Cargo transportation background business management method and system and electronic equipment

Abstract

The invention discloses a general cargo transportation background business management system and method and electronic equipment, the system comprises a data acquisition module, an information management module, a position tracking module and a scheduling management module, and automatic acquisition of cargo position, attribute and safety inspection information is realized through an integrated sensor, an RFID tag technology and an image identification device; the GPS positioning and local wireless network complementary correction technology is utilized to break through the positioning precision limitation in the indoor and outdoor complex environment; based on a distributed database and a real-time data processing engine, high-concurrency query and dynamic information updating are supported; in combination with cargo state and position data, an optimization scheduling instruction is generated through an algorithm, empty driving waiting of loading and unloading equipment is reduced, the method covers intelligent scheduling processes of multi-source data acquisition, standardized processing, real-time tracking and cloud driving, full-life-cycle automatic management of cargos is realized, and electronic equipment realizes full-life-cycle automatic management through a software and hardware collaborative architecture. An existing port management system is compatible, and deployment cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of logistics information management applications, and in particular to a method, system and electronic equipment for managing backend business of piece goods transportation. Background Art

[0002] In traditional breakbulk cargo transportation management, the recording and tracking of cargo information relies primarily on manual processes, such as documenting cargo location, status, and safety inspections using paper documents or simple spreadsheets. This method is inefficient and prone to data errors or omissions due to human negligence. Especially in high-throughput port scenarios, manual recording cannot meet the real-time and accuracy requirements.

[0003] Existing technologies have attempted to incorporate barcode or RFID tagging for cargo tracking. However, existing systems often rely on a single technology. For example, using only RFID or barcodes can lead to tag reading failures or data loss in complex terminal environments, such as densely stacked cargo, metal interference, and indoor and outdoor scene transitions. This can lead to inaccurate cargo location records. Furthermore, cargo status information and environmental parameters still require manual monitoring. This information, including temperature, humidity, and damage, lacks automated collection methods, resulting in significant lags in information updates and makes it difficult to support real-time decision-making.

[0004] Cargo management involves multiple processes, including data collection, storage, tracking, and scheduling. However, existing technologies typically implement these functions in independent systems, lacking efficient collaboration between modules. For example, cargo location data is separated from the scheduling system, resulting in loading and unloading plans relying on manual experience and unable to dynamically respond to changes in cargo status. This leads to high idle equipment rates and low operational efficiency.

[0005] In open areas, GPS positioning technology can provide global location information. However, in areas shielded by satellite signals, such as indoor warehouses, multi-story racks, or densely packed yards, existing technology cannot achieve accurate positioning, requiring manual secondary verification, which significantly increases time costs. Furthermore, the lack of a local network correction mechanism results in fragmented indoor and outdoor positioning data, limiting the ability to track goods in all scenarios.

[0006] Traditional management systems often use a centralized architecture, making it difficult to support high-concurrency data queries and real-time processing. This makes them prone to system lag or crashes when cargo volumes surge. Furthermore, existing systems lack integration with other terminal management platforms, making data interoperability difficult. Deploying a new system requires repeated investment in hardware resources, resulting in high costs. Summary of the Invention

[0007] The present invention provides the following technical solution: a backend business management system for piece goods transportation, comprising: The data collection module uses sensors, RFID tag technology and image recognition devices to collect the placement information, basic information of goods and safety inspection information in real time. Through multi-source data synchronous collection technology, it realizes the comprehensive automatic collection of cargo information, significantly improves data accuracy and real-time performance, and reduces manual recording errors.

[0008] The information management module is connected to the data acquisition module to store, update, and classify the collected cargo information, and perform high-concurrency queries based on the distributed database. Through the efficient storage and dynamic update mechanism of the distributed database, it supports the rapid retrieval and real-time synchronization of large-scale cargo information, meeting the business needs in high-throughput scenarios.

[0009] The location tracking module integrates GPS positioning technology and local wireless network to obtain and update the location information of goods in real time. By combining global positioning with local correction technology, it can accurately track the location of goods in complex environments, improve the efficiency of cargo retrieval and reduce the risk of misplacement.

[0010] The scheduling management module generates a genetic algorithm-based routing plan for cargo loading and unloading operations based on data from the location tracking module and the information management module. This dynamic scheduling strategy, driven by real-time data, optimizes the loading and unloading process, improving terminal resource utilization and operational efficiency.

[0011] Preferably, the data acquisition module includes: RFID readers automatically read RFID tag information on goods; through non-contact tag recognition technology, they can quickly collect the identity information of goods and reduce manual scanning costs.

[0012] The camera and image recognition device capture images of goods and extracts their characteristic data. Combined with image recognition technology, they automatically extract the appearance features of goods, assist in verifying the integrity of goods and identify abnormal conditions.

[0013] Sensor devices detect environmental parameters and physical conditions during cargo loading and unloading.

[0014] Through environmental and physical status monitoring, real-time warning of potential risks during cargo storage or loading and unloading.

[0015] Preferably, the location tracking module achieves precise positioning by: The satellite positioning system provides global position coordinates; the global position calibration of goods in open areas is achieved through satellite signal coverage.

[0016] The local wireless network base station performs local position correction through signal strength triangulation.

[0017] In areas where satellite signals are limited, positioning accuracy is enhanced through local network signals to ensure that the location of the goods can be tracked in all scenarios.

[0018] Preferably, the information management module further includes: The artificial intelligence algorithm unit is used to perform machine learning training on historical cargo data to generate cargo classification models and scheduling prediction models; it uses intelligent algorithms to conduct in-depth mining of cargo data, improve information processing efficiency, and provide predictive support for scheduling decisions.

[0019] The real-time data processing engine cleans, integrates, and standardizes collected cargo information. This enables real-time cleaning and unified formatting of massive amounts of data, ensuring data consistency and availability.

[0020] Preferably, the artificial intelligence algorithm unit includes: A deep learning-based image recognition model is used to extract feature information from cargo images; through automated feature extraction technology, the accuracy and processing speed of cargo image analysis are improved.

[0021] Predictive analysis models that predict storage needs and scheduling priorities based on historical cargo data.

[0022] Optimize warehouse resource allocation and plan scheduling tasks in advance through intelligent prediction based on historical data.

[0023] Preferably, the scheduling management module is implemented based on a microservice architecture and includes the following independent service units: Data aggregation service integrates real-time data from the data acquisition module and location tracking module; achieves global information synchronization through multi-source data aggregation, and provides a complete data foundation for decision-making.

[0024] The decision engine service generates scheduling instructions through the rule engine and AI model; combining rules and intelligent models to generate scheduling instructions improves the scientific nature and response speed of decision-making.

[0025] Early warning service: when it detects that the goods have been stored beyond the expiration date or the location has deviated, an alarm is triggered and pushed to the management terminal.

[0026] Reduce cargo management risks and improve problem handling efficiency through real-time anomaly detection and early warning mechanisms.

[0027] The background business management method for piece and general cargo transportation is based on the above-mentioned background business management system for piece and general cargo transportation, and includes the following steps: The location information, basic information and inspection information of goods are collected in real time through sensors, RFID tags and image recognition technology; through the fusion of multiple technologies, the automatic acquisition of full-dimensional information of goods is achieved, reducing manual intervention.

[0028] Use big data analysis algorithms to clean, integrate and classify collected information to generate standardized cargo files; through standardized data processing procedures, improve information management efficiency and support rapid retrieval.

[0029] Combining GPS positioning technology and local wireless networks, the location data of goods can be tracked and updated in real time; through the dynamic location update mechanism, the visualization and traceability of the goods status can be ensured.

[0030] The processed information is stored in a cloud database to support real-time query and scheduling decisions; through cloud storage and real-time query capabilities, data sharing efficiency and decision-making timeliness are improved.

[0031] Generate optimized loading and unloading operation scheduling plans based on cargo status and location information.

[0032] Improve loading and unloading efficiency and reduce resource waste through data-driven scheduling optimization.

[0033] Preferably, the big data analysis algorithm includes: Cargo handling efficiency optimization algorithm based on time series analysis; predict operation peaks through time series analysis and optimize manpower and equipment allocation strategies.

[0034] Dynamic allocation algorithm of cargo storage areas based on cluster analysis.

[0035] By dynamically clustering and dividing storage areas, we can improve storage space utilization and reduce the problem of mixed goods.

[0036] Preferably, further comprising: Through machine learning models, abnormal cargo status can be predicted to trigger early warning mechanisms; through intelligent predictions, abnormal risks can be identified in advance to enhance the proactiveness and preventive capabilities of cargo management.

[0037] Automatically adjust loading and unloading operation plans based on early warning results.

[0038] Respond quickly to abnormal events through adaptive scheduling mechanisms to reduce the risk of job interruptions.

[0039] electronic devices Electronic equipment includes: processor; a memory storing computer program instructions; When the program instructions are executed by a processor, the method steps described above are implemented.

[0040] Through the collaborative design of software and hardware, it supports the stable operation and efficient processing capabilities of system functions and adapts to the needs of complex logistics scenarios.

[0041] In summary, compared with the prior art, the present invention provides a method, system, and electronic device for managing backend business for piece goods transportation, which have the following beneficial effects: 1. The system of the present invention includes an integrated design of a data acquisition module, an information management module, a location tracking module, and a dispatch management module. By collecting data from multiple sources, such as sensors, RFID tags, and image recognition, and managing a distributed database, it reduces the delay in updating cargo information, significantly improves the accuracy of location records, and thus significantly reduces manual recording errors. Scheduling plans are dynamically generated based on real-time location and cargo status data, shortening waiting times for terminal loading and unloading operations and improving equipment utilization.

[0042] 2. The method of the present invention adopts a collaborative process of data collection, processing, tracking, storage and scheduling decision-making, automatically collecting and processing information on the entire life cycle of goods from entry to departure, reducing manual intervention and thus improving the standardization of operational processes; Combining GPS positioning and local network correction technology, cargo location tracking covers the entire indoor and outdoor scenes of the terminal, supporting precise scheduling in complex environments.

[0043] 3. The coordinated execution of the processor, memory, and program instructions of the electronic device of the present invention supports real-time data collection and analysis, meeting the management needs of port cargo. Moreover, the device is based on a cloud-based architecture and can be seamlessly connected to the existing management system of the terminal, thereby reducing deployment costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a module diagram of the background business management system for piece goods transportation of the present invention.

[0045] Figure 2 It is a step diagram of the background business management method of piece goods transportation of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0047] See also Figure 1 , the backend business management system for piece goods transportation includes the following core modules: Data collection module: uses sensors, RFID tag technology, and image recognition devices to collect cargo information in real time, including placement, basic attributes, and safety inspection records; basic attributes include cargo type and weight.

[0048] The data acquisition module uses sensors, RFID tag technology, and image recognition devices to collect real-time information on cargo location, basic cargo information, and safety inspections. An RFID reader automatically reads the RFID tags attached to the cargo and quickly captures its identity using contactless radio frequency signal recognition. A camera and image recognition device capture images of the cargo and extracts characteristic data, analyzing its appearance using deep learning algorithms. Sensors monitor environmental parameters and physical conditions during cargo loading and unloading, such as temperature, humidity, and vibration intensity.

[0049] By leveraging multi-source simultaneous data collection technology, the system comprehensively captures the physical properties and environmental status of goods, significantly improving the integrity and reliability of data collection and avoiding the limitations of a single technology. RFID technology's contactless reading capabilities significantly shorten the time it takes to enter cargo information while reducing the risk of missing or misreading labels due to manual operation. Image recognition devices automatically extract the appearance of goods, effectively assisting manual inspections and promptly identifying damaged packaging or abnormal stacking, thereby reducing the risk of damage during transportation. The sensor network's real-time monitoring capabilities provide dynamic protection for the cargo storage environment, ensuring the safety of temperature and humidity-sensitive goods.

[0050] Information management module: Connects to the data acquisition module and uses a distributed database to store cargo information, supporting high-concurrency queries and dynamic updates. The distributed database is HBase or Cassandra. The built-in classification algorithm groups cargo by type and priority.

[0051] The information management module connects to the data collection module, storing, updating, and classifying collected cargo information. It also conducts high-concurrency queries based on a distributed database. The artificial intelligence algorithm unit performs machine learning training on historical cargo data to generate cargo classification and scheduling prediction models. The real-time data processing engine cleans, integrates, and standardizes collected cargo information.

[0052] The distributed database's efficient storage and dynamic update mechanism enables the system to handle the real-time writing and querying of massive amounts of cargo information, maintaining smooth operation even in high-throughput port scenarios. The artificial intelligence algorithm analyzes cargo turnover patterns in historical data to automatically optimize classification standards and predict future storage needs, providing data support for resource scheduling. The real-time data processing engine automatically cleans redundant data to ensure information accuracy and consistency, eliminating decision-making errors caused by confusing data formats. The generation of standardized cargo records simplifies cross-departmental collaboration, enabling shippers, warehouses, and transporters to quickly access a unified view of information.

[0053] Position tracking module: Integrates GPS positioning and local wireless network, correcting local position errors through signal strength triangulation, with an accuracy of up to ±0.5 meters; GPS positioning provides global coordinates, and the local wireless network is ZigBee or LoRa.

[0054] The location tracking module integrates GPS positioning technology and local wireless networks to obtain and update cargo location information in real time. The satellite positioning system provides global location coordinates, while the local wireless network base station uses signal strength triangulation to perform local position corrections.

[0055] By combining satellite positioning with local network correction technology, the system overcomes the limitations of traditional positioning methods in complex environments. In open dock areas, GPS technology provides wide-area coverage. In indoor warehouses or multi-story racking scenarios, local wireless network signal strength positioning technology compensates for the lack of satellite signals, ensuring continuous tracking of cargo location information. Dynamic correction algorithms further eliminate interference with positioning signals caused by metal racks or densely stacked cargo, significantly improving positioning accuracy. Real-time updates of location information enable managers to quickly locate target cargo, reducing operational delays caused by misplaced goods.

[0056] Scheduling management module: Based on the real-time location and status data of the goods, a rule engine is used to optimize the loading and unloading plan, such as dynamically allocating the paths of loading and unloading equipment to reduce waiting time.

[0057] The dispatch management module is implemented based on a microservices architecture and includes a data aggregation service, a decision engine service, and an early warning service. The data aggregation service integrates real-time data from the data acquisition module and the location tracking module; the decision engine service generates dispatch instructions using a rules engine and AI models; and the early warning service detects overdue storage or location deviations of goods and triggers alarms.

[0058] The modular design of the microservices architecture enhances the system's flexibility and scalability. Each service unit operates independently and supports dynamic expansion, enabling it to cope with unexpected business peaks. The data aggregation service integrates multi-source information to construct a complete view of the cargo lifecycle, providing a complete data foundation for intelligent scheduling. The decision engine service combines preset rules with the prediction results of machine learning models to generate loading and unloading plans that balance efficiency and safety. For example, it prioritizes cargo nearing its departure time or automatically avoids equipment conflict paths. The real-time monitoring capabilities of the early warning service significantly reduce the risk of cargo detention. By instantly pushing abnormal events to the management terminal, it shortens problem response time and reduces economic losses.

[0059] Data acquisition module refinement RFID readers: Deployed in dock loading and unloading areas and cargo ship hatches, they automatically scan cargo tags and read unique identifiers and attribute information.

[0060] Camera and image recognition device: Use a high-resolution industrial camera to capture cargo images and extract features using the YOLO or ResNet model. The extracted features include damage detection and stacking morphology analysis.

[0061] Sensor device: including temperature and humidity sensors, acceleration sensors and weight sensors; among them, the temperature and humidity sensors are used to monitor the cargo storage environment, the acceleration sensors are used to detect loading and unloading collisions, and the weight sensors are used to verify the integrity of the cargo.

[0062] Position tracking module positioning mechanism Satellite positioning: obtain the initial coordinates of the cargo through GPS / Beidou system; Local correction: The local wireless network base station calculates the signal strength (RSSI) between the cargo and the base station, and uses a multilateration algorithm to compensate for satellite signal obstruction errors, improving positioning accuracy indoors or in densely packed yard environments.

[0063] Intelligent processing of information management module Artificial Intelligence Algorithm Unit: Use random forest or XGBoost models to train historical data and generate cargo classification models (such as dangerous goods identification); Predict storage demand peaks based on the LSTM time series model and dynamically adjust storage space allocation.

[0064] Real-time data processing engine: Use the Apache Flink stream processing framework to clean invalid data, including duplicate RFID reading records, and write the data into the database after standardizing the data format.

[0065] Image recognition model: The Faster R-CNN model is used to identify anomalies in cargo images (such as damaged packaging) with higher accuracy. Predictive analysis model: Combining historical cargo turnover rates and terminal operation plans, a reinforcement learning model is used to generate a scheduling priority list to shorten average processing time.

[0066] Microservice architecture of the scheduling management module Data aggregation service: integrates data from multiple sources (such as RFID data streams and sensor readings) through Kafka message queues; Decision engine service: Built-in Drools rule engine, combined with AI models to output scheduling instructions (such as optimal loading and unloading sequence); Early warning service: When cargo is delayed beyond a threshold or deviates from the scheduled location, an SMS / email alert is triggered and an API is called to adjust the operation plan. Thresholds include, but are not limited to, 48 hours and deviations from the scheduled location greater than 1 meter.

[0067] See also Figure 2The background business management method for piece and general cargo transportation is based on the above-mentioned background business management system for piece and general cargo transportation, and the steps include: Data collection: RFID tags and cameras synchronously collect cargo ID, images and environmental parameters; Information Processing: Cleaning redundant data; redundant data is RFID signals that are read repeatedly; Dynamically divide storage areas through clustering algorithms (such as K-means); Location tracking: GPS and local base station data are integrated, such as setting the location to update every 30 seconds; Cloud storage: Alibaba Cloud OSS is used to store standardized cargo files, supporting SQL / NoSQL hybrid queries; Scheduling optimization: Generate loading and unloading routes based on genetic algorithms to reduce equipment idle driving rates.

[0068] Time series analysis: Use the ARIMA model to predict peak hours for terminal operations and allocate human resources in advance; Cluster analysis: Dynamically allocate storage areas based on cargo size and weight (e.g. heavy cargo is concentrated at the edge of the dock).

[0069] Early warning and adaptive scheduling Use the isolation forest algorithm to detect abnormal cargo status (such as cargo not moving for a long time); After the early warning is triggered, the loading and unloading plan is regenerated through the dynamic programming algorithm and pushed to the terminal operation terminal.

[0070] The method specifically includes: 1. Using sensors, RFID tags, and image recognition technology, the system collects cargo location, basic information, and inspection data in real time. RFID readers automatically scan tags at key points where cargo enters and exits. Image recognition devices continuously capture cargo status during loading and unloading, and a sensor network reports environmental parameters at a preset frequency.

[0071] A multi-technology collaborative collection mechanism enables comprehensive capture of cargo information. RFID technology ensures rapid identification of cargo, image recognition technology complements visual status verification, and sensor networks provide environmental security. This combined collection approach significantly reduces the workload of manual spot checks while avoiding potential blind spots associated with a single data source, such as redundant image verification when an RFID tag becomes detached.

[0072] 2. Utilize big data analysis algorithms to clean, integrate, and categorize collected information to generate standardized cargo files. Time series analysis algorithms optimize cargo handling processes, while cluster analysis algorithms dynamically divide storage areas.

[0073] The data cleansing process automatically filters invalid signals (such as RFID read failures) and corrects outliers (such as sudden changes in temperature and humidity data), ensuring the accuracy of subsequent analysis. Standardization converts heterogeneous data into a unified format, enabling seamless integration of information collected from different devices. Time series analysis algorithms identify peak operating periods to guide the flexible allocation of human and equipment resources. Cluster analysis dynamically optimizes storage layout based on the physical characteristics of goods, reducing interference caused by the mixing of different types of goods.

[0074] 3. Combine GPS positioning technology and local wireless networks to track and update cargo location data in real time. Satellite positioning is preferred in outdoor areas, and automatically switches to wireless network positioning mode when cargo enters indoor warehouses.

[0075] A seamless positioning mechanism ensures visual management of cargo throughout its journey from the dock to the warehouse. Satellite positioning provides a macro-positioning benchmark, while the local network's high-precision correction capabilities ensure precise positioning of cargo in complex yard environments. The continuous updating of location data enables the dispatching system to monitor cargo movements in real time, providing a reliable basis for route planning.

[0076] 4. The processed information is stored in a cloud database, supporting real-time multi-terminal query and scheduling decisions. Based on cargo status and location information, a genetic algorithm is used to generate optimized scheduling plans for loading and unloading operations.

[0077] The cloud-based storage architecture breaks down data silos, enabling port management, transport companies, and cargo owners to access real-time information through a unified platform. Intelligent scheduling algorithms simulate the feasibility of multiple operational paths and select the one with the shortest overall time and highest equipment utilization. Dynamic adjustment mechanisms allow for rapid generation of alternative plans in the event of sudden equipment failures or changes in cargo priorities, minimizing operational disruptions.

[0078] 5. Use machine learning models to predict abnormal cargo conditions, triggering early warning mechanisms and automatically adjusting loading and unloading plans based on the results. For example, predicting the risk of cargo storage overage or equipment overload.

[0079] The proactive early warning mechanism transforms the traditional problem-response model into a risk prevention model. Machine learning models analyze the correlation characteristics of historical abnormal events to proactively identify potential risk points. When an early warning is triggered, the system not only sends an alert but also coordinates adjustments to the task queues of loading and unloading equipment, such as automatically moving high-risk cargo to emergency response areas or reallocating handling resources.

[0080] Electronic equipment: Hardware: Equipped with a multi-core processor (such as Intel Xeon), more than 16GB of memory, and TB-level SSD storage; Software: A hypervisor pre-installed in memory that does the following: Call the sensor driver to collect data; Launch AI models for data prediction and classification; Communicate with the terminal dispatch terminal through RESTful API and issue instructions in real time.

[0081] The electronic equipment implementation plan of this program is specifically as follows: The electronic device includes a processor, memory and communication module. The processor is equipped with a multi-core computing unit to support parallel data processing. The memory uses a high-speed solid-state drive to improve reading and writing efficiency. The communication module integrates 5G and Wi-Fi 6 dual-mode transmission capabilities.

[0082] The multi-core processor's high concurrent computing power meets real-time data processing requirements, significantly reducing response latency in image recognition and positioning algorithm operations. High-speed storage ensures rapid access to massive amounts of cargo information, avoiding system lags caused by I / O bottlenecks. The dual-mode communication design enhances device connection stability in complex electromagnetic environments. 5G networks support high-bandwidth data transmission, while Wi-Fi 6 ensures efficient communication between devices within the local area network.

[0083] The computer program instructions stored in the memory include data acquisition drivers, analysis algorithm libraries, and visualization interface components. When the program instructions are executed, the driver layer coordinates the collaborative operation of sensors and RFID devices, the algorithm layer calls pre-trained AI models for decision analysis, and the interface layer provides a real-time monitoring dashboard and early warning notification panel.

[0084] The software system's layered design decouples functional modules, facilitating future expansion, maintenance, and upgrades. The driver layer's abstracted device management supports plug-and-play integration with sensors from various brands, reducing hardware replacement costs. The algorithm layer's containerized deployment allows for model updates without interrupting system operations, ensuring business continuity. The visual interface, displaying a graphical heat map of cargo distribution and a dashboard of equipment status, enhances managers' situational awareness.

[0085] The electronic equipment is connected to the port's existing management system through the API interface, and middleware technology is used to achieve data format conversion and protocol adaptation.

[0086] The open interface design avoids disruptive changes to existing infrastructure when deploying the new system. The middleware layer converts the system's standardized data format into a compatible format for systems like port ERP and WMS, ensuring smooth migration of historical data and seamless integration of business flows. This design significantly reduces operational training costs and implementation risks during the system transition.

[0087] The system of this solution adopts a modular integrated design to break through the data silo problem in traditional technology, and achieves a qualitative change in global management efficiency through a real-time data closed loop (collection → processing → tracking → scheduling).

[0088] The method of this solution uses standardized processes and algorithm-driven methods to replace manual experience-based decision-making, which increases the response speed of cargo scheduling to minutes and improves the timeliness of abnormal event processing.

[0089] The equipment in this solution adopts a decoupled design of hardware and software, which enables the equipment to support flexible expansion and adapt to the business needs of terminals of different sizes.

[0090] Therefore, this solution has the following technical effects: From collecting cargo entry information to generating departure dispatch instructions, the system replaces traditional manual operations with automated technology, significantly improving the level of operational standardization and reducing cargo mismatches or delays caused by human errors.

[0091] The indoor and outdoor collaborative technology of the positioning module ensures the continuous tracking capability of goods in multiple scenarios such as docks, yards, and warehouses, solving the industry pain point that traditional GPS fails indoors.

[0092] The scheduling algorithm based on real-time data continuously optimizes the working paths and task allocation of loading and unloading equipment, shortens the equipment's idle time, and improves the overall operational throughput of the terminal.

[0093] Through the abnormal prediction and early warning linkage mechanism of the machine learning model, the system establishes multiple protections in the cargo storage, handling and other links, reducing the cargo damage rate and the occurrence rate of operational safety accidents.

[0094] The microservice architecture and cloud-based collaborative design enable the system to expand elastically according to the scale of business while maintaining compatibility with existing management platforms, reducing the marginal cost of the port's intelligent upgrade.

[0095] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0096] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The background business management system for piece goods transportation is characterized by: include: The data acquisition module uses sensors, RFID tag technology, and image recognition devices to collect real-time information on the placement of goods, basic information about goods, and safety inspections; An information management module, connected to the data acquisition module, stores, updates, and classifies the collected cargo information, and performs high-concurrency queries based on a distributed database; Location tracking module, integrating GPS positioning technology and local wireless network, to obtain and update the location information of goods in real time; The scheduling management module generates a loading and unloading path planning scheme based on a genetic algorithm for cargo loading and unloading operations according to the data of the location tracking module and the information management module.

2. The background business management system for piece goods transportation according to claim 1 is characterized in that: The data acquisition module includes: RFID reader, automatically reads the RFID tag information on the goods; Camera and image recognition device to capture cargo images and extract cargo feature data; Sensor devices detect environmental parameters and physical conditions during cargo loading and unloading.

3. The background business management system for piece goods transportation according to claim 1 is characterized in that: The location tracking module achieves precise positioning through the following methods: Satellite positioning systems provide global position coordinates; The local wireless network base station performs local position correction through signal strength triangulation.

4. The background business management system for piece goods transportation according to claim 1 is characterized in that: The information management module further includes: Artificial intelligence algorithm unit, used to perform machine learning training on historical cargo data to generate cargo classification models and scheduling prediction models; Real-time data processing engine cleans, integrates and standardizes collected cargo information.

5. The background business management system for piece goods transportation according to claim 4 is characterized in that: The artificial intelligence algorithm unit includes: A deep learning-based image recognition model is used to extract feature information from cargo images; Predictive analysis models that predict storage needs and scheduling priorities based on historical cargo data.

6. The background business management system for piece goods transportation according to claim 1 is characterized in that: The scheduling management module is implemented based on the microservice architecture and includes the following independent service units: Data aggregation service, integrating real-time data from the data collection module and location tracking module; Decision engine service, which generates scheduling instructions through rule engines and AI models; Early warning service: when it detects that the goods have been stored beyond the expiration date or the location has deviated, an alarm is triggered and pushed to the management terminal.

7. A method for managing backend services for piece cargo transportation, based on the backend services management system for piece cargo transportation according to any one of claims 1 to 6, characterized in that: The following steps are involved: Collect cargo location, basic information, and inspection information in real time through sensors, RFID tags, and image recognition technology; Use big data analysis algorithms to clean, integrate and classify collected information to generate standardized cargo files; Combined with GPS positioning technology and local wireless network, the location data of goods can be tracked and updated in real time; The processed information is stored in a cloud database to support real-time query and scheduling decisions; Generate optimized loading and unloading operation scheduling plans based on cargo status and location information.

8. The background business management method for piece goods transportation according to claim 7 is characterized in that: The big data analysis algorithm includes: Cargo handling efficiency optimization algorithm based on time series analysis; Dynamic allocation algorithm of cargo storage areas based on cluster analysis.

9. The method for managing background business of piece goods transportation according to claim 7, characterized in that: Further including: Use machine learning models to predict abnormal cargo conditions and trigger early warning mechanisms; Automatically adjust loading and unloading operation plans based on early warning results.

10. An electronic device, characterized in that include: processor; a memory storing computer program instructions; When the program instructions are executed by a processor, the method steps according to any one of claims 7 to 9 are implemented.

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