42results about How to "Achieve global optimization" patented technology

This invention provides a three-trailer collaborative aircraft transfer method and system based on multi-sensor fusion. This invention relates to the field of aircraft ground support technology, specifically to a three-trailer collaborative aircraft transfer method based on multi-sensor fusion, applicable to short-haul aircraft ground transport to hangars. Through multi-sensor data fusion and multi-trailer collaborative control, it improves transfer accuracy, safety, and efficiency. The method specifically includes the following steps: system setup and sensor calibration; trailer attitude initialization and collaborative parameter configuration; real-time acquisition and preprocessing of multi-sensor data; multi-sensor data fusion and state estimation; dynamic path planning based on fused data; attitude error quantification and calibration based on complex statistics; multi-trailer collaborative driving control and traction distribution; real-time environmental monitoring and risk warning; precise docking at the hangar entrance and attitude fine-tuning; and parking and system review and optimization.

The present application relates to the technical field of flexible job shop scheduling in intelligent manufacturing and production scheduling, and particularly relates to an optimization method for solving flexible job shop scheduling with adjusted resource constraints. The method comprises the following steps: initializing parameters and randomly generating an initial population; using a crossover operator and a mutation operator in sequence to evolve the current population; executing a hybrid decoding strategy on the current population; sorting the individuals in the current population in ascending order of maximum completion time to form an elite population, and updating the elite population through problem-specific local search; judging whether the evolution condition is met, and if yes, executing mathematical evolution based on CP, and outputting a final solution when the running time reaches the total running time. The present application has the positive effects of reducing resource waiting time, improving machine utilization, and improving the resource utilization efficiency and scheduling performance of the entire workshop production.

The invention relates to the technical field of AGV monitoring, in particular to an AGV single vehicle state monitoring method based on operation state analysis, and the method comprises the steps: calculating a multi-dimensional operation state index based on the multi-source sensor data of an AGV, and generating a comprehensive state score; dynamically adjusting operation parameters of the AGV monitoring system based on the comprehensive state score and the change trend thereof; and based on the link quality index calculated in real time and the comprehensive state score, cooperatively adjusting a data transmission strategy between the AGV and the cloud. When the communication quality is lower than a preset threshold value, optimizing a local state record according to the comprehensive state score; and carrying out grading diagnosis and verification on the detected abnormity in combination with the comprehensive state score.

This invention discloses a spatiotemporal joint anti-interference method in a UAV-ground cooperative network. The method includes: Step 1: Constructing a UAV-ground cooperative network communication scenario; Step 2: Establishing a base station status discrimination mechanism; Step 3: Constructing a satellite link switching model; Step 4: Setting an anti-ping-pong mechanism for heterogeneous link switching; Step 5: Analyzing the communication effectiveness of heterogeneous links; Step 6: Constructing a joint optimization problem; Step 7: Designing an intelligent anti-interference decision-making method based on DDQN; Step 8: Executing the anti-interference decision. This application autonomously optimizes anti-interference strategies and flight trajectories in complex scenarios involving intelligent interference and base station failures, achieving global optimization of the flight path while ensuring the reliability of the communication link.

The application discloses an active defense method, device and equipment for diffusion speech conversion and a medium, and relates to the technical field of speech security. The active defense method comprises the following steps: obtaining source speech and reference speech, introducing a constrained protection disturbance into the reference speech, constructing protected speech, and ensuring that the protection disturbance satisfies a disturbance imperceptibility constraint. The source speech and the protected speech are input into a speech conversion model with the reference speech as a speaker condition, content information analysis, speaker condition constraint acoustic generation and waveform synthesis are completed by the speech conversion model, and a protection generated speech is output. A joint loss function is constructed based on speaker embedding representations of the protection generated speech and the reference speech, gradients of a current disturbance position and a predicted midpoint position are calculated according to the joint loss function and are fused to obtain an update direction, the protection disturbance is iteratively optimized, and projection or clipping is performed to satisfy the disturbance imperceptibility constraint. Finally, the protected speech is output.

This invention relates to the field of intelligent battery swapping recommendation technology, and discloses an intelligent battery swapping recommendation method and system based on historical battery data. The method includes: a vehicle central control system identifying the vehicle from a user-triggered battery swapping request signal to obtain parameter information of the current vehicle; querying historical battery matching data corresponding to the parameter information from a cloud database, and calculating the compatibility score between each battery model and the current vehicle; a battery swapping cabinet control system performing multi-objective collaborative recommendation decision-making with the compatibility score and the inventory batteries to obtain the optimal recommended battery identifier; and the battery swapping cabinet opening the corresponding compartment door according to the optimal recommended battery identifier to execute the battery swapping process and generate a battery swapping operation record. This invention improves the accuracy of battery compatibility assessment, can discover performance correlations between different battery models, and thus improves the automation and intelligence level of the entire battery swapping process.

The present application provides a ladle scheduling method and device based on a heat plan, equipment and storage medium, the method comprises the following steps: obtaining the heat plan and the ladle information; formulating the corresponding ladle plan according to the heat plan; determining the planned start time and the planned end time of the ladle plan based on the time information corresponding to the multiple process stages of the heat plan; establishing a ladle matching model according to the ladle plan and the ladle information; taking the highest ladle utilization rate and the least number of matched new ladles as the optimization target; solving the ladle matching model to obtain the matching result of the ladle and the ladle plan; and matching the heat plan and the ladle based on the matching result and the corresponding relationship between the ladle plan and the heat plan, so as to schedule the ladle based on the heat plan. The method solves the technical problems of low efficiency and easy errors in manual ladle scheduling.

The application relates to a large language model assisted cross-layer optical network routing optimization method. The method comprises the following steps: constructing the architecture of the cross-layer optical network, collecting network physical node composition, link resource parameters and service request characteristics; designing an auxiliary graph model, converting the cross-layer network routing optimization problem into a minimum cost path calculation problem in a virtual topology; building a large language model enabled auxiliary graph model weight optimization framework, and preliminarily generating the edge weight of the auxiliary graph model by the large language model; calculating the minimum cost path based on the initial edge weight of the auxiliary graph, evaluating the network performance index corresponding to the path, inputting the evaluation result into the large language model as feedback data, iteratively adjusting the edge weight until the performance index converges to the optimum; and completing the routing planning and wavelength allocation of all service requests in the auxiliary graph model according to the optimal edge weight. The application combines the large language model and the auxiliary graph model framework, improves the flexibility and multi-objective adaptation capability of the routing optimization, and realizes global optimization.

The invention relates to the technical field of storage intelligent management systems, in particular to a cold chain storage intelligent management system based on the internet of things, which comprises an internet of things network constructed by a plurality of cold chain warehouses, and is characterized in that each cold chain warehouse is internally provided with a temperature adjusting device, an image acquisition device, a temperature acquisition device and a storage and transportation planning module; determining a circulation coefficient according to the space utilization rate of the corresponding cold chain warehouse, determining a to-be-circulated storage object list based on the circulation coefficient and the historical warehouse-out data, and responding to the to-be-circulated storage object list or the to-be-stored object list to obtain the to-be-delivered storage object list so as to match the cold chain warehouse. And a warehousing strategy is determined according to the number of the cold chain warehouses which can be matched. According to the invention, the technical problem that the storage environment of the stored objects is not matched with the actual demand due to the change of the storage environment of the stored objects and the space proportion caused by the fact that the warehouse-in and warehouse-out link of the goods is not comprehensively considered in the prior art is solved.

The energy-saving control system comprises a hardware layer and an algorithm layer, the hardware layer comprises executing mechanisms such as a compressor unit, an evaporator and a condenser, a sensor module and a PLC, and the algorithm layer comprises an upper computer algorithm module; according to the energy-saving control method, system operation data are collected through the sensor module and input into the upper computer algorithm module after being preprocessed, local agent decision making is achieved through multi-agent reinforcement learning MARL, global collaborative optimization is completed in combination with a collaborative genetic algorithm CGA, decision consistency is guaranteed through conflict detection and a coordination mechanism, and energy-saving control is achieved. And finally, the PLC controller executes the control instruction and realizes closed-loop feedback updating. The temperature stability, the energy efficiency ratio and the equipment reliability are comprehensively considered, global optimal control is achieved, energy consumption is effectively reduced, the overall operation efficiency of the refrigeration house group is improved, and the method is suitable for the fields of food, medicine, industrial cold chain storage and transportation and the like.

The invention relates to the technical field of power transmission line planning, in particular to a power transmission line path selection system and method based on a database, and the system comprises a data collection and preprocessing module, a data storage and management module, an algorithm module, an application module and a user interface module. And the data acquisition and preprocessing module is used for acquiring multi-source data of satellite remote sensing data, oblique photography data, laser point cloud data, manual inspection data, meteorological data and geological data. According to the method, the power transmission line path is comprehensively evaluated and optimally selected by constructing the standardized database of multi-source data fusion and combining an intelligent optimization algorithm, so that the optimal path planning scheme can be quickly generated according to different constraint conditions and actual requirements, and dynamic adjustment is realized when a power grid fails or the environment is changed; the global optimization and dynamic adjustment of path planning are realized, and the defects of poor data intercommunity, one-sided evaluation and insufficient adaptability in the prior art are overcome.

The deep learning-based video multi-level adaptive compression cross-layer transmission optimization method and system of the application relates to the technical field of video transmission. By designing a compression level selection model based on deep learning, the optimal compression level is output based on the original video data and network environment parameters, and the corresponding compression strategy of the optimal compression level is obtained. A transmission strategy selection model is designed to generate a transmission strategy based on video quality parameters and network environment parameters. A global utility function is defined to calculate the global utility value when the compression strategy and the transmission strategy are assumed to be executed. An optimization objective function is designed, which inputs the compression strategy and the transmission strategy and outputs the optimized compression strategy and the transmission strategy. A condition judgment logic is set to select the final strategy based on the compression strategy and the transmission strategy before and after optimization, thereby achieving global optimization of video compression and transmission.

The present application relates to dilution refrigerator temperature calibration technical field, and disclose a kind of dilution refrigerator thermometer in situ calibration system based on natural cooling dynamics, comprising: according to the dynamics equation of cooling power and temperature Curvature is calculated, according to the temperature corresponding to thermometer resistance value, the temperature is substituted into temperature curvature, and the calibration function of resistance value and temperature is obtained;Identify the multivalued region of temperature, and evolve multiple branch functions in multivalued region in calibration function, calculate the deviation measure of each branch function and calibration function;In multivalued region, the support degree of each branch function to calibration function is calculated, and real-time data is obtained by adjusting calculation load, to minimize the number of experiments to verify calibration function;According to the branch verification result of multivalued region, dynamically adjust the verification strategy of non-multivalued region, under the premise of not relaxing calibration accuracy, through dynamics branch and load guide, significantly reduce the repeated experiment cost of dilution refrigerator cryogenic calibration.

The invention discloses a method and system for optimizing two-dimensional argon plasma uniformity by introducing a high-energy particle source, and the method comprises the steps: S1, filling a plasma reaction chamber with background gas, and simulating the distribution of neutral particles; s2, initializing the speed and the position of charged particles in the gas in the chamber, enabling the charged particles to be uniformly distributed in the chamber, and determining the intensity, the time sequence, the incident direction and the incident position of high-energy particles emitted by an external particle source; s3, calculation of charged particle density, potential and electric field; s4, calculating the force acting on the charged particles; s5, moving the charged particles; s6, checking and executing collision; and S7, repeating the steps S3 to S6, adjusting the high-energy particle injection time sequence until a steady state is reached, and obtaining the data distribution of the charged particles. Through controllable interaction between an external particle source and plasma or background gas, seed electrons-ions are actively generated in a specific space area or energy distribution is changed, and inherent space non-uniformity of the plasma is compensated.

This invention discloses a method for adjusting the end effector stiffness of a hybrid robot used in the machining of thin-walled structural parts. The method first collects the proportional gain of each active branch servo drive system as the axial servo stiffness; secondly, it establishes a mapping model based on the robot configuration to solve for the equivalent axial servo stiffness of the end effector in the reference coordinate system; subsequently, it introduces stiffness weighting coefficients and combines them with the mechanical stiffness and electromechanical coupling stiffness performance evaluation index; finally, it uses a one-dimensional discrete search algorithm to traverse and filter redundant rotation angles that satisfy displacement and rotation constraints while keeping the end effector pose unchanged, aiming to maximize the electromechanical coupling stiffness index and generate an optimal sequence of redundant rotation angles. This invention establishes an electromechanical coupling model integrating mechanical and servo systems and adaptively adjusts the end effector stiffness using redundant degrees of freedom, which can effectively suppress cutting deformation and chatter during the machining of thin-walled parts while reducing computational complexity, significantly improving machining quality.

The invention discloses a pure highland barley powder extrusion puffing process optimization method, and belongs to the technical field of food extrusion processing. According to the method, the technical problems that the high-fiber highland barley powder is easily subjected to insufficient puffing and too hard texture in the extrusion process, and the retention rate of heat-sensitive functional components such as beta-glucan is low are solved. The method comprises the following steps: optimizing process parameters based on a multi-target response surface method, and determining the water content of materials, the temperatures of a first region, a second region, a third region and a fourth region of a machine barrel and the rotating speed of a screw; carrying out low-temperature superfine grinding on the highland barley powder; a compound emulsifier and edible sodium bicarbonate are added during hardening and tempering; during extrusion, the temperature gradient between the third area and the fourth area is controlled to be smaller than or equal to 20 DEG C; the method is mainly used for producing the high-quality pure highland barley puffed food, the puffing rate and the beta-glucan retention rate of the product can be synergistically improved, the hardness of the product is effectively controlled, and the eating quality and the nutritional value are improved.

The invention provides a federal learning method, a medical data processing method, a medical data processing system and a related device. The method comprises the following steps: acquiring a prediction label carrying a medical sample identifier sent by each participant and a prediction deviation between the prediction label and an original real label; for each medical sample, under the condition that the medical sample is detected to be an abnormal medical sample according to the prediction labels of all participants and the prediction deviation, generating a target real label of the medical sample according to the prediction labels of all participants; and sending a target real label carrying the medical sample identifier to each participant, so that each participant updates a local medical large model according to the target real label corresponding to the medical sample identifier and the prediction label. According to the method, collaborative correction and global optimization of the abnormal medical samples are realized, the interference of the abnormal medical samples on model training is avoided, and the generalization ability and accuracy of the finally generated model are improved.

The invention discloses an adaptive path training system and method based on capability evolution prediction, and the system comprises a data collection module which is used for collecting the real-time behavior data of a target training object in a training process; the capability state construction module is used for constructing capability state data according to the real-time behavior data; the capability trajectory prediction module is used for performing capability evolution prediction on the capability state data according to a preset time sequence prediction model to obtain a behavior evolution trajectory sequence; the training path generation module is used for constructing a target function on the basis of a preset training task set according to the behavior evolution trajectory sequence and in combination with training time and training cost, and solving the target function on the basis of a preset strengthening algorithm to obtain an optimal training path; and the training execution module is used for sequentially executing the corresponding training tasks based on the optimal training path. The technical problem that in the prior art, a training path is fixed, and a training object cannot be dynamically adjusted is solved.

The present application relates to supply chain management method technical field, more specifically, it relates to supply chain management method and system based on intelligent manufacturing, method includes: obtaining the cost information, operation and maintenance information and inventory information of each stage in supply chain;Based on information, obtain the risk value of each stage through hierarchical weighted dynamic risk prediction algorithm;Combined with the risk value and the sales information of goods, the gross profit information of each stage is obtained by using superposition nonlinear gross profit optimization algorithm;According to the gross profit information and the risk value, the optimal shipment allocation of each stage is obtained by using adaptive allocation optimization algorithm;Based on the gross profit accumulation of each stage, the global gross profit optimization information is obtained by using multi-dimensional progressive gross profit accumulation optimization algorithm;Based on the global gross profit optimization information, the feedback adjustment information of supply chain system is formed by using overall feedback closed loop optimization algorithm, the multi-dimensional risk prediction is formed by dynamically obtaining the cost, operation and maintenance and inventory information of each stage in supply chain.

The invention discloses a sign language vocabulary segmentation and recognition method based on continuous integral triggering, which comprises the following steps: (1) acquiring a sign language video frame sequence, and extracting frame-level visual features to obtain a frame-level feature sequence; (2) inputting the frame-level feature sequence into a CIF module, generating a feature vector sequence corresponding to the vocabulary sequence by the CIF module through a continuous integral triggering method, and outputting a time division point corresponding to each vocabulary; (3) inputting the feature vector sequence into a vocabulary decoder, and outputting a vocabulary recognition result; and (4) aligning a vocabulary recognition result with the segmentation time point to form a vocabulary sequence with a timestamp, and outputting the vocabulary sequence. By utilizing the method, the problems of high labeling cost, two-stage error accumulation and lack of explicit segmentation capability of the existing end-to-end method in the traditional method can be effectively solved.

The invention provides a parking path generation method and system, and relates to the technical field of parking control, and the method comprises the steps: responding to a parking request from a target vehicle in the parking process of the target vehicle according to a specified parking path, and generating N alternative paths; for each alternative path in the N alternative paths, deconstructing scene information included in the alternative paths through a scene recognition model to obtain scene scores corresponding to the alternative paths, and determining predicted passing duration of the target vehicle driving the alternative paths according to the scene information included in the alternative paths through a duration prediction model, according to the predicted passing duration and the vehicle state of the target vehicle, determining the predicted energy consumption of the alternative driving path of the target vehicle, and according to the scene score corresponding to the alternative path, the predicted passing duration and the predicted energy consumption, determining the total cost of the alternative path; and determining a target parking path from the N alternative paths according to the N total costs corresponding to the N alternative paths.

The invention relates to the field of water conservancy construction, in particular to a drainage hole and settlement joint arrangement optimization method based on big data assessment, and the method comprises the steps: obtaining regional historical rainfall data, soil attribute data, inverted filter attribute data and drainage hole attribute data; based on regional historical rainfall data, determining the designed drainage amount of the drainage hole; determining the critical flow velocity of soil particles based on the soil attribute data and the inverted filter attribute data; determining the blocking risk of the drain hole based on the design displacement of the drain hole and the critical flow velocity of the soil particles in combination with the attribute data of the drain hole; and determining an optimal arrangement parameter combination of the drainage holes by using a multi-objective optimization algorithm by taking blockage risk minimization as an objective function, and outputting a drainage hole and settlement joint arrangement optimization scheme. According to the retaining wall drainage system, by quantifying the drainage hole blocking risk under the time accumulation effect, the stability of the retaining wall drainage system is effectively improved, and the risks of wall back osmotic pressure rise and wall structure damage caused by hole blocking are reduced.

This invention relates to the field of intelligent decision-making technology and discloses an intelligent decision-making system for agricultural irrigation based on artificial intelligence. The system constructs a collaborative architecture comprising a field perception layer, an edge computing layer, a cloud-based decision-making layer, and an irrigation execution layer, forming an irrigation task context through multi-source perception data. Based on this, a meta-reinforcement learning-driven multi-round agent-based search decision-making mechanism is introduced, combined with explicit self-reflection to achieve iterative optimization of the decision-making process. Simultaneously, a preference-aware reward evaluation model is constructed, achieving multi-objective trade-off modeling through latent variable mirror constraints and inverse autoregressive flow transformation, and globally optimizing the strategy using a cross-round benefit attribution mechanism to generate an irrigation scheduling scheme that meets water-saving, crop water requirement, and energy consumption constraints. This invention can improve the accuracy, stability, and adaptability of irrigation decisions.

The application discloses a cross-domain network transmission scheduling method, a storage medium and an electronic device. The method comprises the following steps: receiving a target service flow in a plurality of autonomous domain corresponding domain policy set, wherein the domain policy set comprises a plurality of candidate domain routing policies of the target service flow in the corresponding autonomous domain, and a plurality of candidate domain routing policies corresponding transmission delay and transmission cost; based on the corresponding domain policy set, the target routing policy and the target transmission delay are determined; the target routing policy is sent to a plurality of domain controllers, which are used for the plurality of domain controllers to control the transmission of the target service flow in the corresponding autonomous domain; and the target transmission delay is sent to the gateway device corresponding to the plurality of autonomous domains, which is used for the gateway device to control the inter-domain transmission of the target service flow. The application solves the technical problem that the related art cross-domain network transmission scheduling is independently scheduled in each autonomous domain, resulting in uncontrollable end-to-end delay, low scheduling efficiency and high resource cost.

The invention provides a multi-refinery-plant-oriented crude oil centralized purchase, storage and pipeline transportation integrated scheduling method, which comprises the following steps: S1, demand collection and analysis: obtaining crude oil types, total demand, time windows and demand priority parameters; s2, constructing a multi-dimensional resource model based on the demand parameters and the full-link resources obtained in S1, wherein the full-link resources comprise purchase resources, transportation resources, port resources, storage resources, pipeline transportation resources and emergency resources; and S3, constructing a multi-objective optimization function, and realizing multi-constraint collaborative optimization. Due to the fact that full-link collaborative modeling is adopted, the limitation of segmented scheduling in the prior art is broken through, information sharing and plan linkage of all links are achieved, information islands can be effectively eliminated, the problems of unshipping congestion, pipe transportation idling and the like caused by link disjunction can be effectively solved, and the supply chain collaborative efficiency is improved.

The application provides a cost constraint retrieval optimization method and system based on MCTS, and relates to the technical field of information retrieval. After receiving a user query, the application first selects a prompt template and personalized parameters according to the query to enhance the request and receives a total budget constraint. Then, parallel preprocessing and iterative planning are performed, the maximum number of iterations is calculated, and the computing resources are allocated. Next, the MCTS retrieval of the cost constraint is executed, the search tree is constructed through multiple iterations, and the selection and expansion of the tree structure are completed according to the node value and the cost in each iteration. After reaching the maximum number of iterations, a preliminary result set is obtained, and the preliminary result set is reordered based on the personalized parameters, and the final retrieval list is output. Finally, according to the user interaction feedback, the selection of the prompt template and the parameters is adaptively optimized, the accuracy and the personalized efficiency of the information retrieval can be improved through intelligent retrieval, cost constraint and feedback optimization.

The invention relates to a defect detection system based on a large projection model, and relates to the technical field of machine vision defect detection. The technical problems that an existing defect detection technology needs to depend on specific scene customization design, the cost is high, and the detection effect is limited are solved. The system comprises a hardware module, a data acquisition module, a model training module and a detection application module, the hardware module forms an imaging facility with a fixed pose by a DLP (Digital Light Processing) and an area-array camera, the data acquisition module obtains a 70-channel initial imaging effect picture and a structured cue word of multi-period fringe projection, and the model training module realizes cooperative training of a projection large model and a detection large model through reinforcement learning. And the detection application module completes full-process automatic defect detection. Manual customization design is not needed, the technical threshold is reduced, time and labor cost are saved, the detection precision and universality are remarkably improved, hardware equipment is small, exquisite and portable, and the method is suitable for detection scenes of various materials, surface types and defect types.

The application discloses a kind of network type converter and electric heat cold energy storage system collaborative optimization control method, contain steps as follows: construct "network type converter-electric heat cold energy storage-flexible load" multi-objective optimization model;Establish the collaborative constraint of network type converter and energy storage system;Establish demand response and network state linkage mechanism;Solve the multi-objective optimization problem by NNC algorithm and combine TOPSIS method to filter optimal operation scheme;According to optimal operation scheme, dynamically adjust the virtual inertia and droop coefficient of network type converter, synchronously dispatch electric energy storage charge-discharge power and cold energy storage and / or heat energy storage electric drive equipment operating state.The method can quickly and accurately suppress new energy output fluctuation and load mutation, strictly constrain system voltage deviation and frequency deviation within a more optimal operating range, fundamentally enhancing the transient stability and dynamic power quality of the grid with high proportion of new energy access.

The invention discloses a reservoir safety intelligent management system, which comprises an acquisition module for acquiring the vibration spectrum of a dam structure and the electromagnetic flux of water flow; the mapping module is used for executing phase change mapping on the vibration spectrum and the electromagnetic flux to obtain an instability sequence parameter of the dam, mapping the instability sequence parameter through Boltzmann distribution to generate a dam break probability distribution value, and mapping the instability sequence parameter into a three-dimensional thermodynamic diagram by using a phase change sensing network; the generation module is used for solving a risk flow density field vector based on the dam break probability distribution value, calculating a risk entropy, performing spatial differential operation on the risk entropy to obtain an entropy change gradient field, and generating a gate opening regulation and control instruction according to the risk flow density field vector; according to the method, through time domain alignment and abnormal value elimination operation of the vibration spectrum and the electromagnetic flux, misjudgment caused by sensor noise or data delay is avoided, and the problem that complex coupling risks cannot be recognized through single-parameter threshold judgment in an existing method is solved.