3464 results about "Transportation cost" patented technology

The invention provides a procurement supply chain collaborative intelligent management method and a procurement supply chain collaborative intelligent management system. Belongs to the technical field of supply chain management. Constructing a purchase demand prediction model; dynamically adjusting the purchasing strategy according to the prediction result; the record and the evaluation result are stored on the block chain; the system automatically sends order information to a supplier; the supplier confirms the order through the block chain platform and updates the production progress and the logistics information; the system adjusts an inventory strategy in real time; the logistics state is monitored in real time; the system analyzes data of each link of the supply chain in real time and identifies potential risks. Through dynamic path planning and real-time transportation optimization, the logistics path can be adjusted in real time according to traffic conditions, environmental factors and emergencies, the transportation time is shortened, the transportation cost is reduced, and therefore the overall efficiency of a supply chain is remarkably improved.

The invention relates to the technical field of supply chains, and discloses an intelligent supply chain and logistics optimization management method and system, and the method comprises the steps: obtaining a real-time order of each supply chain station in a region, and planning a logistics distribution path; data analysis is carried out, the sales volume of orders on the current day is predicted, and the inventory of a supply chain station is predicted in combination with supply volume consumption analysis; and according to calculation of the inventory emergency degree of the supply chain station, a target scheduling station is selected, and an inventory logistics scheduling scheme is generated. According to the invention, full-chain optimization management from order obtaining to logistics distribution path planning, accurate stock prediction and reasonable stock scheduling is realized, the logistics distribution efficiency can be significantly improved, the transportation cost is reduced, refined and scientific management of the stock is realized, the stock cost and the stockout risk are accurately balanced, and the logistics distribution efficiency is improved. The capacity of the supply chain for dealing with market changes and uncertainty is enhanced, the customer satisfaction is improved, the market competitiveness is improved, and efficient, stable and sustainable operation of the supply chain system is guaranteed.

Disclosed in the present application are a container, an energy storage apparatus, an energy storage system and a charging network. The energy storage apparatus comprises containers. There are m containers, where m≥2, and the m containers are arranged in the direction of height of the containers. The size of each container in the direction of length thereof is consistent with the size of a standard container in the direction of length thereof, the size of each container in the direction of width thereof is consistent with the size of a standard container in the direction of width thereof, the size of each container in the direction of height thereof is smaller than the size of a standard container in the direction of height thereof, and the sum of the sizes of m1 adjacent containers among the m containers in the direction of height thereof is equal to the sum of the sizes of n standard containers in the direction of height thereof. The size of m1 containers in the direction of height thereof is equal to the size of n standard containers in the direction of height thereof, such that the space occupied by m1 stacked containers is the same as the space occupied by n standard containers, thereby improving the space utilization rate of container placement, and reducing the container transportation cost, thus reducing the use cost of energy storage apparatuses.

The present invention relates to the technical field of tableware, in particular to an easy-to-transport assembled table, including table legs disposed at each of four corners of a lower end surface of a table top and fixed by bolts, where the table top is assembled by at least one table plate. By adopting the present invention, the technical problem that an existing table is relatively large as a whole, inconvenient to carry, high in transportation cost and inconvenient to transport can be solved.

The present application discloses a container, an energy storage apparatus, an energy storage device, an energy storage system, and a charging network. The energy storage apparatus comprises containers, the number of containers is m, m≥2, and the m containers are arranged in the height direction of the containers. The dimensions of the containers in the length direction thereof are consistent with the dimensions of standard containers in the length direction, the dimensions of the containers in the width direction thereof are consistent with the dimensions of the standard containers in the width direction, the dimension of one container in the height direction is smaller than the dimension of one standard container in the height direction, and the sum of dimensions of adjacent m1 containers among the m containers in the height direction is equal to the sum of dimensions of n standard containers in the height direction. The dimensions of the m1 containers in the height direction are equal to the dimensions of the n standard containers in the height direction, such that the space occupied by the m1 containers when stacked is the same as the space occupied by the n standard containers, improving the utilization of container placement space, and reducing the transportation cost of the containers, thereby reducing the use costs of energy storage apparatuses.

The invention is suitable for the field of logistics transportation, and provides a hazardous chemical substance transportation path planning method and system, and the method comprises the steps: collecting road network information, road condition real-time data, weather data, population density distribution data, environment sensitive point distribution data, and hazardous chemical substance characteristic data; based on the collected data, establishing a risk assessment model, and performing risk assessment on each preset transportation path; constructing a multi-objective function, and distributing a corresponding weight for each objective; and searching and generating an optimal or suboptimal transportation path in the road network based on the multi-objective function, and when the change of the road condition data or the weather data exceeds a preset threshold value, performing risk assessment and path planning again, and regenerating the optimal or suboptimal transportation path. And the transportation data security is ensured. A path can be adjusted in real time to cope with road conditions and weather changes, the transportation risk is reduced, and the transportation cost, time and risk are balanced.

The invention discloses an intelligent engineering vehicle scheduling method based on artificial intelligence, and the method comprises the steps: constructing a scheduling objective function which at least comprises the engineering vehicle transportation cost, the task completion time cost, the path congestion cost and the energy consumption cost; establishing a multi-dimensional task demand prediction model based on historical task data and real-time traffic data, and outputting a task distribution prediction value and a traffic state prediction value in a future time period; inputting the model into an engineering vehicle dynamic scheduling model established based on a deep reinforcement learning algorithm for iterative optimization; and generating a real-time scheduling instruction based on the optimized dynamic scheduling model, dynamically allocating task paths and resources of the engineering vehicle, and monitoring an execution state in real time to adjust a scheduling strategy. According to the method, efficient, economical and environment-friendly intelligent scheduling of the engineering vehicle is realized through construction of a scheduling objective function, multi-dimensional demand prediction, deep reinforcement learning dynamic optimization and real-time scheduling and monitoring.

The invention relates to an artificial intelligence-based cargo transportation path planning method and system for automatic warehouse logistics. The method comprises the steps of performing weighted scoring calculation based on customer historical transaction data and cargo attributes, and generating service label data for distinguishing a high-value hierarchy and a common hierarchy; a differentiation algorithm strategy is called according to the service label, an exclusive transportation path is distributed to the high-value goods, obstacle avoidance is optimized in real time, and adjacent orders are matched for common goods to be merged and distributed; in combination with real-time road conditions and storage scheduling data, through path weight dynamic calculation and genetic algorithm iterative optimization, generating a global transportation scheme considering both time efficiency and cost; customer preference parameters are updated in a closed loop mode based on execution feedback data, and system self-adaptive learning is achieved. According to the method, the problems that differentiated services, dynamic response to environment changes and low-efficiency resource utilization cannot be achieved in a traditional method are solved, the punctuality rate of high-value orders and common orders can be increased, and meanwhile the transportation cost of the common orders can be reduced.

The invention discloses a centralized unloading type power generation robot which comprises a mute box type diesel generator set and further comprises at least two sets of lifting supporting rod assemblies, and the lifting supporting rod assemblies are arranged on the two sides of the mute box type diesel generator set so as to lift the mute box type diesel generator set in the loading and unloading process. According to the centralized unloading type power generation robot, by arranging the lifting supporting rod assembly, the problem that a traditional generator set loading and unloading vehicle depends on hoisting equipment is solved, automatic lifting and unloading of the loading and unloading vehicle can be achieved in the environment without the hoisting equipment, the transportation efficiency is improved, the transportation cost is reduced, and it is ensured that the equipment can be rapidly transferred to the needed place to be put into use.

The invention discloses a fatigue life model of a single-row tapered roller bearing and an optimization method. Contact load, contact stress and fatigue life need to be calculated. Generally speaking, when the service life is reached, fatigue damage occurs at the maximum contact load position, and the failure of the part starts to fail from the maximum contact load position theoretically. The cycle time of the rolling body or the ferrule at the maximum contact load under the maximum stress is calculated according to the formula and is not influenced by the failure of a certain roller (or ferrule) and other parts of the bearing on the service life of the bearing. According to the invention, the fatigue life, the static strength and the static bearing capacity meet the requirements, and the total volume sum of the upwind bearing and the downwind bearing is minimum. On the premise that the performance requirement and the 20-year service life specified by the IEC61400-4 standard are met, the size sum of the two fan main bearings is reduced by 13.68%, the production and transportation cost is reduced, the economic benefits of enterprises are improved, and long-term stable operation of wind power equipment is guaranteed.

The present application discloses an optimization method and apparatus for a transportation strategy under a feeder logistics network, an electronic device and a storage medium. The method comprises: acquiring logistics information under a feeder logistics network, wherein the logistics information comprises information of each logistics node; inputting the logistics information into a digital twin model, by means of the digital twin model and by taking transportation rules of the feeder logistics network as a constraint condition and transportation cost minimization as an objective, performing route planning to generate a transportation strategy to be optimized, and simulating a transportation process of goods between the logistics nodes on the basis of the transportation strategy to be optimized, so as to obtain a simulation result; and on the basis of the simulation result, optimizing the transportation strategy to be optimized, so as to obtain an optimized transportation strategy. In the present application, on the basis of the computing power of the electronic device, the transportation strategy during logistics transportation can be quickly obtained; and by simulating the logistics transportation process, the reliability of the transportation strategy can be improved.

The invention relates to the technical field of cold-chain logistics, in particular to an intelligent scheduling method and system for cold-chain transportation, and the method comprises the steps: achieving the precise scheduling and matching of vehicles and resources according to the information and resource integration of transportation vehicles; carrying out vehicle transportation path planning according to the determined target order; formulating an emergency response mechanism based on the real-time health state of the transport vehicle; according to the invention, through integration of transport vehicle information and resource data, optimization matching is carried out by using an intelligent calling algorithm, and accurate scheduling of vehicles and resources is realized. The resource waste and the unnecessary transportation cost are effectively avoided, and the improvement of the transportation efficiency is ensured; particle swarm optimization is adopted for path planning, and two-step operation of preliminary path generation and dynamic path optimization is combined, so that high efficiency and reliability in the cold chain transportation process are ensured; on the basis of real-time monitoring and historical data, potential faults of vehicles and equipment can be recognized in advance, and quick response can be achieved when abnormity occurs.

The invention provides a cross-border logistics order management method and a cross-border logistics order management system, and the method comprises the steps: carrying out the similarity comparison of package feature data and historical package feature data, and obtaining a similarity comparison result; if the preset similarity threshold is not exceeded, determining that the to-be-sent parcel is a target parcel; inputting the parcel feature data of the target parcel into a pre-trained digital twinborn model to obtain a digital twinborn body; carrying out analog simulation on the digital twinborn body based on the digital twinborn model, and generating a standard logistics track and a standard transportation cost; after the target parcel is delivered, an actual logistics track, actual transportation cost and current logistics environment information are acquired; and when the logistics deviation value exceeds a logistics abnormal threshold value or the cost deviation value exceeds a cost abnormal threshold value, generating alarm information. In the mode, automatic identification of repeated delivery, logistics abnormity and transportation cost abnormity can be realized, so that the intelligent and precise level of cross-border logistics order management is improved.

The invention provides a port multimodal transport path optimization method under an uncertain condition, and relates to the technical field of port freight. The port multimodal transport path optimization method comprises the steps of model construction and model solving. Aiming at the random fluctuation phenomenon of port import and export cargo transportation volume caused by market environment change, the multimodal transport path optimization problem under the condition of uncertain transportation volume is researched, a multimodal transport model with the minimum transportation comprehensive cost upper limit as the target is established, and the model is solved by adopting a genetic algorithm improved based on fuzzy logic. Simulation results show that in the aspect of effectiveness, the transportation comprehensive cost of the constructed model is reduced by about 10% compared with that of a comparison model and is only about 7% higher than that of an optimal decision scheme, and it is proved that the constructed model can remarkably reduce the comprehensive cost of multimodal transportation under the condition that the transportation volume is uncertain.

The invention relates to the technical field of freight scheduling, in particular to a freight scheduling information management method and system and a storage medium. The method comprises the following steps that uncompleted order collection is carried out on a freight platform to obtain an uncompleted order set, and the uncompleted order set comprises a plurality of uncompleted orders; each uncompleted order at least comprises an order ID, order release time, target cargo attribute data, publisher identity data, a departure place, a destination, an order state identifier, an order transportation task schedule, cargo stacking attributes, predicted delivery time and cargo expected arrival time; and performing order state classification and marking on the uncompleted order set to obtain a classified order set. By optimizing the loading space utilization rate of the vehicle, the unloaded and half-loaded conditions of the vehicle can be reduced, so that the fuel consumption and the transportation cost are reduced.

The invention relates to the technical field of intelligent logistics, in particular to a vehicle path planning method and system for tire enterprise distribution, a storage medium and a software product. The method comprises the following steps: firstly, performing entropy weight-fuzzy comprehensive evaluation based on order quantity, frequency and time window width to obtain customer priority, and constructing a target of'transportation cost + priority linkage soft time window punishment '; the method comprises the following steps of: selecting an ALNS damage / repair operator by taking Q-learning as a high layer under the condition of meeting load, volume and time window constraints, and matching greedy initialization, Metropolis acceptance and 2-opt fine tuning solution. The result shows that the total cost and the time window default are remarkably reduced under the same calculation budget, the punctuality rate of high-priority customers and the robustness of the solution are improved, and platform deployment is facilitated.

The invention discloses a double-row steel sheet pile cofferdam construction method, which comprises the following steps of: before construction, measuring the original riverbed elevation of a cofferdam piling area, organizing steel pipes to enter a site according to a cross-section diagram and a construction sequence of a cofferdam, timely removing and recording if stone blocks and the like at the river bottom are measured in the measurement process, and before construction, checking and maintaining all mechanical equipment, so that the construction efficiency is improved. After entering the field, the steel pipes are stacked according to specifications, varieties and classifications, before pile conveying, the models and the number of the steel pipes used during construction are determined, and the steel pipes are arranged strictly according to the designed section. Compared with a construction scheme of adopting a Larsen steel sheet pile cofferdam and a temporary construction platform, the construction cost, the rental cost, the earthwork transportation cost, the earthwork compaction cost, the earthwork filling excavation filling cost and the like of temporary facilities can be saved, building construction is carried out in a water area needing a large area, the construction period is long, and the construction cost is low. The method is more economical and reasonable when being used for constructing structures in water under the conditions of high water level and the like.

The invention relates to the field of logistics vehicle management, in particular to a centralized management method and system for intelligent vehicles. The invention discloses a centralized management system for smart vehicles. The centralized management system comprises a task receiving module, a portrait construction module, a vehicle screening module, a sign-in verification module and a queue management module. According to the method, the vehicle multi-dimensional portrait model is constructed, and the transportation cost, the transportation efficiency and the like are subjected to multi-dimensional matching calculation based on the multi-objective optimization algorithm, so that intelligent vehicle dispatching decision-making from experience driving to data driving is realized; according to the method, the historical operation data, the real-time state and the driver behavior characteristics of the vehicle are comprehensively considered, a global optimal vehicle dispatching scheme can be output, the vehicle dispatching accuracy and the resource utilization efficiency are remarkably improved, and the limitation that a traditional method only depends on a single factor for dispatching is overcome.

A handling truck comprises a mast assembly, a base, a moving assembly, and a connecting member. The base is fixedly disposed at the bottom end of the mast assembly, and the base is provided with a socket portion. The moving assembly includes a leg assembly and a connecting portion, wherein one end of the connecting portion is connected to the leg assembly, and the other end of the connecting portion is configured for socket engagement with the socket portion. The moving assembly is configured to drive the mast assembly to move on a plane. The connecting member is configured to pass through the socket portion and the connecting portion to achieve a detachable connection between the moving assembly and the base. The present application provides a handling truck capable of reducing packaging costs and transportation costs.

The invention relates to the technical field of warehouse management, and discloses a warehouse management system and method based on dynamic inventory prediction. According to the invention, market, sales, supply chain and warehouse internal data are collected in real time, a dynamic inventory prediction model is constructed in combination with a long short-term memory (LSTM) network, the future inventory demand can be accurately predicted, and the prediction precision is significantly improved; an inventory optimization strategy is generated based on a multi-objective optimization algorithm, so that the inventory holding cost, the stockout cost and the transportation cost are effectively reduced; and intelligent warehouse operation instruction generation and real-time monitoring are realized through the execution control module, and the response capability of an enterprise to market fluctuation and emergencies is enhanced. According to the system and the method, the warehouse management efficiency is improved, the operation risk is reduced, and an efficient and reliable solution is provided for modern logistics and supply chain management.

The invention discloses a vehicle and unmanned aerial vehicle low-altitude collaborative distribution path planning method considering a heterogeneous fleet. The method comprises the following steps: firstly, acquiring low-altitude logistics distribution demand data, distribution combination data composed of a distribution vehicle and a heterogeneous unmanned aerial vehicle, and distribution combination data composed of a vehicle and a heterogeneous unmanned aerial vehicle for low-altitude logistics distribution, and initially realizing distribution resource allocation; establishing a vehicle-heterogeneous unmanned aerial vehicle team low-altitude logistics cooperative distribution path optimization model by taking the shortest total distribution time and low total distribution cost as targets, improving the distribution timeliness of all demands of customers and optimizing the cost, and optimizing the vehicle and unmanned aerial vehicle low-altitude logistics cooperative distribution path by adopting an improved adaptive neighborhood search algorithm, so as to improve the distribution timeliness of all demands of the customers. And an overall distribution scheme of the terminal logistics demand is obtained. The method can be compatible with cooperative transportation of different types of unmanned aerial vehicles, so that corresponding unmanned aerial vehicle fleet combinations are configured for different transportation requirements, the transportation cost is saved, and meanwhile, the method has good vehicle-vehicle cooperative path planning solving efficiency.

The invention discloses a dangerous goods transportation charging method based on risk assessment, and relates to the technical field of dangerous goods transportation charging, and the method comprises the following steps: collecting the data of traffic flow, accident occurrence frequency, environmental factors and the like of each road segment in a dangerous goods transportation network, and calculating a dangerous goods transportation charging result based on a population coverage risk model and an accident expected value calculation method; and evaluating the potential risk level of each road section. Through multi-dimensional data fusion and an advanced algorithm, real-time assessment and path optimization of transportation network risks are realized. A population coverage risk model and an accident expected value calculation method are adopted, high-risk road sections are accurately recognized, transportation tasks are reasonably distributed, and safety is improved. Meanwhile, the genetic algorithm is utilized to optimize the transportation path, balance the cost and risk, reduce the transportation cost and guarantee the safety. Global path distribution is optimized through a simulated annealing algorithm, excessive load of high-risk road sections is avoided, task distribution is dynamically adjusted according to risk prediction, reasonable resource allocation is ensured, and transportation efficiency and safety are improved.

An umbrella comprises a column having a middle portion; an upper nest; a lower nest; a plurality of long rods; a plurality of short rods each; a crank handle assembly disposed on the middle portion of the column. The lower nest has a plurality of first long rod grooves on a periphery of the lower nest; the crank handle assembly comprises a housing and a crank handle, correspondingly, the housing has a plurality of second long rod grooves. The umbrella of the present invention is simple and reasonable in structure, greatly reduced in size and convenience to store and carry, and reduces the cost for packaging and transportation.

The invention relates to the technical field of logistics and supply chain management, and discloses a one-station inspection platform-based product management method, which comprises the following steps of S1, constructing an optimal transportation problem model, defining a resource circulation relationship between a supplier and an order, and calculating an optimal scheme allocated to the order by the supplier by taking the minimum transportation cost as a target; s2, based on a game theory model, strategy interaction among a plurality of suppliers is considered, a supplier resource allocation strategy is optimized through a game, and balance of resource scheduling is ensured; s3, a Markov decision process is adopted, and order circulation and resource scheduling strategies are adjusted through real-time feedback so as to adapt to dynamic changes of service requirements; and S4, carrying out multi-objective optimization. According to the invention, through a dynamic optimization and real-time data feedback mechanism based on the one-station inspection platform, efficient resource scheduling and order circulation are realized, the maximization of the resource utilization rate is ensured, the delivery timeliness is improved, and the cost and the service quality are optimized at the same time.

The unmanned material transportation system comprises an intelligent equipment cluster and a remote centralized dispatching system, and the intelligent equipment cluster is connected with the remote centralized dispatching system; the intelligent equipment cluster comprises horizontal transportation intelligent equipment used for same-layer transportation of materials, an intelligent construction elevator used for cross-layer transportation of the materials and a stacking robot used for sorting, taking and placing of the materials. The remote centralized control system comprises a front-end interface and a rear-end server, wherein the front-end interface is used for issuing tasks and monitoring material processes and equipment states, the rear-end server is used for task scheduling, task path planning, task conflict monitoring and management and transmitting collected information to the front-end interface for display, and the front-end interface is connected with the rear-end server. And the intelligent equipment cluster and the back-end server are in two-way communication through the real-time communication module. Material sorting and transporting can be automatically completed, manual operation on a construction site can be reduced, the equipment efficiency is improved, and the transporting cost is reduced.

The invention discloses an intelligent optimization system and method for logistics transportation cost management, and relates to the technical field of intelligent logistics management, and the system comprises a data collection and analysis module which is responsible for collecting basic data of goods and analyzing the data; the constraint condition and transportation characteristic analysis module analyzes the characteristics of various constraint conditions and actual transportation environments in the cargo transportation process; the initial loading scheme generation module is responsible for generating an initial loading scheme according to characteristics and constraint conditions of goods; when an initial loading scheme is generated, three heuristic methods of first adaptation, optimal adaptation and worst adaptation are adopted, historical data are combined to guide generation of the scheme, and meanwhile, a genetic algorithm and a simulated annealing algorithm are introduced to further optimize the scheme, so that an approximate optimal solution is efficiently searched in a solution space.

The invention relates to an intelligent logistics scheduling and resource distribution system based on multi-source data fusion, and relates to the technical field of logistics management technologies, and the system comprises the steps: collecting vehicle operation state data, road condition data, logistics order dynamic data and warehouse inventory change data in real time based on a multi-source heterogeneous interaction platform; constructing a multi-modal traffic logistics time series data set; based on a maximum priority scheduling algorithm, giving priority weights to the logistics orders in combination with logistics order timeliness demands and cargo features; based on the logistics order priority weight, path optimization is carried out by using a space-time hierarchical planning algorithm, and an initial transportation path set is constructed; real-time monitoring of the transportation process is achieved based on the digital twinborn technology, the path condition is sensed dynamically, the optimal transportation path is obtained through continuous adjustment, and an intelligent logistics scheduling and resource allocation scheme based on multi-source data fusion is generated. The method has the advantages that the resource allocation efficiency is improved and the transportation cost is reduced by optimizing the priority path and continuously adjusting the transportation path.

The present invention relates to the technical field of tableware, in particular, to an easy-to-transport three-spliced table. The three-spliced table includes table legs arranged at four corners of a table top, where the table legs and the table top are reinforced by a plurality of groups of reinforcing members; the table top is formed by splicing a first table plate, a second table plate and a third table plate side by side; and the table top of the three-spliced table is divided into three parts when being opened, which are not different from a table with a complete table top after being assembled through screws. During transportation, the packaging volume is reduced, the transportation cost is saved, and the risk of packaging damage and product damage during transportation is reduced.

A disassemblable table is provided, which belongs to the technical field of furniture. The problem of reducing the transportation cost of the table while ensuring the structural stability of the table is solved. The disassemblable table includes a table board and a plurality of table legs, where the disassemblable table further includes a ringlike connecting frame, the connecting frame is located below the table board and disposed among the plurality of table legs, a side wall of each of the table legs is provided with a U-shaped connecting clip, the connecting clips are clamped with an outer side wall of the connecting frame and bolted and locked through fasteners, a bottom face of the table board is provided with a ringlike fixing frame, and an upper end of each of the table legs is joined with an outer wall of the fixing frame and bolted and locked through the fasteners.