1753 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 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.

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 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 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 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 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 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 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.

An autonomous warehouse robotics system integrates a multi-sensor platform, adaptive payload handling, dynamic task reallocation, advanced navigation, energy management, and comprehensive safety features into one mobile robot chassis. The system utilizes LiDAR, stereo vision, ultrasonic sensors, mmWave radar, thermal cameras, and event cameras to perform complete environmental sensing and obstacle detection. Sensor fusion combines adaptive weighting, multi-modal data integration, and statistical filtering to create high-confidence maps for reactive path planning and collision avoidance. The robot's payload system features machine vision for item recognition, telescopic lifts, variable-width grippers, and real-time toolhead verification to handle a variety of goods. Fleet management is achieved through dynamic task reallocation that considers robot location, battery level, and operational delays, all coordinated by a cross-platform middleware architecture that ensures standardized communication, remote monitoring, and over-the-air updates among diverse robot brands. Energy management optimizes power usage via predictive routing, autonomous return-to-charge, and auction-based scheduling. Safety is maintained through proximity detection, behavior-based intervention, and human-robot cohabitation protocols while advanced localization is enhanced by fusing ultra-wideband positioning with visual landmark alignment, inertial sensing, and machine learning to deliver high accuracy in non-line-of-sight conditions. An onboard edge AI module further refines navigation and task prioritization through neural network inference, ensuring robust, adaptive operation in dynamic, unstructured warehouse environments.

The invention provides a vehicle path planning method based on a multi-head adaptive Actor-Critic algorithm, and the method comprises the steps: obtaining urban road network data comprising at least one warehouse center and more than two demand client nodes through a simulation generation technology, obtaining a training and testing data set, carrying out the coding processing of the urban road network node training data, and carrying out the coding processing of the urban road network node training data, obtaining a multi-dimensional vehicle path coding sequence on a two-dimensional plane space [0, 1] * [0, 1]; a deep reinforcement learning framework is built based on the multi-dimensional vehicle path coding sequence, a multi-head adaptive Actor-Critic algorithm is integrated to build an MHAAC model, and a vehicle path planning system is formed; and the constructed MHAAC model is adopted to carry out path solving on urban road network data, and a global optimal path scheme is generated under the condition that all customer demand constraint conditions are met. According to the scheme, the problems of poor adaptivity, prolonged solving time, reduced solution quality and the like caused by limitations of sparse environmental information features, fixed feature embedding, static parameter generation, single decoding strategy and the like are successfully solved.

The broiler processing refrigeration warehouse comprises a refrigeration warehouse body, a warehouse door is installed on the surface of the refrigeration warehouse body, a controller is arranged on one side of the warehouse door, a gas tank is arranged outside the refrigeration warehouse body, a transmission device is arranged on the other side of the warehouse door, and a temperature sensor is installed beside the transmission device; an in-warehouse bin is formed in the refrigeration warehouse, a partition layer is installed in the in-warehouse bin, a door groove is formed in the wall of the in-warehouse bin, a ladder stand is erected on the partition layer, refrigeration equipment is installed in the in-warehouse bin, and a refrigeration evaporator is placed beside the refrigeration equipment. In the using process, the transmission device is started, and the transmission shaft drives the linkage screw to rotate; the linkage screw drives the first cam on the linkage screw to rotate, then the second cam is pushed through the linkage effect, the movement of the second cam promotes the bracket plate to do vertical displacement along the limiting sliding rod, and finally the scraping cover is driven to scrape and remove ice blocks on the fins. The process can effectively avoid the problem that the heat exchange efficiency is greatly reduced due to the fact that ice accumulated between the fins hinders airflow.

The invention provides a multi-task execution method for a steel coil warehouse and a steel coil warehouse management system. The method comprises the steps that a central control terminal determines an initial task list containing at least two initial tasks; determining task elements of each task; calculating the priority of each task based on the task elements; ranking according to the priority from high to low, and determining the task with the highest priority as a target task; screening a target crane according to the target task, and generating an optimal path and a task execution time period for the target crane; sending the optimal path, the task execution time period and the target task to the target crane; and when the execution time period is reached, the target crane executes the target task according to the optimal path. The scheduling flexibility and the overall operation efficiency of the steel coil warehouse in the multi-task state can be effectively improved. In the task state, the task scheduling flexibility and the overall operation efficiency of the steel coil warehouse are improved.

The invention belongs to the technical field of AGV path planning, and particularly relates to an AGV path planning method and system for a production line, and the method comprises the steps: obtaining an instantaneous speed sequence, a planning speed sequence, a current remaining electric quantity, a current load mass, a rated load, and a safe electric quantity of a single transportation task of an AGV; calculating the weight of the current land parcel where the AGV is located in the warehouse area divided into the plurality of land parcels; obtaining the current passing resistance; calculating an electric quantity risk factor; the load condition and the electric quantity risk of the AGV are integrated, the situation passing cost is calculated, the comprehensive cost is calculated based on the geometric distance between the adjacent land parcels and the fluctuation condition of the situation passing cost, and the optimal path is obtained; and performing trajectory interpolation on the optimal path, generating a smooth motion trajectory to control the AGV to run, and feeding back operation data to the central scheduling system to realize optimization. The technical problems of low path planning efficiency and high energy consumption in the prior art are solved.

The utility model provides an automatic plate stereoscopic warehouse, and belongs to the technical field of stereoscopic warehouses, the automatic plate stereoscopic warehouse comprises a conveying line, a stacking machine, a front goods shelf, a rear goods shelf, a ground guide rail and a control device, and the stacking machine comprises a frame body, a lifting frame, a walking mechanism and a lifting mechanism; the conveying line is provided with a first driving wheel, and the lifting frame is provided with a push-pull mechanism and a second driving wheel. The first driving wheel, a second driving wheel on the stacking machine and the push-pull mechanism are controlled and matched by the control device to achieve movement of the tray, the position of the tray can be stably controlled, and the stability of the in-out warehouse process is guaranteed. The push-pull mechanism can completely push the trays into the front goods shelf or the rear goods shelf. The walking mechanism driven by the gear and the rack can avoid slipping, and the supporting strength is guaranteed through the supporting wheels adjusted in the side direction. The lifting mechanism ensures that the lifting frame does not deflect by synchronously lifting the four corners of the lifting frame. And after the cutting and blanking procedures are integrated, the plate in-out warehouse efficiency and the workpiece circulation efficiency of the warehouse are improved.

The invention discloses a project management method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of software development control, and the method comprises the steps: managing the construction path, output path and other information of a nested warehouse project through a unified configuration file, and achieving the centralized definition of a multi-project construction process; the problem of packaging dispersion caused by separate management of the sub-modules is avoided; the unified construction environment is initialized based on project information of centralized configuration, it is ensured that the dependency relationship of all modules and the construction process are coordinated and consistent, and the stability and reliability of the construction process are enhanced; command generation logic is constructed in a standardized mode, configuration complexity and later maintenance cost are reduced, and unified management and control of the process are achieved; through a centralized management mode driven by configuration, the defect of processing multi-warehouse construction deployment depending on manual scripts is eliminated, repeated labor is reduced, and development efficiency and project agility are improved.

The invention discloses a multi-model intelligent management scheduling method and system. The method comprises the following steps: receiving a to-be-registered model and corresponding metadata, and executing integrity check to generate an integrity check result; generating a model identifier for the to-be-registered model according to an integrity check result, and storing the model identifier into a model storage warehouse; receiving a deployment request, and extracting a target model file and target metadata according to the deployment request; selecting a target computing node according to the target metadata; configuring a running environment according to a deployment environment requirement, and combining the target model file with the running environment to obtain a container mirror image; creating a containerized operation instance based on the container mirror image, and configuring a network access endpoint; predicting the page view of the network access endpoint to obtain predicted page view data; and adjusting the number of the containerized operation instances according to the predicted page view data and the system resource use data. By implementing the technical scheme, the problems of hysteresis and lack of perspectiveness of resource adjustment based on a real-time system load are solved.

A method for processing a warehouse order task, comprising: determining a production end time of an order to be processed; determining a production period of a batch task that the order to be processed belongs to; determining whether to issue the order to be processed to a picking pool according to the production end time and the production period; in response to determining not to issue the order to be processed to the picking pool, assigning the order to be processed to a picking terminal according to a backlog quantity of orders to be outbound and an outbound productivity; and in response to determining to issue the order to be processed to the picking pool, assigning the order to be processed to a picking terminal according to an order quantity of the batch task and a batch completion efficiency.

The invention discloses a storage goods allocation dynamic optimization method based on AI, and belongs to the technical field of intelligent storage and logistics management.The method comprises the steps that firstly, multi-dimensional data of goods, storage, business and the like are obtained; then, calculating a time-sensitive goods location-goods matching degree score integrating future delivery frequency of goods, association degree with adjacent goods, physical distance and a dynamic time urgency factor changing along with approaching of expected sorting time; a reinforcement learning model is used, the matching degree score is used as a key decision basis, a composite reward function considering overall layout gain, physical handling cost and high-urgency-degree task reward is used for guidance, and a dynamic goods allocation optimization scheme with globality and perspectiveness is output; according to the method, continuous self-adaptive adjustment of the storage layout can be realized, and the sorting efficiency and the response capability of the warehouse are remarkably improved.

The application discloses an automatic stereoscopic storage warehouse, which comprises a high-precision linear guide rail embedded in a column of a portal lifting guide frame; a horizontal conveying and taking platform is driven by a servo motor to drive a second chain, and the second chain drives a moving plate to move; and the conveying and taking platform is located between the portal lifting guide frames and is displaced up and down by the two symmetrical portal lifting guide frames. In the use process, a dynamic avoidance module calculates data of a radar and infrared distance sensors on both sides of the conveying and taking platform, thereby automatically monitoring a moving obstacle module, avoiding motion interference when the conveying and taking platform moves the moving plate of a storage layer out, avoiding collision accidents caused by the moving plate not being completely moved out of the storage layer, and avoiding damage of the plate; and according to cooperation of a path planning module and distribution of collected plate quality, the best storage position and the fastest processing time are obtained.

The invention provides a vehicle path planning method and system based on a dynamic attention model, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining instance data containing warehouse nodes, client nodes and vehicle capacity information; a dynamic encoder-decoder architecture model is constructed, and an encoder recalculates feature embedding of unaccessed nodes every time a vehicle returns to a warehouse based on a graph attention network so as to respond to dynamic changes of instance states in the path construction process; the decoder selects a next node to be accessed according to the current path state, residual node embedding and vehicle real-time capacity; a REINFORCE algorithm is adopted to take the minimum path length as a target training model, and a greedy strategy baseline is introduced to reduce variance; and finally, the initial path is optimized by combining the local search of the 2OPT, and a final planning scheme is output. According to the method, the problem that a traditional attention model cannot adapt to a dynamic environment due to fixed node embedding is effectively solved, the total cost of a path is reduced, and good generalization and expandability are achieved.

The invention discloses a stereoscopic warehouse goods allocation intelligent binding system and method, and relates to the technical field of stereoscopic warehouse storage management. Comprising the following steps: S1, creating a two-dimensional electronic map of a warehouse, establishing a two-dimensional coordinate system of the warehouse, and based on the two-dimensional coordinate system and shelf positions, establishing coordinates of each shelf by taking a warehouse-out station position as a coordinate origin; s2, the RFID tag is installed at the preset position of the first layer of storage location of each goods shelf, and the coordinates of the goods shelf where the RFID tag is located are pre-stored in each RFID tag; by integrating the advanced navigation technology, the AI intelligent decision-making system and the multi-mode interaction unit, accurate and efficient binding of the goods locations of the stereoscopic warehouse is achieved. The system not only fully considers the three-dimensional space characteristics of the warehouse, but also combines the multi-dimensional factors such as dynamic heat and seasonal demand change of the goods, so that the goods allocation is more scientific and reasonable, and compared with the prior art, the system can dynamically adjust the goods allocation strategy according to the real-time demand.

A symbolic execution framework for consent-verified biosensing and AGI supply chain automation is disclosed. The system governs end-to-end manufacturing, logistics, and deployment using EEG-linked biometric tokens, zero-knowledge consent modules, and treaty-bound robotics. Physical actions within factories, warehouses, and transport fleets are gated by emotional risk filters, oath-indexed execution logs, and symbolic ethics DAGs. Robotic agents and embedded AGI nodes respond only to verified biometric cognition streams or authorized treaty signatures. Edge nodes route instructions based on emotional volatility, identity lineage, and symbolic legality. All physical artifacts—components, packages, vehicles—are traceable via post-quantum consent hashes and symbolic memory. The architecture prevents coercive production, misaligned routing, or unauthorized AGI control. Applications include military-grade manufacturing locks, child-safe warehouse robotics, cognitive-authenticated deliveries, and fully auditable zero-harm logistics. This invention defines the lawful symbolic substrate for real-world AGI logistics, securing biosensed cognition-to-object flows with planetary ethics, memory provenance, and modular revocation logic.

A control system for a warehouse includes a controller for communicating commands for execution by item carrying vehicles, robotic pickers, and human workers. A warehouse simulation performs simulated runs of order picking and replenishment activities. The simulated results and experience data are recorded and stored in storage. The stored data includes operational data including live results and experience data that was recorded while the workers were performing according to the executable commands from the controller. A training module receives the simulation results, the simulated experience data, and the recorded operational data from the storage. The training module trains an algorithm using the simulated data and the operational data. The training module generates an updated algorithm for the controller. Using the updated algorithm, the controller communicates executable commands to the workers.

The system can include: a cell frame structure; a robot; and a motion planner. The system can optionally include a set of cell trays and a warehouse management system. The cell frame structure can include and / or define a set of payload storage cells; a set of transfer cells; a set of cell tray storage cells. However, the system can additionally or alternatively include any other suitable set of components. The system functions to automatically manage the storage of payloads within a grid-based structural system.