388 results about "Logistic management" patented technology

The invention relates to the technical field of logistics transportation scheduling, in particular to a logistics management system based on intelligent scheduling, which comprises a data acquisition module, a demand analysis module, an intelligent scheduling module, a path planning module, a monitoring and feedback module, an optimization module and the like. The data acquisition module is used for collecting information such as goods, vehicles, storage, customer demands and traffic road conditions; the demand analysis module predicts logistics demands by using a time sequence analysis or machine learning algorithm; the intelligent scheduling module adopts a greedy and dynamic programming mixed algorithm to allocate resources; the path planning module calculates an optimal path according to road conditions and starting and ending points; the monitoring and feedback module monitors logistics parameters in real time and feeds back abnormity; and the optimization module optimizes the scheduling scheme according to the fitness function based on the genetic algorithm. According to the invention, data and resources of all links of logistics are effectively integrated, accurate demand prediction, efficient resource configuration, transportation path optimization, real-time monitoring feedback and overall performance improvement of the system are realized, the logistics cost is significantly reduced, and the transportation efficiency and the service quality are improved.

The invention provides an integrated logistics management system for cargo transportation. The integrated logistics management system comprises a multi-mode collaborative perception module, a semantic reasoning module, a scene adaptation decision module, an intelligent execution module and a logistics trust chain module. The multi-mode collaborative sensing module collects logistics full-scene state data through a sensor network; the semantic reasoning module is used for constructing a knowledge graph based on a graph attention network and realizing unified modeling of multi-source information; the scene adaptation decision module is combined with a pulse coupled neural network (PCNN) and a swarm intelligence collaboration mechanism to complete routing optimization; the intelligent execution module adopts a hierarchical task management and dynamic adjustment mechanism, and adjusts a task scheme according to execution abnormity and credit evaluation; the logistics trust chain module constructs a logistics trust system through a hierarchical consensus mechanism, a credit scoring model and a trust propagation and early warning mechanism, and realizes risk prevention and control of the whole process. The system solves the technical problems of a traditional logistics system in the aspects of data integration, dynamic supervision, decision support, collaborative scheduling, safety traceability and the like.

The invention relates to the technical field of logistics management, in particular to an intelligent logistics terminal equipment collaborative management and control system based on AI edge computing, and the system comprises an edge data fusion and state recognition module which is deployed at an edge node, collects multi-source data of an operation state, environment perception, a communication link and the like, and generates an equipment state representation vector through fusion; the intelligent prediction and task scheduling decision module uploads the state vector to a cloud, predicts task completion capability and fault probability, and generates a task scheduling decision packet; the scheduling strategy issuing and edge execution collaboration module issues a scheduling packet through a multi-protocol gateway, and an edge node completes task distribution, communication switching and resource scheduling and caches a key strategy. According to the invention, the real-time sensing of the state of the logistics terminal equipment, the intelligent prediction and resource optimization of task scheduling, and the quick response and fault-tolerant control in a fault scene are realized, and the operation efficiency, the intelligent level and the stability of the system are remarkably improved.

The invention provides an automatic driving vehicle scheduling method and device, equipment and a storage medium, and the method comprises the steps: carrying out the multi-dimensional collection and preprocessing of an automatic driving vehicle, and generating a comprehensive data set; and based on the comprehensive data set, carrying out dynamic allocation and fleet collaborative planning on loading and unloading tasks to generate a task allocation scheme. And through a hierarchical search algorithm and a multi-objective optimization algorithm, the path of the vehicle is planned and optimized in combination with the task allocation scheme and the comprehensive data set, and an optimized path scheme is generated. And according to the task allocation scheme, the optimized path scheme, the integrated data set and the vehicle real-time state data, utilizing a predictive analysis model to evaluate and deal with potential risks in the transportation process, and generating and executing a real-time scheduling instruction. According to the invention, the scheduling method of multi-dimensional data acquisition and processing, dynamic task allocation, path optimization and risk prediction is combined, the scheduling efficiency and safety of the autonomous vehicle are comprehensively improved, and efficient logistics management is realized.

The invention discloses a product logistics management scheduling method based on a block chain, a medium and equipment, and the method comprises the steps: generating an initial transportation path scheme through collecting logistics transportation demand information and supply chain node data; constructing a decentralized logistics resource pool, and collecting and verifying transportation fluctuation parameters to generate a credible state matrix; inputting the initial transportation path scheme and the logistics resource credible state matrix into a graph neural network scheduling model, and outputting a dynamic scheduling scheme; compliance verification of task allocation and scheduling instructions is automatically executed through the smart contract; and finally, updating the scheduling execution result and the logistics resource credible state matrix to a distributed account book, and outputting a logistics management report. Through deep cooperation of the intelligent contract and dynamic path optimization, real-time credible scheduling of the transportation process is realized, the problems of insufficient dynamic response and multi-party cooperation trust deficiency of an existing logistics system are solved, and the reliability and efficiency of cold chain and other sensitive commodity transportation are improved.

The invention, which relates to the technical field of intelligent logistics management, discloses an AI-based intelligent logistics management system comprising a warehouse management unit, a data fusion acquisition unit and a digital twin supervision unit. The warehouse management unit is used for managing warehouse-in and warehouse-out information and cargo information of a warehouse, reminding inventory risks, and carrying out demand prediction and inventory optimization; the data fusion acquisition unit is used for acquiring personnel, goods, equipment and environment information of a warehouse; the digital twin supervision unit comprises an intelligent AI recognition unit and a data analysis processing unit, the intelligent AI recognition unit is used for carrying out recognition analysis and risk reminding on personnel, goods, equipment and environment of a warehouse through an AI intelligent algorithm, and the data analysis processing unit is used for mapping data of an entity warehouse to a digital twin warehouse. And visual interaction management and intelligent scheduling of personnel, goods and equipment are carried out on the digital twin warehouse, so that the warehouse can be comprehensively monitored, and the goods storage and management efficiency is improved.

The invention relates to the technical field of logistics management, in particular to a warehouse material storage location distributing and sorting system based on a genetic algorithm, and the system comprises a data access and analysis module which is connected with a main database of a warehouse management system through a security protocol, synchronously updates the information of materials, orders and employees, carries out the establishment of a data index, and transmits the data index to a server; according to the invention, through the design of the sorting system and the combination of simulated annealing and a genetic algorithm, the intelligent configuration of the material storage position is effectively realized, the sorting path is optimized, the material carrying time and the manual error are reduced, the sorting accuracy is improved, the AGV is managed by adopting an asynchronous algorithm, the task scheduling is optimized, and the sorting efficiency is improved. The material processing speed is accelerated, the response efficiency is improved, the inventory configuration is dynamically adjusted, the market demand change is adapted, the high efficiency and the quick response capability of the inventory are kept, the automation and the intelligence of the inventory management are promoted, and the operation efficiency and the service quality of the whole warehouse are greatly improved.

The invention discloses an intelligent logistics system based on optimal scheduling, and relates to the field of logistics management, and the system comprises the steps: arranging environment sensors at key nodes of a logistics network, and transmitting collected real-time data to edge computing equipment for preprocessing; analyzing the preprocessed data by using a deep learning model, and predicting an environment change trend in a non-period of time; dynamically adjusting a logistics scheduling strategy based on the prediction result; after receiving the adjusted scheduling strategy, defining a state space, an action space and a reward function of the logistics system; training a reinforcement learning algorithm model, and continuously optimizing a decision strategy through interaction with the environment; and calling the reinforcement learning model through an API interface to make a real-time decision, and generating an optimal scheduling scheme. According to the invention, by integrating environment perception, deep learning prediction, adaptive scheduling and reinforcement learning, the defects of an existing intelligent logistics system in the aspects of environment perception, prediction and dynamic scheduling optimization are overcome, and the intelligent level and operation efficiency of logistics management are significantly improved.

The invention relates to the technical field of data analysis, provides a logistics access dynamic management report generation method and system based on an Internet of Things lock, and is used for realizing full-process intelligent generation and optimization of a logistics management report. The method comprises the following steps: acquiring an access event data set of a target logistics area; performing cross-modal feature extraction processing on the access event data set to obtain a dynamic access feature set containing space-time dimension features; based on a preset report type template, inputting the dynamic access feature set into a trained report generation model for multi-task analysis processing, and generating an initial logistics management report set; performing semantic integrity verification and logic conflict detection processing on the initial logistics management report set to generate an optimized target logistics management report; and binding the target logistics management report with an authorized access interface of the user terminal, and performing incremental updating operation on the target logistics management report according to the access event triggered in real time.

The invention discloses a logistics order management system and method, and belongs to the technical field of logistics information management. The method comprises the following steps: step 1, generating a unique digital fingerprint for each logistics order; step 2, performing multi-level security encryption and state synchronization management on order information; 3, constructing a digital twinborn model corresponding to the order, collecting and preprocessing key data in real time and visually displaying the key data in the logistics process, and realizing dynamic monitoring of the whole process; step 4, based on an order state consensus mechanism, automatically matching optimal logistics resources and driving an order processing flow, and generating a verifiable order execution proof chain at the same time; and step 5, performing intelligent analysis and adaptive optimization on the full-life-cycle data of the order, and continuously improving the overall efficiency and precision of logistics management.

The invention relates to the technical field of logistics information management, and discloses a cross-border logistics information management method and system based on a logistics management platform, and the method comprises the steps: carrying out the analysis of a received customer order in a domestic logistics system, and obtaining a labeling parameter set; querying equipment specification information of an overseas logistics system, and converting the labeling parameter set based on the equipment specification information to obtain an overseas execution instruction; the overseas execution instruction is transmitted to overseas warehouse labeling equipment of an overseas logistics system for automatic labeling operation, and labeling process data is obtained; state monitoring is conducted on the labeling process data, a labeling state report is obtained, the labeling state report is transmitted back to the domestic logistics system, and synchronous confirmation information is generated. According to the method, full-process automation and intelligentization of cross-border logistics labeling information coordination processing are realized.

The invention discloses a logistics management system based on artificial intelligence, and the system is used for improving the efficiency and intelligent level of logistics scheduling and resource configuration, and comprises the following steps: building a unified data set through collecting and positioning, sensor and operation log and other types of logistics data; a modeling method is used for fusion to form a data structure containing time-space and transportation state information, the system adopts a causal analysis model to identify key influence relations between logistics events, a cross-modal feature extraction technology is combined to generate representation vectors such as transportation tracks, loads and states, and the logistics events are subjected to self-adaptive analysis through a self-adaptive strategy. And the causal information and the multi-modal features are fused to generate a decision basis, and the fusion proportion is dynamically adjusted according to actual operation feedback, so that transportation path optimization, distribution task planning and reasonable resource allocation are realized. The system has a continuous learning capability, and can keep the accuracy and flexibility of scheduling decisions in a continuously updated data environment.

The invention discloses a production and manufacturing management method and system based on digital twinning, which are used in the field of production and manufacturing management, and the method comprises the following steps: collecting logistics management data in logistics storage and operation data of a production line, and constructing a logistics digital twinning processing model; simulating a logistics management process based on a logistics digital twin processing model, and tracking the positions of goods and transportation tools in real time by using a positioning and identification technology; utilizing a demand prediction model to predict a material demand and a product demand based on the data in the logistics digital twinborn model updated in real time, and adjusting the stock of logistics storage according to a prediction result; and calculating an optimal transportation path by using a path optimization algorithm, making a production and manufacturing plan of logistics storage, and monitoring the production and manufacturing process in real time. According to the bidirectional fast random search tree, a new path can be quickly recalculated, and the accuracy and adaptability of decision making are improved by comprehensively considering various transportation and operation constraints.

The invention discloses a logistics information management method and system based on the Internet of Things, and relates to the technical field of logistics intelligent management, and the method comprises the steps: collecting historical logistics data and real-time logistics data, storing the data on a block chain, carrying out the preprocessing, generating real-time data feedback based on the preprocessing data, and carrying out the management of the logistics information. The method comprises the steps of performing priority classification on storage tasks on the basis of real-time data feedback, adjusting warehouse storage layout on the basis of real-time logistics data, distributing the storage tasks to corresponding areas on the basis of storage task priorities and warehouse storage layout adjustment results, and controlling task execution in real time through remote monitoring. According to the method, intelligent classification and priority evaluation of the storage tasks are realized in combination with the environment characteristics and the change trend of the task priorities, the problems of lag and low efficiency of task allocation and priority judgment in existing logistics management are solved, the flexibility and timeliness of task execution are effectively improved, and the accuracy of task scheduling is enhanced.

The invention discloses a port multi-agent task allocation method and system based on reinforcement learning, and belongs to the field of logistics management, and the method comprises the steps: constructing a task set for various operation tasks in a port multi-task environment; aiming at different types of port intelligent equipment, constructing a dynamic model of the heterogeneous multi-agent; constructing a global environment model based on the task set and the dynamical model; constructing a collaborative multi-agent task allocation mechanism, and performing task allocation and scheduling by using the task allocation mechanism; and constructing a collaborative reinforcement learning model based on the task set, the dynamic model, the global environment model and the task allocation mechanism, and training a task execution strategy of the intelligent agent by using the collaborative reinforcement learning model. Through the hierarchical design of the high-layer strategy network and the low-layer strategy network, a complex task can be decomposed into a plurality of sub-tasks, management and optimization are facilitated, a multi-task environment can be effectively processed, and an intelligent agent can flexibly cope with different task sets.

The invention discloses an intelligent logistics platform electronic signature automatic management method and system, and relates to the logistics management technology field, and a contract signing module of the system integrates a signature request unit, a first processing unit, a second processing unit, an electronic signature obtaining unit and a contract signing unit. Through the units, the effective verification of the user identity is realized, the dynamically encrypted and stored electronic signature is quickly obtained, and the security guarantee is provided for the contract signing process. In addition, the system also comprises a contract template management module, a signature management module, a contract generation module, a log and audit module, a user authority management module and the like, which jointly provide a comprehensive, efficient and safe intelligent logistics platform electronic signature automatic management scheme.

The invention provides an intelligent coal fuel scheduling dynamic early warning system and method, and belongs to the technical field of resource scheduling and logistics management, and the system comprises a data acquisition module which carries out the real-time monitoring of each node in a coal fuel supply chain through a preset multi-sensor network, and obtains the dynamic change data in the coal fuel supply chain; the historical analysis module is used for acquiring and analyzing historical scheduling data of the coal fuel supply chain; the scheme acquisition module is used for constructing a multi-level scheduling model and performing local scheduling at different levels in combination with an analysis result based on historical scheduling data and dynamic change data of a supply chain to generate an overall scheduling scheme and an overall alternative scheduling scheme; the scheme reconstruction module is used for executing coal fuel scheduling based on the overall scheduling scheme and obtaining real-time scheduling data to reconstruct the overall scheduling scheme; and the dispatching early warning module is used for constructing a multi-level early warning mechanism based on the coal fuel real-time dispatching data and carrying out early warning, so that the dispatching efficiency and the supply chain stability are improved.

The invention relates to the technical field of logistics management, in particular to a logistics cargo transportation scheduling method and system based on artificial intelligence. The method comprises the following steps: obtaining logistics node data; performing multi-dimensional logistics data acquisition on the logistics node data to generate multi-dimensional logistics data; a logistics monitoring network is constructed according to the logistics node data as nodes and the multi-dimensional logistics data, and the logistics monitoring network is generated and comprises a storage node, a distribution center node and a transportation tool node; performing data preprocessing on the multi-dimensional logistics data through a logistics monitoring network to generate standard multi-dimensional logistics monitoring data; and performing adjacent logistics storage node position search on the standard multi-dimensional logistics monitoring data through the distribution center node to generate adjacent storage node position information data. According to the invention, through multi-dimensional data acquisition, dynamic path optimization, delay prediction and a full-link feedback mechanism, the efficiency and reliability of logistics cargo scheduling are improved.

The invention discloses a production optimization method and system combined with logistics management, and relates to the technical field of production optimization. The invention discloses a production optimization system combined with logistics management. The production optimization system comprises a personalized order analysis module, a logistics state acquisition module, an internal logistics modeling module, an external logistics optimization module, a supply strategy management module, a joint scheduling optimization module, a scheduling execution module and a congestion monitoring and simulation feedback module. According to the invention, an external logistics resource supply strategy is structured and expressed as a transport time window matrix and a dynamic transport capacity allocation table, and the transport time window matrix and the dynamic transport capacity allocation table are used as boundary conditions to be introduced into a joint scheduling optimization model, so that a scheduling system can dynamically adjust a production and logistics collaborative plan according to an external resource state; and the adaptive capacity of the scheduling strategy to external supply changes is effectively enhanced.

The invention discloses a shipping logistics flow direction prediction method and system based on multi-source data fusion, and relates to the field of logistics prediction, and the method comprises the steps: constructing a shipping network diagram based on shipping data; acquiring trajectory data, marine meteorological data, port log data and cargo electronic shipping bill data of a ship automatic identification system in real time; carrying out logistics flow direction prediction on logistics in transportation, obtaining ship data in a future time period, and perfecting a shipping network diagram; carrying out logistics flow direction prediction on the logistics in berthing, and obtaining the remaining loading and unloading amount and the estimated completion time in a future time period; integrating the logistics transportation prediction model and the logistics loading and unloading prediction model, and constructing an end-to-end logistics flow direction prediction model; and correcting the model based on the error between the actual logistics flow direction data and the prediction data. The method has the advantages that the LSTM and Transform models are combined, the shipping logistics flow direction is accurately predicted, the transportation and loading and unloading efficiency is optimized, the prediction precision is improved, and the shipping logistics management efficiency and decision support are improved.

The invention relates to the technical field of logistics management and control of cloud computing, and particularly discloses a remote logistics management method and system based on cloud big data management and control, and the method comprises the steps: obtaining multi-source data in a logistics transportation process in real time through a data collection module, including the information of road conditions, weather, warehouse states and the like; the real-time data and historical data are compared and analyzed through a data comparison and analysis module, and the optimal logistics transportation path is determined; monitoring the real-time data based on the selected path through a path risk assessment module, assessing the risk existing in the current logistics transportation, and dividing the risk into a high risk or a low risk according to the assessment result; different countermeasures are taken according to the risk levels, that is, the current transportation process continues to be executed for low-risk paths, and for high-risk paths, risk factors are analyzed, and alternative schemes are implemented; according to the invention, functions of real-time data acquisition, intelligent path planning and dynamic risk assessment are integrated, and efficient management of logistics transportation is realized.

The invention provides an intelligent logistics management system based on a large language model, and relates to the field of intelligent logistics management. According to the method, a low-rank adaptation technology general large language model is adopted in advance for fine tuning, and an external LLM service obtained through fine tuning is innovatively and deeply fused into a task decision process, so that deep understanding of user intentions and accurate analysis of complex environment situations can be realized, and an optimal distribution scheme is autonomously generated based on the deep understanding of the user intentions and the accurate analysis of the complex environment situations. Besides, the urban environment management module, the logistics management scheduling module, the simulation engine module and the large language model interface module included in the system do not work independently, but closely cooperate through an event-driven and message-passing mechanism, so that a complete unmanned aerial vehicle logistics management closed loop from environment perception to decision making and execution to event feedback is formed. The architecture can reflect and control the distribution task of the unmanned aerial vehicle in real time, and carries out self-correction on a set scheme, thereby finally remarkably improving the intelligent level and operation efficiency of logistics management.

The present invention relates to the technical field of intelligent logistics management and control. Disclosed are a human-machine collaborative management and control method, system and device involving logistics storage and transportation. The method comprises: on the basis of a logistics storage and transportation task, determining a target operation process and a corresponding target operation item queue; executing said task on the basis of the queue; determining whether the queue belongs to a manual operation item set; if the queue belongs to the manual operation item set, generating and issuing a manual operation notification instruction; determining whether a confirmation signal fed back on the basis of the manual operation notification instruction is received; if yes, updating the state of operation items to 'in progress'; determining whether the queue belongs to a feedback operation item set; if the queue belongs to the feedback operation item set, determining whether a manual feedback completion signal is received; if yes, updating the state of the operation items to 'completed'; and when the label of one of the operation items is the maximum label in the queue, confirming that said task is completed. According to the present invention, an instruction is sent to an operator and a manual feedback signal is received in time, so as to collaboratively manage and control the logistics storage and transportation operation process requiring manual participation.

The invention relates to the technical field of logistics management and control, and provides a community shared logistics management and control method and system, and the method achieves the precise matching of resources and the intelligent planning of a distribution path through integrating mobile resource data, fixed resource data and environment data, constructing a logistics resource map and a logistics passing network, dynamically monitoring the distribution process, and carrying out the timely adjustment. The operation efficiency and the service quality of community logistics are obviously improved on the whole; specifically, the logistics management and control system can optimize resource configuration, reduce idle and waste of resources, reduce logistics operation cost, effectively deal with the problems of traffic congestion, abnormal traffic events and the like by planning a main path and a standby path in advance and combining real-time environment change to perform path adjustment, shorten logistics distribution time, improve distribution timeliness and improve logistics distribution efficiency. The logistics task distribution process is comprehensively monitored and dynamically adjusted, the stability and reliability of the logistics service are guaranteed, the user satisfaction is enhanced, and the sustainable development of community shared logistics is promoted.

The invention relates to the technical field of warehouse logistics management, in particular to a dynamic picking path optimization method and system based on deep reinforcement learning, and the method comprises the steps: obtaining a picker state vector based on warehouse structured coding data analysis; calculating the movement value density of orders in each channel according to the to-be-processed order queue data and the picker state vector; defining a state space according to the picker state vector and the to-be-processed order queue data; based on the state space and the discrete execution action space, modeling a warehouse order selection problem as a Markov decision process; solving a Markov decision process by using a deep reinforcement learning neural network model based on the real-time state and the moving value density of the picker to obtain an optimal order picking path decision; and controlling a picker to execute picking operation according to the optimal picking path decision. According to the method, the Markov decision process is solved in combination with the deep reinforcement learning neural network model, accurate optimization of the order picking path is realized, and the warehouse order picking efficiency is effectively improved.

The invention belongs to the technical field of logistics management, and particularly relates to a monitoring management system for e-commerce logistics, which comprises a parameter acquisition module, a parameter preprocessing module, a parameter processing module, a dynamic scheduling module, an early warning execution module and a strategy optimization module. On the basis of dynamic matching of delivery urgency, cost coefficient and vehicle bearing capacity, transportation resource allocation is optimized, the no-load rate and resource waste are reduced, and the logistics resource utilization rate is remarkably increased; a three-level early warning mechanism and an elastic scheduling strategy quickly cope with load fluctuation, overload and load reduction / low-load capacity expansion, the logistics delay risk is reduced, and the order performance timeliness is guaranteed; through data-driven priority delivery coefficient calculation and simulation optimization threshold adjustment, manual dependence is reduced, and the scientificity and foresight of scheduling decisions are improved; the strategy optimization module constructs a self-adaptive logistics management system through historical data backtracking and bottleneck diagnosis, a dynamic iteration algorithm model and threshold parameters, and improves industry competitiveness for a long time.

The invention discloses a digital intelligent logistics management system, relates to the technical field of logistics management, and solves the technical problems that multi-source data is difficult to fuse, a spatio-temporal feature joint construction model is lacked to predict time and position space, and a deviation analysis and early warning mechanism is imperfect. Comprising an express multi-source data fusion and real-time tracking module, a logistics space-time prediction and intelligent scheduling module, a logistics prediction deviation analysis and evaluation module and a logistics prediction abnormity early warning and optimization module. Parcel coordinates are collected in real time through RFID, and a multi-source express information database is constructed; fusing the spatial features and the time features to construct an artificial intelligence model to predict transportation time and position coordinates; quantitative indexes of the relative time deviation value and the relative position deviation value are calculated, and a dynamic threshold value is set in combination with service requirements; and finally, constructing a transportation time prediction model and a position coordinate prediction model, and respectively optimizing transportation time probability output and position coordinates.

The invention provides a logistics informatization management method based on a circulation box and the circulation box based on basalt fibers, belongs to the technical field of logistics, and aims to improve information safety in a logistics information management process. The method is applied to logistics management equipment of a logistics management center and comprises the steps that the logistics management equipment receives logistics information from a turnover box, and the logistics information comprises information of logistics articles read by the turnover box when the logistics articles are placed in the turnover box and further comprises position information of the turnover box; the logistics management equipment divides the logistics information into N parts of information according to an unstructured information segmentation rule, any part of information in the N parts of information is information without an information logic structure, and N is an integer greater than 2; and the logistics management equipment writes the N pieces of information into different storage areas in a distributed manner.

The invention provides a labor insurance article order dynamic scheduling and inventory management method based on the industrial internet, and relates to the technical field of logistics scheduling, and the method comprises the steps: obtaining the order demand information of each enterprise in an industrial park, and the real-time inventory data and historical delivery records of suppliers, and dynamically constructing a probability density function of the delivery capability of the suppliers, and a gravitation model is combined to calculate the adaptation degree between the supplier and the order demand, so that an accurate order distribution scheme is generated. In this way, order allocation rationality and efficiency can be improved, and order delay and resource waste are reduced. Besides, through an improved isochronous circle division method and a dynamic space-time combination optimization algorithm, clustering analysis and scheduling optimization are performed on the distribution path, so that the timeliness of distribution and the rationality of the path can be remarkably improved, the logistics cost is reduced, and the overall logistics management efficiency of the industrial park is improved.

The invention discloses an e-commerce logistics monitoring method and system based on big data, and relates to the technical field of logistics management, and the method comprises the following steps: S1, constructing a multi-dimensional data model; s2, acquiring and processing real-time data; s3, performing anomaly detection; s4, decision support of logistics; s5, transparent tracking of logistics is carried out; and S6, dynamically optimizing the logistics strategy. The system comprises function modules corresponding to the operation of each step in the e-commerce logistics monitoring method based on big data. According to the big data-based e-commerce logistics monitoring method and system, by constructing the multi-dimensional data model and the logistics decision support model, efficient processing and analysis of real-time data streams can be realized, abnormal conditions can be identified in time, optimization suggestions can be provided, logistics efficiency and customer satisfaction can be effectively improved, and meanwhile, the method and the system have good application prospects. Through integration of multiple data sources, information islands are eliminated, the accuracy and flexibility of prediction are enhanced, and the overall efficiency of e-commerce logistics management is significantly improved.