2636 results about "Storage garage" patented technology

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 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 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 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 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 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 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 an agricultural product supply chain abnormal event tracing method and system based on a knowledge graph, and relates to the technical field of agricultural product supply chain management, and the method comprises the steps: constructing a supply chain knowledge graph, and enabling graph nodes to cover agricultural product batches, production subjects, processing equipment, transport vehicles, storage warehouses and other entities; when an anomaly is detected, performing reverse traversal from an abnormal node based on a graph neural network to determine a candidate propagation path; calculating an abnormal source probability score by integrating the historical abnormal record, the time window goodness of fit and the association strength, and outputting a responsibility subject sequence; the method supports hypothetical reasoning to predict the downstream influence range, and solves the problems that a traditional traceability scheme is difficult to efficiently associate and analyze cross-link data and lacks abnormal path intelligent reasoning ability.

The invention discloses a multi-warehouse logistics path optimization method and related equipment. The method comprises the following steps: acquiring warehouse data and customer data; constructing a distribution graph model according to the obtained data, and defining a Markov decision process; extracting homogeneous features and heterogeneous features of nodes in the distribution graph model, and fusing the extracted features and initial features of the nodes to form super-relation features; inputting the super-relation features into an encoder for feature extraction, and outputting a high-dimensional vector of each node; and the decoder gradually constructs a path decision sequence of vehicle distribution according to the vector provided by the encoder. According to the method, fine description of a heterogeneous relationship is introduced into a neural network model, and end-to-end automatic solution is realized in combination with reinforcement learning. According to the method, the warehouse distribution and path optimization strategy can be automatically learned, the warehouse distribution and path planning strategy can be automatically learned without manual parameter adjustment, a better transportation cost result can be obtained in a large-scale multi-constraint distribution scene, and the method has intelligent and practical values.

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 invention relates to the technical field of cross-border logistics, in particular to an artificial intelligence algorithm for cross-border overseas warehouse location storage and picking path optimization, which comprises the following steps: S1, obtaining order details, commodity characteristics and warehouse environment data, and monitoring warehouse states and cargo storage conditions in real time; s2, data preprocessing is carried out, features are extracted, clustering analysis is carried out, and association rules are mined; s3, performing rule matching, constructing a mixed integer programming model, performing constraint processing, and outputting a commodity storage strategy; and S4, converting the storage strategy into a warehousing operation instruction, and executing the warehousing operation instruction. The method has the beneficial effects that the accuracy of goods classified storage and the space utilization rate of the warehouse location can be improved, and the storage cost of the warehouse location is reduced.

The invention provides a railway automatic goods yard loading and unloading operation method, and relates to the technical field of railway goods yards, and the method is characterized in that a railway automatic goods yard goods loading and unloading operation system comprises a loading and unloading machine tool positioning system, a goods positioning system and a loading and unloading machine tool management system; the railway freight yard is provided with a freight car loading and unloading operation area, a warehouse operation area and a railway freight car loading and unloading operation area, and goods are loaded and unloaded in different areas. The loading and unloading machine tool management system controls the loading and unloading machine tools to carry out truck unloading operation and truck loading operation in the truck loading and unloading area, carry out warehousing operation and ex-warehouse operation in the warehousing operation area and carry out railway loading operation and railway unloading operation in the railway loading and unloading area. According to the method, rapid loading and unloading, efficient stacking and intelligent management of goods can be achieved, and the efficiency and competitiveness of railway transportation are improved; meanwhile, labor cost can be reduced, operation risks are reduced, and the automation degree of the railway freight yard is improved.

The invention discloses a multi-warehouse multi-logistics vehicle path planning method based on a self-adaptive neighborhood ant colony system, provides a self-adaptive warehouse selection strategy based on historical evolution information, autonomously selects a proper warehouse for each logistics vehicle, achieves the purpose of optimizing the position of the warehouse, and improves the path planning efficiency. A customer selection mechanism based on a self-adaptive neighborhood is designed for the ant colony system, so that the complexity of customer selection is effectively reduced; through continuous iterative optimization, a high-quality and high-precision multi-logistics-vehicle service path is finally output, and the service cost of each logistics vehicle is balanced as much as possible while the total service cost of all the logistics vehicles is reduced. According to the invention, solution is carried out through an ant colony optimization algorithm, and a self-adaptive warehouse selection strategy, a customer selection mechanism based on a self-adaptive neighborhood, an ant selection technology based on path estimation service cost and a path combination and segmentation strategy fused with 2-opt are designed, so that the quality of a path planning scheme is remarkably improved; and meanwhile, the time consumed for constructing the planning scheme is greatly shortened.

The invention discloses an overseas warehouse operation intelligent wave order management method and system, and the method comprises the steps: constructing a commodity incidence matrix, mining a frequent item set, determining the commodity correlation degree, carrying out the centralized picking of high-correlation commodities, reducing the round-trip times, shortening the walking distance, improving the picking efficiency, planning a picking path in combination with the warehouse layout and inventory information, and achieving the intelligent wave order management of the overseas warehouse operation. The method comprises the following steps: preferentially selecting short-distance commodities in the same area, further reducing the picking time, allocating priorities according to order emergency degrees and customer levels, ensuring that important orders are processed in time, improving customer satisfaction, dynamically adjusting the sequence in the picking process, avoiding invalid walking caused by stockout, enhancing path flexibility and adaptability, and collecting warehouse-in and warehouse-out information in real time. Inventory is updated immediately, data are ensured to be accurate and timely, labor intensity and operation time of picking personnel are reduced, labor cost is reduced, and storage equipment loss and maintenance cost are reduced.

The invention provides a truck-unmanned aerial vehicle cooperative dynamic scheduling method and system in emergency logistics, and relates to the technical field of unmanned aerial vehicle intelligent scheduling. Constructing a truck-unmanned aerial vehicle cooperative distribution network, wherein each truck is equipped with a cooperative unit comprising a large unmanned aerial vehicle and a small unmanned aerial vehicle; the network comprises warehouses, hub points and demand points; establishing a mixed integer programming model by taking minimization of the total operation cost as a target and taking electric quantity management, service distribution, a time window and collaborative feasibility as constraints; and solving the model by adopting a randomized greedy algorithm, and synchronously deciding a truck path and an unmanned aerial vehicle task in single iteration to obtain a truck path scheme, an unmanned aerial vehicle service distribution scheme and a dynamic electric quantity scheduling scheme. Therefore, the cost optimization and dynamic coordination of emergency logistics scheduling are realized, and the efficiency and feasibility of emergency distribution are effectively improved.

A dynamic marketplace system leveraging store and warehouse mobility features a neuroevolution (NE) engine, an electronic device, and a request handler facilitating communication between the electronic device and the NE engine. The NE engine interfaces with a data storage system and an event handler receiving real-time event data from a public cloud services processor. An intentions handler interprets user intention data to predict user behavior. The NE engine, integrated with a processor, generates a predictive evolutionary model for the marketplace based on request, event, and intention data. An AI agent processor within the NE engine creates a recommendation model for mobile retail vendors, devises route plans, and deploys vendors to strategic locations.

A system and devices for automating and computerizing audits, tracking, and error prevention associated with container packing events and, in particular, multi-container packing events at a storage facility, yard, warehouse, or the like. In some cases, the systems and devices may be configured to monitor and detect errors or issues with packing items and send alerts and control signals to agents, such as autonomous agents, to correct the detected errors.

The invention relates to the technical field of automatic warehouse material, and discloses an intelligent goods allocation distribution system for automatic warehouse logistics, and the system comprises a goods allocation data collection module; a dynamic state detector; a multi-source data fusion unit; a three-dimensional storage modeling module; intelligently distributing a strategy engine; and a PLC control interface. According to the intelligent goods allocation allocation and scheduling system for automatic warehouse logistics, a goods allocation allocation scheme is generated through a multi-target Pareto optimization algorithm, the balance between carrying distance minimization min sigma alpha * Di and space utilization rate maximization Ut is achieved, and compared with a proportional static allocation rule, the warehouse space utilization rate is increased to 82%; a dynamic grid map technology is integrated, and a track cross risk area (red early warning grid) of the stacker and the AGV is marked in real time, so that the equipment collision probability is reduced by 90%; and in combination with a goods relevance knowledge graph, special goods such as medicines are clustered and stored according to temperature zones, and the energy consumption of cross-temperature-zone operation of equipment is reduced by 35%.

The invention discloses a dense warehousing operation optimization system, which takes storage efficiency, warehouse gravity center and goods allocation density as targets, optimizes warehouse goods allocation layout according to the initial goods quantity of a warehouse, reasonably allocates access points in orders, and effectively avoids collision during multi-vehicle collaborative operation while ensuring the safety and high-efficiency storage of the warehouse. The task allocation between the shuttle vehicle and the elevator is reasonably scheduled by taking the minimum order completion time and energy consumption as the optimization target, and the efficient utilization of resources and the balanced distribution of operation loads are ensured, so that the phenomenon that a single vehicle is excessively busy or idle is reduced; according to the method, the shortest path and vehicle avoidance are taken as targets, an intelligent algorithm is adopted to plan an optimal driving route for each vehicle, a lengthy path is avoided, the collision risk and the congestion phenomenon between the vehicles are reduced, and a dynamic avoidance strategy of multi-vehicle collaborative operation is considered in path planning so as to ensure that the vehicles can efficiently complete tasks in a dense operation environment.

A warehouse system and method for fulfilling orders comprising a plurality of products. As pallets containing cases of individual units are received, they are transported to a specific storage location within a warehouse. There, a picker arm is configured to retrieve individual units from the pallets and place in a tote according to a specified order. The totes are transported through the warehouse to the specific storage locations, then sent to a packing station.

The invention discloses a physical asset space management method based on three-dimensional point cloud coordinate mapping, and relates to the field of three-dimensional coordinate reconstruction and warehouse management. The method comprises the following steps: performing three-dimensional point cloud modeling on a warehouse space, and constructing a unified coordinate system; collecting three-dimensional point cloud data of goods, generating a unique identifier and binding the unique identifier with a digital model; the digital model of the goods is anchored into a storage coordinate system through a point cloud registration algorithm, and the attitude angle and the spatial position of the goods are calculated; analyzing the geometrical relationship between the goods and the storage positions, and identifying space states such as border crossing, stacking and posture abnormity; and calculating a space utilization rate based on the cargo volume and the storage location volume, and outputting an optimization suggestion. According to the invention, ontology-level coupling management of the goods and the storage space is realized, space deduction type account checking and checking and anomaly detection are supported, and the method is suitable for complex storage scenes such as special-shaped objects and high-density stacking.

The invention, which relates to the technical field of intelligent inventory management, discloses an intelligent inventory management system for household appliance spare and accessory parts, comprising a problem diagnosis module used for generating an inventory management first-level problem label and an inventory management second-level problem label; the intelligent demand prediction module is used for carrying out classification demand prediction according to the problem label generated by the problem diagnosis module and carrying out dynamic parameter adjustment on a prediction value; the dynamic classification management module is used for carrying out intelligent warehouse division, supplier collaborative optimization and digital agreement on the household appliance spare and accessory parts; and the intelligent storage module is used for upgrading hardware and carrying out software integration operation. According to the invention, historical data, market trend and seasonal fluctuation are utilized to accurately predict the requirements of spare and accessory parts. Classification is performed according to the value and demand fluctuation of the spare and accessory parts, and fine management is implemented. Through a VMI + JMI mixed mode, a supply chain is optimized to be cooperatively combined with an RFID technology and automatic storage equipment, so that the warehouse management efficiency is improved.

The invention provides a warehouse management method for emergency materials of a smart station, and the method comprises the steps: obtaining a production date, an expiration date and an initial environment parameter through the analysis of an electronic tag of the emergency materials, and generating a dynamic temperature suitable range based on the nonlinear mapping of the expiration date and an initial temperature value; generating a dynamic humidity suitable range based on a correlation model of the production date and the initial humidity value; the overlap ratio of the remaining days of the shelf life and the temperature suitable range is calculated to serve as a first priority factor, a second dynamic allocation factor is generated in combination with the real-time load rate of the warehouse, and a material allocation matrix is generated through integration of a priority fusion algorithm; according to the method, target warehouse environment parameters are monitored in real time, a multi-stage adjustment strategy is triggered when the target warehouse environment parameters exceed a suitable range, a parameter self-learning engine is started when verification fails, intelligent regulation and optimization of the storage environment are achieved, and the emergency material storage safety and management efficiency are improved. The method can dynamically adapt to the material characteristic change, and optimizes the warehouse environment control precision.

The invention discloses an external visual system for forklift operation, and relates to the technical field of forklifts. Comprising an acquisition module, a processing module and an output module. The acquisition module stores a warehouse information data set, a cargo information data set, a forklift information data set, an equipment information data set and a problem information data set. According to the invention, through multi-factor decision fusion of a plurality of data sets stored in the acquisition module, a dynamic environment map of the processing module and an analysis model, collaborative optimization of cargo transportation safety and path planning robustness in a storage scene is realized; according to the system, historical accident data, dynamic obstacle contours and warehouse static structures can be fused in real time, high-risk areas are actively avoided, collision accidents and cargo damage risks are remarkably reduced, the problem of path failure caused by dynamic environment changes is effectively solved, and full-process intelligent safety guarantee is provided for high-density storage scenes.

The invention discloses a quick broiler fresh-locking refrigeration house which comprises a refrigeration house body, an in-house opening and closing door is installed on the inner wall of the refrigeration house body, an electric controller is installed on the same side of the in-house opening and closing door, an even number of driving motors are installed on the refrigeration house body, an out-house opening and closing door is installed on the outer wall of the refrigeration house body, and an even number of outdoor condensation units are installed on the top of the refrigeration house body. An in-storage bin is arranged in the refrigeration storage, inner supporting columns are fixed to the four corners of the in-storage bin, and an even number of cold supply assemblies are installed on the top of the in-storage bin. After cold air is input into the conveying air guide cylinder, the cold air is divided into two parts through the flow dividing inner cavity, enters the independent frame inner cavity and is guided to the connecting cover opening through the cavity wall air guide block; meanwhile, the plate side temperature sensor feeds back a temperature signal to the electric controller, the electric controller is triggered to adjust the first electric control motor and the second electric control motor, the air volume and the air outlet angle are changed, and air outlet of the duckbilled covers with the shutters at different heights is matched with the temperature of products placed on the movable goods shelf; and the condition of freshness locking failure caused by slow cooling and large temperature difference of broiler chickens on middle and lower shelves is avoided.

The invention discloses a warehouse goods accurate inventory method based on RFID and unmanned aerial vehicle cooperation, and the method comprises the steps: S1, pasting an RFID tag on a warehouse-in goods, binding the warehouse-in goods, and putting the warehouse-in goods on a shelf, and storing the goods inventory information in a warehouse management system; s2, establishing a three-dimensional model and a space coordinate system of the warehouse, wherein each storage location corresponds to a unique three-dimensional space coordinate; s3, the unmanned aerial vehicle executes an inventory task according to a preset route; s4, the unmanned aerial vehicle binds the RFID tag with the strongest signal when hovering in front of each storage location, so that the storage locations are in one-to-one correspondence with the goods; s5, comparing the RFID signal value acquired when the unmanned aerial vehicle flies to the next storage location with the RFID signal value acquired when the unmanned aerial vehicle flies to the previous storage location, and binding the tag with the strongest signal to the current storage location; and S6, after the checking task is finished and the unmanned aerial vehicle returns, the system automatically generates a checking report, compares the RFID real-time data with the WMS inventory record and marks a difference item. According to the invention, efficient and accurate unmanned inventory checking can be realized, and the inventory quantity and inventory position accuracy of inventory checking can be ensured.

The utility model provides a honeycomb type stereoscopic warehouse feeding mechanism, and relates to the technical field of warehouse feeding, the honeycomb type stereoscopic warehouse feeding mechanism comprises a moving assembly, a lifting mechanism arranged on the top surface of the moving assembly and used for lifting angle steel, and a fixing mechanism arranged on the lifting mechanism and used for fixing the angle steel. According to the honeycomb type stereoscopic warehouse feeding mechanism, the lifting mechanism is arranged so that the height of the fixing mechanism can be conveniently adjusted, angle steel can be conveniently fed, a V-shaped groove can be matched with the shape of the bottom of the angle steel, and a second servo motor is started to drive a two-way lead screw so that two limited lead screw sliding blocks can be close to each other; and the side edges of the stacked angle steel are fixed through the two fixing plates close to each other, and the contact area of the fixing plates and the side edges of the stacked angle steel can be increased by forming the grooves, so that the situation that the angle steel slides off when the angle steel is fed is avoided.

The invention discloses an intelligent warehousing industry Internet of Things system and a control method, and particularly relates to the technical field of intelligent warehousing systems.The intelligent warehousing industry Internet of Things system comprises a sensing module, an information processing module, an automatic operation module, a control decision module, a data storage module and a user interface module; a sensor in the sensing module is at least one of a temperature and humidity sensor, a gas sensor and a vibration sensor, the warehouse environment can be monitored in real time, abnormal conditions can be detected, and a plurality of sensors are selected to be installed at different positions according to the size of a storage space. Data analysis and decision optimization are carried out in combination with an artificial intelligence algorithm, the automation and intelligence level and the actual application effect of the warehousing system are improved, the warehouse environment can be monitored by using various sensors and real-time data, and in combination with an intelligent early warning mechanism, potential risks can be recognized in time, an alarm can be given out, and the safety and stability of the warehousing environment are ensured.