88 results about "Location-allocation" patented technology

The invention relates to the technical field of warehouse management, and discloses a virtual goods allocation dynamic mapping and elastic warehouse space management method and system. The method comprises the following steps: acquiring a storage space multi-source data set including goods allocation physical attributes, goods flow records and storage environment parameters; generating a topological relation graph based on the physical attributes of the goods allocation, generating a virtual goods allocation allocation strategy according to the goods flow record, and processing the storage environment parameters to generate capacity demand fluctuation characteristics; and inputting the result into a multi-dimensional decision model to generate an optimization vector, constructing an elastic adjustment strategy graph through a constraint satisfaction algorithm, and outputting a virtual goods allocation dynamic mapping scheme. The system comprises a multi-source data acquisition module, a topology modeling module, a strategy generation module, a capacity prediction module, a multi-dimensional decision module and an elastic adjustment module. The storage space utilization rate and the operation efficiency can be improved, environmental changes can be effectively coped with, and the enterprise operation cost is reduced.

The invention provides a medical consumable management method and device based on an ant colony algorithm, and relates to the technical field of ant colony algorithms, and the method comprises the steps: constructing a medical consumable turnover frequency weight model, and generating a consumable turnover priority function; generating a goods allocation weight mapping matrix through an ant colony algorithm in combination with a priority function, wherein the mapping matrix is established according to the medical consumables and the access areas; simulating ant colony individuals to perform multi-path search in a hospital channel topological structure, and dynamically updating path pheromones to form a shortest path set; a storage space is constructed into a heterogeneous graph structure with storage locations as graph nodes and channels as graph edges, and an embedding result is fed back to an ant colony optimization algorithm as a goods allocation initial constraint through embedding attribute vectors of each node by a graph neural network; goods allocation initial constraint and a trafficability score and a blocking factor generated by camera identification data are introduced, and a consumable transportation path under an emergent task is dynamically adjusted and realized. According to the invention, full-life-cycle management of the medical consumables can be realized.

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 invention provides a control method, device and equipment for an FMS production line warehouse system based on fault warning and elimination verification and a storage medium. The control method comprises the steps that material attribute information is acquired, a classification model based on machine learning is adopted to conduct classification and recognition on materials, a unique material identification code is generated and bound with an electronic tag, and a real-time material tracking system is established; a storage position distribution model based on multi-objective optimization is constructed, material relevance, use frequency and warehouse constraint conditions are considered, a material storage position distribution scheme is generated, and a corresponding carrying equipment control instruction is generated; a real-time monitoring and exception handling mechanism is established, and when it is detected that the actual state of the material does not accord with the system record, a fault warning process is triggered, an exception handling control instruction is generated, and a correction action is executed; the material demand prediction is carried out based on the neural network model, the replenishment control instruction is generated according to the prediction result, the accuracy of the execution result is ensured through a secondary verification mechanism, and the reliability of the warehouse system is 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.

Dynamic storage location allocation and strategy adjustment system (100) for SAP Extended Warehouse Management (EWM) environments, comprising: a) a real-time warehouse data aggregation module configured to collect SKU attributes, inventory levels and bin location availability from SAP EWM; (b) a dynamic bin allocation engine that applies artificial intelligence algorithms to allocate optimal bin locations based on SKU characteristics, space utilization and operational parameters; c) an adaptive strategy adjustment module that dynamically modifies storage and retrieval strategies in response to real-time storage conditions; (d) a pattern recognition and forecasting module configured to analyze historical trends and predict inventory and demand fluctuations; (e) a warehouse resource optimisation module that aligns decisions on the allocation of storage space with the availability of labour and equipment; f) an SAP EWM integration module that communicates with SAP EWM via APIs or BAdIs to implement changes in real time; and g) a review and feedback learning module that tracks system decisions and results to continuously improve allocation and strategy logic.

The invention relates to the technical field of warehouse logistics, and discloses a warehouse scheduling method based on WMS and AGV cooperation. The method comprises the following steps: constructing a dynamic storage location data center, and integrating storage location occupation states, material turnover rates and other parameters; generating a dynamic storage location allocation scheme by using a multi-factor decision algorithm; according to the real-time warehouse-in and warehouse-out instruction, an AGV carrying task chain is generated according to a task decomposition rule; constructing a multi-AGV cooperative path planning network by using the space-time map; inputting the storage location allocation scheme and the path planning network into the coupling scheduling model to generate an AGV cluster scheduling instruction set; and an AGV driving track is adjusted through an anti-collision optimization algorithm, and a final scheduling strategy is output. According to the invention, intelligentization and high efficiency of storage scheduling are realized, the storage space utilization rate, the operation efficiency and the safety are improved, the operation cost is reduced, and the method is suitable for various storage logistics scenes.

The invention discloses a storage location distribution method for goods warehousing, and the method comprises the steps: dividing goods clusters according to the attribute information of goods; according to the goods cluster, in combination with the warehouse area attribute information, determining a warehouse area distribution result of the goods; and according to the warehouse area distribution result of the goods, in combination with a warehouse location distribution strategy, determining warehouse location coordinates of the goods based on the idle warehouse location information. According to the method, the goods are subjected to clustering analysis, accurate classification is realized according to goods attributes, a scientific basis is provided for warehouse area distribution, the management refinement degree is remarkably improved, and the workload and the error rate of manual classification are reduced. And different types of goods are allocated to the most suitable warehouse area in combination with warehouse area attributes, so that the warehouse layout is optimized, and space resources are utilized to the maximum extent. After the storage area is determined, based on the idle storage location information and the storage location distribution strategy, the storage location coordinates are accurately selected, the in-out storage path time is shortened, the operation efficiency is improved, and the cost is reduced.

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 relates to the technical field of warehouse logistics optimization, in particular to an intelligent storage location allocation optimization method based on deep reinforcement learning, which comprises the following steps: step 1, collecting storage location allocation related data of a target warehousing system, and preprocessing original data; step 2, constructing a mathematical model of a storage location allocation optimization problem of single-objective optimization and multi-objective optimization; step 3, based on a Markov decision process MDP framework, constructing a deep reinforcement learning agent decision model; 4, performing iterative training on the agent decision model, and realizing parameter optimization of the agent decision model through strategy gradient updating; and 5, performing performance evaluation on the trained agent decision model. According to the method, the optimal storage position is selected for the warehoused goods under the condition that physical constraint conditions are met, so that the operation cost is minimized.

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.

The present invention relates to the technical field of warehouse management, and discloses a method and system for virtual location dynamic mapping and elastic warehouse space management. The method includes obtaining a multi-source data set of the warehouse space, including the physical attributes of the locations, the goods flow records, and the warehouse environment parameters; generating a topological relationship graph based on the physical attributes of the locations, generating a virtual location allocation strategy according to the goods flow records, and processing the warehouse environment parameters to generate the capacity demand fluctuation characteristics; inputting the above results into a multi-dimensional decision-making model to generate an optimization vector, and then constructing an elastic adjustment strategy graph through a constraint satisfaction algorithm, and outputting a virtual location dynamic mapping scheme. The system includes a multi-source data acquisition module, a topological modeling module, a strategy generation module, a capacity prediction module, a multi-dimensional decision-making module, and an elastic adjustment module. The present invention can improve the utilization rate of the warehouse space and the operation efficiency, effectively cope with environmental changes, and reduce the enterprise operation cost.

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.

The present application relates to a location allocation method, apparatus, computer equipment, storage medium and computer program product, and relates to the field of financial technology or other related fields. The method comprises: obtaining location allocation demand data, the location allocation demand data including the resource domain, resource subdomain, identification information and type information of the required target server; searching for the target power consumption data corresponding to the type information of the target server in the correspondence between the pre-configured type and the average power consumption data, and determining the grouping feature information of the target server; based on the grouping feature information and the location allocation optimization function, optimizing the multiple location information contained in the target computer room resource information to obtain the optimized location information of the target server. By adopting the location optimization function in the present disclosure to realize the location allocation of server resources, the automatic allocation of server resource locations can be completed quickly.

The invention provides a multi-target goods allocation method and a related device, and belongs to the technical field of goods allocation. According to the method, an NSGA-II algorithm is improved by adopting adaptive crossover and mutation operation and a variable neighborhood search algorithm, and an AVNS-NSGA-II algorithm is obtained; based on the set constraint condition of the goods allocation model, an AVNS-NSGA-II algorithm is adopted to solve the target function of the established goods allocation model, and a Pareto leading edge is obtained; calculating subjective weights and objective weights of the maximized warehouse-in and warehouse-out efficiency objective function, the maximized goods shelf stability objective function and the maximized goods allocation equilibrium distribution objective function; performing coordinated distribution on the subjective weight and the objective weight to obtain a comprehensive weight; and evaluating and sorting each solution in the Pareto frontier based on the comprehensive weight to obtain a goods allocation result so as to complete multi-target goods allocation. According to the invention, the problem of low goods allocation accuracy is solved.

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.

A method for associating environmental data with map features is described. The method includes receiving sensor data and receiving position data. The sensor data is associated with each of a plurality of time intervals and is from at least one sensor on at least one mobile sensor platform. The position data is associated with the plurality of time intervals and is from the at least one mobile sensor platform. Trajectories and corrected locations of the mobile sensor platform are generated for the plurality of time intervals using the position data. A position of the mobile sensor platform is assigned to a corresponding map feature for each of the plurality of time intervals based on the trajectories and corrected locations. The sensor data is also processed to generate sensor data values for each of the plurality of time intervals. The sensor data values are assigned to the corresponding map feature of the position of the mobile sensor platform for each of the plurality of time intervals.

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.