A warehousing logistics scheduling method and system based on cloud computing

Abstract

The present invention relates to a warehousing logistics scheduling method and system based on cloud computing, belonging to the technical field of warehousing logistics scheduling. The method includes: jointly establishing a logistics scheduling model through multiple edge cloud computing nodes, including an edge scheduling scheme generation model and a comprehensive scheduling scheme generation model; determining at least one first optimal edge cloud computing node based on the computing power resource requirements of the warehousing logistics scheduling task; each first optimal edge cloud computing node generates multiple candidate warehousing logistics scheduling schemes through the edge scheduling scheme generation model based on the warehousing logistics scheduling task, the warehousing node status information, and the logistics vehicle status information, screens the multiple candidate warehousing logistics scheduling schemes, and determines multiple target warehousing logistics scheduling schemes; generates an optimal warehousing logistics scheduling scheme through the comprehensive scheduling scheme generation model based on the multiple target warehousing logistics scheduling schemes, and conducts warehousing logistics scheduling, which has the advantage of improving the intelligent level of warehousing logistics scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of warehousing and logistics scheduling, and particularly to a warehousing and logistics scheduling method and system based on cloud computing. Background Art

[0002] In the supply chain link of the circulation industry, storing, warehousing goods and logistics distribution are the main business forms of warehousing logistics, which are composed of a series of supply and demand. In the prior art, during the operation process, loading the goods at each distribution point stored in the warehouse onto the corresponding logistics vehicles and arranging them to be transported to the corresponding distribution points is called the scheduling of logistics vehicles. Currently, the vehicle scheduling of warehousing logistics mainly relies on manual scheduling, that is: manually, according to the current inventory of goods and the condition of the vehicles, specifying which goods are loaded onto which vehicles and transported to which nodes. There are mainly the following defects in manual warehousing and logistics scheduling: low scheduling efficiency. During the scheduling process, only manually allocate according to the warehousing condition of the goods and the condition of the vehicles, without considering the actual transportation situation of the goods and the condition of the vehicles, resulting in low scheduling efficiency and inability to make loading decisions in a timely manner; high logistics cost. There is a certain correlation between the size, weight, destination of the goods and the size, load capacity, transportation route of the vehicles. A better match can greatly reduce the vehicle logistics transportation cost, reduce the transportation route and number of the vehicles, etc., thereby reducing the logistics transportation cost. Manual scheduling, due to unreasonable vehicle loading, such as inability to be fully loaded, overly complex vehicle planned routes, etc., results in a smaller transportation volume under the same number of vehicles, and thus the warehousing rate of the warehouse remains high. For this reason, logistics enterprises have to increase the capacity of the warehouse, which in turn leads to an increase in the operating cost of logistics enterprises. Unreasonable loading leads to an increase in the loading and unloading times of vehicle loading, and thus an increase in the loading and unloading cost.

[0003] Therefore, it is necessary to provide a warehousing and logistics scheduling method and system based on cloud computing to improve the intelligent level of warehousing and logistics scheduling. Summary of the Invention

[0004] The present invention provides a warehousing logistics scheduling method based on cloud computing, including: jointly establishing a logistics scheduling model through multiple edge cloud computing nodes, wherein the logistics scheduling model includes an edge scheduling scheme generation model and a comprehensive scheduling scheme generation model; deploying the logistics scheduling model on each of the edge cloud computing nodes; obtaining a warehousing logistics scheduling task; calculating the computing power resource requirements of the warehousing logistics scheduling task; determining at least one first optimal edge cloud computing node from the multiple edge cloud computing nodes based on the computing power resource requirements of the warehousing logistics scheduling task; obtaining warehousing node status information and logistics vehicle status information; each of the first optimal edge cloud computing nodes generates multiple candidate warehousing logistics scheduling schemes based on the warehousing logistics scheduling task, the warehousing node status information, and the logistics vehicle status information through the edge scheduling scheme generation model; determining a second optimal edge cloud computing node from the at least one first optimal edge cloud computing node based on the device status information of each of the first optimal edge cloud computing nodes; the second optimal edge cloud computing node screens the multiple candidate warehousing logistics scheduling schemes to determine multiple target warehousing logistics scheduling schemes; the second optimal edge cloud computing node generates an optimal warehousing logistics scheduling scheme based on the multiple target warehousing logistics scheduling schemes through the comprehensive scheduling scheme generation model; and performing warehousing logistics scheduling based on the optimal warehousing logistics scheduling scheme to complete the warehousing logistics scheduling task.

[0005] Further, jointly establishing a logistics scheduling model through multiple edge cloud computing nodes includes: establishing an initial logistics scheduling model; deploying the initial logistics scheduling model on each of the edge cloud computing nodes; and jointly training the initial logistics scheduling model through the multiple edge cloud computing nodes to obtain the logistics scheduling model.

[0006] Further, training the initial logistics scheduling model through the multiple edge cloud computing nodes to obtain the logistics scheduling model includes: S11. Each of the edge cloud computing nodes trains the initial logistics scheduling model, obtains the trained initial logistics scheduling model, tests the trained initial logistics scheduling model, and obtains the model performance test data of the trained initial logistics scheduling model; S12. Based on the model performance test data of the trained initial logistics scheduling model, determine whether the training is completed. If so, generate the logistics scheduling model based on the model parameters of the trained initial logistics scheduling model on each of the edge cloud computing nodes. If not, execute S13; S13. Generate an intermediate logistics scheduling model based on the model parameters of the trained initial logistics scheduling model on each of the edge cloud computing nodes; S14. Each of the edge cloud computing nodes trains the intermediate logistics scheduling model, obtains the trained intermediate logistics scheduling model, tests the trained intermediate logistics scheduling model, and obtains the model performance test data of the trained intermediate logistics scheduling model; S15. Based on the model performance test data of the trained intermediate logistics scheduling model, determine whether the training is completed. If so, generate the logistics scheduling model based on the model parameters of the trained intermediate logistics scheduling model on each of the edge cloud computing nodes. If not, execute S16; S16. Update the parameters of the intermediate logistics scheduling model based on the model parameters of the trained intermediate logistics scheduling model on each of the edge cloud computing nodes, deploy the parameters of the updated intermediate logistics scheduling model on each of the edge cloud computing nodes, and execute S14.

[0007] Further, calculating the computing power resource requirements for the warehousing logistics scheduling task includes: establishing a sample requirement database, where the sample requirement database is used to store the computing power resource requirements corresponding to multiple sample warehousing logistics scheduling tasks; calculating the similarity between the warehousing logistics scheduling task and the sample warehousing logistics scheduling tasks, and determining the similar sample warehousing logistics scheduling tasks; calculating the computing power resource requirements for the warehousing logistics scheduling task based on the computing power resource requirements of the similar sample warehousing logistics scheduling tasks.

[0008] Furthermore, the second optimal edge cloud computing node screens the multiple candidate warehousing and logistics scheduling schemes to determine multiple target warehousing and logistics scheduling schemes, including: calculating the scheme similarity between any two of the candidate warehousing and logistics scheduling schemes; clustering the multiple target warehousing and logistics scheduling schemes based on the scheme similarity between any two of the candidate warehousing and logistics scheduling schemes to obtain multiple scheme clustering clusters; the second optimal edge cloud computing node screens the multiple scheme clustering clusters through multiple scheme screening indicators to determine the optimal scheme clustering cluster; the second optimal edge cloud computing node screens the multiple candidate warehousing and logistics scheduling schemes included in the optimal scheme clustering cluster through multiple scheme screening indicators to determine multiple target warehousing and logistics scheduling schemes.

[0009] Furthermore, the second optimal edge cloud computing node screens the multiple candidate warehousing and logistics scheduling schemes included in the optimal scheme clustering cluster to determine multiple target warehousing and logistics scheduling schemes, including: predicting the warehousing node status information and logistics vehicle status information at multiple future time points through a state prediction model based on the warehousing node status information and logistics vehicle status information at multiple historical time points; obtaining weather forecast information and traffic flow information of the target area at multiple historical points; predicting the traffic flow information of the target area at multiple future time points through a road condition prediction model based on the weather forecast information and traffic flow information of the target area at multiple historical points; for each candidate warehousing and logistics scheduling scheme included in the optimal scheme clustering cluster, determining the score of the candidate warehousing and logistics scheduling scheme in multiple scheme screening indicators based on the predicted warehousing node status information, logistics vehicle status information, and traffic flow information of the target area at multiple future time points; screening the multiple candidate warehousing and logistics scheduling schemes included in the optimal scheme clustering cluster based on the scores of each candidate warehousing and logistics scheduling scheme included in the optimal scheme clustering cluster in multiple scheme screening indicators to determine multiple target warehousing and logistics scheduling schemes.

[0010] Furthermore, calculate the fusion weight of each target warehousing and logistics scheduling scheme through the comprehensive scheduling scheme generation model; generate the optimal warehousing and logistics scheduling scheme through the comprehensive scheduling scheme generation model based on the multiple target warehousing and logistics scheduling schemes and the fusion weight of each target warehousing and logistics scheduling scheme.

[0011] The present invention provides a warehousing logistics scheduling system based on cloud computing for implementing the above-mentioned warehousing logistics scheduling method based on cloud computing, including: a model establishment module for jointly establishing a logistics scheduling model through multiple edge cloud computing nodes, wherein the logistics scheduling model includes an edge scheduling scheme generation model and a comprehensive scheduling scheme generation model; a model deployment module for deploying the logistics scheduling model on each of the edge cloud computing nodes; a task acquisition module for acquiring warehousing logistics scheduling tasks; a requirement determination module for calculating the computing power resource requirements based on the warehousing logistics scheduling tasks; a primary screening module for determining at least one first optimal edge cloud computing node from the multiple edge cloud computing nodes based on the computing power resource requirements; an information acquisition module for acquiring warehousing node status information and logistics vehicle status information; a scheme generation module for generating multiple candidate warehousing logistics scheduling schemes based on each of the first optimal edge cloud computing nodes through the edge scheduling scheme generation model, based on the warehousing logistics scheduling tasks, the warehousing node status information, and the logistics vehicle status information; a secondary screening module for determining a second optimal edge cloud computing node from the at least one first optimal edge cloud computing node based on the device status information of each of the first optimal edge cloud computing nodes; a scheme screening module for screening the multiple candidate warehousing logistics scheduling schemes based on the second optimal edge cloud computing node to determine multiple target warehousing logistics scheduling schemes; a scheme optimization module for generating an optimal warehousing logistics scheduling scheme based on the second optimal edge cloud computing node through the comprehensive scheduling scheme generation model, based on the multiple target warehousing logistics scheduling schemes; and a logistics scheduling module for performing warehousing logistics scheduling based on the optimal warehousing logistics scheduling scheme to complete the warehousing logistics scheduling tasks.

[0012] Compared with the prior art, the warehousing logistics scheduling method and system provided in this specification have at least the following beneficial effects:

[0013] 1. Through the collaborative work of multiple edge cloud computing nodes, distributed processing of warehousing logistics scheduling tasks can be achieved, improving the utilization rate of computing resources. According to the device status information and computing power resource requirements of each node, tasks can be reasonably allocated to avoid overloading of a single node, achieve load balancing, and improve the overall efficiency. Deploying the same logistics scheduling model on multiple nodes can increase the fault tolerance of the system. Even if a certain node fails, other nodes can continue to work to ensure the normal progress of scheduling tasks. Each first optimal edge cloud computing node will generate multiple candidate scheduling schemes. Through comprehensive comparison and screening, the optimal scheduling scheme can be selected to optimize the scheduling cost;

[0014] 2. Utilizing the computing power of multiple edge cloud computing nodes for distributed training can accelerate the training process of the logistics scheduling model and improve the training efficiency. Each node may process warehousing and logistics data in different regions. This data diversity helps the logistics scheduling model learn more comprehensive features, thereby improving the generalization ability and accuracy of the logistics scheduling model. Through multiple iterative trainings, the model parameters can be gradually optimized, the model error can be reduced, and the model performance can be improved. Deploying the trained logistics scheduling model on each node to form model redundancy can quickly switch to the logistics scheduling model of other edge cloud computing nodes when the logistics scheduling model of a certain edge cloud computing node fails, ensuring the continuity of services;

[0015] 3. Through the state prediction model and the road condition prediction model, it is possible to predict the states of warehousing nodes, logistics vehicle states, and traffic flow information in the target area at future time points based on data from multiple historical time points. This enables the scheduling plan to more accurately reflect the actual situation in the future, improving the accuracy and reliability of the plan. Based on the real-time predicted information, the scheduling plan can be dynamically adjusted to adapt to changes in the warehousing and logistics environment, such as weather changes and traffic flow changes, ensuring the flexibility and adaptability of the scheduling plan. By comprehensively considering the predicted states of warehousing nodes, logistics vehicle states, and traffic flow information in the target area, the scheduling plan can be formulated more precisely, reducing ineffective transportation and waiting time, and improving the scheduling efficiency. A precise scheduling plan helps to reduce transportation costs, reduce waste of human and material resources, and thus achieve cost control and maximize benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] This specification will be further described by way of exemplary embodiments, which will be described in detail through the accompanying drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0017] Figure 1 is a flowchart of a warehousing and logistics scheduling method based on cloud computing shown in an embodiment of the present application;

[0018] Figure 2 is a flowchart of jointly training an initial logistics scheduling model shown in an embodiment of the present application;

[0019] Figure 3 is a module diagram of a warehousing and logistics scheduling system based on cloud computing shown in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] To more clearly illustrate the technical solutions of the embodiments of this specification, the accompanying drawings required for the description of the embodiments will be briefly introduced below.

[0021] Figure 1It is a flowchart of a warehousing logistics scheduling method based on cloud computing shown in an embodiment of the present application. As Figure 1 shown, a warehousing logistics scheduling method based on cloud computing may include the following steps.

[0022] Step 111: Jointly establish a logistics scheduling model through multiple edge cloud computing nodes.

[0023] Among them, the logistics scheduling model includes an edge scheduling scheme generation model and a comprehensive scheduling scheme generation model.

[0024] Specifically, it includes:

[0025] Jointly establish a logistics scheduling model through multiple edge cloud computing nodes, including:

[0026] Establish an initial logistics scheduling model, where the initial logistics scheduling model can all be Convolutional Neural Networks (CNN) models;

[0027] Deploy the initial logistics scheduling model on each edge cloud computing node;

[0028] Jointly train the initial logistics scheduling model through multiple edge cloud computing nodes to obtain the logistics scheduling model.

[0029] Figure 2 It is a flowchart of jointly training the initial logistics scheduling model shown in an embodiment of the present application. As Figure 2 shown, jointly train the initial logistics scheduling model through multiple edge cloud computing nodes to obtain the logistics scheduling model, including:

[0030] S11: Each edge cloud computing node trains the initial logistics scheduling model, obtains the trained initial logistics scheduling model, tests the trained initial logistics scheduling model, and obtains the model performance test data of the trained initial logistics scheduling model;

[0031] S12: Based on the model performance test data of the trained initial logistics scheduling model, determine whether the training is completed. If so, generate the logistics scheduling model based on the model parameters of the trained initial logistics scheduling model on each edge cloud computing node. If not, execute S13;

[0032] S13: Generate an intermediate logistics scheduling model based on the model parameters of the trained initial logistics scheduling model on each edge cloud computing node;

[0033] S14. Each edge cloud computing node trains the intermediate logistics scheduling model to obtain the trained intermediate logistics scheduling model, and tests the trained intermediate logistics scheduling model to obtain the model performance test data of the trained intermediate logistics scheduling model;

[0034] S15. Based on the model performance test data of the trained intermediate logistics scheduling model, determine whether the training is completed. If so, generate a logistics scheduling model based on the model parameters of the trained intermediate logistics scheduling model on each edge cloud computing node. If not, execute S16;

[0035] S16. Update the parameters of the intermediate logistics scheduling model based on the model parameters of the trained intermediate logistics scheduling model on each edge cloud computing node, deploy the parameters of the updated intermediate logistics scheduling model on each edge cloud computing node, and execute S14.

[0036] Specifically, the model performance test data may include the scores of the trained logistics scheduling model on multiple model performance metrics. The first model performance mean and the first model performance standard deviation can be calculated based on the model performance test data of the trained logistics scheduling model on multiple edge cloud computing nodes. Based on the model performance mean and the model performance standard deviation, calculate the training progress evaluation score. When the training progress evaluation score is less than the training progress evaluation score threshold, it is determined that the training is completed.

[0037] Preferably, the training progress evaluation score can be calculated based on the following formula:

[0038]

[0039] where S t is the training progress evaluation score for the t-th training, μ t is the first model performance mean for the t-th training, μ0 is the preset model performance mean, σ t is the first model performance standard deviation for the t-th training, and σ0 is the preset model performance standard deviation.

[0040] In some embodiments, the loss function for training the logistics scheduling model is:

[0041] L total = β 11 × L G + β 12 × L F

[0042] where L total is, β 11 and β 12 are both preset weights, and β 11 and β 12 are both greater than 0, β11 +β 12 = 1, L G is the loss for scenario generation, L F is the loss for scenario fusion.

[0043] The loss for scenario generation and the loss for scenario fusion are calculated based on the following formula:

[0044]

[0045] where N is the total number of training samples included in a training batch, β 21 and β 22 are both preset weights, and β 21 and β 22 are both greater than 0, β 21 +β 22 = 1, for, σ (n,1) is the standard deviation of the scenario similarity of multiple generated candidate warehousing and logistics scheduling scenarios corresponding to the nth training sample, σ (n,2) is the standard deviation of the scenario similarity of multiple true candidate warehousing and logistics scheduling scenarios corresponding to the nth training sample, P (n,1) is the coverage rate of multiple generated candidate warehousing and logistics scheduling scenarios corresponding to the nth training sample for multiple true candidate warehousing and logistics scheduling scenarios, that is, the intersection of multiple generated candidate warehousing and logistics scheduling scenarios corresponding to the nth training sample and multiple true candidate warehousing and logistics scheduling scenarios, P0 is the preset coverage rate, β 31 and β 32 are both preset weights, and β 31 and β 32 are both greater than 0, β 31 +β 32 = 1, w (n,1) is the weight matrix of multiple generated target warehousing and logistics scheduling scenarios corresponding to the nth training sample, w (n,2) is the weight matrix of multiple true target warehousing and logistics scheduling scenarios corresponding to the nth training sample, R (n,1) is the optimal warehousing and logistics scheduling scenario generated corresponding to the nth training sample, R (n,2) is the true optimal warehousing and logistics scheduling scenario corresponding to the nth training sample.

[0046] For each edge cloud computing node, based on the model performance test data and the model performance mean of the trained intermediate logistics scheduling model during each training of this edge cloud computing node, the parameter adjustment weight corresponding to this edge cloud computing node can be calculated, and based on the parameter adjustment weight corresponding to each edge cloud computing node, the model parameters of the intermediate logistics scheduling model are updated.

[0047] Preferably, the parameter adjustment weight corresponding to the edge cloud computing node can be calculated based on the following formula:

[0048]

[0049] where w i is the parameter adjustment weight corresponding to the i-th edge cloud computing node, p (i,t) is the model performance test data of the trained logistics scheduling model of the i-th edge cloud computing node in the t-th training, T is the total number of completed trainings, J is the total number of edge cloud computing nodes, and p (j,t) is the model performance test data of the logistics scheduling model of the j-th edge cloud computing node after the t-th training.

[0050] Preferably, the model parameters can be updated based on the following formula:

[0051]

[0052] where η t+1 is the parameter of the intermediate logistics scheduling model for the (t + 1)-th training after update, and η (i,t) is the parameter of the intermediate logistics scheduling model of the i-th edge cloud computing node for the t-th training.

[0053] Step 112: Deploy the logistics scheduling model on each edge cloud computing node.

[0054] Step 113: Obtain the warehousing logistics scheduling task.

[0055] The warehousing logistics scheduling task may include the starting point, destination, transportation time requirement, transportation volume, type, size, weight, packaging method of the goods, and whether special treatment (such as refrigeration, shockproof, etc.) is required, etc.

[0056] Step 114: Calculate the computing power resource requirements of the warehousing logistics scheduling task.

[0057] Specifically, it includes:

[0058] Establish a sample demand database, where the sample demand database is used to store the computing power resource requirements corresponding to multiple sample warehousing logistics scheduling tasks;

[0059] Calculate the similarity between the warehousing logistics scheduling task and the sample warehousing logistics scheduling task, and determine the similar sample warehousing logistics scheduling task;

[0060] Based on the computing power resource requirements of the similar sample warehousing logistics scheduling task, calculate the computing power resource requirements of the warehousing logistics scheduling task.

[0061] Specifically, the similarity between the warehousing logistics scheduling task and the sample warehousing logistics scheduling task can be calculated by a similarity determination model, where the similarity determination model can be a Convolutional Neural Networks (CNN) model. The sample warehousing logistics scheduling tasks with a similarity greater than the similarity threshold are used as similar sample warehousing logistics scheduling tasks.

[0062] The computing power resource requirements of the similar sample warehousing logistics scheduling tasks are weighted and summed to calculate the computing power resource requirements of the warehousing logistics scheduling task, where the weight corresponding to the computing power resources of the similar sample warehousing logistics scheduling task is the similarity between the similar sample warehousing logistics scheduling task and the warehousing logistics scheduling task.

[0063] Step 115: Based on the computing power resource requirements of the warehousing logistics scheduling task, at least one first optimal edge cloud computing node is determined from multiple edge cloud computing nodes.

[0064] Specifically, an edge cloud computing node that meets the computing power resource requirements can be selected as the first optimal edge cloud computing node.

[0065] Step 116: Obtain the warehousing node status information and the logistics vehicle status information.

[0066] Specifically, the warehousing node status information may include the goods storage situation in the warehouse, including the types, quantities, storage locations of the goods in the node, and the status of the goods (such as to be out of the warehouse, already out of the warehouse, to be in the warehouse, etc.), and may also include the status information of the warehouse equipment (such as shelves, forklifts, conveyor belts, etc.) (such as whether it is idle, whether it is faulty, whether it needs maintenance, etc.), and may also include weather information, the warehousing logistics scheduling tasks being executed, and the warehousing logistics scheduling tasks to be executed.

[0067] The logistics vehicle status information may include information such as the vehicle model, license plate number, load capacity, volume capacity, the warehousing logistics scheduling tasks being executed, and the warehousing logistics scheduling tasks to be executed.

[0068] Step 117: Each first optimal edge cloud computing node uses an edge scheduling scheme generation model to generate multiple candidate warehousing logistics scheduling schemes based on the warehousing logistics scheduling task, the warehousing node status information, and the logistics vehicle status information.

[0069] Step 118: Based on the device status information of each first optimal edge cloud computing node, a second optimal edge cloud computing node is determined from at least one first optimal edge cloud computing node.

[0070] Specifically, based on the device status information of each first optimal edge cloud computing node, the first optimal edge cloud computing node with the best device performance can be used as the second optimal edge cloud computing node.

[0071] Step 119: The second optimal edge cloud computing node screens multiple candidate warehousing and logistics scheduling plans to determine multiple target warehousing and logistics scheduling plans.

[0072] Specifically, it includes:

[0073] Calculate the plan similarity between any two candidate warehousing and logistics scheduling plans;

[0074] Based on the plan similarity between any two candidate warehousing and logistics scheduling plans, cluster multiple target warehousing and logistics scheduling plans to obtain multiple plan clustering clusters. For example, use the K-means clustering algorithm to cluster multiple target warehousing and logistics scheduling plans based on the plan similarity between any two candidate warehousing and logistics scheduling plans to obtain multiple plan clustering clusters;

[0075] The second optimal edge cloud computing node screens multiple plan clustering clusters through multiple plan screening indicators to determine the optimal plan clustering cluster. For example, for each plan clustering cluster, calculate the scores of the candidate warehousing and logistics scheduling plan corresponding to the cluster center of the plan clustering cluster in multiple plan screening indicators, and use the plan clustering cluster with the highest scores in multiple plan screening indicators as the optimal plan clustering cluster;

[0076] The second optimal edge cloud computing node screens multiple candidate warehousing and logistics scheduling plans included in the optimal plan clustering cluster through multiple plan screening indicators to determine multiple target warehousing and logistics scheduling plans.

[0077] In some embodiments, the second optimal edge cloud computing node screens multiple candidate warehousing and logistics scheduling plans included in the optimal plan clustering cluster through multiple plan screening indicators to determine multiple target warehousing and logistics scheduling plans, including:

[0078] Based on the warehousing node status information and logistics vehicle status information at multiple historical time points, use the state prediction model to predict the warehousing node status information and logistics vehicle status information at multiple future time points. Among them, the state prediction model can be a long short-term memory (LSTM) model;

[0079] Obtain weather forecast information and traffic flow information of the target area at multiple historical points;

[0080] Based on weather forecast information and traffic flow information of the target area at multiple historical points, a traffic condition prediction model predicts the traffic flow information of the target area at multiple future time points. Among them, the traffic condition prediction model can be a Long Short-Term Memory (LSTM) model;

[0081] For each candidate warehousing and logistics scheduling plan included in the optimal plan clustering cluster, based on the predicted warehousing node status information, logistics vehicle status information, and traffic flow information of the target area at multiple future time points, determine the scores of the candidate warehousing and logistics scheduling plan in multiple plan screening metrics. Among them, the multiple plan screening metrics can at least include transportation time-consuming metrics, transportation route length metrics, transportation safety metrics, average load rate metrics of vehicles, and loading and unloading pressure metrics of warehouses, etc.;

[0082] Based on the scores of each candidate warehousing and logistics scheduling plan included in the optimal plan clustering cluster in multiple plan screening metrics, screen the multiple candidate warehousing and logistics scheduling plans included in the optimal plan clustering cluster to determine multiple target warehousing and logistics scheduling plans.

[0083] For example, perform a weighted sum of the scores of each candidate warehousing and logistics scheduling plan included in the optimal plan clustering cluster in multiple plan screening metrics, calculate the comprehensive score of the candidate warehousing and logistics scheduling plan, and use the candidate warehousing and logistics scheduling plan with a comprehensive score greater than the comprehensive score threshold as the target warehousing and logistics scheduling plan.

[0084] Step 120, the second optimal edge cloud computing node uses the comprehensive scheduling plan generation model to generate an optimal warehousing and logistics scheduling plan based on multiple target warehousing and logistics scheduling plans.

[0085] Specifically include:

[0086] Calculate the fusion weight of each target warehousing and logistics scheduling plan through the comprehensive scheduling plan generation model;

[0087] Based on multiple target warehousing and logistics scheduling plans and the fusion weight of each target warehousing and logistics scheduling plan, the comprehensive scheduling plan generation model generates an optimal warehousing and logistics scheduling plan.

[0088] Step 121, based on the optimal warehousing and logistics scheduling plan, perform warehousing and logistics scheduling to complete the warehousing and logistics scheduling task.

[0089] Figure 3 It is a module diagram of a warehousing and logistics scheduling system based on cloud computing shown in an embodiment of the present application, as Figure 3As shown in the figure, a cloud computing-based warehousing and logistics scheduling system may include a model establishment module, a model deployment module, a task acquisition module, a requirement determination module, a primary screening module, an information acquisition module, a solution generation module, a secondary screening module, a solution screening module, a solution optimization module, and a logistics scheduling module.

[0090] The model establishment module can be used to jointly establish a logistics scheduling model through multiple edge cloud computing nodes. Among them, the logistics scheduling model includes an edge scheduling solution generation model and a comprehensive scheduling solution generation model;

[0091] The model deployment module can be used to deploy the logistics scheduling model on each edge cloud computing node;

[0092] The task acquisition module can be used to acquire warehousing and logistics scheduling tasks;

[0093] The requirement determination module can be used to calculate the computing power resource requirements based on the warehousing and logistics scheduling tasks;

[0094] The primary screening module can be used to determine at least one first optimal edge cloud computing node from multiple edge cloud computing nodes based on the computing power resource requirements;

[0095] The information acquisition module can be used to acquire warehousing node status information and logistics vehicle status information;

[0096] The solution generation module can be used to generate multiple candidate warehousing and logistics scheduling solutions based on each first optimal edge cloud computing node through the edge scheduling solution generation model, based on the warehousing and logistics scheduling tasks, warehousing node status information, and logistics vehicle status information;

[0097] The secondary screening module can be used to determine a second optimal edge cloud computing node from at least one first optimal edge cloud computing node based on the device status information of each first optimal edge cloud computing node;

[0098] The solution screening module can be used to screen multiple candidate warehousing and logistics scheduling solutions based on the second optimal edge cloud computing node to determine multiple target warehousing and logistics scheduling solutions;

[0099] The solution optimization module can be used to generate an optimal warehousing and logistics scheduling solution based on the second optimal edge cloud computing node through the comprehensive scheduling solution generation model, based on multiple target warehousing and logistics scheduling solutions;

[0100] The logistics scheduling module can be used to perform warehousing and logistics scheduling based on the optimal warehousing and logistics scheduling solution to complete the warehousing and logistics scheduling task.

[0101] A cloud computing-based warehousing and logistics scheduling system can be used to execute a cloud computing-based warehousing and logistics scheduling method. For more descriptions of a cloud computing-based warehousing and logistics scheduling system, reference can be made to the relevant descriptions of a cloud computing-based warehousing and logistics scheduling method, which will not be elaborated here.

[0102] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other deformations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification can be regarded as consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A warehouse logistics scheduling method and system based on cloud computing, characterized in that: include: A logistics scheduling model is jointly established through multiple edge cloud computing nodes, wherein the logistics scheduling model includes an edge scheduling solution generation model and a comprehensive scheduling solution generation model; The logistics scheduling model is deployed on each edge cloud computing node; Obtain warehousing logistics scheduling tasks; Calculate the computing resource requirements for the warehousing logistics scheduling task; Based on the computing resource requirements of the warehousing logistics scheduling task, determining at least one first optimal edge cloud computing node from the multiple edge cloud computing nodes; Obtain storage node status information and logistics vehicle status information; Each of the first optimal edge cloud computing nodes generates a plurality of candidate warehousing and logistics scheduling schemes based on the warehousing and logistics scheduling tasks, warehousing node status information, and logistics vehicle status information through the edge scheduling scheme generation model; Based on the device status information of each of the first optimal edge cloud computing nodes, determining a second optimal edge cloud computing node from the at least one first optimal edge cloud computing node; The second optimal edge cloud computing node screens the multiple candidate warehousing logistics scheduling solutions to determine multiple target warehousing logistics scheduling solutions; The second optimal edge cloud computing node generates an optimal warehousing and logistics scheduling plan based on the multiple target warehousing and logistics scheduling plans through the comprehensive scheduling plan generation model; Based on the optimal warehousing logistics scheduling plan, warehousing logistics scheduling is carried out to complete the warehousing logistics scheduling task.

2. A cloud computing-based warehousing logistics scheduling method and system according to claim 1, characterized in that: Through multiple edge cloud computing nodes, a logistics scheduling model is jointly established, including: Establish an initial logistics scheduling model; The initial logistics scheduling model is deployed on each edge cloud computing node; The initial logistics scheduling model is jointly trained by the multiple edge cloud computing nodes to obtain the logistics scheduling model.

3. A cloud computing-based warehousing logistics scheduling method and system according to claim 2, characterized in that: The initial logistics scheduling model is jointly trained by the multiple edge cloud computing nodes to obtain the logistics scheduling model, including: S11. Each of the edge cloud computing nodes trains the initial logistics scheduling model to obtain the trained initial logistics scheduling model, tests the trained initial logistics scheduling model, and obtains model performance test data of the trained initial logistics scheduling model; S12. Based on the model performance test data of the trained initial logistics scheduling model, determine whether the training is completed. If so, generate the logistics scheduling model based on the model parameters of the trained initial logistics scheduling model on each edge cloud computing node. If not, execute S13.

4. A cloud computing-based warehousing logistics scheduling method and system according to claim 3, characterized in that: The logistics scheduling model is obtained by jointly training the initial logistics scheduling model through the multiple edge cloud computing nodes, and further includes: S13, generating an intermediate logistics scheduling model based on the model parameters of the trained initial logistics scheduling model on each edge cloud computing node; S14. Each of the edge cloud computing nodes trains the intermediate logistics scheduling model to obtain the trained intermediate logistics scheduling model, tests the trained intermediate logistics scheduling model, and obtains model performance test data of the trained intermediate logistics scheduling model.

5. A cloud computing-based warehousing logistics scheduling method and system according to claim 4, characterized in that: The initial logistics scheduling model is jointly trained by the multiple edge cloud computing nodes to obtain the logistics scheduling model, further comprising: S15, judging whether the training is completed based on the model performance test data of the trained intermediate logistics scheduling model, if so, generating the logistics scheduling model based on the model parameters of the trained intermediate logistics scheduling model on each edge cloud computing node, if not, executing S16; S16. Based on the model parameters of the trained intermediate logistics scheduling model on each of the edge cloud computing nodes, update the parameters of the intermediate logistics scheduling model, deploy the updated parameters of the intermediate logistics scheduling model on each of the edge cloud computing nodes, and execute S14.

6. A cloud computing-based warehousing logistics scheduling method and system according to claim 5, characterized in that: Calculate the computing resource requirements for the warehousing logistics scheduling task, including: Establishing a sample demand database, wherein the sample demand database is used to store computing resource requirements corresponding to multiple sample warehousing logistics scheduling tasks; Calculating the similarity between the warehousing logistics scheduling task and the sample warehousing logistics scheduling task, and determining a similar sample warehousing logistics scheduling task; Based on the computing power resource requirements of the similar sample warehousing and logistics scheduling tasks, the computing power resource requirements of the warehousing and logistics scheduling tasks are calculated.

7. According to the cloud computing-based warehousing logistics scheduling method and system described in claim 5, it is characterized in that: The second optimal edge cloud computing node screens the multiple candidate warehousing logistics scheduling solutions to determine multiple target warehousing logistics scheduling solutions, including: Calculate the similarity between any two candidate warehousing logistics scheduling solutions; Based on the similarity between any two candidate warehousing logistics scheduling solutions, clustering the multiple target warehousing logistics scheduling solutions to obtain multiple solution clusters; The second optimal edge cloud computing node screens the multiple solution clusters through multiple solution screening indicators to determine the optimal solution cluster; The second optimal edge cloud computing node screens multiple candidate warehousing and logistics scheduling solutions included in the optimal solution clustering cluster through multiple solution screening indicators, and determines multiple target warehousing and logistics scheduling solutions.

8. A cloud computing-based warehousing logistics scheduling method and system according to claim 7, characterized in that: The second optimal edge cloud computing node screens multiple candidate warehousing logistics scheduling solutions included in the optimal solution clustering cluster through multiple solution screening indicators, and determines multiple target warehousing logistics scheduling solutions, including: The state prediction model is used to predict the state information of storage nodes and logistics vehicles at multiple future time points based on the state information of storage nodes and logistics vehicles at multiple historical time points. Obtain weather forecast information and traffic flow information for target areas at multiple historical points; Predicting the traffic flow information of the target area at multiple future time points by using a road condition prediction model based on weather forecast information and traffic flow information of the target area at multiple historical points; For each candidate warehousing logistics scheduling solution included in the optimal solution cluster, based on the predicted storage node status information, logistics vehicle status information and vehicle flow information of the target area at multiple future time points, determine the score of the candidate warehousing logistics scheduling solution in multiple solution screening indicators; Based on the scores of each candidate warehousing logistics scheduling solution included in the optimal solution clustering cluster in multiple solution screening indicators, multiple candidate warehousing logistics scheduling solutions included in the optimal solution clustering cluster are screened to determine multiple target warehousing logistics scheduling solutions.

9. A cloud computing-based warehousing logistics scheduling method and system according to any one of claims 1 to 5, characterized in that: The second optimal edge cloud computing node generates an optimal warehousing and logistics scheduling plan based on the multiple target warehousing and logistics scheduling plans through the comprehensive scheduling plan generation model, including: Calculate the fusion weight of each target warehousing logistics scheduling plan through the comprehensive scheduling plan generation model; The comprehensive scheduling scheme generation model generates an optimal warehousing logistics scheduling scheme based on the multiple target warehousing logistics scheduling schemes and the fusion weight of each target warehousing logistics scheduling scheme.

10. A cloud computing-based warehouse logistics scheduling system, characterized in that: A cloud computing-based warehousing logistics scheduling method for executing any one of claims 1 to 9, comprising: A model building module, used to jointly establish a logistics scheduling model through multiple edge cloud computing nodes, wherein the logistics scheduling model includes an edge scheduling solution generation model and a comprehensive scheduling solution generation model; A model deployment module, used to deploy the logistics scheduling model on each edge cloud computing node; Task acquisition module, used to obtain warehousing logistics scheduling tasks; A demand determination module, used to calculate the computing power resource demand based on the warehousing logistics scheduling task; A primary screening module, configured to determine at least one first optimal edge cloud computing node from the plurality of edge cloud computing nodes based on the computing resource requirement; Information acquisition module, used to obtain storage node status information and logistics vehicle status information; A solution generation module, configured to generate a plurality of candidate warehousing and logistics scheduling solutions based on each of the first optimal edge cloud computing nodes through the edge scheduling solution generation model, based on the warehousing and logistics scheduling tasks, warehousing node status information, and logistics vehicle status information; a secondary screening module, configured to determine a second optimal edge cloud computing node from the at least one first optimal edge cloud computing node based on device status information of each of the first optimal edge cloud computing nodes; A solution screening module, used to screen the multiple candidate warehousing logistics scheduling solutions based on the second optimal edge cloud computing node, and determine multiple target warehousing logistics scheduling solutions; A solution optimization module, configured to generate an optimal warehousing and logistics scheduling solution based on the second optimal edge cloud computing node through the comprehensive scheduling solution generation model and based on the multiple target warehousing and logistics scheduling solutions; The logistics scheduling module is used to perform warehousing logistics scheduling based on the optimal warehousing logistics scheduling plan and complete the warehousing logistics scheduling task.

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