38 results about "Order form" patented technology

The system allows for flexible configuration of the decision-making criteria required for order processing, and outputs accurate order information based on the configured criteria. [Solution] A program for operating a computer comprising a processor and memory, the program causing the processor to perform the following steps: receiving setting operations for a plurality of judgment items necessary for a first AI model to execute order processing via a user interface; having the first AI model learn the received plurality of judgment items and tuning the first AI model to output order information relating to the order content based on the judgment results for each of the plurality of judgment items; receiving order information relating to the order content and extracting character information relating to predetermined characters from the order information; and inputting the extracted character information into the first AI model and causing the first AI model to output order information.

An online system receives a request from a client device associated with a user to place an order for pickup from a source location during a timeframe and identifies candidate remedial actions associated with the order based on the timeframe and a current time. The system retrieves user data for the user and accesses a machine-learning model. For each candidate remedial action, the system applies the model to predict, based on the user data and order data for the order, a likelihood the user will pick up the order if the candidate remedial action is taken and computes an associated value based on the likelihood. The system selects a remedial action from the candidate remedial actions based on the values, generates, based on the selected remedial action, a message associated with the order that includes a set of selectable options, and sends the message to the client device.

The application discloses an order task splitting and reorganization processing method and device and a storage medium. The method comprises the following steps: constructing an order processing flow model according to order information of to-be-processed orders; dividing the order processing flow into a plurality of first sub-tasks which can be independently executed, and calculating a first priority, a first dependency matrix and a resource consumption vector for the first sub-tasks; acquiring resource state information, and executing a preconfigured merging and splitting algorithm according to the resource state information and the resource consumption vector of the first sub-tasks to generate second sub-tasks which are adapted to a performance node; updating the first priority and the first dependency matrix to obtain third sub-tasks containing a second priority and a second dependency matrix; executing a preconfigured reorganization algorithm to generate at least one task processing sequence; calculating a path score of each execution path in the task processing sequence, and determining a target execution path according to the path score.

Embodiments of the present disclosure provide an order processing method, apparatus, device, system, medium, and product. The method includes: obtaining a target order to be processed and determining a quantity of types of goods in the target order; dividing the target order into a plurality of sub-orders according to the quantity of types of goods and assigning different workstations for the sub-orders; and sending the sub-orders for which the workstations have been assigned to the corresponding workstations for processing.

The specification discloses a method for constructing a distribution network, which can take the combination of the pickup point and the delivery point of each historical order task as an order node, construct the distribution network, and determine the order aggregation degree of each order node. The higher the determined order aggregation degree is, the easier it is to form an order package in the distribution direction from the pickup point to the delivery point of the order node. Therefore, the distribution time of a to-be-distributed order matched with the order node can be extended to wait for the order package formed by the orders matched with the to-be-distributed order within the distribution time, and then the order package is distributed to the distribution capacity to perform a distribution task. Therefore, the order distribution can be performed according to the constructed distribution network, so that the rationality of the order distribution is improved, the efficiency of the distribution capacity performing the distribution task is improved, and the user experience is improved.

We provide the system. [Solution] A means for analyzing order information received from a user terminal, A means of using a generation device that generates order information based on the order format of each store, A means of transmitting the generated order information to the store's terminal, A means of sending feedback information from the store to the user's terminal, A system that includes this.

This application relates to the field of warehousing and logistics scheduling technology, and discloses an order wave generation method, an order allocation method, an apparatus, a server, a storage medium, and a system. The order wave generation method includes: obtaining a set of orders to be processed; performing a first clustering strategy on the orders in the set of orders to be processed, including: determining multiple candidate order combinations from the set of orders to be processed; calculating the feature similarity of each candidate order combination; selecting the candidate order combination with the highest feature similarity as a seed order combination, constructing the current order wave based on the seed order combination, and removing the orders in the seed order combination from the set of orders to be processed; iteratively adsorbing orders from the remaining orders based on feature similarity and adding them to the current order wave, and removing the adsorbed orders from the set of orders to be processed, until the termination adsorption condition is met. This application improves the efficiency and flexibility of warehousing operations.

An approach is provided for facilitating user access to an obscured online cancellation option. By using natural language processing (NLP) and learned patterns of order systems that employ hostile architecture designs, it is determined that cancellation option(s) that cancel order(s) are obscured by hostile architecture design(s) employed by online platform(s). The order(s) specify service(s) ordered by a user. Cancellation selection(s) are presented in a user interface to provide the user with a direct access to an activation of the cancellation option(s). The direct access provides a user experience that avoids the hostile architecture design(s).

Aspects relate to a server for facilitating processing a plurality of orders for on-demand services, the server comprising: a memory configured to store instructions; a communication interface configured to receive a plurality of orders for on-demand services from a plurality of computing devices; and a processor to execute the stored instructions and configured to: obtain a first indicator related to a rate of allocation of orders received within a particular time slot that are geographically related to the plurality of orders; obtain a second indicator for each order of the plurality of orders related to a likelihood that each order will be allocated to a delivery service provider; select at least one order from the plurality of orders based on the first indicator and the second indicator; and delay allocation of the selected order to a delivery service provider for a predetermined time.

The application discloses a kind of machining order full life cycle management method and system, specifically related to machining production management and industrial informatization technical field, including obtaining order record, resource time period record and event record, according to order mark, resource mark and time period mark association, determine original attribution time period, output order time period dataset;According to order time period dataset, extract the original attribution time period and present attribution time period before and after insert order event, rework event and outsourcing abnormal event, determine the occupied time period, and write the occupied time period into corresponding order to form performance obligation item, output performance obligation result;By comparing the original attribution time period and present attribution time period before and after event, and combining edge computing, the occupied time period is written into corresponding order to form performance obligation item, which can explicit and traceable the subsequent execution opportunity loss caused by shared resource occupation, thereby relatively improve the problem that subsequent performance loss is difficult to close loop digestion by only adjusting current order.

The application relates to a target category object identification method and device, computer equipment and a storage medium. The method comprises the following steps: based on an order object corresponding to a target order, order objects with the same address belonging to the same region are divided into the same group to obtain a plurality of order object groups; target category objects and region information are extracted from each piece of target category information respectively, and target category objects with the same region information are divided into the same group to obtain a plurality of target category object groups; based on the region information corresponding to each order object group and each target category object group, a to-be-matched order object group and a to-be-matched target category object group belonging to the same region are screened out; order objects in the to-be-matched order object group and target category objects in the to-be-matched target category object group are subjected to similarity matching, and whether the order objects are new target category objects is determined according to a matching result. Through the above method, the efficiency of identifying enterprise members is improved.

To effectively manage the outstanding stock of pharmaceutical orders. [Solution] The order backlog management device according to this embodiment includes a product master 106a in which a product code and an internal code for defining the identity of a product are registered in association, and an order backlog processing unit 102c which, in the order backlog data, sets the slip cancellation flag to "slip cancellation" for order numbers for which the set number of days for order backlog deletion has elapsed, and if the slip cancellation flag in the order backlog data is not "slip cancellation", clears the number of undelivered items calculated by subtracting the number of received items from the order backlog data using the number of purchased items, and sets the purchase completion flag to "purchase completion" when the number of undelivered items becomes 0.

This invention discloses an order batch processing method and system for intelligent warehousing, comprising: a modeling step: constructing a semi-Markov decision process model for the dynamic order batch processing decision problem; a state awareness step: collecting warehouse operation data in real time and encoding it into a structured state vector containing order queue characteristics, equipment load characteristics, and global monitoring characteristics; an action decision step: based on a trained deep reinforcement learning agent, adaptively selecting a target heuristic rule from multiple preset underlying order batch processing heuristic rules according to the current state vector; and a task execution step: calling the selected heuristic rule to batch process the current order pool, generating picking tasks and driving the physical system to execute. This invention, through an adaptive decision-making mechanism based on deep reinforcement learning, enables the warehousing system to perceive the environment in real time and dynamically select the optimal batch processing strategy from multiple rules, thereby intelligently balancing efficiency and timeliness.

To make order reception / placement information between companies secret.SOLUTION: In an order reception / placement system that manages order reception / placement, a key management server includes an access right management unit that assigns authentication information of a company that can obtain key shares to the key shares, and a communication unit that distributes the key shares to a key management share server. A terminal includes an information transmission unit that requests the key shares from a key share holding server, a key recovery / generation unit that recovers an encryption key from the obtained multiple key shares, an encryption / decryption unit that encrypts input order placement information and decrypts order reception information using the recovered encryption key, and a display unit that displays the decrypted order reception information. The information transmission unit sends the encrypted order placement information to a zero-order server, and the key share holding server includes an authentication unit that determines whether the key shares can be sent to each terminal based on the authentication information.SELECTED DRAWING: Figure 1

Example implementations relate to order prediction and automated cart creation. In an example, basket features, item features, and order features associated with a profile are inputted into a predictive model and daily order predictions are outputted by the predictive model. The daily order predictions are inputted into a grouping algorithm to output an order predictability. Profiles are segmented into respective order predictability cohorts of a plurality of order predictability cohorts based on respective order predictabilities. A cart may be automatically created for at least one daily order prediction associated with at least one profile when a respective order predictability cohort belongs to a predetermined order predictability cohort.

Embodiments of the present disclosure provide an order processing method, apparatus, and device, a c system, and a storage medium. The order processing method includes: in a case that a second order is detected and a priority of the second order is higher than a priority of at least one first order, obtaining a material container of a first order in a buffer area, where each of the at least one first order is an order that is currently being executed and whose execution is not completed, and the material container of the first order is at least one material container of material containers corresponding to the at least one first order; and determining a first transport strategy of the second order according to the material container of the first order and a requirement of the second order, so as to complete the second order according to the first transport strategy.

This invention discloses a method, apparatus, electronic device, and program product for generating order settlement files, relating to the field of data processing technology. The generation method includes: acquiring order data and constructing an order object based on the order data, wherein the order object includes at least multiple attributes, each attribute corresponding to attribute data; processing all attributes and all attribute data using multiple message generators to generate multiple message data; and combining all message data according to a preset order to generate a target settlement file. This invention solves the technical problem of low accuracy in generating order calculation files in related technologies.

The invention provides an order allocation method and device. The method comprises the following steps: setting constraint conditions including a same-region order-to-order distance constraint, a cross-region order-to-order distance constraint and a maximum percentage constraint of on-road order combination distance increase; performing same-region clustering on orders in the to-be-allocated order set according to geographic information and constraint conditions to form a preliminary allocation result; when the distance between the individual batch order and the cross-regional order accords with the distance constraint between the cross-regional orders, combining the individual batch order with the cross-regional order closest to the individual batch order; when the total driving distance of the individual batch orders and the on-the-way orders accords with the maximum percentage constraint of on-the-way order combining distance increase, combining the individual batch orders with the on-the-way orders; and according to the timeout rate, the average delivery time of each batch and the total batch number, an objective function is set, the preliminary distribution result is optimized, the number of riders is reduced, the delivery time of the riders is shortened, and the user satisfaction is improved.

This application relates to an order early warning method, apparatus, electronic device, and storage medium. The method includes: extracting an order entity subgraph centered on the target order entity from a knowledge graph when the target order entity meets the source risk triggering threshold condition; wherein the target order entity is any order entity in the knowledge graph; determining the associated risk quantification value of each order entity based on the order entity subgraph and the current state of each order entity in the order entity subgraph; comparing the associated risk quantification value with a benchmark risk quantification standard, and issuing an early warning for each order entity based on the comparison result. This method, by determining the associated risk quantification value of each order entity based on the order entity subgraph and the current state of each order entity in the order entity subgraph, and then issuing an early warning for each order entity based on the comparison result of the associated risk quantification value and the benchmark risk quantification standard, can improve the control capability of order entities.

The application discloses an order processing method and device, and relates to the technical field of warehousing. A specific embodiment of the method comprises the following steps: in response to detecting an update instruction for an order delivery process, determining a target node from the order delivery process based on the update instruction; configuring a production mode of the target node as a target mode indicated by the update instruction; and in response to the order delivery process executing to the target node, processing the order based on the target mode. The embodiment effectively improves the flexibility and effectiveness of order processing.

This application relates to a method, system, and medium for querying order communication data based on order status changes. The method includes: monitoring the status of a target order, where the target order status includes an order creation status used to trigger data preloading; when an order creation status is detected, acquiring historical communication data associated with the target order and storing the historical communication data in a preloaded cache area; in response to receiving a compliant query request for the target order, merging the historical communication data with real-time acquired incremental data to obtain the target order communication data; and performing a compliant query on the target order communication data to obtain the query results. This addresses the problem of how to improve the performance of order communication data querying in related technologies.

The application relates to an order synchronization method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a current synchronization time point and a historical synchronization time point of the last time when a server database is updated, the server database being updated based on a preset synchronization interval; if a synchronization time difference between the current synchronization time point and the historical synchronization time point is higher than a synchronization threshold, a first set of to-be-synchronized orders is acquired; if the synchronization time difference is not higher than the synchronization threshold, a second set of to-be-synchronized orders is acquired; the first set of to-be-synchronized orders or the second set of to-be-synchronized orders is saved into the server database, and data in the updated server database is sent to a client, so that the server database is synchronized to a client database by the client. Through the application, the problems of low synchronization efficiency and low query efficiency of order data are solved, the synchronization and query efficiency of orders is improved, the delivery speed of orders is improved, and the use experience of users is optimized.

In one aspect, an example methodology implementing the disclosed techniques includes, by an order prioritization service, receiving information regarding orders that need to be fulfilled and determining, for each one of the orders, one or more relevant features from the information regarding the order, the one or more relevant features influencing prediction of an order priority. The method also includes, by the order prioritization service, predicting, using a machine learning (ML) model, a priority score for each one of the orders based on the determined one or more relevant features, and ranking the orders based on their respective priority scores.

An order support device includes an acquisition unit, an estimation unit, a prediction unit, and an output unit. The acquisition unit acquires reservation information regarding the number of store visitors to a restaurant. The estimation unit estimates a serving amount of a dish, based on the reservation information. The prediction unit predicts an amount of an ingredient to be ordered, based on the estimated serving amount of the dish. The output unit outputs a prediction result on the amount of the ingredient to be ordered.

This application provides an order creation method, apparatus, device, and storage medium based on artificial intelligence, belonging to the field of artificial intelligence. The method includes: acquiring user voice data and invoking a cluster of intelligent agents; performing intent recognition on the voice data through an intent recognition intelligent agent to obtain the user's target intent; selecting a target interaction intelligent agent and a target order generation intelligent agent from the cluster of intelligent agents based on the target intent; conducting order request voice interaction with the user through the target interaction intelligent agent to obtain order voice interaction information; and creating an order based on the order voice interaction information through the target order generation intelligent agent to generate order information for the services required by the user. This application significantly improves the efficiency and accuracy of order creation, thereby enhancing the convenience of users' lives. This application can be applied to the fields of medical technology and financial technology, improving the accuracy and convenience of order generation in financial and medical services.

The application provides a method for generating an invoice to be received and a computing device, the method comprising: determining an invoice allocation file, the invoice allocation file comprising contract information of at least one first order contract and cumulative trigger identification of each first order contract, the first order contract being an order contract that needs to be managed by the invoice to be received, and the cumulative trigger identification being all triggered or not all triggered; querying a second order contract with the cumulative trigger identification not all triggered according to the cumulative trigger identification of each first order contract in the invoice allocation file; and generating the invoice to be received of a current accounting period for the second order contract in the case that the second order contract meets the entry condition of the current accounting period.

We provide a system that can reduce the workload of the client. [Solution] The system of the present invention comprises a storage unit 110 and a control unit 120. The storage unit 110 stores order information relating to the order items. The control unit 120 converts an order image representing the order details of the order items ordered by the orderer into order information. The control unit 120 extracts mismatch information from the converted order information that is not present in the order information stored in the storage unit 110 and provides the orderer with a list of the extracted mismatch information.