45results about How to "Satisfy real-time" patented technology

The invention discloses an electromagnetic transient simulation and mapping method and system oriented to multi-physics field coupling, and belongs to the technical field of substation digital operation and maintenance and electromagnetic simulation. Deploying multiple types of sensors in a transformer substation, preprocessing the acquired multi-physical field data, and fusing with the three-dimensional twinborn model; a coupling algorithm is adopted to carry out multi-physical field coupling analysis, multi-physical quantity display and thermodynamic diagram superposition are realized, and scene evolution change simulation under various typical working conditions is supported; a micro-service architecture is adopted to construct a digital twin system, double closed-loop verification of simulation and actual measurement is performed through actual measurement data and simulation data of a real-model test field, model optimization is completed, and real-time mapping of electromagnetic transient dynamic simulation and a transformer substation physical entity is realized. The method improves the precision of model and entity mapping, meets the requirements of real-time tracking and response of the electromagnetic transient change of the transformer substation, adapts to the digital transformation of different transformer substations, and provides a basis for the operation and maintenance of the transformer substations.

The invention relates to the field of welding seam detection, and discloses a welding seam center line real-time tracking method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a laser tracking evaluation speed, an ultrasonic tracking evaluation speed and a tracking speed threshold value of a to-be-detected weldment; if the laser tracking evaluation speed and the ultrasonic tracking evaluation speed are both larger than the tracking speed threshold value, the weld joint center line of the to-be-detected weldment is tracked according to a laser and ultrasonic simultaneous inspection strategy; and if the laser tracking evaluation speed or the ultrasonic tracking evaluation speed is smaller than or equal to the tracking speed threshold value, the welding seam center line of the to-be-detected weldment is tracked according to a main laser and auxiliary ultrasonic strategy. According to the invention, the obvious limitation of a single laser tracking technology under the conditions of a complex surface and strong illumination is overcome, and high-precision and high-robustness real-time tracking is realized through acousto-optic fusion.

The present application relates to the technical field of unmanned aerial vehicle, and discloses a field splicing method of distributed photoelectric tracking system, first, a frequency spectrum monitor is used to find an unmanned aerial vehicle target and send the azimuth and distance data of the target to a target detection unit, after the target is found by the target detection unit, the target is automatically or manually locked, and the information of the locked target is transmitted to a target tracking unit, the target tracking unit extracts features in the area around the original position of the target in the last frame, finds the position with the highest matching degree of the target features and takes the position as the new position of the target, then adjusts the camera to the new position, automatically or manually controls the linked interference device, and transmits satellite navigation and remote control interference signals to the target area until the unmanned aerial vehicle is driven away. The present application solves the problem of too large target position error of the frequency spectrum monitor, and has accurate positioning, and meets the real-time and success rate requirements of target search.

This invention relates to a physical information neural network-based intelligent performance prediction method and system for automated multi-material fiber placement on complex curved surfaces in aircraft. The invention includes: collecting fiber placement process parameters; performing standardized preprocessing and physical constraint-enhanced sampling to obtain an enhanced training dataset; constructing a high-order nonlinear sparse regression candidate library based on the enhanced training dataset; jointly identifying piecewise smooth physical differential equation systems and their mode switching logic using sparse Bayesian regression and Hidden Markov Models to obtain an embeddable inverse mechanism model; constructing a Physical Information Embedded Generative Adversarial Network (PI-GAN); collaboratively optimizing the generator and discriminator through adversarial training to output surface defect indices and mechanical performance indices; and initiating incremental self-learning when introducing new process scenarios to achieve model self-evolution. This invention achieves end-to-end accurate prediction of multi-dimensional quality indices such as surface defects and mechanical performance.

The invention discloses a trajectory prediction system based on convolution and self-attention. The trajectory prediction system comprises an overall network architecture, a training method and a reasoning output algorithm. The whole network architecture takes a road grating map, a vehicle motion state and target information as inputs, the inputs are processed through a DenseNet layer, a self-attention coding layer, a full-connection coding layer and a decoding output layer in sequence, and a prediction track is output; according to the method, a road grating map containing information such as a collision box and a road marking and a vehicle motion state are used as input, road environment constraints are associated with a self-attention mechanism, a polar coordinate track description mode fitting vehicle kinematics characteristics is adopted, a track parameter value range is limited, common errors and abnormal tracks are effectively avoided, and the method is suitable for popularization and application. Traffic rules and driving safety requirements are met, and the reasonability and safety of formed track prediction are prominent; based on 16000-frame multi-dimensional training data collected by a Carla simulator, errors are reduced in a verification set after optimization training, and errors in a crossing turning scene are reduced.

The invention relates to a multifunctional intelligent aloe bin, and relates to the field of artificial climate boxes, the multifunctional intelligent aloe bin comprises a planting bin, a plurality of brackets vertically arranged in the planting bin at intervals, and planting trays arranged above the brackets, and the planting bin is divided into a plurality of cultivation bin chambers by the planting trays; the environment sensor is arranged in the planting bin and used for detecting environment parameters; the execution mechanism is arranged in the planting bin and used for adjusting the growth environment or supplying growth substances; the control terminal is connected to the environment sensor, is arranged on the planting bin or is in communication connection with the planting bin, comprises a human-computer interaction interface and is configured to establish communication connection with a remote experimenter terminal; a control instruction from the remote experimenter terminal is received and responded to drive the execution mechanism to act; and data detected by the environment sensor is sent to a remote experimenter terminal. According to the invention, the experiment requirements of real-time monitoring and flexible control are met, and the experiment accuracy is improved.

The application discloses a spherical decoding detection method and device, electronic equipment and a readable storage medium, and belongs to the technical field of wireless communication. The application obtains a Q matrix and an R matrix by performing orthogonal triangular QR decomposition on a channel response matrix, wherein each column in the Q matrix is arranged according to the size of channel energy; determines a target equalization signal according to the Q matrix and the R matrix; performs an ML path search process according to the target equalization signal to select N branches with minimum metrics as survivor paths; performs ML complement path search on the survivor paths to obtain ML complement paths; takes the survivor paths with minimum metrics as ML paths, and obtains the likelihood ratio information of each bit of each symbol at each layer according to the ML paths and the ML complement paths. The application can reduce the operation complexity of the spherical decoding algorithm while maintaining the decoding detection performance.

The invention relates to the field of industrial internet-of-things data transmission, in particular to an industrial internet-of-things data secure transmission method, system, device and medium, and the method comprises the steps: pre-registering client information at a server, and generating a client unique identifier; the client generates a client key according to the public key obtained from the server and the unique identifier of the client, and registers the key to the server; the client uses the registered key to initiate security authentication to the server, and after the authentication is passed, the server generates or negotiates with the client to generate a session key for data encryption transmission, and distributes the session key to the client; the client and the server encrypt or decrypt interaction data by using the session key; according to the method, the data transmission security of the industrial Internet of Things can be ensured, the real-time performance, the reliability and the usability are considered, and the method is particularly suitable for industrial control, monitoring and cooperative processing scenes with relatively high requirements on the security and the real-time performance, and has relatively high practical value and popularization prospect.

The embodiment of the invention provides an iridium satellite monitoring and early warning system and method, and belongs to the wireless communication signal monitoring technology. The system comprises a signal acquisition subsystem used for acquiring wireless signals in an iridium communication frequency band; the signal processing subsystem is connected with the signal acquisition subsystem and is used for performing iridium communication characteristic-oriented processing on the wireless signal so as to obtain analyzable effective signal data; the information analysis subsystem is connected with the signal processing subsystem and is used for analyzing the effective signal data and generating communication information for representing iridium communication activity; and the monitoring and early warning subsystem is connected with the information analysis subsystem and is used for executing monitoring analysis based on the communication information and outputting a corresponding monitoring result or early warning information. According to the scheme of the invention, acquisition, processing, analysis, monitoring and early warning of iridium communication signals are integrated in a unified system architecture, and continuous monitoring, structured analysis and timely early warning output of iridium communication activities are realized.

The application discloses a kind of fast simulation method of ion scattering track cluster driven by bypass factor, including constructing basic radiation parameter table;According to the basic radiation parameter table, search discrete energy point, form lookup table;According to the variation of bypass factor of each discrete energy point in lookup table, determine the scattering angle distribution characteristic parameter corresponding to discrete energy point, and generate scattering angle sample based on scattering angle distribution characteristic parameter and store in scattering angle sample pool;Initialize three-dimensional grid, set the initial position and direction of incident ion;Carry out incident ion transport and energy deposition cycle;Parallel execution of the simulation task of multiple incident ions, respectively in independent three-dimensional grid copy incident ion transport and energy deposition cycle, finally all three-dimensional grid copy is obtained Total dose distribution.This method achieves optimal balance between calculation efficiency and accuracy, so as to generate high-fidelity three-dimensional dose distribution chart.

This invention discloses a multifunctional biomimetic robot for field exploration, comprising a body structure, a variable-radius slewing wheel-leg mechanism, a variable-stiffness connection mechanism, a flexible tail mechanism, and a front-end actuator. The body structure includes multiple base plates; the variable-stiffness connection mechanism includes multiple variable-stiffness connection components, with adjacent base plates connected by these components; the variable-radius slewing wheel-leg mechanism includes multiple variable-radius slewing wheel-leg components, each capable of both wheel-based and leg-based movement; the flexible tail mechanism possesses degrees of freedom for circumferential rotation and vertical bending; and the front-end actuator has obstacle removal and / or data acquisition functions. This design achieves high movement speed on flat terrain while simultaneously meeting the requirement for high obstacle-crossing capability on complex terrain. Furthermore, the body stiffness can be automatically adjusted, and the tail structure exhibits high controllability and flexibility.

The invention discloses a side end multi-scene perception control system and method based on mechanical arm vision, and relates to the technical field of intelligent technology. Through the unified software development kit and the intelligent sensing control software with the body, the intelligent terminal can be used for cooperative work with multiple types of mechanical arms, multiple types of depth cameras and multiple types of sensors, and seamless integration and cooperative work of the multiple types of mechanical arms and the depth cameras are achieved; multi-scene intelligent sensing and mechanical arm operation control under the limited computing power of a terminal are achieved, the intelligent sensing and operation control capacity of the system is improved, meanwhile, decoupling type integration of the mechanical arm, the depth camera and multiple sensors is achieved through a unified interface layer and a calling layer, the coupling degree and adaptation cost of the system are remarkably reduced, and plug and play is supported.

This invention discloses a method for inferring pilot stress and emotional states based on video, audio, and QAR data, belonging to the field of intelligent perception technology for multimodal pilot psychological states. The method includes temporal alignment, noise reduction, and data cleaning of the acquired data; unified modeling of multimodal features; outputting results and generating interpretive visualization reports. This invention improves the usability and deployment convenience of the system in real flight or simulated training environments; enhances the accuracy and robustness of psychological state recognition; strengthens the model's cross-human generalization ability; and meets the stringent real-time and reliability requirements of aviation scenarios.

The invention provides an analogue simulation method and device for determining a risk field based on a particle diffusion model, and the method comprises the steps: carrying out the analysis of the current sensing data collected by each target sensing device on a target vehicle in a simulation scene, and determining the risk particles included in each current sensing data; performing particle diffusion model updating processing by using each risk particle and the current environmental influence data, and determining at least one candidate particle diffusion model; for each candidate particle diffusion model, re-updating the candidate particle diffusion model by using effective secondary particles generated by chain reaction after collision to obtain at least one optimal particle diffusion model; fusing all the optimal particle diffusion models to determine a target particle diffusion model; and outputting a target risk field according to particle information in the target particle diffusion model. Therefore, the particle diffusion model and dynamics are combined to realize dynamic interactive modeling, so that the dynamic accurate evaluation of the risk field can be effectively realized.

The application discloses a building image segmentation method and system based on a SAM-GAN, and relates to the technical field of image data processing. The method comprises the following steps: acquiring an original remote sensing image, and preprocessing the original remote sensing image to obtain a preprocessed image; generating a preliminary building segmentation result based on a preset SAM-GAN model according to the preprocessed image; the SAM-GAN model comprises a SAM generator and a double discriminator network, the double discriminator network is used for optimizing the SAM generator, and the double discriminator network is a double discriminator architecture that fuses semantic and topological constraints; and performing geometric rule adaptive optimization post-processing on the preliminary building segmentation result to obtain a final building segmentation result. The application improves the accuracy, quality and generalization ability of the building image segmentation result.

The application discloses a laser radar point cloud object detection method and system, comprising the following steps: obtaining a three-dimensional point cloud of a laser radar, and after filtering interference point clouds and compensating for missing point clouds, obtaining a to-be-segmented point cloud; generating a two-dimensional distance image from the to-be-segmented point cloud; setting an angle constraint; the angle constraint is that the included angle between the line connecting two adjacent points in the two-dimensional distance image and the line connecting the radar center and the point farther away from the radar center among the two adjacent points is not less than an angle threshold; setting a distance constraint; the distance constraint is that the distance between the two adjacent points in the two-dimensional distance image is not greater than a distance threshold; traversing the points in the two-dimensional distance image, judging whether the two adjacent points satisfy the angle constraint or the distance constraint, if any constraint is satisfied, marking the two adjacent points as the same object, otherwise, marking the two adjacent points as different objects, and thus obtaining an object segmentation result. The object can be quickly detected under sparse point cloud data.

The application relates to the field of power system data acquisition and edge computing, and discloses a multi-source heterogeneous data pre-acquisition and edge intelligent processing method, which comprises the following steps: using a modal division mechanism, combining the operation state of power equipment, and performing condition correction on an adaptive sampling frequency to obtain a corrected sampling frequency, so as to acquire multi-source heterogeneous data; extracting fusion core features from multi-dimensional feature vectors; substituting the fusion core features into a general fault judgment model and a time sequence prediction model to respectively output equipment fault prediction results and time sequence prediction results, and obtaining early warning information; based on the fusion core features, the early warning information and the equipment fault prediction results of each acquisition node, and combined with the Lymerzi complexity theory, the spatio-temporal coupling law is identified; and based on the spatio-temporal coupling law and the time sequence prediction results, edge end decision suggestions are provided for source-grid-load-storage collaborative regulation. The application effectively improves the power system data processing efficiency and the power grid operation and maintenance regulation level.

The application provides a power load adaptation and output regulation control method, equipment and storage medium, and belongs to the technical field of power load regulation control, and the method comprises the following steps: calculating dynamic parameters based on load electrical parameters; generating an initial solution population of control parameters based on a chaotic mapping mechanism and a dynamic parameter value boundary; iteratively optimizing the initial solution population by using an improved artificial bee colony algorithm to obtain an optimal control parameter set matched with the current load characteristics; fusing the optimal control parameter set as a feedforward control amount and a feedback correction amount to generate a comprehensive control instruction; adjusting a power output unit according to the instruction; determining whether the power output reaches a target set value within a preset adjustment time window; if not, updating an iterative optimization input condition and repeatedly executing the iterative optimization; and if yes, maintaining the comprehensive control instruction. Based on the method, corresponding equipment and storage medium are also provided. The application realizes accurate and stable output of a power system within the first sine wave cycle after load mutation.

The invention discloses a motor sweeper operation path generation method and device, and the method comprises the steps: constructing a road connected graph based on a to-be-swept region map road boundary and a topological connection relation; coarsening the connected graph according to a preset merging strategy, and merging the fine-grained nodes into logic region nodes; constructing a final cleaning task area based on the coarsened connected graph; planning a sub-region cleaning sequence in combination with the current position of the cleaning vehicle; and constructing a global operation path. According to the invention, whole-process automation of cleaning area division and sequence planning is realized, manual intervention is not needed, and operation efficiency and reliability are improved; complex road topologies such as roundabout and the like are adapted, and sub-region division is more reasonable; a heuristic weight and an improved threshold acceptance algorithm are fused, the solving efficiency and the planning quality are balanced, and the requirements for the real-time performance and the accuracy of outdoor cleaning are met.

The present application relates to the technical field of online learning data security, and relates to an online learning platform data security access control method.The present application calls the historical operation behavior of a user before the user applies for data access after identity verification, analyzes the antecedent association of the applied access operation behavior sequence and the historical operation behavior, extracts the reference access operation behavior sequence of other users with the same attribute data in accessing the current data, compares and outputs the matching degree of the applied access operation behavior sequence, dynamically adjusts the access permission according to the analyzed risk value of the user in accessing the current data, increases the risk recognition accuracy of the user operation behavior, and according to the deviation degree of the access operation data and the user behavior portrait after the adjustment of the user access permission, determines the dynamic trust score in combination with the historical trust score of the user, realizes the quick response means when abnormal access operation occurs, and provides fine user trust management for the shuttle machine in the learning platform.

The invention relates to the technical field of vehicles, in particular to a vehicle safety certification and control system, a vehicle and a control method.The vehicle safety certification and control system comprises a multi-mode biological characteristic quantization processing module which is used for obtaining at least one biological characteristic of a user and conducting coding fusion on the biological characteristics in a hardware operation environment to generate a quantum-state identity label; the dynamic key management and vehicle control module is used for completing identity authentication according to the quantum state identity credential, generating an encryption key of a vehicle control instruction according to the traffic situation information if the identity authentication is successful, and outputting the vehicle control instruction after safely packaging the vehicle control instruction by using the encryption key in response to user operation or a preset event, and the vehicle is controlled to execute corresponding operation. Therefore, the problems that in the related technology, biological characteristic transmission of a vehicle-mounted safety system is prone to being stolen, secret key management is static and rigid and cannot respond to road conditions in real time, and quantum hardware is difficult to stably work in the vehicle-mounted vibration and high-temperature environment are solved.

A deep learning-based method for reconstructing the two-dimensional vector vibration direction of a multi-core optical fiber includes: constructing simulated noise-free data; selecting the same multi-core fiber grating sensor as the simulation, providing the same two-dimensional vibration excitation parameters as the simulation, and collecting measured sensor data; preprocessing the measured sensor dataset to achieve standardized alignment between the measured data and the simulation data; constructing a deep learning model for two-dimensional vibration direction reconstruction; optimizing the deep learning model through a phased training and transfer learning strategy; and reconstructing the two-dimensional vector vibration direction based on the trained deep learning model. This invention automatically extracts complex temporal features from wavelength drift signals by constructing a deep learning model, learns the temporal variation patterns and inter-channel coupling relationships in the signal, effectively mining vibration direction-related information, and overcomes the limitations of traditional geometric models that rely on the assumption of an ideal optical fiber structure, significantly improving the accuracy and precision of vibration direction reconstruction.

The utility model provides a kind of armoured tungsten-rhenium thermocouple, belong to temperature measurement technical field, this armoured tungsten-rhenium thermocouple, including armoured protection pipe, the tungsten-rhenium electrode being arranged in armoured protection pipe inside, insulation material being filled between armoured protection pipe and tungsten-rhenium electrode, terminal box and sealing assembly being arranged in armoured protection pipe one end, pass through double sealing structure, construct full-closed protection system, can effectively resist the invasion of high-temperature molten gas in metallurgical furnace, strong corrosive medium in chemical reaction kettle, make tungsten-rhenium electrode and alumina ceramic insulation layer exempt from corrosion, compared with traditional thermocouple service life improves 2-3 times.Simultaneously, spring-like elastic support piece and ceramic powder insulation layer form double anti-vibration structure.

The application discloses a kind of dynamic decomposition pebble bed type high temperature gas cooled reactor fuel ball burnup distribution prediction methods, it is related to nuclear engineering and artificial intelligence cross technical field, wherein method includes: obtaining initial burnup distribution and unloading instruction of core, normalizing and constructing dynamic mask matrix to distinguish fuel and non-fuel ball layer for burnup data;Multiple cycle burnup data is constructed time series snapshot matrix, feature is extracted by modal decomposition and dynamic operator is calculated;Based on dynamic operator, subsequent period burnup distribution is iteratively predicted by autoregression, is denormalized after covering target period, and then the value of non-fuel layer is zeroed by mask matrix, and the result meeting physical constraint is output.The present application can realize multi-cycle prediction of second level, greatly shorten the calculation time, meet the real-time monitoring demand;Through physical constraint and adjustable mode number, the prediction accuracy and calculation efficiency are considered, to ensure that the result is consistent with the core physical law.

The application aims to provide a fuel cell hybrid electric vehicle Boost circuit double closed loop control method, comprising the following steps: S1, designing an adaptive load estimator to reduce the influence of load disturbance on voltage regulation; S2, using the load estimation value obtained in the S1 step, introducing a double loop feedback control technology based on the fixed time control theory to replace the virtual control rate in the backstepping method, using the fixed time control method to control the current regulation internal loop, and ensuring that the system output voltage is tracked to the set reference voltage within a fixed time; S3, using the particle swarm algorithm to optimize the fixed time controller in the S2 step, finding the global optimal value, and minimizing the target function value. According to the fixed time control theory, the fixed time double closed loop control circuit is designed, the rapid response capability of the Boost converter is improved, and the particle swarm algorithm is used to optimize the controller parameters in the fixed time control strategy, so that the power control effect of the Boost converter is better.

This invention discloses an active noise control system and method based on an adaptive deep neural network. The system includes a reference signal acquisition unit, an error signal acquisition unit, a causal deep neural network controller, a lightweight adaptive module, and a parameter update unit. The method comprises the following steps: pre-training the causal deep neural network controller using an active noise control database; fixing the trained controller parameters and inserting a lightweight adaptive module with initial parameters of zero into the network; inputting the reference signal into the controller to generate a control signal to drive a secondary sound source to generate anti-noise, and using the error signal to construct the loss and calculate the gradient; freezing the main controller parameters during the control process, and only updating the embedded lightweight adaptive module online. This invention can improve robustness and control stability under complex operating conditions, reduce the dependence of deep neural network-based active noise control methods on large-scale training data, and improve noise suppression performance.

The invention provides a face recognition method based on point cloud data and RGB image sequential verification, and the method comprises the following steps: collecting two types of data at the same time, completing the face detection and cutting of an RGB image, combining histogram equalization and illumination compensation, extracting unitized features, comparing the unitized features with a feature library, and carrying out the confidence calibration. And according to the first upper threshold value and the first lower threshold value, primary judgment of passing, re-verification or refusal is made. After the RGB image passes verification, selecting a point cloud corresponding to a human face region, executing voxel downsampling, counting abnormal point removal and normal estimation, calculating local geometric description, performing robust registration, and constructing a single geometric similarity index based on a median absolute deviation of a point-to-surface residual error and quantile statistics; and outputting passing, re-checking or refusing according to the second upper threshold value and the second lower threshold value. Only when the two stages pass, an identity conclusion and confidence information are given; and when any stage is not passed or re-verification is not passed, outputting a rejection result or triggering a warning or auxiliary verification.

The invention discloses a supplier illegal behavior intelligent identification method and system based on a Monte Carlo method, and relates to the technical field of data processing, and the method comprises the steps: carrying out the preprocessing of an original bidding record data set, and obtaining an analysis bidding record data set; extracting multi-dimensional features by a multi-dimensional analysis feature mechanism, and carrying out supplier relation network feature quantitative processing to obtain an initial feature engineering data set; fusing the initial feature engineering data set into an enhanced multi-dimensional feature engineering data set by using multi-granularity time analysis mechanism time sequence mode analysis; constructing an abnormal behavior portrait by using a Monte Carlo simulation model, constructing a multi-level data view, simulating the multi-level data view, and obtaining a quantitative comprehensive risk score; and performing assessment and identification by using a multi-dimensional risk assessment and intelligent identification terminal to obtain an integrity comprehensive risk score, dividing suppliers based on a preset threshold, and generating an illegal behavior adjustment report. According to the invention, differentiated schemes are provided for suppliers with different risks, and the scientificity and pertinence of management and control are improved.

Embodiments of the present disclosure provide an electronic device, an accelerator, a method for generating an executable file and a method for executing the executable file. The electronic device comprises at least one processor configured to: determine a neural network model to be compiled and an encryption level; split the neural network model to be compiled based on the encryption level, and determine a first network layer and a second network layer in the neural network model to be compiled; when performing a compilation process on the first network layer and the second network layer, determine, based on encryption constraint information, a first instruction sequence to be encrypted corresponding to the first network layer and a second instruction sequence corresponding to the second network layer; perform encryption processing on the first instruction sequence to obtain a ciphertext instruction sequence corresponding to the first instruction sequence; and generate an executable file corresponding to the neural network model to be compiled based on the second instruction sequence and the ciphertext instruction sequence. The present disclosure can reduce the computing resources and storage resources occupied by model encryption and decryption, and improve the execution efficiency of the model.

The present application relates to the field of environmental monitoring and remote sensing information processing technology, and discloses a method and system for predicting the concentration of nutrient salts in a breeding area coupled with remote sensing data. The method comprises: constructing a physically constrained ecological hydrodynamic model to generate synthetic remote sensing-nutrient salt pairing data; fusing multi-source remote sensing and measured data to form a high-dimensional feature tensor; separating breeding sensitive features and environmental interference features through an attention-guided domain-invariant feature decoupling network; transferring prior knowledge of the source domain large-scale breeding area using a dynamic weight cross-domain knowledge distillation architecture; and finally outputting real-time high-precision nutrient salt concentration distribution through a lightweight regression head and time series smoothing. The system includes corresponding functional units. The present application improves the prediction accuracy and stability in small sample scenarios, suppresses negative transfer, and provides support for precision breeding and eutrophication early warning.