62 results about "Location-allocation" patented technology

The invention relates to the technical field of warehouse management systems, and discloses an intelligent storage location recommendation method and system based on a warehouse management system, and the method comprises the following steps: S1, obtaining basic data in a warehouse; s2, preprocessing the basic data, and generating a structured cargo-storage location matching data set; s3, based on a multi-dimensional evaluation model, comprehensively analyzing cargo attributes, storage location characteristics and operation efficiency, calculating a matching score of each storage location, and generating an optimal storage location recommendation list; s4, outputting the optimal storage location recommendation list to a user terminal, wherein the recommendation list comprises storage location numbers, matching scores and allocation reasons; and S5, performing storage location allocation according to the storage location recommendation list, and recording an allocation result and operation efficiency to optimize subsequent recommendation. In the invention, a dynamic optimization algorithm (a genetic algorithm or reinforcement learning) is adopted, the storage location distribution is adjusted in real time according to the inventory change and the order demand, and compared with the traditional fixed rule distribution, the efficiency is obviously improved.

The invention relates to the technical field of warehouse management, in particular to an intelligent warehouse management system based on deep learning, which constructs a space-time diagram model comprising nodes and edges by combining historical warehouse-in and warehouse-out records, AGV (Automatic Guided Vehicle) tracks and scheduling logs on the basis of spatial distribution information and channel communication relations of warehouse goods locations and function bits. And through deep space-time diagram convolution modeling, outputting a goods allocation value score and a traffic congestion probability, and identifying a goods allocation area which should be preferentially allocated by the high-frequency SKU. And a goods allocation strategy is generated through a reinforcement learning model, and excessive occupation and deadlock of channels are avoided while high-frequency goods are quickly put in and put out of a warehouse. And finally, generating an AGV scheduling instruction set through a path planning algorithm, and issuing and executing the AGV scheduling instruction set. Dynamic collaborative optimization of goods allocation planning and AGV scheduling is realized, the high-frequency goods operation efficiency is improved, meanwhile, path congestion and conflicts are effectively avoided, and the throughput rate and the operation reliability of the system are improved.

The present disclosure discloses a method and system for formation control for an USV swarm via a CEDRL. The method includes: designing a desired formation pattern based on a formation hierarchical virtual leader strategy, establishing an USV desired location library, and assigning a location index to a desired location of each USV in a formation; updating the desired location of each USV and the corresponding location index via an USV formation local consensus strategy in a case where there is a risk of collision between USVs; and acquiring an actual geolocation of each USV in real time, and adopting a surface vessel control decision-making network to direct the USV toward a latest desired location. An autonomous collaborative formation of a large-scale USV swarm may be realized by the present disclosure.

The invention provides an intelligent storage location optimization method and system, and belongs to the technical field of storage management, and the method comprises the steps: obtaining the data information of commodities in a storage system, constructing a storage location optimization model, comprising a comprehensive objective function taking maximization of space utilization rate and sorting efficiency and minimization of storage cost as objectives and constraint conditions based on physical rules and business rules; solving the storage location optimization model by using an optimization algorithm, and generating a storage location distribution scheme and prediction performance data; executing the storage location allocation scheme, and collecting actual storage location data and actual operation performance data; comparing the actual storage location data with the storage location allocation scheme to obtain a scheme execution coincidence rate; comparing the actual operation performance data with the predicted performance data to obtain a performance deviation rate; and based on the scheme execution coincidence rate and the performance deviation rate, adjusting the constraint condition of the storage location optimization model and / or the weight coefficient of the comprehensive objective function to obtain an updated storage location optimization model, and carrying out storage location allocation.

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.

The invention discloses an automatic storage location allocation method and device based on a warehouse management system, a terminal and a medium, and the method comprises the steps: building a hierarchical mapping relation among a warehouse, a region location and a storage location, configuring a storage location set based on a material attribute, and updating a storage location occupation state in real time; after a warehousing task is received, matching a distributable storage location set according to a material type, and preferentially selecting a continuous empty storage location with the minimum physical distance from the storage location with the same stored material; when a warehouse-out request is responded, the valid period priority rule and the scattered priority rule are executed, and space adjacent storage locations are distributed for the same order; if the storage location is not emptied after unloading, the storage location is marked as a'to-be-continued 'state, and the'to-be-continued' storage location is preferentially allocated to a subsequent task until the storage location is completely emptied. According to the invention, through adjacent distribution and state optimization, the on-shelf / off-shelf time is reduced, personnel movement and equipment loss are reduced, and the operation cost is reduced; human errors are avoided, and the inventory accuracy is improved.

The invention discloses a data writing method, device, equipment, medium and product, when a data writing task is processed, a data writing request containing multiple pieces of to-be-written data is received firstly, and the data writing task is processed by extracting basic attribute characteristics of the request instead of depending on a single data hot and cold classification standard; the data type of the to-be-written data is analyzed in combination with the associated environment characteristics, and the storage position of the data in the storage device is preliminarily determined; meanwhile, position distribution is not independently carried out any more, an adjustment decision is made based on the real-time performance data of the storage device, the initial position is dynamically optimized to obtain the target storage position, and finally data writing is completed. The defect that in the prior art, SLC cache adjustment and data classification are disjointed is overcome through cooperation, and the problem that adaptability is insufficient is solved. And the effects of matching the cache space and the data storage requirement in real time, reducing the waste of cache resources, considering high storage density and high reliability, and improving the operation stability and the data writing efficiency of the whole storage system are achieved.

This invention provides a method for allocating storage locations in an automated three-dimensional warehouse using digital twin technology. The method constructs a warehouse management system through five dimensions: a physical layer, a twin layer, a data layer, a service layer, and a connectivity layer. The physical layer represents the actual warehousing operation scenario; the twin layer maps the equipment and environment of the physical layer to a virtual space, constructing a digital model of the warehouse; the data layer is responsible for the collection, processing, and storage of data from the physical layer, providing accurate input for the service and twin layers; the service layer provides optimization decision-making and human-computer interaction functions for users and system management; and the connectivity layer is responsible for the data and control transmission between the layers, ensuring the system's synergy and real-time performance. By supporting multi-objective optimization algorithms through digital twin technology, intelligent and precise storage location allocation is achieved, providing a novel solution for digital logistics management.

The invention discloses an intelligent storage dynamic goods allocation distribution method based on goods allocation popularity analysis, and belongs to the technical field of intelligent storage management. The method comprises the steps of 1, collecting goods location coordinates, goods attributes and historical and current order data; 2, calculating goods real-time demand popularity and goods shelf position popularity based on the current order, and coupling to obtain goods allocation coupling popularity; 3, by taking maximization of the total goods allocation coupling heat and optimization of the estimated picking path as targets, constructing a dynamic equilibrium model and solving and outputting a goods allocation distribution scheme; and step 4, executing distribution and order picking tasks, collecting actual operation data and feeding back the correction heat calculation model to realize closed-loop optimization. According to the method, the coupling conflict between static goods allocation and dynamic order selection is solved, the linkage decision of storage location optimization and path planning is realized, the method can be adaptive to order fluctuation and operation environment change, and the warehousing operation efficiency is remarkably improved.

The application discloses a high-density stereoscopic warehouse dynamic storage space allocation method for WMS, and relates to the technical field of artificial intelligence. Stereoscopic warehouses are arranged along the direction of the roadway, and stackers, shuttles and hoists are arranged in the stereoscopic warehouses. Field programmable gate array acceleration cores are arranged in the edge nodes of the stereoscopic warehouses. The acceleration cores are interconnected with the WMS through a time-sensitive network. The method comprises the following steps: step 1, the acceleration core constructs a beat dependency graph based on the current carrying instruction issued by the WMS, wherein the carrying motion is taken as an event node, and the action connection relationship is taken as a directed edge; step 2, the acceleration core constructs a colored Petri net; and step 3, the acceleration core constructs a graph attention reinforcement learning decision module and takes a heterogeneous graph as input, and the WMS drives the corresponding carrying equipment to perform the actions of warehousing, moving and replenishment. The application can significantly improve the system throughput, reduce the equipment idle rate and beat jitter under the sub-millisecond end-to-end decision delay.

The application discloses a kind of stereoscopic warehouse goods location allocation optimization method, system, equipment and medium, it is related to intelligent warehousing and logistics scheduling technical field, including: in the process of equipment execution warehousing operation, real-time acquisition equipment actual track point and the track deviation amount between equipment pre-planned track, when the track deviation amount accumulates exceeds preset deviation threshold, with current actual equipment state parameter set as initial state, jump to step S2 and re-perform track pre-planning and goods location allocation.The application is by real-time acquisition equipment actual track point and the track deviation amount between pre-planned track, when cumulative deviation is over threshold, with current actual equipment state parameter set as initial state trigger re-planning, form closed loop deviation feedback mechanism, effectively guarantee the robustness and dynamic adaptability of system operation.

Disclosed are systems and methods for determining swap and replenishment opportunities in a pick area of a storage facility. A method can include receiving, by a computer system, current stock levels for bays in the pick area, identifying a subset of the bays as having a replenishment or swap opportunity based on determining that the current stock levels for the subset of the bays is less than a threshold stock level, and determining, for each bay in the subset of the bays, whether the bay has a swap opportunity or a replenishment opportunity. The method can also include determining whether each bay is a static bay based on pick area build information, which can include a pick area layout, current SKUs in the pick area, date of layout build, static location assignments for the bays, and dynamic location assignments for the bays.

Systems and methods are provided for automatically generating prioritized, color-coded tasks based on user input received through a conversation interface of a task management application. User input provided to an automated software assistant is analyzed along with contextual data from multiple sources, including existing tasks, calendar events, emails, and messages. The system determines task attributes such as title, status, deadline, geographic location, urgency, and frequency, and assigns a color tag based on color assignment parameters including task category, priority, and location. Machine learning algorithms analyze historical user-assigned colors in relation to task attributes to predict color assignments for new tasks and establish intelligent color relationships, thereby dynamically updating a prioritized, color-coded task list displayed in a graphical user interface.

The application discloses an intelligent warehouse storage location allocation method for gas valve parts, and belongs to the technical field of warehouse planning, which comprises the following steps: based on the three-dimensional warehouse rack layout of the gas valve parts, a multi-objective location allocation optimization model is constructed; the weight coefficients of each objective function in the multi-objective location allocation optimization model are determined; the improved mycelium growth optimization algorithm is used to solve the optimal solution of the location allocation corresponding to each objective function; a multi-objective evaluation function is constructed in combination with the weight coefficients of each objective function and the optimal solution of the location allocation corresponding to each objective function, and the improved mycelium growth optimization algorithm is used to solve the multi-objective evaluation function, so as to obtain an optimal location allocation scheme. The IFGO algorithm is used for location allocation in the application, and the constructed objective function is used to realize the optimization of the location allocation target, so that the premature convergence of the algorithm can be effectively avoided, more diversity in the population can be maintained, and therefore a more innovative location allocation scheme can be found.

Systems and methods for generating a business planning recommendation for a project may include generating weighted location metrics based on location metrics, generating a location allocation decision based on the weighted location metrics, wherein the location allocation decision includes an allocation of a percentage of the project to house at each location of at least two locations of an enterprise, generating weighted sourcing metrics based on sourcing input parameters, generating a sourcing allocation decision based on the weighted sourcing metrics, wherein the sourcing allocation decision includes an allocation of a percentage of the project to source internally within the enterprise and externally outside the enterprise, and generating a business plan recommendation including a location and sourcing allocation decision for each location based on a combination of the location allocation decision and the sourcing allocation decision.

The invention discloses a storage location distribution method, device and equipment, a medium and a product. The method comprises the steps that a storage dynamic graph is acquired, and the storage dynamic graph comprises node feature vectors and edge feature vectors; the node feature vectors and the edge feature vectors of the storage dynamic graph are input into a pre-constructed graph neural network model, a storage location popularity score of each storage location is obtained, and the storage location popularity scores are used for representing the access probability of the storage locations; screening the initial storage location set based on the storage location popularity score of each storage location to obtain a candidate storage location set; and based on the candidate storage location set, solving a pre-constructed operational research multi-objective optimization model to obtain a storage location allocation result. According to the technical scheme of the invention, through graph neural network feature learning and operational research multi-objective optimization collaborative decision making, the accuracy and reliability of storage location allocation are improved, the time spent in the storage and taking process of goods is reduced, and the overall warehousing operation efficiency is improved.

The invention provides a warehouse-in and warehouse-out dynamic allocation method and system for a medicine warehouse, and relates to the technical field of inventory management. The method comprises the following steps: firstly, acquiring an initial task set, and determining a predicted job density according to the initial task set; secondly, according to the predicted operation density and the real-time carrying information, a dynamic effective period threshold value is determined; secondly, for each ex-warehouse task, determining a first candidate goods allocation set according to the dynamic expiry date threshold value and the specification of the corresponding medicine; generating a corresponding goods allocation scheme according to attribute information in the first candidate goods allocation set; calculating evaluation parameters of each goods allocation scheme, and determining a target goods allocation combination according to the evaluation parameters; and finally, according to the target goods allocation combination, generating a goods allocation scheduling instruction and updating real-time carrying information. According to the technical scheme provided by the invention, multi-objective optimization of the ex-warehouse efficiency, the inventory turnover and the storage resource utilization is realized, and the overall response efficiency and the inventory turnover quality of a complex and dynamic storage environment are also improved.

The application discloses a data migration method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of storage. The target storage type can indicate a storage mode of data, the target strip size can indicate the length of reconstructed data, and the total quantity can indicate a position allocation mode of data in a storage array. In order to reduce the data migration amount, a first starting address and a first space length of an effective storage space in an original storage array can be acquired, so as to determine the position of effective data. Further, based on the target strip size, the first starting address and the first space length can be adjusted to obtain target position information, so that the space length of a target task space indicated by the target position information is an integer multiple of the target strip size. Finally, based on the target strip size, the target storage type and the total quantity, data of the target task space can be completely migrated to a target storage array, and the migration efficiency can be greatly improved.

The invention relates to the technical field of warehouse logistics management, and discloses a warehouse container automatic operation method and system based on a three-dimensional mechanism. The method comprises the following steps of: counting characteristic parameters of an operation scene, calculating a target weight when the characteristic parameters continuously exceed a threshold value, obtaining a weight on that day according to five-day linear interpolation smooth transition, scoring candidate storage locations according to the weight on that day, selecting an optimal storage location, and selecting an optimal crane by integrating optimal scheduling of a moving distance, operation time and a queue length. According to the invention, the adaptive capability of the storage location allocation decision to the scene change, the smoothness and strategy continuity of the weight adjustment process, and the weight configuration convergence and operation efficiency optimization effect based on feedback correction are improved.

The application discloses an intelligent goods location pre-allocation method and system based on a four-layer closed-loop management architecture. The intelligent goods location pre-allocation method comprises the following steps: a feedforward guidance layer encodes physical world constraints into a structured rule set with rigid / flexible separation and injects the structured rule set into the decision-making context of an AI agent; the AI agent outputs a goods location pre-allocation scheme with a unified confidence; a feedback sensing layer collects actual execution deviations; a self-correction layer automatically selects a correction mode from parameter fine-tuning, strategy rollback and complete re-planning according to a double-threshold three-mode mechanism; an upper system fallback gating layer automatically switches to a default algorithm of an upper system when the unified confidence is lower than a threshold; and a full-link observable layer records data throughout the whole process for auditing. Through the four-layer closed-loop management architecture, the application solves the technical problems of lack of physical constraint guarantee, closed-loop correction capability and full-automatic operation safety when the AI agent is directly applied to goods location allocation, and is suitable for pure single-deep, pure double-deep and mixed three types of goods shelf architectures.

Techniques are disclosed for mapping an indoor space using light fidelity (Li-Fi) communication. An entity may obtain location information for a plurality of Li-Fi devices, that includes at least one mobile Li-Fi device. The entity may determine, based on the location information, a mapping of the indoor space, where the mapping comprises spatial information for one or more continuous areas of the indoor space based on LOS Li-Fi communication between Li-Fi devices of the plurality of Li-Fi devices. The entity may be a Li-Fi device or a server. The entity may assign locations of Li-Fi devices in LOS communication with each other to the same continuous area to help determine the mapping.

The application provides a steel coil storage location allocation method based on multi-objective optimization. It belongs to the field of intelligent storage. The steps are as follows: collecting the storage location information of the steel coil warehouse, the crane operation data and the steel coil scheduling history record, establishing a model aiming at improving the steel coil turnover rate; then, the type information of the in-stock steel coil inventory is counted, a model for improving the uniformity of the steel coil storage location allocation is established; the steel coil turnover rate and the uniformity of the steel coil are comprehensively considered, and the model constraint conditions are established according to the actual situation of the warehouse, a multi-objective optimization model is constructed, and the multi-objective model is weighted, so that the multi-objective problem is converted into a single-objective problem; the model is calculated through the hunting algorithm, and the storage location for the steel coil allocation is obtained. The steel coil storage location allocation method based on multi-objective optimization improves the scheduling speed of the high turnover rate steel coil in the warehouse, and also concentrates the same type of steel coil, improves the steel coil scheduling efficiency, and reduces the warehouse management cost.

The application provides a stereoscopic warehouse goods location allocation and picking scheduling method and system based on digital twinning, relates to the technical field of digital twinning, and comprises the following steps: constructing a three-dimensional digital model of a stereoscopic warehouse; allocating goods locations based on a deep reinforcement learning algorithm, taking commodity storage frequency, associated purchase probability and seasonal demand prediction as input parameters, taking the shortest picking path and associated commodity centralized storage as optimization objectives to generate goods location allocation instructions; performing warehouse entry operations and receiving picking orders; constructing a virtual potential field in the model, determining task priorities based on order urgency and triggering obstacle avoidance when repulsive force between stackers exceeds a threshold; calculating an obstacle avoidance trajectory and generating a collaborative scheduling instruction to perform picking operations. The application realizes efficient goods location allocation and intelligent multi-stacker collaborative scheduling.

A kind of based on crayfish optimization algorithm's warehouse location allocation optimization method, contain steps: S1, obtain stereoscopic warehouse information and cargo information;S2, establish multi-objective warehouse location allocation mathematical model;Respectively establish single-objective warehouse location allocation mathematical model;Through linear weighting method, single-objective warehouse location allocation mathematical model is converted into multi-objective warehouse location allocation mathematical model;S3, calculate optimal warehouse location allocation result;Multi-objective warehouse location allocation mathematical model is solved by crayfish optimization algorithm, and optimal warehouse location allocation result is obtained.The present application can more efficiently find the relatively optimal warehouse location allocation scheme under multi-objective constraint.

Disclosed are systems and methods for determining swap and replenishment opportunities in a pick area of a storage facility. A method can include receiving, by a computer system, current stock levels for bays in the pick area, identifying a subset of the bays as having a replenishment or swap opportunity based on determining that the current stock levels for the subset of the bays is less than a threshold stock level, and determining, for each bay in the subset of the bays, whether the bay has a swap opportunity or a replenishment opportunity. The method can also include determining whether each bay is a static bay based on pick area build information, which can include a pick area layout, current SKUs in the pick area, date of layout build, static location assignments for the bays, and dynamic location assignments for the bays.

The invention relates to the technical field of warehouse management, in particular to a comprehensive management method and system for an ERP warehouse, and the method comprises the steps: collecting storage multi-source data, and generating a structured data set through standardized preprocessing; based on the structured data set, initial storage location allocation parameters are obtained through a multi-parameter weighted adaptation algorithm, inventory early warning parameters are obtained by combining a sequential anomaly correction and threshold derivation algorithm with the initial storage location allocation parameters, and high-precision management and control parameters are generated through target screening; constructing a storage full-process execution model based on the high-precision management and control parameters, and outputting a service operation result; according to a service operation result, judging abnormity through a dynamic verification algorithm, and generating a parameter correction instruction and system optimization data; and inputting the high-precision management and control parameters, the system optimization data and the warehousing service scene label into the collaborative management model, and outputting a warehousing management strategy adaptive to the scene. According to the scheme, a scientific and quantitative storage location distribution mechanism is constructed, so that the operation efficiency is improved.

This invention discloses a method for dynamic warehouse location allocation based on location heat analysis, belonging to the field of intelligent warehouse management technology. The method includes: Step 1: collecting location coordinates, product attributes, historical and current order data; Step 2: calculating the real-time demand heat of products and shelf location heat based on current orders, and coupling them to obtain location coupling heat; Step 3: constructing a dynamic equilibrium model with the goal of maximizing total location coupling heat and optimizing the estimated picking path, and solving to output a location allocation scheme; Step 4: executing allocation and picking tasks, collecting actual operation data, and feeding back to correct the heat calculation model to achieve closed-loop optimization. This invention solves the coupling conflict between static location allocation and dynamic order picking, realizes the linkage decision-making between location optimization and path planning, and can adapt to order fluctuations and changes in the operating environment, significantly improving warehouse operation efficiency.

In one aspect, a method of determining a location of a user within an indoor space, includes emitting a radiofrequency signal into the indoor space, receiving backscattered training radiofrequency signals, including multipath, for at least one location within the indoor space, converting the received training signals into a point cloud for each location of the at least one location, assigning a signature for each location based on the point cloud for each location, receiving additional radiofrequency signals, including multipath, converting the additional radiofrequency signals into an additional point cloud, and determining a location of the user by comparing the additional point cloud to the assigned signatures.

The application discloses a warehouse location dynamic management method and system based on RFID and two-dimensional code double identification, and relates to the technical field of warehouse location management. The method comprises the following steps: calling a historical order sequence and a real-time order sequence; obtaining a plurality of elastic warehouse location units based on elastic warehouse location allocation according to storage convenience; deploying a plurality of RFID warehouse location identification tags; in a storage cross-warehouse location dynamic storage process in response to the real-time order sequence, performing plate single identity process data writing and writing of the plurality of RFID warehouse location identification tags; in a processing process of the real-time order sequence, performing space matching verification on a multi-angle image of warehouse-in remaining materials to update wear-resistant two-dimensional code tags and refresh associated storage information of the plurality of RFID warehouse location identification tags. The technical problem that the warehouse location management in the prior art is difficult to dynamically adjust according to order conditions, resulting in low warehouse scheduling efficiency, is solved, and the technical effect of improving warehouse scheduling efficiency is achieved.

The invention relates to the technical field of artificial intelligence, and discloses an intelligent warehouse management method and system based on artificial intelligence. The method comprises the following steps: acquiring accurate cargo coordinates, and constructing a first correlation degree matrix based on the accurate cargo coordinates and each free cargo allocation coordinate; updating a goods allocation occupation state matrix according to the first correlation degree matrix, generating a first AGV scheduling sequence, and generating a target path by taking the accurate goods coordinates in the first AGV scheduling sequence as a target position; calculating the planning redundancy of the target path and calculating the real-time positioning deviation; and when the real-time positioning deviation exceeds the planning redundancy, updating the first goods allocation weight to obtain a second goods allocation weight, correcting the first association degree matrix by using the second goods allocation weight, and re-executing goods allocation distribution to obtain a second AGV scheduling sequence. According to the invention, the cargo positioning information is improved from pure position sensing to a core variable for driving warehousing decision making, and the operation efficiency and environmental adaptability of the intelligent warehousing management system are improved.