1764 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 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 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 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 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 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 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 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.

The invention discloses a hardware part numerical control machining real-time error control system which comprises the steps that historical machining information of a target numerical control machine tool is collected, error coupling relation analysis is conducted on the basis of the data, and therefore a coupling physical error model is constructed. Meanwhile, a processing error prediction model is established by using an LSTM network, and training is carried out by using historical data, so that the trained prediction model is associated with a physical model to form a composite error analysis model. Processing monitoring information is obtained through a machine tool sensor array, and after preprocessing, the processing monitoring information is input into the composite model for error prediction; and on the basis of a prediction result, an advanced compensation amount is generated by adopting an improved GA algorithm to control the machine tool, and whether self-correction needs to be performed on the composite model is judged according to a correction effect, so that efficient and accurate error control is realized. And processing errors and machine tool part maintenance are associated through historical maintenance information to form a maintenance knowledge graph, so that the state of the numerical control machine tool can be known, and maintenance early warning is realized.

The invention relates to a navigation equipment health management method based on an SAITS algorithm and a digital twin platform. The method comprises the following steps: acquiring navigation equipment operation data; adopting an improved SAITS algorithm to carry out interpolation on missing data, and predicting to obtain future navigation equipment operation data; identifying current and future abnormal states of the navigation equipment by adopting a self-adaptive anomaly detection algorithm; constructing an equipment fault mode knowledge graph according to the identified equipment exception; simulating the operation of the navigation system by using a digital twin platform, verifying the confidence coefficient of equipment abnormity, and generating a health assessment report; if the confidence coefficient is not smaller than a confidence coefficient threshold value, generating an exception coping strategy in combination with an equipment fault mode knowledge graph; incremental learning or retraining is carried out on an adaptive anomaly detection algorithm to improve the detection accuracy. The method has remarkable advantages in the aspects of improving the navigation equipment management efficiency, reducing the fault risk, optimizing the maintenance cost and the like, and is particularly suitable for water transportation scenes with strict requirements on reliability and real-time performance.

The invention discloses a method for determining the distance between blasting unit groups for a surface mine, a rapid adjusting device and a blasting operation system. The determination method comprises the following steps: acquiring multi-dimensional data through a geological radar, a sensor and the like, and constructing a model through normalization processing; a finite element-discrete element coupling algorithm is adopted to carry out three-dimensional geology-blasting coupling modeling, an improved NSGA-III algorithm is combined to solve a multi-objective function to determine an optimal interval, and Kalman filtering is utilized to realize real-time feedback correction. The rapid adjusting device comprises a multi-angle adjusting module, an intelligent navigation module and a cooperative control module, and accurate positioning and rapid adjusting can be achieved. According to the method, a modeling-calculating-adjusting-feedback full-process automatic system is formed, and compared with a traditional technology, the lumpiness qualification rate can be increased by 25% or above, the interval adjusting time is shortened to be within 15 minutes, and the mine blasting efficiency and safety are effectively improved.

The invention relates to the technical field of communication engineering, in particular to a communication optical cable line intelligent inspection fault point rapid positioning method and device, and the method comprises the steps: obtaining optical cable line state data, constructing an intelligent fault detection model, recognizing an abnormal signal through wavelet transform and a threshold determination method, training an LSTM neural network in combination with historical fault data, and pre-judging a fault type. When a fault is detected, an unmanned aerial vehicle inspection unit is triggered, an inspection path is planned based on an improved Dijkstra algorithm according to the fault type and suspected fault area geographic information, an optical cable line is scanned and detected through a laser radar, physical fault features are recognized through a YOLOv5 algorithm, and geographic coordinates of a fault point are calibrated in combination with an inertial navigation system; and fusing sensor data and a detection result, evaluating a fault confidence level by adopting a DS evidence theory, and generating an alarm work order. Therefore, the problems of long positioning time consumption, high omission ratio, difficulty in distinguishing fault types and the like in the prior art are solved.

According to the tray cycle scheduling system and the application method, historical data and a production plan are fused, an improved algorithm is adopted to predict tray requirements, and an attention mechanism is introduced to improve precision; the method comprises the following steps: acquiring multi-dimensional state information of a tray through a multi-modal sensor, eliminating noise by using a data fusion algorithm, and constructing a digital twin model to realize state synchronization; a double-layer optimization architecture is constructed, an upper layer solves a global scheme by combining an improved particle swarm and a simulated annealing algorithm, and a lower layer dynamically adjusts a path through reinforcement learning; an instruction is generated based on a digital twin model, an event triggering mechanism is adopted to reduce communication load, and virtual-real interaction closed-loop control is realized; a real-time evaluation index system is established, a meta-learning algorithm is utilized to quickly adapt to a new environment, and system parameters are continuously optimized. Multi-module collaborative innovation is achieved, the tray scheduling efficiency and the intelligent level are remarkably improved, production logistics whole-process collaborative optimization is achieved, and core support is provided for cost reduction and efficiency improvement of enterprises.

The invention belongs to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle exploration navigation method based on a sequence enhancement improved SAC algorithm, and the method comprises the following steps: obtaining multi-modal perception data, and enabling the multi-modal perception data to form a standardized state vector at each time step; forming a state sequence by the state vector of each time step, inputting the state sequence into an improved Transform network, and carrying out feature extraction to obtain a high-dimensional feature vector; inputting the high-dimensional feature vector into a teacher-student improved SAC network, and outputting an original action by a student network; and judging whether the distance of the nearest obstacle is smaller than a threshold value or not, if so, performing VO safety shield correction on the original action to obtain a safety action, projecting / cutting the safety action to a dynamic feasible set, and issuing and executing the safety action. According to the invention, safe and efficient autonomous exploration and flight of the unmanned aerial vehicle in an unknown environment can be realized.

The invention discloses an automatic driving planning method, device and equipment for a two-wheeled mobile robot and a storage medium, and the method comprises the steps: collecting environment data and vehicle body posture information through a multi-source sensor, and constructing a space-time joint map under a dynamic aerial view coordinate system; a hierarchical trajectory planning architecture is designed based on the four-dimensional state space, an initial path is generated by adopting an improved algorithm, and space-time joint optimization is performed through quadratic programming; a smooth trajectory meeting dynamic constraints is generated in combination with a differential flatness parameterization method, and accurate execution of attitude and motion instructions is realized based on feedforward-feedback cooperative control. According to the method, the problem that the trajectory is not feasible due to neglect of the inclination angle in the traditional automatic driving planning of the two-wheeled vehicle is solved, and the motion stability and safety in a dynamic scene are improved.

The invention relates to a point cloud registration method based on improved ISS-TOLDI features in combination with ICP, and belongs to the technical field of laser point cloud application. The method comprises the steps of obtaining a source point cloud and a target point cloud, and performing preprocessing; extracting feature points from the point cloud data by using an internal shape descriptor ISS algorithm; performing feature description on the extracted feature point set by using an improved TOLDI algorithm; performing coarse registration on the point cloud by using a sampling consistency initial registration algorithm; and performing fine registration on the coarsely registered point cloud data by using an iterative closest point ICP algorithm to complete point cloud registration. According to the method, the TOLDI algorithm is improved, so that the calculation complexity is reduced, the feature integrity is ensured, and the point cloud registration precision is higher.

The invention relates to the technical field of electric power spot transaction, and discloses an electric power spot transaction optimization system. The system comprises a data acquisition module, a dynamic pricing module, a supply and demand prediction module, a transaction optimization model construction module, a real-time scheduling module and the like. The data acquisition module acquires market data; the dynamic pricing module generates a dynamic electricity price strategy based on a deep reinforcement learning algorithm and a mixed integer programming model; the supply and demand prediction module predicts supply and demand through a federated learning framework aggregation model; the transaction optimization model construction module constructs an objective function and constraint conditions by combining the results; and the real-time scheduling module uses an improved algorithm to solve and generate an optimal scheduling scheme. In addition, the system also has the functions of risk control, abnormal transaction detection, intelligent contract management, performance evaluation and the like. The system can optimize electric power spot transaction, improve transaction efficiency, guarantee power grid stability, prevent and control market risks, and promote healthy development of the electric power market.

The invention discloses an underwater target recognition method based on an improved YOLOv8 algorithm, and the method comprises the steps: inputting an underwater image into an improved YOLOv8n model for underwater target detection, and obtaining an output underwater target recognition result. The method has the advantages that the convolution blocks of the P5 layer of the backbone network and the last layer of the neck network adopt DSConv, so that the network complexity is reduced, and the reasoning speed is increased; a fourth C2f module of the backbone network adopts a C2f DiRMB module in which an inverted residual attention mechanism and dual-channel convolution are introduced, so that the capability of capturing key global information of the network is enhanced, training parameters are reduced, and the understanding of a complex scene is improved; and finally, a small target detection head for improving the small target detection capability is additionally arranged in the head network. According to the underwater target identification method, the mAP (at) is 0.5%, the mAP (at) is 0.5-0.95%, and the accuracy and the recall rate are respectively improved by 0.5%, 0.8%, 0.5% and 1.0%.

The invention discloses an AGV path planning algorithm and a conflict-free strategy based on a Dlite algorithm. The algorithm and the strategy are suitable for multi-AGV collaborative operation in dynamic complex environments such as track equipment assembly workshops. The method comprises the following steps: (1) adopting an improved Dlite algorithm to realize dynamic path planning, and shortening the time consumed for updating a single AGV path through an incremental re-planning mechanism to adapt to the real-time change of dynamic obstacles such as personnel flow and equipment displacement; (2) constructing a space-time joint constraint model, dividing a workshop channel into limited grid units, and accurately marking the space-time occupancy state of the AGV through a time window; and (3) designing a hierarchical conflict resolution strategy, and when path crossing is detected, dynamically allocating priorities based on the task emergency degree and the AGV cargo carrying value, and realizing conflict avoidance through a speed adjustment window and path fine adjustment.

The invention discloses a corn germination image segmentation method based on an elite adaptive rime algorithm. Relates to the technical field of agricultural seed detection and image processing, in particular to the technical field of corn germination image segmentation based on an elite adaptive rime algorithm. According to the method, a dual-adaptive weight mechanism and an elite reselection strategy are introduced into a rime optimization algorithm, the convergence capability of the algorithm is enhanced, and multi-threshold segmentation is carried out on the corn kernel germination image in combination with the Kapur entropy. And the image segmentation precision is effectively improved. The method comprises the following steps: acquiring a corn germination image data set; drawing a two-dimensional histogram, and inputting the two-dimensional histogram into a Kapur entropy function to obtain an objective function fobj; an elite solution module is initialized; a dual-adaptive weight mechanism is added; a soft rime strategy and a hard rime strategy are improved; updating the elite solution after the rime search strategy module; and the target function fobj is input into a rime improvement algorithm, and an optimal threshold value is obtained.