363 results about "Sorting algorithm" patented technology

The invention relates to a task scheduling optimization method and device based on reinforcement learning, equipment and a medium. The method comprises the steps that firstly, system resource state data are collected in real time, dynamic environment characteristics are determined through preprocessing and time sequence analysis, task characteristic data are analyzed at the same time, and a task priority sequence and a resource demand vector are generated through a priority ranking algorithm and a resource evaluation model; and then a state space and an action space are constructed by adopting a reinforcement learning algorithm, an optimal task allocation scheme is generated through strategy iteration and reward function optimization, and if the scheme meets a resource balance threshold, scheduling is executed, and performance indexes are collected. And finally, fusing real-time indexes with historical data, and updating parameters of the reinforcement learning model through experience playback and gradient descent to form a closed-loop optimized improved scheduling strategy. By adopting the method, the accurate mapping of the resource state and the task requirement can be realized, and the problem of insufficient adaptability of the traditional static scheduling to a complex scene is solved.

The invention discloses a milk industry knowledge question-answering method and device based on a large model and RAG and a medium, and the method comprises the steps: collecting multi-source heterogeneous data of the milk industry, carrying out the term protection word segmentation processing of the multi-source heterogeneous data, and generating a labeled field corpus; based on a pre-training language model base, through a domain corpus injection and adversarial learning mechanism in a domain corpus, generating an enhanced language model adapted to the dairy industry terminology; receiving a milk industry problem of a user, executing semantic vector retrieval and term extension retrieval in parallel, and screening a multi-modal retrieval result through a dynamic sorting algorithm; and splicing the multi-modal retrieval result and the milk industry question into an enhanced prompt, inputting the enhanced prompt into an enhanced language model, generating a final answer corresponding to the milk industry question, and associating the final answer with a knowledge source.

The invention relates to the technical field of equipment maintenance scheduling, in particular to an equipment fault repair management system, which comprises a dependency identification and modeling module, a fault state analysis module, a task priority evaluation module, a work order scheduling generation module and a resource scheduling execution module. According to the method, a multi-level dependency topology between devices is constructed through a directed graph traversal algorithm, a trigger relation between physical connection and an operation process is quantitatively analyzed, a classification marking model is established in combination with dynamic parameters such as a performance degradation rate, a fusion influence range and a dependency factor are calculated through scalar superposition, and a priority scoring matrix is dynamically generated. A task queue structure is optimized through a sorting algorithm, a data-driven maintenance decision mechanism is formed, the fault positioning precision is improved, response delay caused by manual intervention is reduced, key node equipment maintenance lag is avoided, the resource configuration efficiency is optimized, the collaboration of fault processing and a production system is strengthened, and formulation of a preventive maintenance strategy is supported.

The invention relates to a high-fidelity cloud rendering cluster scheduling method and system, and the method comprises the steps: receiving a rendering task, generating a multi-level priority queue according to the task complexity, timeliness and resource demand classification, and automatically optimizing a scheduling strategy; monitoring the resource state of the heterogeneous computing node in real time; allocating tasks by using preemptive and round-robin scheduling strategies, and adjusting and coping with resource fluctuation in combination with a dynamic code rate; the tasks are decomposed by adopting a spatial blocking and time framing strategy, and an execution sequence is controlled according to a topological sorting algorithm; abnormal nodes are identified through heartbeat detection, and affected subtasks are migrated through incremental task updating. According to the method, efficient resource allocation, scheduling algorithm and idle key frame scheme can be realized, the GPU directly outputs the video stream, the rendering speed is remarkably improved, resource waste and operation cost are reduced through a dynamic resource allocation mechanism, a fault-tolerant mechanism is provided, the task is ensured to be normally completed under the condition of node fault, and the task efficiency is improved. And distributed rendering and synthesis of large-scale high-resolution images are supported.

The invention provides an industrial data online migration method, medium and system of a productivity middle platform, and belongs to the technical field of electrical digital data processing.The method comprises the steps that firstly, historical access records of an industrial data source are collected, and a deep neural network model comprising a multi-head attention layer, a time sequence feature extraction layer and a residual connection layer is constructed; and the prediction of the data change rate is realized. And based on a prediction result, dynamically calculating a data change rate demarcation point by utilizing an optimization equation set, and dividing a data table into different change frequency categories. And then constructing a data dependency relationship matrix, and determining a migration sequence by adopting an improved topological sorting algorithm. Batch migration, incremental migration and real-time synchronization mechanisms are adopted for different types of data, and model updating is triggered through real-time monitoring, so that dynamic optimization of a migration strategy is ensured. Finally, data consistency verification is carried out, migration accuracy is guaranteed, and the technical problem that the online migration efficiency of industrial data is low due to real-time dynamic change of the data change rate is solved.

The invention provides a 3D + 2D high-dimensional feature fusion-based material tracking multi-target identification method and system. The method comprises the steps of performing target identification on a to-be-tracked material by using a YOLO algorithm; performing multi-target real-time tracking on the material based on a Deep SORT algorithm; generating three-dimensional point cloud data of a target by adopting a multi-view data calibration and alignment algorithm, and carrying out noise reduction, sampling and data enhancement preprocessing operation; performing three-dimensional reconstruction on the preprocessed three-dimensional point cloud data by using a Gaussian Splitting method, and introducing time sequence information at the same time; segmenting a dynamic three-dimensional target and a static background through a Mask R-CNN technology; aligning the position of the target in the two-dimensional image with the coordinates in the three-dimensional space by using a feature fusion method; a PNP algorithm is adopted to estimate attitude and depth information of a target in a three-dimensional space, accurate positioning and state restoration of materials are realized, and efficient and reliable technical support is provided for material management and tracking in an industrial environment.

The invention relates to the technical field of photovoltaic systems, and particularly discloses a fault identification method and system for a photovoltaic system, and the method comprises the steps: collecting data in real time through environment, electrical parameters and an equipment state monitoring sensor, carrying out the cleaning and standardization, extracting time domain, frequency domain and time frequency features, and screening a feature subset through a correlation analysis and feature importance sorting algorithm; detecting and classifying faults by using a hybrid model composed of an isolated forest algorithm and a random forest classifier; positioning a fault subsystem and analyzing a root cause by means of a hierarchical diagnosis strategy, a graph neural network and a Bayesian reasoning algorithm; and periodically updating the model based on the new data. The method can quickly and accurately identify and position faults, adapts to a complex environment, reduces the operation and maintenance cost, and improves the operation reliability and operation and maintenance efficiency of a photovoltaic system.

The invention provides a multi-agent system initialization method and device for a question and answer large model, and relates to the technical field of question and answer large language model multi-agent systems. The method comprises the following steps: disassembling a user question and answer query task into a plurality of sub-tasks by adopting a pre-constructed question and answer planning agent, generating an agent corresponding to each sub-task, carrying out standardization processing through a formatting agent to obtain a standardized agent corresponding to each sub-task, and carrying out evaluation through observing the agents to obtain feedback information. Generating a candidate agent set based on multiple iterations; a pre-training text encoder is adopted for encoding, and semantic representation and task representation of each agent are generated; task correlation and agent diversity are calculated, and a multi-objective optimization problem is constructed; and obtaining a Pareto optimal frontier team set by adopting a non-dominated sorting algorithm, and selecting an optimal agent team set by selecting agents. According to the invention, the cooperation efficiency of the multi-agent system can be improved.

The invention provides a household appliance knowledge question-answering method and system based on retrieval enhancement generation. The method comprises the following steps: acquiring household appliance field multi-modal data from a multi-format document library; extracting text information, table information and chart information in the multi-modal data; performing domain term injection processing on the extracted information, and constructing a packet domain enhancement index; receiving a natural language question input by a user; the natural language problem is analyzed through a query optimizer, and semantic retrieval and keyword retrieval are executed in parallel; carrying out fusion processing on the semantic retrieval result and the keyword retrieval result; selecting matched document fragments by adopting a relevancy sorting algorithm; inputting the matched document fragments into a large language model to generate candidate answers; verifying the compliance and traceability of the candidate answers through a credibility evaluation module; outputting a final answer with a reference source; and storing the high-frequency questions and the final answers into a cache library to solve the problems that the answer accuracy of a knowledge question-answering system is reduced and the response efficiency is limited.

The invention provides a user complaint processing method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining user complaint information; based on the first cue word, guiding the knowledge analysis large language model to determine key information, guiding the knowledge analysis large language model to retrieve the key information in the knowledge graph based on a path sorting algorithm, and generating a plurality of candidate solutions based on a retrieval result; performing sentiment analysis on the user complaint information based on a sentiment analysis large language model, and determining the sentiment level of the user complaint information; and based on a second cue word, guiding a knowledge analysis large language model, and based on the emotion level, determining a final solution from the plurality of candidate solutions. According to the method, an automatic complaint processing process from information extraction, knowledge retrieval to scheme generation and emotion adaptation is constructed through division and cooperation of the knowledge analysis large language model and the emotion analysis large language model, a judgment process needing manual intervention is avoided, and complaint processing efficiency is improved.

The invention discloses a short message communication method and system for FTU (feeder terminal unit) equipment of a power distribution network. The method comprises the following steps: acquiring a power state data stream, an equipment fault data stream and an equipment health state data stream in the FTU equipment of the power distribution network; each data stream is preprocessed; calculating the priority of each data stream by using a dynamic priority sorting algorithm based on each preprocessed data stream, and classifying the data streams into a high-priority data stream and a low-priority data stream; monitoring the real-time quality data of the communication link based on the Beidou No.3 satellite system, and predicting the health state of the satellite link by using an extended Kalman filtering algorithm to obtain a link health state prediction result; the real-time quality data comprises link delay, bandwidth and packet loss rate; and calculating a quality score of each link by using a nonlinear programming algorithm based on a link health state prediction result, and selecting the link according to the score to carry out data stream transmission. The reliability and the transmission efficiency of the system can be improved.

The invention discloses a large model-based government affair knowledge retrieval method, apparatus and device, and a medium, and relates to the technical field of natural language processing, and the method comprises the steps of performing information extraction on a to-be-processed government affair work order through a preset government affair large model to obtain target structured information; based on a word processing fusion algorithm constructed by a word frequency-inverse document frequency algorithm and a graph sorting algorithm, performing word processing on the target structured information to obtain work order keywords; converting the work order keyword into a target semantic vector; performing retrieval operation on a preset government affair knowledge base by utilizing a preset retrieval condition and the target semantic vector to obtain a target government affair knowledge document; the preset retrieval condition is a retrieval condition constructed based on time, space and the work order emergency degree. Therefore, the information accuracy and the processing efficiency can be improved through word processing; and in combination with retrieval conditions constructed based on time, space and work order emergency degrees, accurate retrieval can be provided, and the processing effect on various complex government affair work orders is improved.

The invention relates to the technical field of embedded system construction, in particular to a software dependency relationship automatic integration method and system based on meta packaging, and the method comprises the steps: S1, creating a meta packaging framework in an embedded construction system, and defining and constructing a stage hook through a Makefile; s2, automatically deploying the pre-compiled software package and the shared dependency library to a target directory of a construction system through a resource injection script; s3, analyzing a metadata file of the pre-compiled software package, extracting dependency relationship information, and generating a global dependency relationship list based on a directed acyclic graph topological sorting algorithm; and S4, inversely writing the global dependency relationship list into the dependency declaration configuration of the meta-packaging by utilizing the dependency aggregation script. By implementing the scheme, the problems of dependency sequence disorder and metadata auditing failure during embedded system integration are solved, the reliability of system integration is improved, the system construction efficiency is optimized, the safety of system construction is enhanced, and the maintainability and expansibility of system construction are improved.

The invention discloses a fast charging station planning method based on urban dynamic traffic demand prediction and correction, and relates to the technical field of electric vehicle fast charging station planning, and the method comprises the steps: S1, constructing a traffic topology connection matrix according to urban road network topology, and recognizing traffic hub nodes and long-distance nodes as candidate nodes; s2, proposing three dynamic factors including social demands, user public praise and demand elasticity, and correcting the electric vehicle traffic demand prediction value of each path unit; s3, constructing a multi-target fast charging station capacity optimization model taking the minimum planning total cost and prediction error as targets, wherein the multi-target fast charging station capacity optimization model comprises fast charging station self constraints and power grid adaptation constraints; s4, solving the model by adopting a non-dominated genetic sorting algorithm to obtain a Pareto solution set, and selecting a planning result according to the demand of a decision maker; according to the method, the problems of blind site selection and static demand prediction in traditional planning are solved, economy and power grid safety are considered, planning scientificity and decision-making flexibility are improved, and supply and demand of charging resources can be effectively balanced.

The invention provides a user-defined script dynamic editing and executing method and system of a Unity engine. The method comprises the following steps that codes are input and preprocessed; executing text cleaning, word segmentation and annotation preprocessing according to the line feed as a code line set; grammatical analysis and semantic understanding; constructing an abstract syntax tree, identifying a syntax structure, and combining context analysis semantics to generate a symbol table; processing errors in real time; positioning an error line through a multi-stage matching strategy, highlighting an error position through a visual interface, and generating a repair suggestion in combination with a line number mapping table, error propagation analysis and a multi-evidence fusion mechanism; executing dynamic codes; explaining and executing the corrected script, and feeding back an execution result; through interaction with a Unity engine, operation control of a script on a game object and a scene is realized; through three core technologies of semantic weight dynamic matching, an anti-interference navigation state machine and a hybrid sorting algorithm, the long-standing problems of low matching precision, poor interaction experience and the like in the field of code completion are solved.

The invention discloses a regional water environment bearing capacity change evaluation and prediction method and system, and the method comprises the steps: obtaining water resource data, water environment data and social economic data in a target region, and constructing a water environment bearing capacity index system; the index system comprises a plurality of subsystems; evaluating the subsystems through a projection pursuit model based on a sparrow algorithm to obtain subsystem evaluation results; obtaining a comprehensive evaluation result of the water environment bearing capacity through an entropy weight approximation ideal solution sorting algorithm according to the subsystem evaluation result; constructing a Markov transition probability matrix according to the comprehensive evaluation result; and predicting the change probability of the water environment bearing capacity according to the Markov transition probability matrix. The method can improve the evaluation and prediction accuracy of the water environment bearing capacity change, and can be widely applied to the technical field of water environment evaluation and prediction.

The invention discloses a production control method and system based on multi-source data fusion and an intelligent algorithm, and relates to the technical field of automatic control. Multi-dimensional production state information is collected through a sensor, multi-source heterogeneous data is integrated into a data set in a unified format through a data fusion technology, and the multi-dimensional production state information is obtained; a long short-term memory network is used for trend analysis and prediction, a multi-objective optimization algorithm is used for calculating an optimal control parameter, multi-loop cooperative adjustment is realized, an adjustment instruction is issued through a distributed control protocol, a priority ranking algorithm is introduced to ensure system stability, and a game theory negotiation algorithm is used for realizing dynamic resource allocation among production units. The production scheduling plan is optimized through the genetic algorithm, continuous monitoring and optimization can be achieved, control parameters can be adjusted in a self-adaptive mode, the production efficiency and the resource utilization rate are effectively improved, and intelligent automatic control under the complex production environment is achieved.

The invention discloses a multi-dimensional natural language reasoning method based on a safety emergency knowledge graph. The method comprises the following steps: S1, collecting multi-source heterogeneous data, and carrying out standardized cleaning, entity recognition and relationship extraction; s2, constructing an emergency knowledge graph, performing storage and indexing by adopting a graph database, and designing an incremental updating mechanism; s3, intention classification and keyword recognition are conducted on natural language questions input by a user through the deep learning model, and the questions are mapped into initial entity nodes in the graph; s4, performing multi-hop reasoning around an initial entity in the knowledge graph, screening out a most relevant and logically coherent Top-K path in combination with reinforcement learning and a graph sorting algorithm, and realizing deep correlation analysis and answer candidate generation of the question; s5, generating an answer in a structured and natural language mixed form based on a reasoning result, and providing a visual reasoning path for display; a user feedback mechanism is introduced, and continuous optimization of the system and iterative updating of the model are supported.

The invention provides an industrial model visual arrangement method based on fragment operator development. The method comprises the steps that a directed acyclic graph is adopted to define a dependency relationship between operators according to operator module input and output parameter configuration; aiming at the directed acyclic graph, determining an operator execution sequence through a topological sorting algorithm; generating a container arrangement scheduling configuration file based on the operator execution sequence; obtaining the operator module code, and carrying out grammar check and semantic analysis by adopting a static code analysis tool; deploying the operator container by adopting a container arrangement platform according to the container arrangement scheduling configuration file; and carrying out dynamic scheduling and load balancing on the operator task by utilizing a distributed scheduling framework aiming at the operator container.

The invention provides a distributed photovoltaic and energy storage load collaborative optimization operation method and system. The method comprises the following steps: acquiring multi-source data and processing the multi-source data by adopting an improved wave sorting algorithm; constructing a CNN and LSTM hybrid model to predict photovoltaic output; applying a single-index time FPT algorithm and combining with a cactus shrinkage principle to predict load demand and electricity price fluctuation; establishing a collaborative optimization model taking economy and photovoltaic consumption rate as double targets; carrying out distributed iterative optimization by adopting a single-index time FPT algorithm based on a cactus shrinkage principle; and generating a multi-time-scale control instruction and issuing the multi-time-scale control instruction to each device to realize cooperative control. Through the innovative data processing method and optimization algorithm, the efficient cooperative operation of the distributed energy system is realized, the photovoltaic consumption rate and the economic benefit are improved, and the practical value is relatively high.

The invention relates to a calcareous sand unit test broken particle tracking and matching method based on a digital image, which comprises the following steps: S1, taking a calcareous sand unit test picture, and making the taken test picture into a two-dimensional picture with three-dimensional information; s2, performing instance segmentation by using the SAM; s3, the BoT-SORT algorithm is improved; s4, tracking an instance segmentation result in the step S2 by using the BoT-SORT algorithm improved in the step S3; s5, using a PIV algorithm to track the part except for the instance segmentation in the S2; s6, performing feature point extraction on the instance segmentation tracking result in the step S4 by using an SURF, and performing matching on the extracted feature points by using a KNN matching algorithm to obtain a particle crushing matching result; and S7, making a final result graph. According to the method, non-uniform grading particle tracking and crushing matching can be realized through the CNN without training, and the method has extremely high robustness and generalization ability.

The invention provides a lightweight configurable cue word labeling method and system for a Web system, and relates to the technical field of Web front-end development and man-machine interaction, and the system comprises a front-end labeling plug-in module, a cue word content service module, a cue word management module and an intelligent recommendation module. The front-end labeling plug-in captures text content selected by the user by monitoring a global shortcut key, and allows the user to add prompt words of a text, a rich text or a link type; the prompt word content service module provides an API (Application Program Interface) to realize persistent storage of data; the prompt word management module provides a unified management interface for an administrator; and the intelligent recommendation module automatically judges the prompt word display priority through a candidate recall and sorting algorithm based on the user click behavior data, and continuously optimizes the recommendation result by using an online learning mechanism. The method is integrated to an existing Web system in a non-intrusive mode, business core codes do not need to be modified, the use threshold of the system is remarkably lowered, and precipitation and sharing of business knowledge are promoted.

The invention relates to the technical field of mechanical arm obstacle avoidance, and particularly discloses a mechanical arm obstacle avoidance method and system based on vision. The method comprises the following steps: collecting environmental visual data to identify auxiliary potential obstacles and construct an attribute matching list; in combination with characteristics of a task real-time execution stage, an ideal solution sorting algorithm is improved to construct an auxiliary adaptation degree evaluation model, an auxiliary target obstacle is determined after sorting, an auxiliary scheme is designed, and a path planning core constraint is extracted; auxiliary function nodes are positioned, a path is initialized, and a collaborative motion track is output through quadratic programming optimization; and calculating a failure judgment coefficient based on the auxiliary adaptation degree and the demand achievement rate, and carrying out track local reconstruction or updating according to scenes. The system comprises an obstacle recognition module, a path planning module, a trajectory adaptation module and a trajectory updating module. The obstacle is converted into the auxiliary resource from the obstacle avoidance object, and the task efficiency of the mechanical arm can be improved.

The invention discloses a hybrid expert model structured pruning and acceleration method and system based on micro expert sorting, belongs to the technical field of large language models, and solves the problems that an existing MoE pruning method cannot consider the requirements of fine-grained pruning, reasoning acceleration and structural analysis generalization at the same time, coarse-grained expert-level pruning damages the performance of a model, and the reliability of the model is poor. And fine-grained compression lacks speed increase and lacks a unified micro-analysis method. The method comprises the following steps: splitting each expert network in a hybrid expert model into a plurality of micro experts, and modeling the plurality of micro experts, so that the micro experts in different expert networks have comparability; sorting all the micro experts according to the energy indexes of the micro experts by adopting a micro expert sorting algorithm; and processing the sorted micro-experts by adopting a pruning algorithm, selecting a core micro-expert for reservation, and directly deleting the rest of the micro-experts. The method is suitable for application scenes such as edge calculation and multi-task learning.

The invention relates to the technical field of cell detection and tracking, and discloses a cell multi-target tracking method, system and equipment, and the method comprises the steps: constructing a multi-cell video data set; and constructing an improved YOLOv5 target detection model, inputting the multi-cell video data set into the improved YOLOv5 target detection model for target detection to obtain a target detection result, and performing tracking processing on the target detection result in combination with a BoT-SORT algorithm to obtain a cell multi-target tracking result. According to the method, reliable input is provided for tracking by improving YOLOv5, optimizing cell feature extraction and improving low resolution and cell division scene detection precision, complete link optimization is formed through cooperation of a BoT-SORT algorithm, optimizing matching and trajectory management, and the cell tracking precision and efficiency are improved while real-time performance is guaranteed.

The embodiment of the invention discloses an AI application development method and system based on visual arrangement and an equipment medium, and the method comprises the steps that the development system monitors a graphical interface corresponding to a visual arrangement module to obtain a dragging component of a user, and determines a task process of a current to-be-developed AI application based on the dragging component of the user; performing format conversion on the task process to obtain a structured description file, and analyzing the structured description file to generate a corresponding cluster sub-task; determining a dependency relationship of the cluster subtasks on the basis of a topological sorting algorithm, and executing the cluster subtasks in sequence on the basis of the dependency relationship; feedback data in the execution process of the cluster subtasks are obtained in real time, the computing resources are dynamically adjusted based on the feedback data, and development of the current to-be-developed AI application is completed.

The invention provides a performance test and evaluation method, system and device for a power grid credential environment and a medium, and the method comprises the steps: obtaining real-time data of each node through employing a distributed test probe and a data flow distribution strategy, and constructing a digital twinborn body of the power grid credential environment; the digital twin body comprises twin nodes corresponding to the nodes; extracting multi-dimensional performance test data of each twin node under the performance test load by adopting a classification dimension convergence method; and based on the performance test prediction data of each twin node, performing risk assessment on each twin node by adopting an entropy weight-comprehensive sorting algorithm. According to the method, dynamic load balance of each node is ensured through a data traffic distribution strategy, normal operation of the power grid credential environment is prevented from being influenced during test evaluation, and the method is suitable for a complex scene in which the power grid credential environment is easily impacted by a high load; data evaluation is carried out through performance test prediction data, data exception can be captured, and potential performance risk bottlenecks of a power grid credential environment can be found in advance.

The invention discloses a multi-scalar multiplication acceleration method based on resource estimation and a pre-calculation strategy, and provides a systematic solution for the problems of insufficient resource estimation, pre-calculation factor stiffness and low sorting efficiency of a Pippenger algorithm in zero-knowledge proof. A multi-resolution point multiplication table is dynamically generated through a hierarchical displacement pre-calculation strategy, and global memory access delay is remarkably reduced by combining memory layout optimization and asynchronous flow task scheduling of window inner barrel continuous storage. A dynamic resource adaptation mechanism is designed, thread block topology, grid division and sorting algorithm selection are adjusted in real time based on GPU hardware features, and load balancing and cache utilization rate maximization are achieved. Iterative reduction and double temporary bucket strategies are introduced, and data scale is compressed and boundary processing is optimized through multi-round reduction. In the large-scale MSM operation in the block chain and privacy computing field, the computing efficiency and the resource utilization rate can be remarkably improved, and the method is suitable for an elliptic curve cryptography acceleration task.

The invention discloses a regional power load real-time allocation system and method based on artificial intelligence, and belongs to the technical field of power distribution. According to the method, after an electric load data set and an alarm condition data set are integrated, a multi-dimensional feature interaction matrix is decomposed based on a factorization machine, a high-order interaction relation between electric load features and alarm condition features is extracted, hidden variable modeling is introduced, accurate division of risk levels of different areas is further achieved, and the accuracy of the risk levels is improved. Compared with a traditional single-domain feature analysis method, the method has the advantages that complex nonlinear relations can be captured, potential influence rules of regional power anomalies on alarm risks can be mined, and experimental verification shows that the method is high in accuracy and high in reliability. According to the modeling method, the recognition accuracy of the high-correlation region is improved by more than 15%, and a more scientific basis is provided for resource allocation decisions.

The invention belongs to the technical field of logistics distribution, and particularly relates to an unmanned aerial vehicle and truck collaborative distribution method based on multi-target reinforcement learning, and the method comprises the steps: order clustering: a logistics service provider carries out the clustering of orders according to a K-means method, enables the orders with the similar distribution time and address to be divided into one order cluster, and carries out the clustering of the orders according to a K-means method; the unmanned aerial vehicles are delivered by the truck and the unmanned aerial vehicles carried by the truck; according to the NSGA-II algorithm, multiple targets such as path cost, distribution time and carbon emission are considered, and optimal paths of multiple schemes are provided; and planning an optimal path by adopting a reinforcement learning method. According to the cooperative logistics distribution method based on the unmanned aerial vehicle and the truck, the three targets of logistics distribution time, cost and carbon emission are considered, the dynamic conditions of low-altitude traffic restriction of the unmanned aerial vehicle, the road congestion condition of the truck, load constraint and the like are also considered, the optimal path is optimized by adopting a reinforcement learning mode, and the logistics distribution efficiency is improved. And parameter setting of reinforcement learning is automatically updated by using a non-dominated sorting algorithm, so that multi-dimensional optimal path selection can be provided for logistics service providers.