5229 results about "Storage garage" patented technology

The invention relates to the technical field of automatic logistics, in particular to an intelligent warehouse cargo scheduling device, which comprises a dynamic priority adjustment module, a self-adaptive classified storage module, a cargo flow optimization module, a resource allocation module, a fault analysis and maintenance module, a behavior monitoring module, an energy management module and a cost control module. According to the method, the cargo scheduling priority is optimized through a support vector machine algorithm and a decision tree method, the warehouse space utilization rate is improved by utilizing a convolutional neural network and a genetic algorithm, the cargo flow is optimized by adopting linear programming and a network flow analysis algorithm, and the cargo flow trend is estimated through an autoregressive integral moving average model and a recurrent neural network. The system utilizes a long short-term memory network to identify equipment faults and maintain, reduces the risk of accidental shutdown, improves the operation safety through a computer vision technology and a behavior identification algorithm, and utilizes an energy consumption mode analysis and cost-benefit analysis method to reduce the operation cost.

The invention discloses an intelligent warehouse management method, which is applied to a multi-layer shuttle vehicle intensive stereoscopic warehouse, and comprises the steps: predicting warehouse-out orders in a future preset time period according to historical order data, and obtaining order prediction information; according to the order prediction information and a warehouse model of the multi-layer shuttle vehicle intensive stereoscopic warehouse, predicting a cargo storage position of a cargo in a warehousing order and a corresponding carrying equipment path; constructing a plurality of cargo handling distribution schemes based on the handling equipment path, the load balance degree of the handling equipment and the emergency degree of the task; according to the cargo storage position, the corresponding carrying equipment path and the cargo carrying distribution scheme, utilizing an optimization algorithm to calculate a globally optimal cargo storage position, a cargo carrying distribution scheme and a carrying equipment path; and carrying out warehousing scheduling on the goods in the warehousing order according to the globally optimal goods storage position, the corresponding goods carrying distribution scheme and the carrying equipment path.

To predict the working time of sorting work for taking out merchandise and sorting it for each shipping destination.SOLUTION: The prediction device 100 includes a prediction unit 113 that predicts a work time of sorting work, which is work of taking out and sorting articles from a storage that stores the articles, based on a plurality of orders related to the articles (commodities), and calculates the work time as a predicted work time. The prediction unit 113 calculates the predicted working time based on the input information including the type of article to be taken out from the storage, the number of articles to be taken out from the storage, and the number of articles included in each order. The storage may be a storage shelf that the transport robot carries to a work place of the sorting work. The item may be transported along a predetermined path to a work location of a sorting operation. Further, a worker who performs the sorting work may move to a place where the storage is placed and perform the sorting work.SELECTED DRAWING: Figure 3

The invention discloses a storage AGV dynamic path planning system based on multi-objective optimization, and relates to the technical field of storage logistics, and the system comprises a multi-source sensing and data collection module which is used for collecting the operation state, operation environment and external traffic information of an AGV and generating a standardized feature vector; and the cross-modal digital twinning and risk simulation module is used for constructing a virtual twinning body of a warehouse and external traffic and carrying out risk prediction and simulation under the driving of cross-modal sensing data. According to the invention, through the multi-source sensing and data acquisition module, the system can comprehensively acquire the AGV operation state, the operation environment and the external traffic information, and through combination with an advanced data fusion technology, a high-dimensional standardized feature vector is generated, so that an accurate and comprehensive data basis is provided for subsequent path planning and risk prediction; the cross-modal digital twinning and risk simulation module constructs a virtual twinning body of a warehouse and external traffic, and can reflect the dynamic change of the physical world in real time.

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

The invention discloses a cargo warehouse-in and warehouse-out management method and management system, and relates to the technical field of information management. Through dynamic storage position optimization, the warehouse-in and warehouse-out frequency, the associated sales relationship and the storage demand characteristics of cargos are combined with a warehouse three-dimensional space model; and generating a cargo storage location allocation scheme by using a multi-objective optimization algorithm. According to the scheme, the picking time of high-frequency warehouse-in and warehouse-out commodities is remarkably shortened, the average time consumption of single-time commodity picking is reduced, meanwhile, the combined picking error rate of associated commodities is reduced, the problem that in the prior art, storage positions are disjointed with warehouse-in and warehouse-out requirements is solved, through predictive process scheduling, cross congestion of operation channels is effectively avoided, and the working efficiency is improved. Therefore, smooth operation of the in-out warehouse process is realized, the occurrence frequency of congestion events of the in-out warehouse channel is reduced, the delay time is shortened, and the problem of congestion of the in-out warehouse process in the prior art is solved.

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

The invention relates to the technical field of mine warehouse management, in particular to a self-service warehouse-out system and method, and the system comprises a sensing and interaction module, a data processing and storage module, an automatic carrying and storage module, and a safety and monitoring module. The sensing and interaction module realizes functions such as cargo identification and identification; the data processing and storage module manages inventory, user and task data and analyzes and processes the data; equipment of the automatic carrying and storage module carries goods according to instructions, and the intelligent goods shelf can be automatically adjusted; and the safety and monitoring module ensures accurate cargo quantity, monitors the warehouse and controls personnel to enter and exit. The method comprises the steps that a user initiates an ex-warehouse request, a system checks an inventory and calculates an equipment scheduling priority, then cargo carrying and ex-warehouse operation including equipment scheduling, path planning, carrying detail determination and the like is carried out, and meanwhile weight monitoring, image monitoring and the like are carried out. The warehouse-out efficiency, the inventory management accuracy and the cargo handling safety are improved, and the intelligent level of warehouse management is improved.

The invention discloses an automated guided vehicle scheduling optimization method and device, a medium and a terminal, and the method comprises the steps: generating fleet heterogeneous data based on parameter data of a plurality of automated guided vehicles in an intelligent warehouse, and fusing the fleet heterogeneous data into an undirected graph model obtained through undirected graph modeling based on storage environment topological data in advance, the method comprises the steps of obtaining an environment topological graph model, then carrying out graph topology and heterogeneous feature extraction on the environment topological graph model to obtain a multi-constraint path planning model, and dynamically adjusting a single-vehicle route of each automated guided vehicle by utilizing the planning model and real-time storage field state data. And then performing scheduling process simulation on the intelligent warehouse based on the dynamic routing table formed by adjustment and historical scheduling index data of the intelligent warehouse, so that a scheduling system model learns a mapping strategy from a state to a behavior and outputs an optimization decision strategy. The method is used for automatic guide vehicle scheduling, is suitable for storage environments of different scales and complexities, and improves the scheduling efficiency and accuracy.

Systems and methods for automating the management of a warehouse are disclosed. The method includes monitoring. in real-time, a plurality of items in the warehouse, a plurality of tasks associated with the warehouse, and / or incoming vehicles to the warehouse. The method also includes receiving, via a plurality of sensors, image data and / or video data associated with the plurality of items, the plurality of tasks, and / or the incoming vehicles. The method further includes inputting the image data and / or the video data into a machine learning model to automate the management of the warehouse, wherein the machine learning model has been trained based on training data to generate predictions for the inputted data, and wherein the machine learning model utilizes the predictions for the inputted data for retraining to generate predictions for newly inputted data and improve accuracy of error handling.

The invention discloses a warehouse cargo supply chain management method and system based on AI big data, and relates to the technical field of warehouse management, and the method comprises the steps: collecting and preprocessing warehouse multi-source data, and predicting the cargo delivery frequency based on an LSTM neural network; and a multi-target scheduling model combining pickup path time consumption and storage gravity center control is constructed based on a prediction result, a cargo task and AGV path planning are jointly modeled into an open path multi-starting-point asymmetric TSP problem, an FWA-GA hybrid optimization algorithm is used for model solving, and an optimal cargo allocation matrix and an optimal AGV scheduling path table are obtained. Therefore, integrated joint optimization of task allocation, path planning and goods allocation layout is achieved, a supply chain platform is further linked to complete stockout early warning and supplier intelligent selection, and multiple limitations of a traditional method in the aspects of prediction precision, scheduling coupling, structure control, supply collaboration and the like are effectively broken through.

The invention discloses an inventory monitoring management system for a silica gel material storage center, and relates to the technical field of intelligent storage management. According to the inventory monitoring and management system for the silica gel material storage center, real-time and historical inventory data of silica gel materials are collected and stored through the inventory data module, and the inventory shortage index is analyzed by utilizing the inventory detection module in combination with a Markov chain, so that the inventory risk is evaluated. The inventory prediction module predicts future inventory demand and a replenishment trend chart based on an LSTM neural network model, and optimizes a replenishment decision; the abnormity monitoring module generates an inventory abnormity index through the inventory shortage index and the future demand quantity, and early warns abnormal conditions in time. And the inventory distribution module dynamically distributes the inventory of each warehouse according to requirements by using an inventory optimization equation, so that reasonable allocation of resources is realized. The intelligent silica gel material inventory management is realized, and the monitoring efficiency and the response speed are improved.

The invention discloses an intelligent warehouse management method and system for hazardous wastes, and the method comprises the steps: carrying out the real-time monitoring of the state of an object in a hazardous waste warehouse through a deployment sensor network, obtaining the basic data of the position of the object and the environment parameter, and transmitting the basic data to a central processing platform, thereby obtaining a preliminary data set; performing integration processing on multi-source information by adopting a data fusion technology according to the preliminary data set, eliminating redundancy and noise, and determining a standardized data record; aiming at the standardized data record, dynamically analyzing the distribution of goods locations in the warehouse by utilizing a path optimization algorithm, and if the change of the occupation state of the goods locations is detected, adjusting a goods location allocation scheme in real time, and outputting an optimal storage location combination; according to the optimal storage position combination, a dynamic route planning scheme is generated in combination with warehouse layout information, and the optimal moving track of the operation equipment is obtained; according to the invention, intelligent management of the hazardous waste warehouse is realized, and the storage efficiency and safety are improved.

The invention discloses a warehouse-in and warehouse-out path planning system and method applied to stored goods, and relates to the technical field of intelligent storage. The method comprises the steps of establishing a three-dimensional space structure of a warehouse, generating a menu to be stored and matching various storage schemes, carrying out importance degree assignment and priority ranking on goods, calculating the residual space and the total weight of the warehouse, judging and optimizing the space and the weight, and planning and optimizing warehouse-in and warehouse-out paths. Through the mathematical planning method, the total cost is minimized, and the space utilization rate and the access efficiency are maximized. And meanwhile, the operation condition in the warehouse is monitored in real time, the in-out path and the in-out path are dynamically adjusted, and congestion and conflicts are avoided. In addition, a high-definition camera and a sensor are further installed, and safety management in the warehouse is ensured. According to the invention, intelligent warehouse-in and warehouse-out path planning of the stored goods is realized, the warehouse management efficiency and the space utilization rate are improved, and safe storage and efficient warehouse-in and warehouse-out of the goods are ensured.

The invention provides a power grid infrastructure project material collaborative management method, which comprises the following steps: analyzing historical material use, power grid construction progress and external environment data through a reinforcement learning algorithm, dynamically adjusting model parameters, and predicting material demands; a low-power-consumption wide area network and an edge computing technology are used for monitoring and analyzing material states in a warehouse and a transportation process in real time, and a result is transmitted to the central management system; based on the prediction data, an optimized material purchasing plan is automatically generated, and the purchasing strategy is dynamically adjusted by considering the arrival time, the inventory state and the supplier capability; using an improved ant colony algorithm to optimize the transportation route and time of the materials from the warehouse to the construction site; using an intelligent sensor to record the material use condition, and carrying out preliminary data processing and analysis through edge calculation; and an information management platform is established to realize information sharing and cooperative work. According to the method, the intelligence and efficiency of material management are improved by integrating multiple technologies.

The invention proposes a method and system for automatically identifying and controlling a robot to enter the undercarriage of roll carts. Specifically, the method and system proposed in the invention belong to the field of autonomous mobile robots (AMRs) applied in the transportation of goods in warehouses. The invention relates to the field of robot vision and automatic control.

The invention relates to a robot, a server, a warehousing system, a warehouse and a warehouse-out method, and the warehousing system comprises a goods shelf which is provided with a raw material box storage area and an order box storage area; the carrying robot is used for receiving the first dispatching instruction and conveying the raw material box to the first stopping position of the target sorting work station from the first goods position according to the first dispatching instruction; the sorting robot is used for receiving a second dispatching instruction and arriving at a second stopping position of the target sorting work station according to the second dispatching instruction, so that the to-be-sorted goods in the raw material box on the carrying robot at the first stopping position can be sorted to the sorting robot at the second stopping position to become sorted goods; and the sorted goods or the target order boxes containing the sorted goods are conveyed to the second goods allocation from the second parking position. According to the robot, the server, the warehousing system, the warehouse and the warehouse-out method, the warehousing area can be saved, and the flexibility of the warehousing process is improved.

The invention discloses an intelligent logistics digital warehouse management method based on deep learning, and relates to the technical field of warehouse management, and the method comprises the steps: calculating the correlation of SKUs through Graph SAGE, constructing an SKU incidence matrix, optimizing an SKU combination based on an SKU co-occurrence frequency, calculating an SKU storage optimization strategy through Transform, analyzing the storage relation between the SKUs through an attention mechanism, and optimizing a storage layout in combination with GNN. A high-frequency SKU can be located in an optimal storage area, in a sorting task scheduling link, a sorting robot is trained by adopting reinforcement learning, an optimal sorting path is autonomously learned in a warehouse environment, optimal distribution of AGVs and manual tasks is calculated in combination with GNN, collaborative operation of automatic equipment and manual operation is balanced, and path conflicts and invalid movement are effectively reduced; and after the task is executed, recording the sorting time and a path optimization result, and applying feedback data to self-supervised learning to optimize a warehouse management strategy, so that SKU storage, a sorting path and task scheduling can be dynamically adjusted.

The invention relates to the technical field of warehouse management, in particular to an unmanned warehouse management method and system with a real-time inventory function, and the method comprises the steps: building a signal strength-probability statistical model through return signal data generated in a historical inventory process, and carrying out the stability verification of each RFID tag return signal in a current inventory period, and under the condition that the stability is confirmed, carrying out preliminary positioning based on the time difference of arrival and determining the probability of the storage unit. And finally, the preliminary positioning is verified in combination with the probability of the storage unit and the checking information of the last checking period, so that the situation that whether the position of the RFID tag is changed really cannot be confirmed due to the fact that verification cannot be carried out when the positioning jumps is avoided. According to the invention, low-cost and high-reliability material positioning can be realized by using an existing RFID system, so that real-time and accurate checking is realized, and an information basis is provided for management of unmanned warehouses.

The invention discloses a material weighing and conveying control method and system, and relates to the technical field of self-adaptive control systems. The method comprises the following steps: setting a visual positioning identifier in an unloading area, and configuring an identification code for a material; establishing an information management database, associating the identification codes of the materials with material attributes, and generating a material association attribute table; collecting an identification code of the material, and calling a material association attribute table based on the identification code; and based on the information management database, obtaining a production line state table of each production line and a warehouse state table of the current warehouse, generating a candidate transportation list, determining a comprehensive priority score of each candidate transportation target point, and selecting the candidate transportation target point with the highest score as a final transportation target point. Compared with the prior art in which fixed rules are used for scheduling, the material transportation targets can be dynamically distributed according to real-time production requirements, manual intervention is avoided, the logistics efficiency is improved, high-requirement materials are preferentially distributed, and production interruption caused by material shortage is prevented.

The invention provides a dense warehouse adopting embedded elevators and a storage solution of the dense warehouse, the elevators are embedded between storage bins of the dense warehouse, and guide rails with single paths are arranged on the elevators. According to the elevator, the guide rails can directly communicate with the rails of the storage units of all the layers in the dense warehouse, so that cargoes at the inlet position are directly guided into the elevator through the rails during warehousing, and the cargoes are transferred to the corresponding storage layers to be stored; and during warehouse-out, the goods in the storage layer are directly guided into the elevator through the track and are transferred to the corresponding exit position. On the basis of the embedded elevator, the occupied storage space can be effectively reduced, and the storage utilization rate is increased; and front and rear buffer storage positions are formed by front and rear tracks of the lifting platform, so that on one hand, the planning design of a warehouse-in and warehouse-out line can be simplified, on the other hand, the warehouse-in and warehouse-out efficiency of automatic warehouse space can be improved through linkage scheduling of different walking devices, and the construction cost and the construction period are reduced.

The invention relates to the technical field of warehouse cargo management, in particular to a warehouse cargo anomaly detection method and device based on an image recognition technology, and the method comprises the steps: carrying out the image collection of a goods shelf in a target warehouse according to a preset sampling period based on an unmanned plane, so as to obtain a goods shelf storage image, carrying out the preprocessing of the goods shelf storage image, and obtaining a goods shelf anomaly detection result; a standard goods shelf storage image is obtained; and carrying out edge detection on the standard goods shelf storage image to obtain a goods shelf storage contour, and carrying out linear detection on the goods shelf storage contour to obtain a standard goods shelf storage contour tolerance. Through comprehensive detection based on the stockout quantity and the tidiness coefficient, the management condition of the warehouse can be comprehensively evaluated, potential problems can be timely found, the warehousing operation is optimized, the dependence on human resources is reduced, the cost brought by manual inspection is reduced, the safety is improved, and the manual operation risk is reduced.

The invention discloses a high-density stereoscopic warehouse ex-warehouse scheduling and transportation system and method based on RGVs, and relates to the technical field of warehouse ex-warehouse scheduling and transportation, and the system comprises a scheduling management center which is in communication connection with a virtual model construction module, a task analysis and planning module, a task distribution module and a real-time monitoring intervention module. Wherein the modules are connected through electric signals; and the virtual model construction module is used for constructing a virtual model of the warehouse and the RGV. By constructing the virtual models of the warehouse and the RGVs and combining the digital twinning technology, the state of a physical entity can be synchronized in real time, the space-time paths of the multiple RGVs can be accurately simulated, path conflicts and traffic congestion are effectively avoided, the efficiency of warehouse-out operation is remarkably improved, task delay caused by the path conflicts is greatly reduced, and the working efficiency of warehouse-out operation is improved. And the accuracy and timeliness of warehouse-out operation are ensured, so that the overall logistics efficiency is improved.

The invention, which relates to the technical field of artificial intelligence, discloses a warehouse-in and warehouse-out path planning method and system for intelligent warehousing, and the method comprises the steps: generating an optimal operation path of a to-be-operated carrier through a path planning model; the method comprises the following steps: predicting whether a current carrier has a collision risk with all carriers in a warehouse in a time dimension or not through a time block, predicting whether the current carrier has a collision risk with all carriers in a warehouse in a space dimension or not through a space block, and when the time block and the space block simultaneously detect that the current carrier has the collision risk at a certain moment, judging whether the current carrier has the collision risk; the decision block judges that the current carrier has a collision conflict, and determines an optimal avoidance strategy through an action value function; according to the optimal avoidance strategy, the avoidance waiting carrier is determined, the total waiting time and the waiting space position of the avoidance waiting carrier are calculated, and the avoidance strategy is executed. According to the technical scheme, the problem that in-out garage path planning and collision processing cannot be intelligently and accurately carried out in the prior art can be solved.

The invention provides a dispatching method for a four-way vehicle dense warehouse without a conveying line and a warehousing system. The vertical two-way vehicle is used for achieving vertical movement of goods in a single goods lattice, and layer changing in the longitudinal space is executed. And meanwhile, the vertical two-way vehicle is directly communicated with the goods shelf sub-track in the horizontal space, so that the four-way vehicle can freely enter and exit from the vertical two-way vehicle, and the automatic warehouse entering and exiting function of the tray goods is flexibly achieved. According to the invention, a conveying line is removed, the high-flexibility four-way shuttle vehicle can be directly utilized to realize the butt joint with the warehouse-in and warehouse-out point location, the dense storage without the conveying line in the warehouse is realized, the installation, control and debugging work of conveying equipment is avoided, the field implementation process is simplified, the construction cost is reduced, and the construction period is shortened. The warehouse space can be fully utilized, the number of stored goods locations is increased, the production and installation cost is greatly reduced, the density of an automatic warehouse is increased, and the delivery, operation and maintenance difficulty is reduced while the flexibility of a warehousing system is improved.

The embodiment of the invention discloses a path planning method and device, electronic equipment and a storage medium, and relates to the technical field of electric power, and the method comprises the steps: determining a starting point and an ending point of a to-be-planned path in a preset warehouse; based on the current scene information of the preset warehouse, determining the current traffic obstruction level of each sub-region in the preset warehouse; calculating the actual driving cost of each candidate node, and determining a corresponding first target weight based on the current traffic obstruction level of the sub-region where each candidate node is located; calculating the estimated driving cost of each candidate node, and determining a corresponding second target weight based on the current traffic obstruction level of the sub-region where the terminal point is located; and weighting and summing the actual driving cost and the estimated driving cost of the corresponding candidate node based on the first target weight and the second target weight to obtain the comprehensive evaluation cost of the corresponding candidate node, and determining the intermediate node of the to-be-planned path based on the comprehensive evaluation cost, so that the path planning function is realized, and the working efficiency of the robot is improved.

Disclosed in the present invention are a warehousing transportation resource scheduling method and system based on goods quantity prediction, solving the problems of planning and scheduling of warehousing transportation resources in a warehousing environment. The technical solution comprises: collecting data to form a data set; preprocessing the collected data set to ensure the quality and accuracy of the data; constructing seasonal data in the preprocessed data set; introducing a bidirectional LSTM model, an input being a historical feature sequence, and an output being a predicted inbound quantity; using a deep learning framework to build a CTA-LSTM model used for predicting the inbound quantity; using a grey wolf optimizer to initialize parameters in the CTA-LSTM model, and using the grey wolf optimizer to perform parameter optimization to determine an optimal parameter configuration in the optimizer; using an adversarial mechanism to perform improvement and adversarial training on the built CTA-LSTM model so as to predict a warehouse goods quantity; building a warehousing simulation model; solving the built warehousing simulation model; and performing transportation resource scheduling on the basis of an obtained optimal solution.

The invention provides a warehouse site selection method and system. The method comprises the following steps: obtaining order information and target demand information corresponding to a target merchant, and network optimization constraint information corresponding to the target demand information; wherein the target demand information comprises a target scene and a target demand; the target demand is the optimal cost or the optimal time efficiency; if the target scene is a long-term optimization scene, determining at least one warehouse according to the commodity demand and network optimization constraint information of each city in the order information; determining a plurality of warehouse combination schemes according to a preset warehouse range, the at least one warehouse and the network optimization constraint information, and determining the service cost and the timeliness of each warehouse combination scheme; and according to the business cost and the timeliness of each warehouse combination scheme, determining the city corresponding to the preposed warehouse in the warehouse combination scheme with the optimal business cost or the optimal timeliness matched with the target demand in each warehouse combination scheme as a preposed warehouse address.

The invention discloses an intelligent warehousing dynamic optimization system based on a digital twinborn technology. The system comprises the following steps: S1, creating a digital twinborn model: acquiring basic data of an entity warehouse and constructing an accurate digital model; s2, data collection and integration: deploying Internet of Things equipment and realizing real-time transmission and processing of data; s3, constructing an analysis algorithm: developing an advanced analysis algorithm and implementing a machine learning model; s4, dynamic optimization and adjustment: automatically adjusting a storage layout and optimizing a warehouse-in and warehouse-out process; and S5, prediction and early warning: analyzing and predicting potential problems and setting an alarm mechanism based on historical data. According to the invention, the real-time performance and accuracy of data are obviously improved, the environment monitoring capability is enhanced, the cargo safety is guaranteed, and the space utilization rate is greatly improved.

The invention discloses a path planning method and system based on an automatic three-dimensional warehouse four-way shuttle vehicle, and the method comprises the steps: building a storage environment model through a grid method, making traffic rules according to a grid map, and building a map table; based on the map table, generating a task list by using a data memorization processing technology, and sorting according to task priorities; the distributor performs task distribution on the shuttle vehicle according to the task list, performs path planning by using an improved A * algorithm, divides an obtained path into a plurality of road sections, generates time window list information of the road sections, checks whether a time window conflicts with a time window of a planned path node or not, shields the road section corresponding to the time window if the time window conflicts with the time window of the planned path node, and does not shield the path corresponding to the time window if the time window conflicts with the time window of the planned path node. Re-planning the path; otherwise, time window dynamic adjustment is carried out, and the path is executed. According to the invention, conflict judgment is carried out through the time window table of the shuttle vehicle, the possibility of conflict occurrence is further reduced, and the operation efficiency is improved.