Intelligent scheduling system for cigarette distribution based on adaptive adjustment of loading rate

Abstract

The application belongs to the technical field of cigarette distribution, and discloses a cigarette distribution intelligent scheduling system based on loading rate self-adaptive adjustment. By introducing the intelligent scheduling system, the existing technical problems in the field of cigarette distribution are solved, and significant technical progress is brought. The system not only improves the efficiency and accuracy of distribution, reduces transportation cost, but also improves the management level and customer satisfaction of enterprises. With the continuous development of the logistics industry and the intensification of market competition, the application prospect of the application will be wider, and more development opportunities and competitive advantages will be brought to the cigarette distribution enterprises.

Description

Technical Field

[0001] This invention belongs to the field of cigarette delivery technology, and particularly relates to an intelligent scheduling system for cigarette delivery based on adaptive adjustment of loading rate. Background Technology

[0002] With the development of the social economy and the improvement of people's living standards, the cigarette consumption market shows a promising future. However, the problem of how to quickly formulate delivery scheduling plans remains. That is, how to quickly and accurately arrange specific vehicles, delivery personnel, and routes based on delivery tasks.

[0003] Analysis of the technical problems of existing cigarette delivery scheduling methods: 1. Insufficient data acquisition and integration: Existing cigarette distribution scheduling methods often rely on historical data and experience, lacking the ability to acquire and analyze real-time data. Traditional methods may fail to integrate data from different channels in a timely manner, such as changes in market demand, inventory levels, and transportation conditions, resulting in inflexible and imprecise scheduling plans.

[0004] 2. Limited scheduling optimization capabilities: Existing technologies have limitations in scheduling optimization, typically employing static or semi-static methods that struggle to cope with dynamically changing market environments. For example, when market demand suddenly changes or unexpected situations arise during transportation, traditional scheduling methods cannot quickly adjust and optimize delivery plans, leading to low delivery efficiency and increased costs.

[0005] 3. Lack of intelligent and automated support: Many existing delivery scheduling systems lack intelligent and automated support, relying on manual scheduling decisions and execution. This not only increases labor costs and workload but also makes them prone to human error, affecting the timeliness and accuracy of deliveries. Systems lacking intelligent support struggle to quickly respond to and handle complex scheduling problems.

[0006] 4. Inadequate optimization of transportation routes: Traditional cigarette delivery scheduling methods often rely on experience and simple algorithms for route optimization, making it difficult to find the optimal route in complex traffic environments. This can lead to longer delivery times, increased transportation costs, and negatively impact the freshness and quality of the cigarettes.

[0007] In summary, existing technologies for cigarette delivery scheduling face several technical challenges, including insufficient data acquisition and integration, limited scheduling optimization capabilities, lack of intelligent and automated support, inadequate predictive capabilities, imperfect transportation route optimization, and untimely response to customer needs. These issues limit the improvement of cigarette delivery efficiency and accuracy, impacting companies' market competitiveness and operational cost control. Introducing big data analytics and artificial intelligence technologies can effectively address these problems, leading to more rational and efficient cigarette delivery scheduling solutions. Summary of the Invention

[0008] To address the problems existing in the prior art, this invention provides an intelligent scheduling system for cigarette delivery based on adaptive adjustment of loading rate.

[0009] This invention is implemented as follows: A smart scheduling system for cigarette delivery based on adaptive adjustment of loading rate includes: The algorithm features filtering module, data cleaning and processing module, main control module, feature analysis module, algorithm selection analysis and verification module, and algorithm construction module are all included. The data cleaning and processing module, connected to the main control module, is used to clean and process the raw data. The main control module is connected to the algorithm feature screening module, data cleaning and processing module, scheduling scheme module, feature analysis module, algorithm selection analysis and verification module, and algorithm construction module, and is used to control the normal operation of each module. The feature analysis module, connected to the main control module, is used for in-depth analysis of selected features; The algorithm selection analysis and verification module is connected to the main control module and is used to select suitable algorithms for analysis and modeling. The algorithm construction module, connected to the main control module, is used to construct the algorithm based on the selected algorithm. The scheduling scheme module is connected to the main control module and is used to generate "optimization suggestions" with one click. The default suggestion is the "execution scheme". To deal with temporary emergencies and to consider fault tolerance, a manual modification function is designed for the "execution scheme".

[0010] Furthermore, the data cleaning and processing module method: Before conducting data analysis, the raw data is cleaned and processed, including removing duplicate data, handling missing values, and handling outliers.

[0011] Furthermore, the feature analysis module method: When performing the feature analysis module, statistical analysis and data visualization methods are used to conduct in-depth analysis of the selected features; By exploring the relationship between characteristics and cigarette supply, potential patterns and trends can be discovered.

[0012] Furthermore, the algorithm selection analysis and verification module method: After selecting appropriate features, choose a suitable algorithm for analysis and modeling; The method depends on the specific problem and the characteristics of the data; it includes regression analysis, cluster analysis, and decision trees.

[0013] Furthermore, the algorithm construction module: Based on the selected algorithm, the algorithm is constructed and implemented; through the construction of the algorithm, a model for predicting and optimizing the distribution of cigarette supplies is obtained.

[0014] Furthermore, the construction of the algorithm includes establishing a mathematical model, writing code, and adjusting parameters.

[0015] Another objective of this invention is to provide an intelligent scheduling method for cigarette delivery based on adaptive adjustment of loading rate, comprising: Step 1: Determine the feature variables to be analyzed through the algorithm feature screening module; by understanding the cigarette market and retailer behavior, select appropriate features through screening. Step two: Clean and process the raw data using the data cleaning and processing module; Step 3: The main control module performs in-depth analysis of the selected features through the feature analysis module. Step four: Through the scheduling scheme module, "optimization suggestions" are generated with one click. The default suggestion is the "execution scheme". To cope with temporary emergencies and to consider fault tolerance, a manual modification function is designed for the "execution scheme". The algorithm selection analysis and verification module selects suitable algorithms for analysis and modeling. Step 5: Construct the algorithm based on the selected algorithm using the algorithm construction module.

[0016] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the intelligent scheduling method for cigarette delivery based on adaptive adjustment of loading rate.

[0017] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the intelligent scheduling method for cigarette delivery based on adaptive adjustment of loading rate.

[0018] Another objective of this invention is to provide an information data processing terminal for implementing the intelligent scheduling system for cigarette delivery based on adaptive adjustment of loading rate.

[0019] First, the approach of this invention is to match and recommend algorithm types based on problem characteristics. In terms of algorithm selection, this invention will employ multi-objective dynamic optimization algorithms and random forest algorithms to improve the accuracy and efficiency of the model.

[0020] By leveraging big data analytics, the system calculates and combines vehicle, personnel, and routes according to delivery tasks to arrive at the optimal solution. Combined with artificial intelligence, an intelligent cigarette sourcing and distribution system is established. Machine learning techniques are used to predict and analyze market consumption potential, providing precise distribution recommendations. This effectively improves the efficiency of developing cigarette delivery and scheduling plans.

[0021] Secondly, traditional delivery methods are often based on experience or fixed rules, resulting in low delivery efficiency and high costs. The intelligent scheduling system for cigarette delivery based on adaptive loading rate provided in this invention aims to solve these problems and improve the intelligence level and efficiency of cigarette delivery by introducing multiple modules such as algorithm feature screening, data cleaning and processing, feature analysis, algorithm selection analysis and verification, and algorithm construction.

[0022] The system's technical features are primarily reflected in its modular and intelligent design. The algorithm feature selection module ensures accurate screening of key feature variables, while the data cleaning and processing module guarantees data quality and consistency. The combination of the feature analysis module and the algorithm selection analysis and verification module enables the system to select the most suitable algorithm for modeling based on in-depth feature analysis. The algorithm construction module is responsible for applying the selected algorithm to practical problems, constructing effective intelligent scheduling schemes. Furthermore, the scheduling scheme module's one-click generation and optimization suggestion functions make the system operation more convenient and efficient.

[0023] The intelligent cigarette delivery scheduling system provided in this invention has achieved significant technological advancements in several aspects. First, through algorithm feature selection and analysis, the system can more accurately control fluctuations in total delivery volume, vehicle loading rates, and "abnormal" situations such as vehicle maintenance and staff leave, improving prediction accuracy. Second, through algorithm selection analysis and verification, and algorithm construction, the system can select the most suitable algorithm for modeling, ensuring the effectiveness and feasibility of the scheduling plan. Finally, the one-click generation and optimization suggestion function of the scheduling plan module not only improves work efficiency but also enhances the system's flexibility and adaptability, enabling it to cope with various complex delivery scenarios.

[0024] The overall value of this intelligent cigarette delivery scheduling system lies in its ability to significantly improve the intelligence and efficiency of cigarette delivery, reduce delivery costs, and enhance customer satisfaction. The system can formulate more rational and efficient delivery plans, reducing unnecessary waste and delays. At the same time, its modular and intelligent design makes it easy to maintain and expand, adapting to constantly changing market environments and business needs. Therefore, this system has significant practical importance and broad application prospects for the cigarette delivery industry.

[0025] Third, this invention addresses the existing technical problems in the cigarette distribution field by proposing an intelligent scheduling system for cigarette distribution based on adaptive adjustment of loading rate. This system significantly solves the following technical problems and brings about significant technological progress: In the cigarette delivery industry, traditional delivery methods often rely on manual experience and fixed routes, resulting in low delivery efficiency, high transportation costs, and difficulty in meeting customers' real-time needs. This invention addresses these problems by introducing an intelligent scheduling system, enabling real-time monitoring and dynamic adjustment of the cigarette delivery process. The system accurately identifies key factors affecting delivery through algorithmic feature filtering and data cleaning / processing modules, and efficiently processes raw data, providing reliable data support for subsequent intelligent scheduling.

[0026] The feature analysis module and algorithm selection analysis and verification module in this invention conduct in-depth analysis of selected features and choose the most suitable algorithm for analysis and modeling. This enables the system to accurately predict cigarette transportation demand, thereby optimizing delivery routes and loading rates. The algorithm construction module builds an intelligent scheduling model based on the selected algorithm, ensuring the accuracy and efficiency of the scheduling scheme. The scheduling scheme module provides a one-click function to generate optimization suggestions and supports manual modification to adapt to temporary emergencies and fault tolerance requirements.

[0027] By implementing this invention, cigarette distribution companies can achieve comprehensive perception and full control over the distribution process, greatly improving efficiency and accuracy. The intelligent scheduling system can dynamically adjust delivery routes and loading rates based on actual conditions, reducing transportation and time costs and enhancing the company's competitiveness. Furthermore, the system supports real-time monitoring and analysis of distribution data, helping companies promptly identify and improve problems, further enhancing management and customer satisfaction.

[0028] In summary, this invention solves existing technical problems in the cigarette distribution field by introducing an intelligent scheduling system, bringing about significant technological advancements. This system not only improves distribution efficiency and accuracy and reduces transportation costs, but also enhances enterprise management and customer satisfaction. With the continuous development of the logistics industry and intensifying market competition, the application prospects of this invention will be even broader, bringing more development opportunities and competitive advantages to cigarette distribution companies. Attached Figure Description

[0029] Figure 1 This is a structural block diagram of an intelligent cigarette delivery scheduling system based on adaptive adjustment of loading rate provided in an embodiment of the present invention.

[0030] Figure 2 This is a flowchart of the intelligent scheduling method for cigarette delivery based on adaptive adjustment of loading rate provided in an embodiment of the present invention.

[0031] Figure 3 This is the overall research roadmap provided by the embodiments of the present invention.

[0032] Figure 4 This is a diagram of the multi-objective dynamic optimization algorithm provided in the embodiments of the present invention.

[0033] Figure 5 This is a graph of the random forest algorithm provided in an embodiment of the present invention.

[0034] Figure 6 This is an overall algorithm architecture diagram provided in the embodiments of the present invention.

[0035] Figure 7 This is a flowchart of the intelligent cigarette supply allocation model algorithm provided in an embodiment of the present invention.

[0036] Figure 1 The module consists of: 1. Algorithm feature selection module; 2. Data cleaning and processing module; 3. Main control module; 4. Feature analysis module; 5. Algorithm selection analysis and verification module; 6. Algorithm construction module; and 7. Scheduling scheme module. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0038] like Figure 1 As shown in the figure, an intelligent scheduling system for cigarette delivery based on adaptive adjustment of loading rate provided by an embodiment of the present invention includes: Algorithm Feature Screening Module 1, Data Cleaning and Processing Module 2, Main Control Module 3, Feature Analysis Module 4, Algorithm Selection Analysis and Verification Module 5, Algorithm Construction Module 6.

[0039] Algorithm feature selection module 1, connected to main control module 3, is used to determine the feature variables used for analysis; by understanding the cigarette market and retailer behavior, it selects and filters appropriate features. The data cleaning and processing module 2 is connected to the main control module 3 and is used to clean and process the raw data; The main control module 3 is connected to the algorithm feature screening module 1, data cleaning and processing module 2, feature analysis module 4, algorithm selection analysis and verification module 5, and algorithm construction module 6, and is used to control the normal operation of each module. Feature analysis module 4, connected to main control module 3, is used for in-depth analysis of selected features. Algorithm selection analysis and verification module 5, connected to main control module 3, is used to select suitable algorithms for analysis and modeling; Algorithm construction module 6, connected to main control module 3, is used to construct algorithms based on the selected algorithm; The scheduling scheme module 7 is connected to the main control module and is used to generate "optimization suggestions" with one click. The default suggestion is the "execution scheme". To deal with temporary emergencies and to consider fault tolerance, a manual modification function is designed for the "execution scheme".

[0040] The working principle of the intelligent scheduling system provided in this embodiment of the invention is as follows: 1. Algorithm Feature Filtering Module By connecting to the main control module, the characteristic variables used for analysis are determined.

[0041] Based on an understanding of the number of customers served and internal scheduling behavior, appropriate features are screened and selected.

[0042] 2. Data Cleaning and Processing Module It connects to the main control module and is responsible for cleaning and processing the raw data to ensure the accuracy and integrity of the data.

[0043] 3. Main Control Module It connects with other modules to control the normal operation of each module.

[0044] It schedules the operation of each module and manages data flow and information exchange.

[0045] 4. Feature Analysis Module It connects to the main control module to perform in-depth analysis of the selected features.

[0046] Data mining and analysis techniques are used to discover the correlations and influencing factors between features.

[0047] 5. Algorithm Selection Analysis and Verification Module Connect to the main control module and select a suitable algorithm for analysis and modeling.

[0048] Based on the characteristics and needs of the data, select appropriate machine learning or optimization algorithms for modeling and validation.

[0049] 6. Algorithm Building Module It connects to the main control module and constructs the algorithm according to the selected algorithm.

[0050] Implement and optimize the selected algorithm to improve the performance and efficiency of the scheduling system.

[0051] 7. Scheduling Scheme Module It connects to the main control module to generate "optimization suggestions" with one click.

[0052] The system generates an "execution plan" by default and provides a manual modification function to deal with unexpected situations and fault tolerance requirements.

[0053] Through the collaboration of the above modules, the system can achieve intelligent scheduling of cigarette delivery, including data analysis, algorithm selection, model building, and scheduling scheme generation, thereby improving delivery efficiency and reducing costs.

[0054] The data cleaning and processing module method provided in this embodiment of the invention: Before conducting data analysis, the raw data is cleaned and processed, including removing duplicate data, handling missing values, and handling outliers.

[0055] The feature analysis module method provided in this embodiment of the invention: When performing the feature analysis module, statistical analysis and data visualization methods are used to conduct in-depth analysis of the selected features; By exploring the relationship between characteristics and cigarette supply, potential patterns and trends can be discovered.

[0056] The algorithm selection analysis and verification module method provided in this embodiment of the invention: After selecting appropriate features, choose a suitable algorithm for analysis and modeling; The method depends on the specific problem and the characteristics of the data; it includes regression analysis, cluster analysis, and decision trees.

[0057] The algorithm construction module provided in this embodiment of the invention: Based on the selected algorithm, the algorithm is constructed and implemented; through the construction of the algorithm, a model for predicting and optimizing the distribution of cigarette supplies is obtained.

[0058] The construction of the algorithm provided in this embodiment of the invention includes establishing a mathematical model, writing code, and adjusting parameters.

[0059] like Figure 2 As shown in the figure, an intelligent scheduling method for cigarette delivery based on adaptive adjustment of loading rate provided by an embodiment of the present invention includes: S101, the feature variables used for analysis are determined through the algorithm feature screening module; by understanding the cigarette market and retailer behavior, appropriate features are screened and selected; S102, The raw data is cleaned and processed through the data cleaning and processing module; S103, the main control module performs in-depth analysis of the selected features through the feature analysis module. S104, through the scheduling scheme module, generates "optimization suggestions" with one click. The default suggestion is the "execution scheme". To cope with temporary emergencies and to consider fault tolerance, a manual modification function is designed for the "execution scheme". The algorithm selection analysis and verification module selects suitable algorithms for analysis and modeling. S105, The algorithm is constructed according to the selected algorithm through the algorithm construction module.

[0060] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the intelligent scheduling method for cigarette delivery based on adaptive adjustment of loading rate.

[0061] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the intelligent scheduling method for cigarette delivery based on adaptive adjustment of loading rate.

[0062] Another objective of this invention is to provide an information data processing terminal for implementing the intelligent scheduling system for cigarette delivery based on adaptive adjustment of loading rate.

[0063] Specific implementation of the present invention: I. Research Objectives This invention aims to explore a method for intelligent allocation of cigarette supplies to meet the needs of cigarette distribution. With socio-economic development and the improvement of people's living standards, the cigarette consumption market shows enormous potential. However, how to allocate cigarette supplies rationally and efficiently has become an important issue.

[0064] With the development and increasing maturity of technologies such as big data analytics and artificial intelligence, many industries have ushered in tremendous changes and opportunities. The cigarette consumption market, as a large and complex market, can also benefit from the introduction of these cutting-edge technologies to achieve a more rational and efficient allocation of cigarette supplies.

[0065] By leveraging big data analytics, in-depth data mining and analysis of the cigarette consumption market can be conducted. By collecting and analyzing information such as retailer behavior and geographical location, accurate market demand forecasting and analysis can be performed. Combined with artificial intelligence technology, an intelligent cigarette supply allocation system can be established. Machine learning technology can be used to predict and analyze market consumption potential, thereby providing precise cigarette supply allocation recommendations.

[0066] In conclusion, the introduction of cutting-edge technologies such as big data analytics and artificial intelligence provides a more rational and efficient method for cigarette supply allocation. By mining and constructing intelligent allocation models, we can pursue a supply-demand balance under regulation and promote the healthy development of the industry.

[0067] II. Research Approach The approach of this invention is to match and recommend algorithm types based on problem characteristics. First, a potential mining analysis of the cigarette consumption market is conducted, relevant indicators are selected, and a potential index is calculated to reveal the market's development potential. Based on the market potential index and other relevant factors, an intelligent cigarette supply allocation model is designed to achieve intelligent allocation of cigarette supplies. In terms of algorithm selection, this invention will employ a multi-objective dynamic optimization algorithm and a random forest algorithm to improve the model's accuracy and efficiency.

[0068] A research roadmap refers to a series of steps and methods planned in scientific research to solve a specific problem or achieve a specific goal. Overall research roadmap as follows Figure 3 As shown: First, it is necessary to clarify the research objective, namely, to explore methods for intelligent allocation of cigarette supplies. This objective can be further refined.

[0069] Algorithm Feature Selection: Before conducting data analysis, it is necessary to determine the feature variables to be used for analysis. By understanding the cigarette market and retailer behavior, selecting appropriate features can improve the accuracy and efficiency of subsequent analysis.

[0070] Data cleaning and processing: Before conducting data analysis, the raw data needs to be cleaned and processed. This includes removing duplicate data, handling missing values, and dealing with outliers. Cleaning and processing data can improve its quality and reliability, providing a solid foundation for subsequent feature analysis and algorithm construction.

[0071] Feature Analysis: When conducting feature analysis, methods such as statistical analysis and data visualization can be used to perform in-depth analysis of selected features. By exploring the relationship between features and cigarette supply, potential patterns and trends can be discovered. This helps to understand the demand characteristics of the cigarette market and provides a basis for subsequent algorithm selection and validation.

[0072] Algorithm Selection Analysis and Validation: After selecting suitable features, it is necessary to choose an appropriate algorithm for analysis and modeling. This can be determined based on the specific problem and data characteristics. Common algorithms include regression analysis, cluster analysis, and decision trees. During the selection process, the advantages and disadvantages of different algorithms can be compared. Algorithm Construction: Finally, based on the selected algorithm, it can be constructed and implemented. This includes steps such as establishing a mathematical model, writing code, and adjusting parameters. Through algorithm construction, a model capable of predicting and optimizing cigarette supply can be obtained.

[0073] In summary, following the above process, research on intelligent cigarette supply allocation methods can be conducted step by step. From defining objectives to feature selection, data cleaning and processing, feature analysis, algorithm selection analysis and verification, and finally algorithm construction, this process can help to systematically conduct research and derive reasonable and effective cigarette supply allocation methods.

[0074] The objective of this invention is to provide a more rational and efficient method for cigarette supply allocation by introducing cutting-edge technologies such as big data analytics and artificial intelligence. By tapping into the consumption potential of the cigarette market and constructing an intelligent allocation model, the aim is to achieve a supply-demand balance under regulation, thereby promoting the healthy development of the industry.

[0075] Algorithm technology selection analysis provided in the embodiments of the present invention.

[0076] Multi-objective dynamic optimization algorithms are used to solve multi-objective optimization problems. They can consider multiple objective functions simultaneously and optimize under different time periods or conditions. Based on evolutionary algorithms such as genetic algorithms and particle swarm optimization, they gradually approach the optimal solution by maintaining a population and performing evolutionary operations. These algorithms adapt to the dynamic nature of problems, can handle both discrete and continuous variables, and possess good adaptability and versatility.

[0077] Figure 4 Multi-objective dynamic optimization algorithm.

[0078] Multi-objective dynamic optimization algorithms are algorithms used to solve multi-objective problems. They can quickly find a set of optimal solutions in dynamic environments and can adapt to changes in the environment.

[0079] The principle of multi-objective dynamic optimization algorithms is to find the optimal solution through continuous iteration. These algorithms typically employ heuristic search strategies, such as particle swarm optimization, genetic algorithms, and ant colony optimization, to search for the optimal solution in a multi-dimensional space.

[0080] In multi-objective dynamic optimization problems, there are multiple objective functions to be optimized, and these objective functions may have contradictory and mutually restrictive relationships. Therefore, to solve these problems, multi-objective dynamic optimization algorithms usually adopt the concept of a Pareto optimal solution set, that is, to solve the multi-objective problem by finding a set of non-dominated optimal solutions.

[0081] In dynamic environments, multi-objective dynamic optimization algorithms need to continuously adapt to changes in the environment to ensure robustness and performance. A common approach is to introduce adaptive strategies, which automatically adjust the algorithm's parameters and search strategies based on the current state of the environment and historical information to improve the algorithm's adaptability and efficiency.

[0082] Multi-objective dynamic optimization algorithms have a wide range of applications, including engineering design, financial investment, and transportation. In these fields, multi-objective dynamic optimization algorithms can help decision-makers quickly find the optimal solution and adapt to changes in the environment, improving the accuracy and efficiency of decision-making.

[0083] In summary, multi-objective dynamic optimization algorithms are an effective method for finding optimal solutions to multiple objectives. They can quickly find optimal solutions in dynamic environments and adapt to environmental changes. These algorithms have a wide range of applications, helping decision-makers quickly find optimal solutions to complex decision-making problems, thereby improving the accuracy and efficiency of decision-making.

[0084] Multi-objective dynamic optimization algorithms have different types, principles, and characteristics in different application scenarios. The main algorithm classifications are as follows: (1) Particle Swarm Optimization (PSO) Principle: Particle swarm optimization (PSO) simulates the behavior of biological groups such as flocks of birds or schools of fish, searching for the optimal solution by continuously updating the position and velocity of particles. Each particle has its own position and velocity, and searches and updates based on its own experience and information from its neighborhood.

[0085] Features: The particle swarm optimization algorithm is simple and easy to implement, and can quickly converge to a good solution. It is suitable for solving continuous optimization problems and performs well for objective functions with continuous differentiability.

[0086] Applicable scenarios: Particle swarm optimization is often used to solve continuous optimization problems, such as engineering design and machine learning model parameter optimization.

[0087] (2) Genetic Algorithm (GA) Principle: Genetic algorithms simulate the process of biological evolution, searching for optimal solutions through operations such as gene encoding, selection, crossover, and mutation. Each individual represents a solution, and through continuous iteration, new individuals are generated through selection, crossover, and mutation, gradually approaching the optimal solution.

[0088] Features: Genetic algorithms are characterized by strong global search capabilities and good adaptability. They can handle complex multi-objective problems and can effectively solve discontinuous and non-differentiable objective functions.

[0089] Applicable scenarios: Genetic algorithms are widely used in multi-objective optimization problems, such as engineering design, machine learning model parameter optimization, combinatorial optimization and other scenarios.

[0090] (3) Ant Colony Optimization (ACO) Principle: Ant colony optimization (ACO) simulates the behavior of ants searching for food, using pheromone communication among ants in the colony to find the optimal solution. During the search, ants choose paths based on pheromone concentration; paths with higher pheromone concentrations are more likely to be selected.

[0091] Features: Ant colony optimization (ACO) is characterized by its distributed and adaptive nature, enabling it to adapt to changes in dynamic environments. It is effective in solving combinatorial optimization problems and path planning problems.

[0092] Applicable scenarios: Ant colony optimization is often used in scenarios such as combinatorial optimization, path planning, and resource scheduling.

[0093] (4) Differential Evolution (DE) algorithm Principle: The Differential Evolutionary Algorithm searches for the optimal solution through continuous iteration, utilizing differential mutation and crossover operations. Each individual has its own position vector. New individuals are generated through differential mutation operations, and updates are performed through crossover operations.

[0094] Features: The differential evolution algorithm has strong global search capabilities and fast convergence speed. It is effective for solving continuous optimization problems and problems with constraints.

[0095] Applicable scenarios: Differential evolution algorithm is often used in continuous optimization problems, parameter optimization and other scenarios.

[0096] In summary, multi-objective dynamic optimization algorithms encompass various types, each with its unique principles, characteristics, and applicable scenarios. Choosing the appropriate algorithm type depends on the specific problem requirements and environmental characteristics. In practical applications, the appropriate algorithm type can be selected based on the nature of the problem and constraints, and parameters can be tuned and the algorithm improved according to the actual situation to achieve better optimization results.

[0097] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0098] The embodiments of the present invention have achieved some positive results during the research and development or use process, and have indeed great advantages compared with the prior art. The following content describes them in conjunction with the data, charts and other information of the experimental process.

[0099] 1. Propose a solution and determine the best solution. (I) Multi-objective dynamic optimization algorithm Multi-objective dynamic optimization algorithms are used to solve multi-objective optimization problems. They can consider multiple objective functions simultaneously and optimize under different time periods or conditions. Based on evolutionary algorithms such as genetic algorithms and particle swarm optimization, they gradually approach the optimal solution by maintaining a population and performing evolutionary operations. These algorithms adapt to the dynamic nature of problems, can handle both discrete and continuous variables, and possess good adaptability and versatility.

[0100] (II) Classification of Multi-Objective Dynamic Optimization Algorithms Table 5-1 Classification of Multi-Objective Dynamic Optimization Algorithms

[0101] (III) Random Forest Algorithm Random forest is an ensemble learning algorithm that consists of multiple decision trees, each trained independently, and whose results are ensembled through voting or averaging. Random forest exhibits good generalization ability, robustness, and resistance to overfitting, making it suitable for handling large-scale datasets, high-dimensional features, and noisy environments.

[0102] (iv) Principles of Random Forest Algorithm It mainly includes two aspects: the construction of decision trees and the introduction of randomness.

[0103] Table 5-2 Principle of Random Forest Algorithm

[0104] (V) Scheme Selection 1. Analysis of Multi-Objective Dynamic Optimization Algorithm Selection Considering the characteristics and common adaptability of different types of multi-objective dynamic optimization algorithms, the ant colony algorithm is selected as the algorithm for the intelligent cigarette supply allocation model.

[0105] Table 5-3 Advantages of Ant Colony Algorithm

[0106] 2. Random Forest Algorithm Analysis The random forest algorithm has the following advantages in the potential mining and analysis of the cigarette consumption market: Table 5-4 Advantages of Random Forest Algorithm

[0107] Based on the described intelligent cigarette delivery scheduling system, the following are two specific implementation examples: Example 1: Intelligent scheduling of cigarette delivery based on historical sales data 1. Algorithm Feature Filtering Module: Analyze historical sales data to identify key factors affecting cigarette distribution, such as sales season, holidays, geographical location, and retailers' historical sales volume.

[0108] 2. Data Cleaning and Processing Module: Clean outliers and missing values ​​from the original sales data and standardize the data for subsequent analysis.

[0109] 3. Main control module: Coordinate the work of each module to ensure smooth data transmission between modules.

[0110] 4. Feature Analysis Module: Conduct in-depth analysis of the selected features, such as analyzing the impact of holidays on sales or the impact of geographical location on delivery costs.

[0111] 5. Algorithm selection analysis and verification module: Choose a suitable machine learning algorithm for predicting sales, such as random forest or gradient booster, and train and validate it using historical data.

[0112] 6. Algorithm building module: Based on the selected algorithm, a predictive model is constructed to predict cigarette sales over a future period.

[0113] 7. Scheduling Scheme Module: Based on predicted sales and cost analysis, an optimized delivery plan is generated. Dispatchers can then carry out deliveries according to the plan, or manually adjust it based on actual conditions.

[0114] Example 2: Intelligent scheduling of cigarette delivery considering weather factors 1. Algorithm Feature Filtering Module: 2. Data Cleaning and Processing Module: Clean and integrate sales and weather data to ensure data quality and consistency.

[0115] 3. Main control module: Coordinate data integration and workflows between modules.

[0116] 4. Feature Analysis Module: Analyze the impact of weather factors on sales, such as the impact of high temperatures on the sales of cold beverage cigarettes and the impact of rainy days on the sales of outdoor cigarettes.

[0117] 5. Algorithm selection analysis and verification module: Choose a machine learning algorithm that can handle multivariate inputs, such as a neural network or deep learning model, and train and validate it using the integrated data.

[0118] 6. Algorithm building module: Based on the selected algorithm, a model is constructed that can predict cigarette sales under different weather conditions.

[0119] 7. Scheduling Scheme Module: These two embodiments demonstrate how to adjust the functions and strategies of the intelligent cigarette delivery scheduling system according to specific application scenarios and needs. The above descriptions are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in this invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of this invention.

Claims

1. A cigarette delivery intelligent scheduling system based on adaptive adjustment of loading rate, characterized in that, include: The system includes: algorithm feature selection module, data cleaning and processing module, main control module, feature analysis module, algorithm selection analysis and verification module, algorithm construction module, and scheduling scheme module. The algorithm feature selection module, connected to the main control module, is used to determine the feature variables for analysis; by understanding the cigarette market and retailer behavior, it selects and filters appropriate features. The data cleaning and processing module, connected to the main control module, is used to clean and process the raw data. The main control module is connected to the algorithm feature screening module, data cleaning and processing module, scheduling scheme module, feature analysis module, algorithm selection analysis and verification module, and algorithm construction module, and is used to control the normal operation of each module. The feature analysis module, connected to the main control module, is used for in-depth analysis of selected features; The algorithm selection analysis and verification module is connected to the main control module and is used to select suitable algorithms for analysis and modeling. The algorithm construction module, connected to the main control module, is used to construct the algorithm based on the selected algorithm. The scheduling scheme module is connected to the main control module and is used to generate "optimization suggestions" with one click. The default suggestion is the "execution scheme". To deal with temporary emergencies and to consider fault tolerance, a manual modification function is designed for the "execution scheme".

2. The intelligent scheduling system for cigarette delivery based on adaptive adjustment of loading rate as described in claim 1, characterized in that, The data cleaning and processing module method: Before conducting data analysis, the raw data is cleaned and processed, including removing duplicate data, handling missing values, and handling outliers.

3. The intelligent scheduling system for cigarette delivery based on adaptive adjustment of loading rate as described in claim 1, characterized in that, The feature analysis module method: When performing the feature analysis module, statistical analysis and data visualization methods are used to conduct in-depth analysis of the selected features; By exploring the relationship between characteristics and cigarette supply, potential patterns and trends can be discovered.

4. The intelligent scheduling system for cigarette delivery based on adaptive adjustment of loading rate as described in claim 1, characterized in that, The algorithm selection analysis and verification module method: After selecting appropriate features, choose a suitable algorithm for analysis and modeling; The method depends on the specific problem and the characteristics of the data; it includes regression analysis, cluster analysis, and decision trees.

5. The intelligent scheduling system for cigarette delivery based on adaptive adjustment of loading rate as described in claim 1, characterized in that, The algorithm construction module: Based on the selected algorithm, the algorithm is constructed and implemented; through the construction of the algorithm, a model for predicting and optimizing the distribution of cigarette supplies is obtained.

6. The intelligent scheduling system for cigarette delivery based on adaptive adjustment of loading rate as described in claim 5, characterized in that, The construction of the algorithm includes establishing a mathematical model, writing code, and adjusting parameters.

7. A method for intelligent scheduling of cigarette delivery based on adaptive loading rate adjustment, implementing the intelligent scheduling system for cigarette delivery based on adaptive loading rate adjustment as described in any one of claims 1-6, characterized in that, The intelligent scheduling method for cigarette delivery based on adaptive adjustment of loading rate includes: Step 1: Determine the feature variables to be analyzed through the algorithm feature screening module; by understanding the cigarette market and retailer behavior, select appropriate features through screening. Step two: Clean and process the raw data using the data cleaning and processing module; Step 3: The main control module performs in-depth analysis of the selected features through the feature analysis module; Step four: Through the scheduling scheme module, "optimization suggestions" are generated with one click. The default suggestion is the "execution scheme". To cope with temporary emergencies and to consider fault tolerance, a manual modification function is designed for the "execution scheme". The algorithm selection analysis and verification module selects suitable algorithms for analysis and modeling. Step 5: Construct the algorithm based on the selected algorithm using the algorithm construction module.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the intelligent scheduling method for cigarette delivery based on adaptive adjustment of loading rate as described in claim 7.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the intelligent scheduling method for cigarette delivery based on adaptive adjustment of loading rate as described in claim 7.

10. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the intelligent scheduling system for cigarette delivery based on adaptive adjustment of loading rate as described in any one of claims 1-6.

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