1182 results about "Utilization rate" patented technology

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention discloses a computing power scheduling method and system based on dynamic load prediction and resource priority ranking. The computing power scheduling method comprises the following steps: collecting historical load data, task submission data and resource state data of each node in a computing power cluster; on the basis of the preprocessed multi-dimensional load feature data set, constructing an improved hybrid prediction model, optimizing model parameters through training, and predicting the load change trend of each computing power node in a future preset time period by using the trained model to obtain a node load prediction result; extracting a service level protocol parameter, a resource demand type and historical execution efficiency data of a to-be-scheduled task, and establishing a multi-dimensional resource priority evaluation index system; according to the computing power scheduling method, the problems of low resource utilization rate and high task response delay caused by low load prediction precision and mismatching of resource allocation and task priority in a traditional computing power scheduling method are solved, and the overall operation efficiency and service quality of a computing power cluster are improved.

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention relates to the technical field of intelligent scheduling, in particular to an enterprise production real-time monitoring and intelligent scheduling system based on artificial intelligence, which comprises a multi-source heterogeneous data fusion unit, a priority resource coupling decision unit, a bottleneck prediction and tracing unit and a scheduling instruction generation unit, the multi-source heterogeneous data fusion unit collects multi-dimensional data such as equipment vibration, temperature, order delivery time and the like in real time and constructs a joint feature vector, and the priority resource coupling decision unit dynamically adjusts task priority and resource allocation through a dual-channel depth Q network to cope with order insertion tasks and equipment health degree fluctuation. The bottleneck prediction and tracing unit predicts production bottlenecks and traces root causes by using a process dependency graph, a multi-modal fusion model and a causal discovery algorithm, and supports preventive maintenance and dynamic scheduling, and the scheduling instruction generation unit synthesizes a preorder result to generate an adaptive scheduling instruction. And enterprise production equipment utilization rate and production efficiency are improved.

The invention provides an intelligent boxing method and system. The intelligent boxing method comprises the steps of obtaining boxing task information and generating a boxing scheme. The boxing task information comprises the size, the weight, the stacking limitation, the number and the priority of each cargo, and the size, the bearing capacity and the occupation state of each loading unit. And under the condition that the size, bearing and stacking constraint conditions are met, a plurality of candidate boxing combinations are generated, the loading utilization rate, the residual space distribution and the shape matching degree are evaluated based on a preset optimization target, and an optimal scheme is selected. The system comprises an input terminal, a task information receiving module, a data storage module, a task and scheme storage module, an operation processing module and a final boxing scheme generation module. According to the method, multi-dimensional constraint matching and weight balance are considered, and space waste and transportation risks are avoided; the maximum greedy algorithm and the adaptive large neighborhood search optimization are combined to realize the combination of automation and experience. The method can be expanded to batch task scheduling and grouping sequencing, so that the calculation complexity is reduced, and the overall boxing and transportation efficiency is improved.

The invention relates to the technical field of packaging, in particular to an intelligent metering method for remnant base paper rolls of a corrugated paper board assembly line. The intelligent metering method for the remnant base paper rolls of the corrugated paper board assembly line comprises steps as follows: a plurality of base paper feeding positions are arranged in the conveying direction of the assembly line at intervals, a base paper feeding frame is arranged in each base paper feeding position, and two base paper feeding rolls located on two sides of each base paper feeding frame are arranged on the base paper feeding frame; each base paper feeding roll is provided with lap recording sensors and incremental encoders, the lap recording sensors are used for monitoring the number of paper feeding laps of base paper clamped on the corresponding base paper feeding roll, the incremental encoders are used for monitoring the length of each lap after the base paper on the base paper feeding roll passes by one lap, and the lap recording sensors and the incremental encoders are connected with a controller. Intelligent metering can be realized, the operation cost of an enterprise is reduced, labor is saved, and the utilization rate of enterprise resources is increased.

The invention discloses an order-driven cross-factory collaborative production system, and relates to the technical field of intelligent manufacturing and supply chain collaboration, and the system obtains the productivity data, logistics cost and tax policies of a plurality of production bases such as Ningbo, Thailand and America in real time, and carries out the intelligent splitting and distribution of orders through a multi-base productivity game algorithm. And dynamic optimal matching of the order and the productivity is realized. Meanwhile, the system integrates WMS inventory data and third-party logistics real-time quotation, a transportation scheme with the lowest total cost is generated by adopting a genetic algorithm, and cross-border logistics and tax expenditure are remarkably reduced. The system overcomes the problems of information isolated island, response lag, extensive cost control and the like in traditional multi-factory production, realizes global productivity collaborative optimization and supply chain integrated intelligent decision, and improves the enterprise order performance efficiency and the overall resource utilization rate.

The invention relates to the technical field of cloud computing, and discloses an enterprise resource planning data efficient processing method based on cloud computing, and the method comprises the steps: collecting system data of finance, supply chain, production management and the like through a multi-source interface module, and carrying out the preprocessing through a hierarchical fusion framework, thereby obtaining a standardized data set; the method comprises the following steps of: selecting a multi-target scheduling model, inputting the multi-target scheduling model into a task decomposition model, generating task decomposition parameters based on a block processing mechanism and a service association rule, constructing the multi-target scheduling model aiming at the shortest processing duration and the minimum resource occupation, and outputting an optimal scheduling scheme by adopting a dynamic allocation algorithm. A hierarchical execution control model comprising a strategy layer, a coordination layer and an execution layer is established, the strategy layer performs global task planning, the coordination layer performs local resource adjustment, and the execution layer tracks tasks based on a load balancing algorithm and outputs a processing control instruction to realize efficient processing. The method improves the data processing efficiency and the resource utilization rate, and provides support for enterprise decision making.

The invention discloses a charging pile management method and system based on data analysis, and belongs to the field of charging management.The charging pile management method based on data analysis comprises the following steps that a time sequence prediction model is constructed based on historical charging session data, environmental factors, special time period information and the real-time occupancy state of a charging pile, predicting peak periods and valley periods of charging demands in different areas from a few hours to one day in the future; the method has the beneficial effects that the charging service price is dynamically adjusted by predicting the charging demand peak period and the charging demand valley period and combining the overall utilization rate prediction result of the charging piles in the region in the period, so that the regional charging price in the charging demand peak period is relatively high, and the regional charging price in the charging demand valley period is relatively low; therefore, the user is guided to actively perform peak shifting charging or select other areas for charging, the load distribution of the charging piles is optimized, the queuing pressure in the peak period of the charging demand is relieved, and the infrastructure utilization rate in the valley period of the charging demand is improved.

The invention discloses an intelligent electric meter box operation and maintenance management method and system, and relates to the field of electric power operation and maintenance management. The intelligent electric meter box operation and maintenance management method comprises the steps of multi-source data acquisition and preprocessing, equipment health state dynamic evaluation, fault root cause positioning and prediction, maintenance task dynamic scheduling, adaptive maintenance execution and closed-loop optimization, feedback of a maintenance result to a health evaluation model, updating of a dynamic weight coefficient, and monthly generation of an equipment health white book. And continuously optimizing the operation and maintenance strategy. According to the intelligent electric meter box operation and maintenance management method, the fault prediction model is established based on the LSTM neural network, the typical fault mode is matched in combination with the knowledge graph, the fault prediction accuracy is improved, and non-planned power failure is reduced. The improved genetic algorithm improves the utilization rate of maintenance resources and shortens the emergency response time. Predictive maintenance replaces regular maintenance, invalid inspection is reduced, and the spare part inventory turnover rate is increased. Human errors are reduced through AR assistance; and the maintenance operation accuracy is improved.

The invention discloses an intelligent-based multi-task self-adaptive scheduling system for a pension accompanying robot, and belongs to the technical field of multi-task self-adaptive scheduling. Comprising a multi-modal task perception fusion module, a dynamic task evaluation module, a dynamic task priority generation module, a quantum causal reasoning module, a quantum optimization task scheduling module, a multi-task collaborative optimization module and a man-machine collaborative interaction module. Task features are mapped to a multi-dimensional weight space, a time attenuation coefficient is introduced to dynamically adjust the influence of historical causal association, a dynamic adjustment mechanism ensures timely execution of high-priority tasks, a quantum superposition state is utilized to parallelly search a global optimal solution through a quantum optimization task scheduling module, and the task scheduling efficiency is improved. Through a dynamic priority queue and a conflict solution feedback mechanism, the scheduling scheme is iteratively optimized, and the feasibility and the resource utilization rate of the scheduling scheme are remarkably improved.

The invention relates to the technical field of textile production scheduling and resource allocation planning, in particular to a textile equipment scheduling management and optimization system of a textile factory, which comprises a data perception and integration module, a scheduling optimization decision module, a plan execution and equipment control module and a closed-loop feedback and self-learning module. The data sensing module collects and fuses order data, equipment operation state data and production environment data in real time, and a unified real-time data view is generated through cleaning and alignment processing; the scheduling module runs a mixed integer programming dynamic model, and minimizes the comprehensive cost and synchronously optimizes the equipment utilization rate and energy consumption in combination with rolling horizon optimization under the condition of meeting the process constraints of order delivery time limit and process dependency matrix representation; the plan execution module analyzes the scheduling instruction into an equipment executable instruction, drives equipment operation and collects execution deviation; and the closed loop module triggers rescheduling when the deviation exceeds the limit or the order is plugged. The scheduling accuracy and adaptability are improved, the cost is reduced, and efficient and stable production is guaranteed.

The invention discloses an EPC project resource dynamic scheduling method based on multi-objective optimization, and the method comprises the steps: extracting project starting time and activity attributes from an EPC project, constructing a project activity list and a project resource list, constructing a multi-objective function, a decision variable and a constraint condition, and carrying out the dynamic scheduling of the EPC project resource. And processing the project activity list, the project resource list and the project starting time by using a multi-objective optimization algorithm containing crossover and mutation operation to obtain an optimal resource scheduling scheme containing the project construction period, the cost and the resource utilization rate, and after the scheme is issued and executed, obtaining the progress, the cost and the resource use condition in real time, thereby realizing the resource scheduling. A progress deviation rate, a cost performance index and a resource load rate are calculated and normalized, a comprehensive evaluation value is obtained through a linear weighting model, and whether a multi-objective optimization algorithm is optimized or not is judged according to the comprehensive evaluation value, namely, the weight of each objective function is adjusted according to the highest index after normalization, and a scheme is regenerated. According to the invention, resource dynamic optimization scheduling is realized, and project management efficiency is improved.

The invention provides a method for checking dynamic allocation of reservation resources, which belongs to the technical field of intelligent allocation and comprises the following steps: defining a reservation resource set and a reservation request set; a three-layer conflict detection and cooperative compensation mechanism is established, conflict detection is carried out on each reservation request to obtain a conflict set of each reservation request, and the conflict set comprises various conflicts of each reservation resource matched with the corresponding reservation request; determining the time utilization rate of each matching resource based on all the reservation requests and the capacity utilization rate of each request time moment, and establishing an idle possible set of each request time moment according to a preset triggering condition; and according to the conflict set of the reservation requests and a corresponding reservation request configuration priority obtained according to the conflict set of the reservation requests, performing mapping matching on the idle possible set to realize dynamic allocation. The accuracy of appointment resource allocation is ensured, and the user experience and the resource operation efficiency are improved.

The invention provides a heterogeneous computing power resource pooling scheduling platform which comprises a task and resource tensor construction module, a scheduling decision module, a breakpoint state generation module and a rescheduling and recovery module. According to the platform, task requirements and node resources are modeled into a tensor structure in a unified mode, and accurate initial scheduling is achieved through a structured scoring function containing conditional penalty terms. In the task running process, interruption judgment is carried out based on the node dynamic state and the scheduling score, and a breakpoint state tensor containing an execution state is generated. When interruption occurs, a proper node is selected through compatibility screening, and task seamless migration and execution recovery are realized by utilizing the stored state. According to the method, the technical problems of non-uniform resource expression, lack of scene adaptability and incapability of realizing uninterrupted migration in a heterogeneous computing power environment are solved, and the resource utilization rate and the service continuity are remarkably improved.

The invention discloses a production process scheduling method, system, equipment and medium based on the industrial Internet of Things, and relates to the technical field of industrial Internet of Things data processing, and the method comprises the steps: obtaining a to-be-scheduled production process, obtaining a preset production queue which is composed of a current production task and a plurality of to-be-processed production tasks, obtaining completion time and production information; obtaining environment trend information, obtaining a dynamic correction index, obtaining a process utilization rate, and obtaining a process utilization index based on the correction model, the process utilization rate and the dynamic correction index; obtaining inventory unit time cost and delivery deadline, and obtaining a scheduling index based on the scheduling model, the completion time, the process utilization index, the inventory unit time cost and the delivery deadline; and sequentially arranging the plurality of production tasks to be processed according to the scheduling indexes from small to large, generating a scheduling queue, and executing the production tasks according to the scheduling queue. The method has the advantages of dynamic environment self-adaption, multi-target collaboration and good scheduling effect.

The invention discloses a large model task scheduling optimization method and device, and the method comprises the steps: analyzing a task type and a service quality requirement corresponding to a large model reasoning task when a scheduling request of the large model reasoning task is received; determining current state information and historical execution information corresponding to the large model reasoning task; inputting the task type, the service quality requirement, the current state information and the historical execution information into a pre-trained scheduling agent, so that the scheduling agent outputs a scheduling action corresponding to the large model reasoning task; executing a scheduling action on the large model reasoning task, and recording corresponding performance data of the scheduling action in an execution process; and inputting the performance data into the scheduling agent, so that the scheduling agent updates a weight parameter of the scheduling agent according to the performance data and a preset reward function. The problem that a traditional static scheduling strategy cannot adapt to a complex heterogeneous environment is effectively solved, and the system response efficiency, the resource utilization rate and the user satisfaction degree are remarkably improved.

The invention relates to the technical field of cross-border logistics, in particular to an artificial intelligence algorithm for cross-border overseas warehouse location storage and picking path optimization, which comprises the following steps: S1, obtaining order details, commodity characteristics and warehouse environment data, and monitoring warehouse states and cargo storage conditions in real time; s2, data preprocessing is carried out, features are extracted, clustering analysis is carried out, and association rules are mined; s3, performing rule matching, constructing a mixed integer programming model, performing constraint processing, and outputting a commodity storage strategy; and S4, converting the storage strategy into a warehousing operation instruction, and executing the warehousing operation instruction. The method has the beneficial effects that the accuracy of goods classified storage and the space utilization rate of the warehouse location can be improved, and the storage cost of the warehouse location is reduced.

The invention provides a calculation and power collaboration system and method, and belongs to the field of calculation and power collaboration, and the system comprises a data collection module which collects power data, calculation power data and price data, and constructs a power supply time sequence diagram and a calculation power demand time sequence diagram; a prediction module which generates a power supply predicted value and a computing power demand predicted value based on the power supply sequence diagram and the computing power demand sequence diagram; the intelligent matching module is used for generating a matching decision scheme based on the power supply predicted value and the computing power demand predicted value; the task scheduling module is used for performing differential scheduling based on the matching decision scheme; and the feedback optimization module is used for evaluating the task scheduling result and feeding back the evaluation result to the prediction module and the intelligent matching module. According to the method, the power supply and the computing power demand are predicted, the active scheduling decision is generated based on the prediction result, and the task scheduling module executes space-time flexible scheduling of the task, so that the consumption utilization rate of the new energy power and the supply stability of the computing power service are remarkably improved.

The invention relates to the technical field of data processing, in particular to a fault repair method and system based on big data analysis. The method comprises the following steps: in response to a received new repair request, obtaining a state deviation degree based on a difference between a real-time operation parameter and each type of historical operation parameter; converting the text information in the new repair request into a real-time fault vector, and calculating the semantic similarity between the real-time fault vector and the historical text vector of each fault type; for any fault type, performing weighted fusion on the state deviation degree and the semantic similarity corresponding to the fault type to obtain a comprehensive evaluation index of the fault type, and determining the fault type with the highest evaluation index as a diagnosis result of current repair; and according to the diagnosis result, intelligent order sending is carried out on the repair task. The method has the effects of improving the maintenance timeliness and improving the human resource utilization rate.

The invention discloses an e-commerce supply chain intelligent scheduling optimization system and method based on big data, and relates to the field of intelligent logistics scheduling, and the system comprises a demand prediction module, a balance optimization module, a path optimization module, a collaborative replenishment module and a feedback driving module. According to the method, the deep learning model and the time sequence decomposition method are combined, and multi-level modeling is performed on the historical order data and the real-time sales data, so that supply chain imbalance caused by prediction deviation is avoided. A dynamic replenishment plan is generated based on the prediction result and the inventory early warning information, real-time matching of the inventory and the demand is achieved, the stockout rate and inventory redundancy are effectively reduced, and the storage resource utilization rate is improved. By establishing the multi-constraint path optimization model and comprehensively considering order distribution, vehicle load and real-time traffic data, the distribution path can be dynamically adjusted, the vehicle utilization rate and the distribution time efficiency are improved, and the energy consumption and the cost caused by empty driving and detour are reduced.

The invention discloses an intelligent scheduling method based on task optimization. According to the method, firstly, the task load of each time period is predicted through historical data, and then an M / M / c queuing model is established to quantify a task queuing duration expected value as an optimization target. A two-stage optimization strategy is adopted: in the first stage, the optimal number of service desks in each time period is determined by applying dynamic planning, and accurate distribution of human resources is realized; in the second stage, specific employees are distributed to shifts through constraint planning, and meanwhile, complex constraints such as total working time fixing, continuous working duration, shift intervals and skill matching are met. According to the system, a queuing theory, constraint optimization and a prediction model can be combined, and on the premise of ensuring the feasibility of a scheduling scheme, the task processing waiting time is effectively shortened, and the service quality and the human resource utilization rate are improved.

The invention relates to the technical field of intelligent production management, and discloses an intelligent production comprehensive management method and system for realizing timed feeding, and the method comprises the steps: obtaining biological characteristic data of a fed object, modeling to generate feeding demand model data, and confirming timed and quantitative or dynamically adjusting a feeding mode; setting and optimizing an initial plan based on different modes, and generating time period adjustment data and adaptation range data; dynamic feeding parameter adjustment data are generated through prediction and analysis, and a control strategy is constructed; feedback data is collected to evaluate efficiency and optimize the strategy. The system comprises a feeding mode recognition module, a timed feeding plan module, a dynamic feeding adjustment module and a strategy optimization module. Through multi-dimensional data integration and dynamic adjustment, accurate feeding management is achieved, the feeding efficiency and the feed utilization rate are improved, the method is suitable for the field of intelligent production management, and the problem that a traditional feeding mode is insufficient in flexibility and accuracy is solved.

The invention discloses a data storage method and device and a computer storage medium, and the method comprises the steps: obtaining a system storage capacity, a new request resource capacity and a system used capacity, and obtaining a system capacity utilization rate based on the system storage capacity and the system used capacity; in response to the fact that the system capacity utilization rate reaches a threshold value and the current residual capacity does not meet the new request resource capacity, determining a storage capacity needing to be expanded based on the system storage capacity, the new request resource capacity, the used capacity and a preset target utilization rate, creating an expansion space corresponding to the storage capacity, and updating the system storage capacity; based on the updated system storage capacity, determining data to be stored of each node, and determining a data block to be migrated; migrating the to-be-migrated data block to the target node, and updating the data storage state of the current node; through the method, the change of the storage capacity can be dealt with more intelligently.

The invention relates to the technical field of automatic warehouse material, and discloses an intelligent goods allocation distribution system for automatic warehouse logistics, and the system comprises a goods allocation data collection module; a dynamic state detector; a multi-source data fusion unit; a three-dimensional storage modeling module; intelligently distributing a strategy engine; and a PLC control interface. According to the intelligent goods allocation allocation and scheduling system for automatic warehouse logistics, a goods allocation allocation scheme is generated through a multi-target Pareto optimization algorithm, the balance between carrying distance minimization min sigma alpha * Di and space utilization rate maximization Ut is achieved, and compared with a proportional static allocation rule, the warehouse space utilization rate is increased to 82%; a dynamic grid map technology is integrated, and a track cross risk area (red early warning grid) of the stacker and the AGV is marked in real time, so that the equipment collision probability is reduced by 90%; and in combination with a goods relevance knowledge graph, special goods such as medicines are clustered and stored according to temperature zones, and the energy consumption of cross-temperature-zone operation of equipment is reduced by 35%.

The invention provides a test task scheduling method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a test case set and defect level information corresponding to the test case set in response to a test task request; based on the defect level information and the historical execution time interval, the initial priority of each test case is calculated, and the initial priority is calculated based on a time attenuation coefficient; the test cases are pushed into a task queue according to the initial priorities, case priority adjustment is conducted on the test cases in the task queue based on a priority aging model, and the priority aging model dynamically adjusts the case priorities according to task waiting time; and based on the adjusted use case priority and the comprehensive load weight of each execution node, distributing a test task corresponding to the test use case to a target execution node of which the comprehensive load weight is greater than a distribution threshold value. The utilization rate of system resources is improved; the influence of old data on the current task priority is gradually weakened, and the accuracy and timeliness of priority calculation are improved.

The invention discloses a financial intelligent backbone network flow scheduling method and system based on SRv6, and relates to the technical field of network scheduling, and the method comprises the following steps: S1, carrying out the recognition of the ingress service flow, and dividing the ingress service flow into predefined service types; s2, matching a corresponding SRv6 traffic tunnel model for each service type; step S3, distributing differentiated QoS strategies for different types of service flows, and executing corresponding queue scheduling; s4, aiming at the SLA requirements of different service types, constructing a corresponding optimization objective function to carry out path calculation; s5, when it is detected that the link utilization rate exceeds a preset threshold value, an intelligent tuning process is automatically triggered; and S6, issuing the optimal SRv6 strategy obtained by calculation to entrance equipment for execution. According to the invention, an intelligent and efficient financial backbone network architecture is constructed, and a refined traffic scheduling strategy is realized, so that the utilization rate of network resources and the operation and maintenance efficiency are improved.

The invention provides a multi-objective optimization intelligent stereoscopic warehouse scheduling method and system, a medium and computer equipment. The method comprises the steps that S1, attribute data of goods in a warehouse are collected in real time; s2, based on the attribute data, weights of a plurality of optimization targets are dynamically determined through a self-adaptive weight adjustment mechanism, and the optimization targets at least comprise the space utilization rate, the access efficiency and the goods shelf gravity center; s3, carrying out multi-objective optimization on the shelf layout by adopting a particle swarm optimization algorithm, and carrying out weighted calculation on an optimization objective by the particle swarm optimization algorithm according to the dynamically adjusted weight in the step S2 to generate an optimal shelf layout scheme; s4, feeding back the optimal shelf layout scheme to a warehouse management system for execution, continuously adjusting weights and optimizing strategies according to cargo attribute data collected in real time, and dynamically balancing the space utilization rate, the access efficiency, the shelf stability and other targets through the combination of an adaptive weight adjustment mechanism and a particle swarm optimization algorithm, so as to achieve the optimal shelf layout scheme. And meanwhile, the algorithm convergence speed and the system real-time response capability are improved.

The invention discloses a method for automatically generating a matching and cutting result of a clothing and luggage manufacturing industry order, and the method comprises the steps: receiving the original demand data of a plurality of orders, and obtaining an order data set which is aggregated according to a version group; according to the version group order data set, an initial matching and cutting scheme set with excess material identification is obtained; according to the initial matching and cutting scheme set with the excess material identification, bed number / version number multi-objective optimization processing is carried out through a resource constraint scoring model, and an optimal matching and cutting scheme is obtained; according to the optimal matching and cutting scheme, performing cutting piece physical parameter packaging by utilizing an instruction translator to obtain a structured cutting piece instruction set which can be identified by a typesetting system; and according to the structured cutting instruction set, carrying out cutting bed separation task construction through a task work order generator to obtain an automatic cutting bed task order with a work order identifier. By utilizing the embodiment of the invention, full-link automation from data to production can be realized, the resource utilization rate of the cutting bed is improved, and the manual intervention cost is reduced.

The invention relates to the technical field of data processing, and discloses an operation and maintenance task processing intelligent distribution management system and method based on data analysis, and the system comprises a data collection module; a life cycle analysis module; a task plan making module; an operation and maintenance personnel task distribution module; and a path planning and dynamic adjusting module. Based on data analysis, in combination with the equipment operation state, the skill of the operation and maintenance personnel and the load condition, the execution sequence and route of the operation and maintenance tasks are optimized through intelligent allocation and route planning, task allocation and route can be dynamically adjusted according to the health condition of the equipment, the task priority and the real-time information of the operation and maintenance personnel, and the operation and maintenance efficiency is improved. The operation and maintenance task is efficiently and accurately completed, and the overall working efficiency and the resource utilization rate are improved.