352results about How to "Avoid idling" patented technology

The invention relates to the technical field of logistics transportation scheduling, in particular to a logistics management system based on intelligent scheduling, which comprises a data acquisition module, a demand analysis module, an intelligent scheduling module, a path planning module, a monitoring and feedback module, an optimization module and the like. The data acquisition module is used for collecting information such as goods, vehicles, storage, customer demands and traffic road conditions; the demand analysis module predicts logistics demands by using a time sequence analysis or machine learning algorithm; the intelligent scheduling module adopts a greedy and dynamic programming mixed algorithm to allocate resources; the path planning module calculates an optimal path according to road conditions and starting and ending points; the monitoring and feedback module monitors logistics parameters in real time and feeds back abnormity; and the optimization module optimizes the scheduling scheme according to the fitness function based on the genetic algorithm. According to the invention, data and resources of all links of logistics are effectively integrated, accurate demand prediction, efficient resource configuration, transportation path optimization, real-time monitoring feedback and overall performance improvement of the system are realized, the logistics cost is significantly reduced, and the transportation efficiency and the service quality are improved.

The invention discloses a data analysis method, system and device based on distributed communication and a medium, and relates to the technical field of data processing, and the method comprises the steps: monitoring a node state in real time based on a distributed communication network, obtaining a node resource state data set, building a node performance evaluation model through a machine learning algorithm, and obtaining a node performance evaluation result; computing resources of the data analysis tasks are dynamically allocated to optimize resource allocation, a task allocation strategy is designed according to requirements and node performance of the data analysis tasks, and a distributed data processing framework is established; the method comprises the following steps: performing distributed decomposition and executing a calculation data analysis task, aggregating and collecting distributed calculation results, and dynamically adjusting and optimizing resource task allocation to obtain a final data analysis result, so that a node state can be monitored in real time, and a resource allocation scheme can be intelligently adjusted; according to the advantages of dynamically optimizing task allocation according to the communication condition and the computing power of the node, the problems of insufficient dynamic resource management and poor task scheduling flexibility in the prior art are solved.

The invention provides an MBB multi-channel local aggregation superposition transmission method and an MBB multi-channel local aggregation superposition transmission system. Belongs to the technical field of communication. The method comprises the following steps that: a plurality of physical links are established in a wireless communication system, and the system selects the plurality of optimal physical links for aggregation according to a current network environment, a current equipment state and a current user demand; dividing data to be transmitted into a plurality of data packets, packaging each data packet according to the characteristics of the selected physical link, and transmitting the packaged data packets in parallel through the selected physical link; in the transmission process, the system monitors the transmission state of each link in real time, and dynamically adjusts the transmission rate and power distribution of each link according to the current network load and equipment state. By transmitting a plurality of physical links in parallel, the network bandwidth can be fully utilized, the data transmission rate is improved, and the transmission delay is reduced.

The invention relates to task scheduling, in particular to an intelligent task optimization method based on a self-adaptive multi-dimensional dependency graph and dynamic scheduling, which comprises the following steps of: inputting task information and resource information; constructing a self-adaptive multi-dimensional dependency graph according to the task information; in the task execution process, the task state of each task is monitored in real time, and the self-adaptive multi-dimensional dependency graph is dynamically updated; calculating the effective priority of the current task, and updating the effective priority of the current task according to the updated adaptive multi-dimensional dependency graph; dynamically scheduling the current task by adopting a dynamic scheduling algorithm; monitoring a task execution state and a resource use condition of each task in real time; according to the task execution state and the resource use condition, whether dynamic scheduling needs to be adjusted or not is judged until all tasks are executed; according to the technical scheme, the defect that in the prior art, accurate and efficient task optimization scheduling is difficult to carry out under the multi-dimensional dependency relationship between the tasks can be effectively overcome.

The invention relates to the technical field of computing power optimization, and provides a computer and cloud computing power optimization method based on multi-task collaboration. The method comprises the following steps: receiving Q to-be-executed tasks of a local cloud collaborative network; performing priority constraint on the Q to-be-executed tasks according to the multivariate priority evaluation factors, and establishing a task chain; collecting callable computing resource information of a local cloud collaborative network, and establishing a resource space; based on the resource space, performing task chain resource allocation by utilizing a computing resource scheduling optimization algorithm to obtain a scheduling strategy chain; performing cloud computing power balance optimization according to the scheduling strategy chain to obtain an optimized chain; and executing the task chain according to the optimization chain based on the cloud collaborative network. The technical problems of unbalanced scheduling and low resource utilization rate between the cloud and the local computing resources are solved, and the technical effects of improving the cooperative work efficiency and the resource utilization rate of the cloud and the local computing resources through priority constraint and computing resource scheduling optimization are achieved.

The invention discloses a self-adaptive resource scheduling method for improving cluster reconstruction efficiency, and relates to the field of resource scheduling distribution, and the method comprises the steps: monitoring the resource use data of each node in a cluster in real time through a monitoring agent, and obtaining the health state of the node through a heartbeat mechanism; collecting input task requests, predicting the resource demand quantity of each task, and performing preliminary queue processing on the tasks to obtain a task queue group; setting a resource competition constraint according to the task queue group and the resource use data and the health state of each node, obtaining dynamic resource scheduling corresponding to each task by using an optimization algorithm, and allocating the tasks to each node; a resource scheduling dynamic graph is constructed, a visual interface is set, and the resource scheduling dynamic graph is arranged on the visual interface and used for displaying resource scheduling, providing a user-friendly interface and realizing high-efficiency scheduling of adaptive resources in a cluster reconstruction process.

The invention discloses a 5G communication data transmission management system, which relates to the technical field of communication data transmission and comprises a real-time resource monitoring module, an intelligent evaluation module, a feature extraction module, a transmission process division module, a normal transmission management module and an overload transmission regulation and control module, through a deployed network monitoring tool and performance monitoring software, the resource occupation condition in the video monitoring data transmission process is monitored in real time. According to the invention, the system intelligently evaluates the resource occupation condition through real-time monitoring and a machine learning model, and dynamically regulates and controls the resources, reduces the delay, accelerates the analysis speed and guarantees the emergency response in the case of high load by using the queue expansion index, the link fluctuation index and the queue theory model. Through transmission process division, efficient management in a normal stage and dynamic optimization in an overload stage are realized, timely response and resource release are ensured, waste is avoided, and the stability and efficiency of the system under various loads are improved.

The invention provides an edge computing resource allocation method and system based on multi-objective optimization, and relates to the field of data processing, and the method comprises the steps: determining the resource demand feature of each unmanned retail store and the resource demand association feature between any two unmanned retail stores according to the historical computing resource demand information of a plurality of unmanned retail stores, determining an edge calculation layout scheme, and setting a plurality of edge calculation devices; predicting future computing resource demands of the plurality of unmanned retail stores according to the resource demand feature of each unmanned retail store, the resource demand association feature between any two unmanned retail stores and the demand prediction auxiliary information; acquiring resource allocation auxiliary information; according to the method, the edge computing resource allocation scheme is determined according to the future computing resource demands and the resource allocation auxiliary information of the unmanned retail stores, the edge computing resources are allocated to the unmanned retail stores, and the method has the advantage of improving the flexibility of edge computing resource allocation in an unmanned retail scene.

The invention relates to the technical field of natural language processing, in particular to a method and system for achieving intelligent number asking based on a large model, and the method comprises the following steps: integrating data from various data sources including a database, a data warehouse, WEB service and Excel files; the data is cleaned, converted, standardized and normalized through the data management center, so that the data quality is ensured, and basic data support is provided for subsequent intelligent question analysis; the beneficial effects are that the method for realizing intelligent number asking based on the large model provided by the invention responds to complex and changeable data requirements in real time, helps an enterprise to quickly make a data-based decision, and reduces the time cost of waiting for the assistance of an IT department or a data analysis team; through effective integration and intelligent search of mass data resources, various data in an enterprise can be fully mined and utilized, and idleness and waste of the data resources are avoided.

The invention discloses a self-service rental vehicle intelligent scheduling and dynamic battery distribution method, system and device and a storage medium. The self-service rental vehicle intelligent scheduling and dynamic battery distribution method comprises the following steps: S1, predicting a battery demand of a rental network in a future set time period through a linear regression model, and feeding back a prediction result to a cloud scheduling platform; s2, a leasing user requests to lease at a leasing network through a user side application program; s3, the cloud scheduling platform extracts the inventory number information of the lease network in the S2; s4, the cloud scheduling platform continues to extract the historical lease information of the lease user in the S2; s5, the cloud scheduling platform performs judgment through a linear regression model in combination with the detailed information of the batteries and the vehicles in the renting network in the S3 and the historical renting information of the renting users in the S4; and S6, the cloud scheduling platform notifies the battery replacement cabinet system to open a corresponding battery compartment door. The method has the advantages of intelligent scheduling and distribution, improved distribution efficiency, prolonged battery life and the like.

The invention discloses a multi-key index fusion service resource scheduling method based on AI large model driving, and relates to the technical field of intelligent resource scheduling, when new service resources are generated, a scheduling process is automatically triggered, and service task information of the service resources is identified and collected; and collecting latest engineer related data, and integrating engineer data and task information to obtain a multi-dimensional task data set. According to the multi-key index fusion service resource scheduling method based on AI large model driving, through comprehensive consideration of factors such as working saturation and distance, overfatigue of engineers and unnecessary path time waste are avoided, resources can be quickly responded, and the average duration of resource processing is reduced, so that the overall service efficiency is improved, and the service quality is improved. And the resources are allocated according to the skills and historical good reputation rates of the engineers, so that the resources are ensured to be processed by the engineers with corresponding capabilities and high service quality, and the success rate of resource processing and the customer satisfaction are improved.

The invention belongs to the field of intelligent manufacturing, and provides an intelligent recommendation method and system based on a shared robot, and the method comprises the steps: receiving a picture and a processing demand provided by a user; determining the processing attribute of the target workpiece according to the picture; determining a processing sub-task according to the processing attribute; determining an optimal shared robot combination according to the processing subtasks and the processing requirements; and displaying the optimal shared robot combination to the user.

The invention relates to the field of earth-rock engineering, and discloses an organization design optimization method for improving the work efficiency of earth-rock engineering machinery, and the method comprises the following steps: S1, carrying out the modeling of each process in the earth-rock engineering through a directed acyclic graph; s2, calculating the earliest starting time and the latest starting time of each process by using a critical path method based on the process relation model, identifying a critical path of the project, and determining a critical process which affects the project construction period; s3, on the basis of the identified key process, performing process scheduling optimization by using mixed integer linear programming to ensure that the project period is minimized under resource limitation, and generating an optimized process scheduling scheme; and S4, based on the optimized process scheduling scheme, establishing a multi-stage queuing theory model, and optimizing the scheduling of the mechanical equipment. By optimizing procedure scheduling and mechanical equipment scheduling, the construction efficiency of earth-rock engineering is remarkably improved, the construction period and resource waste are reduced, and it is ensured that a project is efficiently completed on schedule.

The invention discloses a federal learning algorithm based on adaptive aggregation and dynamic training, and belongs to the field of deep learning, and the learning algorithm specifically comprises the following steps: S1, each client receives an initial global model parameter, and carries out model training by using local data; s2, locally updating the weight of the model by adopting stochastic gradient descent; s3, after local training is completed, the client side calculates a model loss value of the current round, and the loss is reserved to serve as a reference for subsequent adaptive aggregation; s4, on the client side, each client predicts the time demand of the next round of training by adopting a dynamic training strategy according to the historical training time of the client; according to the method, the accuracy and generalization ability of the model are improved, resource idling is avoided, it is ensured that computing resources of all clients are fully utilized, the convergence speed of the global model is increased, the computing overhead is reduced, resource utilization is optimized, and the cost is reduced.

The invention relates to a heterogeneous GPU (Graphics Processing Unit) resource scheduling method and system based on the combination of meta learning and reinforcement learning, and aims to improve the utilization efficiency of computing resources in large model training and reasoning and improve the adaptive capacity of tasks and the response speed of the system. According to the method, efficient scheduling of GPU resources is achieved through the five steps of task feature extraction, meta-learning strategy initialization, reinforcement learning scheduling optimization, task scheduling execution and dynamic strategy adjustment. Wherein the meta-learning module generates an initial scheduling strategy by utilizing knowledge accumulated by historical tasks, so that the exploration time is shortened; and the reinforcement learning module continuously optimizes a scheduling strategy through environment feedback to realize efficient distribution of tasks. The system can adaptively adjust strategies according to different task requirements and dynamic changes of GPU hardware resources, and therefore higher resource utilization rate and overall performance optimization are achieved.

The invention provides a model training method and device, electronic equipment and a storage medium, and relates to the technical field of distributed computing. The method comprises the steps of dividing a context into context tasks based on structural analysis of a to-be-trained model; performing dependency relationship analysis on the context task to determine a task dependency graph; configuring priorities for the context tasks according to the task dependency graph, distributing the context tasks with the configured priorities to corresponding computing nodes, training according to an execution sequence, and obtaining load information and task execution information of the computing nodes; and according to the load information and the task execution information of the computing nodes, scheduling context tasks among the computing nodes, adjusting the execution sequence of the context tasks in the computing nodes, and outputting a training result. According to the real-time load condition of each node, task allocation is adjusted, load balancing of the computational nodes is realized, the utilization efficiency of computational resources is improved, a context task scheduling strategy is optimized, the utilization rate of the computational resources is improved, and the training time is shortened.

The invention belongs to the technical field of engineering project material intelligent distribution management, and discloses an intelligent building material distribution management system, which comprises a monitoring area division module, a construction progress analysis module, a material demand identification module, a distribution route planning module, a distribution sequence planning module, a distribution process monitoring module and a distribution abnormity identification module. Whether material distribution needs to be carried out or not is judged based on the construction progress of each construction unit, smooth construction can be guaranteed through the analysis mode, material supply and construction requirements can be accurately matched, resource allocation is greatly optimized, and inventory overstock is reduced. According to the invention, during material distribution, the abnormal material weight condition and the abnormal equipment operation condition in the material distribution process corresponding to each target construction unit are analyzed, whether the material distribution process corresponding to each target construction unit is abnormal is judged, the weight abnormity can be accurately identified, the material allocation can be optimized, the supply and demand balance is ensured, and the material distribution efficiency is improved. Stock overstock and waste are reduced, and cost is reduced.

The invention discloses a database AI question answering system and method based on a large language model and retrieval enhancement, and relates to the technical field of database retrieval. The system comprises a front-end interaction module, a distributed cache management module, a task scheduling center, a large language model processing sub-module, a retrieval enhancement sub-module, an answer optimization sub-module and a database management sub-module. All the sub-modules are mutually independent and work cooperatively, through an efficient task scheduling mechanism, the system can monitor the load and resource states of all the sub-modules in real time, accurate allocation of tasks is conducted according to an intelligent algorithm, in a large-scale concurrent request scene, single-point faults and resource waste are effectively avoided, stable operation of the system is ensured, and meanwhile, the system can be widely applied to large-scale concurrent requests. The distributed cache management module further optimizes the storage and access of the data, reduces repeated data query and transmission, and improves the overall performance.

The invention discloses a virtual environment modeling method for an OHT system of a semiconductor factory, and belongs to the technical field of semiconductor production, and the method comprises the following steps: data collection and preprocessing: collecting field facility data of a semiconductor factory production line, operation related data of an OHT vehicle, and product information data, and carrying out the preprocessing of the data, so as to obtain basic data; virtual environment modeling: constructing a virtual environment model according to the basic data; simulation and optimization: carrying out simulation operation based on the constructed virtual environment model, and carrying out performance evaluation; then, optimization is carried out according to a simulation result and a performance evaluation condition; and verifying and applying a result. According to the method, accurate modeling and simulation are carried out on the OHT system, the configuration and scheduling strategy of the OHT system is optimized, resource utilization can be optimized, the cost can be reduced, and the stability, reliability and flexibility of the system can be enhanced.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a cluster unmanned aerial vehicle task dual planning method, which comprises the following steps of: constructing a layered task planning framework comprising a global planning subsystem and a local planning subsystem, and enabling the global planning subsystem to execute task subarea division and task distribution; based on the local planning subsystem, task refinement and path planning are carried out on the unmanned aerial vehicle in each task subarea divided by the global planning subsystem, and information is fed back to the global planning subsystem; based on feedback information, through a global planning subsystem, a task planning strategy of the cluster unmanned aerial vehicle is adjusted again, a layered task planning architecture is adopted to get rid of excessive dependence on a single central control node, a complex task can be decomposed into a plurality of relatively independent sub-tasks, the task planning complexity is reduced, the task calculation amount and planning time are reduced, and the task planning efficiency is improved. The resource of each unmanned aerial vehicle is fully utilized, and the resource utilization rate, the information processing capability and the decision scientificity of the cluster unmanned aerial vehicles are improved.

The invention relates to a method, a device, equipment and a medium for optimizing an SSD (Solid State Disk) write buffer area, and the method comprises the following steps: acquiring a newly written operation request to obtain written data; judging whether the new write-in is host write-in or not; if so, writing the write-in data into a data preparation area; judging whether the data volume in the data preparation area reaches a concurrent write-in threshold value or not; if yes, concurrence data is formed, the concurrence data prepared in the data preparation area is transferred into the data programming area, and the concurrence data is transmitted to a specified Page of each DIE specified WL; whether loading of the Page data corresponding to the WL is completed or not is judged; and if the loading is completed, initiating a programming command of the WL corresponding to each DIE until the data programming is completed. According to the invention, the key data can be processed in time, the delay of data processing is reduced, the idle or excessive allocation of resources is avoided, and the optimal configuration of the resources is realized.

The invention relates to the technical field of electronic digital data processing, in particular to a data responsibility traceability method and system based on edge computing, and the method comprises the steps: collecting the surrounding environment data of a production line, and obtaining a regional environment influence feature value through processing; obtaining operation data of each edge device, processing the operation data to obtain a performance evaluation index of each edge device, and carrying out bearable data volume matching on each edge device; basic data of each edge device is collected, and abnormal characteristic values of each edge device are obtained through processing; performing comprehensive analysis to obtain a fault risk assessment index of each edge device, and performing risk assessment and feedback on each edge device; according to the data responsibility tracing method and system based on the edge computing, the demand processing data of each edge device is obtained, data distribution is carried out on each edge device and the cloud processing device, the data can be ensured to be effectively processed in time, and the overall efficiency of data processing and the overall utilization rate of resources are improved.

The heat dissipation motor comprises a motor main body, an air cooling mechanism and a liquid cooling mechanism, the motor main body comprises a shell, a stator, a rotor, a rotating shaft and a driver, the inner part of the shell is provided with a first chamber and a second chamber, the stator and the rotor are both arranged in the first chamber, and the shell is provided with a ventilation opening; the air cooling mechanism comprises fan blades, the fan blades are fixedly connected to the rotating shaft and located at the ventilation opening, the fan blades are provided with a first air channel and a second air channel, the second air channel surrounds the first air channel, a plurality of first blades are arranged in the first air channel, a plurality of second blades are arranged in the second air channel, and the rotating direction of the second blades is opposite to that of the first blades; the liquid cooling mechanism comprises a radiator, a refrigerator and an oil pump, the radiator is arranged on the driver and used for absorbing heat of the driver, the oil pump is connected with an oil pipe, the radiator and the refrigerator are in closed-loop connection through the oil pipe, and the refrigerator is used for cooling oil in the oil pipe. The heat dissipation efficiency is improved; and the energy consumption is reduced.

The invention relates to an AI-based CDN resource intelligent scheduling cost optimization method and device. Obtaining the current resource use condition of each resource node in the distribution network and the cost strategy of each resource affiliation party to which each resource node belongs; adopting an AI cost strategy model to analyze an estimated resource demand required by a demand side in a prediction time period, a current resource use condition of each resource node and a cost strategy of each resource affiliation side to which each resource node belongs, and obtaining a target node selected by the AI cost model from each resource node; and providing corresponding resources for the demand side in the prediction time period by adopting the target node.

The invention belongs to the technical field of server cluster load balancing, and discloses a server cluster load balancing method based on edge computing, which comprises the following steps: acquiring task data, transmission data between nodes and load data of server nodes; constructing a load topological graph based on the load data and the transmission data, and performing multi-dimensional aggregation on the load data based on the load topological graph to obtain node state description; performing priority evaluation on the task data by adopting an analysis algorithm to obtain a task score; performing task optimal matching on node state description based on task scores to obtain an optimal allocation scheme, predicting the optimal allocation scheme based on the constructed load prediction model to obtain overload allocation, and performing local task migration on the overload allocation to obtain a local optimization scheme; and the overall processing capability of the server cluster is greatly improved.

The invention relates to injection molding equipment for producing traceless thin-wall injection molded parts, and belongs to the field of injection molding machines, the injection molding equipment comprises an injection system connected with a heating system for hot melting of plastics, and is characterized by comprising a mold closing box body, the injection system is connected with the mold closing box body, and the heating system is connected with the mold closing box body; a movable mold and two static molds are arranged in the mold closing box body, the injection system injects hot melting plastic into the static molds, and a flow dividing assembly used for guiding the hot melting plastic to only flow to one static mold each time is arranged in the mold closing box body. And a mold closing assembly which is used for driving the movable mold to be separated from one static mold and then immediately transferring the movable mold to the other static mold for mold closing is arranged in the box body. The injection mold has the beneficial effects that the movable mold is prevented from being idle in the material taking process of the mechanical arm, and therefore the production efficiency of injection products is improved.

The invention relates to a cross-network exchange service flow strategy control method, which comprises the following steps of: S1, constructing a strategy controller and limiting identities of two communication parties; s2, the strategy controller maintains a resource pool based on analysis and prediction; s3, after the transmitting end initiates the transmission service actively, the strategy controller dynamically adjusts the flow control strategy; s4, the cross-network switching processing system processes the transmission service according to a strategy; wherein the strategy controller comprises a strategy management module, a name address directory module, a behavior analysis module and a resource management module. According to the invention, through design oriented to future IPv6, transmission services of corresponding levels are provided for users, services and applications, and the problem of flow control strategies in cross-network transmission is solved.

The embodiment of the invention discloses an interface current-limiting threshold determination method and device, equipment and a medium, and the method comprises the steps: determining a total current-limiting threshold of a target system based on physical resource information corresponding to the target system and a system health degree evaluation function; determining at least one transaction volume prediction scheme corresponding to the target system based on the total current limiting threshold; wherein each transaction volume prediction scheme comprises a predicted transaction volume corresponding to each interface in the target system; and determining a target transaction volume prediction scheme from the transaction volume prediction schemes based on the benefit evaluation results of the transaction volume prediction schemes, so as to determine an interface current-limiting threshold corresponding to each interface in the target system based on the target transaction volume prediction scheme, thereby improving the precision, accuracy and rationality of the current-limiting threshold of the system interface, and improving the user experience. Therefore, the no-load probability of the system is reduced, the idle physical resources of the stock of the system and the loss of service flow are avoided, and the resource utilization rate of the system is increased.

The invention discloses an LLM data acquisition processing method and system based on an automatic capture technology, and relates to the technical field of artificial intelligence, and the method comprises the steps: distributing a data acquisition task for each crawling node in a distributed node architecture; the crawling node accesses different data sources based on the data acquisition task to obtain acquired data; after the crawling node completes a round of data acquisition, task allocation is adjusted based on the acquisition progress and the acquisition efficiency; performing first data processing on the acquired data, and performing data quality evaluation, invalid information removal and text correction on the acquired data after the first data processing; according to the method and the device, the collected data which is from different data sources and is subjected to the first data processing are integrated to obtain the integrated data, and the integrated data is subjected to the second data processing to obtain the LLM data, so that the problem that the data of various data sources for LLM cannot be efficiently collected and processed with high quality in the prior art is solved.

The invention provides an automatic charging method and device for an auxiliary robot, equipment and a storage medium, and the method comprises the steps: obtaining a state data set of a charging station, and generating a charging station state matrix according to the state data set; performing long-short-term memory network model training and prediction processing on the charging station state matrix to obtain expected utilization rate data of the charging station in a preset time period; attribute data of the auxiliary robots are obtained, weighted evaluation is carried out on the attribute data, and a dynamic priority matrix of each auxiliary robot is obtained; performing deep learning network training and optimization processing on the charging station state matrix and the dynamic priority matrix to obtain an optimal charging scheduling strategy; determining a target charging path of the auxiliary robot according to preset three-dimensional map information, the expected utilization rate data and the optimal charging scheduling strategy, wherein the preset three-dimensional map information comprises three-dimensional position information of the charging station; and controlling the auxiliary robot to perform automatic charging according to the target charging path.