351 results about "Allocation algorithm" patented technology

The invention discloses a supply chain warehouse management system and method based on AI digital intelligence, and relates to the technical field of intelligent warehousing, and the method comprises the steps that a data processing unit collects supply chain system data through a data processing module, and constructs an associated data network containing goods information, shipping space information and order information based on the supply chain system data; the goods allocation unit executes a goods allocation algorithm according to the associated data network, extracts an association score based on goods information, analyzes a collaborative ex-warehouse frequency, and calculates an affinity matrix; the storage scheme unit generates a storage position scheme based on the affinity matrix; the job scheduling unit constructs a scheduling model, performs task optimization and outputs a task allocation sequence; the order picking navigation unit is provided with an order picking path planning module, presents a path and guidance, collects execution data, and optimizes the system through analysis. The problems that in traditional warehouse management, the goods allocation efficiency is low, task scheduling is not optimized, and the goods picking path is unreasonable are solved.

The invention discloses an AGV task allocation method and system, and relates to the technical field of automatic navigation trucks, and the method comprises the steps: carrying out the initialization of AGV state information; acquiring environment information in real time through ground sensor equipment; performing priority evaluation on the task instructions, and adjusting task priorities in real time according to emergencies; task merging and splitting are carried out; task allocation is carried out by using an ant colony algorithm, and a task allocation result is output to the corresponding AGV to guide the AGV to execute operation according to the allocated task; an optimal path is planned for each AGV, and path planning is dynamically adjusted in the task execution process of the AGVs; and optimizing a task allocation algorithm by adjusting a pheromone volatilization coefficient. Limited AGV resources are reasonably scheduled by arranging a task instruction obtaining module, a task information processing module and a task planning and distributing module, high environment perception and adaptability are achieved, and task priorities and path planning can be rapidly adjusted.

The invention discloses a power equipment intelligent early warning and maintenance management system based on an AI Agent, and relates to the technical field of power equipment maintenance management, and the system comprises a data preprocessing and weight correction module, a feature extraction and denoising module, a dynamic prediction and fault identification module, a time sequence analysis and intelligent early warning module, and a fault simulation and maintenance optimization module. And the data preprocessing and weight correction module is used for preprocessing the operation data of the power equipment and carrying out weighted analysis on the data of the multi-source sensor by adopting a self-adaptive weight distribution algorithm. According to the method, the hidden deviation problem of the sensor is solved through the multi-source data correction and dynamic weight distribution technology, and key features are accurately extracted and noise is eliminated in combination with a deep learning feature enhancement and denoising method. And a dynamic prediction model and an intelligent early warning generation method are utilized to realize accurate fault prediction and graded early warning, a maintenance strategy is optimized, and the reliability and safety of a power system are improved.

The invention relates to the technical field of data processing, in particular to an anti-interference six-dimensional sensor acquisition system, which comprises a multi-modal signal fusion module for receiving original acquisition data of a six-dimensional sensor and realizing fusion of the acquisition data of all sensors based on a self-adaptive dynamic weight distribution algorithm; the intelligent error compensation module is used for performing forward compensation on errors in the six-dimensional sensor data so as to improve the accuracy of the data; the correction feedback module is used for carrying out self-correction on the data acquired by the six-dimensional sensor at an application end and carrying out feedback adjustment on sampling parameters according to a correction result; according to the method, a self-adaptive covariance matched filtering algorithm is adopted, a noise covariance matrix is dynamically adjusted and measured in combination with Kalman filtering, the denoising effect is improved, and the optimal signal quality is ensured to be kept under different noise levels by setting a dynamic smoothing factor and adaptively adjusting a data smoothing process.

The invention provides a network slice resource allocation method oriented to an industrial internet and related equipment. Comprising the following steps: designing an SDN-based network slice resource allocation management and control architecture which comprises a control plane and a data plane; an intelligent agent is deployed for each switch node in the control plane, and each switch node is provided with an IT slice queue and an OT slice queue; designing a path planning and network slice resource allocation algorithm; modeling joint path planning and network slice resource allocation as a multi-agent Markov decision process; defining a state space and an action space; designing a reward function; constructing and training a multi-agent deep reinforcement learning model; and inputting a network global view collected in the SDN-based network slice resource allocation management and control architecture and demand information of each slice into the deep reinforcement learning agent obtained by training, and outputting a traffic forwarding path and a network slice resource allocation scheme. And the bearable flow quantity of the network can be improved to the maximum extent.

The invention discloses a remote monitoring and control method based on an intelligent engineering management system, and relates to the technical field of the Internet of Things, and the method comprises the steps: carrying out the data collection of an engineering object, carrying out the weight adjustment of multi-modal data through a dynamic weight distribution algorithm, and carrying out the fusion, and obtaining an engineering state feature vector; constructing a prediction model by using historical data and the engineering state feature vector, and calculating and analyzing dynamic characteristics through phase-space reconstruction and a Lyapunov exponent to obtain a chaos prediction model; performing anomaly prediction on the engineering object, calculating a Lyapunov index and identifying an abnormal mode to obtain an anomaly prediction result; performing fault-tolerant analysis on the abnormal prediction result and the network state by using an AI model, predicting a fault risk, switching a control mode, and obtaining a fault-tolerant operation state; and analyzing the monitoring data by using an AI algorithm to generate a control instruction. According to the method, the data acquisition quality is adaptively optimized, so that the reliability under complex working conditions is improved.

The invention relates to the technical field of data management, and provides a cross-enterprise closed-loop order transfer and data cooperative processing method, which comprises the steps of collecting multi-modal data, generating a unique distributed hash identifier for the multi-modal data based on a block chain technology, and performing intelligent association on the multi-modal data in combination with transfer node information; constructing a cross-enterprise order flow graph model by using the multi-modal data fusion; detecting an abnormal mode in real time by combining a graph embedding algorithm with a deep neural network; on the basis of the order flow graph model, a dynamic responsibility allocation algorithm is utilized, and in combination with real-time environmental factors when an anomaly occurs, a specific responsibility node where the anomaly occurs is positioned, and a corresponding responsibility weight is allocated; and generating an adaptive repair strategy based on a multi-objective optimization algorithm according to the responsibility weight. According to the invention, the transparency and cooperation efficiency of cross-enterprise order transfer are significantly improved, and the stability and efficiency of a supply chain system are ensured.

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

The invention discloses a large vision-language model collaborative reasoning method for a cloud side end system, which is characterized by comprising the following steps: deploying a high-performance cloud LVLM model and a cloud archive library at a cloud end; an edge LVLM model and an edge archive library are deployed at an edge end, a retrieval enhancement generation algorithm is introduced into the edge LVLM model, and the edge archive library and a cloud archive library can perform dynamic knowledge interaction; and the task scheduler performs complexity evaluation on the received query task based on a task allocation algorithm, and allocates the query task to an edge end for local processing or allocates the query task to a cloud end for processing according to a complexity evaluation result. According to the method, the reasoning efficiency in the cloud edge end system can be improved, meanwhile, high precision is kept, and the technical problem that the reasoning efficiency is low due to the problems of limited computing resources, communication window intermittency, bandwidth limitation and the like of edge equipment is effectively solved.

The invention discloses a real-time and efficient basin flood inundation simulation method and system, and relates to the technical field of flood simulation. Multi-source data such as hydrology, remote sensing, weather and terrain are collected in real time, and cleaning, interpolation processing and format standardization are carried out; a data fusion technology and a dynamic weight distribution algorithm are utilized, the system integrates multi-source data to generate a high-precision data set, and the weight of each data source is dynamically adjusted according to the real-time performance, the precision and the space coverage degree of the data; factors such as gradient, elevation and land utilization types are considered, correlation coefficients are quantified, the inundation range and water depth distribution in the drainage basin are estimated, and visual display and early warning release are carried out through the GIS technology. In addition, the system assists in decision making, submerging risk index calculation, emergency evacuation path planning and resource scheduling scheme generation; the real-time performance and accuracy of flood simulation are effectively improved, the flood early warning and emergency response capabilities are enhanced, and powerful support is provided for decision makers.

The invention discloses an electric power engineering project management talent intelligent evaluation and dynamic training system, and the system comprises a multi-dimensional quality capability model construction module which constructs a four-dimensional capability framework based on the characteristics of an electric power engineering industry, generates a differentiated post capability model through a dynamic weight distribution algorithm, and carries out the dynamic training of the differentiated post capability model; constructing an industry ability dictionary library by utilizing a knowledge graph technology; the intelligent evaluation and matching module integrates a multi-modal evaluation tool to collect dominant and implicit capability data, dynamically adjusts capability index weights through a pre-trained AI model based on project characteristic parameters, and calculates man-post adaptation indexes by adopting a cosine similarity algorithm; and the dynamic training and development module is used for generating a personalized training scheme by quantifying the capability difference, tracking the training effect in a closed-loop manner based on a Korotkoff four-level evaluation model, and dynamically optimizing a training path by adopting a Bayesian network. The technical defects of single evaluation dimension, low man-post matching degree and insufficient cultivation efficiency of traditional power engineering human resource management can be solved.

The embodiment of the invention provides a multivariate data processing method and system.The method comprises the steps that original data are collected from multiple data sources by calling multiple APIs, and multi-source heterogeneous data are obtained; formatting the multi-source heterogeneous data according to a preset format conversion rule, wherein the formatting comprises timestamp alignment, space coordinate system conversion and key frame extraction; performing multi-dimensional feature extraction on the unified data to generate space-time aligned feature vectors, and performing cross-modal data fusion on the feature vectors based on a dynamic weight distribution algorithm to generate a fusion data packet; and performing data quality evaluation on the fused data packet, including verifying the integrity, consistency and real-time performance of the data, and dynamically adjusting a transmission path of a data acquisition interface according to a verification result. According to the method, an efficient and reliable solution is provided for collection, processing and fusion of multi-source heterogeneous data, and the data processing efficiency and the decision support capability are remarkably improved.

The invention provides an agent development platform and method, a medium and equipment, and relates to the technical field of intelligent algorithm development. The platform comprises a hybrid cloud heterogeneous computing resource management module used for monitoring a plurality of integrated computing resources and scheduling and distributing the plurality of computing resources; the algorithm model automatic development module is used for providing a target development environment and a target algorithm template for a user and generating an initial intelligent algorithm model in response to an editing instruction of the user for the target algorithm template in the target development environment; the intelligent game distributed training module is used for performing distributed training on the initial intelligent algorithm model based on the target data set to obtain a target intelligent algorithm model; and the intelligent algorithm model evaluation module is used for evaluating the target intelligent algorithm model based on the evaluation index and verifying the effectiveness of the target intelligent algorithm model. Therefore, a user is comprehensively assisted in developing a new algorithm model, and the research and development threshold and time cost of intelligent task planning are reduced.

The invention provides a self-discovery ad-hoc network control method based on a swan-gap operating system, and relates to the technical field of data processing, and the method comprises the steps: based on the swan-gap operating system, extracting the identification information and communication parameters of equipment from all to-be-networked equipment; carrying out compatibility judgment on the communication protocol version and the maximum support bandwidth between the devices; determining a control relationship between the master device and the slave device; dynamically allocating communication resources to each device by adopting a distributed channel allocation algorithm in combination with task requirements of the devices and network bandwidth information; periodically synchronizing the resource state of each device; when the slave device sends a networking quitting request, the master device updates the networking candidate data and re-determines the networking control relationship after releasing the corresponding communication resources; when the master device exits, the device with the highest priority is selected from the slave devices to automatically take over the control role and complete the reconstruction of the networking control relation; according to the invention, the autonomy and accuracy of networking are improved.

The invention provides a task parallel processing intelligent scheduling method and system oriented to large model services, and belongs to the technical field of service computation.The method comprises the steps that semantic features of instructions and texts of parallel subtasks are extracted through a LaBSE model, embedded vectors are generated, the vector dimension of the embedded vectors is reduced through a compression module, and the embedded vectors are distributed in parallel; inputting the compressed embedded vector into a regression device to predict the generation length; constructing a segmentation algorithm based on semantic integrity constraint, and segmenting the text into a text fragment list; dividing the segmented text fragments in the text fragment list into different batches of tasks by adopting a multi-constraint task batching algorithm according to a similar length constraint and a same instruction constraint to obtain a batch task set; and optimizing the batch task set by adopting a task allocation algorithm oriented to heterogeneous resources, and determining an optimized sub-task scheduling scheme. According to the method, efficient parallel processing of large model services is realized, and the reasoning speed, the resource utilization rate and the intelligent level of task scheduling are remarkably improved.

The invention discloses a multi-modal large-model multi-agent collaborative scheduling and distribution method, and relates to the technical field of agent deploying.The method comprises the steps that a complex task input by a user is received, the complex task is disassembled into atomic-scale subtasks, and a task decomposition graph is generated; establishing an intelligent agent capability evaluation model, and obtaining a comprehensive capability score of each intelligent agent according to the capability behavior of each intelligent agent in the candidate intelligent agent set; the intelligent agent set generation module is used for dynamically matching and distributing roles through a decision function according to subtask types and intelligent agent comprehensive capability scores, and generating a candidate intelligent agent set; and according to the task decomposition graph and the comprehensive capability score of each agent, constructing a task-agent ant colony allocation algorithm, and solving an allocation scheme. The problems that an existing multi-agent system lacks a dynamic role adjustment mechanism, flexible cooperation cannot be achieved according to task progress and agent states, and the efficient and flexible task processing requirement of a multi-mode large model application development platform is difficult to meet are solved.

The invention discloses a dynamic business data updating and querying method and system, and particularly relates to the technical field of information processing, and the method comprises the following steps: S1, real-time data acquisition and preprocessing; s2, updating the dynamic data; s3, intelligent query optimization; s4, result feedback and visualization are carried out; and S5, performing anomaly detection and self-repairing. According to the method, the real-time processing capability and query efficiency of massive heterogeneous data are remarkably improved, the distributed sensor network is combined with lightweight encryption and a differential privacy mechanism to realize localized cleaning and feature extraction of multi-source data, the load of a central server is effectively reduced, and the risk of sensitive information leakage is avoided; an incremental synchronization mechanism depends on a message queue and Hash chain log technology to ensure the atomicity and traceability of data updating in a high-concurrency scene, and by combining a dynamically generated space-time multi-dimensional index and an adaptive weight distribution algorithm, the system can intelligently analyze query semantics and optimize path selection, and the response time of complex query is greatly shortened.

The invention relates to the technical field of robots, and discloses a heterogeneous multi-machine cooperation method, a terminal and a readable storage medium, and the method comprises the steps: obtaining environment perception data, and obtaining a multi-modal fusion scene prompt according to the environment perception data; distributing a role for each agent according to the multi-modal fusion scene prompt, and generating an action plan of each agent according to the multi-modal fusion scene prompt and the role of each agent; and controlling each agent to execute the respective corresponding action plan. According to the method, the multi-modal fusion scene prompt is extracted according to the environment perception data, the parameters of different scenes are matched according to the multi-modal fusion scene prompt, the role of each agent is allocated through the role allocation algorithm, and the method does not depend on predefined roles and the parameters specific to the scenes; and the task execution capability of the system in a dynamic scene is improved.

The invention relates to the technical field of component management, and discloses an electronic component material identification management method, which comprises the following steps: synchronously acquiring a grayscale image, a three-dimensional point cloud and weight data through X-ray imaging, a laser radar and a weight sensor, and fusing and generating a multi-dimensional feature matrix based on a dynamic weight distribution algorithm; extracting physical characteristic parameters corresponding to the electronic components based on the multi-dimensional characteristic matrix, generating unique identification codes through multi-parameter matching in combination with a material physical characteristic database, and outputting material types and specifications; for a high-temperature or strong electromagnetic interference scene, the X-ray detector adjusts gain and sampling frequency in real time, image distortion is corrected through point cloud spatial correlation, and a high-fidelity image is output; the OCR technology automatically identifies label information to realize inventory dynamic updating; and through geometric consistency verification of the high-fidelity image and the multi-dimensional features, if conflicts occur, an early-warning material physical characteristic database is triggered. According to the invention, the precision and adaptability of electronic component identification management can be improved.

The invention discloses an enterprise carbon emission management system based on a block chain technology. The enterprise carbon emission management system comprises a carbon emission data storage module, a carbon emission quota distribution module and a carbon emission cross-regional transaction module. The method has the advantages that the data processing efficiency and the intelligent analysis capability of the system under different time dimensions are enhanced through time weighting, dynamic compression and environmental data embedding on the carbon data storage and management level. The intelligence, the flexibility and the fairness of enterprise carbon quota allocation are ensured by utilizing a dynamic quota allocation algorithm through adaptive learning, game theory optimization and a market feedback mechanism, and the method adapts to continuously changing carbon markets and enterprise behaviors. A cross-regional carbon transaction mechanism is established, and through automatic execution of an inter-chain interoperation protocol and an intelligent contract, the current situation of isolation of carbon transactions in different regions is broken, and global carbon transaction market integration is realized. The mechanism improves the transaction transparency and the market efficiency, and promotes the development and standardization of the carbon transaction market.

The invention discloses a hydropower station AGC intelligent optimization control method and system based on multi-unit dynamic load distribution and prediction and early warning, and relates to the technical field of AGC control, and the method comprises the steps: initializing system parameters, including a unit power range and upper and lower limits of a vibration region, and designing a dynamic vibration region crossing control algorithm, a prediction model and a multi-unit intelligent load distribution algorithm; acquiring real-time load and operation data, inputting the real-time load and operation data into the prediction model to obtain a prediction result, and triggering a load adjustment or early warning mechanism according to the prediction result; calling a vibration area crossing algorithm, and adjusting the unit power to a safe range; and a multi-unit intelligent load distribution algorithm is used, multi-unit loads are dynamically distributed, a control instruction is output, and real-time adjustment is completed. The method reduces manual intervention, improves the operation efficiency, prolongs the service life of equipment, reduces the operation cost through the optimization model, improves the economic benefits of a power plant, adapts to various working conditions, is short in response time, and adapts to complex scheduling scenes.

The invention discloses a computing task scheduling method and system based on time sequence diagram network resource state prediction, and relates to the technical field of computing resource allocation, and the method comprises the steps: collecting computing power resource state data based on computing power nodes, and modeling the computing power resource state data into time sequence heterogeneous diagram data; constructing a time sequence heterogeneous graph attention network THGAT model, and performing offline training on the THGAT model by taking node feature and edge feature prediction loss as a loss function and taking minimization of a prediction error as a target; inputting the time sequence heterogeneous graph data into a THGAT model, and predicting the resource state of a future time window; the calculation task is converted into a calculation demand vector, and a multi-objective optimization resource allocation THGATRM algorithm is adopted to perform calculation power resource allocation on the calculation task according to the resource state of the corresponding time window; and executing the computing task according to the computing power resource allocation scheme. Through the technical scheme of the invention, the accuracy of computing power resource prediction is improved, and efficient allocation and optimization of resources are realized.

The invention discloses a load balancing optimization method and device, electronic equipment and a storage medium, and relates to the technical field of load balancing optimization, and the method comprises the steps: carrying out the differentiation processing of a data migration task according to a file storage threshold value, and combining a migration performance index and server resource occupation information collected in real time, the task allocation strategy is adjusted through the dynamic task allocation algorithm, so that the reasonable allocation of the files with different sizes and the dynamic load optimization in the migration process are realized. The technical problems that in the prior art, server overload and resource idling coexist due to a single migration server or a static task allocation strategy, high delay and data inconsistency risks are generated due to network congestion and uneven task allocation in the migration process, and the overall performance and reliability of a system are affected can be solved. The technical effects of optimizing the load balance degree, reducing overload and idle conditions of the server, reducing migration delay, ensuring data consistency and improving the overall performance and reliability of the system are achieved.

The invention provides an intelligent computing center-oriented heterogeneous task scheduling method and system and electronic equipment. The method comprises the following steps of: acquiring computing power resource information of each computing power node in an intelligent computing center; determining task information and computing power resource requirements of each heterogeneous task; according to a preset scheduling rule, on the basis of the computing power resource information, the task information and the computing power resource demand, generating a scheduling strategy, and on the basis of the scheduling strategy, scheduling each heterogeneous task to each corresponding target computing node; wherein the preset scheduling rule comprises at least one of the following items: based on a priority allocation algorithm, performing computing power resource allocation on each heterogeneous task; based on a heuristic algorithm, carrying out computing power resource allocation on each heterogeneous task; and according to the preset load threshold value of each computing power node, carrying out computing power resource allocation on each heterogeneous task. According to the method, a proper scheduling mode can be flexibly selected for task scheduling, the computing power resource utilization rate is optimized, the service quality of large-scale heterogeneous tasks can be guaranteed, and the user experience is improved.

The invention relates to the technical field of intelligent manufacturing, particularly provides an AGV task allocation method, electronic equipment and a storage medium, and aims to solve the problems of low AGV starting rate and low production efficiency caused by the fact that an existing AGV scheduling system rarely pays attention to optimization of a dynamic task allocation algorithm. In order to achieve the purpose, the method comprises the steps that operation stages, including a first stage, a second stage and a third stage, of a current AGV task allocation scene are obtained; determining corresponding task allocation strategies based on the operation stage, wherein the task allocation strategies comprise an event-driven allocation strategy, a Hungary algorithm allocation strategy and a heuristic algorithm allocation strategy; and carrying out AGV task allocation based on the determined task allocation strategy. Through the above implementation mode, a coupling scheme of multiple task allocation algorithms is adopted, different task allocation strategies can be adopted in different operation stages, the requirement for dynamic adjustment of production tasks is met, the tasks can be reasonably formulated according to the production plan, and the AGV starting rate and the production efficiency are improved.

The invention discloses a service upgrading system, an electronic device, a method, a storage medium and a product, and relates to the technical field of computers, and the service upgrading system comprises a processor and a controller connected with the processor. Wherein the controller divides a monitored thread to obtain a first thread for carrying out an upgrade service and a second thread for carrying out a non-upgrade service, and the controller also obtains a total time slice and a retention time slice preset by the processor, and dynamically allocates an algorithm based on the total time slice, the retention time slice and the preset time slice; and determining a time slice allocated to the first thread, wherein the allocated time slice is used for representing the duration of the processor resources allocated to the first thread. And the processor generates a required clock signal in the time slice allocated by the first thread, wherein the clock signal is used for carrying out service upgrading. The stability of the clock signal generated by the controller is improved, and the success rate of service upgrading is further improved.

The invention provides a multi-satellite collaborative regional target observation scheduling method based on stripe coverage and ant colony improvement, and relates to the field of multi-satellite collaborative observation. According to the method, firstly, a to-be-observed task list and satellite resources are obtained, and a task conflict matrix is constructed; secondly, performing discretization processing on the regional target based on the task conflict matrix, and generating a stripe coverage scheme and a stripe priority; thirdly, generating a multi-satellite cooperative task allocation scheme by adopting a self-adaptive ant colony task allocation algorithm; thirdly, generating an optimization target based on the stripe priority and a multi-satellite cooperative task allocation scheme, and constructing a scheduling decision model in combination with an energy constraint, a time window constraint and a task unique execution constraint; and finally, solving the scheduling decision model by using an improved branch and bound algorithm, and obtaining a global optimal solution for deciding whether each satellite observes a task or not at different moments. According to the invention, by improving the ant colony algorithm and the dynamic stripe coverage strategy, the task execution efficiency and the resource utilization rate of multi-satellite collaborative observation are significantly improved.

The invention discloses an assembly interface shape error grouping method for three-dimensional matching, and relates to the field of precision assembly, and the method comprises the steps: obtaining the point cloud data of an assembly interface, and calculating five-dimensional morphology parameters; a greedy distribution K-Means algorithm is adopted to carry out clustering grouping on the five-dimensional morphology parameters; performing three-dimensional visualization on a grouping result in combination with principal component analysis; and based on a three-dimensional visualization result, selecting and matching reference parts and matching parts by adopting a grouping and selecting and matching method, and realizing assembling interface shape error grouping oriented to three-dimensional selecting and matching. The problem that an existing size grouping method faces a two-dimensional size chain model and is difficult to adapt to three-dimensional matching requirements is solved.

The invention provides a laboratory detection task intelligent allocation method and system based on big data, and relates to the technical field of laboratory management, and the method comprises the steps: collecting detection task data, carrying out the detachable analysis to construct a collaborative flow chart, calculating a collaborative efficiency index and a task priority, and extracting personnel collaboration features to generate an association weight matrix; and allocating task queues by adopting a double-layer adaptive task allocation algorithm, performing density peak clustering on the first layer to divide task clusters, and optimizing an allocation scheme in combination with personnel ability and cooperative affinity on the second layer. According to the invention, the distribution efficiency and cooperation quality of complex laboratory detection tasks are improved.

The invention provides a multi-unmanned aerial vehicle motion planning method based on a multivariate universe optimization algorithm, and the method employs a Munkres distribution algorithm as a task distribution algorithm of unmanned aerial vehicles, selects an MVO algorithm to plan the path of each unmanned aerial vehicle, employs a parameter polynomial fitting method for track generation, and selects a PID control method as a flight control method of the unmanned aerial vehicles. According to the invention, the MVO algorithm is applied to the multi-unmanned aerial vehicle motion planning problem. Numerical experiments and actual case studies show that the algorithm has high applicability, and the algorithm is of great significance for efficiently completing motion planning of multiple unmanned aerial vehicles in a dynamic and complex environment. An algorithm system of an existing multi-unmanned-aerial-vehicle path planning problem solving method can be enriched and perfected, and meanwhile the multi-unmanned-aerial-vehicle path planning problem is solved with the MVO algorithm as a basic framework for solving the problems that a classic algorithm is low in convergence speed, low in solving precision and the like.