87 results about "Order set" patented technology

The invention discloses a production scheduling method and device based on digital twinning, equipment and a medium, and relates to the field of intelligent manufacturing, and the method comprises the steps: collecting production data, generating an initial production state, defining an initial production scheduling constraint condition, and constructing a digital twinning model; predicting the available capacity of the production resources through a digital twinborn model, identifying a risk period, and updating the twinborn state of the production resources; coupling the predicted production resource available capacity with the order set, generating a task priority queue and updating production scheduling constraint conditions; executing scheduling optimization and simulation detection conflicts in the digital twin model, if conflicts exist, executing local rearrangement based on conflict information until requirements are met, and generating an executable scheduling scheme; and issuing the executable scheduling scheme to a production and logistics link, monitoring execution deviation, rescheduling an affected task, completing a production task and updating the digital twin database. According to the invention, the real-time performance and accuracy of scheduling response are improved.

The invention discloses a multi-objective optimization-based multi-commodity order fulfillment method and device, equipment and a medium, and relates to the technical field of supply chain management. The multi-commodity order fulfillment method comprises the following steps: acquiring order and inventory information including a current order set, a commodity set and a warehouse inventory set; and modeling an order fulfillment problem based on an actual demand of fulfillment of a multi-commodity order in the order and inventory information. The established model is a multi-objective optimization problem containing three objectives. Generating an initial population by adopting a heuristic method based on priority and inventory feasibility; and carrying out iterative optimization on the initial population by adopting a rapid non-dominated sorting genetic algorithm with elitism based on a double-layer elitism strategy to obtain a Pareto non-dominated solution set. And selecting a final order fulfillment scheme from the Pareto non-dominated solution set according to the preference of a decision maker. According to the method, three core operation indexes are synchronously improved under the condition of meeting feasibility constraints such as inventory, processing capability and demand.

The invention relates to the technical field of textile production scheduling, in particular to a textile production full-process intelligent scheduling system, which comprises a data acquisition module for setting a target production cycle and collecting basic information of all orders in the target production cycle to form an order set; the basic information of the order comprises an order type, an order quantity and order delivery time; and the working procedure determination module divides the production process of the textiles produced by the orders into a plurality of working procedure links based on the order types and the textile production process conditions, the working procedure links comprise key working procedures and auxiliary working procedures, and the auxiliary working procedures are auxiliary processing procedures of the key working procedures. According to the textile production full-process intelligent scheduling system provided by the invention, personalized prediction can be carried out on the actual production line productivity of each order in the target period, an extensive historical average value is abandoned, and the accuracy of productivity prediction is remarkably improved.

A method for providing a zero-knowledge membership proof is provided. The method comprises defining public parameters to be used for computing and verification of proof by a prover and a verifier, respectively. The method comprises computing, by the prover, a commitment to a given ordered set X using the public parameters, and sending it to the verifier. The method further comprises receiving, by the prover, a query from the verifier for proving membership of a subset S sampled from the given ordered set X. The method further comprises computing, by the prover, a proof of membership, including an inner product, commitments of polynomials defining the inner product and a proof of the inner product being correctly computed, and sending the proof of membership to the verifier. The method further comprises verifying, by the verifier, the proof of membership based on the defined public parameters.

The invention discloses an intelligent production scheduling method for a copper coil pipe coiling and drawing process. The intelligent production scheduling method comprises the following steps: step 1, converting a monthly order into a daily production order; 2, translating order specifications to obtain an order set; and step 3, scheduling production by using an intelligent algorithm, carrying out frame material division, batch distribution and machine scheduling, and arranging dynamic shutdown to reduce the power cost. According to the intelligent production scheduling method for the copper coil pipe coil drawing process, frame material process similarity clustering and multi-target optimization scheduling are introduced, automatic production scheduling of the copper coil pipe coil drawing process is achieved on the basis of order data, process parameters and equipment information, and therefore the production efficiency of the copper coil pipe coil drawing process is improved while it is guaranteed that an order production task is completed. The working efficiency of the disc drawing equipment is improved, and energy conservation and cost reduction are achieved while production scheduling is optimized.

The invention provides an online car-hailing aggregation mode multi-stage order dispatching method and device and a storage medium. The method comprises the following steps: performing one-order-to-multi-driver matching calculation on each order record according to a first matching rule, selecting a target driver according to an order attribute and a driver attribute, and generating a first order sending result; comprehensively evaluating the matching condition of the driver with the multiple associated order records under the same travel demand, and when a preset adjustment condition is satisfied, adjusting the matching object of the driver from the order record in the first matching relationship to the target order record determined by evaluation, and generating a second order sending result; and comprehensively evaluating the matching condition of the driver and the candidate order set according to a scheduling strategy, and when a preset adjustment condition is satisfied, adjusting a matching object of the driver to a new target order record to obtain a final matching relationship. According to the method, the order sending response timeliness can be improved, the driver order matching quality is improved, and the multi-platform scheduling flexibility and the transport capacity utilization efficiency are enhanced.

The application provides a logistics transportation management method and system based on artificial intelligence, relates to the field of logistics transportation, and solves the technical problems that the prior art is difficult to balance real-time performance, distribution feasibility and resource utilization efficiency in the sudden interruption scene of a logistics vehicle, resulting in slow scheduling response, frequent secondary conflicts or failure of high-value order fulfillment. The method comprises the following steps: obtaining a set of orders to be delivered by a target vehicle at present and real-time position information; based on a preset hard constraint condition, screening candidate vehicles and constructing a task migration feasibility graph; inputting the task migration feasibility graph into a pre-trained lightweight graph neural network model to output the compatibility scores of each candidate vehicle for each order to be delivered in the order set; based on the compatibility scores, evaluating the urgency of the order to be delivered; and based on the urgency of the order to be delivered, updating the delivery task scheme of each candidate vehicle. The application is used in the process of logistics transportation.

The invention discloses a multivariate factor optimization production feeding scheduling method and system based on a fuzzy operator, and the method comprises the steps: 1), obtaining an order set and the stock of various materials, and carrying out the ascending sorting according to the sorting reference value of orders, and obtaining an initial order queue; 2) traversing each order in the initial order queue, and performing state updating and state subtraction to obtain an intermediate order queue in which the total consumption of different materials of each traversed order is not greater than the stock and the comprehensive priority score of each traversed order is accumulated to be the highest; and 3) selecting the intermediate order queue corresponding to the highest accumulation of the comprehensive priority scores of all the orders from all the intermediate order queues as an optimal order queue so as to carry out production supply scheduling. The method has the advantages of improving the order punctual delivery rate, improving the comprehensive priority score, shortening the scheduling response time and the like.

The invention discloses an authority management method, system and device, a storage medium and a program product, and the method comprises the steps that a service platform determines the service data of a first service provider according to a first historical order set of the first service provider; if it is determined that the permission reduction condition is met according to the service data, cancelling the configuration permission of the first service provider to the first delivery element according to the service data; under the condition that a first configuration request of a first service provider for a second delivery element is received, configuring the second delivery element according to the first configuration request; the second delivery element is a delivery element different from the first delivery element in the delivery element set. According to the invention, fine-grained management of the configuration permission is realized, and the service quality of the service provider is improved.

The application provides a dynamic adjustment method for digital economic policy response, comprising: performing clearance backwriting lag evaluation on the time dislocation span, identifying orders exceeding the normal backwriting cycle to form an order composition backlog order set; determining the normal fluctuation range of trade volume without policy intervention based on the same period historical trade volume before policy implementation to construct a baseline interval; performing growth significance evaluation on the trade volume after stripping the delay interference by comparing the trade volume after stripping the delay interference with the baseline interval to identify a true growth signal; performing intersection operation on the time interval corresponding to the true growth signal and the concentrated backwriting period, if the intersection is not empty, then deducting the part occupied by the concentrated release amount in the concentrated backwriting period from the trade volume increment corresponding to the true growth signal to obtain a trade policy response evaluation result reflecting the actual incentive effect of the policy.

The invention discloses an article sorting and warehouse-out method and device, and relates to the technical field of warehouse logistics. A specific embodiment of the method comprises the following steps: based on a hierarchical clustering algorithm, performing order collection according to a correlation degree between orders in a to-be-sorted order set to obtain at least one collection order; for each set order, generating at least one picking task according to the storage positions of the articles included in the set order; and in response to the fact that the order picking task is detected to be completed, an order sorting and warehouse-out instruction is generated, and the order sorting and warehouse-out instruction is used for sorting the articles corresponding to the order picking task included in the collection order to the lattice of the distribution rechecking table corresponding to the order to which the articles belong by using the article distribution wall so as to carry out article sorting and warehouse-out. According to the embodiment of the invention, the method can better meet the demands of order distribution and re-checking work, reasonably distributes the field resources, optimizes the order picking line, improves the order picking and ex-warehouse efficiency, saves the labor cost, reduces the work intensity of store workers, reduces the operation complexity, and reduces the error rate of order re-checking.

The invention discloses an order processing method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the steps of determining a declaration priority of each warehouse based on received warehouse productivity data corresponding to a to-be-declarated order set; according to the declaration priority of each warehouse, setting an order declaration strategy of each warehouse; and performing declaration processing on the to-be-declarated orders in the to-be-declarated order set based on the order declaration strategy of each warehouse. According to the embodiment of the invention, the declaration priority of each warehouse is dynamically adjusted according to the warehouse productivity data, the declaration strategy is dynamically adjusted according to the declaration priority, order declaration is carried out based on the productivity gap, the order declaration efficiency is improved, warehouse order overstock is avoided, and the warehouse operation speed is improved.

The application discloses a denture manufacturing scheduling control method, device, equipment and medium, and relates to the field of production and manufacturing. The method comprises the following steps: acquiring a batch order set; acquiring a preset process node sequence of each denture manufacturing order and a priority score based on multi-dimensional characteristics based on order information of the batch order set to obtain an order priority set queue; the higher the priority score is, the higher the urgency of completing the corresponding denture manufacturing order is; establishing an MDCN decision tree based on process types and corresponding resources; and matching corresponding resources for each preset process node based on preset rule constraints and the MDCN decision tree to obtain a feasible scheme set, wherein the feasible scheme set comprises at least one feasible scheme, and each feasible scheme comprises resources matched by each preset process node and operation time of the resources; and performing optimization screening on the feasible scheme set to obtain a target scheme. The application can improve the synergy between resources and the comprehensive utilization rate of resources.

The invention relates to the technical field of logistics, and provides a sorting processing method and device for logistics orders, equipment and a storage medium. The method comprises the steps of obtaining first order information in a preset duration, wherein the first order information comprises at least one order; judging whether emergency orders exist in the orders or not; if yes, an initial sorting path of each emergency order is generated; wherein each emergency order at least comprises an initial picking path; clustering the emergency orders with the same initial sorting path to obtain at least one emergency order set; and based on a preset emergency order set sorting method, sorting articles corresponding to each emergency order set. According to the method, the problem of delay caused by queuing waiting of high-priority orders in a traditional mode is effectively solved.

The invention provides a scheduling duration adjustment method and device based on a gain model, a product and a medium. The method comprises the steps of obtaining a to-be-distributed delivery order set with an association relationship; constructing an order relation graph of the order set; inputting the order relation graph into a gain model, so that the gain model extracts a graph embedding vector of the order relation graph based on a graph neural network, and predicts a gain value corresponding to each order under one or more predefined duration adjustment strategies for each order based on the graph embedding vector; the duration adjustment strategy comprises a strategy for performing duration adjustment on the initial scheduling duration; the gain value characterizes the probability gain that the order and other orders in the order set are distributed to the same delivery capacity relative to the non-execution of the order execution duration adjustment strategy; and determining a final adjustment strategy of the initial scheduling duration of the order according to the gain value of the order.

The invention provides an e-commerce order self-operation and crowdsourcing transport capacity integrated distribution method and electronic equipment. The method comprises the steps of obtaining a to-be-allocated order set, a commodity set, inventory data, transport capacity data and time window data of an e-commerce platform; constructing a cost optimization model of the to-be-allocated order set, wherein the cost optimization model comprises a front transport capacity cost parameter, a self-operation transport capacity cost parameter and a crowdsourcing transport capacity cost parameter; determining a decision variable and a target constraint condition of the cost optimization model; based on the to-be-allocated order set, the commodity set, the inventory data, the transport capacity data and the target constraint condition, solving a decision variable of the cost optimization model to obtain a target variable value of the decision variable; and outputting a target allocation strategy of the to-be-allocated order set based on the target variable value of the decision variable. According to the invention, the overall timeliness of the order set can be improved, the overall delivery cost is reduced, and the order timeliness default problem is reduced.

The invention discloses an express order pushing method, device and system and a storage medium. The method comprises the following steps: in response to a detected order pushing instruction, obtaining at least one uncompleted express order in the order pushing instruction; according to an uncompleted order address in each uncompleted express order, executing a clustering operation on each uncompleted express order to obtain at least one initial order set; determining a current push order set according to the current address of the courier and the set reference point address corresponding to each initial order set; and at least one push express order in the current push order set is pushed to a courier client, so that the courier client outputs and displays the received push express orders. According to the embodiment of the invention, the problem of low query efficiency of the express order in a full-amount push mode is solved, and the working efficiency of the courier is improved.

Provided are a processing device, a processing program, and a processing method that enable more efficient ordering of financial products. 【Solution means】 For a financial product whose market price fluctuates, by receiving an operation input from a user, price range information that specifies a price range for permitting a buy order and order setting information for setting conditions for permitting the buy order are received from a terminal device usable by the user, a reference price is calculated based on the buy order price and the sell order price for the financial product at an arbitrary point in time, and when the reference price calculated at the arbitrary point in time is included in the range specified by the price range and satisfies the conditions set by the order setting information, a process for enabling a buy order for the financial product is executed.

The embodiment of the invention provides an order processing method and device, electronic equipment and a storage medium, and belongs to the technical field of supply chains. The method comprises the steps of obtaining a target order set of a target supplier, and obtaining a to-be-processed order screening rule and an order sorting rule corresponding to the target supplier; performing order matching on the target order set according to the to-be-processed order screening rule to obtain a plurality of first to-be-processed orders matched with the to-be-processed order screening rule; according to each order sorting rule and the rule priority corresponding to the order sorting rule, multiple rounds of sorting are carried out on the multiple first to-be-processed orders, and order processing is carried out on a to-be-processed order queue obtained in each round in sequence; and after the multiple rounds of sorting operation are completed, if it is determined that a second to-be-processed order which is not matched with the to-be-processed order screening rule exists in the target order set, sorting the second to-be-processed order according to a preset order processing rule. According to the embodiment of the invention, the accuracy of processing abnormal orders can be improved.

The present application provides a method and device for recalling ride-sharing orders, wherein the method includes: obtaining an itinerary issued by a target car owner; encoding the itinerary according to a preset coding rule to obtain a first code; matching a preset number of order codes with the highest similarity to the first code from a vector similarity search database as a target order code set; searching for user orders corresponding to each order code in the target order code set, and pushing them to the target car owner as a target order set. The above solution solves the existing problem of being unable to accurately and efficiently match driver and passenger orders, and achieves the technical effect of accurately and efficiently matching driver and passenger orders to improve the conversion rate of ride-sharing orders.

The embodiment of the invention provides an order processing method and device, equipment and a storage medium. The method comprises the following steps: receiving at least one order to be processed; determining a transportation scheduling result of the at least one order, wherein the transportation scheduling result at least indicates a transportation timeliness requirement and / or a transportation tool corresponding to the at least one order; determining a target order set corresponding to the at least one order based on the order information corresponding to the at least one order, the transportation scheduling result and a predetermined screening rule; and based on each order in the target order set, a picking task for the target goods corresponding to each order in the target order set is generated, and the picking task indicates execution of a picking operation for the target goods. Therefore, the optimized order processing scheme is generated through intervention of an intelligent algorithm in combination with the transportation scheduling result, so that warehouse operation can be arranged more accurately, and the cooperation efficiency of the whole warehousing and logistics process is improved.

The application provides a kind of goods inventory allocation method and device, which comprises: obtaining the current remaining delivery time and a plurality of historical delivery times of goods in the order to be allocated;Fitting is carried out on the plurality of historical delivery times of goods, and the cumulative distribution function of the delivery time of goods is obtained;According to the current remaining delivery time and the cumulative distribution function of the delivery time, the delivery time parameter of the goods in the order to be allocated is determined, wherein the delivery time parameter decreases with the increase of the current remaining delivery time;According to the delivery time parameter of the goods in each order to be allocated, the order to be allocated is sorted, and the target order set after sorting is obtained;Inventory allocation is carried out on the target order set.The application can reduce the inventory turnover period.

The invention provides an order allocation method and device, electronic equipment, a readable storage medium and a program product, and relates to the technical field of computers and the field of intelligent logistics, and the method comprises the steps: dividing a to-be-processed order set into at least one commodity set; calculating the volume performance of the commodity set; in response to the fact that the volume performance of the commodity set meets a preset parameter range, the commodity set is split according to a preset numerical value condition, a first kind of commodity subset and a second kind of commodity subset meeting the preset numerical value condition are obtained, and the preset numerical value condition includes that the volume performance is equal to a preset volume value; and under the condition that the second type of commodity subset does not meet the preset numerical value condition, orders corresponding to the second type of commodity subset are allocated to a target batch order set, and the generation time of the target batch order set is later than the generation time of the to-be-processed order set.

The present invention belongs to the technical field of big data analysis. The present invention discloses a supplier price management system and method based on big data analysis, including: collecting core data and auxiliary data to construct a multi-dimensional data cube; dynamically calculating the grid availability index to form a dynamic spatiotemporal grid benchmark library, and synchronously generating a supply chain topology map; receiving and quantifying business target instructions, and selecting paths through a rule engine; generating procurement strategy recommendations with confidence ratings and a dynamic priority supplier list; dynamically adjusting permissions through an ABAC model, and triggering a smart contract engine to generate orders or bargaining invitations; parallel approval through a workflow engine to generate a procurement order set and a permission change log; receiving real-time data streams and a historical case library of the Internet of Things to trigger disposal logic; matching similar historical cases to push historical optimal strategies, and synchronously updating weight rules, data cube structures, and supplier credit score thresholds to form a full-link closed-loop iteration.

The application relates to an order type determination method and device, computer equipment and a storage medium. It relates to the payment and finance field, and comprises the following steps: obtaining order characteristics of a target order based on the characteristic values of the target order in multiple preset order characteristic dimensions; determining an aggregation characteristic value of the target order based on the order characteristics of the target order and the order characteristics of historical orders in a historical order set; the aggregation characteristic value is used to represent the aggregation degree of the target order and the historical orders in the case of distribution according to the order characteristics; combining the characteristic values of the target order in at least one preset order characteristic dimension and the aggregation characteristic value to obtain a target combination feature; and predicting the order type to which the target order belongs by using the target combination feature. The method can improve the prediction accuracy of the order type.

The invention provides a material supply distribution method, electronic equipment and a storage medium, and the method comprises the steps: obtaining a demand list from a customer, and determining a target material, a target material demand quantity and expected delivery time; the current stock amount of the target material is obtained, when the current stock amount is smaller than the target material demand amount, a first committed work order set from a supplier is obtained, and the expected delivery time of work orders in the first committed work order set is equal to or earlier than the expected delivery time; obtaining committed material information in the first committed work order set, determining committed work orders corresponding to committed materials capable of replacing the target materials, and obtaining a second committed work order set; and determining supply distribution information to the customer according to the second committed work order set. According to the method, the committed work order is introduced, so that excessive concentration of material requirements can be avoided, supply consumption equalization is realized, and production interruption or inventory overstock caused by insufficient or excessive material supply is avoided.

The embodiment of the invention discloses an intelligent production scheduling method and related equipment, which are used for performing intelligent production scheduling under the condition of improving the production scheduling efficiency. The method comprises the steps that configuration information input in an interface is acquired, the configuration information comprises productivity configuration of a production line and constraint conditions of production scheduling, a large model is utilized to analyze order data of multiple target plan orders in a target batch order set according to preset cue words and the configuration information, and order data of multiple target plan orders in the target batch order set are obtained; obtaining initial production scheduling strategies corresponding to a plurality of target plan orders output by the large model, and analyzing order data of the plurality of target plan orders by using the small model based on the initial production scheduling strategies, the configuration information, the current residual capacity of the current production line and an optimization algorithm, and obtaining a target production scheduling strategy corresponding to the plurality of target plan orders output by the small model, thereby performing production scheduling on the plurality of target plan orders based on the target production scheduling strategy.

The invention discloses a commission management system in the field of e-commerce, and relates to the field of e-commerce, and the system comprises an order obtaining module which is used for obtaining orders from an e-commerce platform, and recording and updating the state of each order in real time; the order matching module is used for matching a corresponding business session and business personnel for each order and sending the order to a commission pool of the business personnel; the statistics module is used for performing statistics on states of orders in a commission pool of business personnel in a current management period to obtain a to-be-processed order set in a state of confirmation of delivery and a new order set in a state of refund after delivery; the commission management module is used for calling a corresponding management template from the rule configuration module according to the business personnel, and processing a to-be-processed order set of the business personnel by adopting the management template to obtain a processing result; the processing result represents the business commission of the business personnel. The commission management accuracy and management efficiency of the business personnel can be improved.

Embodiments of the present disclosure disclose a picking task execution method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises: determining the number of orders of an order set for a target order item and inventory information of the target order item; determining item storage warehouse information corresponding to each order in the order set according to the number of orders and the inventory information, to obtain a set of item storage warehouse information; generating at least one order subset according to the set of item storage warehouse information and the order set; generating a set of picking task order information corresponding to each order subset in the at least one order subset, to obtain a plurality of sets of picking task order information; and controlling a picking device to execute a plurality of sets of picking tasks corresponding to the plurality of sets of picking task order information. The embodiment is related to intelligent logistics and can efficiently complete various picking tasks.

The invention relates to the technical field of packaged drinking water distribution, and discloses a packaged drinking water distribution path optimization scheduling system and method. A data acquisition unit of the system obtains a distribution area order set and path information, and paths are divided according to an order distribution sequence and a distance measurement result to obtain sub-road sections; the feature recognition unit recognizes real-time traffic data to obtain path features of each sub-road section, and marks the sub-road sections meeting optimization conditions; a parameter generation unit generates distribution optimization parameters in combination with the sub-road section scheduling information of the fixed nodes and the marked nodes and the road section intervals; the database construction unit associates and integrates the optimization parameters to construct a comprehensive scheduling database; and the optimization unit determines a basic scheduling program through the path control model according to the customer group information and the real-time traffic index curve, and outputs an optimized scheduling program after optimization of the path optimization model. The system can improve the distribution efficiency, improve the service quality and reduce the operation cost.