Regional intelligent logistics coordination system

Abstract

The invention discloses a regional intelligent logistics coordination system, and relates to the technical field of logistics coordination. According to the invention, key information is accurately captured through an Internet of Things sensor network deployed on logistics nodes, and preliminary data cleaning and preprocessing are carried out by using an edge computing technology, so that high efficiency and real-time performance of acquisition are ensured; historical logistics data and real-time sensor information are integrated through a deep learning model, potential bottlenecks are predicted, and strategy parameters are dynamically adjusted through a reinforcement learning mechanism to deal with sudden demand fluctuation or environmental interference; the central coordination unit overall plans global logistics resource distribution, dynamically monitors the operation state of each regional subsystem, generates a cross-regional coordination instruction according to the strategy output of the coordination decision engine, optimizes the overall transportation efficiency and cost, and issues a scheduling command to each logistics regional subsystem in real time through a communication interface. And meanwhile, data of the monitoring and feedback module are integrated, the system performance is evaluated, and parameters are adaptively adjusted to cope with large-scale logistics demand fluctuation.

Description

Technical Field

[0001] This invention relates to the field of logistics coordination technology, specifically to a regional intelligent logistics coordination system. Background Technology

[0002] The development of smart logistics benefits from the integration and innovative application of various advanced technologies, especially the progress in information technology, data analysis, and communication technology. With the rapid development of globalization and e-commerce, the application of the Internet of Things (IoT) has brought revolutionary changes to the logistics industry. Through sensors installed in goods, transportation vehicles, and warehousing facilities, IoT technology can achieve real-time monitoring and data collection of the entire logistics process, making logistics information more transparent and enabling precise tracking and dynamic scheduling of every link in the transportation process.

[0003] Traditional systems lack effective information sharing mechanisms, leading to untimely and inaccurate information transmission, which in turn affects the timeliness and accuracy of logistics decisions. Manual scheduling and decision-making processes are cumbersome and time-consuming, making it difficult to cope with large-scale, high-frequency logistics demands. Especially during peak periods or when emergencies occur, manual scheduling often cannot make rapid adjustments, resulting in transportation delays and resource waste. Due to the lack of real-time monitoring and optimized allocation of logistics resources, traditional systems cannot maximize the utilization of resources such as vehicles, warehouses, and personnel. This not only increases operating costs but may also lead to resource idleness or overuse. To address this, we propose a regional intelligent logistics coordination system. Summary of the Invention

[0004] The purpose of this invention is to provide a regional intelligent logistics coordination system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention specifically adopts the following technical solution:

[0006] The regional smart logistics coordination system includes:

[0007] The data acquisition system is used to collect real-time data on logistics flow, inventory status, and transportation environment.

[0008] Regional division units are used to dynamically divide logistics regions based on geographical distribution, logistics node density, and business needs.

[0009] Multiple logistics area subsystems are used to independently handle specific logistics operations in each area, such as parcel sorting, route optimization, local resource scheduling and abnormal event response. They also synchronize data with the coordination decision engine in real time through communication interfaces to ensure efficient collaboration between areas and consistency of the overall system.

[0010] The coordination and decision-making engine unit analyzes multi-source data based on artificial intelligence algorithms to generate optimized routes, resource allocation, and emergency dispatch strategies.

[0011] The central coordination unit is used to coordinate the overall allocation of logistics resources, dynamically monitor the operating status of subsystems in various regions, generate cross-regional collaborative instructions based on the strategy output of the coordination decision engine, optimize overall transportation efficiency and cost, and issue scheduling commands to each logistics subsystem in real time through the communication interface. At the same time, it integrates data from the monitoring and feedback modules, evaluates system performance, and adaptively adjusts parameters to cope with large-scale fluctuations in logistics demand.

[0012] The monitoring and feedback unit tracks coordination effectiveness in real time, displays key metrics through a visual dashboard, and automatically adjusts strategies to improve overall efficiency.

[0013] Furthermore, the data acquisition system includes a sensor network and IoT devices. The sensor network is used to monitor parameters such as temperature, humidity, location coordinates, movement speed, vibration intensity, and ambient light in real time at key nodes of the logistics network, including warehouses, transport vehicles, and distribution centers. It also tracks changes in cargo status and equipment operation to ensure the accuracy, real-time performance, and comprehensiveness of the collected data. The IoT devices are used to achieve real-time data transmission through wireless communication networks, perform preprocessing operations such as data cleaning, noise reduction, and aggregation, conduct preliminary analysis using edge computing nodes, generate concise data summaries to reduce network load, and seamlessly connect with the coordination decision engine through standardized interfaces.

[0014] Furthermore, the preliminary analysis in the IoT device includes anomaly detection, trend prediction, and feature extraction. Anomaly detection is used to identify abnormal patterns in logistics data in real time and generate alarm signals or automatically trigger preliminary response strategies to coordinate the decision engine to quickly intervene and handle potential risk events. Trend prediction is used to build time series models based on historical data, analyze the periodic changes in logistics flow, inventory consumption rates, and the evolution of the transportation environment, and output future demand forecasts to guide resource pre-allocation. Feature extraction is used to mine key indicators from raw data and generate high-information-density summaries through dimensionality reduction algorithms, providing simplified input for artificial intelligence algorithms to improve decision-making efficiency and system adaptability.

[0015] Furthermore, the logistics area subsystem includes a logistics information collection module, a data processing module, and a communication interface module. The logistics information collection module is used to collect logistics data in real time; the data processing module is used to analyze and process the collected information and generate local decisions; the communication interface module is used to realize efficient data transmission between the logistics area subsystem and the central coordination unit and other external systems, supporting standard protocols such as MQTT or HTTP to ensure real-time and reliable information exchange and timely response to coordination instructions.

[0016] Furthermore, the logistics area subsystem also includes a warehouse management module. This module dynamically optimizes warehouse layout and storage location allocation through intelligent algorithms, monitors inventory status and cargo location in real time, and supports the collaborative operation of automated equipment to achieve efficient inbound and outbound operations. The warehouse management module also includes an inventory forecasting unit, which generates replenishment strategies based on historical turnover data and real-time demand changes. It also synchronizes warehouse capacity utilization, cargo turnover rate, and abnormal storage information to the central coordination unit through a communication interface module, providing a basis for decision-making in global resource scheduling. Simultaneously, it integrates environmental monitoring functions, linking temperature and humidity sensors in the data acquisition system to implement closed-loop control of the cargo storage environment, ensuring compliance with special cargo storage conditions.

[0017] Furthermore, the central coordination unit includes a data processing server, an AI analysis engine, and a real-time monitoring module. The central coordination unit is used to integrate multi-source logistics data using the data processing server; the AI ​​analysis engine is used to analyze the integrated multi-source logistics data and apply artificial intelligence algorithms to generate optimized route planning, resource allocation strategies, and emergency dispatch plans; the real-time monitoring module is used to collect operational status data and abnormal event information of each logistics area subsystem in real time and feed it back to the data processing server through a communication interface to support the dynamic monitoring and adaptive adjustment of the central coordination unit.

[0018] Furthermore, the central coordination unit also includes an anomaly handling mechanism. This mechanism is based on real-time monitoring of the operational status data and anomaly event information of each logistics regional subsystem, combined with a preset anomaly judgment rule base, to automatically trigger multi-level response strategies: when a local anomaly is detected, a regional autonomous repair instruction is generated first through the coordination decision engine to guide the local subsystem to use redundant resources for rapid handling; if the anomaly affects multiple regions or exceeds local processing capacity, a cross-regional collaboration mechanism is immediately activated, and the central coordination unit reallocates global resources and generates an emergency dispatch plan. At the same time, the monitoring and feedback unit evaluates the handling effect in real time and dynamically adjusts the response strategy until the system returns to steady state; after the handling is completed, an anomaly analysis report is automatically generated and the strategy base is updated to improve the system's ability to predict and self-heal similar events.

[0019] Furthermore, it also includes a data storage database, which is used to persistently store multi-source logistics data generated during system operation, including real-time collected environmental parameters, historical operation records, optimization strategy libraries, and performance indicators. The data storage database includes a distributed storage cluster and a data management module. The distributed storage cluster is used to ensure high data availability and disaster recovery capabilities through a redundant backup mechanism, supporting layered storage of structured and unstructured data. The data management module is used to implement data indexing, compression, and encryption operations. It seamlessly connects with the coordination decision engine unit and the central coordination unit through standardized APIs, providing efficient data query services to support the training and decision optimization of artificial intelligence algorithms, while ensuring data security and privacy compliance based on access control mechanisms.

[0020] Furthermore, the data management module in the data storage database includes a query optimization engine and a data cleaning unit. The query optimization engine is used to accelerate multi-dimensional data analysis requests based on load balancing algorithms, such as polymer stream traffic statistics by time range, region, or event type. The data cleaning unit is used to perform secondary noise reduction and consistency verification before data is stored, eliminating sensor acquisition errors or transmission interference, ensuring the accuracy, integrity, and consistency of stored data, and updating the strategy library of the coordination decision engine through a real-time synchronization mechanism to improve the reliability of emergency dispatch decisions.

[0021] Furthermore, it also includes a user interface for providing a visual platform for user interaction with the system, comprising: an instruction input module, a report generation module, an alarm notification module, and a permission management subsystem. The instruction input module receives manual dispatch commands from users and synchronizes with the central coordination unit in real time via a standardized API to ensure accurate execution and feedback of instructions. The report generation module automatically generates logistics performance analysis reports based on historical and real-time data from the data storage database, supports exporting to PDF and CSV formats, and shares them with external systems via a communication interface module. The alarm notification module integrates anomaly detection results from the monitoring and feedback unit, automatically pushes real-time alarms to user terminals when preset thresholds are triggered, and provides a one-click response option to quickly activate the emergency strategy of the coordination decision engine. The permission management subsystem assigns access permissions based on roles, controls the scope of different users' operations on interface functions, and ensures data security and operation audit traceability through encryption authentication mechanisms.

[0022] The beneficial effects of this invention are as follows:

[0023] 1. This invention uses an Internet of Things (IoT) sensor network deployed at logistics nodes to accurately capture key information such as cargo flow rate, warehouse inventory level, transport vehicle location, temperature, humidity, and vibration parameters. Edge computing technology is used for preliminary data cleaning and preprocessing to ensure efficient and real-time data collection. At the same time, the system supports multi-source data fusion, integrating GPS positioning, RFID tag, and API interface data to achieve dynamic monitoring of the entire logistics chain, providing a reliable data foundation for subsequent analysis and decision-making.

[0024] 2. This invention utilizes a deep learning model to integrate historical logistics data with real-time sensor information to predict potential bottlenecks and dynamically adjusts strategy parameters through a reinforcement learning mechanism to cope with sudden demand fluctuations or environmental interference. At the same time, this unit interacts with the regional subsystem through a highly reliable communication protocol to ensure consistency in strategy execution, and optimizes the algorithm model based on a federated learning framework to improve global resource utilization and response timeliness.

[0025] 3. The central coordination unit of this invention coordinates the overall allocation of logistics resources, dynamically monitors the operating status of subsystems in each region, generates cross-regional collaborative instructions based on the strategy output of the coordination decision engine, optimizes overall transportation efficiency and cost, and issues scheduling commands to each logistics subsystem in real time through the communication interface. At the same time, it integrates data from the monitoring and feedback modules, evaluates system performance, and adaptively adjusts parameters to cope with large-scale fluctuations in logistics demand. Attached Figure Description

[0026] Figure 1 This is a block diagram of the present invention;

[0027] Figure 2 This is a block diagram of the data acquisition system in this invention;

[0028] Figure 3 This is a working block diagram of the logistics area subsystem in this invention;

[0029] Figure 4 This is a block diagram of the central coordination unit in this invention;

[0030] Figure 5 This is a flowchart illustrating the working process of the data storage database in this invention.

[0031] Figure 6 This is a flowchart of the user interface in this invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0033] Please see Figure 1 - Figure 6 This invention provides a regional intelligent logistics coordination system, comprising:

[0034] The data acquisition system is used to collect real-time data on logistics flow, inventory status, and transportation environment. Through an IoT sensor network deployed at logistics nodes, it accurately captures key information such as cargo flow rate, warehouse inventory level, transport vehicle location, temperature, humidity, and vibration parameters. Edge computing technology is used for preliminary data cleaning and preprocessing to ensure efficient and real-time data collection. At the same time, the system supports multi-source data fusion, integrating GPS positioning, RFID tag, and API interface data to achieve dynamic monitoring of the entire logistics chain, providing a reliable data foundation for subsequent analysis and decision-making.

[0035] The regional division unit is used to dynamically divide logistics regions based on geographical distribution, logistics node density, and business needs. By integrating real-time logistics flow, inventory status, and environmental parameters provided by the data acquisition system, the regional division unit automatically optimizes regional boundaries using intelligent algorithms (such as cluster analysis or machine learning models) to adapt to seasonal fluctuations or emergencies, thereby improving logistics efficiency and reducing operating costs. At the same time, the division process considers the synergy between regions, ensuring that high-density node areas receive key resource allocation, while remote areas achieve balanced coverage through dynamic merging or splitting strategies, enhancing the overall system's responsiveness and scalability.

[0036] Multiple logistics subsystems independently handle specific logistics operations within their respective areas, such as parcel sorting, route optimization, local resource scheduling, and anomaly response. They synchronize data in real-time with the coordination and decision-making engine via communication interfaces, ensuring efficient inter-regional collaboration and overall system consistency. During parcel sorting, the subsystems utilize edge computing units to execute real-time image recognition algorithms, accurately classifying parcels and optimizing sorting routes. The route optimization module dynamically calculates the most economical route based on real-time traffic flow data, reducing transportation delays. Local resource scheduling automatically allocates warehousing equipment and personnel through intelligent algorithms to respond to sudden changes in demand. The anomaly response mechanism integrates a sensor early warning system to quickly detect cargo damage or environmental anomalies and trigger preset processing procedures. Simultaneously, data synchronization employs high-concurrency communication protocols (such as MQTT or AMQP) to ensure low-latency transmission, supporting basic operation of regional subsystems even offline. Machine learning models continuously optimize local decisions, improving system robustness and overall efficiency. Furthermore, the subsystems support modular expansion, facilitating the addition of new functions such as drone delivery or automated warehouse management, further enhancing regional autonomy and global collaboration.

[0037] The coordination and decision-making engine unit analyzes multi-source data based on artificial intelligence algorithms to generate optimized routes, resource allocation, and emergency dispatch strategies. It integrates historical logistics data and real-time sensor information using deep learning models to predict potential bottlenecks and dynamically adjusts strategy parameters through reinforcement learning mechanisms to cope with sudden demand fluctuations or environmental disturbances. At the same time, this unit exchanges instructions with the regional subsystem through a highly reliable communication protocol to ensure the consistency of strategy execution, and optimizes the algorithm model based on a federated learning framework to improve global resource utilization and response timeliness.

[0038] The central coordination unit is responsible for coordinating the overall allocation of logistics resources, dynamically monitoring the operational status of subsystems in various regions, generating cross-regional collaborative instructions based on the strategy output of the coordination decision engine, optimizing overall transportation efficiency and costs, and issuing scheduling commands to each logistics subsystem in real time through communication interfaces. It also integrates data from monitoring and feedback modules to evaluate system performance and adaptively adjust parameters to cope with large-scale fluctuations in logistics demand. Furthermore, the central coordination unit processes massive amounts of logistics information through an integrated big data analytics platform, uncovering potential optimization opportunities, and uses reinforcement learning algorithms to adjust resource allocation strategies in real time to respond to dynamic market changes. Simultaneously, it combines a federated learning framework to share local model updates from each subsystem, improving the accuracy and robustness of global decision-making. In addition, the unit has a built-in adaptive fault-tolerance mechanism that automatically switches to a redundant backup system when communication interruptions or subsystem failures are detected, ensuring the continuity and reliability of instruction execution. Through data fusion from the real-time monitoring and feedback modules, it generates performance evaluation reports and drives parameter optimization, further reducing operating costs and enhancing the system's resilience and efficiency in large-scale logistics networks.

[0039] The monitoring and feedback unit tracks coordination effectiveness in real time, displays key indicators through a visual dashboard, and automatically adjusts strategies to improve overall efficiency. Specific tracking indicators include logistics delay rate, resource utilization rate, cost-effectiveness ratio, and customer satisfaction score. The dashboard supports multi-dimensional data drill-down and real-time alerts. When abnormal fluctuations are detected, an adaptive algorithm is triggered to optimize resource scheduling paths. Simultaneously, based on historical data trend analysis and predictive models, inter-regional collaboration strategies are dynamically adjusted to ensure decision-making response speed and accuracy, further enhancing the system's throughput and fault tolerance during peak periods.

[0040] In this embodiment, preferably, the data acquisition system includes a sensor network and IoT devices. The sensor network is used to monitor parameters such as temperature, humidity, location coordinates, movement speed, vibration intensity, and ambient light in real time at key nodes in the logistics network, including warehouses, transport vehicles, and distribution centers. It also tracks changes in cargo status and equipment operation, ensuring the accuracy, real-time nature, and comprehensiveness of the collected data. The IoT devices are used to achieve real-time data transmission via wireless communication networks, perform preprocessing operations such as data cleaning, noise reduction, and aggregation, conduct preliminary analysis using edge computing nodes, generate concise data summaries to reduce network load, and seamlessly interface with the coordination decision engine through standardized interfaces. High-precision temperature and humidity sensors, multi-axis accelerometers, and GPS positioning modules are deployed in the logistics network. A timestamp synchronization mechanism ensures spatiotemporal consistency of multi-source data. The IoT devices have built-in data encryption modules, connect to the sensor network using industrial-grade RS485 / CAN bus interfaces, and achieve low-latency data transmission based on the MQTT protocol. The edge computing nodes integrate adaptive filtering algorithms to identify abnormal vibration patterns of equipment and cargo tilting risks, trigger local alarms in real time, and generate compressed data packets. Key feature values ​​are then transmitted to the cloud analysis platform via a 5G private network. The data acquisition system is also compatible with the OPC UA communication standard, supporting two-way data interaction with third-party warehouse management systems and providing reliable data support for global scheduling.

[0041] In this embodiment, preferably, the preliminary analysis in the IoT device includes anomaly detection, trend prediction, and feature extraction. Anomaly detection is used to identify abnormal patterns in logistics data in real time and generate alarm signals or automatically trigger preliminary response strategies to coordinate the decision engine to quickly intervene and handle potential risk events. Trend prediction is used to build time series models based on historical data, analyze the periodic changes in logistics flow, inventory consumption rate, and the evolution of the transportation environment, and output future demand forecast results to guide resource pre-allocation. Feature extraction is used to mine key indicators from the raw data and generate high information density summaries through dimensionality reduction algorithms to provide simplified input for artificial intelligence algorithms to improve decision-making efficiency and system adaptability. Furthermore, the anomaly detection module can integrate machine learning-based dynamic threshold algorithms, such as isolated forests or long short-term memory networks (LSTM). By analyzing sensor data streams and comparing them with preset patterns in real time, it can effectively identify scenarios such as equipment failure, cargo damage, or sudden environmental changes, thereby shortening risk response time and reducing false alarm rates. The trend prediction part further combines seasonal decomposition and regression analysis models, such as ARIMA or Prophet algorithms, to accurately predict logistics throughput, warehouse turnover cycle, and climate impact factors during peak periods. It outputs visual reports to assist in dynamic route planning and inventory optimization, while continuously updating model parameters through an online learning mechanism to cope with market fluctuations. Feature extraction uses dimensionality reduction techniques such as principal component analysis (PCA) or t-distributed neighborhood embedding (t-SNE) to efficiently condense multi-dimensional raw data (such as temperature and humidity sequences, vibration spectra) into low-dimensional feature vectors. This not only reduces the training overhead of cloud-based AI models but also enhances the system's generalization ability and adaptive adjustment speed for sudden events, ultimately realizing a closed-loop feedback mechanism for collaborative optimization of the entire logistics chain.

[0042] In this embodiment, preferably, the logistics area subsystem includes a logistics information collection module, a data processing module, and a communication interface module. The logistics information collection module specifically includes various sensor devices, such as temperature and humidity sensors, GPS positioning devices, and RFID tag readers, covering key indicators such as cargo location, environmental parameters, and transportation status, and collecting logistics data in real time. The data processing module is used to analyze and process the collected information and generate local decisions. The data processing module is further subdivided into a data preprocessing unit and a decision engine unit. The data preprocessing unit is responsible for data cleaning, outlier filtering, and normalization. The decision engine unit integrates lightweight machine learning models (such as decision trees or random forests) to generate local optimization strategies based on real-time analysis results, such as route adjustment or inventory warnings. The communication interface module is used to realize efficient data transmission between the logistics area subsystem and the central coordination unit and other external systems. It supports standard protocols such as MQTT or HTTP to ensure real-time and reliable information interaction and timely response to coordination instructions. The communication interface module also supports data compression, two-way authentication encryption mechanisms, and redundant link design to adapt to stable transmission in high-concurrency scenarios. It can also seamlessly connect to third-party platforms through an API gateway to improve the interoperability and collaborative efficiency of the system in heterogeneous environments.

[0043] In this embodiment, preferably, the logistics area subsystem also includes a warehouse management module. The warehouse management module is used to dynamically optimize the warehouse layout and storage location allocation through intelligent algorithms, monitor the inventory status and cargo location in real time, support the collaborative operation of automated equipment to achieve efficient inbound and outbound operations, and further integrate cargo sorting optimization functions. It uses real-time path planning algorithms (such as A* or Dijkstra's algorithm) to guide automated sorting equipment and combines RFID tag readers to achieve accurate cargo location identification, which greatly shortens order processing time and reduces manual intervention.

[0044] The warehouse management module also includes an inventory forecasting unit that generates replenishment strategies based on historical turnover data and real-time demand changes. It synchronizes warehouse capacity utilization, inventory turnover rate, and abnormal inventory information to the central coordination unit via a communication interface module, providing a basis for global resource scheduling decisions. Simultaneously, it integrates environmental monitoring functions, linking temperature and humidity sensors in the data acquisition system to implement closed-loop control of the goods storage environment, ensuring compliance with special goods storage conditions. The inventory forecasting unit also incorporates time series analysis models (such as ARIMA or LSTM neural networks) to perform multi-period demand forecasting based on seasonal fluctuations and market trends, generating dynamic safety stock thresholds and replenishment strategies. The system establishes a delivery time window and shares forecast results with the central coordination unit via a communication interface module, supporting cross-regional collaborative procurement decisions. The environmental monitoring function enhances the adaptive control mechanism; when temperature and humidity sensors detect anomalies, the system automatically triggers adjustments to the refrigeration or humidification systems and generates real-time alarm logs, which are uploaded to the central coordination unit to ensure full compliance for special goods such as cold chain or hazardous materials. Furthermore, the warehouse management module supports a visual inventory dashboard. Through a decision engine unit integrating the data processing module, it generates real-time warehouse location heatmaps and turnover efficiency reports, assisting managers in optimizing layouts and further improving warehouse space utilization and operational response speed.

[0045] In this embodiment, preferably, the central coordination unit includes a data processing server, an AI analysis engine, and a real-time monitoring module. The central coordination unit is used to integrate multi-source logistics data using the data processing server; the AI ​​analysis engine is used to analyze the integrated multi-source logistics data and apply artificial intelligence algorithms to generate optimized route planning, resource allocation strategies, and emergency dispatch plans; the real-time monitoring module is used to collect operational status data and abnormal event information of each logistics area subsystem in real time and feed them back to the data processing server through a communication interface to support the dynamic monitoring and adaptive adjustment of the central coordination unit. Furthermore, the central coordination unit, through deep integration with the inventory forecasting unit, utilizes the dynamically generated safety stock thresholds and replenishment time windows to optimize resource allocation strategies and emergency dispatch plans, thereby improving the efficiency of cross-regional resource collaboration. The AI ​​analysis engine further integrates time series analysis models (such as ARIMA or LSTM neural networks) to generate high-precision demand forecasts based on seasonal fluctuations and market trends in multi-source logistics data, supporting global route planning. The real-time monitoring module automatically identifies abnormal events when collecting operational status data and, through the communication interface, links with environmental monitoring functions to trigger adaptive adjustments in the refrigeration or humidification systems, ensuring compliance with regulations for the storage of special goods. Simultaneously, this unit also supports data integration from a visual inventory dashboard, generating real-time warehouse location heatmaps and turnover efficiency reports to assist managers in making layout optimization decisions, thereby comprehensively improving warehouse space utilization and operational response speed, and achieving adaptive closed-loop control of regional logistics coordination.

[0046] In this embodiment, preferably, the central coordination unit also includes an anomaly handling mechanism. This mechanism, based on real-time monitoring of the operational status data and anomaly event information of each logistics area subsystem, and combined with a pre-defined anomaly judgment rule base, automatically triggers multi-level response strategies: When a local anomaly is detected, a regional autonomous repair instruction is generated first through the coordination decision engine, guiding the local subsystem to quickly handle the situation using redundant resources; if the anomaly's impact spans multiple areas or exceeds local processing capacity, a cross-regional collaboration mechanism is immediately activated, with the central coordination unit reallocating global resources and generating an emergency dispatch plan. Simultaneously, the monitoring and feedback unit evaluates the handling effect in real time and dynamically adjusts the response strategy until the system returns to a steady state; after processing, an anomaly analysis report is automatically generated and the strategy base is updated, improving the system's ability to predict and self-heal similar events. Furthermore, the anomaly handling mechanism continuously optimizes the anomaly judgment rule base through deep learning algorithms, trains a prediction model based on historical event data, and identifies potential risk patterns; for example, when temperature fluctuations or equipment failure trends are detected, the system automatically generates preventative maintenance instructions to intervene in advance and prevent anomaly escalation. Simultaneously, this mechanism is tightly integrated with an AI analytics engine, using time series analysis to predict seasonal anomaly peaks and dynamically adjust the priority of response strategies. The monitoring and feedback unit collects key indicators during the handling process in real time, such as response latency and resource utilization, and generates performance reports through data visualization, which are then fed back to the central coordination unit for strategy iteration. The anomaly analysis report not only records event details but also correlates with global route planning data, identifies system bottlenecks, and provides decision support for cross-regional resource collaboration, thereby further strengthening the system's adaptive closed-loop control capabilities.

[0047] In this embodiment, preferably, it also includes a data storage database. This database is used to persistently store multi-source logistics data generated during system operation, including real-time collected environmental parameters, historical operation records, optimization strategy libraries, and performance indicators. The data storage database includes a distributed storage cluster and a data management module. The distributed storage cluster ensures high data availability and disaster recovery capabilities through a redundant backup mechanism, supporting layered storage of structured and unstructured data. The data management module implements data indexing, compression, and encryption operations. It seamlessly interfaces with the coordination decision engine unit and central coordination unit through standardized APIs, providing efficient data query services to support the training and decision optimization of artificial intelligence algorithms. Simultaneously, it ensures data security and privacy compliance based on access control mechanisms. Furthermore, the data storage database supports real-time data stream access and batch processing task scheduling. By optimizing data pipelines and coordinating with monitoring feedback units, it ensures low-latency data synchronization and high-throughput processing capabilities. The data management module further integrates intelligent data cleaning algorithms to automatically filter noisy data and repair outliers, improving data quality to meet the training requirements of artificial intelligence models. Simultaneously, it employs role-based access control (RBAC) to refine permission management, combined with audit logs to track all data operation behaviors, strengthening compliance supervision. The distributed storage cluster also implements dynamic resource allocation strategies, automatically adjusting storage nodes according to load changes to optimize query efficiency and cost-effectiveness, and supports cross-regional data sharing and collaborative analysis.

[0048] In this embodiment, preferably, the data management module in the data storage database includes a query optimization engine and a data cleaning unit. The query optimization engine accelerates multi-dimensional data analysis requests based on load balancing algorithms, such as aggregated flow statistics by time range, region, or event type. The data cleaning unit performs secondary denoising and consistency checks before data is stored, eliminating sensor acquisition errors or transmission interference to ensure the accuracy, integrity, and consistency of stored data. It also updates the strategy library of the coordination decision engine through a real-time synchronization mechanism, improving the reliability of emergency dispatch decisions. The query optimization engine significantly reduces the response time of complex queries by intelligently caching hot data and pre-calculating commonly used aggregation results. Simultaneously, the data cleaning unit, combined with a predefined data quality rule library, performs format standardization, missing value imputation, and logical conflict detection on the data, forming a high-quality data foundation.

[0049] In this embodiment, preferably, it also includes a user interface for providing a visual platform for user interaction with the system, comprising: an instruction input module, a report generation module, an alarm notification module, and a permission management subsystem. The instruction input module receives manual dispatch commands from users and synchronizes with the central coordination unit in real time via a standardized API to ensure accurate execution and feedback of instructions. The report generation module automatically generates logistics performance analysis reports based on historical and real-time data from the data storage database, supports exporting to PDF and CSV formats, and shares them with external systems via a communication interface module. The alarm notification module integrates anomaly detection results from the monitoring and feedback unit, automatically pushes real-time alarms to the user terminal when a preset threshold is triggered, and provides a one-click response option to quickly activate the emergency strategy of the coordination decision engine. The permission management subsystem assigns access permissions based on roles, controls the scope of different users' operations on the interface functions, and ensures data security and operation audit traceability through an encryption authentication mechanism. In addition, the user interface integrates a real-time data visualization module to dynamically display key indicators of the logistics network, including cargo tracking routes, vehicle operating status, and warehouse throughput. It supports multi-dimensional chart rendering and interactive zooming to help users quickly identify operational bottlenecks. Simultaneously, the system includes a built-in collaboration tool module that supports concurrent multi-user operations and messaging. By integrating instant messaging protocols, it enables team task allocation and progress synchronization, ensuring efficient execution of coordinated decisions. Finally, the interface optimization unit employs responsive design technology to adapt to different terminal device screen sizes and records user behavior data through performance monitoring logs for continuous optimization of the user experience and system response speed.

[0050] Working principle and usage process of this invention:

[0051] First, upon system startup, the central coordination unit establishes connections with each logistics area subsystem via the communication interface module, loading historical strategy databases and operational parameters from the data storage database. Each logistics area subsystem's logistics information collection module (such as temperature and humidity sensors, GPS positioning devices, and RFID tag readers) continuously collects data on cargo location, environmental conditions, and transportation processes. The collected raw data undergoes cleaning, outlier filtering, and normalization by the data processing module's data preprocessing unit, forming a standardized data stream.

[0052] Subsequently, part of the standardized data stream is uploaded in real time to the data processing server of the central coordination unit for integration via the communication interface module; the other part is input into the local decision engine unit. The local decision engine unit integrates lightweight machine learning models (such as decision trees or random forests) to perform preliminary analysis of the data and generate local optimization strategies (such as real-time path fine-tuning or inventory warnings) to support regional autonomous decision-making.

[0053] The central coordination unit's data processing server integrates multi-source logistics data from various regions and inputs it into the AI ​​analysis engine. Based on the integrated data, the AI ​​analysis engine uses time series analysis models (such as ARIMA or LSTM) for trend prediction, combines feature extraction techniques (such as PCA or t-SNE) to generate high-information-density summaries, and applies artificial intelligence algorithms to perform global optimization tasks, including generating cross-regional route planning, resource allocation strategies, and potential emergency dispatch plans. Simultaneously, the real-time monitoring module continuously receives operational status data from subsystems in each region and uses anomaly detection modules (such as dynamic threshold algorithms based on isolated forests or LSTM) to compare against preset patterns in real time, identifying abnormal events such as equipment failure, cargo damage, or sudden environmental changes.

[0054] When the real-time monitoring module detects an anomaly:

[0055] If the anomaly is localized and its impact is controllable, the central coordination unit's anomaly handling mechanism prioritizes generating regional autonomous repair instructions through the coordination decision engine, which are then distributed to the corresponding logistics area subsystem via the communication interface. This subsystem utilizes local redundant resources (such as automated equipment in the warehouse management module or environmental control units) to perform rapid response.

[0056] If the impact of an anomaly spans multiple regions or exceeds local processing capacity, the anomaly handling mechanism immediately activates a cross-regional collaboration mechanism. Based on the optimization results generated by the AI ​​analysis engine, the central coordination unit reallocates global resources (such as transportation capacity and inventory), generates and distributes emergency dispatch plans. The monitoring and feedback unit collects key indicators (such as response latency and resource utilization) in real time during the handling process, evaluates the handling effectiveness, and dynamically adjusts response strategies until the system returns to a steady state.

[0057] After the incident is resolved, the system automatically generates a detailed anomaly analysis report, updates the optimization strategy library in the strategy library and data storage database, and improves the system's ability to predict and self-heal similar events.

[0058] Throughout the operation, users can monitor and intervene through the user interface:

[0059] The instruction input module allows users to input manual scheduling commands, which are then synchronized to the central coordination unit for execution via a standardized API.

[0060] The real-time data visualization module dynamically displays key indicators of the logistics network (cargo tracking routes, vehicle status, warehouse throughput).

[0061] When an abnormality triggers a preset threshold, the alarm notification module pushes a real-time alarm to the user terminal and provides a one-click response option to activate emergency strategies.

[0062] The report generation module automatically generates logistics performance analysis reports based on historical and real-time data from the data storage database, which users can export or share.

[0063] The access control subsystem ensures that users with different roles can only access functions and data within their authorized scope.

[0064] At the same time, the system has the ability to continuously learn and optimize:

[0065] The trend prediction model uses an online learning mechanism to continuously update its parameters using new inflow data, adapting to market fluctuations.

[0066] The anomaly detection rule base is optimized by analyzing historical event data using deep learning algorithms to improve the ability to identify potential risks.

[0067] The data management module regularly performs intelligent data cleaning to improve the quality of stored data and provide a reliable foundation for AI model training and decision optimization.

[0068] Ultimately, through the collaborative operation of the aforementioned multi-modules and closed-loop feedback mechanism (data acquisition -> local / global processing -> decision execution -> effect monitoring -> strategy update), the system achieves dynamic coordination of regional logistics resources, rapid response to risk events, and continuous optimization of end-to-end efficiency, thus constructing an efficient, intelligent, and adaptive smart logistics coordination system.

[0069] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A regional intelligent logistics coordination system, characterized in that: include: The data acquisition system is used to collect real-time data on logistics flow, inventory status, and transportation environment. Regional division units are used to dynamically divide logistics regions based on geographical distribution, logistics node density, and business needs. Multiple logistics area subsystems are used to independently handle specific logistics operations in each area, such as parcel sorting, route optimization, local resource scheduling and abnormal event response. They also synchronize data with the coordination decision engine in real time through communication interfaces to ensure efficient collaboration between areas and consistency of the overall system. The coordination and decision-making engine unit analyzes multi-source data based on artificial intelligence algorithms to generate optimized routes, resource allocation, and emergency dispatch strategies. The central coordination unit is used to coordinate the overall allocation of logistics resources, dynamically monitor the operating status of subsystems in various regions, generate cross-regional collaborative instructions based on the strategy output of the coordination decision engine, optimize overall transportation efficiency and cost, and issue scheduling commands to each logistics subsystem in real time through the communication interface. At the same time, it integrates data from the monitoring and feedback modules, evaluates system performance, and adaptively adjusts parameters to cope with large-scale fluctuations in logistics demand. The monitoring and feedback unit tracks coordination effectiveness in real time, displays key metrics through a visual dashboard, and automatically adjusts strategies to improve overall efficiency.

2. The regional intelligent logistics coordination system according to claim 1, characterized in that, The data acquisition system includes a sensor network and IoT devices. The sensor network is used to monitor parameters such as temperature, humidity, location coordinates, movement speed, vibration intensity, and ambient light in real time at key nodes of the logistics network, including warehouses, transport vehicles, and distribution centers. It also tracks changes in cargo status and equipment operation to ensure the accuracy, real-time performance, and comprehensiveness of the collected data. The IoT devices are used to achieve real-time data transmission through wireless communication networks, perform preprocessing operations such as data cleaning, noise reduction, and aggregation, conduct preliminary analysis using edge computing nodes, generate concise data summaries to reduce network load, and seamlessly connect with the coordination decision engine through standardized interfaces.

3. The regional intelligent logistics coordination system according to claim 2, characterized in that, The preliminary analysis in the IoT device includes anomaly detection, trend prediction, and feature extraction. Anomaly detection is used to identify abnormal patterns in logistics data in real time and generate alarm signals or automatically trigger preliminary response strategies to coordinate the decision engine to quickly intervene and handle potential risk events. Trend prediction is used to build time series models based on historical data to analyze the periodic changes in logistics flow, inventory consumption rate, and the evolution of the transportation environment, and output future demand forecast results to guide resource pre-allocation. Feature extraction is used to mine key indicators from raw data and generate high information density summaries through dimensionality reduction algorithms to provide simplified input for artificial intelligence algorithms to improve decision-making efficiency and system adaptability.

4. The regional intelligent logistics coordination system according to claim 1, characterized in that, The logistics area subsystem includes a logistics information collection module, a data processing module, and a communication interface module. The logistics information collection module is used to collect logistics data in real time; the data processing module is used to analyze and process the collected information and generate local decisions; the communication interface module is used to realize efficient data transmission between the logistics area subsystem and the central coordination unit and other external systems, supporting standard protocols such as MQTT or HTTP to ensure real-time and reliable information exchange and timely response to coordination instructions.

5. The regional intelligent logistics coordination system according to claim 1, characterized in that, The logistics area subsystem also includes a warehouse management module. This module dynamically optimizes warehouse layout and storage location allocation through intelligent algorithms, monitors inventory status and cargo location in real time, and supports the collaborative operation of automated equipment to achieve efficient inbound and outbound operations. The warehouse management module also includes an inventory forecasting unit, which generates replenishment strategies based on historical turnover data and real-time demand changes. It also synchronizes warehouse capacity utilization, cargo turnover rate, and abnormal storage information to the central coordination unit through a communication interface module, providing a basis for decision-making in global resource scheduling. At the same time, it integrates environmental monitoring functions, linking temperature and humidity sensors in the data acquisition system to implement closed-loop control of the cargo storage environment, ensuring compliance with special cargo storage conditions.

6. The regional intelligent logistics coordination system according to claim 1, characterized in that, The central coordination unit includes a data processing server, an AI analysis engine, and a real-time monitoring module. The central coordination unit is used to integrate multi-source logistics data using the data processing server; the AI ​​analysis engine is used to analyze the integrated multi-source logistics data and apply artificial intelligence algorithms to generate optimized route planning, resource allocation strategies, and emergency dispatch plans; the real-time monitoring module is used to collect operational status data and abnormal event information of each logistics area subsystem in real time and feed it back to the data processing server through a communication interface to support the dynamic monitoring and adaptive adjustment of the central coordination unit.

7. The regional intelligent logistics coordination system according to claim 1, characterized in that, The central coordination unit also includes an anomaly handling mechanism. This mechanism is based on real-time monitoring of the operational status data and anomaly event information of each logistics regional subsystem, combined with a preset anomaly judgment rule base, to automatically trigger multi-level response strategies: when a local anomaly is detected, the coordination decision engine first generates a regional autonomous repair instruction to guide the local subsystem to use redundant resources for rapid handling; if the anomaly affects multiple regions or exceeds local processing capacity, the cross-regional collaboration mechanism is immediately activated, the central coordination unit reallocates global resources and generates an emergency dispatch plan, and the monitoring and feedback unit evaluates the handling effect in real time, dynamically adjusting the response strategy until the system returns to steady state; after the handling is completed, an anomaly analysis report is automatically generated and the strategy base is updated to improve the system's ability to predict and self-heal similar events.

8. The regional intelligent logistics coordination system according to claim 1, characterized in that, It also includes a data storage database, which is used to persistently store multi-source logistics data generated during system operation, including real-time collected environmental parameters, historical operation records, optimization strategy libraries, and performance indicators. The data storage database includes a distributed storage cluster and a data management module. The distributed storage cluster is used to ensure high data availability and disaster recovery capabilities through a redundant backup mechanism, supporting layered storage of structured and unstructured data. The data management module is used to implement data indexing, compression, and encryption operations. It seamlessly connects with the coordination decision engine unit and the central coordination unit through standardized APIs, providing efficient data query services to support the training and decision optimization of artificial intelligence algorithms, while ensuring data security and privacy compliance based on access control mechanisms.

9. The regional intelligent logistics coordination system according to claim 8, characterized in that, The data management module in the data storage database includes a query optimization engine and a data cleaning unit. The query optimization engine is used to accelerate multi-dimensional data analysis requests based on load balancing algorithms, such as aggregate flow statistics by time range, region, or event type. The data cleaning unit is used to perform secondary noise reduction and consistency verification before data is stored, eliminating sensor acquisition errors or transmission interference, ensuring the accuracy, integrity, and consistency of stored data, and updating the strategy library of the coordination decision engine through a real-time synchronization mechanism to improve the reliability of emergency dispatch decisions.

10. The regional intelligent logistics coordination system according to claim 1, characterized in that, It also includes a user interface for providing a visual platform for user interaction with the system, comprising: an instruction input module, a report generation module, an alarm notification module, and a permission management subsystem. The instruction input module receives manual dispatch commands from users and synchronizes with the central coordination unit in real time via a standardized API to ensure accurate execution and feedback of instructions. The report generation module automatically generates logistics performance analysis reports based on historical and real-time data from the data storage database, supporting export to PDF and CSV formats and sharing with external systems via a communication interface module. The alarm notification module integrates anomaly detection results from the monitoring and feedback unit, automatically pushing real-time alarms to user terminals when preset thresholds are triggered, and providing a one-click response option to quickly activate the emergency strategy of the coordination decision engine. The permission management subsystem assigns access permissions based on roles, controlling the scope of different users' operations on interface functions, and ensures data security and operation audit traceability through encryption authentication mechanisms.

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