446 results about "Terminal operation" patented technology

The invention provides an enterprise employee behavior analysis and safety risk early warning monitoring method and system, and relates to the technical field of enterprise risk management, and the method comprises the steps: collecting employee terminal operation behavior data, building an activity thermal distribution diagram based on office area grid behavior association intensity, and training a behavior evaluation model. And inputting the behavior scoring matrix into a deep neural network to extract target behavior characteristics, calculating an abnormal behavior risk weight coefficient in combination with a department security policy, performing classification and analyzing a risk diffusion probability, and generating a risk situation index to determine an early warning level. Starting a response strategy according to the early warning level, blocking high-risk early warning in real time, tracking associated accounts, establishing a risk association map, identifying potential risk propagation nodes and performing active protection, finally generating an early warning report and feeding back effective protection rules to a behavior baseline model, and realizing continuous optimization of a risk early warning mechanism. And the accuracy and effectiveness of safety risk early warning in the enterprise are improved.

The invention relates to the technical field of intelligent scheduling, and discloses an automatic wharf AGV intelligent scheduling method and system. The method comprises the steps of collecting container task data through a wharf operation management system, performing multi-dimensional evaluation processing to obtain a task priority evaluation matrix, constructing an AGV state vector according to the matrix, performing load capacity prediction based on historical data, and performing intelligent matching on the task matrix and the AGV state vector through a Port-MTCSA algorithm to obtain a whole-process task chain. And performing time sequence analysis based on the task chain to predict wharf load distribution, and executing AGV resource dynamic re-planning when the load exceeds a threshold value. The technical problem that an existing automatic wharf AGV scheduling method lacks dynamic priority evaluation, load prediction, task chain optimization and prospective resource allocation in a multi-task concurrent scene is solved.

The invention relates to the technical field of emergency command, and discloses an emergency command system and method based on a BIM digital base, and the method comprises the steps: building a unified data model through expanding an IFC standard, carrying out the semantic mapping and entity alignment of BIM building information, GIS geographic space data, monitoring data and an emergency plan document, and carrying out the construction of a unified data model. The method comprises the steps of generating an emergency knowledge graph with BIM building information as nodes, coupling physical simulation and an LSTM prediction algorithm, injecting monitoring data into a BIM model to deduce a disaster process, outputting a risk thermodynamic diagram and an evacuation forbidden zone, and calculating space-time accessibility of emergency resources through a reinforcement learning model according to a deduction result. An optimization scheme including an unmanned aerial vehicle path, a personnel evacuation route and an equipment scheduling instruction is generated, the instruction is synchronized to an augmented reality individual soldier terminal and a mobile command shelter through an edge computing node, a resource deployment state is labeled in real time in a BIM model, multi-terminal operation is synchronized, the timeliness and accuracy of disaster situation sensing are remarkably improved, and the disaster situation sensing efficiency is improved. And disaster loss is effectively reduced.

The invention provides a feeder terminal fault detection method, system and device, a medium and a program product, and the method comprises the steps: obtaining multi-source heterogeneous data comprising feeder terminal operation data, a topological graph structure of a power distribution network and external sensing data, the multi-source heterogeneous data comprises at least one kind of structured or unstructured time sequence data, and the external sensing data comprises at least one kind of structured or unstructured time sequence data; the external sensing data comprises meteorological data, geographic space information and historical fault records; and preprocessing the multi-source heterogeneous data, inputting the preprocessed time-aligned multi-source heterogeneous data into the trained time-space diagram neural network model to extract features and perform joint modeling, and outputting a fault type classification result and a fault probability distribution result corresponding to each feeder terminal node in the power distribution network. According to the method, a multi-source heterogeneous data fusion and time-space diagram neural network modeling mechanism is introduced, so that the model has a dynamic response capability to complex environment changes, and the identification precision of a potential fault mode is effectively enhanced.

The invention discloses a multi-objective optimization algorithm-based wharf operation resource dynamic allocation method, which comprises the following steps of S1, acquiring wharf operation data, and preprocessing the acquired data; s2, constructing a Dreamer reinforcement learning model, and predicting a wharf operation state through a world model of the Dreamer reinforcement learning model; s3, optimizing the prediction process of the world model by using a water circulation algorithm; s4, training a reinforcement learning module agent, and learning a wharf operation resource allocation strategy; s5, using a water circulation algorithm to optimize the training process of the reinforcement learning module agent; s6, calculating a wharf operation resource allocation deviation and a wharf operation resource allocation scheme; and S7, updating parameters of the Dreamer reinforcement learning model by adopting incremental learning, and adjusting a wharf operation resource allocation scheme. According to the method, the Dreamer reinforcement learning model and the water circulation algorithm are fused, dynamic optimization allocation of the wharf operation resources is achieved, and the method has the advantages of being fast in response, high in adaptability and high in multi-target coordination capacity.

The invention belongs to the technical field of power resource optimization, and particularly relates to a power supply station service architecture optimization method based on digital power grid construction achievements, which comprises the following steps: investigating power supply station service requirements, constructing a power supply station service architecture, and determining a power supply station application architecture; an intelligent Internet of Things terminal operation system based on an electric power gap base and a platformization solution are introduced into the application architecture, and a platformization and perception fused service operation system is constructed; core technologies such as an AI large model, a high-perception terminal, a security authentication mechanism and a 5G slicing network are introduced into asset management, production management, resource management and technical management, and an integrated service execution system with intelligent perception, efficient cooperation, security, credibility and standardized operation is constructed. According to the invention, the problems of scattered architecture, slow response, weak cooperative capability and the like in a traditional power grid system are solved, and reliable support and a technical path are provided for efficient, safe and intelligent development of a power grid.

The embodiment of the invention discloses an intelligent front-end rendering optimization method and system based on real-time environment perception. Obtaining user behavior data and environment perception data corresponding to the terminal; determining a resource loading variable according to the environment perception data based on the equipment weight model, the network weight model and the rendering weight model; performing priority weighting processing on the resource loading variable, and determining priority data corresponding to the terminal; a preset resource loading strategy is sent to the terminal according to the priority data, and the terminal performs rendering optimization on the page based on the preset resource loading strategy according to a progressive rendering template and a parallel resource scheduling method; and determining a multi-terminal operation conflict probability based on the user behavior data, determining a multi-terminal high-risk operation type, and executing intelligent front-end rendering optimization of a conflict reduction strategy based on real-time environment perception according to the multi-terminal high-risk operation type. The loading speed and rendering efficiency of the front-end application are improved, and smoother interaction experience is provided for the user.

The invention discloses a ship shore-end intelligent management method, system and device and a storage medium, and the method comprises the steps: carrying out the ship position prediction based on an electronic chart, the current navigation state data of a ship, and the current environment data, and obtaining the information of a to-be-docked ship according to the future position information of the ship, constructing a port operation knowledge graph based on the information of the ships to arrive at the port, the equipment data of the ships approaching and leaving the port, the historical tug operation data and the port data, and predicting the tug workload demand based on the port operation knowledge graph; taking tug operation data as a state space, taking a tug scheduling strategy and a cargo stacking layout adjustment strategy as an action space, taking maximization of wharf operation efficiency as a reward target, and adopting a reinforcement learning method and a simulated annealing method to carry out wharf operation scheduling optimization to obtain an optimal wharf operation scheduling strategy. According to the method, the port working efficiency is improved, manual interference is not needed when the wharf operation scheduling strategy is obtained, and autonomous decision-making can be completely achieved.

The invention relates to the technical field of software upgrading, in particular to a software upgrading method and system for an Internet of Things terminal. The method comprises the following steps: acquiring software upgrade package information and terminal operation data, performing version verification, and generating terminal version difference data so as to determine software upgrade tasks, performing channel allocation on the tasks, distinguishing independent software tasks from dependent software tasks, performing chain type sorting on the dependent tasks, and generating terminal version difference data; and the head task is upgraded based on a multiplexing scheme, and meanwhile, the subsequent task and the independent software task are preloaded for parallel upgrading, and the dependent task and the independent task are synchronously scheduled, so that efficient Internet of Things terminal software upgrading is realized. The software upgrading method for the Internet of Things terminal is more efficient and more flexible.

The invention provides a terminal operation and maintenance management method and platform for a medical big data platform, and the method comprises the steps: firstly collecting multi-mode historical operation and maintenance data of a platform terminal in a normal working state, and constructing a time sequence feature vector sequence after preprocessing and fusion; a deep sequence learning model training process is then utilized to learn the potential representation to capture the context state of the normal workflow mode and establish a reconstructed baseline model. Acquiring real-time operation and maintenance data of the terminal, constructing a feature vector, extracting potential context representation through the trained model, and calculating a reconstruction error; a current workflow state is identified based on the potential representation, and a context-aware anomaly metric value is calculated in conjunction with state information and reconstruction errors. And analyzing the time evolution characteristic of the abnormal metric value and comparing the time evolution characteristic with a preset abnormal mode criterion to judge whether the terminal has workflow abnormality, and if so, generating an early warning signal containing abnormal evolution characteristic description. The method has the effect of improving the operation and maintenance detection accuracy of the terminal.

The invention relates to the technical field of the Internet of Things, and discloses a campus smart life terminal comprehensive management and control system based on big data, and the system comprises a data collection module, a big data analysis module, a comprehensive management and control module and an execution feedback module. Operation data of different systems are integrated in real time, cross-platform data sharing and consistency of a canteen, an access control and an energy terminal are guaranteed, the problem of information isolated island caused by independent operation of terminal data is avoided, meanwhile, association analysis is carried out on collected environmental factors and user behavior information, the risk of terminal operation abnormity and resource conflict can be found in real time, and the user experience is improved. The method is advantaged in that high efficiency and reliability of terminal cooperation management are guaranteed, fault response delay is further reduced, a shunting scheme is automatically generated when sudden demand increase occurs, a condition of student waiting time prolonging is prevented, and dynamic adaptive capability and service quality of the campus life terminal are guaranteed.

The invention provides a container shipping stowage method and system based on a multi-stage optimization algorithm, is applied to the technical field of operational research and port logistics, and decomposes a complex stowage process into a plurality of optimization stages based on a four-stage mixed integer programming method. The method comprises the following steps: firstly splicing 20-foot boxes into 40-foot equivalent boxes, quickly locking a shipping space by taking the minimum box area-ship-shellfish connection number and weight deviation as targets, then eliminating vertical overweight and transverse unbalance layer by layer, and finally finely adjusting the shipping space through local neighborhood search, thereby reducing the box turnover rate and improving the dispatching efficiency. Therefore, higher efficiency, lower conflict rate and better process matching degree of container space distribution are realized, the adaptability and efficiency of actual wharf operation are remarkably improved while the safety of a ship structure is guaranteed, and intelligent, systematized and efficient development of a port operation system is promoted.

The disclosure relates to a communication technique for converging Internet of things (IoT) technology with 5th generation (5G) communication systems designed to support a higher data transfer rate beyond 4th generation (4G) systems, and a system therefor. The disclosure may be applied to intelligent services (e.g., smart homes, smart buildings, smart cities, smart cars or connected cars, healthcare, digital education, retail business, security and safety-related services, etc.) on the basis of 5G communication technology and IoT-related technology. A terminal operation method related to each universal subscriber identity module (USIM) when dual connectivity is established for a terminal supporting multi-USIM, and an apparatus therefor are provided.

The invention discloses a power distribution terminal upgrading method, system and device based on an SD card and a medium. The method specifically comprises the steps of generating an incremental package of a Linux kernel and / or a root file system, calculating a hash value file of the incremental package, and generating a block chain certificate with a timestamp; encrypting and packaging the incremental package, the hash value file and the block chain certificate into a container mirror image, performing digital signature to form an upgrade package, and writing the upgrade package into an SD card; after it is detected that the SD card is inserted, mounting a boot partition, analyzing metadata of an upgrade package, and setting an upgrade flag bit and an upgrade operation type of Boot; and performing digital signature verification, hash value file verification and block chain certificate verification on the upgrade package in the SD card, if the verification is passed, backing up user parameters to the encrypted storage area, and restarting to enter the Recovery OS to complete replacement upgrade of the Linux kernel and / or the root file system. According to the method, safe, efficient and flexible upgrading of the operating system of the power distribution automation terminal is achieved, the rollback function is achieved, and the upgrading risk and cost are effectively reduced.

A method by which a first device performs wireless communication, and a device for supporting same are provided. The method may comprise the steps of: acquiring a configuration related to inter-UE coordination (IUC) information reporting; initiating an IUC report timer on the basis that the IUC information reporting is triggered; performing listen before talk (LBT); and stopping the IUC report timer on the basis of (i) the generation of an IUC information medium access control (MAC) control element (CE), and on the basis of (ii) the success of the LBT.

The invention discloses an electronic ticket ordering and payment application system based on a block chain, relates to the technical field of block chains, and is used for ticket business whole-process digital management, and a user interaction module supports multi-terminal operation and information management; the block chain order management module is responsible for order chaining and state updating; the multi-mode payment module supports double-track payment and encrypted transmission; the comprehensive matching module is linked with the block chain order management module and comprises a meal subsystem, a tourism subsystem, a hotel reservation subsystem and the like, and the passenger ticket verification module realizes multi-mode verification; the data storage module stores key data; and the intelligent contract scheduling module automatically executes the pre-deployed contract. The problems that an existing system is poor in data safety, low in efficiency and insufficient in adaptability are solved, ticket transaction safety and process automation are improved, data synchronization is guaranteed, multiple languages and multiple currencies are supported, user privacy is protected, multiple scenes are adapted, and travel and supervision requirements are met.

The invention discloses a virtual power plant cloud side end operation control method and system, and belongs to the field of cloud side end operation control methods, and the method comprises the following steps: 1, data collection and preprocessing; step 2, edge calculation and quick response: aggregating operation data of the charging piles in each area at edge nodes, and executing local optimization and quick response; step 3, cloud global optimization and scheduling: a cloud platform predicts the charging demand in the next 24 hours based on a long and short term memory model, and solves a multi-objective optimization model to generate a scheduling plan; 4, issuing and executing an instruction; and 5, performing dynamic feedback and closed-loop optimization. According to the invention, through the cloud side-end collaborative architecture and the multi-level optimization strategy, the problems of response delay, local optimization deficiency, poor multi-target collaboration and the like of an existing virtual power plant technology in a large-scale charging pile scene are solved, and an innovative solution is provided for efficient operation of a smart power grid and bidirectional interaction of user requirements.

The invention relates to the field of new-generation information systems, and discloses an operating system adaptation method for accessing an IMS system terminal, which comprises the following steps of: acquiring network state information and hardware resource state information of the IMS system terminal, constructing a game model of bandwidth resource allocation on the basis of the network state information and the hardware resource state information, and determining a video transmission strategy adapted to a WebRTC communication application; and on the basis of the obtained video transmission strategy, further combining with the hardware resource state information to establish a multi-resource constraint optimization model, and calculating a hardware resource optimal allocation scheme in a terminal operating system for supporting the implementation of the video transmission strategy. By adopting a cross-layer state sensing and scheduling cooperation mechanism, the technical effects of multi-level information synchronization and dynamic scheduling in the video transmission process are achieved. Compared with a scheme of performing strategy adjustment only based on single-layer network feedback in the prior art, the problems of low response speed and single adaptive scene are solved.

The invention discloses an information security protection system and method, and relates to the technical field of information security protection, and the system comprises a behavior modeling module which is used for collecting and standardizing terminal operation behaviors, and constructing a dynamic behavior chain model; the risk assessment module performs real-time risk analysis based on the behavior chain and outputs a grading result; the access control module dynamically adjusts access authentication and authority configuration according to the risk level, and intercepts abnormal access; and when an abnormal mode or a high-risk state is detected, the abnormity repairing module automatically formulates a repairing decision and executes a repairing operation to recover the safety of the equipment. According to the method, the identity verification mode and the permission configuration strategy of the access request are dynamically adjusted according to the risk level, the resource access boundary can be flexibly controlled in different security states, and better effects are achieved in the aspects of security, accuracy and timeliness.

The invention relates to the technical field of form construction, in particular to a form construction method and system based on dragging operation and a storage medium. The method comprises the following steps: identifying a target form dragged by a mouse of a user, and performing form content structure deep analysis to obtain a form information structure identification result; obtaining a user historical form operation data set to perform operation behavior semantic mining, and performing historical page layout behavior clustering analysis on a form information structure identification result to obtain form layout preference features; performing layout demand prediction according to the form layout preference features, and performing feasibility evaluation to obtain an accurate layout demand prediction result; and identifying an affiliation page to which the user drags the target form, performing spatial layout constraint analysis, and performing available space size evaluation, thereby obtaining the available space of the current page. According to the method, efficient and convenient form self-adaptive construction is realized, the user development efficiency is improved, and multi-terminal operation is adapted.

The invention discloses a data encryption method applicable to open source gap terminal equipment, which comprises the following steps of: firstly, establishing communication connection based on a gap distributed soft bus technology, and generating a trust relationship through identity identification authentication; during data transmission, a national cryptographic algorithm, a symmetric or asymmetric encryption algorithm, a hybrid encryption algorithm and the like are dynamically selected according to data types, importance, transmission scenes and equipment performance. After being encrypted, the data are transmitted to the target device through the soft bus, and the target device decrypts and verifies the data. And the terminal equipment is integrated with the hardware security module to realize hardware encryption and key storage. Security module adaptation development is carried out based on an HDF library, and one-time development and multi-terminal operation are achieved. In the encryption process, a fragmentation encryption strategy can be adopted, multiple devices can perform collaborative encryption and decryption, and the encryption efficiency is improved by defining the task priority and sequence and utilizing a distributed task scheduling mechanism and reasonably allocating resources.

The invention belongs to the technical field of bulk cargo wharf operation and maintenance situation awareness, and relates to a bulk cargo wharf operation and maintenance situation awareness method and system. The residual life of the equipment is calculated through the residual life probability distribution of the equipment predicted by the constructed residual life probability distribution prediction model, and the failure rate of the equipment is predicted through the constructed situation awareness model according to the health degree index of the equipment and the residual life of the equipment, so that equipment alarm signals of different grades are obtained. An auxiliary management scheme is generated and executed according to the equipment alarm signal, the equipment user maintenance time is reasonably planned, and the equipment utilization rate and the field production efficiency are improved. Health state monitoring and operation and maintenance situation awareness of the bulk cargo wharf are achieved, the equipment utilization rate and the field production efficiency are improved, and the turnover rate and turnover efficiency of wharf cargos are improved.

The invention relates to the technical field of front-end development, in particular to a method and a system for generating a multi-end table data display page. Comprising a table definition module, a table rendering module, a data processing module, a function extension module and a state management module, the table definition module is used for finely defining the structure and behavior of a table, the table rendering module is used for converting the configured table definition into a multi-terminal adaptive visual interface and optimizing rendering performance and interactive experience, and the data processing module is used for efficiently processing table data and carrying out format conversion, cache management and paging loading. The function expansion module provides flexible function expansion capability and meets interaction requirements of complex business scenes, the state management module uniformly manages interaction states of tables and ensures consistency and persistence of multi-terminal operation, and by means of the mode, the problems that table components are limited in function, insufficient in performance and poor in expansibility are solved, and the user experience is improved. Therefore, the table component has good multi-terminal adaptation capability and can meet complex business requirements.

The invention discloses an intelligent key response method, a user terminal, a storage medium and a program product, and relates to the field of electric digital data processing.The method comprises the steps that based on first time sequence touch data obtained from a sample user and collected when a mouse device does not generate a displacement signal, a two-hand mistaken touch recognition model is obtained through training; based on second time sequence touch data acquired from the sample user and acquired during the period that the mouse device generates the displacement signal, training to obtain a single-hand mistaken touch recognition model; during the operation period of the user terminal, monitoring a target displacement signal of the mouse equipment; according to the target displacement signal, determining one of a two-hand mistaken touch recognition model and a single-hand mistaken touch recognition model as a current effective recognition model; inputting the real-time touch data into the current effective recognition model for calculation to obtain a key position effective score; and when the key position effective score is lower than a preset effective threshold value, preventing a corresponding error key pressing signal. By implementing the method and the device, the misjudgment detection accuracy on the high-sensitivity keyboard can be improved.

The invention discloses an industrial equipment monitoring system based on the Internet of Things, which comprises the following modules: a video monitoring deployment module, an anti-interference intelligent transmission sub-module, a multi-terminal collaborative scheduling module, an equipment intelligent diagnosis and prediction module, a user mobile control terminal module and a video monitoring display module. The problems that data transmission is prone to interference and packet loss delay in a complex industrial environment are solved through edge computing node preprocessing caching and self-adaptive frequency hopping interference resistance, real-time and accurate transmission of equipment state data is guaranteed, a reliable data foundation is built for monitoring management, meanwhile, instruction conflicts are avoided by means of a distributed lock, and the data transmission efficiency is improved. Terminal operation is responded in order in combination with priority scheduling, the multi-terminal access and interaction process is standardized, the multi-terminal cooperative monitoring efficiency is improved, different role terminals are made to cooperate efficiently, the monitoring management experience is optimized, the equipment data value is deeply mined by applying a machine learning algorithm, fault early warning is achieved, and the equipment fault shutdown loss is reduced.

The invention relates to the technical field of port automation, in particular to a bulk cargo wharf operation whole-process management and control system based on multi-dimensional parameter perception. Comprising a multi-source parameter acquisition unit; a multi-dimensional parameter adaptive fusion unit; a process node dynamic analysis unit; an intelligent decision and instruction generation unit; and a closed-loop control and verification unit. According to the invention, multi-dimensional data are comprehensively acquired through the multi-source parameter acquisition unit; meanwhile, based on an adaptive confidence model improved by Kalman filtering, a constraint credible time window is configured for various parameters, time dimension calibration is completed in combination with a time attenuation confidence weight and a working condition reliability factor, and precise fusion of multi-source heterogeneous data is achieved with assistance of physical constraint noise reduction and electromagnetic environment calibration; finally, the process node dynamic analysis unit completes multi-scene accurate adaptation by pre-storing standard process maps of various types of operation such as ship loading and unloading, train loading and unloading and automobile gathering and evacuation ports, and the defect that the whole service scene is not covered in the prior art is overcome.

The invention provides a cable terminal assembly and a cable terminal abnormal heating monitoring system, and relates to the technical field of power cables, and the method comprises the steps: collecting the operation data and environment auxiliary data of a conductor connection point, an insulating layer, a shielding layer and a grounding terminal in a preset monitoring period, and carrying out the time alignment processing, and obtaining the standardized multi-dimensional time series data. Time domain features are calculated, frequency domain features are extracted through fast Fourier transform, and the time domain features and the frequency domain features form single-dimensional time sequence features. Constructing a data association matrix, screening strong association data pairs according to Pearson's correlation coefficients and mutual information values, and calculating dynamic covariance by adopting a sliding window method to form cross-dimension association features. And splicing the two types of features and performing dimension reduction to obtain fusion feature vectors, splicing the fusion feature vectors of the current monitoring period and the previous monitoring period and the next monitoring period into a time sequence input sequence, and inputting the time sequence input sequence into an improved LSTM model to output an abnormal probability value, thereby realizing abnormal heating monitoring of the cable terminal.

The invention discloses an exit-entry license management system and method based on an intelligent terminal, and the system comprises a management platform which comprises a system client, a management background, a core database and a rule engine, and is used for achieving the closed-loop management of online application approval and offline terminal operation; the certificate management terminal equipment is used for reading the certificate key information and performing matching verification on the certificate key information and the employee identity information in the core database; the early warning equipment is used for sending an early warning prompt to a certificate holder and a manager based on a rule engine detection result; and the block chain equipment comprises an intelligent contract and an oracle machine, the oracle machine is used for encrypting the dynamic certificate data and then transmitting the encrypted dynamic certificate data to the block chain to trigger the intelligent contract, and the intelligent contract automatically executes early warning judgment on the dynamic data based on a built-in coding early warning rule and generates an on-chain early warning event. According to the system, the certificate management efficiency is improved.

Disclosed are a communication technique which merges, with IoT technology, a 5G communication system for supporting a data transmission rate higher than that of after a 4G system, and a system therefor. The present disclosure can be applied to intelligent services (for example, smart homes, smart buildings, smart cities, smart cars or connected cars, healthcare, digital education, retail, security- and safety-related services, and the like) on the basis of 5G communication technology and IoT-related technology. The present disclosure relates to an operation method of a terminal in a wireless communication system and, particularly, to a method and a device for performing same, the method comprising the steps of: generating master cell group (MCG) failure information, transmitting the MCG failure information to a base station of a secondary cell group (SCG), receiving a radio resource control (RRC) reconfiguration message from the base station, and transmitting an RRC reconfiguration completion message on the basis of the RRC reconfiguration message, wherein, if the RRC reconfiguration message is included in a downlink information transfer message so as to be received via signaling radio bearer (SRB) 3 of the SCG, the RRC reconfiguration completion message is transmitted via configured SRB1 on the basis of the RRC reconfiguration message.

The invention provides a wharf container truck scheduling method and device, and relates to the technical field of wharf operation, and the method comprises the steps: determining whether a container transported by a container truck needs to be subjected to lock operation or not according to the transportation task type of the container truck in combination with a ship stowage plan and / or a lock detection result; the method comprises the following steps: determining that a container truck does not need to pass through lock stations and directly arrives at a destination under the condition that a container does not need lock operation, determining a target lock station according to a queuing index of each lock station and wharf environment data under the condition that the container needs lock operation, and determining a queue index of each lock station according to a graph neural network model and a large language model. Determining candidate paths from the container truck to the destination through the target lock station and corresponding time nodes; determining a target path from the candidate paths according to a relationship between the time nodes corresponding to the candidate paths and a preset time window; and the target path and the corresponding time node are issued to the container truck and the target lock station, so that the wharf container truck scheduling efficiency is improved.