142 results about "Initial distribution" patented technology

The invention relates to a heterogeneous unmanned ship task dynamic optimization method based on intelligent distributed CBBA, and relates to the technical field of unmanned autonomous cooperative control, the method comprises the following steps: firstly, using an intelligent distributed CBBA algorithm to realize task initial allocation, and constructing a cost function integrating distance cost, time cost and time window default penalty; and a task sequence is adjusted through a greedy strategy so as to meet strict time window constraints. And secondly, in the task execution process, a real-time monitoring and dynamic re-planning mechanism is established, and emergencies can be rapidly dealt with. A redistribution mechanism is introduced, the weight of a cost function is dynamically adjusted through a genetic algorithm, and loss and scheme optimization benefits caused by task execution subject change are balanced. Compared with the prior art, the task execution efficiency, reliability and adaptability of the heterogeneous unmanned ship cluster in a complex dynamic environment are effectively improved, and the method is suitable for complex application scenes such as maritime search and rescue, material delivery, patrol monitoring and the like.

The invention relates to the technical field of vending machine control, in particular to an intelligent vending machine control method and system. The method comprises the following steps: obtaining the sales flow and the goods types of the vending machine, sorting the goods according to the sales volume to design an initial distribution position, carrying out goods racking simulation based on the sales flow and the initial position, monitoring the goods quantity in real time, and calculating the sales volume of each type of goods. Through real-time sales volume data, pole pairing of cargo types and distribution position adjustment are carried out to optimize layout, a shortest redistribution path is calculated according to a new distribution position, cargo redistribution is implemented, replenishment requirements are evaluated, a replenishment prompt mechanism is constructed, cooperative control of cargo redistribution and replenishment is realized, and the operation efficiency of the vending machine is improved. Cooperative replenishment control of the vending machine is achieved, and efficient operation and management of the vending machine are promoted.

The invention discloses an ice cream machine tool bit monitoring method which comprises the following steps: according to an initial distribution state, carrying out filtering operation on distance data by adopting a signal processing module, removing noise interference, and determining a stable distance value between a tool bit and a container wall; the stable distance value is compared with a preset threshold range, if it is detected that the distance value exceeds the threshold range, an abnormal signal is triggered, and it is judged that the position of the tool bit deviates; according to the abnormal signal, current tool bit operation parameters are obtained, the adjustment amount is calculated through a pre-established correction model, and a correction instruction of the tool bit position is obtained; according to the adjusted position state, the change trend of the distance between the tool bit and the container wall is continuously collected, and whether the change of the distance tends to be stable or not is judged through a time sequence analysis method; and for the final stable data, recording an association log of the tool bit position and the spacing change, and storing the association log to a system database through a data storage module for subsequent production batch reference.

The invention discloses a multi-strategy fusion algorithm suitable for edge computing task scheduling, and belongs to the field of edge computing and intelligent scheduling. According to the algorithm, a three-stage co-evolution mechanism of dynamic parameter regulation and control, resource perception disturbance and feedback type guide updating is constructed aiming at the problems of parameter static property, low search efficiency, non-uniform load distribution and the like in the prior art. Initial distribution is guided through a dynamic adaptation degree function of task calculation density and node load, search diversity is enhanced by adopting a disturbance kernel function of local aggregation degree perception, and a task completion state and a feedback evaluation index of delay response are introduced to optimize a scheduling behavior. According to the algorithm, multi-strategy staged collaboration is realized by fusing a collaboration evolution mode, and the resource utilization rate, the task response timeliness and the system stability of edge nodes are effectively improved. According to the method, the problems that a traditional algorithm is prone to local optimum and unbalanced in load in a heterogeneous environment are solved, and an intelligent and self-adaptive task scheduling solution is provided for an edge computing scene.

The invention relates to the field of combinatorial optimization and artificial intelligence, and discloses a group logistics transportation scheduling method and system based on a role interaction graph neural network, and the method comprises the following steps: S1, dividing agent nodes and position nodes, and generating initial features; s2, iteratively updating node embedding by using a multi-channel attention mechanism of a graph neural network; s3, generating a delivery point distribution probability based on node embedding, and determining an initial distribution scheme; s4, local redistribution optimization is performed on the delivery points with low confidence distribution; and S5, performing parallel path planning on the optimal scheme, and outputting a result for reinforcement learning feedback. In the invention, through modeling of a graph neural network multi-channel attention mechanism on a complex interaction relationship and a synergistic effect of local redistribution optimization and parallel path planning, a second-level generation of a high-quality scheduling scheme is realized, cross-scale scene migration of the model is achieved, and group logistics transportation scheduling efficiency and robustness are improved.

The invention provides an irrigation district water diversion quantity multistage distribution method under total quantity index constraint, comprising the following steps: dividing an irrigation district into a plurality of calculation units based on an administrative division boundary and a canal system network; respectively calculating the initial water demand of each calculation unit under the living water demand, the industrial water demand, the agricultural water demand and the ecological water demand; based on the initial water demand and according to multiple water resource rigid constraint indexes, performing at least one round of checking and pressure reduction on the initial water demand of each calculation unit to obtain an initial distribution water amount of each calculation unit; and carrying out secondary distribution on surplus water generated after checking and pressure reduction, and compensating the calculation units of which the water demand is not met to obtain the final distribution water of each calculation unit. According to the method, fair, reasonable and efficient distribution of water resources in a complex system with cross coupling of administrative division and canal system networks is realized, and the problems of conflict between a scheme and actual management requirements, water consumption contradiction and the like caused by a traditional single-dimension distribution method are avoided.

The invention relates to the technical field of wireless communication, in particular to a dynamic power distribution method for a power chip, which comprises the following steps of: 1, acquiring dynamic parameters in real time; 2, calculating a power demand weight; 3, dynamically adjusting a power distribution proportion: generating an initial distribution proportion based on the power demand weight, and compensating and correcting the initial proportion through a residual interference predicted value of a previous adjacent time slot; 4, closed-loop control power output: the corrected power distribution proportion is converted into an actual transmitting power value, and the power output precision is ensured through feedback calibration; and 5, time slot switching transition and parameter updating: adjusting a power change rate based on channel coherence time during time slot switching, and updating an interference compensation parameter according to an actual power error. The stability, the power efficiency and the adaptability of a communication system are improved by combining real-time parameters and a refined power calculation and control mechanism.

The present disclosure provides a method for managing allocation of compute resources in a distributed database. Each request on every task is allocated a certain number of tokens when it arrives, such as by using a token bucket algorithm. This initial allocation may be the per-request maximum usage permitted in the system. This request is made to a central quota server in the region to coordinate allocations between tasks. As the task runs, the system evaluates how much of the limit the request is using, and adjusts its limit up or down to keep the quota allocation tight on the actual usage of the request. If there aren't enough tokens to keep the request active, the request can be evicted.

The invention discloses a multi-target agricultural product logistics distribution path optimization method based on an ant colony algorithm, and particularly relates to the technical field of distribution path optimization, and the method comprises the steps: constructing an initial distribution graph with time window constraint based on the geographic coordinates of a distribution center and a delivery point, and monitoring the distribution sequence state of agricultural products in real time in the distribution process, thereby achieving the optimal distribution path optimization of the agricultural products. The method can accurately identify the upside-down distribution sequence caused by emergency, calculates the frequent opening and closing influence coefficient of the compartment door according to the obtained opening and closing information of the compartment door when the distribution sequence is in an abnormal state, and calculates the frequent starting and stopping influence coefficient of the cold-chain equipment according to the starting and stopping information of the cold-chain equipment. And constructing an agricultural product corrosion rate influence evaluation model, outputting an agricultural product corrosion rate influence evaluation index, evaluating the influence rise degree of the cold chain environment on the agricultural product corrosion rate, and improving the existing ant colony algorithm based on the agricultural product corrosion rate influence evaluation index to optimize the logistics distribution path of the agricultural product.

The invention provides an SDN (Software Defined Network) controller redistribution method based on a particle swarm optimization algorithm, which relates to the technical field of satellite internet, and comprises the following steps: firstly, calculating the average propagation time delay and the maximum propagation time delay of a controller in a period, and carrying out weighted distribution on the average propagation time delay and the maximum propagation time delay; the minimum weighted allocation result is used as a static deployment objective function of the SDN controller and is solved, and static deployment of the initial controller and initial allocation of the switch are completed, so that frequent switch migration is avoided; the method comprises the following steps: receiving a data stream sent by user equipment by using a switch, sending a flow table request instruction to an initial controller allocated by the switch, and if a processing load of the flow table request instruction exceeds a preset load threshold value of the initial controller, optimizing an allocation proportion of the flow table request instruction by using a particle swarm optimization algorithm, and distributing the flow table request instruction to the controller in the set range for the switch according to the optimized distribution proportion, thereby realizing load balancing between the SDN controllers.

The present application belongs to the technical field of determining the load of loading nodes in a static test of a component, and specifically relates to a method for determining the load of loading nodes in a static test of a component, comprising: determining the loading nodes in the static test of the component; determining the initial distribution of the loading load of each loading node; constructing a finite element model of the static test of the component, performing finite element analysis, obtaining the sensitivity coefficient of the assessment target to each loading node, and then constructing a sensitivity coefficient matrix; constructing a finite element model of a theoretical state of the component, performing finite element analysis, obtaining the assessment target response error under the static test and theoretical state finite element model of the component, and then constructing a response error matrix; using the sensitivity coefficient matrix and the response error matrix, calculating the increment of the loading load of each loading node, updating the loading load of each loading node, until the assessment target response error under the static test and theoretical state finite element model of the component meets the requirements, and obtaining the loading load of each loading node.

The utility model relates to the technical field of liquid distributors, in particular to a flow-adjustable liquid distributor which comprises an initial distribution module arranged at the top of a filler, the initial distribution module comprises a spraying mechanism and a flow regulation and control mechanism, a liquid inlet is formed in the spraying mechanism, and a liquid outlet is formed in the flow regulation and control mechanism. A plurality of adjustable liquid outlets are formed in the bottom of the liquid storage tank in an array manner; the flow regulation and control mechanism comprises a plurality of movable plugs and a driving structure, the movable plugs correspond to the adjustable liquid outlets one to one, and the driving structure is used for driving the movable plugs to move so that each movable plug can change the opening degree of the corresponding adjustable liquid outlet. In the working process of the flow-adjustable liquid distributor, the opening degree of the adjustable liquid outlet can be adaptively adjusted according to the fluctuation change of the gas flow, so that the flow of liquid entering the filler can be controlled in real time, the uniformity of gas-liquid distribution is ensured, and the absorption effect of a packed tower is improved.

The embodiment of the invention provides an unmanned aerial vehicle distribution path planning method, device and equipment, and the method comprises the steps: constructing a plurality of distribution paths according to a distribution region of an unmanned aerial vehicle and distribution points included in the distribution region; obtaining topographic data, coordinate information of a plurality of distribution points, cargo weight information of the plurality of distribution points, initial environment data and unmanned aerial vehicle initial state data; according to the coordinate information of each distribution point and the cargo weight information of each distribution point, screening an initial distribution path from the distribution paths based on a load near-point priority strategy; determining cost information of each initial distribution path according to an arrangement sequence of distribution points included in each screened initial distribution path, topographic data, coordinate information of each distribution point, cargo weight information of each distribution point, initial environment data and unmanned aerial vehicle initial state data; and determining a target distribution path according to the cost information of each initial distribution path. The optimal distribution efficiency of the unmanned aerial vehicle under the complex road condition is ensured, the energy consumption of the unmanned aerial vehicle is lowest, and the distribution is safer.

The invention discloses a resource allocation method and system based on a product technology guarantee index, and relates to the technical field of maintenance guarantee, and the method comprises the following steps: 1, based on a task demand set, an available guarantee resource set, a priority weight set, a task time factor and an initial strategy set, defining a task utility function, and obtaining a game model; 2, solving a Nash equilibrium point of resource allocation based on a game model through a hybrid optimization method, and generating an initial resource allocation scheme; step 3, optimizing the initial allocation scheme through a reinforcement learning feedback mechanism and a dynamic priority adjustment method to generate an optimized resource allocation scheme; and step 4, evaluating the optimized resource allocation scheme based on the task completion rate and the resource utilization rate to obtain a final resource allocation scheme. According to the invention, dynamic optimization and fair distribution of technical support indexes are realized based on resource competition and cooperative distribution of the game model.

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 building construction resource constraint allocation optimization method based on big data, particularly relates to the field of building engineering resource management, and is used for solving the problems of on-duty waiting, equipment idling, channel congestion and scheduling and field rhythm departure caused by the fact that material in-place, working plane admission, channel release and mutual exclusion control are not synchronously met in the existing scheduling. Reading an operation window through a unified time mark, and subscribing a material in-place event to generate a time anchor point; according to anchor point extraction constraint fragments, transcription channel release records and mutual exclusion rules, forming a constraint fragment set for a working plane admission condition; constructing a resource feasible region, and executing a four-state closed decision to screen candidate time periods and output an initial allocation table; checking the initial distribution table in a landing manner, marking mismatch and writing the mismatch back to the constraint fragment set; and according to mismatch, triggering rolling recalculation, retaining a local rearrangement non-executed segment of the main closed segment, outputting a stable distribution table, and solidifying criterion reference.

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.

The present invention belongs to the field of service migration technology and discloses a service migration method based on priority and spatiotemporal trajectory prediction, including: constructing service resource requirements and initial distribution feature vectors and initial user feature vectors, calculating service priorities, constructing user interaction relationship trajectory graphs, constructing multi-space user interaction relationship trajectory graphs, constructing time-series user interaction relationship trajectory graphs, processing the spatiotemporal trajectory graphs through a Transformer model to output feature representations, and concatenating the feature vectors to obtain feature vectors. An MLP decoder is used to convert the feature vectors into two-dimensional coordinates in the real world to obtain the user's future movement trajectory; and based on the service priority and the user's future movement trajectory, service migration decisions are made through reinforcement learning. This method can predict the user's movement trajectory and, at the same time, make migration decisions based on the priority of the service required by the user to meet the user's low-latency, high-reliability service needs.

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 provides an air volume dynamic cooperative control method and system for a multi-scene dust removal system, and the method comprises the steps: collecting multi-source detection data in real time, inputting the multi-source detection data into an initial distribution model, and outputting an initial air volume distribution strategy through the initial distribution model; the initial air volume distribution strategy is optimized through an optimization model in the two dimensions of the minimum total power consumption and the minimum dust removal efficiency, and an optimized distribution strategy is obtained; adjusting corresponding equipment according to the optimal distribution strategy, and obtaining feedback data; performing optimization iteration on the initial distribution model and the optimization model through the feedback data; through the design, efficiency and energy consumption are both considered in the dust removal scene, and the air volume distribution scheme is dynamically adjusted according to the external environment change, so that a relatively excellent scheme is effectively output, the dust removal efficiency in the dust removal scene is effectively improved, and the energy consumption is reduced.

The invention relates to a resource slicing method and device, equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring a resource demand of each resource slice in a power service; for each resource slice, performing comparative analysis on the resource demand of the resource slice and an initial allocation strategy, and determining a resource utilization state of the resource slice; and determining a target allocation strategy of the resource slices according to the resource utilization state. By adopting the method, the requirements of low time delay and high QoS guarantee service can be met.

The invention discloses a preparation method and a preparation device of a graphitized composite porous carbon skeleton. The preparation device comprises a base, a lower template is fixed to the top of the base, a mold is fixed to the top of the lower template, and a mounting table is fixed to the top of the lower template. The invention relates to the technical field of material preparation. According to the preparation method and the preparation device of the graphitized composite porous carbon skeleton, through the arrangement of the quantitative discharging, uniform mixing and circulating mechanism and the driving assembly, quantitative and small-amount discharging is achieved, meanwhile, uniform mixing is conducted on a mixture through uniform mixing blades, and the situation that a large number of materials are put into mixing equipment at a time in a traditional mixing mode, and the mixing efficiency is greatly improved is effectively avoided. The problems that materials are relatively dispersed in initial distribution and difficult to quickly and uniformly mix are solved, meanwhile, after the materials are fully and uniformly mixed before hot pressing, the flowability of the mixture is effectively improved, and the probability of forming defects such as material shortage and bubbles of the precursor is effectively reduced through a small amount of multiple times of uniform mixing operation.

The invention discloses a multi-agent cooperative grouping and income distribution method, which comprises the following steps of: introducing an improved HCS algorithm to reasonably initialize tasks and each agent, calculating sub-task priorities according to skill requirements and prediction workload distribution, optimizing cross-task resource distribution by combining income prediction and cost constraint in an inter-task competition stage, and optimizing the task resource distribution. And calculating the marginal contribution of the intelligent agent based on a Shapley value, carrying out income distribution in combination with a dynamic excitation coefficient, adjusting an income distribution proportion according to parameters, and carrying out iterative strategy updating on a formed initial grouping result until the initial grouping result is converged to Nash equilibrium after the individual rational constraint and the overall income maximization are met. According to the method, initial allocation is intelligentized, the resource utilization rate and the total task income are remarkably improved based on skill demand prediction and the workload priority, fair and efficient income allocation is ensured, and the method is suitable for industrial automation, smart cities and other scenes.

The invention relates to an intelligent decision-making and collaborative guidance integrated design method, in particular to an intelligent decision-making and collaborative guidance integrated design method. The method aims at solving the problems that in an existing task scene that multiple interception projectiles cooperatively intercept multiple targets, the overall interception efficiency of a projectile group and a target group cannot be maximized, the calculation complexity is high, iteration time consumption is remarkable, and guidance instruction updating is lagged. The method comprises the following steps of: 1, obtaining a reachable domain of each interception bullet for each target; 2, averaging the three-dimensional coordinates of each interception missile for the reachable domain range of each target, and solving the aiming point of each target; based on the aiming point of each target, the total overload of the interception projectile is obtained; thirdly, target distribution is conducted on the interception bombs; 4, constructing a hybrid optimization index based on the strike benefit index and the overload index; and 5, further adjusting the initial allocation result in the step 3 to obtain a global optimal allocation scheme. The method is applied to the field of integration of intelligent decision and collaborative guidance.

The present invention provides a method and system for optimizing the bandwidth allocation of a content delivery network in an offline scenario. The method includes: obtaining a user task set; performing an initial allocation on the user task set to obtain an initial allocation decision for each user task in each time period within a billing cycle; setting a rolling time window on the billing cycle; iteratively executing the following steps until an iteration stop condition is reached: solving a target optimization function to obtain an optimized allocation decision for each user task in each time period within the time window, where the target optimization function aims to minimize the total billed bandwidth of the content delivery network within the time window; updating the allocation decision of the user tasks in each time period within the time window to the optimized allocation decision; rolling the time window on the billing cycle according to a step size; and when the iteration stop condition is reached, outputting the allocation decision of each user task in the billing cycle in the last iteration. The present invention can complete bandwidth allocation within a controllable and limited time, and optimize the bandwidth cost.

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 present application relates to a logistics distribution management method, device, equipment and medium. A logistics distribution management method includes extracting initial weight features in customer order information, matching a corresponding first optimal distribution path based on a well-established intelligent matching model, and generating a visual path corresponding to the optimal distribution path; extracting identity features in customer order information, customizing and automatically printing initial distribution labels, and real-time updating the current distribution point on the visual path; obtaining dynamic weight features in real time in the user front-end application, matching a second optimal distribution path according to the dynamic weight features, or automatically printing optimized distribution labels at the next distribution point of the current distribution point in the first optimal distribution path, and automatically matching the optimal distribution path through the intelligent matching model, improving the distribution efficiency, and adjusting the distribution plan by extracting dynamic weight features. It can ensure that the distribution can still proceed smoothly even when the external environment changes.