597results about How to "Guaranteed robustness" patented technology

The invention provides an intrusion detection and response method and system for a satellite internet target range, and relates to the technical field of network security technologies, and the method comprises the steps: carrying out the multi-dimensional real-time collection of the network flow of the satellite internet target range, carrying out the self-adaptive multi-scale feature extraction, constructing a network behavior baseline model, and positioning and recognizing the abnormal flow. Deploying an anomaly detection node based on edge calculation, performing preliminary screening at a track end, performing multi-angle association, and outputting an anomaly event report; a distributed intelligent honeypot is activated, deep interaction is induced, the information collection amount is maximized, multi-modal data is obtained, and multi-dimensional attack feature vectors and behavior sequences are generated; historical defense empirical data are integrated, a method of combining reinforcement learning and a genetic algorithm is utilized, a defense strategy is coordinated, a defense process is generated, and the defense process is recorded through a block chain and fed back to a network behavior baseline model.

The invention discloses an intelligent water quality monitoring system and method based on a turbidity sensor, and relates to the technical field of water quality monitoring. Data acquisition nodes are arranged in a water area to be monitored, data are acquired by using the turbidity sensor and an intelligent water quality monitoring equipment group, an environment data set S is constructed, and preprocessing is performed; acquiring a turbidity change coefficient # imgabs0 # according to a turbidity data set in the preprocessed environment data set S, performing primary evaluation, marking abnormal data acquisition nodes, acquiring a particulate matter dynamic index KL and a pollution index WR according to a particulate matter data set and an organic pollutant data set in the environment data set S, and performing secondary evaluation according to the turbidity change coefficient # imgabs1 # and the turbidity change coefficient # imgabs0 # in combination with the particulate matter dynamic index KL and the pollution index WR. Acquiring a comprehensive water quality score Q, performing secondary evaluation, and constructing a water quality prediction model to predict the water quality change and regulate and control the water quality for the data acquisition nodes which are normally evaluated for the second time; the intelligent level of water quality monitoring is improved, the accuracy of water quality monitoring is enhanced, and the safety of water quality is ensured.

The invention discloses a power efficiency optimization method based on intelligent load management. The method comprises the following steps: S1, generating an initial load characteristic matrix; s2, decomposing the initial load characteristic matrix into a short-term behavior characteristic matrix and a long-term trend characteristic matrix through pseudo-symmetry decoupling; s3, multi-level load demand prediction is carried out through a fusion structure of the multi-scale convolution and the recurrent neural network; s4, coding a cognitive gene sequence, triggering gene mutation and recombination operation when significant change occurs, and generating a load distribution strategy; s5, a load distribution strategy is transmitted into a causal reasoning and self-supervision feedback control module, and if the deviation between feedback data and a prediction result exceeds a threshold value, a self-learning mechanism is triggered; s6, dynamically distributing the load priority, and generating a decision path of load management; and S7, performing adaptive optimization through a multi-layer feature feedback closed-loop optimization system. The intelligent load management method is adopted, and the power efficiency is optimized through multi-layer feature decoupling and closed loop.

The invention discloses an automatic ship berthing and leaving method based on video monitoring and image recognition, which comprises the following steps: S1, acquiring real-time video data, and carrying out image preprocessing; s2, extracting target feature information by using a target detection model based on an OfficientDet algorithm; s3, generating environment sensing information by using a multi-modal sensor fusion technology; s4, based on the environment perception information, constructing and updating a three-dimensional dynamic map of the surrounding environment of the ship in real time; s5, performing global path planning by adopting a fast extension random tree-star algorithm; s6, local dynamic obstacle avoidance is carried out in combination with a two-stage neural network algorithm; s7, based on the global path planning result and the local dynamic obstacle avoidance strategy, the adaptive control system accurately adjusts the speed, course and attitude of the ship according to the real-time environment change; and S8, performing big data analysis and historical data mining by using the cloud computing platform. According to the invention, image recognition and deep learning technologies are utilized to realize automatic berthing and unberthing of the ship, and the method has the advantages of high efficiency and accuracy.

The invention, which relates to the technical field of social emotion analysis, discloses a university social negative emotion characterization analysis system comprising a data acquisition and preprocessing module, a feature extraction and anomaly detection module, an intelligent perception and negative emotion characterization module, and an anomaly risk assessment and response module. Emotional feature information is acquired in real time based on a multi-dimensional data source, and the acquired emotional feature information is cleaned and preprocessed. According to the method, by calculating the data consistency offset coefficient and the strong correlation coefficient, the multi-collinearity problem among multi-dimensional data features is solved, and instability of the model regression coefficient is avoided. And the system identifies abnormal correlation in real time and combines risk threshold classification processing to ensure robust model output. An analysis set is established by continuously collecting data, long-term emotion trend analysis is supported, personalized responses such as reminding, intervention or emergency measures are provided according to risk levels, and accurate management and timely adjustment of student emotional states are achieved.

The invention discloses a method and system for improving satellite remote sensing inversion precision by fusing hyperspectral proximity, and belongs to the technical field of water environment and water ecology monitoring. Acquiring a water body hyperspectral near-sensing reflectivity spectral data set, a satellite remote sensing surface reflectance data set and a water environment actual measurement data set, and performing preprocessing; a synchronous data set containing # imgabs0 # and water quality and a synchronous data set containing # imgabs1 # and water quality are constructed; based on the synchronous data of the # imgabs2 # and the water quality, synchronous data of the # imgabs3 # and the water quality are generated, and synchronous data of the # imgabs4 # and the # imgabs5 # are obtained; the # imgabs6 # is normalized to obtain # imgabs7 #, and the quantitative conversion relation between the # imgabs7 # and the # imgabs8 # is constructed band by band to eliminate errors; generating a fused satellite-ground data set, and constructing a training water quality parameter inversion model; and updating corresponding methods and data sets in real time by repeating specified steps, and optimizing the model. According to the method, the inversion result is displayed and downloaded according to the query instruction, the satellite spectrum precision and the satellite-ground synchronous data set scale are improved, and the method is used for high-precision water environment monitoring.

The embodiment of the invention discloses a bridge crack width monitoring method. The method comprises the following steps: acquiring a continuous video image of a bridge surface; generating a pixel-level segmentation mask image of the crack by using the target detection model; when the target detection model is pre-trained, optical flow features between adjacent frames in the first data set are extracted through an optical flow calculation algorithm, and deep fusion is performed on the optical flow features and the image features; contour pixel points of the crack are extracted based on the crack segmentation mask pattern, and the width change information and the width change rate of the crack are obtained; according to the width change information and the width change rate, performing trend prediction on the crack width change by using a time sequence prediction model to obtain a prediction result of the crack width change; in the training process of the time sequence prediction model, a basic model and a complexity capture module are designed, and the weight of a main model is optimized by using a weighted fusion strategy. According to the invention, efficient and accurate crack width monitoring is realized through crack identification and prediction.

The invention discloses an integral type fast nonsingular self-adaptive super-spiral sliding mode control method for an energy storage converter, provides a solution for the problem of low accuracy of a traditional interference state observer, and designs a super-spiral interference observer. A superhelix algorithm is adopted to accurately estimate disturbance and uncertainty information of external changes and internal parameters of the energy storage converter system. Secondly, a finite time observer is adopted to accurately observe the output current of an inverter system, an integral type fast nonsingular self-adaptive super-spiral sliding mode control method is adopted, the controller weakens the buffeting problem, the control gain parameter design of the super-spiral sliding mode control algorithm depends on interference and uncertain boundary information, and the control gain parameter design of the super-spiral sliding mode control algorithm depends on the uncertain boundary information. The information is relatively difficult to obtain in the actual situation, and the adaptive gain can enable the control gain to be adaptively adjusted so as to ensure the stability and robustness of the system.

The invention provides a micro-grid robust optimization scheduling method considering a segmented multi-interval uncertainty set, and the method comprises the steps: carrying out the segmentation fitting of the source load prediction power and error data of wind, light and loads based on a Gaussian mixture model, and combining with a box-type interval uncertainty set to build a multi-interval uncertainty set of power segmentation segments; the uncertainties are used for describing wind, light and load uncertainties; constructing a microgrid robust optimization scheduling model considering the segmented multi-interval uncertainty set and the carbon emission by taking the minimization of the operating cost and the carbon emission cost of the microgrid as a target; and solving the micro-grid robust optimization scheduling model by adopting a column sum constraint generation algorithm. According to the method, the problems of uncertainty modeling and carbon emission of the micro-grid can be solved, the conservative property of a robust optimization scheduling strategy is reduced, and the economical efficiency of micro-grid scheduling is improved.

The invention relates to an underwater image enhancement method and device based on generative adversarial network multi-scale fusion, and the method comprises the steps: obtaining an original underwater image, and carrying out the preprocessing of the original underwater image; inputting a preprocessing result into an underwater image enhancement model based on generative adversarial network multi-scale fusion to obtain an enhanced underwater image; the underwater image enhancement model comprises a generator and a discriminator, and the generator is obtained by replacing the last downsampled convolution module in an encoder of the U-Net network with a multi-scale feature fusion module; and the multi-scale feature fusion module is used for extracting multi-scale fusion features by adopting multi-scale convolution, residual connection, dense connection and a global attention mechanism. The method can adapt to different underwater scenes and enhance the image quality, the color cast and brightness recovery of the underwater image are improved, the robustness of the image is kept, and color distortion and noise artifacts are avoided.

The invention discloses a load prediction method and device based on a hybrid model in a container cloud environment, and the method comprises the steps: collecting the historical load data of a container application from the container cloud environment in real time, and storing the historical load data in a time sequence database Prometheus; preprocessing the historical load data, correcting abnormal values and filling missing values; designing a load prediction model based on the hybrid model architecture, training the load prediction model by using the preprocessed historical load data, adjusting network parameters of the load prediction model according to evaluation indexes of the load prediction model in the training process, and obtaining a trained load prediction model; and using the trained load prediction model to obtain load data at a future moment. According to the invention, the trend and periodicity of load change can be learned from the historical data of the load, and the load condition at the future moment can be accurately predicted; the multi-level features of the time series data can be fully captured, and the accuracy and robustness of prediction are greatly improved.

The invention relates to the field of signal coding, and discloses a method for generating a 3D image watermark based on AIGC and a model framework, aiming at the problem that the watermark is easy to damage when being attacked by noise because the intensity of the embedded watermark cannot be enhanced at different scales in the existing 3D image watermark embedding method. According to the method, the NDOU module is embedded into the encoder based on the U-net, the multi-modal features of the middle viewpoint image and the depth map are extracted at the same time, watermarks are guided to be distributed on the middle viewpoint image at different scales and depths according to the internally-arranged WAM module, and the robustness of the embedded watermarks is improved; a discriminator and an encoder are arranged to adjust the parameters of each other in a confrontation manner, so that the visual effect and invisibility of the embedded watermark are ensured. A noise layer is arranged to improve the ability of the model to resist corresponding noise attacks, and a decoder is arranged to train the ability of an encoder to extract watermarks, so that the model can finally generate a watermarking-containing coded image with excellent robustness and invisibility.

The invention provides a robot-assisted wound suturing method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the robot-assisted wound suturing method comprises the steps: obtaining multi-modal perception data of a wound area, constructing multi-modal three-dimensional representation of a wound, and determining a plurality of suturing points based on the multi-modal three-dimensional representation of the wound; for each suture point, calculating a candidate suture point, and for the adjacent suture points, generating a needle tip track of a needle tip in the wound, and sampling on the needle tip track to obtain a suture scheme adapted to the wound form and the tissue attribute; based on the obtained stitching scheme, an optimal control sequence of the stitching robot is obtained, meanwhile, a robust compensation control law is constructed through coordinate transformation and a feedback control law, and the optimal control sequence and the robust compensation control law are integrated into a stitching robot real-time control system; the output of a real-time control system of the suturing robot is converted into an actuator control instruction, and the suturing robot is controlled to assist in wound suturing.

The invention provides a hyperspectral and point cloud data integrated tree identification system based on an attention mechanism convolutional neural network, relates to the technical field of tree identification, and realizes efficient tree classification through a series of fine processing and deep learning methods. The system firstly collects hyperspectral data and point cloud data, and corrects and de-noising the collected data. And then, performing feature extraction on the hyperspectral data and the point cloud data by using a convolutional neural network and a graph convolutional network, and introducing an attention mechanism to analyze features and obtain an attention weight matrix, thereby realizing effective fusion of the two data features. And finally, inputting the fusion features into a classifier for probability calculation of tree categories, and outputting a final category vector. The system improves the accuracy and efficiency of tree identification, and is expected to play an important role in the fields of ecological monitoring, resource management and the like.

The invention discloses an output feedback sliding mode prediction control method of a piezoelectric driving active and passive integrated vibration isolation system. The invention aims to solve the problems that the input and output characteristics of an actuator are neglected in the control of a vibration isolation system, the system state is not measurable, and the vibration suppression performance is influenced by mismatching disturbance, and optimization and input constraints are considered in a control algorithm. According to the method, an output feedback sliding mode predictive control algorithm based on a control guide model is designed by considering the neglected actuator input and output characteristics in an active and passive integrated vibration isolation system, the unmeasurable state of the system and the vibration suppression effect of the sliding mode predictive control algorithm which is limited by unconsidered mismatching disturbance; therefore, the suppression of the vibration signal is realized. According to the invention, the vibration suppression performance is improved, and the practical engineering application is promoted.

The invention discloses a ship collision avoidance decision method based on two-stage safety reinforcement learning, and the method comprises the following steps: constructing a first-stage reinforcement learning ship collision avoidance decision model which is used for enabling a ship to take collision avoidance actions when the ship has other ships or obstacles within a first threshold value around the ship; a second-stage safety reinforcement learning ship collision avoidance decision model is constructed and used for limiting the ship action space to ensure the ship navigation safety when the ship has other ships or obstacles within a surrounding second threshold value; under a first threshold condition, adopting the trained first-stage reinforcement learning ship collision avoidance decision model to carry out collision avoidance decision in the ship running process; and when the ship and other ships or obstacles are in a second threshold condition, adopting the trained second-stage safety reinforcement learning ship collision avoidance decision model to carry out collision avoidance decision in the ship driving process. According to the method, the safety and economy of ship navigation are improved, and a safety guarantee is provided for a reinforcement learning collision avoidance algorithm.

The invention discloses an enterprise annual report intelligent question and answer method and system based on a generative large model, and the method comprises the following steps: obtaining and preprocessing an enterprise annual report text, and constructing a theme tag, an emotion tag and an annual report knowledge graph based on the preprocessed enterprise annual report text; carrying out keyword extraction and extension on the preprocessed enterprise annual report text, storing the enterprise annual report text into a keyword library, carrying out classification and layering processing on keywords in the keyword library, and constructing a keyword knowledge graph; analyzing a question text input by a user, matching the question text with a keyword library, and querying the keyword knowledge graph and the annual report knowledge graph according to a matching result to locate related enterprise annual report text fragments; and generating a final answer corresponding to the question text input by the user by calling a generative large model based on the positioned enterprise annual report text fragment and the theme tag and the emotion tag thereof. According to the method, efficient, accurate and comprehensive enterprise annual report intelligent question answering can be realized, and annual report data analysis in multiple fields is supported.

The invention discloses an LDI target high-precision positioning method based on deep learning, and relates to the field of image processing, and the method comprises the steps: carrying out the edge detection of a to-be-positioned LDI target image through an edge detection model; and further carrying out gray gradient analysis on candidate edge pixel points in the obtained detection result image, and screening out target edge pixel points to carry out circle fitting so as to realize high-precision positioning. A pooling module and a parallel jump connection module in the edge detection model are designed by introducing wavelet transform into the edge detection model, so that the problem of sampling high-frequency detail loss under the edge detection model can be avoided, and the multi-level feature extraction and utilization capability of the edge detection model is improved; in combination with a method of first coarse positioning and then fine positioning, high accuracy and robustness of edge positioning can be ensured, so that the method can stably detect the edge under complex background and changeable image conditions, and the detection effect and practicability are greatly improved.

The invention discloses a heavy commercial vehicle yaw control system with delay compensation and slip suppression. A state sensing unit collects signals and sends data to a yaw control unit and a braking force control unit. The yaw control unit acquires a desired yaw moment and a braking moment. A yaw velocity tracking module calculates a desired yaw velocity. And the wheel slip suppression module obtains a brake torque for avoiding slip through control. The braking force control unit addresses braking force distribution under yaw moment control and wheel slip suppression, and compensates for the delay of the pneumatic brake system. And the braking force cooperative distribution module calculates the expected braking chamber pressure of each wheel according to the required yawing moment and the braking moment. The braking force delay compensation module calculates and predicts the pressure of a braking chamber through a prediction model, and obtains control input of a braking actuator through a PID controller so as to compensate delay. According to the system, the driving safety of the heavy commercial vehicle under the complex road condition is improved through cooperative control.

The invention discloses a tunnel fire thermodynamic response reasoning method based on MGraphFomer. The method comprises the following steps: constructing a tunnel structure fire thermodynamic response database; according to tunnel classification design and structural damage judgment principles, structural thermal response grade judgment standards of different types of tunnels are constructed in a self-adaptive manner by means of a branch convolutional neural network, and structural thermal response grades of different types of tunnels are combined according to the structural thermal response grade judgment standards. Obtaining a structural unit mechanical response coarseness judgment model; based on a heterogeneous graph processing method of double encoders and a structural unit mechanical response coarse-grained judgment model, a structural unit multi-scale response fine-grained model is constructed, and a tunnel fire structure thermal response digital-intelligent prediction model is obtained; and the tunnel fire structure thermal response is predicted by using the tunnel fire structure thermal response digital intelligent prediction model, and reasoning of the tunnel fire structure thermal response is realized. The thermal response prediction method is high in accuracy and higher in generalization ability.

The invention provides a multi-modal large model content security assessment method and device, and the method comprises the steps: inputting a multi-modal harmful data set into a to-be-assessed multi-modal large model, and obtaining an output response of the multi-modal large model; initializing a plurality of large-scale language models, and endowing the plurality of large-scale language models with different role features to form a heterogeneity evaluation system; performing independent evaluation on the multi-modal large model in the aspect of content security by utilizing a plurality of large language models; a plurality of large language models are used for jointly reviewing independent evaluation results, and consistent evaluation suggestions are achieved through cooperative discussion among the models; and obtaining a final evaluation result by using a hard voting mechanism and a soft voting mechanism. According to the method, a plurality of large language models with different role features are introduced for cooperative assessment, and multi-view and multi-level security risk assessment is realized. And through a mode of combining independent evaluation and collaborative review, diversified view angles are effectively integrated, and the reliability and consistency of evaluation results are remarkably improved.

The invention relates to a fault prediction method for a mining high-voltage power distribution device. Temperature, current, voltage and gas leakage sensors are installed in the mining high-voltage power distribution device; integrating the multi-dimensional data by using a multi-sensor data fusion algorithm to form a real-time operation portrait of the power distribution device; extracting time sequence characteristics of temperature and voltage key variables by combining an operation rule of the high-voltage power distribution device; capturing abnormal fluctuation of the key characteristic parameters by using a sequence analysis model, and identifying a specific fault type in the high-voltage environment; constructing a feature mode database based on a time sequence, and matching new data with a historical fault mode; applying a feature selection algorithm to extract representative fault prediction features from the sensor data; establishing a fault prediction risk level model according to the hazard degrees of different fault modes; based on the fault risk level predicted in real time, an optimized self-adaptive maintenance scheme is generated, and the maintenance period and strategy are dynamically adjusted in combination with the actual operation state of the equipment.

The invention relates to the technical field of power grid side large-scale wind and light and energy storage configuration, in particular to a wind and light storage capacity distribution robust optimization configuration method, device and equipment and a medium. Establishing a power grid side wind and light energy storage optimization configuration model considering inertia constraint; uncertain factors in the optimal configuration model are considered, and a probability distribution fuzzy set based on a mixed norm is constructed; establishing a two-stage distribution robust optimization configuration model based on data driving; converting the two-stage distribution robust optimization configuration model into a mixed integer linear programming model by using a linearization technology; and solving the mixed integer linear programming model by using a column and number generation algorithm to obtain an optimal configuration and operation scheme of each device. According to the method, the economical efficiency and load fluctuation can be balanced, and the problems of frequency instability and the like of a large-scale wind and light energy grid-connected power grid are avoided by considering inertia constraint.

The invention provides a substation safety management and control method and system based on a three-dimensional reconstruction model, and relates to the technical field of safety management and control, and the method comprises the steps: obtaining real-time point cloud data inputted by a laser point cloud sensor and a real-time image collected by a camera; comparing the real-time point cloud data with a background point cloud model, and extracting a changed dynamic region by using a background difference algorithm; the dynamic reconstruction module is used for identifying a first position of a first dynamic target in the dynamic region based on the real-time point cloud data; calculating a second position of a second dynamic target contained in the real-time image; if the first position is successfully matched with the second position and the sparse structure of the real-time point cloud data is successfully matched with dense pixels of the real-time image, determining that the first dynamic target and the second dynamic target are both dynamic flight targets; and determining the position of the dynamic flight target according to the first position and the second position, and marking in the three-dimensional model. The safety management and control precision of the transformer substation can be improved.

The invention discloses a high-frequency vehicle track map matching method, which comprises the following steps of: (1) dividing a track T to be matched into a plurality of sub-tracks based on a reference line distance, and extracting key track points in the sub-tracks; (2) extracting a road where the key track point is located and a road type of the road, filtering the extracted road based on the road type, and storing filtered road data by adopting an R-Tree structure; and (3) matching an optimal matching path of the track T to be matched from the road data stored in the R-Tree structure through a hidden Markov probability matching model. From the perspective of reducing the scale of original data, trajectory and road network data are simplified. Then, space and direction analysis is carried out on the simplified trajectory, a micro-region splicing strategy is adopted to accelerate shortest path search, and a path deviation function is designed to improve the topology of a matching result; in addition, the extraction process of track motion key points is refined by fusing more road semantic information, so that a better matching result is obtained.

A novel knowledge distillation method from binocular parallax to monocular depth comprises the following steps: S1, training a parallax depth conversion module: inputting first training data into a pre-trained binocular stereo parallax teacher model to obtain a parallax map; the first training data comprises a left eye color image and a right eye color image; inputting the disparity map and the original disparity reference map into a disparity depth conversion module to obtain a pseudo label depth map; calculating a loss function according to the pseudo-label depth map, and updating parameters of a parallax depth conversion module; s2, mixing the pseudo-label depth map output by the parallax depth conversion module with the original true label depth map to obtain a mixed label depth map; s3, training a monocular depth estimation student model: training the monocular depth estimation student model through the second training data; the second training data comprises a mixed label depth map and a corresponding original reference map, and the method can achieve the training on a data set with a lower data size, and improves the global precision.

The invention discloses a jitter calibration system and method for a superconducting nanowire single-photon detector. Comprising an FPGA platform used for generating periodic pulse calibration signals, a radio frequency pulse amplifier, a lithium niobate electrooptical modulator and a bias voltage controller thereof, a 1550nm 3kHz linewidth distributed feedback laser, a program control optical attenuator, a three-paddle polarization controller, an SNSPD single photon detector, a multichannel TDC module and an upper computer used for processing data. The calibration method is simple and low in cost, can calibrate a correction curve of the relation between SNSPD detection photon additional jitter and detection recovery time caused by pulse accumulation, compensates additional jitter, and can effectively solve the problem of jitter deterioration generated by SNSPD under a high counting rate in high-speed detection application.

The invention discloses a relocation method and device based on a 3D laser radar and a prior map, and a medium, and the method comprises the steps: carrying out the point cloud preprocessing and point cloud segmentation of laser radar data and inertial measurement unit data, and obtaining a to-be-registered point cloud; establishing a multi-resolution pyramid grid map according to the 2.5 D grid map; step 4, judging whether initial registration needs to be performed on the to-be-registered point cloud, and if yes, entering step 4; otherwise, obtaining an initial relocation result, and entering the step 5; performing 2D coarse registration based on branch and bound search according to the pyramid grid map and the to-be-registered point cloud, performing 3D fine registration based on ICP registration according to the 2D coarse registration result, the to-be-registered point cloud and the 3D point cloud map, and completing initial relocation after iterative optimization convergence to obtain an initial relocation result; and performing incremental frame-graph registration according to the initial relocation result and the 3D point cloud map to obtain a continuous relocation result. The robustness is ensured, and the calculation efficiency is improved.

The invention discloses a wind-light-water storage active power cooperation real-time control method and system, and solves the problems that a wind-light output correlation output curve cannot be considered in an existing method, and a power distribution network operation state model only depends on a wind power photovoltaic output curve, a load curve and the state of energy storage equipment; the method comprises the following steps: in combination with a wind and light probability distribution function, improving a dynamic time bending algorithm to quantitatively describe wind and light output correlation; constructing a cooperative scheduling model based on wind and light output correlation and cascade failure simulation; according to the method, a wind-solar probability distribution function of active power output of wind power generation and photovoltaic power generation is defined through a probability distribution method, and the wind-solar output correlation is quantitatively described based on an improved dynamic time bending algorithm, so that a wind-solar output correlation output curve which is quantitatively expressed is effectively combined and considered when a collaborative scheduling model adaptively searches an optimal solution parameter; and the change of wind and light output can be predicted and simulated more accurately.

The invention discloses an intelligent meat duck weight estimation method combining deep learning and multi-modal data, and the method comprises the steps: S1, obtaining the visual modal data of a meat duck based on a multi-view camera, S2, collecting the non-visual modal data of the meat duck, and S3, fusing the visual modal data and the non-visual modal data based on a deep learning network, s4, performing meat duck key point detection based on the fusion data, and correcting the posture and shielding error; S5, estimating the weight of the meat duck by using the corrected key point data; according to the intelligent meat duck weight estimation method combining deep learning and multi-modal data, real-time weight estimation of mass meat ducks is realized, the manual operation time is saved, the production management efficiency is remarkably improved, different postures of the meat ducks and complex breeding scenes in which the meat ducks are mutually shielded can be dealt with, and the meat duck weight estimation efficiency is improved. The adaptability and robustness of the weight estimation system in a real breeding environment are enhanced, and the stability performance under various conditions is ensured.