214 results about "Transportation scheduling" patented technology

The invention relates to the technical field of logistics transportation, in particular to a logistics transportation route optimization method and system based on digital twinning. The method comprises the following steps: constructing a digital twin logistics base through multi-source heterogeneous data fusion and dynamic logistics network modeling; carrying out regional logistics distribution feature extraction and dynamic time sequence prediction based on a digital twinborn substrate to obtain a predicted regional transportation demand; performing real-time state evaluation on the line, generating a dynamic line performance matrix, and performing vehicle resource allocation in combination with the order task priority; and carrying out multi-objective solution and dynamic adjustment on the transportation scheduling scheme to finally obtain an optimal transportation scheduling scheme. And performing iterative optimization on the digital twin model through real-time deviation analysis and a feedback mechanism, and dynamically adjusting a transportation plan to form a closed-loop optimization process. Through the logistics transportation route optimization technology, the logistics transportation efficiency is remarkably improved, the cost is reduced, and the toughness of the system is enhanced.

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 discloses a container tracking management system and method. The method comprises the following steps: S1, carrying out dynamic encryption binding and multi-dimensional data acquisition on an RFID chip; s2, realizing real-time position data acquisition of the container under different environmental conditions through a multi-source signal fusion and error correction algorithm; s3, various sensor modules are installed inside and outside the container, and data processing is carried out through distributed edge nodes; s4, performing on-chain storage on total data generated in the transportation process through a Hash encryption algorithm, and realizing automatic data verification, state tracking and abnormity alarm in combination with an intelligent contract; s5, carrying out real-time dynamic modeling analysis on the transportation path and resource allocation, and dynamically adjusting the transportation plan by using a distributed scheduling strategy; and S6, integrating the real-time state data, the path optimization model and the transportation scheduling decision, and providing a report for a user. The method has the advantages of high positioning precision, high data security, high transportation efficiency and good real-time performance.

The invention discloses a smart city comprehensive transportation junction passenger flow control optimization method comprising the following steps: collecting data of a sensor, a camera and a mobile device, cleaning and preprocessing the data, using a data mining technology to identify a passenger flow rule, and using the data mining technology to determine the passenger flow rule; and establishing a passenger flow prediction model based on time sequence analysis, a regression model and a neural network, and training and verifying the accuracy of the model through historical data. The invention provides a passenger flow management and control optimization method for a comprehensive transportation junction of a smart city, and aims to realize accurate prediction and real-time regulation and control of passenger flow by integrating multiple data sources and applying artificial intelligence and machine learning technologies. And according to the prediction result, dynamically adjusting the scheduling schemes of the entrance and exit channels of the transportation hub and the transportation means, providing transfer path suggestions and reducing the transfer time of passengers.

The invention provides a logistics transportation optimization method and system based on a traffic logistics large model, and the method comprises the steps: carrying out the element extraction through receiving a basic scheduling instruction of a logistics transportation task, generating core element information through semantic scene analysis, and carrying out the transportation scene adaption based on the core element information. Generating a scene influence factor set in combination with a depth feature mining result of the historical transportation scene database; then, calling a pre-trained traffic logistics large model to carry out collaborative path decision on the scene influence factor set, and outputting a candidate path scheme population containing a path topological structure and cost feature distribution through interactive iteration of a path generation unit and a cost evaluation unit in the model; and performing multi-round evolution screening on the candidate path scheme population according to a preset optimization target, determining an optimal path scheme meeting transportation requirements, and generating a transportation scheduling instruction set to be pushed to a transportation execution terminal after conversion. According to the invention, the adaptability, decision-making precision and reliability in the logistics transportation optimization process can be effectively improved.

The invention, which relates to the technical field of coal mine transportation scheduling, discloses a coal mine intelligent transportation scheduling optimization system comprising a map construction module, a data acquisition module, a communication module, a monitoring module, an intelligent scheduling module, an emergency processing module and a man-machine interaction module. The system has the advantages that the operation states of coal mine collection equipment and transportation equipment are monitored in real time through the equipment monitoring unit and the environment monitoring unit, the environment state of a mining area is comprehensively monitored, equipment abnormity and environment risks can be found and early warned in time, safety accidents are prevented, and the system is safe and reliable. The service life prediction unit can predict the residual service life of the equipment by monitoring and analyzing the operation time and the accumulated workload of the equipment, and gives out early warning when the equipment is close to the end point of the service life, so that potential safety hazards caused by equipment aging are avoided, and the safety of coal mine transportation is improved.

The invention provides a vulnerable cargo transportation scheduling method and device based on intelligent logistics and a medium, and belongs to the technical field of intelligent logistics. The method comprises the following steps: according to multi-dimensional transportation environment data collected by preset transportation environment monitoring equipment in real time, determining transportation cargo state information corresponding to a corresponding in-transit logistics transportation vehicle group; based on the transported cargo state information, the historical driving record and the planned driving path, generating a corresponding cargo loss prediction curve; determining a cargo demand quantity corresponding to each arrival storage node, and determining a cargo reissuing risk value of the corresponding arrival storage node according to the cargo loss prediction curve and the cargo demand quantity; and on the basis of each cargo reissuing risk value and the position information of each arrival storage node, generating an undetermined replenishment path corresponding to the storage node needing replenishment, and sending the undetermined replenishment path to a corresponding transportation scheduling management terminal so as to schedule a corresponding schedulable in-transit vehicle to replenish the storage node needing replenishment.

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 embodiment of the invention provides a dike breach blocking method and system, electronic equipment and a storage medium, and belongs to the technical field of automatic emergency rescue. The method comprises the following steps: performing virtual three-dimensional space reconstruction according to collected water surface flow data in a breach area to obtain a three-dimensional flow field model; performing target feature extraction on the three-dimensional flow field model to obtain a flow field feature matrix; a first target function is used as an optimization target, throwing decision optimization is carried out according to the flow field characteristic matrix, the regional throwing state data and the available material data, an optimal throwing scheme is obtained, and the first target function represents at least one of the minimum flow velocity energy or the minimum material loss amount; and carrying out transportation decision optimization according to the optimal throwing scheme and the transportation resource data by taking a second target function as an optimization target, so as to obtain an optimal transportation scheduling scheme, carrying resource scheduling is carried out according to the optimal transportation scheduling scheme, and the second target function represents maximum transportation efficiency. The dike breach plugging efficiency can be improved.

This application provides a method and system for analyzing cold chain transportation demand based on big data prediction. First, a historical cold chain order dataset containing transportation records of temperature-sensitive goods and corresponding environmental parameters in multiple time periods in the target area is obtained. Then, a set of transportation demand characteristics is extracted from it, covering characteristics such as the correlation between the type of goods and temperature sensitivity, the balance between transportation timeliness and equipment energy consumption, and the matching between regional climate and storage conditions. Subsequently, this set of characteristics is input into a pre-trained spatio-temporal prediction model to generate multi-dimensional cold chain demand parameters for the target area within a specified future period, such as the tolerance threshold of temperature fluctuations. Finally, based on these multi-dimensional cold chain demand parameters, a dynamic cold chain transportation scheduling plan with timestamps is generated. This method can accurately analyze cold chain transportation demand by integrating multi-dimensional factors, achieve scientific and dynamic scheduling, and improve the efficiency and quality of cold chain transportation.

The invention provides a logistics scheduling path intelligent recommendation method and system based on an AI large model, and a medium, and the method comprises the steps: firstly collecting a transportation path file of a transportation driver, and constructing a driver road familiarity portrait according to the transportation path file; when a new logistics transportation scheduling demand exists, a total scheduling distance is extracted, and then two travel schemes, namely an efficiency travel scheme (including a first predicted total duration) of a theoretical shortest path and an experience travel scheme (including a second predicted total duration) according to driver experience, are determined. And if the second predicted total duration does not exceed the set proportion of the first predicted total duration, setting the experience travel scheme as a final scheduling scheme, finally distributing an optimal driver in combination with a driver road familiarity portrait, and pushing the scheme to a corresponding driver terminal device. The scheduling refers to the optimal theory, the actual experience of the driver is considered, the driver familiar with the route is matched, the decision deviation is reduced, the transportation efficiency and reliability are improved, and intelligent and efficient scheduling is realized.

The invention discloses an urban garbage collection and transportation scheduling method and system based on path optimization, and relates to the technical field of urban garbage collection and transportation and intelligent scheduling optimization, and the method comprises the steps: collecting multi-source original data, carrying out the standardization and time-space registration, and generating the structured observation data of vehicles, roads and collection and transportation points; constructing a dynamic garbage quantity prediction model according to the structured observation data, and predicting garbage increment and liquid level trend of each collection and transportation point; generating a task package and establishing a mapping relation based on a dynamic garbage quantity prediction model result in combination with road passage, vehicle load and time window constraints; and constructing a multi-target path optimization model on the basis of the mapping relation and the constraint, outputting an optimal driving path of the vehicle, and converting a path optimization result into a scheduling instruction. According to the method, the task package is generated on the basis of the dynamic garbage quantity prediction result in combination with road traffic, vehicle load and time window constraints, hierarchical matching of task targets and vehicle resources is achieved, and therefore the reasonability of task allocation and operation feasibility are improved.

The invention relates to the field of combinatorial optimization and artificial intelligence, and discloses a group logistics transportation scheduling method and system based on a role interaction graph neural network, and the method comprises the following steps: S1, dividing agent nodes and position nodes, and generating initial features; s2, iteratively updating node embedding by using a multi-channel attention mechanism of a graph neural network; s3, generating a delivery point distribution probability based on node embedding, and determining an initial distribution scheme; s4, local redistribution optimization is performed on the delivery points with low confidence distribution; and S5, performing parallel path planning on the optimal scheme, and outputting a result for reinforcement learning feedback. In the invention, through modeling of a graph neural network multi-channel attention mechanism on a complex interaction relationship and a synergistic effect of local redistribution optimization and parallel path planning, a second-level generation of a high-quality scheduling scheme is realized, cross-scale scene migration of the model is achieved, and group logistics transportation scheduling efficiency and robustness are improved.

The invention relates to the technical field of transportation scheduling, and particularly discloses an unmanned Internet of Things workshop cargo transportation robot scheduling method and system, and the method comprises the steps: receiving demand point locations inputted by an administrator, calculating the virtual distance between the demand point locations, and creating a point location distribution map; obtaining task parameters of each demand point location based on a preset matrix to obtain a task parameter table, and calculating a delivery level of each demand point location based on the task parameter table; selecting a distribution path in a point location distribution diagram according to the distribution level, and feeding back the distribution path to an administrator; and correcting the distribution path based on the feedback information of the administrator, and generating a scheduling instruction according to the corrected distribution path. According to the method, the task parameter table is obtained by counting the task parameters of each demand point location based on the matrix, the task parameter table is subjected to self-identification, the dynamic weight of each index is determined, and then the path selection process is adjusted, so that the integrating degree of the path and the actual situation is greatly improved.

The invention discloses a port and navigation ship traffic transportation scheduling method and system based on big data, and belongs to the technical field of port and navigation traffic, and the method specifically comprises the steps: carrying out the fusion construction of a coverage channel-berth-ship coupling state space; channel microscopic features and ship dynamic response features are excavated, and a channel subsection hydrodynamic disturbance feature set and a ship control response feature set are established; generating an expected energy consumption increment and a steering wear equivalent of each ship in each channel segment based on the two feature sets, and forming a ship-channel coupling cost set; a joint scheduling parameter set is constructed by combining berth space-time constraint and a ship arrival time sequence, a berth allocation scheme and a channel passing time sequence are solved by taking channel disturbance and ship sensitivity matching as targets, and cooperative scheduling is realized. According to the method, channel characteristics and ship response characteristics are quantitatively matched, and port energy consumption and mechanical wear are reduced. Quantitative matching of channel characteristics and ship response characteristics is realized, and port energy consumption and mechanical wear are reduced.

The invention relates to a truck-unmanned aerial vehicle combined transportation scheduling method and system based on a disaster relief scene in the technical field of transportation scheduling. According to the truck-unmanned aerial vehicle combined transportation scheduling method, H trucks and H unmanned aerial vehicles are used for cooperatively transporting materials, the h truck and the h unmanned aerial vehicle form a cooperative group, and the scheduling method of the h cooperative group comprises the steps that the number of rescue points and the positions of the rescue points are determined; according to the number of rescue points, the positions of the rescue points and constraint conditions, generating an initial truck path based on a greedy principle of the shortest distribution distance; generating an initial unmanned aerial vehicle path based on the initial truck path; and optimizing the initial truck path and the initial unmanned aerial vehicle path to obtain an optimal material transportation path. A truck-unmanned aerial vehicle collaborative transportation mechanism is innovatively constructed, the traditional efficiency bottleneck is broken through through a collaborative three-dimensional transportation system of transportation tools, and the emergency material delivery efficiency, reliability and timeliness in a complex catastrophe environment are remarkably improved.

The invention provides a supply chain scheduling optimization method based on transport outsourcing and time window constraint, and relates to the technical field of supply chain scheduling. On the basis of a strict time window constraint framework and under the condition that self transport capacity cannot completely meet order transport requirements, collaborative scheduling is carried out on order production and transport processes, the earnings of order autonomous transport and outsourcing transport are balanced, a production scheduling scheme and a transport scheduling scheme under the outsourcing condition of part of transport tasks are comprehensively considered, and the scheduling efficiency is improved. According to the method, the approximate optimal solution of the problem is dynamically and accurately solved, and collaborative optimization and configuration can be powerfully carried out on resources in two stages of production and transportation of the supply chain, so that the overall cost of the supply chain is reduced to the greatest extent, and the resource collaboration efficiency and management capability of the enterprise supply chain are improved.

The invention discloses an underground ore transportation path optimization method and system based on deep reinforcement learning, and belongs to the technical field of underground mine transportation scheduling and path planning. According to the method, the dynamic state space is constructed by acquiring the underground mine map, the mine car state data and the transportation task information, and the path optimization decision is performed in combination with the deep reinforcement learning algorithm, so that the mine car path and task allocation can be intelligently selected, the transportation efficiency is improved, the no-load rate is reduced, and the safety and stability of mine car operation are ensured. The system comprises a multi-modal data acquisition module, a state space construction module, an action space definition module, a reward function design module, a deep reinforcement learning model training module and the like, and through real-time monitoring and feedback adjustment functions, paths and task scheduling can be dynamically optimized to adapt to underground complex environment changes. The application of the method in underground ore transportation management verifies the effectiveness and practicability of the method, and the method has certain popularization value in the field of underground mine ore transportation management.

The invention discloses a new energy truck charging path collaborative scheduling optimization system, and relates to the technical field of logistics transportation scheduling, and the system comprises a data fusion processing unit, an AI model calculation unit, and a scheduling decision unit. The data fusion processing unit is used for acquiring vehicle position, residual electric quantity, charging station state and power grid load data and performing integration processing on the data; the AI model calculation unit is used for constructing a dynamic demand prediction model, a multi-objective optimization scheduling model and a power grid-charging station-truck cooperative control model, and calculating truck charging dynamic demand information, optimization information and a control path based on each model; and the scheduling decision unit is used for generating an optimal charging path and a scheduling strategy, receiving feedback results after the vehicle-mounted navigation system and the charging pile control system execute the scheduling instruction, and carrying out visual monitoring on the charging information and the charging station data of each truck. The power grid load is more stable; and meanwhile, the transportation cost is reduced.

The invention discloses a strip mine truck multi-target low-carbon dispatching optimization method, system and device based on a two-stage evolution selection mechanism and a medium, and the method comprises the steps: constructing a strip mine multi-target truck low-carbon dispatching optimization model, and constructing an NSGA-II-TS algorithm based on the two-stage evolution selection mechanism, solving the multi-target low-carbon scheduling model of the strip mine by using an NSGA-II-TS algorithm to obtain a strip mine truck multi-target low-carbon scheduling optimization result based on a two-stage evolution selection mechanism; the system, the equipment and the medium are used for realizing the strip mine truck multi-target low-carbon scheduling optimization method based on the two-stage evolution selection mechanism. According to the method, the transportation distance and the transportation frequency in the truck dispatching process can be effectively reduced, the transportation cost of enterprises is remarkably reduced, the mine production efficiency can be improved on the premise of effectively guaranteeing the ore grade, the economic benefits of mine enterprises are remarkably improved on the basis of reducing the carbon emission, and the economic benefits are increased. And the actual requirements of the current strip mine transportation scheduling can be met.

The invention provides a trackless rubber-tyred vehicle scheduling method and system based on a modular auxiliary transportation mode, and relates to the field of coal mine underground auxiliary transportation, and the method comprises the steps: obtaining material demand information and modular loading container information; generating a material transportation task according to the material demand information and the modular loading container information; storing the shortest distance path matrix of each node of the ground and the underground coal mine by adopting a Floyd algorithm; based on an improved adaptive genetic algorithm, generating a vehicle scheduling scheme according to the shortest distance path matrix, the vehicle information and the material transportation task; and carrying out auxiliary transportation scheduling according to the vehicle scheduling scheme. According to the embodiment of the invention, the trackless rubber-tyred vehicles are matched with modular transportation of multiple carriers, the vehicles do not need to wait for a long time through loading and unloading of the carriers, and the multiple trackless rubber-tyred vehicles can efficiently complete underground coal mine auxiliary transportation tasks; a combined strategy genetic algorithm conforming to a modularized auxiliary transportation mode is designed, and the material auxiliary transportation efficiency of the coal mine is remarkably improved.

The invention discloses a building material transportation scheduling method based on edge calculation, and the method comprises the following steps: S1, deploying an edge sensing terminal, and collecting and standardizing multi-source state data; s2, detecting an abnormal event at an edge node and outputting event information; s3, inputting the event information into a disturbance attention affine transformation unit to generate scheduling input; s4, generating and broadcasting a local scheduling scheme by adopting a distributed asynchronous game mechanism; s5, establishing a multi-order memory pool, recording data and schemes, and retrieving similar scene adjustment parameters; and S6, the scheme and the optimization result are uploaded to the cloud, and the edge node is optimized through strategy return. According to the invention, through edge calculation, multi-source data fusion and a distributed cooperative scheduling mechanism, real-time anomaly perception and adaptive optimization scheduling in a building material transportation process are realized.

The invention provides a waste transportation scheduling method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the recognition of images of a plurality of different types of to-be-recovered wastes in a park, and determining the weight information of the plurality of different types of to-be-recovered wastes; based on the weight information, the position information of the plurality of cooperative vehicles away from the park and the carrying capacity information of each cooperative vehicle, determining a plurality of transport vehicles for transporting wastes from the plurality of cooperative vehicles, and generating scheduling information of each transport vehicle going to the park; and sending the scheduling information of each transport vehicle to a driver end of the corresponding transport vehicle, so that after receiving the scheduling information, the driver end goes to the park to transport a corresponding type of waste to be recovered based on the scheduling information. By means of automatic image recognition and dispatching transportation processes, errors and negligence of manual participation and manual operation are reduced, and the efficiency and reliability of waste transportation are improved.

The invention discloses a vehicle informatization management and control method, device and equipment, a medium and a product. The method comprises the following steps: acquiring transportation scheduling information of a ship construction area in a future duration; the road condition information of the ship building area at the current moment and the first vehicle attribute information of the first to-be-dispatched vehicle serve as initial simulation parameters, and analogue simulation is conducted on the transportation dispatching information; in response to an event of which the analog simulation result does not meet a preset condition, adjusting the transportation scheduling information according to a time limit constraint condition and a cost constraint condition to obtain adjusted transportation scheduling information; and sending scheduling control information to the second to-be-scheduled vehicle according to the adjusted transportation scheduling information, so that the second to-be-scheduled vehicle executes a transportation task based on the received scheduling control information. According to the technical scheme, the transportation scheduling information conforming to the actual situation is determined in advance in combination with analogue simulation and optimization adjustment modes, and the to-be-scheduled vehicle is dispatched according to the transportation scheduling information.

The embodiment of the invention relates to the technical field of river-sea combined transportation scheduling, in particular to a resource dynamic configuration method and system applied to river-sea combined transportation, and the method comprises the steps: firstly obtaining a historical operation data set containing ship transportation, port operation and channel passing records, and then carrying out the extraction of resource correlation features, and a multi-dimensional collaborative feature set reflecting the ship-port-channel collaborative relationship is obtained. And then calling a pre-constructed dynamic configuration model to perform resource allocation mode learning on the set, and generating prediction distribution information including ship scheduling, port berth allocation and channel passing priority schemes in different transportation periods. And finally, generating a dynamic resource configuration instruction according to the information, and feeding back to a river-sea combined transportation management system to execute resource adjustment operation, thereby realizing dynamic and accurate configuration of river-sea combined transportation resources, and improving the overall operation efficiency.

The invention belongs to the technical field of food transportation management, and particularly relates to a food end transportation scheduling management system based on big data, which comprises a data fusion and convergence module, a dynamic traffic sensing module, an intelligent order analysis module, a vehicle state evaluation module, a scheduling strategy generation module and a transportation scheduling management end. According to the invention, traffic perception analysis information, order analysis information and vehicle state evaluation information obtained through analysis are sent to a scheduling strategy generation module through a dynamic traffic perception module, an intelligent order analysis module and a vehicle state evaluation module; and the scheduling strategy generation module performs comprehensive analysis based on various types of information to generate an optimal food transportation scheduling strategy, so that optimal configuration of transportation resources is realized, intelligent and refined management of food terminal transportation scheduling is facilitated, the transportation efficiency is comprehensively improved, the transportation cost is reduced, and the food transportation safety is guaranteed.

The present invention provides a scheduling method for AGVs in an automated terminal, comprising the following steps: extracting relevant information to obtain the path information for the transportation scheduling of the AGVs in the automated terminal; establishing the objective function and constraint conditions of the AGV scheduling method in the automated terminal; establishing the overall AGV scheduling objectives and principles; generating the sparrow optimization algorithm; improving the identity transformation rule and position transformation rule of the sparrow optimization algorithm; performing iterative operations until the current iteration number reaches the set maximum iteration number; and outputting the optimal AGV scheduling scheme and the optimal value of the corresponding objective function. The AGV scheduling method provided by the present invention extracts the delivery time of the terminal goods, the node information of the shelves, and the vehicle operation information, etc., comprehensively considers the factor of the lowest empty load rate, establishes a problem model regarding cost, time, and empty load rate, and considers factors such as penalty cost to establish a reasonable scheduling principle for multiple AGVs.

The invention discloses an intelligent bulk commodity freight logistics management method and system, and belongs to the technical field of logistics management, and the method comprises the steps: obtaining the multi-dimensional information of a transportation task, and generating a task semantic tag set; collecting state data and performance behaviors of the transportation units to construct a resource feature vector set; constructing a multi-model scheduling system including transportation risks, node passing trends and loading and unloading waiting prediction, and generating candidate scheduling strategies; performing game optimization on the strategy set in a dynamic environment through a reinforcement learning optimizer, and outputting an optimal scheduling strategy; deploying a continuous monitoring mechanism at edge nodes, and realizing adaptive reconfiguration of paths and resources according to abnormal scores; the intelligent level and the response capability of transportation scheduling can be remarkably improved, the delay risk is reduced, the resource utilization rate is improved, and the method is suitable for a bulk commodity multi-path multi-node combined transportation scene.

The present invention relates to the technical field of tunnel construction transportation scheduling, and specifically to a data fusion-based intelligent scheduling decision-making system for tunnel construction. The present invention uses a work surface demand prediction module to collect data in real time and, in combination with historical curves and schedules, predict concrete demand. This data is then combined with the concrete supply of the mixing station within a future time window to evaluate the supply and demand balance index of the mixing station. When the supply and demand balance index deviates from a threshold, a multi-factor combination is used to determine and generate a feed priority. A set of schedulable work surfaces is screened based on the feed priority and concrete demand of each work surface. Based on the last concrete transport time of all schedulable work surfaces and the concrete loading time of the mixing station, a transport scheduling target is dynamically generated for all concrete transport vehicles to be loaded at the mixing station. This provides precise decision-making support for intelligent scheduling of tunnel construction, improves transport efficiency and construction continuity, and enhances the flexibility and adaptability of transport scheduling.

The invention relates to the technical field of logistics distribution, and discloses a prefabricated building prefabricated part transportation scheduling system and method.The prefabricated building prefabricated part transportation scheduling method comprises the steps that the shape information of prefabricated parts is coded to obtain numerical feature vectors, carrying out grid processing on the vehicle space structure information to obtain a discretized space unit set; calculating the matching degree of the prefabricated part and the vehicle; analyzing the matching degree of the vehicle based on a deep learning model to obtain an optimal transportation matching scheme of the prefabricated part and the vehicle; according to the method, the matching degree of the prefabricated part and the vehicle space is accurately calculated, so that the vehicle loading space is efficiently utilized. And meanwhile, an intelligent matching mechanism based on deep learning can optimize a combination scheme of the prefabricated part and the vehicle, and the comprehensive transportation cost is reduced.