Application method and system of large model in intelligent logistics based on multi-modal adaptive learning

Abstract

The invention relates to the technical field of artificial intelligence, in particular to an application method and system of a large model in intelligent logistics based on multi-modal adaptive learning, and the method comprises the following steps: multi-modal data collection, data preprocessing, data conversion and data fusion. The method has the beneficial effects that the decision accuracy is improved: the multi-modal data fusion module can process and integrate various types of data, more comprehensive information support is provided, the model can comprehensively consider more factors during decision making, and the decision accuracy and reliability are improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and specifically to a method and system for applying a large model based on multimodal adaptive learning in smart logistics. Background Art

[0002] With the rapid development of e-commerce, smart logistics, as an important component supporting the e-commerce ecosystem, faces a series of challenges. Traditional logistics systems have many shortcomings in cargo tracking, warehouse management, and delivery route optimization, mainly manifested in the following aspects:

[0003] Data diversity: The logistics industry generates a rich variety of data types, including text (order details, customer feedback), images (product photos, warehouse monitoring), videos (transportation monitoring), and geolocation information (GPS data). Effectively integrating and utilizing this multimodal data is a key issue in improving logistics efficiency.

[0004] Limitations of Fixed Algorithm Models: Existing logistics management systems mostly rely on a single data source and fixed algorithm models, making them difficult to adapt to rapidly changing market demands and technological advances. This limitation results in poor performance when responding to emergencies and complex scenarios.

[0005] Inefficient resource utilization: Resource allocation in the logistics industry is often uneven, especially during peak periods, which can lead to insufficient or idle capacity. Optimizing resource allocation and improving overall operational efficiency through intelligent means is a pressing issue.

[0006] Security and privacy protection: The logistics process involves a large amount of sensitive information, such as customer addresses and cargo details. Efficiently processing multimodal data while ensuring user information security and complying with relevant laws and regulations is also a key consideration.

[0007] In order to solve the above problems, the present invention proposes a large model based on multimodal adaptive learning, which aims to improve the intelligence level and operation efficiency of the smart logistics system through advanced data processing and learning technologies. Summary of the Invention

[0008] The purpose of the present invention is to provide a method and system for applying a large model based on multimodal adaptive learning in smart logistics to solve the problems raised in the above background technology.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for applying a large model based on multimodal adaptive learning in smart logistics, comprising the following steps:

[0010] Multimodal data collection: Through a variety of sensors, cameras, and GPS devices, multimodal data such as text, images, videos, and geographic location information in smart logistics scenarios are collected;

[0011] Data preprocessing: Clean, normalize, and standardize the collected multimodal data to remove noise, unify the data range and format, and ensure data quality and consistency;

[0012] Data conversion: Convert data of different modalities into a unified feature representation, specifically converting image data into feature vectors and performing word embedding on text data;

[0013] Data fusion: Multimodal fusion technologies, such as attention mechanism and multi-layer perceptron, are used to fuse the converted data of different modalities together to form a comprehensive feature representation for subsequent smart logistics-related analysis and processing.

[0014] Preferably, the method further comprises the following steps:

[0015] Environmental modeling: Build a simulation model of the logistics environment, covering key elements such as warehouses, transportation routes, and distribution points, to simulate the actual operation scenarios of smart logistics;

[0016] Reward function design: Define an appropriate reward function for smart logistics scenarios. This function is used to evaluate the model's performance under different decision-making conditions. Evaluation indicators include but are not limited to delivery time, cost, and customer satisfaction.

[0017] Application of Deep Reinforcement Learning Algorithms: Using Deep Q-Network (DQN) and Policy Gradient Method deep reinforcement learning algorithms, based on the constructed environment model and designed reward function, the model is trained to make optimal decisions in a dynamic smart logistics environment;

[0018] Online learning: enables the model to continuously learn new data during the actual operation of smart logistics, and gradually optimize its decision-making strategy based on the new data to adapt to changes in actual application scenarios.

[0019] Preferably, the method further comprises the following steps:

[0020] Task prioritization: Prioritize tasks based on their urgency and importance in smart logistics;

[0021] Resource allocation algorithm application: Genetic algorithm and simulated annealing algorithm optimization algorithm are used to dynamically allocate logistics resources based on task priority and the availability of logistics resources to ensure that key tasks can be completed first;

[0022] Real-time scheduling: By real-time monitoring of the status of the smart logistics system, including task execution progress and resource usage, the task scheduling plan can be dynamically adjusted to respond to emergencies and changes in the logistics environment, maximize resource utilization, and improve overall operational efficiency.

[0023] Preferably, the method further comprises the following steps:

[0024] Data encryption: Encrypt sensitive data collected and processed in smart logistics scenarios, using appropriate encryption algorithms to ensure data security during transmission and storage;

[0025] Application of differential privacy technology: Introducing noise during the release and analysis of smart logistics data, using differential privacy technology to protect individual privacy while maintaining the overall statistical characteristics of the data for data analysis;

[0026] Access control: Establish a strict access control mechanism to ensure that only authorized personnel can access and process sensitive data in smart logistics through identity authentication and permission management.

[0027] A system for applying a large model based on multimodal adaptive learning in smart logistics, comprising:

[0028] Data collection subsystem: equipped with a variety of sensors, cameras, and GPS devices to collect multimodal data such as text, images, videos, and geographic location information in smart logistics scenarios;

[0029] Data preprocessing module: Cleans the multimodal data collected by the data acquisition subsystem to remove noise and duplicate data; performs normalization to adjust the data range to a unified interval; and standardizes the data to have a unified format and distribution to ensure data quality and consistency;

[0030] Data conversion unit: It has the function of converting different modal data into a unified feature representation. Specifically, it converts image data into feature vectors through a convolutional neural network model and uses a word embedding algorithm to perform word embedding processing on text data.

[0031] Data fusion component: Using multimodal fusion technology, such as attention mechanism or multi-layer perceptron, the different modal feature representations output by the data conversion unit are fused together to form a comprehensive feature representation, providing comprehensive data input for subsequent smart logistics-related modules.

[0032] Preferably, it includes: an environmental modeling module: building a simulation model of the logistics environment, which covers key elements such as warehouse layout, transportation route planning, and distribution point location, and can simulate the actual operation scenarios of smart logistics, providing an environmental foundation for deep reinforcement learning;

[0033] Reward Function Design Unit: Defines an appropriate reward function for smart logistics scenarios. This function comprehensively considers delivery time, cost, and customer satisfaction indicators to evaluate the model's performance under different decision-making conditions and provide feedback for model training.

[0034] Deep reinforcement learning algorithm engine: Using deep Q-network and policy gradient method deep reinforcement learning algorithm, based on the simulation model built by the environment modeling module and the reward function defined by the reward function design unit, the training model makes optimal decisions in a dynamic smart logistics environment;

[0035] Online learning module: enables the system to continuously receive new data during the actual operation of smart logistics, use the new data to update and optimize the model, and gradually improve the model's decision-making strategy to adapt to changes in actual application scenarios.

[0036] Preferably, it includes: a task priority division module: based on the urgency, importance, and delivery time requirements of tasks in smart logistics, it uses preset rules or algorithms to prioritize various tasks and provide a basis for resource allocation;

[0037] Resource allocation algorithm unit: uses genetic algorithm and simulated annealing algorithm optimization algorithm, combined with the results of the task priority division module and the availability of logistics resources, to dynamically allocate logistics resources to ensure that key tasks can obtain the required resources and be completed first;

[0038] Real-time scheduling system: By monitoring the status of the smart logistics system in real time, including task execution progress, resource usage, and traffic conditions, it uses intelligent algorithms to dynamically adjust task scheduling plans, respond promptly to emergencies and changes in the logistics environment, maximize resource utilization, and improve overall operational efficiency;

[0039] Resource status database: stores detailed information about logistics resources, including resource type, quantity, location, and availability, and provides data support for the resource allocation algorithm unit and real-time scheduling system.

[0040] Preferably, it includes: a data encryption module: encrypting sensitive data collected and processed in smart logistics scenarios, such as customer information and cargo information, using a symmetric encryption algorithm or an asymmetric encryption algorithm to ensure the security of the data during transmission and storage;

[0041] Differential Privacy Technology Component: During data publishing and analysis, differential privacy technology is applied by introducing noise to protect individual privacy while maintaining the overall statistical characteristics of the data for data analysis.

[0042] Access control subsystem: Establish a strict access control mechanism, including user identity authentication, permission management, and audit tracking functions, to ensure that only authorized personnel can access and process sensitive data in smart logistics, preventing data leakage and abuse;

[0043] Security Audit Module: Regularly audit the security status of the system, check the effectiveness of data encryption, differential privacy technology application and access control mechanism, and promptly discover and repair security vulnerabilities.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The application method and system of the large model based on multimodal adaptive learning in smart logistics proposed in this invention improve the accuracy of decision-making: the multimodal data fusion module can process and integrate various types of data, provide more comprehensive information support, and enable the model to comprehensively consider more factors when making decisions, thereby improving the accuracy and reliability of decisions.

[0046] Enhanced system flexibility: The adaptive learning framework uses deep reinforcement learning technology to enable the model to automatically adjust its learning strategy based on changes in actual application scenarios, quickly adapt to new market demands and technological advances, and improve the flexibility and robustness of the system.

[0047] Optimize resource utilization: The efficient resource scheduling mechanism dynamically allocates logistics resources through intelligent algorithms to ensure that key tasks are handled first, maximize resource utilization, reduce resource waste, and improve overall operational efficiency.

[0048] Ensure information security: Security and privacy protection measures effectively protect the security of user information through data encryption and differential privacy technology, prevent data leakage, comply with relevant laws and regulations, and enhance user trust.

[0049] Improve service quality: In application scenarios such as cargo tracking and management, warehouse automation, and delivery route optimization, this invention can provide more accurate services, shorten delivery time, reduce operating costs, and improve customer satisfaction.

[0050] Promoting green development: Through intelligent scheduling and optimized route planning, unnecessary transportation and waiting time can be reduced, fuel consumption and carbon emissions can be lowered, and the sustainable development of the logistics industry can be promoted. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a diagram of the architecture of the method of the present invention. DETAILED DESCRIPTION

[0052] In order to clearly and completely describe the objectives and technical solutions of the present invention and make the advantages more clearly understood, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, not all of them, and are only used to explain the embodiments of the present invention, not to limit the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] For example 1, please refer to Figure 1 The present invention provides a technical solution: a method for applying a large model based on multimodal adaptive learning in smart logistics, comprising:

[0054] 1. Multimodal data fusion module

[0055] Function: Process and integrate various types of data, including text, images, videos, and geographic location information, and convert data from different sources into a unified format for further analysis and processing.

[0056] Specific implementation:

[0057] Data collection: Collect multimodal data through various sensors, cameras, GPS devices, etc.

[0058] Data preprocessing: Clean, normalize and standardize the collected data to ensure data quality and consistency.

[0059] Data conversion: Convert data of different modalities into a unified feature representation, such as converting image data into feature vectors and embedding text data into words.

[0060] Data fusion: Use multimodal fusion technology (such as attention mechanism, multi-layer perceptron, etc.) to fuse data from different modalities to form a comprehensive feature representation.

[0061] 2. Adaptive Learning Framework

[0062] Function: Using deep reinforcement learning technology, the model can automatically adjust its learning strategy according to changes in actual application scenarios, improving decision-making accuracy and response speed.

[0063] Specific implementation:

[0064] Environmental modeling: Build a simulation model of the logistics environment, including warehouses, transportation routes, distribution points, etc.

[0065] Reward function design: Define a suitable reward function to evaluate the model's performance under different decision conditions, such as delivery time, cost, customer satisfaction, etc.

[0066] Deep reinforcement learning algorithms: Use algorithms such as Deep Q-Network (DQN) and policy gradient methods (such as PPO) to train models to make optimal decisions in dynamic environments.

[0067] Online learning: The model can continuously learn new data during actual operation and gradually optimize its decision-making strategy.

[0068] 3. Efficient resource scheduling mechanism

[0069] Function: Design a set of intelligent scheduling algorithms to ensure that critical tasks are prioritized while maximizing resource utilization and improving overall operational efficiency.

[0070] Specific implementation:

[0071] Task prioritization: Prioritize tasks based on factors such as urgency and importance.

[0072] Resource allocation algorithm: Use optimization algorithms such as genetic algorithm and simulated annealing algorithm to dynamically allocate logistics resources to ensure that key tasks are completed first.

[0073] Real-time scheduling: By monitoring the system status in real time, dynamically adjust the task scheduling plan to respond to emergencies and changes.

[0074] 4. Security and privacy protection measures

[0075] Function: Differential privacy technology is added during data processing to ensure user information security and comply with relevant laws and regulations.

[0076] Specific implementation:

[0077] Data encryption: Encrypt sensitive data to ensure data security during transmission and storage.

[0078] Differential privacy technology: Introduces noise during data publishing and analysis to protect individual privacy while maintaining the overall statistical characteristics of the data.

[0079] Access control: Establish strict access control mechanisms to ensure that only authorized personnel can access sensitive data.

[0080] Application Scenario

[0081] Cargo tracking and management:

[0082] Real-time location tracking: Combining image recognition and GPS data to achieve real-time cargo location tracking.

[0083] Anomaly detection: By analyzing video data during transportation, abnormal situations can be discovered and handled in a timely manner.

[0084] Warehouse automation:

[0085] Automated picking: Combining visual recognition and robotic arm control to achieve automated picking and packaging, improving operational efficiency.

[0086] Inventory management: Use image recognition technology to read barcodes or QR codes and update inventory information in real time.

[0087] Delivery route optimization:

[0088] Dynamic route planning: Based on traffic congestion, weather forecasts and historical delivery records, delivery routes are dynamically adjusted to reduce waiting time and fuel consumption.

[0089] Multi-objective optimization: Comprehensively consider multiple factors such as time, cost, and customer satisfaction to achieve multi-objective optimization.

[0090] Through the above technical solutions, the present invention aims to improve the intelligence level and operational efficiency of the smart logistics system, solve the shortcomings of the existing system, and provide strong support for the digital transformation of the logistics industry.

[0091] The technical solution of the present invention includes a multimodal data fusion module, an adaptive learning framework, an efficient resource scheduling mechanism and security and privacy protection measures. The multimodal data fusion module converts data from different sources into a unified format by collecting, preprocessing and converting various types of data such as text, images, videos, geographic locations, etc., to facilitate further analysis and processing. The adaptive learning framework adopts deep reinforcement learning technology, which enables the model to automatically adjust the learning strategy according to changes in actual application scenarios, thereby improving the accuracy and response speed of decision-making. The efficient resource scheduling mechanism dynamically allocates logistics resources through intelligent algorithms to ensure that key tasks are prioritized and resource utilization is maximized. Security and privacy protection measures ensure user information security through data encryption and differential privacy technology, in compliance with relevant laws and regulations.

[0092] Through these key technologies, the present invention can effectively integrate and utilize multimodal data, realize intelligent decision-making and resource optimization, improve the overall operating efficiency and intelligence level of the smart logistics system, solve the shortcomings of the existing system, and provide strong support for the digital transformation of the logistics industry.

[0093] Example 2. Based on Example 1, a system for applying a large model based on multimodal adaptive learning in smart logistics is proposed, including: a data acquisition subsystem: equipped with a variety of sensors, cameras, and GPS devices for collecting multimodal data of text, images, videos, and geographic location information in smart logistics scenarios; a data preprocessing module: cleaning the multimodal data collected by the data acquisition subsystem to remove noise and duplicate data; performing normalization processing to adjust the data range to a unified interval; and standardization processing to make the data have a unified format and distribution to ensure data quality and consistency; a data conversion unit: having the function of converting different modal data into a unified feature representation form, specifically converting image data into feature vectors through a convolutional neural network model, and using a word embedding algorithm to perform word embedding processing on text data; a data fusion component: using multimodal fusion technology, such as an attention mechanism or a multi-layer perceptron, to fuse the different modal feature representations output by the data conversion unit to form a comprehensive feature representation, providing comprehensive data input for subsequent smart logistics related modules.

[0094] Environmental modeling module: Builds a simulation model of the logistics environment, which covers key elements such as warehouse layout, transportation route planning, and distribution point location. It can simulate the actual operation scenarios of smart logistics and provide an environmental foundation for deep reinforcement learning; reward function design unit: Defines a suitable reward function for smart logistics scenarios. This function comprehensively considers delivery time, cost, and customer satisfaction indicators, and is used to evaluate the performance of the model under different decisions, providing feedback for model training; deep reinforcement learning algorithm engine: Adopts deep Q network and policy gradient method deep reinforcement learning algorithm, based on the simulation model constructed by the environmental modeling module and the reward function defined by the reward function design unit, to train the model to make optimal decisions in a dynamic smart logistics environment; online learning module: Enables the system to continuously receive new data during the actual operation of smart logistics, uses new data to update and optimize the model, and gradually improves the model's decision-making strategy to adapt to changes in actual application scenarios.

[0095] Task prioritization module: Based on the urgency, importance, and delivery time requirements of tasks in smart logistics, the preset rules or algorithms are used to prioritize each task, providing a basis for resource allocation; Resource allocation algorithm unit: Using genetic algorithms and simulated annealing algorithm optimization algorithms, combined with the results of the task prioritization module and the availability of logistics resources, logistics resources are dynamically allocated to ensure that critical tasks can obtain the required resources and be completed first; Real-time scheduling system: By real-time monitoring of the status of the smart logistics system, including task execution progress, resource usage, and traffic conditions, intelligent algorithms are used to dynamically adjust task scheduling plans, respond to emergencies and changes in the logistics environment in a timely manner, maximize resource utilization, and improve overall operational efficiency; Resource status database: Stores detailed information on logistics resources, including resource type, quantity, location, and availability, providing data support for the resource allocation algorithm unit and real-time scheduling system.

[0096] Data encryption module: Sensitive data collected and processed in smart logistics scenarios, such as customer information and cargo information, are encrypted using symmetric encryption algorithms or asymmetric encryption algorithms to ensure the security of data during transmission and storage; Differential privacy technology component: In the process of data publishing and analysis, differential privacy technology is applied by introducing noise to protect individual privacy while maintaining the overall statistical characteristics of the data for data analysis; Access control subsystem: Establish a strict access control mechanism, including user identity authentication, authority management, and audit tracking functions to ensure that only authorized personnel can access and process sensitive data in smart logistics, and prevent data leakage and abuse; Security audit module: Regularly audit the security status of the system, check the effectiveness of data encryption, differential privacy technology application and access control mechanism, and promptly discover and repair security vulnerabilities.

[0097] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for applying a large model based on multimodal adaptive learning in smart logistics, characterized by: The following steps are involved: Multimodal data collection: Through a variety of sensors, cameras, and GPS devices, multimodal data such as text, images, videos, and geographic location information in smart logistics scenarios are collected; Data preprocessing: Clean, normalize, and standardize the collected multimodal data to remove noise, unify the data range and format, and ensure data quality and consistency; Data conversion: Convert data of different modalities into a unified feature representation, specifically converting image data into feature vectors and performing word embedding on text data; Data fusion: Multimodal fusion technologies, such as attention mechanism and multi-layer perceptron, are used to fuse the converted data of different modalities together to form a comprehensive feature representation for subsequent smart logistics-related analysis and processing.

2. The method for applying a large model based on multimodal adaptive learning in smart logistics according to claim 1 is characterized by: The following steps are also included: Environmental modeling: Build a simulation model of the logistics environment, covering key elements such as warehouses, transportation routes, and distribution points, to simulate the actual operation scenarios of smart logistics; Reward function design: Define an appropriate reward function for smart logistics scenarios. This function is used to evaluate the model's performance under different decision-making conditions. Evaluation indicators include but are not limited to delivery time, cost, and customer satisfaction. Application of Deep Reinforcement Learning Algorithms: Using Deep Q-Network (DQN) and Policy Gradient Method deep reinforcement learning algorithms, based on the constructed environment model and designed reward function, the model is trained to make optimal decisions in a dynamic smart logistics environment; Online learning: enables the model to continuously learn new data during the actual operation of smart logistics, and gradually optimize its decision-making strategy based on the new data to adapt to changes in actual application scenarios.

3. The method for applying a large model based on multimodal adaptive learning in smart logistics according to claim 2 is characterized by: It also includes the following steps: Task prioritization: Prioritize tasks based on their urgency and importance in smart logistics; Resource allocation algorithm application: Genetic algorithm and simulated annealing algorithm optimization algorithm are used to dynamically allocate logistics resources based on task priority and the availability of logistics resources to ensure that key tasks can be completed first; Real-time scheduling: By real-time monitoring of the status of the smart logistics system, including task execution progress and resource usage, the task scheduling plan can be dynamically adjusted to respond to emergencies and changes in the logistics environment, maximize resource utilization, and improve overall operational efficiency.

4. The method for applying a large model based on multimodal adaptive learning in smart logistics according to claim 3 is characterized by: The following steps are also included: Data encryption: Encrypt sensitive data collected and processed in smart logistics scenarios, using appropriate encryption algorithms to ensure data security during transmission and storage; Application of differential privacy technology: Introducing noise during the release and analysis of smart logistics data, using differential privacy technology to protect individual privacy while maintaining the overall statistical characteristics of the data for data analysis; Access control: Establish a strict access control mechanism to ensure that only authorized personnel can access and process sensitive data in smart logistics through identity authentication and permission management.

5. A system for the method of applying a large model based on multimodal adaptive learning in smart logistics according to claim 4, characterized in that: include: Data collection subsystem: equipped with a variety of sensors, cameras, and GPS devices to collect multimodal data such as text, images, videos, and geographic location information in smart logistics scenarios; Data preprocessing module: Cleans the multimodal data collected by the data acquisition subsystem to remove noise and duplicate data; performs normalization to adjust the data range to a unified interval; and standardizes the data to have a unified format and distribution to ensure data quality and consistency; Data conversion unit: It has the function of converting different modal data into a unified feature representation. Specifically, it converts image data into feature vectors through a convolutional neural network model and uses a word embedding algorithm to perform word embedding processing on text data. Data fusion component: Using multimodal fusion technology, such as attention mechanism or multi-layer perceptron, the different modal feature representations output by the data conversion unit are fused together to form a comprehensive feature representation, providing comprehensive data input for subsequent smart logistics-related modules.

6. A system according to claim 5, characterized in that: include: Environmental modeling module: Builds a simulation model of the logistics environment, covering key elements such as warehouse layout, transportation route planning, and distribution point locations. This model can simulate the actual operation scenarios of smart logistics and provide an environmental foundation for deep reinforcement learning. Reward Function Design Unit: Defines an appropriate reward function for smart logistics scenarios. This function comprehensively considers delivery time, cost, and customer satisfaction indicators to evaluate the model's performance under different decision-making conditions and provide feedback for model training. Deep reinforcement learning algorithm engine: Using deep Q-network and policy gradient method deep reinforcement learning algorithm, based on the simulation model built by the environment modeling module and the reward function defined by the reward function design unit, the training model makes optimal decisions in a dynamic smart logistics environment; Online learning module: enables the system to continuously receive new data during the actual operation of smart logistics, use the new data to update and optimize the model, and gradually improve the model's decision-making strategy to adapt to changes in actual application scenarios.

7. A system according to claim 6, characterized in that: include: Task prioritization module: Based on the urgency, importance, and delivery time requirements of tasks in smart logistics, it uses preset rules or algorithms to prioritize tasks and provide a basis for resource allocation; Resource allocation algorithm unit: uses genetic algorithm and simulated annealing algorithm optimization algorithm, combined with the results of the task priority division module and the availability of logistics resources, to dynamically allocate logistics resources to ensure that key tasks can obtain the required resources and be completed first; Real-time scheduling system: By monitoring the status of the smart logistics system in real time, including task execution progress, resource usage, and traffic conditions, it uses intelligent algorithms to dynamically adjust task scheduling plans, respond promptly to emergencies and changes in the logistics environment, maximize resource utilization, and improve overall operational efficiency; Resource status database: stores detailed information about logistics resources, including resource type, quantity, location, and availability, and provides data support for the resource allocation algorithm unit and real-time scheduling system.

8. A system according to claim 7, characterized in that: include: Data encryption module: Use symmetric or asymmetric encryption algorithms to encrypt sensitive data collected and processed in smart logistics scenarios, such as customer information and cargo information, to ensure data security during transmission and storage; Differential Privacy Technology Component: During data publishing and analysis, differential privacy technology is applied by introducing noise to protect individual privacy while maintaining the overall statistical characteristics of the data for data analysis. Access control subsystem: Establish a strict access control mechanism, including user identity authentication, permission management, and audit tracking functions, to ensure that only authorized personnel can access and process sensitive data in smart logistics, preventing data leakage and abuse; Security Audit Module: Regularly audit the security status of the system, check the effectiveness of data encryption, differential privacy technology application and access control mechanism, and promptly discover and repair security vulnerabilities.

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