551 results about "Order processing" patented technology

The invention relates to a logistics order processing efficiency analysis method based on cloud computing, and the method comprises the steps: constructing a multi-dimensional and multi-node behavior log and environment variable synchronous collection system, and obtaining standardized structured data through timestamp calibration, abnormal value elimination, semantic normalization and desensitization processing; and based on node features, sequence features and environment embedding, generating a multi-node multi-scene feature vector, and inputting the multi-node multi-scene feature vector to a multi-order causal nested graph neural network for dynamic causal modeling among nodes, events and constraints. Through combination of anti-fact inference and multi-source evidence fusion, hidden bottleneck automatic identification, attribution report output and optimization suggestion generation are realized; through expert and user feedback mechanisms and working condition and rule change adaptive network structure adjustment, long-term accurate optimization and dynamic closed-loop updating of the bottleneck diagnosis model are realized. According to the invention, the intelligence and adaptive level of order flow bottleneck identification can be improved, and a high-quality data basis and intelligent decision support are provided for flow optimization in a complex logistics scene.

The invention provides an integral shopping mall order rolling processing optimization method and system based on big data, and the method comprises the steps: employing a dynamic priority adjustment mechanism for a conflict order list, and adjusting the order processing priority based on the user behavior track data, including historical exchange preference, and obtaining a resource distribution scheme; integrating the order sequence through a queue reordering mechanism, reordering based on the resource allocation scheme to obtain a final processing sequence, preferentially executing the high-priority request, and maintaining the queue stability at the same time; if residual conflicts exist in the final processing sequence, supplementary resources are extracted from a preset standby inventory through a standby resource set scheduling mechanism, a complete execution plan is obtained, and the plan covers all order processing paths; and on the basis of the complete execution plan, monitoring the order processing progress, and obtaining a stable operation order flow through progress tracking and anomaly detection, real-time state feedback and iterative adjustment.

The embodiment of the invention provides a maintenance work order processing method and system, and relates to the technical field of intelligent household appliances. The method comprises the following steps: acquiring work order text information in maintenance work order data, extracting maintenance key information of the work order text information, matching at least one alternative maintenance personnel portrait according to the maintenance key information and all maintenance personnel portraits, and determining the maintenance personnel portraits according to the alternative maintenance personnel portraits and the maintenance key information. And respectively calculating a matching score of each alternative maintenance personnel portrait, and taking the alternative maintenance personnel corresponding to the alternative maintenance personnel portrait with the highest matching score as the push maintenance personnel. Therefore, accurate identification of maintenance requirements and automatic pushing of optimal technicians can be realized, and work order processing efficiency, distribution accuracy and after-sales service response speed are remarkably improved.

The invention provides an intelligent customer service work order processing method based on a large language model, a medium and electronic equipment. Full-process automatic processing of user service requirements from receiving to feedback is achieved through modular design. The method comprises the following steps: receiving and preprocessing demand information input by a user; performing semantic understanding on the normalized input by using a large language model, identifying a demand category, and extracting key parameters such as a room number and demand details; selecting a corresponding work order generation tool from a preset tool library based on the identified demand category, and matching a required business interface; a required information list is preset according to different requirement categories, whether the extracted key parameters are complete or not is automatically checked, and if not, a prompt is generated to guide a user to supplement information; after the information is complete, calling a corresponding work order template according to the demand category, filling the key parameters into the template to generate a standard work order, and pushing the standard work order to a back-end business processing system; and receiving a back-end processing result, combining the back-end processing result with basic reply content generated by the tool, and performing retouching through a large language model to form a final reply to be fed back to the user. Through the above process, specific demands of users in the vertical field can be accurately understood, the standard work order can be automatically generated and pushed, and the processing result can be acquired in real time and fed back to the user, so that the problems that existing intelligent customer service cannot extract complete demands, work order generation lacks specifications and the processing result cannot be fed back in a closed loop mode are solved; and the service efficiency and the user experience are greatly improved.

The invention relates to the technical field of logistics information processing, and particularly discloses a high-quality and high-efficiency logistics order management system. The system comprises a registration and login terminal, an intelligent review terminal, an order processing terminal and a block chain evidence storage terminal, and is modularly deployed based on a distributed architecture. The intelligent review terminal introduces an image recognition and rule engine to realize automatic order splitting and suggestion merging; the order processing terminal combines a scoring model and a path optimization algorithm to realize dynamic payment routing and multi-target path planning; the block chain evidence storage terminal generates a non-tampering credible receipt based on hash and zero knowledge verification, and supports cross-chain auditing. The system supports GeoHash fragmentation, Flink parallel computing, high-availability container scheduling and hot and cold data hierarchical storage, and the order processing efficiency, the system elasticity and the data security are remarkably improved. The logistics order full-life-cycle management method is suitable for logistics order full-life-cycle management in a high-concurrency and high-credibility scene.

The invention belongs to the technical field of customer service work order processing, and discloses an event priority ranking distribution method and system, and the method comprises the steps: converting historical multi-round dialogue data into a numeralized historical work order emotion score sequence set; the Hidden Markov model is trained by using a Boem-Weili algorithm, and core dynamic parameters of customer emotional evolution are learned; an optimal risk score threshold value after calibration is obtained through calibration; for each work order in progress, the probability distribution of each current hidden state is deduced in real time, and a forward-looking negative upgrading risk score is calculated; and when the negative upgrading risk score exceeds the calibrated risk score threshold, automatically triggering priority adjustment, and generating an updated work order record. According to the invention, a quantifiable framework based on probability prediction is provided, and customer support is converted from passive and post-response type work order processing into an active and pre-warning type risk management normal form.

The invention discloses a real-time performance retail system based on urban entity supermarket distributed inventory, which organically integrates a plurality of functional modules such as inventory data acquisition, geographic index construction, target supermarket determination, state feedback, scheduling, task issuing and the like. And a whole-process automatic and intelligent processing system from commodity inventory information acquisition to order fulfillment completion is formed. The system can automatically complete inventory matching, supermarket screening, path optimization and task allocation in a city retail scene with high concurrency of orders and dynamic change of the inventory, so that manual participation links are effectively reduced, and the response time of order processing and distribution is shortened; meanwhile, based on a distributed inventory geographic index and an intelligent scheduling algorithm, the method can dynamically adapt to real-time changes of supermarket inventory and geographic environment, so that the timeliness, accuracy and user experience of order fulfillment are remarkably improved.

The invention discloses an intelligent switching method and system based on multi-platform overseas payment, and the method comprises the following steps: accessing a plurality of overseas third-party payment platforms, and building a unified order processing flow through a transaction service; a monitoring point is embedded in the payment interaction process of the application program and the transaction service, payment success data and payment failure data are collected, and corresponding counters are maintained respectively; regularly pulling the counter in a parallel task mode, and counting the failure rate of each payment mode in the first time window T1; when it is detected that the failure rates of a certain payment mode in continuous N first time windows T1 all exceed a threshold theta, triggering exception handling, including marking the payment mode as abnormal, and automatically switching to a standby payment mode; and when it is detected that the failure rate of the off-shelf payment mode in the second time window T2 is recovered to be below the threshold value, automatically executing recovery operation, including re-shelving the payment mode.

The invention relates to the technical field of e-commerce logistics and supply chain management, and discloses a multi-channel e-commerce platform order processing optimization system and method, and the method comprises the steps: correcting physical inventory data through a Lagrange dual variable fed back in a previous period, calculating a virtual inventory, and receiving a discrete order flow; constructing a space-time hypergraph model of the to-be-processed order according to the commodity homogeneous attribute and the spatial neighborhood attribute; performing constraint optimization solution on the hypergraph model based on a Lagrangian relaxation algorithm, and generating an optimal order allocation matrix meeting productivity constraints and a Lagrangian dual variable of a current period; and analyzing the distribution matrix to generate an operation instruction, issuing the operation instruction to the physical warehouse, and feeding back the dual variables to the inventory calculation module. According to the method, collaborative operation benefits are mined through the space-time hypergraph, and a negative feedback closed loop with physical capacity pointing to front-end sales is constructed by using dual variables, so that performance cost optimization and flow adaptive control are realized.

The invention relates to the technical field of information processing, in particular to a full-automatic work order processing information generating and pushing method based on a fault knowledge base. The method comprises the following steps: acquiring an initial decision tree; identifying a core fault work order in a plurality of fault work orders corresponding to the target fault type; pruning the initial decision-making tree based on the core fault work order to obtain a target decision-making tree, the number of nodes of the target decision-making tree being smaller than the number of nodes of the initial decision-making tree; and processing a plurality of fault work orders corresponding to the target fault type according to the target decision tree to obtain work order processing information of each fault work order. According to the technical scheme of the invention, a lighter target decision tree is obtained through decision tree pruning, and batch processing is carried out on the plurality of fault work orders corresponding to the target fault type, so that the overall time consumption of the plurality of fault work orders corresponding to the target fault type during processing is reduced, and the overall response efficiency of a large batch of fault work orders is improved.

The invention relates to the technical field of e-commerce, and discloses a cross-border e-commerce abnormal order processing method and system based on reinforcement learning, and the method comprises the steps: collecting historical transaction records and real-time behavior records of a user, and obtaining an original data set; performing format unification according to the original data set to obtain a structured data set, and performing multi-dimensional feature extraction to obtain multi-dimensional behavior features; according to the multi-dimensional behavior characteristics, abnormal sequence analysis and risk level classification are carried out, and risk control adjustment parameters are matched; according to the risk control adjustment parameters and the structured data set, environment grouping, risk order identification and abnormal feature extraction are carried out to obtain abnormal distribution features; according to the abnormal distribution characteristics, comprehensive risk analysis is carried out through a pre-constructed neural network model, and a comprehensive risk score is obtained; and performing abnormal order judgment and risk control processing according to the comprehensive risk score and the historical transaction record, and optimizing a neural network model. The method improves the accuracy of abnormal order detection.

The invention relates to the field of artificial intelligence, and particularly discloses a cooperative office intelligent agent using method and device and a medium, the method comprises the following steps: receiving an input instruction from a user, and analyzing the input instruction to obtain an analysis result; generating a tool calling request based on the analysis result; based on the tool calling request, calling the target MCP Server to generate a tool execution result of the input instruction; and converting the tool execution result into a return result, and sending the return result to the user. And for an enterprise administrator, the intelligent scene access of the service can be completed only by additionally configuring the information of the MCP Server when the light application is originally added. Not only can the scene requirements of enterprise process automation, intelligent work order processing, cross-system data integration and the like be effectively met for enterprises, but also the MCP Server can be automatically expanded for enterprise users, and all the requirements of user office are met.

The invention discloses a new energy automobile rental order-charging collaborative energy consumption optimization system and method, and aims to solve the problems of high operation cost, poor user experience and difficult power grid adaptation in the new energy automobile rental industry. The system adopts a layered architecture of a hardware layer, a data layer, a business logic layer and an application layer, the hardware layer collects data of vehicles, charging piles and the like, the business logic layer realizes collaborative management and control through order management, charging scheduling, energy consumption prediction and a strategy optimization module, and the application layer serves enterprises, users and a power grid. According to the method, through data acquisition, order processing, energy consumption prediction, charging plan optimization and dynamic adjustment, peak-valley charging cost reduction, efficient charging pile utilization and power grid load balance are realized. According to the scheme, the enterprise operation cost can be reduced, the user satisfaction degree is improved, the power grid stability is guaranteed, the industry intelligent development is promoted, and remarkable economic and social benefits are achieved.

The invention discloses a client event work order processing method and device and a program product, and relates to the field of artificial intelligence, and the method comprises the steps: extracting feature data of a plurality of data types from an event work order, and fusing the feature data of the plurality of data types through a cross-modal attention mechanism to obtain multi-modal data; inputting the multi-modal data and the target knowledge graph into a graph neural network, and generating candidate solutions of the event work order through the graph neural network; under the condition that the event work order does not involve the financial risk information, reply information is generated according to the candidate solutions, and the event work order is responded according to the reply information; and otherwise, sending the event work order and the candidate solution to the target object so as to process the event work order. Through the method and the device, the problem of high work order assignment error rate caused by manual assignment of the event work order and processing of the event work order when a financial institution processes the event work order proposed by a client in the related technology is solved.

A method and an Internet of Things (IoT) system for allocating maintenance tasks based on smart gas are provided. A smart gas management platform of the Internet of Things (IoT) system is configured to: obtain call consultation data information; generate first data information from the call consultation data information; generate one or more maintenance feature vectors of a maintainer to be evaluated; determine a first maintenance evaluation value for the one or more maintenance feature vectors; generate second data information; determine and input user-side gas feature data, gas composition features, gas entry features, and gas upstream transportation features into a gas fault prediction model to predict a gas fault type; generate a location accuracy of one or more location feature vectors; adjust a work order processing scope of the maintainer to be evaluated based on a multi-dimensional maintenance evaluation value; and allocate subsequent maintenance tasks.

The invention provides an equipment exception work order intelligent management method and device, equipment and a storage medium, and the method comprises the steps: carrying out the multi-source fusion collection and semantic standardization processing of equipment exception data, and obtaining the standardized exception work order data; performing space-time correlation analysis on the abnormal work order data, and obtaining an equipment abnormal correlation network and a propagation path by calculating space-time correlation intensity and applying a causal inference algorithm; performing state prediction on the equipment anomaly association network and the propagation path by applying a graph neural network model, and obtaining work order state probability distribution and a change trend by adopting a graph convolutional network, an attention mechanism and a belief propagation algorithm; and executing dynamic priority ranking and resource allocation on the work order state probability distribution and the change trend, and obtaining an optimal work order processing sequence and a resource allocation scheme according to the network influence index and the resource matching degree. According to the invention, through abnormal correlation analysis and state probability prediction, intelligent management of the abnormal work order is realized.

The invention provides a clustering aggregation repetitive operation and maintenance order dispatching method, system and device and a medium, and belongs to the technical field of work order processing, and the method comprises the steps: obtaining a work order, and carrying out the preprocessing; extracting work order features from the preprocessed work order; unsupervised clustering is carried out on the feature vectors, the cosine similarity of any two feature vectors is calculated, and a neighborhood radius threshold value and the minimum point number are set; identifying a core point, and attributing all points in a neighborhood of the core point to an initial cluster where the core point is located; recursively checking the neighborhood of the points in the neighborhood, and finally forming a preliminary clustering result; clustering parameters are optimized according to the evaluation result, and a final clustering result is determined; common features of the feature vectors in the cluster are analyzed, and an interpretation text is generated; and merging the same cluster work order and sending the same cluster work order to a corresponding processing party. Through cooperation of a plurality of steps, full-process automation from original operation and maintenance work order data processing to efficient order sending is realized. The accuracy and efficiency of work order processing are effectively improved.

The invention relates to the field of computer software and information system engineering, in particular to a multi-account asynchronous bookkeeping data consistency collaboration system based on instruction driving, which comprises the following steps of: packaging a request into a standard instruction and routing according to an account to ensure ordered processing of the request and improve the processing efficiency and expandability of the system; repeated requests are avoided through idempotent verification, business is mapped into atomic operation through rule analysis, and the accuracy of business logic and data consistency are guaranteed; atomic operation is executed in a single transaction, asynchronous retry is performed during conflict, the atomicity of cross-account operation is ensured, and the concurrent processing capacity is improved; by buffering high-frequency account change and batch aggregation updating, write conflicts are reduced, the system performance is stabilized, and the throughput is improved; through account checking based on daily switching snapshots, pipeline playback and reverse account flushing are supported, and a rapid repair capability is provided; through dynamic configuration and versioned management of the bookkeeping rule, visual monitoring is provided, the operation and maintenance complexity is reduced, and the system flexibility is enhanced.

The invention relates to the field of information technology service management, and discloses an automatic work order processing method and system based on a process orchestration engine, and the method comprises the following steps: S1, carrying out semantic analysis, and obtaining feature information; s2, constructing a processing flow based on the feature information; s3, performing resource scheduling and automatic execution on the processing flow; s4, carrying out case recording; and S5, performing iterative optimization on at least one of the first preset model, the second preset model and the third preset model. The system comprises a work order analysis module, a process arrangement engine, a process execution module, a case recording module and a model optimization module. According to the method, work order semantic analysis is performed through the pre-training language model, and atomic operation utility is dynamically evaluated in combination with the Bayesian network model, so that the service intention can be automatically identified, the key entity can be extracted, and the optimal execution path can be reasoned and selected.

The invention belongs to the technical field of intelligent production, and particularly relates to an intelligent station production method and system based on the AI computer vision technology, and the method comprises the steps: obtaining order information, automatically extracting order data, and generating a structured order task; on the basis of the order tasks, order task distribution of the production line is carried out, and equipment operation parameters are correspondingly adjusted; physical station data are obtained, a digital twin virtual station simulation model is constructed in combination with the distributed order task and the standard operation process on the physical station, and the working state of each station is mapped in real time; performing product verification and operation detection based on the product state of the production line and the operation state of each physical station, and performing order task allocation optimization and employee operation track optimization in combination with the real-time operation state of each physical station; and on the basis of the verified product, electronic order signing is generated, and block chain evidence storage is carried out, so that full-link intelligence, automation and high efficiency of order processing, path planning, job execution and quality control are realized.

The invention relates to the field of take-out order processing, in particular to an intelligent take-out order processing method and system combining a virtual digital human and a large model. The method comprises the following steps: collecting an initial voice audio stream of a user based on a virtual digital person, carrying out real-time emotional state analysis and service personality parameter optimal matching of the user, and outputting a virtual personality model of optimal emotional matching; carrying out instant user order processing interaction analysis based on the virtual personality model, and carrying out deep demand mining to obtain a user order interaction demand; dynamic allocation processing is carried out based on user order interaction demands, and a digital human agent order processing strategy is constructed; and performing user potential operation intention prediction and service request interface preloading to obtain a service request preloading strategy. According to the invention, the real-time emotional state and the user demand of the user are matched based on the virtual human, the service intelligence and the adaptive ability of take-out order processing are enhanced, and the order processing efficiency and the user satisfaction are improved.

The invention provides an order processing method and device based on a large model agent, and electronic equipment, and relates to the technical field of voice interaction. The method comprises the steps of obtaining a to-be-processed order created by a service requester of an order processing system, and determining an order identifier and key content of the to-be-processed order; afterwards, the order identifier and the key content are integrated, first service information is generated, and the first service information is sent to the large model agent for processing. And obtaining a first target instruction generated by the large model agent based on the first service information, executing the first target instruction and generating a first instruction execution result. And sending the first instruction execution result to a service executor of the order processing system, obtaining a voice response initiated by the service executor according to the first instruction execution result, and performing order processing based on the voice response. The order processing efficiency can be improved.

The invention provides an e-commerce integrated order management system, and relates to the technical field of e-commerce, and the system comprises an order processing module which is used for receiving, splitting, merging and updating order states, automatically optimizing order circulation, improving the order processing efficiency, guaranteeing the real-time synchronization of the order states, and reducing the processing delay. In the invention, the data analysis module analyzes user behavior data (such as browsing, purchasing and returning) and market sales data in real time and predicts future commodity demands and sales trends by using the prediction model, and the system can automatically provide a real-time report for an operation team based on the data, so that the user experience is improved. The module helps to adjust the inventory replenishment cycle, sales promotion activities and commodity shelving plans, and has the core advantages that market changes are identified through big data analysis, response measures are automatically generated, the inventory management precision is improved, and the inventory overstock and stockout conditions are reduced.

The invention discloses an approximate multiplier, an operation method thereof, a processor and a chip, and belongs to the field of integrated circuits. The approximate multiplier comprises a high-order processing circuit, a low-order processing circuit, a high-low-order fusion circuit and an error compensation circuit, and the high-order processing circuit adopts a plurality of negative deviation approximate addition units, performs approximate addition on a high-order area of a partial product step by step and outputs an error signal; the low-order processing circuit adopts a positive deviation addition unit to process the low-order area to generate an intermediate result of positive deviation; the fusion circuit merges the high and low position results and outputs an initial multiplication result; the error compensation circuit compensates the result high order according to the error signal to offset the deviation. According to the approximate multiplier provided by the invention, the comprehensive performance of the multiplication unit in the aspects of area, power consumption and speed can be improved while the basic calculation precision is ensured.

The invention discloses an order anomaly detection and compensation method based on a message queue, and relates to the technical field of order processing, and the method comprises the steps: obtaining the operation time sequence data of a target downstream service associated with an abnormal order; generating order processing success probability distribution of the target downstream service through a time sequence prediction model; calculating the optimal retry time of the abnormal order through a target function in combination with a preset business constraint condition; delivering to a message queue for delayed retry; key events and corresponding attributes in the abnormal order processing process are obtained, and an abnormal track event sequence is constructed; mapping the abnormal trajectory event sequence into an abnormal trajectory fingerprint vector with a fixed dimension in response to the abnormal order reaching a compensation triggering condition; and performing matching in a preset abnormal mode library, and executing a corresponding compensation strategy according to a matching result. According to the invention, through prediction driving scheduling of the order abnormity retry time and automatic selection of the compensation strategy, the accuracy and stability of order abnormity processing are improved.

The invention relates to a client work order management method and system based on intelligent template dynamic configuration, and the method comprises the following steps: S1, obtaining a business demand document, historical work order data and a field rule library, extracting all business scene universal fields, and storing the universal fields in the field rule library; s2, constructing template metadata according to the field rule base; s3, dynamically generating a work order instance; s4, after the work order is generated, the whole work order processing cycle is driven according to the flow rule, and each work order node and operation action are dynamically executed according to the template flow chart configuration; and S5, regularly and automatically counting the field utilization rate, common process bottlenecks and high-frequency branches through data analysis, generating template optimization suggestions in combination with user feedback and user satisfaction survey data, automatically classifying new problem descriptions by the system according to a pre-trained machine learning model, recommending the most suitable template, and finally, carrying out data analysis on the new problem descriptions. And a work order dispatching strategy is optimized in combination with historical efficiency data. According to the invention, high flexibility and intelligence of work order management are realized.

The invention discloses a unified social credit code data quality control method based on a traceability technology, and relates to the technical field of quality control, and the method comprises the steps: collecting an original data set with a unified social credit code as an index, calculating the activeness, and forming a data stream with an activeness label; constructing a tree-shaped credit primitive structure through a depth-first algorithm, and determining an authoritative data source in combination with a Gaussian integral minimization model; conflict resolution is completed by using dynamic weight distribution, and a consistent credit file is output; sequentially executing structured verification, semantic consistency verification and business logic comparison, identifying deviation conditions of the key fields, and generating a difference report; based on the difference report, consistency verification is completed through version number verification and block chain evidence storage; and if the problem is not solved overtime, turning to manual work order processing. According to the method, risks caused by multi-source data conflicts and deviations are effectively reduced, and an accurate and credible data basis is provided for credit information management, financial supervision and decision support.

This application discloses an order processing method, apparatus, device, and storage medium. The order processing method includes: acquiring an augmented reality image of a sample product based on product order information from a product customizer and a product manufacturer, wherein the sample product is a product manufactured based on customized product information corresponding to the product order information; sending a product confirmation instruction to the product customizer, the product confirmation instruction including the augmented reality image, the product confirmation instruction being used to instruct the product customizer to determine whether the sample product meets the customization conditions based on the augmented reality image; and, upon receiving first feedback information from the product customizer, sending a batch production instruction to the product manufacturer, the batch production instruction being used to instruct the product manufacturer to batch produce the products customized by the product customizer based on the sample product. The order processing method provided according to the embodiments of this application can reduce transaction costs for both trading parties in related technologies and improve the efficiency of goods transactions.

A mail-order drug delivery system includes an order processing device configured to determine a first shipping mode and a corresponding first shipping carrier, generate an expected shipping duration associated with delivery of a drug, determine an origin forecasted temperature, determine a destination forecasted temperature, associate a shipping container with the drug to contain the drug, access temperature model data associated with the shipping container; and determine a predicted temperature of the drug at the shipping destination. The determination is based on forecasted temperatures, the expected shipping duration, the temperature model data, a next pickup time, a storage location of the drug, and a time difference between packing and the next pickup time. The system includes a packing device configured to receive the drug from a transport mechanism and selectively package the drug within the shipping container in response to the predicted temperature meeting a temperature-related storage requirement of the drug.

The application discloses a group order processing system and method, and belongs to the technical field of computers.The system comprises a first terminal, which is configured to send an order placing request to a server through a group order placing portal in response to an order placing operation; the server is configured to send an order placing notification to a second terminal; and the second terminal is configured to update order status information corresponding to a group identifier in an order status interface based on the order placing notification, and instruct a printer to print labels of goods of multiple orders belonging to the group in order from small to large according to the order picking identifiers in response to a label printing operation on a label of any group in the order status interface; wherein the picking identifiers of the multiple orders belonging to the group are assigned to the multiple orders according to the order information of the multiple orders belonging to the group, and the size of the picking identifiers is positively correlated with the number of goods categories.The system improves the efficiency of goods checking, packing and distribution.