An integrated platform for supply chain whole-process collaborative management

Abstract

The application discloses an integrated platform for whole-process collaborative management of a supply chain, and belongs to the technical field of supply chain management.The platform comprises a dispatch center and a data center.The dispatch center is used for monitoring the logistics transportation state and the warehouse operation state in real time.Once an abnormality is found, the corresponding processing scheme is called from the data center and pushed to the management personnel.The data center is used for storing an updatable knowledge base and historical data, and is used for dynamically counting the abnormality occurrence rate of each link.When the abnormality rate reaches a preset warning threshold, the dispatch center automatically sends a hierarchical early warning information to the management personnel.The platform also has a self-learning and optimizing capability based on feedback, and can continuously improve the processing strategy.The integrated platform for whole-process collaborative management of the supply chain realizes closed-loop abnormality management and intelligent early warning of the logistics and warehouse links of the supply chain, and effectively improves the collaborative efficiency, response speed and operation reliability of the supply chain.

Description

Technical Field

[0001] This invention relates to the field of supply chain management technology, and more specifically, to an integrated platform and its implementation method for collaborative management of the entire supply chain process, which is particularly suitable for real-time monitoring, intelligent handling of anomalies, and forward-looking early warning in logistics, warehousing, and other links. Background Technology

[0002] In modern supply chain management, logistics and warehousing are crucial for ensuring the efficient flow of products. However, traditional management models have significant drawbacks: First, information collection on the status of logistics transportation and warehousing is often fragmented and delayed, creating information silos; second, when anomalies occur, they rely primarily on manual discovery and experience-based handling, resulting in slow response times and uncertain effectiveness of solutions; third, there is a lack of in-depth analysis of historical anomaly data and the ability to predict trends, hindering proactive warnings and process optimization for high-frequency anomalies. While some existing information systems have implemented partial data recording functions, they generally lack the collaborative management capabilities that integrate real-time monitoring, intelligent decision-making, closed-loop handling, and adaptive learning.

[0003] Therefore, there is an urgent need for an integrated platform that can integrate multi-source information, achieve automated anomaly response and intelligent early warning, in order to improve the overall resilience, efficiency and reliability of the supply chain. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide an integrated platform for collaborative management of the entire supply chain process to offer a solution.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: An integrated platform for collaborative management of the entire supply chain process, including a dispatch center for real-time monitoring and management of logistics and warehousing status in the supply chain; The data center, which is connected in communication with the dispatch center, is used to store and update supply chain-related data, rules, and anomaly handling schemes. When the dispatch center detects an abnormal state in the logistics or warehousing process, it automatically retrieves the corresponding abnormality handling plan from the data center and sends the abnormality handling plan to the management personnel responsible for the corresponding process.

[0006] Furthermore, the data center includes: The data update module is used to receive and integrate the latest operational data from all parts of the supply chain; The knowledge base module stores historical anomaly handling cases, industry rules, and dynamically optimized decision-making models.

[0007] Furthermore, the scheduling center includes: The monitoring module is used to acquire real-time information on logistics transportation routes, timeliness data, environmental parameters, as well as warehouse inventory levels, storage location status, and operational efficiency. The anomaly detection module is used to determine whether the acquired state data deviates from the normal range based on preset rules or machine learning models, thereby identifying anomalies.

[0008] Furthermore, the platform also includes: The early warning module is connected to the data center and the dispatch center; The data center also includes a statistical analysis module for calculating the historical and real-time anomaly rates of specific stages of logistics or warehousing. The early warning module is used to compare the anomaly occurrence rate with a preset warning rate threshold. When the anomaly occurrence rate exceeds the warning rate threshold, an early warning message is generated and sent to the management personnel of the corresponding link through the dispatch center.

[0009] Furthermore, the warning rate threshold is a multi-level threshold, corresponding to different warning levels; the content of the warning information and the urgency of its transmission are adjusted according to different warning levels.

[0010] Furthermore, the platform also includes: The feedback tracking module is used to record the confirmation, execution status and processing results of the anomaly handling plan by the management personnel, and to feed back the execution effect to the data center for optimization of subsequent anomaly handling plans.

[0011] Furthermore, the platform also includes: The self-learning optimization module is used to dynamically adjust and optimize the decision model, processing rules, and warning rate thresholds stored in the data center based on the execution effect data provided by the feedback tracking module and newly emerging abnormal cases.

[0012] Furthermore, the platform also includes: The communication and interface module is used to achieve data connection with external logistics tracking equipment, warehouse management system and enterprise resource planning system, and supports information synchronization and command issuance across multiple terminals.

[0013] Furthermore, the communication and interface module supports application programming interface calls, message queues, and data stream transmission communication methods to ensure low latency and high reliability of data acquisition and command push.

[0014] A collaborative management method for the entire supply chain process includes the following steps: The logistics and warehousing status of the supply chain are monitored in real time through the dispatch center; When an anomaly is detected, the system automatically retrieves a pre-set or intelligently generated anomaly handling solution from the data center. The aforementioned exception handling plan will be pushed to the relevant management personnel; The anomaly rate at each stage is statistically analyzed through the data center; When the abnormality rate in a certain process exceeds the preset warning rate, an early warning message is generated and sent.

[0015] The beneficial effects of this invention are as follows: It achieves closed-loop automation of management: linking the discovery, diagnosis, handling and tracking of anomalies into an automated closed loop, greatly reducing the delay of manual intervention.

[0016] It has improved the intelligence level of decision-making: by introducing a rule engine and machine learning model, the anomaly detection is more accurate and the recommended handling plan is more scientific.

[0017] A forward-looking early warning mechanism has been established: transforming passive response into proactive prevention, and issuing early warnings before problems worsen by analyzing the trend of anomaly rates.

[0018] It possesses the ability to continuously evolve: through a feedback learning mechanism, the platform accumulates experience and knowledge, and its decision-making and early warning capabilities continue to improve over time.

[0019] Enhanced supply chain collaboration efficiency: The unified platform breaks down information silos, enabling efficient and transparent collaboration in logistics, warehousing, and even upstream and downstream links. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a structural block diagram of an integrated platform for collaborative management of the entire supply chain, as described in an embodiment of this application. Figure 2 This is a schematic diagram of a supply chain end-to-end collaborative management method for an integrated platform used for end-to-end collaborative management in an embodiment of this application; Figure 3 This is a flowchart illustrating a specific implementation method of an integrated platform for collaborative management of the entire supply chain, as described in this application.

[0022] In the diagram: 1-Dispatch Center, 2-Data Center, 3-Communication and Interface Module. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] In the description of this disclosure, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," "top," and "bottom," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0025] It should also be noted that all standard parts used in this application are commercially available, and can be custom-made according to the description and drawings. Unless otherwise expressly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this disclosure based on the specific circumstances, and unless explicitly limited, machinery, parts, and equipment can all adopt conventional models in the prior art.

[0026] In this document, the term "comprising" is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0027] Example: Reference Figure 1-3 The integrated platform shown is used for collaborative management of the entire supply chain process. It mainly includes a dispatch center 1, a data center 2, and a communication and interface module 3.

[0028] The dispatch center 1 is the core control unit of the platform, responsible for real-time monitoring and coordinated command of the supply chain status. Its internal components mainly include: Monitoring module: Through communication and interface module 3, it continuously collects real-time location, estimated arrival time, environmental parameters, and warehouse inventory, storage space occupancy rate, and inbound / outbound flow data from GPS, temperature and humidity sensors, RFID readers deployed on transportation vehicles and warehouse management systems.

[0029] Anomaly detection module: This module incorporates a built-in rule engine and a machine learning model. The rule engine makes initial judgments based on preset thresholds. The machine learning model, trained on historical normal data, can identify more complex and hidden anomaly patterns. Once monitored data triggers a rule or model-determined an anomaly, this module generates an anomaly event.

[0030] Instruction Generation and Push Module: Upon the generation of an abnormal event, this module immediately sends a request to Data Center 2 to retrieve a handling plan that matches the current abnormality type and stage. The handling plan can be drawn from a preset contingency plan library or dynamically generated by the data center's intelligent model. Subsequently, this module pushes the handling plan, containing specific operation instructions, to the terminal devices of the managers responsible for that stage via multiple channels such as SMS, in-app messages, and email.

[0031] Feedback Tracking Module: This module tracks the response status of administrators to the processing solutions and can collect feedback on the results after processing. This feedback data will flow back to Data Center 2 for evaluating the effectiveness of the solutions.

[0032] Data Center 2 serves as the platform's data hub and intelligent brain, responsible for data storage, analysis, and knowledge evolution. Its internal components primarily include: Data storage and update module: This module uses a distributed database to store real-time data, historical operational data, and product master data from the scheduling center. It supports both scheduled and triggered updates to ensure data timeliness.

[0033] Knowledge base module: Stores structured exception handling plans, industry best practice rules, and processing knowledge graphs abstracted from historical cases.

[0034] Statistical Analysis Module: Regularly calculates and statistically analyzes the anomaly rate in the transportation and warehousing operations of logistics.

[0035] The intelligent analysis engine integrates a self-learning optimization algorithm. On one hand, it scores and optimizes the plans in the knowledge base based on the execution effect data of the solutions returned by the feedback tracking module. On the other hand, based on the output of the statistical analysis module, it uses methods such as time series analysis to dynamically adjust the "alarm rate threshold" of each stage, making the early warning system more adaptable.

[0036] The communication and interface module 3 serves as a bridge for the platform to interact with the outside world. It provides various API interfaces to achieve seamless integration with external ERP systems, TMS transportation management systems, IoT sensing devices, and more. This module ensures real-time data acquisition and high reliability of command issuance.

[0037] The core workflow of this platform, combined with Figure 2-3 The explanation is as follows: Status monitoring and data acquisition: The dispatch center 1 continuously acquires real-time status data of each node in the supply chain through the communication interface module 3.

[0038] Anomaly detection: The anomaly detection module performs real-time analysis on the incoming data and immediately triggers an event once the anomaly conditions are met.

[0039] Intelligent processing: 1. Dispatch Center 1 requests a handling plan from Data Center 2.

[0040] 2. Data Center 2 matches the optimal solution from the knowledge base module or generates a new solution in real time by the intelligent analysis engine.

[0041] 3. Dispatch Center 1 will push the plan to the designated management personnel.

[0042] Warning triggered: 1. The statistical analysis module of Data Center 2 periodically calculates the anomaly rate of each stage.

[0043] 2. Compare the calculation results with the dynamic alert rate threshold maintained by the intelligent analysis engine.

[0044] 3. If the anomaly rate in a certain link exceeds the threshold, Data Center 2 will generate an early warning signal and analysis report.

[0045] 4. Dispatch Center 1 sends the early warning information to the relevant link and its superior management personnel, prompting them to conduct process checks or interventions.

[0046] Closed-loop learning and optimization: 1. The results of management personnel's handling of the anomalies and their feedback are recorded.

[0047] 2. This data flows back to the intelligent analytics engine in Data Center 2.

[0048] 3. Based on this, the engine optimizes the accuracy of subsequent handling plan recommendations and adjusts the sensitivity of the early warning mechanism, enabling the platform to evolve itself.

[0049] Through the above methods, this invention constructs an intelligent collaborative management platform integrating perception, decision-making, execution, and learning, significantly improving the agility and proactivity of the supply chain in responding to abnormal events. It ensures the stable and efficient operation of the supply chain as a whole, connecting the discovery, diagnosis, handling, and tracking of anomalies into an automated closed loop, greatly reducing the delay of manual intervention. By introducing a rule engine and machine learning model, anomaly judgment becomes more accurate, and the recommended handling solutions become more scientific, transforming passive response into proactive prevention. Through trend analysis of the anomaly rate, it can issue early warnings before problems escalate. Through a feedback learning mechanism, the platform's accumulated experience and knowledge are continuously enriched, and its decision-making and early warning capabilities are continuously enhanced over time. The unified platform breaks down information silos and achieves efficient and transparent collaboration in logistics, warehousing, and even upstream and downstream links.

[0050] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An integrated platform for collaborative management of the entire supply chain process, characterized in that: include: The dispatch center is used to monitor and manage the logistics and warehousing status in the supply chain in real time. The data center, which is connected in communication with the dispatch center, is used to store and update supply chain-related data, rules, and anomaly handling schemes. When the dispatch center detects an abnormal state in the logistics or warehousing process, it automatically retrieves the corresponding abnormality handling plan from the data center and sends the abnormality handling plan to the management personnel responsible for the corresponding process.

2. The integrated platform for collaborative management of the entire supply chain process according to claim 1, characterized in that, The data center includes: The data update module is used to receive and integrate the latest operational data from all parts of the supply chain; The knowledge base module stores historical anomaly handling cases, industry rules, and dynamically optimized decision-making models.

3. The integrated platform for collaborative management of the entire supply chain process according to claim 1, characterized in that, The dispatch center includes: The monitoring module is used to acquire real-time information on logistics transportation routes, timeliness data, environmental parameters, as well as warehouse inventory levels, storage location status, and operational efficiency. The anomaly detection module is used to determine whether the acquired state data deviates from the normal range based on preset rules or machine learning models, thereby identifying anomalies.

4. The integrated platform for collaborative management of the entire supply chain process according to claim 1, characterized in that, The platform also includes: The early warning module is connected to the data center and the dispatch center; The data center also includes a statistical analysis module for calculating the historical and real-time anomaly rates of specific stages of logistics or warehousing. The early warning module is used to compare the anomaly occurrence rate with a preset warning rate threshold. When the anomaly occurrence rate exceeds the warning rate threshold, an early warning message is generated and sent to the management personnel of the corresponding link through the dispatch center.

5. An integrated platform for collaborative management of the entire supply chain process according to claim 4, characterized in that, The warning rate threshold is a multi-level threshold, corresponding to different warning levels; the content of the warning information and the urgency of its transmission are adjusted according to different warning levels.

6. An integrated platform for collaborative management of the entire supply chain process according to claim 1, characterized in that, The platform also includes: The feedback tracking module is used to record the confirmation, execution status and processing results of the anomaly handling plan by the management personnel, and to feed back the execution effect to the data center for optimization of subsequent anomaly handling plans.

7. An integrated platform for collaborative management of the entire supply chain process according to claim 1, characterized in that, The platform also includes: The self-learning optimization module is used to dynamically adjust and optimize the decision model, processing rules, and warning rate threshold stored in the data center based on the execution effect data provided by the feedback tracking module and newly emerging abnormal cases.

8. An integrated platform for collaborative management of the entire supply chain process according to claim 1, characterized in that, The platform also includes: The communication and interface module is used to achieve data connection with external logistics tracking equipment, warehouse management system and enterprise resource planning system, and supports information synchronization and command issuance across multiple terminals.

9. An integrated platform for collaborative management of the entire supply chain process according to claim 8, characterized in that, The communication and interface module supports application programming interface calls, message queues, and data stream transmission communication methods, ensuring low latency and high reliability for data acquisition and command push.

10. A method for collaborative management of the entire supply chain process based on the platform described in any one of claims 1 to 9, characterized in that, Includes the following steps: The logistics and warehousing status of the supply chain are monitored in real time through the dispatch center; When an anomaly is detected, the system automatically retrieves a pre-set or intelligently generated anomaly handling solution from the data center. The aforementioned exception handling plan will be pushed to the relevant management personnel; The anomaly rate at each stage is statistically analyzed through the data center; When the abnormality rate in a certain process exceeds the preset warning rate, an early warning message is generated and sent.

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