information, and real-time customer price reception information are input into the delivery price minimization optimization model to calculate the real-time optimal delivery price for each real-time order in the real-time logistics delivery data.

6. The intelligent logistics scheduling method based on delivery price optimization according to claim 1, characterized in that, Using the ant colony algorithm as the original delivery scheme optimization algorithm, and introducing a delivery price influencing factor to update the original delivery scheme optimization algorithm, a delivery scheme optimization algorithm is obtained, including: Ant colony optimization algorithm was used as the original delivery scheme optimization algorithm, and the population size, maximum number of optimization iterations and pheromone of the ant colony were preset. Extract the delivery location and the distance between each real-time order from the real-time logistics delivery data in the logistics delivery data. Use the delivery location of each real-time order as a node and the distance between the delivery locations of each real-time order as the distance to establish a node distance matrix. Based on the preset pheromone and the node distance matrix, an original pheromone matrix is ​​established, wherein each element in the original pheromone matrix is ​​used to represent the influence of each node path on the ant colony. For each element in the original pheromone matrix, a corresponding delivery price influence factor is calculated. The delivery price influence factor is calculated based on the real-time optimal delivery price of each real-time order. The delivery price influence factor serves as a pheromone heuristic factor to represent the degree of influence of pheromones on the ant colony when the ant colony selects paths for each node. The original pheromone matrix is ​​updated using the delivery price influencing factors to obtain the pheromone matrix, and a delivery scheme optimization algorithm is formed based on the pheromone matrix.

7. The intelligent logistics scheduling method based on delivery price optimization according to claim 6, characterized in that, Based on the real-time optimal delivery price of each real-time order in the real-time logistics delivery data, the globally optimal real-time logistics scheduling scheme is calculated using the delivery scheme optimization algorithm, and then the globally optimal real-time logistics scheduling scheme is distributed to each delivery vehicle for execution to complete the logistics scheduling, including: The global real-time logistics scheduling scheme is iteratively calculated using the delivery scheme optimization algorithm. When the number of iterations reaches the maximum number of optimization iterations, the global real-time logistics scheduling scheme obtained from the last iteration is output and used as the global optimal real-time logistics scheduling scheme. Based on the real-time logistics delivery data in the logistics delivery data, the delivery vehicles participating in the delivery are determined, and the global real-time logistics scheduling plan is distributed to each of the delivery vehicles participating in the delivery to complete the logistics scheduling.

8. An intelligent logistics scheduling system based on delivery price optimization, characterized in that, The intelligent logistics scheduling method based on delivery price optimization as described in any one of claims 1 to 7 includes: The data acquisition unit acquires logistics and distribution data, which includes historical logistics and distribution data and real-time logistics and distribution data. The model optimization unit is used to obtain a preset delivery price minimization model and optimize and train the delivery price minimization model based on historical logistics delivery data in the logistics delivery data to obtain a delivery price minimization optimization model. The price calculation unit is used to input the real-time logistics delivery data in the logistics delivery data into the delivery price minimization optimization model, so as to calculate the real-time optimal delivery price for each real-time order in the real-time logistics delivery data. The algorithm update unit is used to update the original delivery scheme optimization algorithm by introducing a preset delivery price influence factor, using the ant colony algorithm as the original delivery scheme optimization algorithm, to obtain the delivery scheme optimization algorithm. The scheme optimization unit is used to calculate the globally optimal real-time logistics scheduling scheme based on the real-time optimal delivery price of each real-time order in the real-time logistics delivery data, using the delivery scheme optimization algorithm, and then distribute the globally optimal real-time logistics scheduling scheme to each delivery vehicle for execution to complete the logistics scheduling.

9. An electronic device, characterized in that, The system includes a memory, a processor, and a transceiver that are sequentially and communicatively connected. The memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the intelligent logistics scheduling method based on delivery price optimization as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the intelligent logistics scheduling method based on delivery price optimization as described in any one of claims 1 to 7.

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