53 results about "Initial distribution" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to the medical field and the financial science and technology field, and discloses a state diagram-based task cooperation method, device and equipment and a storage medium, and the method comprises the steps: receiving initial task data and the real-time states of a plurality of agents through a global management and control node, and creating an initial task; performing semantic analysis on the initial task data to obtain a task demand, and determining a task allocation object; routing the initial task to a corresponding task processing node according to the task allocation object through the initial allocation condition edge; processing the initial task through the task processing node to obtain a processing result, and updating a task state based on the processing result; and judging the updated task state through the routing control condition edge, and if the updated task state is judged to be a blocked state, performing task processing again until the task state becomes a completed state. The method has the remarkable advantages of dynamically adapting to task demand changes and improving the task execution reliability.

The invention discloses a wafer grinding control method and system, and the method comprises the steps: carrying out the prediction of an initial grinding parameter through a pre-established thickness-grinding rate model according to the thickness initial distribution data of a wafer; the thickness-grinding rate model is constructed according to grinding process data of a wafer with a groove isolation structure; sending the initial grinding parameters to grinding equipment so as to control the grinding equipment to grind the grinding film layer; monitoring thickness allowance distribution data of the grinding film layer in the grinding process; the initial grinding parameters are adjusted according to the thickness allowance distribution data, and adjusted grinding parameters are obtained; and the adjusted grinding parameters are sent to the grinding equipment so as to control the grinding equipment to dynamically adjust the grinding rate. By adopting the method, the grinding parameters are dynamically adjusted in the grinding process, the process stability is improved, and the chip yield is improved.

Method and system for DC power distribution for charging stations based on a chaotic topology. The invention relates to a method for DC power distribution for charging stations based on a chaotic topology, comprising: according to user requirements, establishing an initial chaotic topology distribution network for charging station power based on a matrix topology network (S1); using a Gaussian kernel function to extend one dimension of the initial distribution network to a high-dimensional surface to obtain a distribution network with multiple charging nodes (S2); based on the distribution network, the required energy of each charging point, and an energy distribution strategy, controlling the opening and closing of contactor switching units to direct the output energy from power conversion units (S3). Figure 1

The invention belongs to the technical field of computers, and particularly discloses an intelligent distribution system for distributing dynamic red envelopes for consumers, which comprises a grade management module, a fund pool configuration module, a matching distribution module, a fund monitoring module and a dynamic allocation module, according to the method, the initial budget allocation weight of each consumption level is calculated based on the red envelope conversion efficiency, and the initial allocation amount of each level fund pool is obtained by combining the total budget amount, so that marketing funds are preferentially input to a user group with higher red envelope conversion efficiency; meanwhile, by comparing the predicted fund depletion time point with the activity end time point, a gap and a surplus fund pool are judged, according to the comparison result of the total fund gap amount and the total surplus fund amount, sufficient allocation or shortage allocation is triggered, and a corresponding priority ranking and fund allocation algorithm is executed; the problems that traditional budget distribution is rigid and market changes cannot be responded in real time are solved.

This invention relates to the fields of distributed computing and artificial intelligence, and particularly to a task scheduling method, system, device, and medium for edge-cloud intelligent agents. The method includes: constructing a global load topology and an intelligent agent capability feature set; decomposing the target task into multiple sub-tasks and determining temporal dependencies and execution constraints; inputting a pre-trained policy model to output an initial allocation policy; constructing a causal graph based on the allocation policy to generate an interpretable basis chain; performing simulation pre-runs of the policy; if the policy fails verification, analyzing the basis chain to locate local optimal interference and triggering policy self-correction for iterative pre-runs; if the policy passes verification, issuing the policy and adjusting routing weights in real time to implement traffic smoothing and task filling. Therefore, this invention, by constructing a three-level collaborative architecture of edge-cloud, not only achieves deep collaboration from macro-architecture to micro-algorithm, but also significantly improves the flexibility and scalability of equipment management in complex scenarios such as smart stations and industrial parks.

The invention discloses a high-precision wind tunnel nozzle profile design method, and belongs to the technical field of wind tunnel nozzle profile design based on a computer. The problems that in the prior art, a traditional wind tunnel nozzle molded surface design method is poor in process feasibility, and the number of molded surface correction iterations is large are solved. According to the method, modeling software is adopted to construct an initial profile mathematical model, geometric constraints are integrated, a wall thickness initial distribution model is established, and wall thickness distribution is optimized; s2, quantizing an objective function according to the constraint and the model parameters in the step S1, optimizing the objective function, and refining the constraint to obtain refined constraint conditions; and according to the wall thickness initial distribution model and the wall thickness parameters, design software is combined with the refined constraint conditions to carry out parametric modeling, a process feedback mechanism is constructed to carry out correction compensation on initial molded surface modeling, and high-precision wind tunnel nozzle molded surface design is completed. The pneumatic performance and the material utilization rate are effectively improved, and the method can be applied to high-precision pneumatic verification tests.

The invention provides a big data task scheduling optimization method and system based on heterogeneous computing, and relates to the technical field of big data process.The big data task scheduling optimization method comprises the steps that multi-source big data and transmission parameters of corresponding transmission links are obtained, and associated information is extracted from the multi-source big data; obtaining a target transmission link based on the transmission parameter; based on the associated information, obtaining a plurality of processing sub-items with identifiers and a corresponding execution sequence; obtaining an initial allocation scheme based on the associated information; obtaining running state information of the heterogeneous computing resources to obtain a verification result, and adjusting the initial allocation scheme in combination with the execution sequence to obtain a final allocation scheme; and based on the target transmission link and the final allocation scheme, the processing subitems with the identifiers are processed through the heterogeneous computing resources, and efficient, safe and ordered scheduling optimization of the multi-source big data in the heterogeneous computing environment is realized.

This application provides a method, apparatus, equipment, and medium for production line load balancing. The method includes the following steps: generating an initial allocation scheme; selecting an initial optimized allocation scheme; searching and updating to generate a search-optimized allocation scheme; comparing to obtain a search-determined scheme; performing advanced updates to generate an advanced optimized allocation scheme; comparing to obtain an advanced determined scheme; and iteratively executing until a target number of iterations is reached to generate a target determined scheme. This application generates initial allocation schemes for multiple production devices based on a task list; and performs multiple global, local, and advanced updates and optimizations on the initial allocation schemes to select the final target determined scheme. This method can achieve effective global and local balance; it can dynamically adjust task allocation according to the actual load of the production line, ensuring load balance among production devices, and has significant effects on improving production efficiency and balancing the load of various production devices, and is applicable to production lines of different sizes.

The invention relates to a task information processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring initial distribution task information of a first distribution object; under the condition that the initial distribution task information indicates that the initial distribution task meets the task cooperation condition, obtaining a task cooperation rule of the first distribution object; performing task allocation on the initial delivery task according to a task cooperation rule to obtain a first delivery task of the first delivery object and a second delivery task of the second delivery object; wherein the second delivery object and the first delivery object have a task cooperation relationship; sending the first distribution task information to the first distribution object, and sending the second distribution task information to the second distribution object; the first distribution task information is used for prompting the first distribution object to execute the first distribution task, and the second distribution task information is used for prompting the second distribution object to execute the second distribution task. By adopting the method, the logistics distribution efficiency can be improved.

The invention provides a data scheduling method and device of a distributed storage system, electronic equipment, a storage medium and a program product, and relates to the technical field of big data management. The method comprises the following steps: predicting a state change trend of each node based on multi-source state data of the nodes of the distributed storage system; determining an initial allocation scheme and a reward score of the target task to determine a task allocation scheme; and scheduling the data blocks of the distributed storage system based on the task allocation scheme. According to the method, the state change trend of the node is predicted according to the multi-source state data, the state data of the node in multiple different dimensions are fully considered, and accurate prediction of the current state and the future state of the node is realized. The task allocation scheme is determined through the state change trend and the features of the target task, the task allocation scheme is made in advance in combination with the future resource demand change of the node and the features of the to-be-allocated task, and the utilization rate of the node at the current moment and in the future time period can be improved.

The application provides a kind of agricultural product order dynamic processing and distribution method based on real-time data, and relates to logistics and supply chain management technical field, it is clustered to order distribution address, and initial distribution cluster is obtained, then initial distribution cluster is sorted according to freshness decay rate, then vehicle is planned in combination with knapsack algorithm, to generate optimal distribution route. In this way, it can reduce the rotting of agricultural products caused by uneven temperature in the carriage during distribution, and the problem of high loss of fresh agricultural products in the distribution link caused by not considering freshness priority when loading agricultural products in the distribution vehicle.

The invention discloses an exhaust system control method. The exhaust system control method comprises the steps that the total exhaust amount Qreq needing to be completed currently of an exhaust system is obtained; the working period of the exhaust system is called; the exhaust air rate is distributed based on the exhaust capacity of the exhaust system and the initial distribution strategy, and the first exhaust air rate Q1 in the valley period and the second exhaust air rate Q2 in the peak period are obtained; based on the total air exhaust amount Qreq, the first air exhaust amount Q1 and the second air exhaust amount Q2, the third air exhaust amount Q3 of the ordinary period is obtained; performing executable judgment on the third exhaust air rate Q3, and respectively confirming target exhaust air rates in a low ebb period, a peak period and a normal period on the basis of a judgment result; and the working time of the exhaust system is monitored, and the corresponding target exhaust air rate is executed according to the working time period to which the working time belongs. According to the invention, on the premise that the exhaust demand is realized, excessive power resources and power consumption cost are not wasted additionally, and under the condition that the distribution strategy is executable, the power consumption peak avoidance demand is considered, and the power consumption safety is further realized.

This invention relates to the field of space debris environment modeling technology, specifically to a short-term evolution method for continuum disintegration debris based on numerical methods. The method includes: Step 1, establishing an initial distribution model of disintegration debris, including a disintegration debris velocity increment model and a numerical model of the initial distribution of disintegration debris; Step 2, sampling feature points of the continuum using a gradient-based sampling method, selecting the top N samples with the highest L2 norm scores of the gradient vectors as feature points of the continuum to be evolved; Step 3, processing the continuum equations using numerical solutions, considering the six orbital roots and four perturbation factors to perform short-term evolution of feature points; Step 4, reconstructing the final state distribution of disintegration debris using a Student's t-mix model. This invention fills the gap in previous research in this field, which only focused on ultra-short-term and long-term evolution, and has the beneficial technical effects of requiring fewer samples, having lower computational costs, shorter computation time, higher result accuracy, and stronger robustness.

The application discloses a reverse link resource allocation method and system, a base station device and a storage medium. The method comprises the following steps: when reverse resource allocation is performed on a reverse sub-band combination, a quality of service priority dominant scheduling mode is used to perform scheduling share allocation on the reverse sub-band combination, so that an initial allocation result is obtained; and a sub-band balance dominant scheduling mode is used to perform scheduling share allocation on the initial allocation result, so that a target allocation result is obtained. The application solves the technical problem of low scheduling fairness and low resource utilization rate in the reverse link resource allocation method provided in the related art.

The invention belongs to the technical field of material processing simulation, and particularly relates to a simulation method for inclusion distribution in a high-temperature alloy deformation process. The simulation method covers the dynamic evolution of the distribution rule of the inclusions in the high-temperature alloy in the thermal processing and thermal deformation process. The method comprises the following steps: firstly, based on actual detection or early-stage simulation, acquiring the surface density distribution condition of the inclusions in the cast ingot, generating a data file representing the initial distribution of the inclusions, providing the data file to Deform software to calculate the position movement condition of the inclusions in the machining process, and performing nuclear density estimation on the distribution of the inclusions after exporting a calculation result; and obtaining a probability density distribution function of the inclusions in the deformation process of the high-temperature alloy. According to the method, the yield, the quality consistency and the service reliability of the deformation high-temperature alloy component are improved, and the method has important engineering application value and strategic significance for promoting high-quality and high-stability manufacturing of key components in the fields of aerospace, energy power and the like in China.

The invention discloses a vertical optical element palletizing collaborative access method and system based on intelligent scheduling, and relates to the field of precise optical element automatic warehousing, S1, an intelligent collaborative control module comprising a three-level cache management module, a layered mixed intelligent scheduling module and a collaborative time sequence control module is constructed; s2, a double-vision positioning module of the truss manipulator generates an initial distribution point cloud picture matched with the optical element; s3, the real-time response layer generates a corresponding scheduling decision to obtain a cache position corresponding to a first-level cache cargo inlet and outlet; s4, the mechanical arm grabs the optical elements, the grabbed optical elements are placed in the transfer cache positions of the second-level cache trays, and the RFID information and element counting of the trays are updated; and S5, judging whether the current tray is full or not, if not, returning to S3, and otherwise, triggering a tray-level transfer process. According to the method, the throughput of the system is not limited by the slowest operation period of single equipment, and the overall efficiency of the system is greatly improved.

The application relates to the technical field of transportation, in particular to a layout planning method and device for a component type mobile charging facility, wherein the method comprises the following steps: acquiring actual distribution conditions and actual charging standards of charging equipment of multiple operators; initializing the actual distribution conditions and the actual charging standards to obtain initial distribution conditions and initial charging standards, so as to calculate total revenue of the multiple operators based on the initial distribution conditions and the initial charging standards; and based on the total revenue, iteratively optimizing the initial distribution conditions and the initial charging standards by using a particle swarm algorithm and an evolutionary game strategy to obtain target distribution conditions meeting a preset iteration stop condition. Thus, the problems in the related art that due to the lack of effective pricing strategies, the fierce market competition cannot be effectively responded to, the market share is easily lost, the profit is reduced, and due to the unreasonable distribution of charging facilities, the utilization rate of the charging equipment is possibly reduced and the user experience is affected are solved.

One embodiment of the present invention sets forth a technique for processing requests. The technique includes retrieving, from one or more data sources, a plurality of reservations and a plurality of available rooms associated with a first time frame, and generating a set of valid assignments of one or more available rooms to each reservation. The technique also includes generating, based on (i) the sets of valid assignments for the plurality of reservations and (ii) a plurality of matching objectives, a plurality of initial assignments of at least a portion of the available rooms to the plurality of reservations. The technique further includes performing a check-in process for a first reservation based on the plurality of initial assignments.

The invention relates to the technical field of new energy, and discloses a hybrid energy storage power two-stage optimization distribution method, system and device and a medium. The method comprises the following steps: establishing a grid-connected power optimization model by taking minimum error between grid-connected power and original new energy power as a target and considering power fluctuation, hybrid energy storage power and charge state limitation so as to solve and obtain target grid-connected power; taking the difference between the original new energy power and the grid-connected power as the to-be-compensated power, and decomposing the to-be-compensated power into a plurality of mode components and residual terms by adopting a continuous variation mode; sequencing the modal components according to the frequency and setting a cutoff point, accumulating the power before the cutoff point as the battery power, and then taking the residual error as the flywheel power, and obtaining the initial distribution power of multiple groups of batteries and flywheels; and establishing a power distribution optimization model by taking the battery distribution power, the respective charge state deviation of the battery and the flywheel and the minimum power distribution deviation as targets, and solving to obtain the target distribution power of the multiple groups of batteries and the flywheel of the battery and the flywheel by taking the multiple groups of initial distribution power as initial solutions.

This invention discloses a distribution network restoration control method and system, belonging to the field of smart distribution network control and protection technology. The method involves: acquiring real-time measurement data and the initial distribution network topology based on the distribution network to be restored; constructing a restoration path optimization model and iteratively solving it to obtain multi-objective optimization function values ​​and iteration counts; if a preset convergence condition is not met but a preset iteration threshold is not exceeded, obtaining a node restoration priority sequence and power allocation strategy, and generating switch control commands and distributed power generation commands; integrating the above data into an operation proposal package and sending it to an intelligent agent for security verification and feedback of the verification results; if all preset intelligent agent verification conditions are met, issuing commands to the distribution network to be restored to restore power supply. This invention provides a distribution network restoration control method that enables security verification of the distribution network restoration path, ensuring stable and reliable power restoration of the distribution network.

This invention relates to the field of distribution network optimization, specifically providing a method, apparatus, and electronic device for distribution network optimization scheduling. The method includes: generating an initial distribution strategy set; calculating the acceptance degree of one distribution network scheduling vector in the initial distribution strategy to another distribution network scheduling vector, and updating the distribution network scheduling vector when the acceptance degree is greater than or equal to a preset acceptance degree; selecting the M distribution network scheduling vectors with the highest cost data as benchmark scheduling vectors, and updating the N distribution network scheduling vectors with the lowest cost data using any one of the benchmark scheduling vectors to obtain a target distribution strategy set; repeating the above steps iteratively for the distribution network scheduling vectors in the target distribution strategy set until a preset iteration termination condition is met, and determining the distribution network scheduling vector with the lowest cost data in the current target distribution strategy set as the power grid scheduling strategy for a preset time period. The technical solution provided by this invention can improve the efficiency of distribution network scheduling optimization.

This application provides a distribution planning method, system, and device based on the ReAct framework. The method includes: acquiring and processing business indicators of the power system; inputting the business indicators into a large model, where the large model identifies problems within the business indicators; introducing the problems into the ReAct framework, and using the ReAct framework to retrieve knowledge content matching the problems from a knowledge base; monitoring the operation of the knowledge content in the power system, feeding the operational status back to the ReAct framework, where the ReAct framework generates an initial distribution scheme; feeding the initial distribution scheme and its execution results back to the ReAct framework's thinking phase, updating the distribution scheme using the ReAct framework, updating the distribution scheme multiple times, and recording each updated distribution scheme and its execution results, generating a final distribution scheme and an interpretable reasoning report. This solves the problems of low accuracy and processing efficiency in distribution network planning analysis.

The invention discloses a reverse link resource allocation method and system, base station equipment and a storage medium. The method comprises the following steps: when reverse resource allocation is carried out on a reverse sub-band combination, scheduling share allocation is carried out on the reverse sub-band combination by adopting a service quality priority dominant scheduling mode to obtain an initial allocation result; and performing scheduling share allocation on the initial allocation result by adopting a sub-band balanced dominant scheduling mode to obtain a target allocation result. According to the invention, the technical problems of low scheduling fairness and low resource utilization rate of the resource allocation method of the reverse link provided in the related technology are solved.

The invention relates to an optimization design method for a metasurface optical coupler, and belongs to the field of optimization methods, and the method comprises the steps: importing initial dielectric constant initial distribution, determining system parameters and an optimization target, and carrying out the filtering and gradient binarization of an initial structure; the value of a current system evaluation function is calculated, forward and adjoint field calculation is carried out, and the gradient is solved; gradient optimization is carried out, distribution of dielectric constants is updated according to the current gradient, and filtering and gradient binarization are carried out on the updated structure; continuously solving an evaluation function of the new structure, and updating a weight coefficient in the evaluation function according to a current value and a previous value; and calculating forward and adjoint fields, solving the gradient, and repeating the process until the evaluation function meets the design requirement. According to the invention, the metasurface optical coupler can be rapidly designed, and the time required by the design is reduced.

The invention relates to the field of artificial intelligence, in particular to a multi-robot task allocation method and system based on mixed dialogue negotiation, and the method comprises the steps: executing natural language task analysis processing, and obtaining a structured task graph; according to the structured task graph, performing adaptive task grouping and hybrid negotiation to obtain an initial allocation scheme; performing multi-round dialogue negotiation on the initial allocation scheme to obtain a negotiated allocation scheme; carrying out confidence fusion and re-negotiation on the negotiation distribution scheme to obtain a feasible distribution scheme; and performing distribution verification on the feasible distribution scheme to obtain a task distribution result. According to the method, natural language analysis, multi-round dialogue negotiation, confidence fusion, re-negotiation and the like are carried out through the large language model to meet task allocation requirements in a multi-robot environment, and the execution efficiency and the allocation accuracy of an allocation scheme are improved.

This invention discloses an optimization method for VAR melting and casting ingots based on spatial coordinate transformation, belonging to the field of high-end metal material vacuum melting technology. The method first obtains the initial distribution of material state parameters of a single ingot after a first VAR melting process along its axial spatial position; based on this distribution, it determines the cutting position and cuts the single ingot into two or more ingot segments; then, according to the bottom-up solidification segregation law of VAR, it performs spatial transformation on the ingot segments to match the high and low element regions of the single ingot; finally, it combines the transformed ingot segments and performs a second VAR melting process. This invention solves the problem that the traditional head-and-tail interchange method cannot completely improve ingot segregation. Through cutting and spatial transformation, it achieves the reconstruction of the segregation pattern, significantly improving the compositional uniformity and microstructure consistency of the ingot. It also has the advantages of clear principle, feasible operation, and easy integration into existing VAR processes.

This invention provides a smart power plant management method and system based on distributed management. The method includes the following steps: an initial allocation phase, in which the central node performs initial training on a first central model and a second central model based on an initial training dataset, and distributes the initially trained model parameters to each edge node; the edge nodes determine whether to perform manual inspection based on real-time collected environmental parameters and equipment operating parameters; and an iterative update phase, in which the central node obtains the inspection results of manual inspection, stores the inspection results of each monitoring location within a preset time period as an iterative training dataset, and the central node performs iterative training on the first central model and the second central model based on the iterative training dataset, and distributes the iteratively trained model parameters of the first central model and the second central model to each edge node.

This application discloses a dehydration control method, apparatus, clothing processing equipment, and storage medium, relating to the field of smart home technology. The method includes: when a dehydration program is detected, acquiring the current distance between the inner and outer drums of the clothing processing equipment; determining an initial distribution time for uniformly distributing the clothes in the inner drum during the dehydration program; if the absolute value of the difference between the current distance and the stable distance is greater than a preset distance value, adjusting the initial distribution time to obtain a target distribution time, and executing the dehydration program based on the target distribution time. The technical solution provided by this application can ensure the accuracy of the distribution time setting, quickly and efficiently achieve the expected clothing distribution effect, and improve the user experience of using the clothing processing equipment.

The invention provides a multi-unmanned aerial vehicle task allocation method and system based on a multi-target blue whale hunting mechanism, and belongs to the field of unmanned aerial vehicle task allocation. The problems that an existing unmanned aerial vehicle multi-target task allocation method is prone to falling into local optimum, and later convergence precision is insufficient are solved. Segmented initialization is applied during initialization, initialization is performed according to task allocation in the first stage, so that an initial blue whale group is more reasonable, and a chaotic mapping idea is adopted in the second stage, so that initial distribution of the blue whale group has higher randomness and ergodicity; an elitism strategy is applied, excellent individuals of each generation are reserved, and the convergence speed is increased; a simulated annealing thought is introduced in each iteration process, so that a relatively high mutation probability exists in the initial stage of iteration to accept a relatively poor solution so as to jump out of local optimum; the blue whale positions are searched in a continuous space through continuous value coding, threshold conversion and an optimization algorithm, a blue whale group is guided to evolve to an excellent solution through fitness function feedback, and finally an actually executable task allocation scheme is obtained through mapping.

This invention provides a distribution network partitioning method and apparatus, relating to the field of power system planning technology. The invention employs the DPC algorithm to determine cluster centers and the number of partitions based on relative distance and local density, obtaining an initial distribution network partitioning network diagram. The LPA algorithm is then used to determine the distribution network partitioning results based on the initial distribution network partitioning network diagram. This invention ensures the objectivity and scientific rigor of distribution network partitioning initialization and solves problems such as partitioning difficulties and giant partitions, ensuring the rationality of distribution network partitioning.