A management system for digitalized logistics supply chain

Abstract

The application belongs to the technical field of logistics management, and particularly relates to a management system of a digital logistics supply chain, which comprises an intelligent route planning module, a warehouse Internet of Things monitoring module and a warehouse Internet of Things monitoring module; the improved genetic algorithm is used to generate a multi-objective optimal driving route containing time window constraints, a dynamic crossover probability and a mutation probability adjustment mechanism are introduced, parameters are dynamically adjusted according to a current population diversity index and an individual fitness value distribution state, the global search capability is enhanced, and falling into a local optimal solution is avoided; meanwhile, a path weight factor based on historical transportation efficiency is introduced, factors such as a completion timeliness of historical transportation tasks, a cost deviation and an abnormal event occurrence rate are fully considered, the generated route is more in line with actual transportation requirements, in addition, a time window constraint penalty function is set, the route scheme is ensured to meet a preset delivery time window requirement, transportation efficiency is improved, and transportation cost is reduced.

Description

Technical Field

[0001] This invention belongs to the field of logistics management technology, specifically a digital logistics supply chain management system. Background Technology

[0002] In today's globalized and information-driven business environment, logistics and supply chain management plays a crucial role in a company's operational efficiency and competitiveness. Traditional logistics and supply chain management systems mainly rely on manual planning and experience-based decision-making, which has the following shortcomings: In terms of route planning, traditional methods often only consider simple distance factors and cannot take into account factors such as real-time traffic conditions, cargo weight and volume, etc., making it difficult to generate the optimal driving route, resulting in low transportation efficiency, increased transportation costs, and difficulty in meeting the requirements of time window constraints. In the warehouse management process, the traditional method mainly relies on manual inventory counting, which is not only inefficient and prone to errors, but also cannot keep track of the entry and exit of goods and their location information in real time, making it difficult to achieve refined inventory management and easily causing inventory backlog or stockouts. In terms of demand forecasting, traditional methods are usually based on simple historical sales data for linear forecasting. They cannot fully consider multi-dimensional information such as time series characteristics, product correlation characteristics, and external event characteristics, resulting in inaccurate forecast results and failing to provide a reliable basis for enterprises' production and replenishment decisions.

[0003] Therefore, the present invention provides a management system for digital logistics supply chain. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0005] The technical solution adopted by this invention to solve its technical problem is: a digital logistics supply chain management system as described in this invention, comprising: The intelligent route planning module receives the start point, destination, weight, and volume parameters of a cargo transportation task, and combines them with real-time traffic data to generate a multi-objective optimal driving route with time window constraints through an improved genetic algorithm. The improved genetic algorithm adopts a dynamic crossover probability and mutation probability adjustment mechanism and introduces a path weight factor based on historical transportation efficiency. The warehouse IoT monitoring module includes a multimodal sensor array deployed in the warehouse to automatically collect information on the time, quantity, and location of goods entering and leaving the warehouse, and to perform data preprocessing through edge computing nodes; The sensor array includes an RFID reader, UWB positioning tags, pressure sensors, and an image recognition camera; The intelligent inventory management subsystem, based on IoT data fusion technology, performs spatiotemporal alignment processing on the collected goods status information and automatically generates inventory discrepancy alarm signals by comparing the difference between theoretical inventory and actual perceived data. The demand forecasting module uses an integrated learning framework to perform feature engineering on historical order data, constructs a composite forecasting model that includes time series features, product association features, and external event features, and outputs the sales trend forecast results and market demand fluctuation range within a preset period. The central control unit establishes a secure and encrypted communication link with each module to coordinate data interaction between modules and trigger corresponding logistics operation instructions according to preset business rules. Business rules include, but are not limited to, inventory warning thresholds, route adjustment trigger conditions, and replenishment strategy priorities.

[0006] A further improvement of the present invention is that the dynamic adjustment mechanism of the improved genetic algorithm specifically includes: Establish a multi-objective optimization function that includes path length, transportation time, energy consumption cost, and risk coefficient; The crossover probability is dynamically adjusted based on the current population diversity index. When the population diversity is lower than the preset threshold, an adaptive crossover operator is used to enhance the global search capability. The mutation probability is adjusted according to the distribution of individual fitness values, and high-intensity mutation operations are performed on individuals with fitness values ​​lower than the population average. A path weighting factor based on historical transportation efficiency is introduced, which is calculated by analyzing the completion time, cost deviation and abnormal event occurrence rate of similar historical transportation tasks. Set a time window constraint penalty function to apply adaptive decay to route plans that violate the preset delivery time window.

[0007] A further improvement of the present invention is that the deployment scheme of the multimodal sensor array satisfies: The RFID reader adopts a distributed grid layout, covering all warehouse operation areas, and the reading and writing distance is adjustable from 3 to 8 meters; UWB positioning tags are attached to cargo packaging units; Pressure sensors are installed on the load-bearing parts of the shelf; The image recognition camera uses a combination of a wide-angle lens and a zoom lens, and supports a nighttime infrared imaging mode. Each sensor type uses a time synchronization protocol to align data timestamps.

[0008] A further improvement of the present invention is that the data processing flow of the intelligent inventory subsystem includes: The data from multiple sensors is cleaned to remove outliers and duplicate records. The Kalman filter algorithm is used to smooth the cargo location data; Establish a three-dimensional spatial coordinate system and spatially map the UWB positioning data with the warehouse layout map; A preliminary inventory report is generated by comparing the differences between RFID reading records and the theoretical inventory in the WMS system. For goods with positional deviations, the image recognition module is invoked for secondary verification; Based on a preset reliability threshold, inventory discrepancies are automatically flagged and alarm signals are generated.

[0009] A further improvement of the present invention is that the integrated learning framework of the demand forecasting module includes: a data preprocessing layer, including outlier detection, missing value imputation and time series decomposition; The feature engineering layer extracts features including: time features, statistical features, product features, and external features. The model training layer uses a stacking ensemble strategy to combine the following base models: Long Short-Term Memory Network, Gradient Boosting Decision Tree, Support Vector Regression, and Bayesian Structured Time Series. The output layer generates a composite prediction result that includes point prediction values ​​and probability prediction intervals.

[0010] A further improvement of the present invention is that the business rule engine of the central control unit includes: Dynamic inventory allocation rules automatically allocate storage locations based on inventory turnover rate, shelf life, and storage conditions; Intelligent replenishment rules calculate the optimal replenishment quantity and timing based on demand forecasts and current inventory levels. Route optimization trigger rules: routes are replanned when the following conditions are detected: The traffic congestion index exceeds the preset threshold. The actual transportation time deviated from the planned value by more than 20%; A new emergency transport mission has emerged; Vehicle load utilization rate is less than 60%; The anomaly handling rules automatically activate backup plans and notify relevant personnel in response to emergencies such as equipment failures and sudden weather changes.

[0011] A further improvement of the present invention is that it also includes a security protection subsystem, which comprises: The data encryption module uses the national cryptographic algorithm SM4 to encrypt the transmitted data; The access control module establishes a multi-level permission management system based on the RBAC model; The intrusion detection module identifies abnormal access behavior by analyzing network traffic characteristics; The audit trail module fully records all system operation logs and supports source tracing analysis; The physical security module includes environmental monitoring sensors and an electronic fence system.

[0012] A further improvement of this invention is that the system adopts a microservice architecture design, with each functional module deployed in a containerized environment, and achieves the following through a service mesh: service discovery and load balancing, circuit breaking, rate limiting and fault tolerance, distributed transaction coordination, dynamic configuration updates and service call chain tracing.

[0013] A further improvement of the present invention is that it also includes a visualization decision support module, which provides: A 3D digital twin interface for warehousing displays the real-time storage status of goods and the operating status of equipment; A dynamic transportation route simulation system that supports multi-scheme comparison and conflict warning; A visual dashboard of forecast results displays sales trends, inventory levels, and demand fluctuations. The abnormal event alarm center notifies relevant personnel through audible and visual alarms and mobile push notifications; Operate the performance analysis dashboard to statistically analyze key indicators such as transportation timeliness and inventory accuracy.

[0014] A further improvement of the present invention is that it supports data interaction with external systems, including: Synchronize master data with the ERP system via API interface; Use EDI standards to connect with supplier systems for order processing; Connect to the OBD device of the transport vehicle via IoT protocol; Connect to the meteorological service API to obtain real-time weather data; Connect to the government's traffic management platform to obtain road condition information; It integrates with the financial system to enable automatic settlement.

[0015] The beneficial effects of this invention are as follows: 1. This invention employs an improved genetic algorithm to generate multi-objective optimal travel routes with time window constraints. It introduces dynamic crossover and mutation probability adjustment mechanisms, dynamically adjusting parameters based on the current population diversity index and individual fitness value distribution, thus enhancing global search capabilities and avoiding getting trapped in local optima. Simultaneously, it introduces a path weight factor based on historical transportation efficiency, fully considering factors such as the completion time, cost deviation, and abnormal event occurrence rate of historical transportation tasks, making the generated routes more consistent with actual transportation needs. Furthermore, a time window constraint penalty function is set to ensure that the route plan meets the preset delivery time window requirements, thereby solving the problems of traditional route planning methods. The system addresses the challenges of comprehensively considering multiple factors, generating optimal routes, and meeting time window constraints, thereby improving transportation efficiency, reducing transportation costs, and mitigating transportation risks. The sensor array employs a rational deployment scheme to ensure comprehensive coverage of the warehousing operation area. Each sensor type uses a time synchronization protocol to align data timestamps, guaranteeing data accuracy and consistency. This overcomes the shortcomings of traditional warehousing management, such as reliance on manual inventory checks and untimely or inaccurate information acquisition. It achieves automation and intelligence in warehousing management, enabling real-time monitoring of cargo status information and providing accurate data for inventory management and logistics operations, effectively improving the efficiency and precision of warehousing management. 2. This invention, based on IoT data fusion technology, performs spatiotemporal alignment processing on collected goods status information. By comparing the difference between theoretical inventory and actual perceived data, it automatically generates inventory discrepancy alarm signals, thus solving the problems of low efficiency and error susceptibility in traditional inventory counting methods. It can quickly and accurately detect inventory discrepancies and take timely measures to adjust them, improving the accuracy and reliability of inventory management. An ensemble learning framework is used to perform feature engineering processing on historical order data, constructing a composite prediction model that includes time series features, product association features, and external event features. Furthermore, the ensemble learning framework uses a stacking ensemble strategy to combine multiple base models, fully leveraging the advantages of each model to output sales trend predictions and market demand fluctuation ranges within a preset future period. Based on this, it overcomes the problem of inaccurate predictions in traditional demand forecasting methods, comprehensively considering multi-dimensional information to improve the accuracy and reliability of prediction results. This provides a scientific basis for enterprise production and replenishment decisions, optimizes inventory levels, and reduces inventory costs. Attached Figure Description

[0016] The invention will now be further described with reference to the accompanying drawings.

[0017] Figure 1 This is the overall architecture diagram of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 , This embodiment provides: a digital logistics supply chain management system, including: The intelligent route planning module receives the start point, destination, weight, and volume parameters of a cargo transportation task, and combines them with real-time traffic data to generate a multi-objective optimal driving route with time window constraints through an improved genetic algorithm. The improved genetic algorithm adopts a dynamic crossover probability and mutation probability adjustment mechanism and introduces a path weight factor based on historical transportation efficiency. The dynamic adjustment mechanism of the improved genetic algorithm specifically includes: Establish a multi-objective optimization function that includes path length, transportation time, energy consumption cost, and risk coefficient; The crossover probability is dynamically adjusted based on the current population diversity index. When the population diversity is lower than the preset threshold, an adaptive crossover operator is used to enhance the global search capability. The mutation probability is adjusted according to the distribution of individual fitness values, and high-intensity mutation operations are performed on individuals with fitness values ​​lower than the population average. A path weighting factor based on historical transportation efficiency is introduced. The weighting factor is calculated by analyzing the completion time, cost deviation and abnormal event occurrence rate of similar historical transportation tasks. Set a time window constraint penalty function to apply adaptive decay to route plans that violate the preset delivery time window.

[0020] It should be noted that in genetic algorithms, fixed crossover and mutation probabilities are difficult to adapt to the search requirements at different stages. The dynamic adjustment mechanism adaptively adjusts the crossover and mutation probabilities based on the population diversity index and the distribution of individual fitness values, thereby enhancing the global search capability, avoiding getting trapped in local optima, and increasing the probability of finding the optimal path. For example, when the population diversity is low, an adaptive crossover operator is used to enhance the search capability and prevent premature convergence. Historical transportation data contains a wealth of valuable information, such as completion time, cost deviation, and abnormal event occurrence rate. By analyzing this data to calculate path weight factors, the algorithm can refer to historical experience when planning routes, prioritizing routes with high transportation efficiency, low cost, and low risk, thereby improving the rationality and reliability of route planning. For example, if a route has a high historical abnormal event occurrence rate, the algorithm will reduce the probability of it being selected. Many freight transportation services have strict requirements for delivery time. Violating the time window can lead to decreased customer satisfaction or even additional costs. By setting a penalty function, an adaptive decay is applied to route plans that violate the preset delivery time window, so that the algorithm prioritizes meeting the time window constraint when optimizing routes, ensuring that goods are delivered on time. For example, if the estimated delivery time of a route plan exceeds the time window, its fitness value will decrease, and the probability of it being eliminated during the evolution process will increase. The warehouse IoT monitoring module includes a multimodal sensor array deployed in the warehouse to automatically collect information on the time, quantity, and location of goods entering and leaving the warehouse, and to perform data preprocessing through edge computing nodes; The sensor array includes an RFID reader, UWB positioning tags, pressure sensors, and an image recognition camera; The deployment scheme for the multimodal sensor array satisfies: The RFID reader adopts a distributed grid layout, covering all warehouse operation areas, and the reading and writing distance is adjustable from 3 to 8 meters; UWB positioning tags are attached to cargo packaging units; Pressure sensors are installed on the load-bearing parts of the shelf; The image recognition camera uses a combination of a wide-angle lens and a zoom lens, and supports a nighttime infrared imaging mode. Each sensor type uses a time synchronization protocol to align data timestamps; The intelligent inventory management subsystem, based on IoT data fusion technology, performs spatiotemporal alignment processing on the collected goods status information and automatically generates inventory discrepancy alarm signals by comparing the difference between theoretical inventory and actual perceived data. The data processing flow of the intelligent inventory subsystem includes: The data from multiple sensors is cleaned to remove outliers and duplicate records. The Kalman filter algorithm is used to smooth the cargo location data; Establish a three-dimensional spatial coordinate system and spatially map the UWB positioning data with the warehouse layout map; A preliminary inventory report is generated by comparing the differences between RFID reading records and the theoretical inventory in the WMS system. For goods with positional deviations, the image recognition module is invoked for secondary verification; Based on a preset reliability threshold, the system automatically marks inventory discrepancies and generates alarm signals. It should be noted that different types of sensors have different functions. Their combined use can comprehensively and accurately collect cargo information. The distributed grid layout of RFID readers ensures coverage of all warehouse operation areas, and the adjustable reading distance meets the needs of different scenarios. UWB positioning tags are attached to cargo packaging units for precise location tracking. Pressure sensors are installed on the load-bearing parts of the shelves to monitor changes in cargo weight. The combination of wide-angle and zoom lenses in the image recognition camera supports multi-scenario monitoring, and the night-time infrared imaging mode ensures normal operation at night. All sensors use a time synchronization protocol to align data timestamps, ensuring data consistency over time and providing an accurate foundation for subsequent analysis and processing. For example, RFID readers can quickly read cargo entry and exit information, while UWB positioning tags can track the real-time location of goods within the warehouse. Multi-source sensor data may contain outliers and duplicate records; data cleaning can improve data quality. Kalman filtering smooths cargo location data, reducing noise interference and improving accuracy. A three-dimensional spatial coordinate system maps UWB positioning data to the warehouse layout map, visually displaying cargo location. A preliminary inventory report is generated by comparing RFID read records with the theoretical inventory in the WMS system, quickly identifying discrepancies. For goods with location deviations, an image recognition module is used for secondary verification, improving inventory accuracy. Inventory discrepancies are automatically marked and alarm signals are generated based on preset confidence thresholds, promptly notifying management for handling. For example, if RFID read records show a certain item in a certain area, but UWB positioning shows a large location deviation, an alarm signal is generated after confirmation through image recognition. The demand forecasting module uses an integrated learning framework to perform feature engineering on historical order data, constructs a composite forecasting model that includes time series features, product association features, and external event features, and outputs the sales trend forecast results and market demand fluctuation range within a preset period. The integrated learning framework for the demand forecasting module includes: a data preprocessing layer, which includes outlier detection, missing value imputation, and time series decomposition; The feature engineering layer extracts features including: time features, statistical features, product features, and external features. The model training layer uses a stacking ensemble strategy to combine the following base models: Long Short-Term Memory Network, Gradient Boosting Decision Tree, Support Vector Regression, and Bayesian Structured Time Series. The output layer generates a composite prediction result that includes point prediction values ​​and probability prediction intervals; It should be noted that historical order data is complex and diverse, and a single model can hardly accurately capture its features. The ensemble learning framework improves data quality through outlier detection, missing value imputation, and time series decomposition in the data preprocessing layer; the feature engineering layer extracts multiple features to comprehensively reflect the inherent patterns of the data; the model training layer uses a stacking ensemble strategy to combine multiple base models, leveraging the advantages of different models to improve prediction accuracy; and the output layer generates composite prediction results containing point prediction values ​​and probability prediction intervals, providing more comprehensive information for decision-making. For example, Long Short-Term Memory networks are good at processing time series data, and gradient boosting decision trees have strong capabilities for feature interaction processing; using them together can improve prediction accuracy. The central control unit establishes a secure and encrypted communication link with each module, coordinates the data interaction between the modules, and triggers corresponding logistics operation instructions according to preset business rules. The business rule engine includes dynamic inventory allocation rules, intelligent replenishment rules, route optimization triggering rules, and exception handling rules, which can automatically adjust and optimize logistics operations according to different business scenarios. This solves the problem of lack of effective coordination and integration between modules in traditional logistics supply chain management systems, realizes global system optimization and intelligent decision-making, and improves the overall operating efficiency and response speed of the system. It should be noted that the dynamic inventory allocation rules automatically allocate storage locations based on inventory turnover rate, shelf life, and storage conditions, improving warehouse space utilization and inventory management efficiency; the intelligent replenishment rules calculate the optimal replenishment quantity and timing based on demand forecasts and current inventory levels, avoiding inventory backlog or stockouts; the route optimization trigger rules replan routes in cases of traffic congestion, delivery time deviations, emergency tasks, or low vehicle load utilization, ensuring the smooth completion of transportation tasks; and the anomaly handling rules automatically activate backup plans and notify relevant personnel in response to emergencies such as equipment failures and sudden weather changes, reducing the impact of emergencies on the logistics supply chain. For example, when the traffic congestion index is detected to exceed a preset threshold, the route is replanned to avoid congested sections. The system is equipped with a security protection subsystem, including a data encryption module, an access control module, an intrusion detection module, an audit tracing module, and a physical security module. It uses the national cryptographic SM4 algorithm to encrypt transmitted data, establishes a multi-level access control system based on the RBAC model, identifies abnormal access behavior by analyzing network traffic characteristics, records all system operation logs completely and supports source tracing analysis, and is equipped with environmental monitoring sensors and an electronic fence system to ensure the physical security of the system. This solves the problem of poor security in traditional logistics supply chain management systems, effectively protects the system's data security and operational security, prevents data leakage and unauthorized access, and ensures the stable and reliable operation of the system. It should be noted that there is a risk of data leakage during data transmission. The SM4 algorithm, a national cryptographic algorithm, is an encryption algorithm independently developed in my country. It has high security. Using this algorithm to encrypt transmitted data can effectively prevent data from being stolen or tampered with during transmission and ensure data security. Different personnel have different system operation permissions. Based on the RBAC model, permissions are assigned according to user roles to achieve multi-level permission management, ensuring that only authorized personnel can access the corresponding functions and data, preventing unauthorized access and operation, and ensuring the safe and stable operation of the system. Various attack behaviors exist in the network environment. The intrusion detection module analyzes network traffic characteristics, such as traffic volume, access frequency, and access source, to identify abnormal access behavior, detect potential attacks in a timely manner, take measures to prevent attacks, and protect the network security of the system. The system operation log records all operations. Complete operation logs can be used for source analysis when problems occur, find the cause of the problem, clarify responsibilities, and help to discover potential security risks and take timely preventive measures. Physical environmental security is crucial for the stable operation of the system. Environmental monitoring sensors can monitor environmental parameters such as temperature, humidity, and smoke, and detect environmental anomalies in a timely manner. Electronic fence systems can prevent unauthorized personnel from entering the system area and ensure the physical security of the system. The system adopts a microservice architecture, with each functional module deployed in a containerized environment. It utilizes a service mesh to achieve service discovery and load balancing, circuit breaking and fault tolerance, distributed transaction coordination, dynamic configuration updates, and service call chain tracing, thus improving the system's scalability and flexibility. Simultaneously, a visual decision support module is included, providing a 3D warehouse digital twin interface, a dynamic transportation route simulation system, a prediction result visualization dashboard, an anomaly event alarm center, and an operational performance analysis dashboard. This allows managers to monitor system operation in real time and make informed decisions, thereby addressing the poor scalability and low decision-making efficiency issues of traditional logistics supply chain management systems. The system can quickly adapt to business changes and improve the efficiency and accuracy of managerial decisions. It also includes a visualization decision support module, which provides: A 3D digital twin interface for warehousing displays the real-time storage status of goods and the operating status of equipment; A dynamic transportation route simulation system that supports multi-scheme comparison and conflict warning; A visual dashboard of forecast results displays sales trends, inventory levels, and demand fluctuations. The abnormal event alarm center notifies relevant personnel through audible and visual alarms and mobile push notifications; Operate the performance analysis dashboard to statistically analyze key indicators such as transportation timeliness and inventory accuracy.

[0021] A 3D warehouse digital twin interface displays the real-time storage status of goods and equipment operation, enabling managers to intuitively understand the warehouse situation; a dynamic transportation route simulation system supports multi-scheme comparison and conflict warning, helping to select the optimal transportation route; a visual dashboard of forecast results displays sales trends, inventory levels, and demand fluctuations, providing intuitive basis for decision-making; an abnormal event alarm center notifies relevant personnel through audible and visual alarms and mobile push notifications to promptly handle abnormal situations; an operational performance analysis dashboard statistically analyzes key indicators such as transportation timeliness and inventory accuracy, evaluating logistics operation performance and providing a reference for optimizing management; for example, the dynamic transportation route simulation system allows for a direct view of the differences in transportation time and cost under different traffic conditions for different routes; The system supports data interaction with external systems, synchronizes master data with ERP systems via API interfaces, connects with supplier systems for order processing using EDI standards, connects with OBD devices on transport vehicles via IoT protocols, obtains real-time weather data by connecting to meteorological service APIs, obtains road condition information by connecting to government traffic management platforms, and integrates with financial systems to achieve automatic settlement functions. This solves the problem of information silos in traditional logistics supply chain management systems, achieves seamless integration and data sharing with external systems, promotes collaborative cooperation between upstream and downstream enterprises in the supply chain, and improves the operational efficiency and competitiveness of the entire supply chain. It should be noted that synchronizing master data with the ERP system ensures data consistency between the logistics supply chain system and the enterprise management system; using EDI standards to connect with supplier systems for order processing enables rapid and accurate transmission of order information; connecting with the OBD devices of transport vehicles via IoT protocols obtains real-time vehicle operating status information; connecting with meteorological service APIs to obtain real-time weather data provides a reference for transportation route planning; connecting with government traffic management platforms to obtain road condition information allows for timely adjustments to transportation routes; and integrating with the financial system enables automatic settlement functions, improving financial processing efficiency; for example, after connecting with the meteorological service API, if severe weather is expected, transportation plans can be adjusted in advance.

[0022] The terms "front," "back," "left," "right," "top," and "bottom" all refer to the figures in the accompanying drawings. Figure 1 Based on the perspective of the observer, the side of the device facing the observer is defined as the front, the left side of the observer is defined as the left, and so on.

[0023] In the description of this invention, it should be understood that the terms "center", "longitudinal", "lateral", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the scope of protection of this invention.

[0024] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital logistics supply chain management system, characterized in that, include: The intelligent route planning module receives the start point, destination, weight, and volume parameters of a cargo transportation task, and combines them with real-time traffic data to generate a multi-objective optimal driving route with time window constraints through an improved genetic algorithm. The improved genetic algorithm adopts a dynamic crossover probability and mutation probability adjustment mechanism and introduces a path weight factor based on historical transportation efficiency. The warehouse IoT monitoring module includes a multimodal sensor array deployed in the warehouse to automatically collect information on the time, quantity, and location of goods entering and leaving the warehouse, and to perform data preprocessing through edge computing nodes; The sensor array includes an RFID reader, UWB positioning tags, pressure sensors, and an image recognition camera; The intelligent inventory management subsystem, based on IoT data fusion technology, performs spatiotemporal alignment processing on the collected goods status information and automatically generates inventory discrepancy alarm signals by comparing the difference between theoretical inventory and actual perceived data. The demand forecasting module uses an integrated learning framework to perform feature engineering on historical order data, constructs a composite forecasting model that includes time series features, product association features, and external event features, and outputs the sales trend forecast results and market demand fluctuation range within a preset period. The central control unit establishes a secure and encrypted communication link with each module to coordinate data interaction between modules and trigger corresponding logistics operation instructions according to preset business rules. Business rules include, but are not limited to, inventory warning thresholds, route adjustment trigger conditions, and replenishment strategy priorities.

2. The digital logistics supply chain management system according to claim 1, characterized in that: The dynamic adjustment mechanism of the improved genetic algorithm specifically includes: Establish a multi-objective optimization function that includes path length, transportation time, energy consumption cost, and risk coefficient; The crossover probability is dynamically adjusted based on the current population diversity index. When the population diversity is lower than the preset threshold, an adaptive crossover operator is used to enhance the global search capability. The mutation probability is adjusted according to the distribution of individual fitness values, and high-intensity mutation operations are performed on individuals with fitness values ​​lower than the population average. A path weighting factor based on historical transportation efficiency is introduced, which is calculated by analyzing the completion time, cost deviation and abnormal event occurrence rate of similar historical transportation tasks. Set a time window constraint penalty function to apply adaptive decay to route plans that violate the preset delivery time window.

3. The management system for a digital logistics supply chain according to claim 1, characterized in that: The deployment scheme of the multimodal sensor array satisfies: The RFID reader adopts a distributed grid layout, covering all warehouse operation areas, and the reading and writing distance is adjustable from 3 to 8 meters; UWB positioning tags are attached to cargo packaging units; Pressure sensors are installed on the load-bearing parts of the shelf; The image recognition camera uses a combination of a wide-angle lens and a zoom lens, and supports a nighttime infrared imaging mode. Each sensor type uses a time synchronization protocol to align data timestamps.

4. The management system for a digital logistics supply chain according to claim 1, characterized in that: The data processing flow of the intelligent inventory subsystem includes: The data from multiple sensors is cleaned to remove outliers and duplicate records. The Kalman filter algorithm is used to smooth the cargo location data; Establish a three-dimensional spatial coordinate system and spatially map the UWB positioning data with the warehouse layout map; A preliminary inventory report is generated by comparing the differences between RFID reading records and the theoretical inventory in the WMS system. For goods with positional deviations, the image recognition module is invoked for secondary verification; Based on a preset reliability threshold, inventory discrepancies are automatically flagged and alarm signals are generated.

5. The digital logistics supply chain management system according to claim 1, characterized in that: The integrated learning framework of the demand forecasting module includes: a data preprocessing layer, including outlier detection, missing value imputation, and time series decomposition; The feature engineering layer extracts features including: time features, statistical features, product features, and external features. The model training layer uses a stacking ensemble strategy to combine the following base models: Long Short-Term Memory Network, Gradient Boosting Decision Tree, Support Vector Regression, and Bayesian Structured Time Series. The output layer generates a composite prediction result that includes point prediction values ​​and probability prediction intervals.

6. The management system for a digital logistics supply chain according to claim 1, characterized in that: The business rule engine of the central control unit includes: Dynamic inventory allocation rules automatically allocate storage locations based on inventory turnover rate, shelf life, and storage conditions; Intelligent replenishment rules calculate the optimal replenishment quantity and timing based on demand forecasts and current inventory levels. Route optimization trigger rules: routes are replanned when the following conditions are detected: The traffic congestion index exceeds the preset threshold. The actual transportation time deviated from the planned value by more than 20%; A new emergency transport mission has emerged; Vehicle load utilization rate is less than 60%; The anomaly handling rules automatically activate backup plans and notify relevant personnel in response to equipment failures or sudden weather changes.

7. The management system for a digital logistics supply chain according to claim 1, characterized in that: It also includes a security protection subsystem, which comprises: The data encryption module uses the national cryptographic algorithm SM4 to encrypt the transmitted data; The access control module establishes a multi-level permission management system based on the RBAC model; The intrusion detection module identifies abnormal access behavior by analyzing network traffic characteristics; The audit trail module fully records all system operation logs and supports source tracing analysis; The physical security module includes environmental monitoring sensors and an electronic fence system.

8. The management system for a digital logistics supply chain according to claim 1, characterized in that: The architecture adopts a microservices approach, with each functional module deployed in a containerized environment and implemented through a service mesh: service discovery and load balancing, circuit breaking, rate limiting and fault tolerance, distributed transaction coordination, dynamic configuration updates and service call chain tracing.

9. A digital logistics supply chain management system according to claim 1, characterized in that: It also includes a visualization decision support module, which provides: A 3D digital twin interface for warehousing displays the real-time storage status of goods and the operating status of equipment; A dynamic transportation route simulation system that supports multi-scheme comparison and conflict warning; A visual dashboard of forecast results displays sales trends, inventory levels, and demand fluctuations. The abnormal event alarm center notifies relevant personnel through audible and visual alarms and mobile push notifications; Operate the performance analysis dashboard to statistically analyze key indicators such as transportation timeliness and inventory accuracy.

10. A digital logistics supply chain management system according to claim 1, characterized in that: Supports data interaction with external systems, including: Synchronize master data with the ERP system via API interface; Use EDI standards to connect with supplier systems for order processing; Connect to the OBD device of the transport vehicle via IoT protocol; Connect to the meteorological service API to obtain real-time weather data; Connect to the government's traffic management platform to obtain road condition information; It integrates with the financial system to enable automatic settlement.

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