A method for managing the whole life cycle of express consumables

Abstract

The application relates to a whole life cycle circulation management method of express consumables. The method constructs a decision node, an application node and a device node management architecture, introduces the application node as a regional hub, realizes an abnormal message broadcast mechanism among the device nodes, uploads the abnormal message to the application node when the device node identifies a warning trend and generates an abnormal message, and more importantly, the abnormal message is broadcast to other device nodes in the corresponding region, promotes the detection of the same abnormality by the device nodes, enables the devices in the region to form linkage, realizes distributed collaborative detection, significantly improves the efficiency and coverage of abnormality discovery, and further realizes front-end warning and regional cooperation of the device nodes of the management method, the application node is responsible for the summarization and preliminary processing of the abnormal messages in the region, the decision node performs global deep analysis and decision, effectively reduces the processing burden of the cloud decision node, and improves the real-time performance and accuracy of the abnormal response.

Description

Technical Field

[0001] This application relates to the field of express delivery consumables management technology, and in particular to a method for full life cycle management of express delivery consumables. Background Technology

[0002] With the continuous implementation of China's green logistics system, recyclable express delivery consumables have been widely applied across all business scenarios, including express pickup, transit, delivery, and recycling, becoming a core carrier for cost reduction and carbon reduction in the industry. Express delivery consumables are characterized by dispersed network distribution, frequent cross-regional transfers, complex lifecycle stages, and high levels of hidden risks, placing extremely high demands on intelligent and refined closed-loop management throughout the entire process. Therefore, building an efficient and collaborative full lifecycle recycling management system for consumables is an important direction for current logistics technology upgrades.

[0003] Currently, mainstream consumables management technologies in the industry generally adopt a two-tier architecture of centralized cloud control and independent data collection by on-site devices. This solution relies on a cloud platform to store consumables ledgers and configure basic control rules. Each site's data collection device independently completes consumables identification, data recording, and anomaly detection. After detecting issues such as consumable damage, timeouts, or inventory anomalies, the devices directly upload alarm data point-to-point to the cloud. The cloud then centrally completes risk assessment and instruction issuance, enabling basic consumables ledger statistics and simple early warning control.

[0004] However, this two-tier flat architecture has overall core technology defects and is difficult to adapt to distributed and regional consumable management scenarios. Summary of the Invention

[0005] To achieve the above objectives, a first aspect of the present invention provides a method for the full lifecycle cyclical management of express delivery consumables, the method comprising: Construct a management architecture; wherein the management architecture includes: Decision nodes are located in the cloud; Multiple application nodes are configured, with one application node set up in each region, and each of the multiple application nodes is communicatively connected to the decision node. Multiple device nodes are provided, with at least one device node in each region, and the multiple device nodes are respectively connected to the application nodes in the corresponding regions. The management architecture enables full lifecycle management of express delivery consumables. Specifically, if a warning trend emerges at any stage of the express delivery consumables' lifecycle, the corresponding device node compares the warning trend with a pre-set warning threshold set by the decision node. If an anomaly is detected, an anomaly message is generated and broadcast to all other device nodes in the corresponding region, enabling them to detect similar anomalies. The anomaly message is then uploaded to the corresponding application node, which aggregates all anomaly messages within the region and reports them to the decision node, which then executes the corresponding decision.

[0006] The first aspect of this application provides a method for the full lifecycle cyclical management of express delivery consumables, which has at least the following beneficial effects: This method constructs a multi-level management architecture, realizing front-end early warning of device nodes, collaborative detection of devices within a region, regional aggregation of application nodes, and global decision-making by decision nodes. This hierarchical collaborative mechanism enables timely detection, rapid response, and effective handling of anomalies in express delivery consumables throughout their entire lifecycle. Specifically, by constructing a three-tier management architecture of decision nodes, application nodes, and device nodes, an application node is introduced as a regional hub, and an anomaly message broadcasting mechanism is implemented between device nodes. When a device node identifies an early warning trend and generates an anomaly message, the message is not only uploaded to the application node, but more importantly, it is broadcast to other device nodes in the corresponding region, prompting these device nodes to detect similar anomalies. This key technology enables devices within the region to form linkages, achieving distributed collaborative detection and significantly improving the efficiency and coverage of anomaly detection. In addition, the device nodes in this management method perform front-end early warning and regional collaboration, the application nodes are responsible for the aggregation and preliminary processing of anomaly messages within the region, and the decision nodes perform global in-depth analysis and decision-making. This multi-level processing mode effectively reduces the processing burden on cloud-based decision nodes and improves the real-time performance and accuracy of anomaly response.

[0007] The second aspect of this application provides a device for the full life-cycle management of express delivery consumables, the device comprising: An architecture generation module is used to build a management architecture; wherein the management architecture includes: Decision nodes are located in the cloud; Multiple application nodes are configured, with one application node set up in each region, and each of the multiple application nodes is communicatively connected to the decision node. Multiple device nodes are provided, with at least one device node in each region, and the multiple device nodes are respectively connected to the application nodes in the corresponding regions. The cycle management module is used to perform full lifecycle management of express delivery consumables based on the management architecture. Specifically, if a warning trend appears at any stage of the express delivery consumables' lifecycle, the corresponding device node in the region compares the warning trend with a pre-set warning threshold by the decision node. If an anomaly is identified, an anomaly message is generated and broadcast to all other device nodes in the corresponding region, enabling them to detect similar anomalies. The anomaly message is also uploaded to the corresponding application node, which aggregates all anomaly messages within the region and reports them to the decision node, which then executes the corresponding decision.

[0008] To achieve the above objectives, a third aspect of the present invention provides an electronic device, comprising: at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, the instructions being executed by the at least one control processor to enable the at least one control processor to perform the above-described method for full lifecycle management of express delivery consumables.

[0009] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the above-described method for the full lifecycle management of express delivery consumables.

[0010] It is understood that the beneficial effects of the second to fourth aspects compared with the related technologies are the same as the beneficial effects of the first aspect compared with the related technologies. Please refer to the relevant description in the first aspect above, which will not be repeated here. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of a method for the full life-cycle cyclical management of express delivery consumables provided in an embodiment of this application; Figure 2 This is a schematic diagram of a lifecycle management device for express delivery consumables provided in an embodiment of this application; Figure 3 This is a schematic diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0014] Traditional express delivery consumables management technologies employ a two-tier architecture: centralized cloud control and independent data collection by on-site devices. Device nodes independently identify consumables, record data, and determine anomalies, uploading alarm data point-to-point to the cloud. However, this architecture lacks a mechanism for information sharing between device nodes within a region. This prevents similar anomalies from spreading and being collaboratively handled in real time, leading to duplicate detection and processing delays, impacting the real-time performance and accuracy of consumables lifecycle management. Furthermore, this problem stems from the fact that device nodes only perform local anomaly determinations and directly upload data to the cloud, without establishing broadcast communication paths between devices within the region. This limits anomaly risk assessment to single-point data, hindering regional collaborative prevention and control capabilities and weakening the overall effectiveness of the closed-loop lifecycle management system.

[0015] like Figure 1 As shown in the embodiments of this application, a method for full lifecycle cyclical management of express delivery consumables is provided, the method including: Step S100, construct the management architecture; wherein the management architecture includes: Decision nodes are located in the cloud; Multiple application nodes are configured, with one application node set up in each region, and each application node communicates and connects with the decision node. Multiple device nodes, with at least one device node set up in each area, and the multiple device nodes communicating with the application nodes in their respective areas; Step S200: Perform full lifecycle management of express delivery consumables based on the management architecture; wherein, if an early warning trend appears at any stage of the express delivery consumables' lifecycle, the corresponding device node in the region compares the early warning threshold set in advance by the decision node with the early warning trend of the express delivery consumables, and if an anomaly is identified in the comparison, an anomaly message is generated and broadcast to all other device nodes in the corresponding region so that other device nodes can detect similar anomalies; and the anomaly message is uploaded to the corresponding application node, which aggregates all anomaly messages in the region and reports them to the decision node, which then executes the corresponding decision.

[0016] In this embodiment, constructing a management architecture is the foundation for realizing the full lifecycle cyclical management of express delivery consumables. The management architecture is designed as a multi-level distributed structure to adapt to the characteristics of express delivery business outlets being scattered and frequently transferring across regions. Specifically, the management architecture includes decision nodes, multiple application nodes, and multiple device nodes.

[0017] One implementation approach is to deploy decision nodes on high-performance server clusters, using load balancing technology to ensure high availability and processing power. Another approach is to deploy decision nodes on distributed cloud computing platforms, leveraging the elastic scaling capabilities of cloud services to dynamically adjust resources based on business needs. As the brain of the entire management system, the decision node is responsible for overall data analysis, strategy formulation, and command issuance.

[0018] Each region is assigned an application node, which communicates with the decision node. One implementation approach is to deploy application nodes in a regional data center or on a regional server, interacting with the decision node in the cloud via encrypted network channels (e.g., HTTP / HTTPS connections based on the TLS protocol). Another approach is to configure application nodes as lightweight containerized applications, running on edge computing devices within the region, and communicating asynchronously with the decision node via a message queue service. As a regional-level management hub, the application node is responsible for aggregating and initially processing data within its jurisdiction.

[0019] Each area is equipped with at least one device node, which communicates with the application nodes within its corresponding area. One implementation method is to use embedded modules integrated into smart parcel lockers, sorting machines, or handheld scanning devices, transmitting data with the application nodes within the area via a local area network (e.g., Wi-Fi or Ethernet). Another implementation method is to use vehicle-mounted terminals deployed on logistics vehicles, communicating with the application nodes via a cellular network. The device nodes serve as units for collecting and initially judging on-site data.

[0020] If an early warning trend emerges at any stage of the entire lifecycle of express delivery consumables, the corresponding regional device node compares the early warning trend of the consumables with the early warning threshold pre-set by the decision node. As one implementation, the device node can periodically collect real-time status data of the consumables (e.g., location, weight, temperature, number of uses, etc.) and compare it with locally stored fixed early warning thresholds. For example, if the weight of the express box is lower than a preset minimum threshold, it may indicate damage or loss. Alternatively, the device node can use built-in sensors to monitor the physical parameters of the consumables in real time and perform trend analysis with the dynamic early warning thresholds issued by the decision node. For example, it can use moving average or exponential smoothing algorithms to determine whether the rate of change of the consumables' status exceeds the allowable range.

[0021] Upon identifying anomalies, an anomaly message is generated and broadcast to all other device nodes in the corresponding area. This allows other device nodes to detect similar anomalies. One implementation involves a device node generating a structured message containing information such as the anomaly type, occurrence time, unique consumable identifier, and anomaly location. This message is then sent to other device nodes within the same area via multicast or broadcast mechanisms within the local area network. For example, if one device node detects damage to a parcel locker, other device nodes, upon receiving the anomaly message, can perform additional, more detailed inspections on subsequent parcel lockers of the same batch or type. Another implementation involves device nodes publishing anomaly messages to a shared message queue within the area. Other device nodes can subscribe to this message queue to obtain and process similar anomaly information.

[0022] As one implementation, device nodes can send exception message data to application nodes in their respective regions via HTTP POST requests. As another implementation, device nodes can use lightweight messaging protocols (such as MQTT) to push exception messages to application nodes in real time.

[0023] One approach is for application nodes to aggregate all abnormal messages within their region and report them to the decision node. After receiving abnormal messages from all device nodes within the region, the application node can store them in a local database, perform initial deduplication and merging, and then periodically package and upload the aggregated data to the decision node. Another approach is for application nodes to receive abnormal messages in real time, immediately parse and initially classify them, and then forward key abnormal information to the decision node in real time via a message bus.

[0024] The decision-making node executes the corresponding decision. As one implementation, after receiving the aggregated anomaly report from the application node, the decision-making node can perform in-depth analysis and risk assessment of the anomaly based on a preset rule engine or machine learning model, and automatically generate corresponding handling instructions (e.g., requiring the isolation, repair, or scrapping of the abnormal consumables). Alternatively, the decision-making node can display the anomaly information to the system administrator, who can then manually judge and issue handling instructions.

[0025] The following example will provide a more detailed explanation of the above technical solution: Suppose a courier company needs to manage a batch of newly received reusable parcel lockers in its warehouse at location A.

[0026] First, the management approach constructs a three-tier management architecture, including a decision node in the cloud, an application node at location A, and multiple device nodes deployed in the warehouse (e.g., intelligent weighing equipment, RFID scanning equipment, and visual inspection equipment).

[0027] When a batch of parcel lockers passes through the inbound channel, the intelligent weighing equipment (as one of the equipment nodes) collects the weight data of each locker in real time. The equipment node compares the collected real-time weight data with the warning threshold set in advance by the decision-making node. For example, the warning threshold may be set to the empty locker weight being between 1.5kg and 1.8kg. If the real-time weight of a certain parcel locker is 1.2kg, which is significantly lower than the warning threshold, the equipment node will immediately identify a warning trend of abnormal weight in the parcel locker.

[0028] Upon detecting an anomaly, the intelligent weighing device immediately generates an anomaly message, containing the unique identifier of the parcel box, the anomaly type (weight anomaly), the time of the anomaly, and the specific abnormal value. This anomaly message is then broadcast to other device nodes within area A, such as RFID scanners and visual inspection devices. When these device nodes receive the anomaly message, they immediately adjust their detection strategies, conducting more rigorous inspections on subsequent parcel boxes of the same batch or type. For example, the visual inspection device might activate a high-resolution scanning mode to check for any visible damage. This collaborative detection within the area improves the efficiency and accuracy of anomaly detection.

[0029] Meanwhile, the intelligent weighing equipment will also upload abnormal messages to the application node at location A. The application node at location A receives and aggregates abnormal messages uploaded from all device nodes in the area. For example, if, in addition to weight abnormalities, the visual inspection device also reports that there are external damages to the same batch of express boxes, the application node will integrate these related abnormal messages.

[0030] The application nodes report the aggregated anomaly messages to the decision nodes in the cloud. Upon receiving these messages, the decision nodes perform a global analysis. For example, they might correlate the current anomaly at location A with historical anomaly data or compare it with similar anomalies across different regions to determine whether the anomaly is an isolated incident or a widespread problem. Based on the analysis results, the decision nodes execute corresponding decisions. For instance, they might issue instructions requiring warehouse management personnel at location A to conduct a comprehensive manual inspection of the batch of parcel boxes and isolate any damaged boxes to prevent them from entering subsequent logistics stages.

[0031] As can be seen from the above examples, this management method, by constructing a multi-level management architecture, achieves front-end early warning of device nodes, collaborative detection of devices within a region, regional aggregation of application nodes, and global decision-making by decision nodes. This layered collaborative mechanism enables timely detection, rapid response, and effective handling of anomalies in express consumables throughout their entire lifecycle. Specifically, by constructing a three-tiered management architecture of decision nodes, application nodes, and device nodes, application nodes are introduced as regional hubs, and an anomaly message broadcasting mechanism is implemented between device nodes. When a device node identifies an early warning trend and generates an anomaly message, the message is not only uploaded to the application node, but more importantly, it is broadcast to other device nodes in the corresponding region, prompting these device nodes to detect similar anomalies. This key technology enables devices within the region to form linkages, achieving distributed collaborative detection and significantly improving the efficiency and coverage of anomaly detection. Furthermore, this management method uses device nodes for front-end early warning and regional collaboration, application nodes are responsible for aggregating and initially processing anomaly messages within the region, and decision nodes perform global in-depth analysis and decision-making. This multi-level processing mode effectively reduces the processing burden on cloud-based decision nodes and improves the real-time performance and accuracy of anomaly response. In some embodiments of this application, the entire lifecycle includes the consumable warehousing stage, the requisition stage, the in-transit stage, the recycling stage, the maintenance and reuse stage, and the scrapping stage.

[0032] In some embodiments of this application, the decision node performs a corresponding decision, including: The decision node integrates and correlates at least one abnormal message received this time with historical abnormal messages and / or similar abnormal messages across regions to obtain the analysis results; Decision nodes generate corresponding handling guidelines based on the analysis results; The decision-making node will distribute the handling instructions to the application nodes in the corresponding areas; The decision-making node records at least one abnormal message, analysis results, and handling instructions in the ledger.

[0033] In this process, the decision node integrates and correlates at least one received anomalous message with historical anomalous messages and / or similar anomalous messages from other regions to obtain the analysis results. This means that the decision node performs in-depth mining and comprehensive evaluation of the received anomalous information. Integrated correlation analysis can be understood as comparing and analyzing current anomalous events with similar past events and similar events in other regions from multiple dimensions and levels to reveal the underlying causes, development trends, or potential correlations of the anomalies. For example, data mining techniques, such as cluster analysis and classification algorithms, can be used to identify patterns, trends, or causal relationships between anomalous messages; alternatively, expert systems or rule engines can be used to pre-set a series of correlation rules, triggering corresponding correlation analysis logic when an anomalous message matches a specific rule.

[0034] Decision nodes generating corresponding handling guidance based on analysis results refers to the process by which decision nodes formulate specific and actionable response strategies based on insights gained from integrated correlation analysis. Handling guidance is a series of suggestions, instructions, or operational procedures provided to operators or automated systems for specific anomalies, aiming to effectively resolve the anomalies and prevent their recurrence. For example, a standard operating procedure (SOP) library can be pre-set, and the most suitable SOP can be matched as handling guidance based on the analysis results; alternatively, machine learning models can be used to predict and generate optimal handling guidance based on current analysis results by learning from historical anomaly handling cases and their effects.

[0035] The decision-making node's distribution of handling guidelines to the corresponding application nodes means that the decision-making node promptly and accurately conveys the formulated handling guidelines to the regional application nodes responsible for specific implementation. This ensures the effective implementation and execution of the decision. For example, asynchronous communication can be achieved through a message queue service, where the decision-making node publishes the handling guidelines as messages to a specific topic, and the corresponding regional application nodes subscribe to the topic and receive the messages; alternatively, remote procedure calls or RESTful API interfaces can be used, where the decision-making node directly calls the interface of the corresponding application node, passing the handling guidelines as parameters.

[0036] The decision-making node records at least one abnormal message, analysis result, and handling guidance in a ledger, meaning that the decision-making node meticulously records and archives the entire abnormal event handling process. The establishment of this ledger aims to create a complete historical record of abnormal event handling, facilitating subsequent auditing, traceability, experience learning, and system optimization. For example, this information can be structured and stored in a relational database, creating an abnormal event log table containing fields such as abnormal message ID, analysis result summary, handling guidance content, timestamp, and processing status; alternatively, a distributed file system or blockchain technology can be used to ensure the immutability and traceability of the records.

[0037] This embodiment, through the aforementioned hierarchical management architecture, allows decision nodes to proactively integrate and correlate received anomaly reports from various application nodes when they receive such reports. Instead of passively receiving information, the system actively integrates and correlates these reports with historical and / or cross-regional anomaly reports of the same type. This enables the system to identify potential patterns, trends, or common problems from isolated anomalies, resulting in more comprehensive and accurate analysis. Based on these results, decision nodes can generate targeted and effective handling guidelines, ensuring that measures address the core issues. These guidelines are then promptly distributed to application nodes in the corresponding regions, guaranteeing rapid communication and efficient execution of decisions. Simultaneously, anomaly reports, analysis results, and handling guidelines are recorded in a ledger, creating a complete historical archive of anomaly handling, providing a solid data foundation for subsequent tracing, auditing, learning from experience, and system optimization. In this way, the entire express consumables lifecycle management system is upgraded from an anomaly reporting mechanism to a system with intelligent analysis, precise decision-making, and closed-loop management capabilities, significantly improving the efficiency and accuracy of anomaly handling.

[0038] In some embodiments of this application, a comparison is made between a pre-set warning threshold for the decision node and a warning trend for express delivery consumables, including: The equipment nodes collect real-time status data of express delivery consumables throughout their entire lifecycle; Equipment nodes extract historical status data of express delivery consumables throughout their entire lifecycle; The device node retrieves the pre-configured early warning threshold from the decision node; When the deviation between real-time status data and historical status data exceeds the warning threshold, an anomaly is identified.

[0039] Specifically, the device nodes are responsible for acquiring various operational parameters or attribute information of the express delivery consumables at the current moment. This real-time status data can reflect the immediate usage, wear and tear, location information, and environmental conditions of the consumables. For example, the device nodes can use sensors (such as RFID readers, barcode scanners, weight sensors, temperature sensors, humidity sensors, etc.) to monitor the physical state, usage frequency, turnover time, and environment of the consumables in real time and digitize this data. Alternatively, the device nodes can receive the latest status updates on the consumables from other systems (such as logistics management systems and warehouse management systems), such as the current location of the consumables and whether they are being used.

[0040] Device nodes need to access and obtain the status records of express consumables over a period of time. This historical status data is an important basis for analyzing the operating trend and health status of consumables. For example, device nodes can retrieve the past usage records, maintenance records, turnover paths, wear history, etc. of specific consumables from local storage or cloud databases via the network. In addition, device nodes can also obtain the historical archives of consumables they maintain from application nodes or decision nodes through periodic synchronization or on-demand querying.

[0041] Warning thresholds are key parameters for measuring whether the status of consumables deviates from the normal range. For example, device nodes can periodically receive an updated list of warning thresholds from the decision node and store it in a local cache. Alternatively, when performing anomaly detection, device nodes can send real-time requests to the decision node to obtain currently valid warning thresholds. These thresholds may include the upper limit of consumable lifespan, the upper limit of single use duration, the upper limit of cumulative use, and specific environmental parameter ranges. Finally, device nodes determine whether anomalies exist by comparing current real-time data with historical data and combining this with preset thresholds. For example, device nodes can calculate the difference between real-time status data and historical averages, historical maximum / minimum values, or historical trend lines. When this difference (deviation) exceeds the warning threshold set by the decision node, it is judged as an anomaly. This can include situations such as real-time use far exceeding the historical average, a sudden increase in real-time wear and tear, and an abnormally long real-time turnaround time.

[0042] Specifically, the device nodes are first responsible for collecting real-time status data throughout the entire lifecycle of express delivery consumables. This data reflects the current immediate condition of the consumables. Simultaneously, the device nodes also extract historical status data throughout the entire lifecycle of the express delivery consumables. This historical data provides a benchmark for judging the normal operating mode and trends of the consumables. Based on this, the device nodes further retrieve pre-configured warning thresholds from the decision nodes. These thresholds are quantitative standards for judging anomalies. Subsequently, the device nodes compare the collected real-time status data with the extracted historical status data and calculate the deviation between the two. When this deviation exceeds the pre-set warning threshold, the device nodes can identify the abnormal situation of the express delivery consumables. In this way, real-time data is dynamically compared with historical data, and combined with adjustable warning thresholds, anomaly detection becomes more accurate and flexible, effectively avoiding false alarms and missed alarms, thereby improving the warning accuracy and response efficiency of the entire express delivery consumables lifecycle management system.

[0043] In some embodiments of this application, the application node aggregates all abnormal messages within the region and reports them to the decision node, including: The application node receives at least one exception message uploaded by all device nodes in the region; Application nodes deduplicate and merge at least one abnormal message; Application nodes perform risk classification on at least one abnormal message based on the scope and severity of the anomaly's impact. Application nodes prioritize and encapsulate at least one abnormal message based on the risk classification ranking results. Application nodes will prioritize and select at least one exception message to report to the decision node.

[0044] In this system, application nodes act as regional aggregation centers, responsible for receiving abnormal messages from all device nodes within their jurisdiction. Deduplication and merging aim to eliminate duplicate or highly similar abnormal messages and reduce data redundancy. One implementation method is for application nodes to maintain a cache of recent abnormal messages. When a new abnormal message is received, it compares key fields (e.g., unique consumable identifier, abnormal type, timestamp, location) to determine if a duplicate exists. If a duplicate is identified, the new message can be discarded or merged with existing messages, for example, by updating the count or latest occurrence time of existing messages. Another implementation method is to use a hash algorithm to fingerprint the message content, merging messages with the same hash value as duplicate messages.

[0045] Risk classification is the process of assessing the importance of abnormal messages in order to carry out differentiated processing in the future. This can be achieved through a pre-set rule engine. For example, based on factors such as the type of abnormality (e.g., damaged, lost, or contaminated consumables), the value of the consumables involved, the criticality of the business processes affected, and potential environmental hazards, abnormalities can be classified into different risk levels such as urgent, high, medium, and low. In addition, machine learning models can be used to automatically learn and predict the risk classification of new abnormalities by analyzing historical abnormal data and corresponding processing results.

[0046] Priority encapsulation involves appending the risk classification results to the exception message in a structured manner, enabling it to be identified and utilized during transmission and processing. This can be represented by adding a priority field to the exception message's data structure, such as an integer value (1 representing the highest priority, 5 representing the lowest priority) or an enumeration type.

[0047] Priority-based reporting means that application nodes no longer indiscriminately forward all abnormal messages, but instead filter and schedule them according to their importance. For example, application nodes can be configured to immediately report all urgent and high-risk abnormal messages, while medium-risk messages can be reported in batches at regular intervals. Low-risk messages may only be recorded locally or reported under specific conditions. This selective reporting mechanism ensures that decision-making nodes can prioritize the processing of the most critical abnormal information.

[0048] This method effectively solves the problems of redundancy and unclear priority in original abnormal messages by introducing a refined abnormal message processing mechanism at the application node level. When device nodes within the region detect abnormal trends in express delivery consumables and generate abnormal messages, these messages first converge to the corresponding application nodes. Upon receiving these messages, the application nodes do not forward them indiscriminately, but first perform deduplication and merging processing. By identifying and integrating duplicate or similar abnormal events, the amount of data to be processed is significantly reduced, ensuring the conciseness of the information. On this basis, the application nodes further classify the messages into risk levels based on the scope and severity of the anomalies, distinguishing anomalies of different severities. Subsequently, the application nodes encapsulate the risk level information into the abnormal messages, forming messages with clear priorities. Finally, the application nodes sort the messages according to these priorities and selectively report the most important and urgent abnormal messages to the decision-making nodes first. This progressive processing flow ensures that the abnormal information received by the decision-making nodes has been preprocessed, deduplicated, classified, and prioritized, thereby enabling more efficient identification of key issues, avoiding being overwhelmed by a large amount of redundant information, and ensuring the timeliness and accuracy of decision-making.

[0049] In some embodiments of this application, after the decision node executes the corresponding decision, the method further includes: The decision-making node continuously collects the historical operational status and abnormal statistics of express delivery consumables in all regions; The decision-making node adaptively adjusts the early warning threshold based on the historical operational status and abnormal statistical status of all regions. The decision-making node will send the adjusted warning threshold to the device nodes in each region.

[0050] The decision-making node's continuous collection of historical operational status and anomaly statistics for express delivery consumables across all regions refers to its role as the central control unit of the entire management architecture. This involves gathering and storing detailed data from various regions regarding the long-term operational performance and anomalies of express delivery consumables. This can include periodically or in real-time receiving aggregated data reported by all application nodes, such as operational data on the usage frequency, average lifespan, loss rate, and recycling rate of express delivery consumables in each region, as well as statistical data on various anomaly messages, such as anomaly type, frequency of occurrence, handling results, and false alarm rate.

[0051] The decision-making node adaptively adjusts the early warning threshold based on historical operational status and anomaly statistics across all regions. This means that the decision-making node utilizes the rich historical data it collects, through intelligent analysis and learning mechanisms, to dynamically optimize the early warning threshold used for anomaly detection. This can be achieved using machine learning algorithms. For example, the decision-making node can deploy anomaly detection models (such as clustering-based LOF algorithms, density-based DBSCAN algorithms, or isolation-based Isolation Forest algorithms) or time series prediction models to learn from historical operational data, identify normal fluctuation ranges and potential anomaly patterns under different scenarios, and dynamically calculate or update the early warning threshold accordingly. Another approach is for the decision-making node to use statistical analysis methods, such as moving averages, exponential smoothing, and standard deviation analysis, combined with indicators such as the frequency of historical anomaly events, false alarm rates, and false negative rates, to periodically or event-drivenly adjust the early warning threshold. For example, if a certain threshold leads to a large number of false alarms in a specific region or time period, the decision-making node can appropriately relax the threshold; conversely, if false negatives occur frequently, the threshold can be appropriately tightened to improve detection accuracy.

[0052] The decision node distributes the adjusted warning thresholds to device nodes in each region. This means that the decision node promptly deploys the optimized warning thresholds to the device nodes that actually execute warning judgments, ensuring that the latest warning logic takes effect. This can be achieved by the decision node using a message queue service or API interface to push the new warning thresholds as configuration files to all application nodes, which are then responsible for forwarding and loading these updated thresholds to the device nodes within their jurisdiction. Alternatively, device nodes can be configured to periodically send query requests to the decision node to check for updated warning thresholds, and automatically download and apply these new thresholds when updates are detected.

[0053] This method continuously collects historical operational and anomaly statistics of express delivery consumables across all regions after the decision-making node executes a decision. Based on this data, it adaptively adjusts the warning threshold and then distributes the adjusted threshold to each device node. This mechanism creates a closed-loop self-optimizing process for the entire express delivery consumables management system. The decision-making node, as the central intelligent unit, no longer relies solely on static preset thresholds but learns from a global perspective and long-term operation, continuously optimizing its anomaly detection capabilities. Through in-depth analysis of historical data, it can identify true anomaly patterns and normal fluctuation ranges in different regions, time periods, and consumable types, thereby dynamically adjusting the warning standards. This dynamic adjustment ensures that the warning threshold remains synchronized with actual operational conditions, significantly improving the accuracy and robustness of anomaly detection, effectively avoiding false alarms and missed alarms caused by inappropriate thresholds, and enabling device nodes to perform more accurate anomaly detection locally.

[0054] In some embodiments of this application, deduplication and merging of at least one abnormal message includes: Based on the unique identifier of the express delivery consumables, the type of abnormality, and the location of the abnormality, at least one abnormal message is matched and classified according to its source. Duplicate abnormal messages identified as originating from the same source are integrated and merged to eliminate data redundancy.

[0055] The unique identifier for express delivery consumables refers to the code or mark used to uniquely identify each consumable. For example, it could be a globally unique serial number, an RFID tag code, a QR code, or a barcode. Through this unique identifier, the system can accurately track and distinguish different express delivery consumables, ensuring accurate identification of anomalies related to specific consumables. The anomaly type refers to the label used to classify anomalies occurring with express delivery consumables. For example, it could include damage, loss, delay, contamination, or expired use. By standardizing the definition of anomaly types, the system can effectively distinguish and statistically analyze anomalies of different natures. The anomaly occurrence location refers to the specific geographical or physical location where the anomaly was detected. For example, it could be a specific warehouse number, sorting center code, GPS coordinates of the transport vehicle, or an identifier for a specific area. By recording the anomaly occurrence location, the system can pinpoint the location of the anomaly, providing geographic information support for subsequent investigation and processing. Same-source matching and classification refers to the application node comparing and grouping received anomaly messages based on the above three key pieces of information (such as unique identifier, anomaly type, and anomaly occurrence location). If multiple abnormal messages are completely identical in these key information points or highly similar within a preset tolerance range, they are determined to originate from the same abnormal event and classified into one category. Consolidation and merging refers to merging multiple duplicate abnormal messages that have been classified as originating from the same source into a single, representative abnormal message. During the merging process, the earliest or latest message information can be retained, or all relevant information can be summarized to form a more comprehensive abnormal record. Eliminating data redundancy refers to reducing the amount of duplicate data stored and processed in the system through consolidation and merging operations. This helps optimize storage space, reduce network transmission load, and improve the efficiency and accuracy of data processing.

[0056] This method, after receiving abnormal messages from all device nodes within the region, first performs refined source matching and classification on these messages. Specifically, the application node extracts the unique identifier of the express consumables, the abnormality type, and the location of the abnormality in each abnormal message. Subsequently, the application node compares and analyzes all received abnormal messages based on these three key dimensions, identifying messages with the same unique identifier, the same abnormality type, and the same location of the abnormality as source messages and grouping them together. This multi-dimensional matching mechanism ensures that only duplicate messages truly originating from the same abnormal event are identified. Once duplicate abnormal messages from the same source are identified, the application node integrates and merges them, simplifying multiple duplicate messages into a single, representative abnormal message. In this way, the system can effectively eliminate data redundancy caused by multiple device nodes repeatedly detecting or reporting the same abnormal event. This processing mechanism enables subsequent application nodes to perform risk classification and priority encapsulation of abnormal messages based on more accurate and refined data, avoiding resource waste and judgment errors caused by redundant information. This provides decision-making nodes with high-quality abnormal information, ensuring the timeliness and effectiveness of decision-making.

[0057] like Figure 2 One embodiment of this application provides a device for the full lifecycle management of express delivery consumables, the device comprising: Architecture generation module 1001 is used to build the management architecture; wherein the management architecture includes: Decision nodes are located in the cloud; Multiple application nodes are configured, with one application node set up in each region, and each application node communicates and connects with the decision node. Multiple device nodes, with at least one device node set up in each area, and the multiple device nodes communicating with the application nodes in their respective areas; The cycle management module 1002 is used to perform full lifecycle management of express delivery consumables based on the management architecture. Specifically, if a warning trend appears at any stage of the express delivery consumables' lifecycle, the corresponding device node in the region compares the warning trend with the warning threshold pre-set by the decision node. If an anomaly is identified, an anomaly message is generated and broadcast to all other device nodes in the corresponding region, enabling other device nodes to detect similar anomalies. The anomaly message is also uploaded to the corresponding application node, which aggregates all anomaly messages in the region and reports them to the decision node, which then executes the corresponding decision.

[0058] It should be noted that the express delivery consumables full life cycle recycling management device provided in this embodiment and the express delivery consumables full life cycle recycling management method described above are based on the same inventive concept. Therefore, the content of the express delivery consumables full life cycle recycling management method described above is also applicable to the content of the express delivery consumables full life cycle recycling management device in this embodiment, and will not be repeated here.

[0059] like Figure 3 This application also provides an electronic device, which includes: At least one memory; At least one processor; At least one program; The program is stored in memory, and the processor executes at least one program to implement the above-described method for the full lifecycle management of express consumables.

[0060] Electronic devices can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), and in-vehicle computers.

[0061] The electronic devices according to embodiments of this application will now be described in detail.

[0062] The processor 1600 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention. The memory 1700 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1700 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1700 and is called and executed by the processor 1600 to implement the express delivery consumables full lifecycle cyclic management method of this invention.

[0063] The input / output interface 1800 is used to implement information input and output. The communication interface 1900 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 2000 transmits information between various components of the device (e.g., processor 1600, memory 1700, input / output interface 1800, and communication interface 1900); The processor 1600, memory 1700, input / output interface 1800 and communication interface 1900 are connected to each other within the device via bus 2000.

[0064] This invention also provides a storage medium, which is a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the above-described method for the full lifecycle management of express delivery consumables.

[0065] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0066] The embodiments described in this invention are intended to more clearly illustrate the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0067] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present invention, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0068] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0069] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0070] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0071] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0072] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0073] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0075] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes multiple instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0076] The above is a detailed description of the preferred embodiments of this application. However, the embodiments of this application are not limited to the above-described implementation methods. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the embodiments of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of the embodiments of this application.

Claims

1. A method for the full lifecycle cyclical management of express delivery consumables, characterized in that, The method includes: Construct a management architecture; wherein the management architecture includes: Decision nodes are located in the cloud; Multiple application nodes are configured, with one application node set up in each region, and each of the multiple application nodes is communicatively connected to the decision node. Multiple device nodes are provided, with at least one device node in each region, and the multiple device nodes are respectively connected to the application nodes in the corresponding regions. The management architecture enables full lifecycle management of express delivery consumables. Specifically, if a warning trend emerges at any stage of the express delivery consumables' lifecycle, the corresponding device node compares the warning trend with a pre-set warning threshold set by the decision node. If an anomaly is detected, an anomaly message is generated and broadcast to all other device nodes in the corresponding region, enabling them to detect similar anomalies. The anomaly message is then uploaded to the corresponding application node, which aggregates all anomaly messages within the region and reports them to the decision node, which then executes the corresponding decision.

2. The method for full lifecycle cyclical management of express delivery consumables according to claim 1, characterized in that, The entire life cycle includes the consumables warehousing stage, the requisition stage, the in-transit stage, the recycling stage, the maintenance and reuse stage, and the disposal stage.

3. The method for full lifecycle cyclical management of express delivery consumables according to claim 2, characterized in that, The decision node executes the corresponding decision, including: The decision node integrates and correlates at least one abnormal message received this time with historical abnormal messages and / or similar abnormal messages across regions to obtain the analysis results. The decision node generates corresponding handling guidance based on the analysis results; The decision node will send the handling instructions to the application nodes in the corresponding areas; The decision node records at least one of the abnormal messages, the analysis results, and the handling instructions in a ledger.

4. The method for full lifecycle cyclical management of express delivery consumables according to claim 1, characterized in that, The comparison between the pre-set early warning threshold based on the decision node and the early warning trend of the express delivery consumables includes: The device node collects real-time status data of the express consumables throughout their entire lifecycle; The device node extracts historical status data of the entire lifecycle of the express consumables; The device node retrieves the pre-configured early warning threshold from the decision node; An anomaly is identified when the deviation between the real-time status data and the historical status data exceeds the warning threshold.

5. The method for full lifecycle cyclical management of express delivery consumables according to claim 4, characterized in that, The application node aggregates all abnormal messages within the region and reports them to the decision node, including: The application node receives at least one abnormal message uploaded by all the device nodes in this area; The application node performs deduplication and merging on the at least one abnormal message; The application node performs risk classification on the at least one abnormal message based on the scope and severity of the abnormality. The application node performs priority encapsulation on the at least one abnormal message according to the sorting result of the risk classification; The application nodes will select, according to priority, to report at least one abnormal message to the decision node.

6. The method for full lifecycle cyclical management of express delivery consumables according to claim 5, characterized in that, After the decision node executes the corresponding decision, the method further includes: The decision node continuously collects the historical operating status and abnormal statistics of the express delivery consumables in all regions; The decision node adaptively adjusts the early warning threshold based on the historical operating status and abnormal statistical status of all regions. The decision node will send the adjusted early warning threshold to the device nodes in each region.

7. The method for full lifecycle cyclical management of express delivery consumables according to claim 5, characterized in that, Deduplication and merging of the at least one abnormal message includes: Based on the unique identifier, anomaly type, and anomaly location of the express consumables, the at least one abnormal message is classified by same-source matching. Repeated abnormal messages that are determined to be from the same source are integrated and merged to eliminate data redundancy.

8. A device for the full life-cycle recycling management of express delivery consumables, characterized in that, The device includes: An architecture generation module is used to build a management architecture; wherein the management architecture includes: Decision nodes are located in the cloud; Multiple application nodes are configured, with one application node set up in each region, and each of the multiple application nodes is communicatively connected to the decision node. Multiple device nodes are provided, with at least one device node in each region, and the multiple device nodes are respectively connected to the application nodes in the corresponding regions. The cycle management module is used to perform full lifecycle management of express delivery consumables based on the management architecture. Specifically, if a warning trend appears at any stage of the express delivery consumables' lifecycle, the corresponding device node in the region compares the warning trend with a pre-set warning threshold by the decision node. If an anomaly is identified, an anomaly message is generated and broadcast to all other device nodes in the corresponding region, enabling them to detect similar anomalies. The anomaly message is also uploaded to the corresponding application node, which aggregates all anomaly messages within the region and reports them to the decision node, which then executes the corresponding decision.

9. An electronic device, characterized in that, include: At least one control processor and a memory for communicatively connecting to the at least one control processor; The memory stores instructions that can be executed by the at least one control processor, which, when executed by the at least one control processor, enables the at least one control processor to perform the express delivery consumables full life cycle management method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which are used to cause a computer to perform the method for full life cycle management of express delivery consumables as described in any one of claims 1 to 7.

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