Multi-line collaborative express loading and unloading management method and loading and unloading management platform

Abstract

The multi-line collaborative express loading and unloading management method and loading and unloading management platform provided by this application relate to the technical field of express loading and unloading management. It obtains express loading and unloading process information, extracts key nodes, and conducts endpoint deployment analysis to obtain N express control thread nodes. It obtains express logistics data information through the logistics information center and maps and diverts it to the control thread nodes to obtain N-node express logistics data. Based on the express control thread nodes, it conducts distributed operation analysis on the node express logistics data to obtain N sets of node loading and unloading operation analysis parameters, and encrypts and integrates them into the loading and unloading control center for operation collaborative analysis to obtain express loading and unloading collaborative operation parameters, and then conducts express loading and unloading collaborative management, solving the technical problems of insufficient response speed and accuracy in express loading and unloading management in a dynamic operation environment, inability to flexibly adjust resulting in resource waste and service delays, and poor collaborative operation.

Description

Technical Field

[0001] The present invention relates to the technical field of express loading and unloading management, and particularly to a multi-line collaborative express loading and unloading management method and a loading and unloading management platform. Background Art

[0002] In the modern logistics industry, the management efficiency of express loading and unloading operations directly affects logistics costs and service quality. With the rapid development of e-commerce, the challenges faced by the logistics industry include how to handle a large number of express packages, and how to optimize the loading and unloading process to improve efficiency and reduce errors. These challenges require the logistics system to not only process a large amount of logistics information, but also make a quick response in a dynamically changing environment. The existing technologies mainly rely on fixed flowcharts and preset nodes to manage express loading and unloading operations. This method is highly efficient in dealing with standard operations, but lacks flexibility and scalability when dealing with peak-hour traffic or emergencies. Moreover, although the existing systems can collect and process express data, there are often delays in information updates, which cannot reflect the current logistics status in real time, resulting in inaccurate or untimely data for decision-making, thereby affecting subsequent operation management.

[0003] In summary, the existing express loading and unloading management often has technical problems such as insufficient response speed and accuracy in a dynamic operation environment, inability to flexibly adjust, resulting in waste of resources and service delays, and poor collaborative operation. Summary of the Invention

[0004] This application provides a multi-line collaborative express loading and unloading management method and a loading and unloading management platform, which are used to solve the technical problems existing in the existing express loading and unloading management, such as insufficient response speed and accuracy in a dynamic operation environment, inability to flexibly adjust, resulting in waste of resources and service delays, and poor collaborative operation.

[0005] In view of the above problems, this application provides a multi-line collaborative express loading and unloading management method and a loading and unloading management platform.

[0006] In the first aspect, this application provides a multi-line collaborative express loading and unloading management method, and the method includes:

[0007] Obtain the express loading and unloading process information, extract the key nodes from the express loading and unloading process information to obtain N express loading and unloading process nodes; perform endpoint deployment analysis based on the N express loading and unloading process nodes to obtain N express control thread nodes; obtain the express logistics data information through the logistics information center, map and divert the express logistics data information to the N express control thread nodes to obtain N-node express logistics data; perform distributed operation analysis on the N-node express logistics data based on the N express control thread nodes to obtain N-node loading and unloading operation analysis parameter sets; encrypt and integrate the N-node loading and unloading operation analysis parameter sets into the loading and unloading control center for operation collaborative analysis to obtain express loading and unloading collaborative operation parameters, and perform express loading and unloading collaborative management based on the express loading and unloading collaborative operation parameters.

[0008] In a second aspect, the present application provides an express loading and unloading management platform with multi-line collaboration, and the platform includes:

[0009] A node extraction module, configured to obtain the express loading and unloading process information, extract the key nodes from the express loading and unloading process information to obtain N express loading and unloading process nodes; an endpoint deployment module, configured to perform endpoint deployment analysis based on the N express loading and unloading process nodes to obtain N express control thread nodes; a mapping and diversion module, configured to obtain the express logistics data information through the logistics information center, map and divert the express logistics data information to the N express control thread nodes to obtain N-node express logistics data; a distributed analysis module, configured to perform distributed operation analysis on the N-node express logistics data based on the N express control thread nodes to obtain N-node loading and unloading operation analysis parameter sets; a collaborative management module, configured to encrypt and integrate the N-node loading and unloading operation analysis parameter sets into the loading and unloading control center for operation collaborative analysis to obtain express loading and unloading collaborative operation parameters, and perform express loading and unloading collaborative management based on the express loading and unloading collaborative operation parameters.

[0010] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] The multi-line collaborative express loading and unloading management method provided by this application obtains express loading and unloading process information, extracts key nodes from the express loading and unloading process information to obtain N express loading and unloading process nodes; performs endpoint deployment analysis based on the N express loading and unloading process nodes to obtain N express control thread nodes; obtains express logistics data information through the logistics information center, maps and diverts the express logistics data information to the N express control thread nodes to obtain N-node express logistics data; performs distributed operation analysis on the N-node express logistics data based on the N express control thread nodes to obtain N-node loading and unloading operation analysis parameter sets; encrypts and integrates the N-node loading and unloading operation analysis parameter sets into the loading and unloading control center for operation collaborative analysis to obtain express loading and unloading collaborative operation parameters, and performs express loading and unloading collaborative management based on the express loading and unloading collaborative operation parameters, solving the technical problems of insufficient response speed and accuracy in the existing express loading and unloading management in a dynamic operation environment, inability to flexibly adjust resulting in resource waste and service delays, and poor collaborative operation, achieving the technical effects of improving the processing speed and accuracy of express loading and unloading operations, optimizing resource allocation, enhancing dynamic adjustment capabilities, and operation collaborative efficiency. Description of the Drawings

[0012] Figure 1 It is a schematic flowchart of the multi-line collaborative express loading and unloading management method provided by this application.

[0013] Figure 2 It is a schematic structural diagram of the multi-line collaborative express loading and unloading management platform provided by this application.

[0014] Description of the reference numerals: Node extraction module 11, Endpoint deployment module 12, Mapping and diversion module 13, Distributed analysis module 14, Collaborative management module 15. Detailed Description of the Embodiment

[0015] In order to make the objectives, technical solutions, and advantages of this application clearer, the following further details this application in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0016] Embodiment 1, as Figure 1 shown, the multi-line collaborative express loading and unloading management method provided by this application includes:

[0017] Obtain express loading and unloading process information, extract key nodes from the express loading and unloading process information to obtain N express loading and unloading process nodes.

[0018] Specifically, obtaining express delivery loading and unloading process information refers to collecting various data on the loading and unloading of express items from an express delivery management system or a real-time logistics tracking system, including but not limited to information such as loading and unloading time, location, operators, and logistics status. This information provides the basic data for subsequent processing. Immediately following, key nodes are extracted from the express delivery loading and unloading process information. In this step, by analyzing the collected express delivery loading and unloading process information and using data processing algorithms such as classification, clustering, or association rule mining, key operation nodes are identified. Key nodes refer to those nodes that have a significant impact on the entire loading and unloading process, such as the time points when goods arrive at the distribution center and when loading or unloading begins. The identification of these nodes helps to optimize the management of the entire process. Through the above extraction process, N express delivery loading and unloading process nodes can be obtained. These nodes represent the key control points in the entire express delivery loading and unloading process, and their quantity and specific nature depend on the complexity of the actual process and the depth of data analysis applied. For example, if an express package is sent from a warehouse, arrives at a sorting center, and then is sent to the final destination, the key nodes may include the time point when the package leaves the warehouse, the time point when it arrives at the sorting center, and the time point when it is finally loaded onto the vehicle for the destination, etc. This series of steps makes the express delivery management more accurate and efficient, provides better process control and optimization possibilities, and thus ensures that express items can be processed and transferred with the highest efficiency in the shortest time.

[0019] Endpoint deployment analysis is performed based on the N express delivery loading and unloading process nodes to obtain N express delivery control thread nodes.

[0020] Optionally, after obtaining N express loading and unloading process nodes, endpoint deployment analysis is performed to optimize the efficiency of express loading and unloading operations. This step mainly focuses on how to implement effective control and resource allocation based on the identified express loading and unloading process nodes. The purpose of endpoint deployment analysis is to arrange the most suitable control resources and operating facilities according to the geographical location, operation characteristics of each express loading and unloading process node, and its role in the entire logistics chain. This analysis involves the application of computational and decision support systems to determine the type and quantity of resources required for each node, and how to schedule these resources to maximize operational efficiency. When implementing endpoint deployment analysis, the operation requirements and time sensitivity of each express loading and unloading process node are first evaluated. For example, for a major sorting center node, efficient automated sorting equipment and sufficient manpower may need to be deployed to handle a large number of packages during peak hours. For smaller local distribution centers, the focus may be on optimizing fast goods transfer and distribution strategies. Through this analysis, N express control thread nodes can be determined. Each express control thread node represents an optimized control point, at which express loading and unloading operations can be carried out according to predefined standards and procedures. The determination of control thread nodes relies on advanced algorithm models and real-time data, which help managers accurately predict operation requirements and deploy corresponding resources. For example, if the process node indicates that a certain location will receive a large number of international express deliveries during a specific period, endpoint deployment analysis may indicate adding temporary workstations and labor at this node to ensure the rapid processing of the increased volume of goods and guarantee the timeliness and quality of express services. In summary, by performing endpoint deployment analysis on N express loading and unloading process nodes and finally obtaining N express control thread nodes, resource allocation can be effectively optimized, express loading and unloading efficiency can be improved, resource waste can be reduced, and service quality can be enhanced. These control thread nodes, as the key execution points of express loading and unloading management, are the cornerstone for ensuring the flexible response and efficient operation of the entire logistics system.

[0021] Obtain express logistics data information through the logistics information center, map and divert the express logistics data information to the N express control thread nodes to obtain N-node express logistics data.

[0022] Furthermore, express delivery logistics data is collected through a logistics information center. The logistics information center is a centralized data processing and communication hub responsible for collecting and storing in real-time data from express transportation activities across various regions. This data includes, but is not limited to, package tracking information, transportation vehicle locations, cargo status updates, timestamps, etc. The logistics information center utilizes advanced information technology systems to ensure that all collected data is up-to-date and complete, providing a reliable input source for subsequent data processing. Next, the express delivery logistics data information is mapped and diverted to the N express control thread nodes. This process involves a complex data routing and allocation algorithm aimed at distributing the collected express delivery logistics data to the corresponding express control thread nodes according to pre-set rules and parameters. The mapping and diversion process not only considers the type and importance of the data but also optimizes the allocation based on the geographical location and processing capabilities of the control thread nodes to ensure that the data is delivered to the correct nodes at the right time. For example, if a control thread node is dedicated to handling urgent parcels, then all express delivery logistics data marked as "urgent" will be preferentially mapped and diverted to that node. Such an allocation strategy ensures that critical tasks can obtain immediate data support, thereby improving processing efficiency and response speed. Through the above steps, N-node express delivery logistics data is finally obtained. Each control thread node receives a dataset customized for its specific operations, and these datasets include all the necessary information to enable the node to make independent decisions and operations without waiting for centralized instructions. This data allocation method enhances the flexibility and scalability of the entire express delivery logistics system, enabling each node to perform fast and accurate loading and unloading operations based on real-time data. In summary, the process of obtaining express delivery logistics data through the logistics information center and mapping and diverting it to each express control thread node is a key link in ensuring the efficient and flexible operation of the express loading and unloading management system. This process not only improves the efficiency of data processing but also enhances the response ability and service quality of the entire logistics network.

[0023] Based on the N express control thread nodes, distributed job analysis is performed on the N-node express delivery logistics data to obtain N sets of loading and unloading operation analysis parameters.

[0024] Exemplarily, the execution of distributed job analysis is based on each express control thread node independently processing the express logistics data it receives. This analysis method allows each node to make decisions and optimizations using its local data, thereby enhancing the efficiency and response speed of the overall system. Distributed job analysis involves comprehensive processing of data, including but not limited to data screening, classification, priority ranking, and resource allocation. For example, if an express control thread node is responsible for express distribution in a major urban area, the data analysis of this node will focus on optimizing the express routes and scheduling the work plans of delivery personnel to reduce traffic delays and improve delivery efficiency. The node determines the optimal route and time window through algorithms to ensure that the express can arrive within the promised time. Subsequently, through these independently conducted analyses, each node will generate a set of loading and unloading operation analysis parameters. These parameter sets contain key information required for efficient loading and unloading operations, such as the best loading and unloading time points, resource configuration suggestions, expected operation durations, etc. These parameters are obtained through the processing of machine learning models and heuristic algorithms based on real-time data and historical performance data, aiming to maximize the operation efficiency and resource utilization rate of each node. Finally, the N sets of loading and unloading operation analysis parameters obtained through the distributed job analysis of the N express control thread nodes on the N-node express logistics data are personalized and refined result outputs. These outputs not only improve the speed and quality of express processing but also enhance the adaptability of the entire express network to changing market demands. In this way, the express loading and unloading management system can flexibly respond to various operation challenges while maintaining high operation standards.

[0025] Encrypt and integrate the N sets of loading and unloading operation analysis parameters of the nodes into the loading and unloading control center for job collaboration analysis to obtain express loading and unloading collaboration operation parameters, and perform express loading and unloading collaboration management based on the express loading and unloading collaboration operation parameters.

[0026] Specifically, the analysis parameter sets of the loading and unloading operations generated by each node are encrypted. The importance of this step lies in ensuring the security during data transmission and the protection of data privacy. The encryption uses the Advanced Encryption Standard (AES) or other equivalent security technologies to ensure that the data is not accessed or tampered with by unauthorized parties when transmitted to the loading and unloading control center. Subsequently, the encrypted parameter sets are sent to the loading and unloading control center. The loading and unloading control center is the core of the express delivery loading and unloading management system, responsible for coordinating the operations of each node and optimizing the entire express delivery loading and unloading process. Here, the integrated parameter sets are used for operation coordination analysis. This analysis utilizes the data of each node for global optimization, aiming to find the best operation plan to improve the operational efficiency of the entire network. For example, the loading and unloading control center may analyze the expected loading and unloading times, resource requirements, and availability of each node, and adjust the flow of express deliveries or reallocate human resources through algorithms to relieve the pressure on certain nodes and speed up the processing speed. This process involves multiple algorithms and models, such as linear programming and network flow analysis, to ensure that the proposed solutions are both feasible and economical. Through this operation coordination analysis, the loading and unloading control center obtains a set of express delivery loading and unloading coordination operation parameters, which cover the key decision-making points of cross-node operations, including but not limited to operation priorities, resource sharing strategies, and time window adjustments. The obtained coordination operation parameters not only reflect the operation requirements of individual nodes but also consider the coordination efficiency and effectiveness of the entire network. Finally, based on the obtained express delivery loading and unloading coordination operation parameters, the loading and unloading control center executes the coordinated management of express delivery loading and unloading, which includes sending the coordination operation parameters to relevant nodes, implementing specific operation instructions, and monitoring the execution process and results to ensure that each operation is executed according to the optimal strategy, thereby significantly improving the speed and quality of overall express delivery processing, reducing the error rate and operation costs. In summary, by encrypting the analysis parameter sets of the loading and unloading operations of N nodes and integrating them into the loading and unloading control center for coordinated analysis to obtain the express delivery loading and unloading coordination operation parameters, efficient and secure coordinated management of express delivery loading and unloading can be achieved, effectively optimizing the operational efficiency and service quality of the entire logistics network.

[0027] Furthermore, obtaining the N express delivery control thread nodes includes:

[0028] Collect and obtain the express delivery loading and unloading database, divide and integrate the express delivery loading and unloading database based on the N express delivery loading and unloading process nodes to obtain N node express delivery loading and unloading data sets; obtain the computing power resource allocation strategy, extract evaluation indicators from the computing power resource allocation strategy to determine the computing power allocation decision index set; conduct multi-dimensional evaluation on the N node express delivery loading and unloading data sets based on the computing power allocation decision index set to determine the computing power decision index parameters of the N nodes; perform computing power allocation and endpoint deployment on the N express delivery loading and unloading process nodes based on the computing power decision index parameters of the N nodes to obtain the N express delivery control thread nodes.

[0029] Furthermore, collect and obtain the express loading and unloading database, which stores various types of information related to express loading and unloading operations, such as key data like the flow direction of goods, operation time, loading and unloading points, processing speed, etc. These information are collected from various touchpoints of the entire logistics network, providing a basis for subsequent data analysis and decision-making. Immediately afterwards, based on the identified N express loading and unloading process nodes, divide and integrate the express loading and unloading database, that is, group the information in the database according to the specific requirements and characteristics of the process nodes, thereby forming N node express loading and unloading data sets. Each data set focuses on reflecting the specific operation data of the corresponding node, facilitating targeted analysis and optimization.

[0030] Next, obtain and implement the computing power resource allocation strategy. In this link, first determine the overall allocation strategy of computing power resources, which involves evaluating existing computing resources such as server performance, network bandwidth and their matching degree with the needs of each node. Based on these strategies, further extract key evaluation indicators to form a computing power allocation decision index set. These indicators include data processing demand, real-time response ability, resource consumption rate, etc. Each indicator aims to ensure process efficiency and resource optimization.

[0031] After that, conduct a multi-dimensional evaluation of the N node express loading and unloading data sets based on the computing power allocation decision index set. This multi-dimensional evaluation analyzes the performance and resource requirements of each node under different operating conditions, determines the optimal computing power and resource configuration required for each node. This step uses complex data analysis models, such as machine learning algorithms, to ensure the accuracy of the evaluation and the optimization of operations. Finally, based on the node computing power decision index parameters obtained from the multi-dimensional evaluation, perform precise computing power allocation and endpoint deployment for the N express loading and unloading process nodes. This means that each node obtains the corresponding resource configuration according to its operation requirements and evaluation results, such as computing power, storage space and network bandwidth. After completing this step, each node is given the resources required to execute its specific loading and unloading tasks, forming N fully functional express control thread nodes.

[0032] Through this series of steps, not only the effective management and processing of express loading and unloading data are realized, but also the efficiency and response speed of express loading and unloading operations are greatly improved through fine resource allocation. This systematic method ensures the high efficiency and reliability of the express loading and unloading management system when dealing with peak loads and complex scenarios.

[0033] Furthermore, the determination of the N node computing power decision index parameters includes:

[0034] Perform associated data mapping on the N-node express loading and unloading data sets based on the computing power allocation decision index set to obtain N-node computing power index loading and unloading data sets; perform multi-dimensional index evaluation on the N express loading and unloading process nodes respectively according to the N-node computing power index loading and unloading data sets to obtain an N-node computing power index evaluation matrix set; perform critical evaluation on the computing power allocation decision index set through express loading and unloading information processing requirements to generate a computing power index criticality matrix; perform weighted calculation and correction on the N-node computing power index evaluation matrix set based on the computing power index criticality matrix to determine the N-node computing power decision index parameters.

[0035] Specifically, in order to accurately configure the required computing resources for each node to improve the loading and unloading efficiency and data processing capabilities, perform associated data mapping on the N-node express loading and unloading data sets based on the computing power allocation decision index set. Here, the computing power allocation decision index set includes indicators such as the historical processed data volume of the node, data type, and data processing accuracy, which crucially reflect the computing performance requirements and characteristics of each node. The task of associated data mapping is to combine these decision-making indicators with the actual computing and data processing records of each node to form an N-node computing power index loading and unloading data set.

[0036] Next, perform multi-dimensional index evaluation on the obtained N-node computing power index loading and unloading data sets. In this step, the data set of each node is used to generate a computing power index evaluation matrix set, which details the performance and requirements of each node on different computing power indexes, such as computing speed, storage capacity, and network bandwidth. The multi-dimensional evaluation provides decision-making support for subsequent resource allocation by considering the relative importance of each index and the specific requirements of the node. Thereafter, perform critical evaluation of the express loading and unloading information processing requirements to generate a computing power index criticality matrix. This evaluation considers the sensitivity and urgency of the computing power requirements in express loading and unloading operations, such as the necessity of real-time data processing and the complexity of big data processing. The criticality matrix helps determine which computing power indexes are crucial for maintaining the operation efficiency of the node and which can be appropriately de-prioritized in case of resource constraints.

[0037] Finally, perform weighted calculation and correction on the N-node computing power index evaluation matrix set based on the computing power index criticality matrix. This correction process involves adjusting the original computing power allocation to make it more in line with the actual operation requirements and strategic goals of the node. For example, for nodes with higher processing requirements, the allocation of their computing resources may be increased, while for nodes handling simple tasks, the resources can be appropriately reduced to ensure the overall optimal allocation of resources.

[0038] Through these detailed analysis and evaluation steps, the computing power decision index parameters of each express loading and unloading node can be determined. These parameters ultimately guide the specific allocation and optimization of resources, ensuring the efficient operation of the overall express loading and unloading system and maximizing the data processing capacity.

[0039] Furthermore, the obtaining of the N-node loading and unloading operation analysis parameter sets includes:

[0040] Analyze the operation tasks of the N express control thread nodes in sequence to construct N express node loading and unloading operation task lists; cluster the N-node express loading and unloading data sets according to the N express node loading and unloading operation task lists to obtain N-node loading and unloading operation task data sets; perform distributed training on the N-node loading and unloading operation task data sets respectively to obtain N-node loading and unloading operation analysis model sets; use the N-node loading and unloading operation analysis model sets to perform distributed operation analysis on the N-node express logistics data respectively to obtain the N-node loading and unloading operation analysis parameter sets.

[0041] Optionally, conduct a detailed operation task analysis on the N express control thread nodes. This analysis involves identifying and recording the specific tasks undertaken by each node in the express loading and unloading operation, such as loading, unloading, sorting, transshipment, etc. Based on these analysis results, construct N express node loading and unloading operation task lists, and each list details key information such as the task type, task frequency, and time sensitivity of the corresponding node's tasks, providing a basis for subsequent data clustering and model training.

[0042] Next, cluster the N-node express loading and unloading data sets according to their respective express node loading and unloading operation task lists. The purpose of this step is to classify and organize the operation data of each node according to the operation type, thereby forming N-node loading and unloading operation task data sets. Data clustering uses advanced clustering algorithms such as K-means or hierarchical clustering to ensure that the information in each data set is optimized for specific operation tasks.

[0043] Subsequently, perform distributed training on the formed N-node loading and unloading operation task data sets to obtain N-node loading and unloading operation analysis model sets. At this stage, use machine learning techniques such as deep learning or support vector machines to analyze each data set and train models that can accurately predict and optimize the performance of the loading and unloading operation. These models can predict operation bottlenecks based on real-time data, propose improvement measures, and predict future operation requirements.

[0044] Finally, the N-node loading and unloading operation task analysis model set is used to perform distributed operation analysis on the express logistics data of the N nodes respectively. This analysis stage applies the trained model to the actual operation data to evaluate and optimize the operation efficiency and resource allocation of each node. Through the calculation and analysis of the model, a set of loading and unloading operation analysis parameters for the specific operation requirements of each node can be obtained.

[0045] Through the above steps, not only the operation response ability and efficiency of each node are enhanced, but also the performance of the entire express loading and unloading system is greatly improved through fine data processing and intelligent decision support. These sets of loading and unloading operation analysis parameters provide a powerful decision-making tool for the management level, ensuring that the express loading and unloading operations can be carried out under optimal conditions, thereby achieving the maximization of resource utilization and the minimization of operation costs.

[0046] Furthermore, obtaining the N-node loading and unloading operation task analysis model set includes:

[0047] Perform task characteristic analysis on the N-node loading and unloading operation task data sets respectively to obtain N-node loading and unloading task processing characteristic sets; construct a deep learning model list, match according to the N-node loading and unloading task processing characteristic sets and the deep learning model list, and determine the N-node operation task deep learning model set; use the N-node operation task deep learning model set to perform distributed training on the N-node loading and unloading operation task data sets respectively to obtain N initial node operation task analysis model sets; verify, optimize and integrate the N initial node operation task analysis model sets to obtain the N-node loading and unloading operation task analysis model set.

[0048] Exemplarily, first perform task characteristic analysis on the N-node loading and unloading operation task data sets. In this step, the data sets of each node are analyzed in detail to identify the core characteristics of its operation tasks, such as task frequency, complexity, data volume, processing time, and required computing resources, etc. Through this analysis, N-node loading and unloading task processing characteristic sets are obtained, and these characteristic sets provide a detailed description and requirement overview of each node when performing its operation tasks, providing a basis for the subsequent model matching and training.

[0049] Next, construct a list of deep learning models that includes various different deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), or other models suitable for processing time series data and classification tasks. Based on the obtained N-node handling task processing characteristic sets, these models are carefully matched to ensure that the job task characteristics of each node can be processed by the most suitable model, thereby maximizing the analysis efficiency and accuracy. Subsequently, use the matched set of N-node job task deep learning models to perform distributed training on each node's handling job task dataset. At this stage, each selected model is trained on its respective node using the corresponding dataset to adjust the model parameters and optimize its prediction and analysis capabilities. This process usually requires a large amount of computing resources and may involve multiple rounds of iterative training to ensure that each model can accurately reflect the job requirements and environmental changes of the node. Finally, verify, optimize, and integrate the set of N initial node job task analysis models. In this step, each trained model will go through a series of verification processes, including cross-validation and comparison with actual historical data, to test its effectiveness and accuracy. After verification, all optimized models are aggregated and integrated into the final set of N-node handling job task analysis models, and these model sets will be deployed in actual operations to guide and optimize the handling operations of each node.

[0050] Through the above steps, it is ensured that each node can effectively process its handling operations through customized deep learning models, thereby improving the efficiency and response speed of the overall express handling system, while reducing the error rate and enhancing the adaptability of the operations. These model sets are data-driven decision-making tools based on actual operation requirements and provide strong support for express handling management.

[0051] Furthermore, the obtaining of the express handling collaborative operation parameters includes:

[0052] Respectively perform multi-dimensional encryption degree evaluations on the N express control thread nodes to obtain N express node encryption coefficients; use the N express node encryption coefficients to encrypt and integrate the N-node handling operation analysis parameter sets into the handling control center; perform node handling interaction analysis on the N express control thread nodes to generate an express handling node interaction flow network; based on the express handling node interaction flow network, perform job collaboration analysis on the N-node handling operation analysis parameter sets to obtain express handling collaborative operation parameters.

[0053] In a specific embodiment, a multi-dimensional encryption degree evaluation is performed on the N express control thread nodes. This step involves analyzing the encryption requirements and capabilities of each node when processing sensitive data to ensure the security of data during transmission and processing. The result of the evaluation is the encryption coefficients of the N express nodes, which reflect the encryption strengths and policies of each node, ensuring that each node can maintain data integrity and privacy when exchanging data with other nodes.

[0054] Next, the N node handling operation analysis parameter sets are encrypted and integrated into the handling control center using the encryption coefficients of the N express nodes. In this step, the operation analysis parameters of each node are first encrypted before being sent to the handling control center, and the encryption strength used is determined according to the encryption coefficients obtained from the previous evaluation. This is done to protect the data during transmission from unauthorized access or tampering, while ensuring that the handling control center can receive all necessary operation information for subsequent processing.

[0055] Subsequently, a node handling interaction analysis is performed on the N express control thread nodes to generate an express handling node interaction flow network. This analysis task is to understand and optimize the collaborative operation process between nodes, including logistics paths, data exchange, and resource sharing, etc. Through the analysis, the generated express handling node interaction flow network details the interaction patterns and dependencies between each node, providing a basis for formulating more effective operation collaboration strategies.

[0056] Finally, an operation collaboration analysis is performed on the N node handling operation analysis parameter sets based on the express handling node interaction flow network. In this step, advanced analysis tools and algorithms are used to comprehensively consider and optimize the operation analysis parameters of each node according to the interaction characteristics and operation requirements between nodes. Through this in-depth collaboration analysis, express handling collaborative operation parameters are obtained, which guide the operation execution of the entire express network, ensuring the efficient cooperation of each node's operation and the maximization of the overall performance.

[0057] In summary, through these detailed steps, not only the data security and privacy protection of the system are ensured, but also the operation efficiency and response speed of the entire express handling network are significantly improved through in-depth interaction analysis and collaborative operation strategy optimization between nodes. These collaborative operation parameters are indispensable materials for management decision-making and daily operations, providing solid data support and intelligent guidance for the smooth operation of the entire logistics network.

[0058] Furthermore, the obtaining of the express handling collaborative operation parameters includes:

[0059] Obtain the express loading and unloading operation objectives, extract and evaluate and fit the indicators of the express loading and unloading operation objectives, and construct the fitness function of the express loading and unloading operation effect; construct the express loading and unloading strategy space, and respectively perform strategy parameter analysis and optimization on the N-node loading and unloading operation analysis parameter sets in the express loading and unloading strategy space based on the fitness function of the express loading and unloading operation effect to obtain N-node loading and unloading strategy parameter sets; based on the express loading and unloading node interaction flow network, perform associated impact analysis on the N express control thread nodes to determine the N-node loading and unloading associated impact parameter sets; based on the N-node loading and unloading associated impact parameter sets, perform collaborative integration analysis on the N-node loading and unloading strategy parameter sets to obtain the express loading and unloading collaborative operation parameters.

[0060] Specifically, obtain the express loading and unloading operation objectives, which includes determining the specific objectives of each express loading and unloading operation, such as minimizing the loading and unloading time, maximizing the throughput, optimizing resource utilization, etc. Extract and evaluate and fit the indicators of these operation objectives, which means extracting quantifiable performance indicators from the operation objectives and evaluating the degree of fit between the existing operation situation and these indicators. This step is carried out by analyzing historical data and current operation efficiency to ensure that the extracted indicators are accurate and practical. Then, construct the fitness function of the express loading and unloading operation effect. This function is designed based on the evaluation results of the above indicators to quantify the adaptability and impact of each possible strategy adjustment on the operation objectives. The fitness function usually uses mathematical modeling techniques to implement and can score and rank the expected results under different operation strategies.

[0061] Subsequently, construct the express loading and unloading strategy space. This space contains all possible strategy combinations, and each combination represents a set of possible operation parameter settings. Based on the fitness function of the express loading and unloading operation effect, respectively perform strategy parameter analysis and optimization on the N-node loading and unloading operation analysis parameter sets in the express loading and unloading strategy space. This process uses optimization algorithms, such as genetic algorithms or simulated annealing, to find the optimal strategy combination to achieve the best fitness of the loading and unloading operation objectives. Further, based on the express loading and unloading node interaction flow network, perform associated impact analysis on the N express control thread nodes. This analysis considers the dependencies and operation interaction characteristics between nodes and evaluates the potential impact of different node strategy adjustments on the overall network. This helps to determine the N-node loading and unloading associated impact parameter sets, which include parameters that may affect the operation efficiency and resource requirements of adjacent nodes.

[0062] Finally, based on the set of N-node loading and unloading correlation influence parameters, a collaborative integration analysis is performed on the set of N-node loading and unloading strategy parameters. This step integrates the strategy optimization results of all nodes and adjusts and optimizes the loading and unloading operations of the entire network through collaborative analysis to ensure the consistency and complementarity of all strategies. Through this integration analysis, express loading and unloading collaborative operation parameters are obtained, which provide a coordinated and optimized operation strategy guidance for the entire express loading and unloading system.

[0063] Through the above steps, not only is the efficiency and effectiveness of express loading and unloading operations maximized, but also through in-depth data analysis and optimization algorithms, the collaboration and data security between operation nodes are strengthened, thus significantly improving the operation performance of the entire express network.

[0064] Through the technical solutions of the above embodiments, the multi-line collaborative express loading and unloading management method provided by the present application solves the technical problems existing in the existing express loading and unloading management, such as insufficient response speed and accuracy in a dynamic operation environment, inability to flexibly adjust resulting in resource waste and service delays, and poor collaborative operation. It achieves the technical effects of improving the processing speed and accuracy of express loading and unloading operations, optimizing resource allocation, enhancing the dynamic adjustment ability and operation collaborative efficiency.

[0065] Embodiment 2, based on the same inventive concept as the multi-line collaborative express loading and unloading management method in the foregoing embodiment, as Figure 2 shown, the present application provides a multi-line collaborative express loading and unloading management platform, which includes:

[0066] A node extraction module 11, configured to obtain express loading and unloading process information, extract key nodes from the express loading and unloading process information, and obtain N express loading and unloading process nodes.

[0067] An endpoint deployment module 12, configured to perform endpoint deployment analysis based on the N express loading and unloading process nodes to obtain N express control thread nodes.

[0068] A mapping and shunting module 13, configured to obtain express logistics data information through a logistics information center, map and shunt the express logistics data information to the N express control thread nodes to obtain N-node express logistics data.

[0069] A distributed analysis module 14, configured to perform distributed operation analysis on the N-node express logistics data based on the N express control thread nodes to obtain a set of N-node loading and unloading operation analysis parameters.

[0070] A collaborative management module 15, configured to encrypt and integrate the set of N-node loading and unloading operation analysis parameters into a loading and unloading control center for operation collaborative analysis to obtain express loading and unloading collaborative operation parameters, and perform express loading and unloading collaborative management based on the express loading and unloading collaborative operation parameters.

[0071] Furthermore, the endpoint deployment module 12 is also used to perform the following steps:

[0072] Collect and obtain the express loading and unloading database, divide and integrate the express loading and unloading database based on the N express loading and unloading process nodes, and obtain N node express loading and unloading data sets.

[0073] Obtain the computing power resource allocation policy, extract evaluation indicators from the computing power resource allocation policy, and determine the computing power allocation decision index set.

[0074] Perform multi-dimensional evaluation on the N node express loading and unloading data sets based on the computing power allocation decision index set, and determine the computing power decision index parameters of the N nodes.

[0075] Perform computing power allocation and endpoint deployment on the N express loading and unloading process nodes based on the computing power decision index parameters of the N nodes to obtain the N express control thread nodes.

[0076] Furthermore, the endpoint deployment module 12 is also used to perform the following steps:

[0077] Perform associated data mapping on the N node express loading and unloading data sets based on the computing power allocation decision index set to obtain N node computing power index loading and unloading data sets.

[0078] Perform multi-dimensional index evaluation on the N express loading and unloading process nodes respectively according to the N node computing power index loading and unloading data sets to obtain an N node computing power index evaluation matrix set.

[0079] Perform critical evaluation on the computing power allocation decision index set through the express loading and unloading information processing requirements to generate a computing power index criticality matrix.

[0080] Perform weighted calculation and correction on the N node computing power index evaluation matrix set based on the computing power index criticality matrix to determine the computing power decision index parameters of the N nodes.

[0081] Furthermore, the distributed analysis module 14 is also used to perform the following steps:

[0082] Analyze the job tasks of the N express control thread nodes in sequence to construct an N node express loading and unloading job task list.

[0083] Cluster the N node express loading and unloading data sets according to the N node express loading and unloading job task list to obtain N node loading and unloading job task data sets.

[0084] Perform distributed training on the N node loading and unloading job task data sets respectively to obtain an N node loading and unloading job task analysis model set.

[0085] Using the described N-node loading and unloading operation task analysis model set to perform distributed operation analysis on the N-node express logistics data respectively, and obtaining the N-node loading and unloading operation analysis parameter set.

[0086] Furthermore, the distributed analysis module 14 is also used to perform the following steps:

[0087] Perform task characteristic analysis on the N-node loading and unloading operation task data sets respectively, and obtain the N-node loading and unloading task processing characteristic set.

[0088] Construct a deep learning model list, match the N-node loading and unloading task processing characteristic set with the deep learning model list, and determine the N-node operation task deep learning model set.

[0089] Using the N-node operation task deep learning model set to perform distributed training on the N-node loading and unloading operation task data sets respectively, and obtaining the N initial node operation task analysis model set.

[0090] Verify, optimize and integrate the N initial node operation task analysis model set, and obtain the N-node loading and unloading operation task analysis model set.

[0091] Furthermore, the collaborative management module 15 is also used to perform the following steps:

[0092] Perform multi-dimensional encryption degree evaluation on the N express control thread nodes respectively, and obtain the N express node encryption coefficients.

[0093] Use the N express node encryption coefficients to encrypt and integrate the N-node loading and unloading operation analysis parameter set into the loading and unloading control center.

[0094] Perform node loading and unloading interaction analysis on the N express control thread nodes, and generate an express loading and unloading node interaction flow network.

[0095] Based on the express loading and unloading node interaction flow network, perform operation collaboration analysis on the N-node loading and unloading operation analysis parameter set, and obtain express loading and unloading collaboration operation parameters.

[0096] Furthermore, the collaborative management module 15 is also used to perform the following steps:

[0097] Obtain the express loading and unloading operation target, perform index extraction and evaluation fitting on the express loading and unloading operation target, and construct an express loading and unloading operation effect fitness function.

[0098] Construct a delivery loading and unloading strategy space, and respectively perform strategy parameter analysis and optimization on the N-node loading and unloading operation analysis parameter sets in the delivery loading and unloading strategy space based on the delivery loading and unloading operation effect fitness function, so as to obtain N-node loading and unloading strategy parameter sets.

[0099] Based on the delivery node interaction flow network, perform an associated influence analysis on the N delivery control thread nodes to determine an N-node loading and unloading associated influence parameter set.

[0100] Based on the N-node loading and unloading associated influence parameter set, perform a collaborative integration analysis on the N-node loading and unloading strategy parameter sets to obtain the delivery loading and unloading collaborative operation parameters.

[0101] Through the foregoing detailed description of the multi-line collaborative delivery loading and unloading management method in this specification, those skilled in the art can clearly know the multi-line collaborative delivery loading and unloading management platform in this embodiment. For the platform disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For related parts, refer to the description in the method part.

[0102] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-line collaborative express loading and unloading management method, characterized in that The method comprises: Acquire express loading and unloading process information, extract key nodes from the express loading and unloading process information, and obtain N express loading and unloading process nodes; Perform endpoint deployment analysis based on the N express loading and unloading process nodes to obtain N express control thread nodes; Obtain express logistics data information through the logistics information center, map and divert the express logistics data information to the N express control thread nodes, and obtain N node express logistics data; Performing distributed operation analysis on the express logistics data of the N nodes based on the N express control thread nodes to obtain a set of analysis parameters for loading and unloading operations of the N nodes; Encrypting and integrating the N node loading and unloading operation analysis parameter sets into the loading and unloading control center for operation collaborative analysis, obtaining express loading and unloading collaborative operation parameters, and performing express loading and unloading collaborative management based on the express loading and unloading collaborative operation parameters; The obtaining of N express control thread nodes includes: Acquire an express loading and unloading database, divide and integrate the express loading and unloading database based on the N express loading and unloading process nodes, and obtain N-node express loading and unloading data sets; Obtaining a computing power resource allocation strategy, extracting evaluation indicators for the computing power resource allocation strategy, and determining a computing power allocation decision indicator set; Performing a multi-dimensional evaluation on the N-node express loading and unloading data set based on the computing power allocation decision indicator set to determine the N-node computing power decision indicator parameters; Based on the N node computing power decision indicator parameters, computing power is allocated and endpoints are deployed for the N express loading and unloading process nodes to obtain the N express control thread nodes; Determining N node computing power decision indicator parameters includes: Performing correlation data mapping on the N-node express loading and unloading data sets based on the computing power allocation decision indicator set to obtain the N-node computing power indicator loading and unloading data sets; Perform multi-dimensional indicator evaluation on the N express loading and unloading process nodes according to the N node computing power indicator loading and unloading data sets to obtain an N node computing power indicator evaluation matrix set; Performing a criticality evaluation on the computing power allocation decision indicator set based on the express loading and unloading information processing requirements to generate a computing power indicator criticality matrix; Based on the computing power indicator criticality matrix, a weighted calculation correction is performed on the N node computing power indicator evaluation matrix set to determine the N node computing power decision indicator parameters.

2. The multi-line collaborative express loading and unloading management method according to claim 1, characterized in that The step of obtaining the N node loading and unloading operation analysis parameter sets includes: Performing job task analysis on the N express control thread nodes in turn, and building a list of loading and unloading job tasks for the N express nodes; Clustering the N-node express loading and unloading data sets according to the N-node loading and unloading task lists to obtain the N-node loading and unloading task data sets; Distributed training is performed on the N node loading and unloading task data sets to obtain an N node loading and unloading task analysis model set; The N-node loading and unloading operation task analysis model set is used to perform distributed operation analysis on the N-node express logistics data respectively to obtain the N-node loading and unloading operation analysis parameter set.

3. The multi-line collaborative express loading and unloading management method according to claim 2, characterized in that The step of obtaining a set of N node loading and unloading task analysis models includes: Analyze the task characteristics of the N node loading and unloading operation task datasets respectively to obtain N node loading and unloading task processing characteristic sets; Construct a list of deep learning models, match the N node loading and unloading task processing characteristic sets with the list of deep learning models, and determine the N node operation task deep learning model sets; Use the N node operation task deep learning model sets to perform distributed training on the N node loading and unloading operation task datasets respectively to obtain N initial node operation task analysis model sets; Verify, optimize, and integrate and summarize the N initial node operation task analysis model sets to obtain the N node loading and unloading operation task analysis model sets.

4. The multi-line collaborative express loading and unloading management method according to claim 1, wherein, The obtaining of the express loading and unloading collaborative operation parameters includes: Evaluate the multi-dimensional encryption degree of the N express control thread nodes respectively to obtain N express node encryption coefficients; Use the N express node encryption coefficients to encrypt and integrate the N node loading and unloading operation analysis parameter sets into the loading and unloading control center; Conduct node loading and unloading interaction analysis on the N express control thread nodes to generate an express loading and unloading node interaction flow network; Based on the express loading and unloading node interaction flow network, conduct operation collaboration analysis on the N node loading and unloading operation analysis parameter sets to obtain express loading and unloading collaborative operation parameters.

5. The multi-line collaborative express loading and unloading management method according to claim 4, wherein The obtaining of the express loading and unloading collaborative operation parameters includes: Obtain the express loading and unloading operation target, extract and evaluate and fit the indicators of the express loading and unloading operation target, and construct an express loading and unloading operation effect fitness function; Construct an express loading and unloading strategy space, and based on the express loading and unloading operation effect fitness function, perform strategy parameter analysis and optimization on the N node loading and unloading operation analysis parameter sets in the express loading and unloading strategy space respectively to obtain N node loading and unloading strategy parameter sets; Based on the express loading and unloading node interaction flow network, conduct associated influence analysis on the N express control thread nodes to determine N node loading and unloading associated influence parameter sets; Based on the N node loading and unloading associated influence parameter sets, conduct collaborative integration analysis on the N node loading and unloading strategy parameter sets to obtain the express loading and unloading collaborative operation parameters.

6. The express loading and unloading management platform with multi-line collaboration is characterized in that For implementing the multi-line collaborative express loading and unloading management method according to any one of claims 1-5, the platform includes: A node extraction module, configured to obtain express loading and unloading process information, extract key nodes from the express loading and unloading process information, and obtain N express loading and unloading process nodes; An endpoint deployment module, configured to perform endpoint deployment analysis based on the N express loading and unloading process nodes to obtain N express control thread nodes; A mapping and shunting module, configured to obtain express logistics data information through a logistics information center, map and shunt the express logistics data information to the N express control thread nodes to obtain N node express logistics data; A distributed analysis module, configured to perform distributed operation analysis on the N node express logistics data based on the N express control thread nodes to obtain N node loading and unloading operation analysis parameter sets; A collaborative management module, which is used to encrypt and integrate the N-node loading and unloading operation analysis parameter sets into a loading and unloading control center for operation collaborative analysis, obtain express loading and unloading collaborative operation parameters, and perform express loading and unloading collaborative management based on the express loading and unloading collaborative operation parameters.

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