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

Abstract

The invention provides a multi-line collaborative express loading and unloading management method and a loading and unloading management platform, and relates to the technical field of express loading and unloading management. Express loading and unloading process information is acquired for key node extraction and endpoint deployment analysis, and N express control thread nodes are acquired; the method comprises the following steps: acquiring express logistics data information through a logistics information center, mapping and distributing the express logistics data information to a control thread node to obtain N node express logistics data, and performing distributed operation analysis on the node express logistics data based on an express control thread node to obtain N node loading and unloading operation analysis parameter sets, according to the method, the problems of resource waste and service delay caused by insufficient response speed and accuracy of express loading and unloading management in a dynamic operation environment and incapability of flexible adjustment are solved; and the collaborative operation is not good.

Description

Technical Field

[0001] The present invention relates to the technical field of express delivery loading and unloading management, and in particular to a multi-line coordinated express delivery 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 parcels 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 large amounts of logistics information, but also respond quickly in a dynamically changing environment. Existing technologies mainly rely on fixed flowcharts and preset nodes to manage express loading and unloading operations. This method is efficient when handling standard operations, but lacks flexibility and scalability when dealing with peak traffic or emergencies. In addition, although the existing system can collect and process express data, there is often a delay in information update, which cannot reflect the current logistics status in real time, resulting in the data based on which the decision is based being inaccurate or outdated, which in turn affects the 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 dynamic operating environments, inability to flexibly adjust, resulting in waste of resources and service delays, and poor collaborative operations. Summary of the invention

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

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

[0006] In a first aspect, the present application provides a multi-line collaborative express loading and unloading management method, the method comprising: 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, and obtain N express control thread nodes; acquire express logistics data information through a 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; perform distributed operation analysis on the N node express logistics data based on the N express control thread nodes, and 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, 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.

[0007] In the second aspect, the present application provides a multi-line collaborative express loading and unloading management platform, the platform comprising: A node extraction module is used 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 is used to perform endpoint deployment analysis based on the N express loading and unloading process nodes, and obtain N express control thread nodes; a mapping and diversion module is used to obtain express logistics data information through a 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; a distributed analysis module is used to perform distributed operation analysis on the N node express logistics data based on the N express control thread nodes, and obtain N node loading and unloading operation analysis parameter sets; a collaborative management module is used 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, 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] One or more technical solutions provided in this application have at least the following technical effects or advantages: The multi-line collaborative express loading and unloading management method provided by the present application obtains express loading and unloading process information, extracts key nodes from the express loading and unloading process information, and obtains 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 a logistics information center, maps and diverts the express logistics data information to the N express control thread nodes, and obtains 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, obtains 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, thereby solving the technical problems of 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 in the existing express loading and unloading management, thereby achieving the technical effects of improving the processing speed and accuracy of express loading and unloading operations, optimizing resource allocation, and enhancing dynamic adjustment capabilities and operation collaborative efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flow chart of a multi-line collaborative express loading and unloading management method is provided for this application.

[0010] Figure 2 A structural diagram of a multi-line collaborative express loading and unloading management platform is provided for this application.

[0011] Explanation 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

[0012] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0013] Embodiment 1, as Figure 1 As shown, the present application provides a multi-line collaborative express loading and unloading management method, the method comprising: The express loading and unloading process information is obtained, key nodes are extracted from the express loading and unloading process information, and N express loading and unloading process nodes are obtained.

[0014] Specifically, obtaining express loading and unloading process information refers to collecting various data on the loading and unloading of express items from the express management system or real-time logistics tracking system, including but not limited to information such as loading and unloading time, location, operator, logistics status, etc., which provide basic data for subsequent processing. Next, the key nodes of the express loading and unloading process information are extracted. In this step, by analyzing the collected express loading and unloading process information, data processing algorithms such as classification, clustering or association rule mining are used to identify key operation nodes. Key nodes refer to those nodes that have a significant impact on the entire loading and unloading process, such as the time when the goods arrive at the distribution center and start loading or unloading. The identification of these nodes helps to optimize the management of the entire process. Through the above extraction process, N express loading and unloading process nodes can be obtained. These nodes represent the key control points in the entire express loading and unloading process. The number and specific nature of these nodes depend on the complexity of the actual process and the depth of the 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 when the package departs from the warehouse, the time when it arrives at the sorting center, and the time when it is finally loaded to the destination. This series of steps makes express loading and unloading management more precise and efficient, provides better process control and optimization possibilities, and ensures that express items can be processed and transported with the highest efficiency in the shortest time.

[0015] Based on the N express loading and unloading process nodes, endpoint deployment analysis is performed to obtain N express control thread nodes.

[0016] Optionally, after obtaining N express loading and unloading process nodes, an endpoint deployment analysis is performed to optimize the efficiency of express loading and unloading operations. This step focuses on how to implement effective control and resource allocation based on the identified express loading and unloading process nodes. The purpose of the endpoint deployment analysis is to arrange the most appropriate control resources and operating facilities based on the geographical location, operating characteristics and role of each express loading and unloading process node in the entire logistics chain. This analysis involves the application of computing and decision support systems to determine the type and quantity of resources required for each node and how to schedule these resources to maximize operating efficiency. When implementing the endpoint deployment analysis, the operational requirements and time sensitivity of each express loading and unloading process node are first evaluated. For example, for a major sorting center node, it may be necessary to deploy efficient automated sorting equipment and sufficient manpower to handle a large number of packages during peak hours. For smaller local distribution centers, the focus may be on optimizing rapid cargo transfer and distribution strategies. Through this analysis, N express control thread nodes can be determined, each of which represents an optimally configured control point where express loading and unloading operations can be carried out according to predetermined standards and procedures. The determination of control thread nodes relies on advanced algorithm models and real-time data, which help managers accurately predict job requirements and deploy corresponding resources. For example, if a process node indicates that a certain location will receive a large number of cross-border express parcels during a specific period of time, endpoint deployment analysis may indicate the addition of temporary workstations and labor at this node to ensure rapid processing of the increased volume of goods and to ensure the timeliness and quality of express delivery services. In summary, by performing endpoint deployment analysis on N express loading and unloading process nodes and ultimately obtaining N express control thread nodes, it is possible to effectively optimize resource allocation, improve express loading and unloading efficiency, reduce resource waste, and improve service quality. These control thread nodes, as key execution points for express loading and unloading management, are the cornerstone for ensuring the flexible response and efficient operation of the entire logistics system.

[0017] The express logistics data information is obtained through the logistics information center, and the express logistics data information is mapped and diverted to the N express control thread nodes to obtain N node express logistics data.

[0018] Furthermore, express logistics data is collected through the logistics information center. The logistics information center is a centralized data processing and communication hub responsible for real-time collection and storage of data from express transportation activities in various places, including but not limited to package tracking information, transportation vehicle location, cargo status update, time stamp, etc. The logistics information center uses advanced information technology systems to ensure that all collected data are up-to-date and complete, providing a reliable input source for subsequent data processing. Next, the express logistics data information is mapped and diverted to the N express control thread nodes. This process involves a complex data routing and distribution algorithm, the purpose of which is to distribute the collected express 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 distribution according to the geographical location and processing capacity of the control thread node to ensure that the data is delivered to the correct node at the right time. For example, if a control thread node is responsible for handling urgent items, then all express logistics data marked as "urgent" will be mapped and diverted to the node first. Such a distribution strategy ensures that critical tasks can obtain immediate data support, thereby improving processing efficiency and response speed. Through the above steps, N node express logistics data are finally obtained. Each control thread node receives a data set customized for its specific operation, which includes all the necessary information to enable the node to make decisions and operations independently without waiting for centralized instructions. This data distribution method enhances the flexibility and scalability of the entire express logistics system, allowing each node to perform fast and accurate loading and unloading operations based on real-time data. In short, the process of obtaining express logistics data through the logistics information center and mapping it to each express control thread node is a key link to ensure 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 responsiveness and service quality of the entire logistics network.

[0019] Based on the N express control thread nodes, distributed operation analysis is performed on the express logistics data of the N nodes to obtain a set of analysis parameters for loading and unloading operations of the N nodes.

[0020] 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 use its local data to make decisions and optimize, thereby improving the efficiency and responsiveness of the overall system. Distributed job analysis involves comprehensive processing of data, including but not limited to data screening, classification, priority sorting and resource allocation. For example, if an express control thread node is responsible for express delivery in a major metropolitan area, the data analysis of the node will focus on optimizing 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 an algorithm 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 job analysis parameters, which contain key information required for efficient loading and unloading operations, such as optimal loading and unloading time points, resource allocation recommendations, and expected operation duration. These parameters are based on real-time data and historical performance data processed by machine learning models and heuristic algorithms, aiming to maximize the operational efficiency and resource utilization of each node. Finally, the N-node loading and unloading operation analysis parameter sets obtained by the distributed operation analysis of the N-node express logistics data based on the N express control thread nodes are personalized and refined result outputs, which not only improve the speed and quality of express processing, but also enhance the adaptability of the entire express network to the changing market demand. In this way, the express loading and unloading management system can flexibly respond to various operational challenges while maintaining high operating standards.

[0021] The N-node loading and unloading operation analysis parameter sets are encrypted and integrated into the loading and unloading control center for operation collaborative analysis, to obtain express loading and unloading collaborative operation parameters, and to perform express loading and unloading collaborative management based on the express loading and unloading collaborative operation parameters.

[0022] Specifically, the loading and unloading operation analysis parameter sets generated by each node are encrypted. The importance of this step lies in ensuring security and data privacy during data transmission. 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 persons when transmitted to the loading and unloading control center. Then, the encrypted parameter set is sent to the loading and unloading control center. The loading and unloading control center is the core of the express loading and unloading management system, responsible for coordinating the operations of each node and optimizing the entire express loading and unloading process. Here, the integrated parameter set is used for operation coordination analysis, which uses the data of each node for global optimization 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 time, resource requirements and availability of each node, and adjust the flow of express or reallocate human resources through algorithms to relieve the pressure on certain nodes and speed up processing. This process involves multiple algorithms and models, such as linear programming and network flow analysis, to ensure that the proposed solution is both feasible and economical. Through this collaborative analysis of operations, the loading and unloading control center obtains a set of express loading and unloading collaborative operation parameters, which cover the key decision points of cross-node operations, including but not limited to operation priority, resource sharing strategy, time window adjustment, etc. The obtained collaborative operation parameters not only reflect the operation requirements of a single node, but also take into account the collaborative efficiency and effect of the entire network. Finally, based on the obtained collaborative operation parameters of express loading and unloading, the loading and unloading control center performs collaborative management of express loading and unloading, which includes sending collaborative operation parameters to relevant nodes, implementing specific operation instructions, and monitoring the execution process and results to ensure that each step of the operation is executed according to the optimal strategy, thereby greatly improving the speed and quality of the overall express processing and reducing the error rate and operation cost. In summary, by encrypting the loading and unloading operation analysis parameter set of N nodes and integrating them into the loading and unloading control center for collaborative analysis and obtaining the collaborative operation parameters of express loading and unloading, efficient and safe collaborative management of express loading and unloading can be achieved, effectively optimizing the operational efficiency and service quality of the entire logistics network.

[0023] Further, the obtaining of N express control thread nodes includes: Acquire 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; obtain the computing power resource allocation strategy, extract evaluation indicators for the computing power resource allocation strategy, and determine the computing power allocation decision indicator set; perform multi-dimensional evaluation on the N node express loading and unloading data sets based on the computing power allocation decision indicator set, and determine the N node computing power decision indicator parameters; perform computing power allocation and endpoint deployment on the N express loading and unloading process nodes based on the N node computing power decision indicator parameters, and obtain the N express control thread nodes.

[0024] Furthermore, the express loading and unloading database is collected and obtained. The database stores various information related to express loading and unloading operations, such as cargo flow, operation time, loading and unloading points, processing speed and other key data. This information is collected from various touchpoints in the entire logistics network, providing a basis for subsequent data analysis and decision-making. Next, the express loading and unloading database is divided and integrated based on the N identified express loading and unloading process nodes, that is, the information in the database is grouped according to the specific needs and characteristics of the process nodes, thereby forming N node express loading and unloading data sets. Each data set focuses on reflecting the specific operational data of the corresponding node, which is convenient for targeted analysis and optimization.

[0025] Next, we acquire and implement the computing power resource allocation strategy. In this step, we first determine the overall allocation strategy for computing power resources, which involves evaluating existing computing resources such as server performance, network bandwidth, and how well they match the needs of each node. Based on these strategies, we further extract key evaluation indicators to form a set of computing power allocation decision indicators, including data processing demand, real-time response capability, resource consumption rate, etc. Each indicator is aimed at ensuring process efficiency and resource optimization.

[0026] Afterwards, a multi-dimensional evaluation is performed on the N-node express loading and unloading data sets based on the computing power allocation decision indicator set. This multi-dimensional evaluation analyzes the performance and resource requirements of each node under different operating conditions, and 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 indicator parameters obtained from the multi-dimensional evaluation, the N express loading and unloading process nodes are accurately allocated computing power and deployed at the endpoints, which means that each node obtains the corresponding resource configuration, such as computing power, storage space, and network bandwidth, according to its operational requirements and evaluation results. After completing this step, each node is given the resources required to perform its specific loading and unloading tasks, forming N fully functional express control thread nodes.

[0027] Through this series of steps, not only the effective management and processing of express loading and unloading data is achieved, but also the efficiency and response speed of express loading and unloading operations are greatly improved through refined resource allocation. This systematic approach ensures the efficiency and reliability of the express loading and unloading management system when handling peak loads and complex scenarios.

[0028] Furthermore, the determination of N node computing power decision indicator parameters includes: Based on the computing power allocation decision indicator set, the N-node express loading and unloading data sets are associated with data mapping to obtain N-node computing power indicator loading and unloading data sets; according to the N-node computing power indicator loading and unloading data sets, multi-dimensional indicator evaluation is performed on the N express loading and unloading process nodes respectively to obtain N-node computing power indicator evaluation matrix sets; the computing power allocation decision indicator set is critically evaluated according to the express loading and unloading information processing requirements to generate a computing power indicator criticality matrix; based on the computing power indicator criticality matrix, the N-node computing power indicator evaluation matrix set is weightedly calculated and corrected to determine the computing power decision indicator parameters of the N nodes.

[0029] Specifically, in order to accurately configure the required computing resources for each node to improve loading and unloading efficiency and data processing capabilities, the N-node express loading and unloading data sets are associated with data mapping based on the computing power allocation decision indicator set. Here, the computing power allocation decision indicator set includes indicators such as the amount of historical data processed by the node, the data type, and the data processing accuracy. These indicators critically reflect the computing performance requirements and characteristics of each node. The task of associated data mapping is to combine these decision indicators with the actual computing and data processing records of each node to form N-node computing power indicator loading and unloading data sets.

[0030] Next, a multi-dimensional indicator evaluation is performed on the obtained N node computing power indicator loading and unloading data sets. In this step, the data set of each node is used to generate a computing power indicator evaluation matrix set, which details the performance and requirements of each node on different computing power indicators, such as computing speed, storage capacity, and network bandwidth. The multi-dimensional evaluation provides decision support for subsequent resource allocation by considering the relative importance of each indicator and the specific needs of the node. After that, a critical evaluation of the express loading and unloading information processing needs is performed to generate a computing power indicator criticality matrix. This evaluation takes into account the sensitivity and urgency of 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 indicators are critical to maintaining node operation efficiency and which can be appropriately lowered in priority when resources are tight.

[0031] Finally, based on the computing power indicator criticality matrix, the N node computing power indicator evaluation matrix set is weighted and calculated. This correction process involves adjusting the original computing power allocation to make it more in line with the actual operation needs and strategic goals of the node. For example, for nodes with higher processing requirements, their computing resource allocation may be increased, while for nodes processing simple tasks, resources can be appropriately reduced to ensure the overall optimal configuration of resources.

[0032] Through these detailed analysis and evaluation steps, the computing power decision indicator parameters of each express loading and unloading node can be determined. These parameters ultimately guide the specific allocation and optimization of resources, ensuring the overall efficient operation of the express loading and unloading system and maximizing data processing capabilities.

[0033] Furthermore, 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 constructing a list of N express node loading and unloading job tasks; clustering the N node express loading and unloading data sets according to the N express node loading and unloading job task lists, and obtaining N node loading and unloading job task data sets; performing distributed training on the N node loading and unloading job task data sets, and obtaining an N node loading and unloading job task analysis model set; using the N node loading and unloading job task analysis model set to perform distributed job analysis on the N node express logistics data, and obtaining the N node loading and unloading job analysis parameter set.

[0034] Optionally, a detailed job task analysis is performed on the N express control thread nodes, which involves identifying and recording the specific tasks each node undertakes in the express loading and unloading operations, such as loading, unloading, sorting, and transshipment. Based on these analysis results, a list of N express node loading and unloading task is constructed, each list describing in detail the key information such as the task type, task frequency, and time sensitivity of the task that the corresponding node is responsible for, providing a basis for subsequent data clustering and model training.

[0035] Next, the N-node express loading and unloading data sets are clustered according to their respective express node loading and unloading task lists. The purpose of this step is to classify and organize the operation data of each node according to the job type, thereby forming N-node loading and unloading 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 a specific job task.

[0036] Subsequently, the N node loading and unloading task data sets are trained in a distributed manner to obtain a set of N node loading and unloading task analysis models. In this stage, machine learning techniques such as deep learning or support vector machines are used to analyze each data set to train models that can accurately predict and optimize loading and unloading operation performance. These models can predict operation bottlenecks based on real-time data, propose improvement measures, and predict future operation needs.

[0037] Finally, the N node loading and unloading task analysis model set is used to perform distributed operation analysis on the express logistics data of the N nodes. This analysis stage is to apply 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.

[0038] Through the above steps, not only the operational response capability and efficiency of each node are enhanced, but also the performance of the entire express loading and unloading system is greatly improved through sophisticated data processing and intelligent decision support. These loading and unloading operation analysis parameter sets provide management with powerful decision-making tools to ensure that express loading and unloading operations can be carried out under optimal conditions, thereby maximizing resource utilization and minimizing operating costs.

[0039] Furthermore, obtaining the N-node loading and unloading task analysis model set includes: Perform task characteristic analysis on the N node loading and unloading task data sets respectively to obtain N node loading and unloading task processing characteristic sets; construct a deep learning model list, match the N node loading and unloading task processing characteristic sets with the deep learning model list, and determine 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 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 task analysis model sets.

[0040] Exemplarily, the task characteristic analysis is first performed on the N node loading and unloading task data sets. In this step, the data set of each node is analyzed in detail to identify the core characteristics of its task, such as task frequency, complexity, data volume, processing time, and required computing resources. Through this analysis, the N node loading and unloading task processing characteristic sets are obtained. These characteristic sets provide a detailed description and requirement overview of each node when performing its task, providing a basis for subsequent model matching and training.

[0041] Next, a list of deep learning models is constructed, which contains a variety of 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 loading and unloading task processing feature sets, these models are carefully matched to ensure that the operation task characteristics of each node can be processed by the most suitable model, thereby maximizing the analysis efficiency and accuracy. Subsequently, the matched N node operation task deep learning model sets are used to perform distributed training on the loading and unloading operation task data sets of each node. In this stage, each selected model is trained on its respective node using the corresponding data set to adjust the model parameters and optimize its prediction and analysis capabilities. This process usually requires a lot of computing resources and may involve multiple rounds of iterative training to ensure that each model can accurately reflect the node's operation requirements and environmental changes. Finally, the N initial node operation task analysis model sets are verified, optimized, and integrated. In this step, each trained model will go through a series of verification processes, including cross-validation and actual historical data comparison, to test its effectiveness and accuracy. After verification, all optimized models are summarized and integrated into the final N-node loading and unloading task analysis model set, which will be deployed in actual operations to guide and optimize the loading and unloading operations of each node.

[0042] Through the above steps, it is ensured that each node can effectively handle its loading and unloading operations through customized deep learning models, thereby improving the efficiency and responsiveness of the overall express loading and unloading system, while reducing the error rate and improving the adaptability of operations. These model sets are decision-making tools based on actual operational needs and data-driven, providing strong support for express loading and unloading management.

[0043] Furthermore, the obtaining of express loading and unloading collaborative operation parameters includes: Perform multi-dimensional encryption degree evaluation on 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; perform node loading and unloading interaction analysis on the N express control thread nodes to generate an express loading and unloading node interaction flow network; perform operation collaboration analysis on the N node loading and unloading operation analysis parameter sets based on the express loading and unloading node interaction flow network to obtain express loading and unloading collaborative operation parameters.

[0044] In a specific embodiment, a multi-dimensional encryption degree assessment 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 assessment is N express node encryption coefficients, which reflect the encryption strength and strategy of each node, ensuring that each node can maintain data integrity and privacy when exchanging data with other nodes.

[0045] Next, the N express node encryption coefficients are used to encrypt and integrate the N node loading and unloading operation analysis parameter sets into the loading and unloading control center. In this step, the operation analysis parameters of each node are first encrypted before being sent to the loading and unloading control center, and the encryption strength used is determined according to the encryption coefficient obtained by the previous evaluation. This is done to protect the data during transmission from unauthorized access or tampering, while ensuring that the loading and unloading control center can receive all necessary operation information for subsequent processing.

[0046] Subsequently, the node loading and unloading interaction analysis is performed on the N express control thread nodes to generate an express loading and unloading 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. Through analysis, the generated express loading and unloading node interaction flow network describes in detail the interaction mode and dependency relationship between each node, providing a basis for formulating more effective operation collaboration strategies.

[0047] Finally, based on the express loading and unloading node interactive flow network, the N node loading and unloading operation analysis parameter sets are subjected to operation collaborative analysis. 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 collaborative analysis, express loading and unloading collaborative operation parameters are obtained. These parameters guide the operation execution of the entire express network, ensuring the efficient coordination of operations at each node and maximizing overall performance.

[0048] In summary, through these detailed steps, not only the data security and privacy protection of the system are guaranteed, but also the operational efficiency and response speed of the entire express loading and unloading network are significantly improved through in-depth interaction analysis and collaborative operation strategy optimization between nodes. These collaborative operation parameters are indispensable data for management decision-making and daily operations, providing solid data support and intelligent guidance for the smooth operation of the entire logistics network.

[0049] Furthermore, the obtaining of express loading and unloading collaborative operation parameters includes: Acquire the express loading and unloading operation target, extract indicators and evaluate and fit the express loading and unloading operation target, and construct the express loading and unloading operation effect fitness function; construct the express loading and unloading strategy space, and based on the express loading and unloading operation effect fitness function, 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 to obtain N node loading and unloading strategy parameter sets; based on the express loading and unloading node interaction flow network, perform correlation influence analysis on the N express control thread nodes to determine the N node loading and unloading correlation influence parameter sets; based on the N node loading and unloading correlation influence 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.

[0050] Specifically, the express loading and unloading operation objectives are obtained, which includes determining the specific objectives of each express loading and unloading operation, such as minimizing loading and unloading time, maximizing processing volume, optimizing resource utilization, etc. Indicators are extracted and evaluated for fitting these operation objectives, which means extracting quantifiable performance indicators from the operation objectives and evaluating the fit of the existing operation conditions with these indicators. This step is carried out by analyzing historical data and current operating efficiency to ensure that the extracted indicators are accurate and practical. Next, a fitness function for the express loading and unloading operation effect is constructed. 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 is usually implemented using mathematical modeling techniques, which can score and rank the expected results under different operation strategies.

[0051] Subsequently, an express loading and unloading strategy space is constructed. This space contains all possible strategy combinations, and each combination represents a set of possible operating parameter settings. Based on the express loading and unloading operation effect fitness function, the strategy parameter analysis and optimization are performed on the loading and unloading operation analysis parameter sets of the N nodes in the express loading and unloading strategy space. This process uses optimization algorithms, such as genetic algorithms or simulated annealing, to find the most optimized strategy combination to achieve the best fitness for the loading and unloading operation objectives. Furthermore, based on the express loading and unloading node interaction flow network, the N express control thread nodes are subjected to an associated impact analysis. This analysis takes into account the dependencies and job interaction characteristics between the nodes, and evaluates the potential impact of different node strategy adjustments on the overall network. This helps determine the N node loading and unloading associated impact parameter sets, which include parameters that may affect the operating efficiency and resource requirements of adjacent nodes.

[0052] Finally, the N node loading and unloading strategy parameter sets are collaboratively integrated and analyzed based on the N node loading and unloading association impact parameter sets. 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 integrated 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.

[0053] Through the above steps, not only the efficiency and effectiveness of express loading and unloading operations are maximized, but also through in-depth data analysis and optimization algorithms, the coordination and data security between various operation nodes are strengthened, thereby significantly improving the operational performance of the entire express network.

[0054] Through the technical solutions of the above-mentioned embodiments, the multi-line collaborative express loading and unloading management method provided by the present application solves the technical problems of the existing express loading and unloading management in a dynamic operating environment, such as insufficient response speed and accuracy, inability to flexibly adjust resulting in waste of resources and service delays, and poor collaborative operations, and achieves the technical effect of improving the processing speed and accuracy of express loading and unloading operations, optimizing resource allocation, and enhancing dynamic adjustment capabilities and operational collaborative efficiency.

[0055] Embodiment 2, based on the same inventive concept as the multi-line collaborative express loading and unloading management method in the previous embodiment, Figure 2 As shown, the present application provides a multi-line collaborative express loading and unloading management platform, the platform comprising: The node extraction module 11 is used 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.

[0056] The endpoint deployment module 12 is used to perform endpoint deployment analysis based on the N express loading and unloading process nodes to obtain N express control thread nodes.

[0057] The mapping and diversion module 13 is used to 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.

[0058] The distributed analysis module 14 is used to perform distributed operation analysis on the express logistics data of the N nodes based on the N express control thread nodes to obtain the N node loading and unloading operation analysis parameter sets.

[0059] The collaborative management module 15 is used 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, 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.

[0060] Furthermore, the endpoint deployment module 12 is further configured to perform the following steps: An express loading and unloading database is collected and acquired, and the express loading and unloading database is divided and integrated based on the N express loading and unloading process nodes to obtain N-node express loading and unloading data sets.

[0061] Obtain a computing power resource allocation strategy, extract evaluation indicators for the computing power resource allocation strategy, and determine a computing power allocation decision indicator set.

[0062] Based on the computing power allocation decision indicator set, a multi-dimensional evaluation is performed on the N-node express loading and unloading data set to determine the computing power decision indicator parameters of the N nodes.

[0063] Based on the N node computing power decision indicator parameters, computing power allocation and endpoint deployment are performed on the N express loading and unloading process nodes to obtain the N express control thread nodes.

[0064] Furthermore, the endpoint deployment module 12 is further configured to perform the following steps: Based on the computing power allocation decision indicator set, the N-node express loading and unloading data sets are associated with data mapping to obtain the N-node computing power indicator loading and unloading data sets.

[0065] According to the N node computing power indicator loading and unloading data sets, multi-dimensional indicator evaluation is performed on the N express loading and unloading process nodes respectively to obtain an N node computing power indicator evaluation matrix set.

[0066] The computing power allocation decision indicator set is critically evaluated based on the express loading and unloading information processing requirements to generate a computing power indicator criticality matrix.

[0067] 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 computing power decision indicator parameters of the N nodes.

[0068] Furthermore, the distributed analysis module 14 is further configured to perform the following steps: Perform job task analysis on the N express control thread nodes in turn, and build a list of loading and unloading job tasks for the N express nodes.

[0069] The N-node express loading and unloading data sets are clustered according to the N-node loading and unloading operation task lists to obtain the N-node loading and unloading operation task data sets.

[0070] Distributed training is performed on the N node loading and unloading task data sets respectively to obtain an N node loading and unloading task analysis model set.

[0071] 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.

[0072] Furthermore, the distributed analysis module 14 is further configured to perform the following steps: Task characteristic analysis is performed on the N node loading and unloading task data sets respectively to obtain N node loading and unloading task processing characteristic sets.

[0073] 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.

[0074] The N node operation task deep learning model sets are used 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.

[0075] The N initial node operation task analysis model sets are verified, optimized and integrated to obtain the N node loading and unloading operation task analysis model sets.

[0076] Furthermore, the collaborative management module 15 is further configured to perform the following steps: Multi-dimensional encryption degree evaluation is performed on the N express control thread nodes respectively to obtain N express node encryption coefficients.

[0077] The N express node encryption coefficients are used to encrypt and integrate the N node loading and unloading operation analysis parameter sets into the loading and unloading control center.

[0078] Perform node loading and unloading interaction analysis on the N express control thread nodes to generate an express loading and unloading node interaction flow network.

[0079] Based on the express loading and unloading node interactive flow network, the N node loading and unloading operation analysis parameter sets are subjected to operation collaborative analysis to obtain express loading and unloading collaborative operation parameters.

[0080] Furthermore, the collaborative management module 15 is further configured to perform the following steps: Obtain express loading and unloading operation objectives, extract indicators and evaluate and fit the express loading and unloading operation objectives, and construct the express loading and unloading operation effect fitness function.

[0081] Construct an express loading and unloading strategy space, and 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 express loading and unloading operation effect fitness function to obtain the N node loading and unloading strategy parameter sets.

[0082] Based on the express loading and unloading node interactive flow network, the N express control thread nodes are subjected to correlation impact analysis to determine the N node loading and unloading correlation impact parameter sets.

[0083] Based on the N node loading and unloading associated influencing parameter sets, the N node loading and unloading strategy parameter sets are collaboratively integrated and analyzed to obtain the express loading and unloading collaborative operation parameters.

[0084] Through the above-mentioned detailed description of the multi-line collaborative express loading and unloading management method in this specification, those skilled in the art can clearly know the multi-line collaborative express 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, and the relevant parts can be referred to the method part description.

[0085] 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 apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform 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; Acquire 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; Based on the N express control thread nodes, distributed operation analysis is performed on the express logistics data of the N nodes to obtain a set of analysis parameters for loading and unloading operations of the N nodes; The N-node loading and unloading operation analysis parameter sets are encrypted and integrated into the loading and unloading control center for operation collaborative analysis, to obtain express loading and unloading collaborative operation parameters, and to perform express loading and unloading collaborative management based on the express loading and unloading collaborative operation parameters.

2. The multi-line coordinated express loading and unloading management method according to claim 1, characterized in that: 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; Based on the computing power allocation decision indicator set, a multi-dimensional evaluation is performed on the N-node express loading and unloading data set to determine the N-node computing power decision indicator parameters; Based on the N node computing power decision indicator parameters, computing power allocation and endpoint deployment are performed on the N express loading and unloading process nodes to obtain the N express control thread nodes.

3. The multi-line coordinated express loading and unloading management method according to claim 2, characterized in that: Determining the N node computing power decision indicator parameters includes: Based on the computing power allocation decision indicator set, the N-node express loading and unloading data set is associated with data mapping to obtain the N-node computing power indicator loading and unloading data set; According to the N node computing power index loading and unloading data sets, multi-dimensional index evaluation is performed on the N express loading and unloading process nodes respectively to obtain an N node computing power index evaluation matrix set; Performing a critical 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 computing power decision indicator parameters of the N nodes.

4. The multi-line coordinated express loading and unloading management method according to claim 2, characterized in that: The obtaining of N node loading and unloading operation analysis parameter sets includes: Performing job task analysis on the N express control thread nodes in turn, and constructing a loading and unloading job task list 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 respectively 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.

5. The multi-line coordinated express loading and unloading management method according to claim 4, characterized in that: The step of obtaining a set of N node loading and unloading task analysis models includes: Performing task characteristic analysis on the N node loading and unloading task data sets respectively to obtain N node loading and unloading task processing characteristic sets; 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; Using the N node operation task deep learning model sets to respectively perform distributed training on the N node loading and unloading operation task data sets to obtain N initial node operation task analysis model sets; The N initial node operation task analysis model sets are verified, optimized and integrated to obtain the N node loading and unloading operation task analysis model sets.

6. The multi-line coordinated express loading and unloading management method according to claim 1, characterized in that: The obtaining of express loading and unloading collaborative operation parameters includes: Perform multi-dimensional encryption degree evaluation on the N express control thread nodes respectively to obtain N express node encryption coefficients; The N express node encryption coefficients are used to encrypt and integrate the N node loading and unloading operation analysis parameter sets into the loading and unloading control center; Perform 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 interactive flow network, the N node loading and unloading operation analysis parameter sets are subjected to operation collaborative analysis to obtain express loading and unloading collaborative operation parameters.

7. The multi-line coordinated express loading and unloading management method according to claim 6, characterized in that: The obtaining of express loading and unloading collaborative operation parameters includes: Obtain express loading and unloading operation targets, extract indicators and evaluate and fit the express loading and unloading operation targets, and construct the express loading and unloading operation effect fitness function; Constructing an express loading and unloading strategy space, and performing 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 express loading and unloading operation effect fitness function, to obtain the N node loading and unloading strategy parameter sets; Based on the express loading and unloading node interactive flow network, the N express control thread nodes are subjected to correlation impact analysis to determine the N node loading and unloading correlation impact parameter set; Based on the N node loading and unloading associated influencing parameter sets, the N node loading and unloading strategy parameter sets are collaboratively integrated and analyzed to obtain the express loading and unloading collaborative operation parameters.

8. A multi-line collaborative express loading and unloading management platform, characterized by: The platform is used to implement the multi-line collaborative express loading and unloading management method according to any one of claims 1 to 7, comprising: A node extraction module is used 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, used 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 is used to obtain express logistics data information through a 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; A distributed analysis module, used to perform distributed operation analysis on the express logistics data of the N nodes based on the N express control thread nodes, and obtain a set of analysis parameters for loading and unloading operations of the N nodes; The collaborative management module is used 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, 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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