2119 results about "Improved algorithm" patented technology

The invention relates to the technical field of energy management, and discloses a multi-source photovoltaic energy storage collaborative management method and system. The method comprises the steps of obtaining a multi-source photovoltaic system data generation task set; constructing a multi-dimensional data fusion model, and defining a collaborative optimization coordinate system; determining an initial cooperative constraint condition; co-scheduling simulation is executed based on the model to generate a preliminary strategy; and dynamically correcting and generating a multi-source collaborative optimization strategy by using a self-adaptive optimization algorithm. The method also relates to refining processing of each axis in the model, adoption of an improved algorithm optimization strategy, a calculation framework and a fault-tolerant mechanism in simulation, anomaly detection and re-optimization, construction of a scene library for predicting conflicts and the like. The system comprises a data acquisition and classification module, a model construction module, a constraint setting module, a simulation scheduling module and a strategy optimization module. The energy utilization efficiency of the multi-source photovoltaic energy storage system can be effectively improved, stable operation of the system is guaranteed, and equipment loss and cost are reduced.

The invention discloses a concrete surface intelligent construction method and system based on multi-modal sensing. The method comprises the following steps: firstly, receiving multi-source sensing data, and generating standardized feature data and defect feature vectors through standardization processing and feature extraction; performing time sequence alignment processing on the sensing data, and generating time sequence alignment data in combination with multi-scale analysis; constructing a state evaluation model based on the dynamic Bayesian network, and generating an environment state vector and a construction difficulty graph; carrying out region segmentation and optimizing task allocation by adopting a spectral clustering algorithm, and generating a task allocation scheme and a collaboration strategy; then, construction parameters are determined through multi-objective optimization, tool configuration is planned, and an optimization parameter set and a tool configuration scheme are generated; and finally, trajectory planning is carried out based on an improved A * algorithm, and a multi-robot cooperative construction trajectory is generated through comprehensive optimization. Full-process control of intelligent construction of the concrete surface is achieved, and the construction quality and efficiency are improved.

The invention discloses a mechanical arm positioning and grabbing method based on machine vision, and relates to the technical field of machine vision and mechanical arm control, the method comprises the following steps: synchronously acquiring RGB-D images of a target scene through a multi-view camera array, and generating three-dimensional point cloud data through data fusion; an improved LSD algorithm and a PnP algorithm are adopted to calculate the initial pose of the target object, illumination distortion is eliminated in combination with the generative adversarial network, and three-dimensional coordinates are output; a mechanical arm motion error transfer model is constructed based on Monte Carlo simulation, and a candidate grabbing scheme set is generated through reinforcement learning; and an optimal grabbing scheme is screened through a preset priority evaluation rule, and a mechanical arm joint movement track and a control instruction set are generated. Through multi-modal data fusion and a nonlinear optimization algorithm, the technical problems of large target positioning deviation and sensitive illumination interference in a complex environment are solved, and the grabbing precision and robustness of the mechanical arm are improved.

The invention discloses a drilling key parameter automatic regulation and control method based on an improved DDPG algorithm. The method comprises the following steps: collecting and preprocessing drilling data, and constructing a complete data set for training; a drilling speed (ROP) time sequence prediction model is constructed and trained on the basis of a self-attention mechanism (self-attention); building a simulation environment by using the trained ROP prediction model, and defining a state space, an action space and a multi-target reward function of a reinforcement learning DDPG algorithm model; a deep deterministic strategy gradient algorithm (DDPG) is adopted to train a reinforcement learning model, through an Actor-Critic network structure, an intelligent agent learns an optimal bit pressure (WOB) and rotating speed (RPM) regulation strategy in a simulation environment, and dynamic optimization of the drilling speed (ROP) is achieved. According to the method, through automatic and intelligent parameter regulation and control, the efficiency and stability of the drilling process are improved, the drill bit wear rate and the operation cost are reduced, and the method has good adaptability and multi-target optimization capacity and is suitable for drilling operation under the complex stratum condition.

The invention discloses a financial data risk control system and method based on big data, and relates to the technical field of financial science and technology, and the system comprises a multi-source data collection module which achieves cross-mechanism safety collection through federal learning; the data cleaning and preprocessing module is used for processing abnormal values and missing values by using an improved algorithm; the knowledge graph construction module is used for constructing a dynamic knowledge network based on an innovative algorithm; the risk assessment engine fuses various models to assess risks; the real-time monitoring and early warning module is used for realizing second-level response by utilizing multi-scale analysis; the decision support module is used for optimizing a strategy based on reinforcement learning; and the audit tracking module guarantees evidence storage and privacy through zero-knowledge proof, and all the modules cooperate to improve the risk control capability. According to the financial data risk control system and method, risks are accurately recognized, real-time monitoring and early warning are achieved, data security sharing is achieved, risk control strategies are dynamically optimized, risks and business development are balanced, the risk prevention and control capacity and economic benefits of financial institutions are improved, and data privacy and risk control transparency are guaranteed.

The invention relates to the field of unmanned aerial vehicle automation, in particular to a three-dimensional modeling and unmanned aerial vehicle landing area address selection method based on laser point cloud data. The method comprises the steps of collection and dynamic compensation of laser radar point cloud data, multi-scale adaptive registration of three-dimensional point cloud, topographic feature extraction and evaluation, selection of an optimal landing point, path planning based on an improved A algorithm, and real-time environmental data updating and dynamic adjustment mechanism. According to the method, point cloud data is dynamically compensated through Kalman filtering, a 3D-NDT algorithm is utilized to perform high-precision three-dimensional environment modeling, key topographic features are extracted through an RANSAC algorithm to comprehensively evaluate topographic complexity and safety distance, an optimal landing point is selected, an optimal path of an unmanned aerial vehicle is planned in combination with an improved A algorithm, and path safety and flight energy efficiency are ensured. By updating environment data in real time and dynamically adjusting a flight path and a landing point, the unmanned aerial vehicle is adapted to sudden environment changes, and safe and stable autonomous landing of the unmanned aerial vehicle in a complex and changeable environment is ensured.

The invention discloses a monorail crane inspection robot intelligent test method based on data analysis, and relates to the technical field of intelligent detection, and the method comprises the following steps: synchronously collecting track images, point cloud and attitude data, carrying out time alignment and preprocessing, and outputting a standardized data packet; detecting an inspection target by using a YOLO detection network, and outputting a multi-scale feature vector in combination with a point cloud feature hierarchy extraction network and a time sequence convolutional network; predicting a fault development trend in combination with an improved A-star algorithm and a long short-term memory network, optimizing an inspection path through reinforcement learning, and outputting a maintenance decision scheme; the maintenance decision scheme is converted into a control instruction, the robot is driven to execute an inspection task and feed back the operation state in real time, incremental learning and point cloud reconstruction are combined, and a visual diagnosis report is output. According to the method, the dynamic threshold algorithm is adopted for self-adaptive analysis, and the key geometric indexes are calculated in combination with the cross-modal attention mechanism, so that the recognition capability of structural anomalies is improved.

The invention discloses an intelligent scheduling and optimization method for an industrial electrical automation system, and the method comprises the following steps: deploying a distributed sensor network to collect electrical parameters, an equipment vibration spectrum and production work order data in real time, and constructing a unified feature vector based on time-space alignment and confidence weighting; a production system-energy management-external power grid three-layer interaction model is established, and a dynamic carbon emission calculation engine and process deadlock detection module is embedded; an improved NSGA-III algorithm is adopted to solve a multi-target Pareto leading edge, and energy consumption, productivity and carbon emission target priorities are adjusted in real time in combination with a dynamic weight mechanism; distributed optimization is executed through an edge-cloud federated architecture, cross-system instruction synchronization is achieved, and closed-loop dynamic feedback is formed. According to the method, the limitation of traditional single system optimization is broken through, the energy consumption is reduced by 15%-30%, the carbon emission intensity is reduced by 12%-18%, the abnormal response speed is increased to 3 seconds, and intelligent decision making and green transformation in a complex industrial scene are supported.

The invention provides a robot path planning method and system based on fusion of an improved ant colony algorithm and DWA, and the method comprises the steps: building a grid map of the movement of a robot, and generating a global path of the robot in the grid map through employing the ant colony algorithm; transmitting the global path to a DWA dynamic window model to generate a dynamic window; adding unknown static and dynamic obstacles into the grid map, and setting a local target point by using a local target point setting method based on the global path; based on the local target point and the robot movement information, robot speed sampling is carried out in the dynamic window, and a simulation track of robot movement is obtained; evaluating the simulation track by using an obstacle avoidance evaluation function, and if the robot reaches a target point, obtaining an optimal path; and if the robot does not arrive at the target point, regenerating a simulation track for iterative calculation until the robot arrives at the target point. The improved algorithm can effectively improve the performance and adaptability of robot path planning in different environments.

The invention discloses a segmentation method for rail defect detection based on improved YOLOv10 and SETR, and the method comprises the steps: S1, collecting image data, carrying out the enhancement processing of the image data, carrying out the segmentation processing, and making a data set; s2, an improved YOLOV10 algorithm model is built and trained; s3, using the improved YOLOV10 model weight to detect image data to be detected, judging whether defects exist or not, if the defects are detected, storing detection frame information of the defects, and outputting a target detection frame and confidence; and S4, transmitting the detection frame information as prompt information into the SETR model, segmenting the target by adopting the SETR model to obtain a segmentation result, and outputting a mask pattern. The improved YOLOv10 algorithm and the SETR algorithm are adopted to detect the rail defects, the calculation complexity and the efficient detection speed are kept, the detection precision is improved, and the robustness of the model is improved.

The invention relates to the technical field of autonomous navigation and intelligent control of an ocean unmanned system, in particular to a dynamic obstacle avoidance system in unmanned ship path planning, which comprises a multi-source sensing module, a global path planning module, a remote obstacle avoidance decision module, a short-range dynamic obstacle avoidance module and a multi-stage cooperative control unit, and is also provided with a semi-physical verification module. The multi-source sensing module fuses multi-source data to construct a layered map; the global path planning module generates and optimizes a path by adopting an improved algorithm; the long-range and short-range obstacle avoidance modules cope with far and near obstacles based on different algorithms; the multi-stage cooperative control unit coordinates the output of each module; the functions of all the modules are achieved through specific algorithms and formulas, and the semi-physical verification module simulates a real environment to conduct system testing. According to the invention, the environment can be sensed in all directions, intelligent path planning and multi-stage obstacle avoidance decision are realized, an optimal instruction is output through cooperative control, the reliability is improved by combining virtual verification, and safe and efficient navigation of the unmanned ship in a complex water area is effectively ensured.

The invention discloses an unmanned aerial vehicle route planning method based on an improved grey wolf optimization algorithm. The method comprises the following steps: 1, environment modeling and parameter setting; 2, designing constraint conditions and a target function; 3, initializing an algorithm and generating a population; 4, improving an algorithm and updating a track; and step 5, iteration termination and result output. The flight path of the unmanned aerial vehicle is efficiently coded in a spherical vector coding mode, and the flexibility and the calculation efficiency of flight path representation are remarkably improved; by introducing the Levy flight strategy, the global search capability of the algorithm is enhanced, and falling into a local optimal solution is avoided; meanwhile, a parameter adaptive adjustment strategy is combined, so that the convergence speed and precision of the algorithm are remarkably improved. According to the method, the problem of flight path planning in a complex mountain environment and a threat area is effectively solved, a safer and more efficient flight path is planned for the unmanned aerial vehicle, and the method has higher practicability and reliability.

The invention discloses a wind turbine generator data analysis and fault diagnosis method and system based on big data and artificial intelligence. According to the method, a blade image, a vibration signal, audio data and operation parameters are synchronously acquired through an unmanned aerial vehicle multi-mode sensor and a ground monitoring system, and a multi-source heterogeneous data set is constructed; after the data is classified and preprocessed, image features, vibration time-frequency domain features and operation parameter key value pairs are extracted respectively; dimensionality reduction is carried out by using an auto-encoder, feature-level space-time alignment is realized through an improved DTW algorithm, and a multi-dimensional fault feature matrix is generated; a hierarchical diagnosis model including a GRU auto-encoder, an MLP network and an attention mechanism CNN is constructed, and training is carried out by taking minimization of sub-model deviation as an optimization target; and finally, fusing multi-source features to realize fault classification, and generating a visual diagnosis report. According to the method, efficient fusion and accurate diagnosis of multi-source heterogeneous data are realized, and the accuracy and the real-time performance of fault detection of the wind turbine generator are remarkably improved.

The invention discloses an intelligent job shop scheduling method. The method comprises the following steps: S1, establishing a personalized improved NSGA-II algorithm module and a DQN-based adaptive decision algorithm module; s2, establishing a mathematical model taking the maximum completion time, the total load of equipment and the total energy consumption of a workshop as optimization targets; s3, in a personalized improved NSGA-II algorithm module, considering a workshop optimization target, adopting multiple strategies for initialization, and adding a user personalized demand to screen a target population; s4, in a DQN-based adaptive decision algorithm module, population features are extracted to construct an agent state space, and a deep Q network experience playback mechanism is utilized to train the relationship between agent capture scheduling information and a scheduling target to perform adaptive action decision; and S5, returning the optimal action predicted by the DQN model to the NSGA-II, adjusting the optimal parameter proportion according to the real-time state, and improving the multi-target solution quality.

The invention discloses an intelligent news broadcasting method and system based on artificial intelligence, and the method comprises the following steps: S1, obtaining data through a news crawling tool, and generating structured event data; s2, generating an event knowledge graph by using an improved BERT model, and performing clustering analysis by using an improved K-Medoids clustering algorithm; s3, generating a dynamic broadcast script through the multi-modal generative adversarial network; s4, analyzing the emotional state of the user, and generating personalized voice broadcast content; s5, generating an enhanced news abstract in combination with an event-driven mechanism and a time sensitivity optimization rule; s6, distributing dynamic weights by using a hierarchical attention mechanism, and dynamically adjusting a news broadcasting sequence in combination with a time sensitivity optimization rule; s7, performing logic consistency detection on the news broadcast content; and S8, adjusting the broadcast content and generating updated news broadcast content in real time. According to the method, personalized news broadcasting is realized by combining an improved algorithm, sentiment analysis, a dynamic broadcasting technology and the like.

The invention belongs to the technical field of target detection, and particularly relates to a student classroom behavior detection method based on deep learning. The method comprises the following steps: training an improved YOLOv11 model, and pruning the trained improved YOLOv11 model to obtain a student classroom detection model; acquiring a to-be-detected student classroom video and inputting the to-be-detected student classroom video into the student classroom detection model for processing to obtain a student classroom behavior detection result; the improved DeepSORT algorithm is used for processing the student classroom behavior detection result, tracking of the student classroom behaviors is achieved, the global information capturing capacity of the network is enhanced, the performance of the network under the shielding and multi-scale change scene is improved, and therefore the robustness and detection precision of the network are improved, and the method has good application prospects.

The invention discloses a transformer substation inspection unmanned aerial vehicle path planning method based on a fusion A *-grey wolf algorithm, and aims to solve the problem of unmanned aerial vehicle path planning in a transformer substation inspection scene, and the method fully considers coupling of task point traversal and obstacle avoidance, thereby ensuring that an inspection path allocated for unmanned aerial vehicle inspection accords with a real transformer substation scene. A transformer substation virtual environment model is established through a grid map of an A * algorithm, and path implementation of the algorithm is simplified; a population is initialized by improving Tent chaotic mapping, a grey wolf algorithm is improved by introducing an adaptive position updating strategy and an elite grey wolf optimization strategy, and an initial traversal path is generated; and refining through an A * algorithm dynamically weighted by an evaluation function to obtain a final path. Simulation results prove that the feasibility and effectiveness of the improved fusion algorithm in obstacle avoidance and path planning in a substation simulation scene, and the improved algorithm is more suitable for path planning in a complex environment with multiple task targets and multiple obstacles.

The invention belongs to the technical field of path planning, and particularly relates to a robot path planning method based on an RRT improved algorithm, and the method comprises the steps: obtaining environment space information needing path planning, defining parameters, and obtaining the minimum distance between a coordinate point and an obstacle; a bidirectional random tree is constructed and initialized, and sampling points are obtained through a sampling function; finding a nearest neighbor point, selecting a new node, carrying out safety distance judgment, and if not, carrying out sampling again; generating a discretization path; setting a cost function during local node expansion; performing loop iteration to generate discrete point cost, taking a minimum cost value, searching an optimal node and inserting the optimal node into the current tree; an overall path Path after local optimization is obtained; and discretizing the whole Path, setting a cost function of nodes during global planning, selecting the node with the minimum cost for reconnection, and finally obtaining a smooth and effective optimal path. According to the method, the optimality and smoothness of the path are effectively improved.

The invention discloses a GIS disconnecting switch multi-state intelligent sensing system, and relates to the field of GIS disconnecting switch state monitoring. A self-calibration multi-mode sensor is deployed for multi-source data acquisition, and novel sensors including terahertz imaging and the like are included; in data preprocessing, deep learning noise reduction is applied, fuzzy entropy is used for dynamic weighted fusion, and abnormal values are processed by an improved algorithm; feature extraction is combined with a plurality of frontier algorithms to process vibration signals, and CRNN is used to analyze acoustic signals; the QPSO evidence theory is adopted for state fusion perception, the node relation is learned by means of GNN, and the weight is adjusted according to the information gain rate; state assessment and early warning are based on transfer learning, GAN and LSTM-attention mechanisms, and early warning priorities are ranked by FAHP. According to the invention, multi-mode accurate acquisition, intelligent data processing, deep feature mining, innovative fusion perception, accurate evaluation and early warning and efficient fault diagnosis and positioning are realized, the state of the GIS isolation switch can be comprehensively and accurately perceived, the system is self-learned and optimized, the equipment safety is guaranteed, the risk of a power system is reduced, and the power operation and maintenance benefits are improved.

The invention relates to the field of navigation, and more particularly discloses a gradient optimization driven unmanned aerial vehicle real-time obstacle avoidance multi-stage trajectory planning method, which comprises the following steps of: in a trajectory initialization stage, firstly, searching a collision-free geometric path considering steering limitation of an unmanned aerial vehicle on an occupied grid map by utilizing an improved algorithm to generate an initial B-spline control point; then, in a trajectory optimization stage, constructing a multi-target trajectory optimization problem, introducing an obstacle avoidance constraint based on an Euclidean distance field map, and optimizing the initial control point set in combination with unmanned aerial vehicle parameters and optimization weights to obtain a trajectory meeting an obstacle avoidance requirement; and finally, for the optimized trajectory, performing dynamic feasibility evaluation based on unmanned aerial vehicle kinematics limitation in a trajectory correction stage, and if the trajectory does not meet the constraint, performing correction based on the minimum curvature constraint on the control point to ensure that the finally generated trajectory is not only obstacle-avoiding but also feasible in dynamics, and finally, determining that the trajectory does not meet the constraint. Therefore, the real-time obstacle avoidance capability of the unmanned aerial vehicle in a complex environment is effectively improved.

The invention discloses a path planning method based on an improved Hybrid A * algorithm and a DWA algorithm. The path planning method specifically comprises the following steps: establishing a grid map for a working environment and setting task starting and ending points; improving a heuristic function in the A * algorithm; the cost function of the Hybrid A * algorithm is improved, the path cost in the improved A * algorithm is used as the complete heuristic constraint cost with obstacles, the extension distance of the Dubins curve is used as the incomplete constraint heuristic cost without obstacles, and a variable step size search strategy based on the obstacle distance is adopted; smoothing the path searched by the global path planning algorithm; extracting path key nodes; optimizing a DWA local path planning algorithm; and outputting the optimal motion path. The efficiency of path planning of the workshop inspection robot in the unstructured complex environment is effectively improved, the real-time performance and safety are enhanced, and the workshop inspection robot can dynamically avoid obstacles while finding the optimal path in the complex environment.

The invention discloses an image detection method based on an improved YOLO algorithm. The method comprises the following steps: collecting a nuclear magnetic resonance image with a focus; a target detection network model is constructed: basic framework improvement is carried out based on YOLOv8, an improved backbone network comprises a C2f module, a convolution module and an improved multichannel spatial pyramid pooling module SPPMC, and the image feature extraction capability is enhanced; the improved neck network comprises an up-sampling layer, a splicing layer, a C2f module, a convolution layer and an embedded attention mechanism UECA module, multi-scale feature map fusion is carried out, and the feature fusion efficiency is improved; the improved header network adopts an improved bounding box regression loss function L (iS-IoU), the convergence speed of the model is increased, and the positioning precision is improved; inputting a training image set into the target detection network model for training; and performing image focus detection by using the trained target detection network model. The method has the characteristic of effectively improving the robustness and the detection performance of the target detection model.

The invention discloses an online prediction method for a transient frequency track of a power grid under coexistence of wind power low voltage ride through and off-grid, and belongs to the technical field of operation and control of a power system. A multi-source heterogeneous data fusion monitoring system is constructed, power grid and wind turbine generator data are collected, faults are recognized through an improved algorithm, and feature vectors are output; building an energy flow model based on a fault result, calculating a trajectory divergence index by using technologies such as phase-space reconstruction, estimating power vacancy, and obtaining a power unbalance sequence; designing a prediction algorithm by using the sequence, predicting a frequency trajectory in combination with an improved K-nearest neighbor algorithm and a trajectory feature library, and introducing a confidence coefficient to evaluate a correction error; and finally, establishing a three-level control response system, and implementing multi-time scale cooperative control according to a prediction result. The method can accurately predict the frequency trajectory, effectively deal with the wind power fault, improve the stability of the power grid and the wind power consumption capability, and provide powerful guarantee for the safe and stable operation of the power grid.

The invention belongs to the technical field of computer communication, and particularly relates to a communication link dynamic channel allocation method based on a swan gap system. The method comprises the implementation steps that a cooperative communication monitoring module is established at a gap communication node, multi-dimensional link state parameters such as channel idle degree are collected in a distributed mode, and the collection period is dynamically adjusted along with state changes; constructing an evaluation model by using an improved LSTM algorithm, fusing the space coordinate frequency domain features and the time sequence parameters, and generating a channel state evaluation matrix; dynamically allocating channels in combination with channel stability, task priorities and data types; and a rapid re-evaluation mechanism is set, and when the channel score deviates from a threshold value, re-allocation is performed, so that the communication stability is guaranteed. Through dynamic monitoring, intelligent evaluation and self-adaptive distribution, the channel resource utilization rate and communication stability are improved, the method is suitable for a distributed internet of things scene of the swan mongolian system, and the problems of channel competition and real-time scheduling under high-density deployment are solved.

The invention discloses an unmanned aerial vehicle path planning method based on an improved RRT algorithm, and relates to the technical field of path planning, and the method mainly comprises the steps: taking a current starting point / target point as a starting point, taking a current target point / starting point opposite to the starting point as an end point, and carrying out the dynamic ellipsoid sampling in combination with a target deviation strategy; node expansion facing respective terminal points is carried out according to sampling points obtained through sampling, and node expansion is carried out by adopting an improved artificial potential field method combining repulsive force of a starting point and attraction force of the sampling points under the condition of expansion failure; according to the expanded node set, selecting a nearby node pair set of which the inter-node distance is smaller than a random threshold value; and calculating a point position switching probability based on the obstacle density and the path collision frequency, and randomly selecting a group of nearby nodes to carry out path planning under the update of the starting point and the target point. According to the method, the expansion direction is dynamically adjusted in a complex environment, so that the problem of low dual-tree intersection efficiency of a traditional bidirectional RRT algorithm in a dense obstacle area is solved.

The invention discloses an Internet of Things card anomaly detection method and system based on an improved K-means algorithm. The method comprises the following steps: S1, collecting and preprocessing feature data of an Internet of Things card; s2, performing clustering analysis on the preprocessed feature data by using an improved K-means algorithm; s3, calculating a distance threshold value of abnormal judgment according to a clustering result; and S4, judging whether the to-be-detected Internet of Things card is abnormal or not according to the threshold value. The system comprises a data preprocessing module, a clustering analysis module and an anomaly detection module. The invention aims to provide an efficient and intelligent anomaly detection method for the use behavior of the Internet of Things card through an improved K-means algorithm, and by automatically identifying the abnormal use behavior of the Internet of Things card in different application scenes, the accuracy of management and safety monitoring is improved, and malicious abuse and illegal behaviors are reduced.

The invention relates to the field of data processing, in particular to an operation state monitoring method and system for a connector production machine, and the method comprises the steps: obtaining operation state data of the connector production machine, carrying out the preprocessing, taking preset local reference data as a benchmark, determining the relative change degree of data of each dimension, and calculating the abnormal fluctuation degree. And meanwhile, performing first-order difference symbolization processing on the local reference data, and counting symbol continuous lengths to form a sequence so as to analyze the abnormal tendency. And combining the abnormal fluctuation degree and the tendency to construct a fault risk index, and calculating a local abnormal weighting factor at each moment. And finally, based on the factor, improving an LOF anomaly detection algorithm and calculating and correcting an anomaly score, thereby effectively monitoring the operation state of the connector production machine. According to the method, the LOF algorithm is improved, the defects of a traditional algorithm in time sequence and multi-type data anomaly detection are overcome, the monitoring accuracy and the fault early warning capacity are improved, and the production efficiency and reliability are improved.

The invention relates to the technical field of target detection and tracking, and discloses a multi-modal real-time target detection and tracking system, which is characterized in that a multi-modal data acquisition module integrates a high-definition camera, a millimeter-wave radar, a laser radar and an infrared sensor and is used for acquiring target information from multiple dimensions such as visual images, distance, speed and angle, three-dimensional point cloud and thermal radiation; and the data preprocessing module is used for carrying out denoising, enhancement, normalization, filtering, coordinate conversion and temperature correction processing on the original data acquired by the multi-modal data acquisition module. Multiple sensors are integrated to collect multi-dimensional data, after preprocessing, efficient fusion is achieved through hierarchical fusion and an attention mechanism, the detection module is combined with an improved algorithm and a dynamic threshold value, the tracking module fuses multiple features and has the online learning ability, and the system control module achieves intelligent management. The system greatly improves the accuracy, the real-time performance and the stability of detection and tracking, has remarkable advantages of an innovative technology, and provides a new scheme for related fields.

The invention discloses an electric power unmanned aerial vehicle obstacle avoidance method and system based on multi-modal perception and reinforcement learning, and the method comprises the steps: firstly starting an unmanned aerial vehicle, collecting environment and own motion data in real time, constructing an initial three-dimensional environment map after preprocessing fusion, and recognizing an obstacle in the map; based on the flight task, performing global path planning by using an improved algorithm, and generating a preliminary flight path from the starting point to the target; then, dynamically detecting the path obstacle; if no obstacle exists, working along the path; if the obstacle exists, calculating values of different obstacle avoidance actions through a reward function, and selecting an optimal action to generate a second flight path; and the unmanned aerial vehicle adjusts the attitude and speed to execute operation according to the second path and the optimal action. According to the method, the obstacle avoidance response speed of the unmanned aerial vehicle is higher, the route prediction accuracy is greatly improved, the collision risk is effectively reduced, the power inspection efficiency and safety are improved, and the method has remarkable technical advantages and application value.

The invention discloses a multi-dimensional computing power dynamic perception routing decision-making method and system based on SRv6 driving. The method comprises the following steps: firstly, screening an optimal target computing power node based on a service demand and a node real-time load; and then, based on the dynamic network topological graph fusing the service sensitivity, calculating an optimal network path reaching the computing power node by using an improved SPFA algorithm, and realizing a global collaborative decision of selecting the most suitable computing point and searching the most efficient connection path. Meanwhile, based on an SRv6 driving service chain dynamic generation and closed loop execution method, a target computing power node and necessary network functions are abstracted into a programmable SID, an SID sequence (service chain) is dynamically constructed according to a double-stage decision result and is packaged in an SRH head, second-level path issuing and state monitoring are achieved through a programmable controller, and a real-time monitoring result is obtained. According to the method, the problems of poor real-time performance, single dimension, weak cooperative capability and the like in the prior art are solved, and efficient, stable and intelligent development of a future-oriented distributed intelligent computing network system can be promoted.