1634 results about "Performance estimation" patented technology

The invention relates to the technical field of computer networks, and discloses a computing power network real-time performance evaluation method based on an analytic hierarchy process, and the method comprises the steps: collecting a node operation state and task demand data through a sensor, and generating a local performance index in combination with an edge quantum algorithm; simulating a future network state by using digital twinning, and fusing to generate a multi-dimensional performance data set; the AHP weight is dynamically adjusted based on resource deviation and a geological classification model, high-frequency updating is started for high load / fault, and the weight range is expanded for low load; introducing a risk assessment algorithm to quantify a performance-cost-carbon effect conflict level, and triggering resource recovery, optimization prompt or single index suggestion; scheduling strategies are triggered in a grading mode according to evaluation results, and active intervention is started in combination with anomaly detection; the AHP weight is dynamically updated through reinforcement learning, and quantum-classical hybrid algorithm parameters and block chain verification weight are automatically optimized. The real-time response efficiency and the resource utilization rate of the computing power network can be improved.

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

The invention discloses a non-energy-storage off-grid photovoltaic hydrogen production system and a control method. The control method comprises five steps of environmental data acquisition and preprocessing, photovoltaic cell output power estimation, electrolytic cell power management, converter control parameter optimization and system performance evaluation and adaptive adjustment. The method comprises the following steps: firstly, processing environmental data by adopting an isolation forest algorithm, Kriging interpolation and an empirical mode decomposition method, and extracting time sequence features; and then combining with the estimated photovoltaic output power. And then the genetic algorithm and dynamic programming are utilized to realize electrolytic cell power optimization distribution. And then converter control parameters are optimized through subspace identification, pole assignment and a hybrid optimization algorithm. And finally, system performance evaluation and adaptive adjustment are realized by adopting a sliding time window method, singular spectrum analysis and a fuzzy prediction control method. The technical problems of the energy-storage-free photovoltaic hydrogen production system in the aspects of environmental data processing, power estimation, energy management, control optimization and the like are solved, and the operation efficiency and reliability of the system are improved.

The invention provides a storage battery pack performance evaluation method and system based on big data, and relates to the technical field of storage battery performance evaluation, and the method comprises the steps: obtaining the charging and discharging data of a to-be-evaluated storage battery under different working conditions, carrying out the denoising processing of the charging and discharging data, carrying out the adaptive normalization processing, and obtaining the standard charging and discharging data, equalization processing is carried out, time domain features and frequency domain features are extracted respectively, and high-dimensional feature vectors are obtained through combination; performing dimension reduction operation on the high-dimensional feature vector to obtain low-dimensional embedded representation, generating an initial data set, dividing the initial data set into a training data set and a test data set, constructing an initial prediction model and performing hyper-parameter optimization to obtain a first prediction model; and acquiring real-time charging and discharging data, constructing a real-time feature vector, adding the real-time feature vector into the first prediction model to generate a real-time performance parameter, filtering the real-time performance parameter, constructing a health state prediction model for prediction, and obtaining a comprehensive performance evaluation result.

The invention provides an intelligent short message scheduling method and device based on multi-dimensional dynamic optimization, and the method comprises the steps: obtaining the performance data of a plurality of short message channels, and calculating a channel health score based on a weight dynamic adjustment model; determining a scheduling strategy according to the priority identifier of the to-be-sent message, and performing channel screening and optimal matching; executing message sending and monitoring a sending state; terminal state detection is carried out on the failure message through operator base station signaling, and a decision tree model is applied to determine a retry strategy; and performing Huffman coding compression processing on the P2-level marketing messages which fail in retry, and performing batch sending in an idle window. According to the method, a comprehensive performance evaluation index and reward function model is also constructed, and parameter optimization is performed by applying a reinforcement learning algorithm. According to the invention, multi-dimensional dynamic channel scoring, intelligent retry decision making based on terminal state perception, batch processing with balanced cost-time efficiency and a closed-loop self-optimization system are realized, the short message delivery rate is obviously improved, and the invalid retry rate and the sending cost are reduced.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

The invention relates to the technical field of fluid machinery design, and discloses a fluid machinery through-flow component intelligent design simulation system, which comprises a multi-physics field modeling module, which is used for constructing fluid mechanics, structural mechanics and heat conduction models based on preset boundary conditions and physical attributes; the intelligent optimization decision module is used for constructing and defining a through-flow performance optimization objective function; the cooperative calculation module is used for constructing a comprehensive performance evaluation model based on the candidate design parameter set; the virtual experiment feedback module is used for reconstructing a corresponding through-flow component structure model according to the multi-target evaluation result; and the scheduling control module is used for controlling algorithm path selection and computing resource allocation in the optimization process. According to the method, a method of combining multi-physics field coupling modeling and a dynamic feedback mechanism is adopted, and design parameters are continuously adjusted through simulation data fed back in real time, so that the optimization process can be dynamically updated according to actual operation conditions, and the optimization convergence speed and the overall performance are improved.

The invention relates to the technical field of cloud computing and artificial intelligence, in particular to a disaster recovery drill and performance evaluation system and method for multi-cluster distributed training. In order to solve the problem of disaster recovery and performance evaluation in multi-cluster distributed training, the method comprises the steps of environment analysis and modeling, fault injection and drill control, cross-cluster scheduling and resource monitoring, performance data acquisition and index aggregation, training result evaluation and tuning closed loop and the like. Multi-cluster environment elements are identified, a fault and index mapping relation is constructed, faults are injected by using a fault injection tool, and task scheduling, resource monitoring and performance data acquisition and analysis are realized by means of a multi-cluster management and monitoring tool, so that an adjusting and optimizing strategy is formed, and closed-loop management is realized. According to the method, faults can be dynamically simulated in a multi-cluster environment, the training performance is monitored, the disaster recovery and recovery efficiency is evaluated, the training efficiency and robustness of the system are improved, and the requirements for high availability and high performance in the fields of finance, automatic driving and the like are met.

The invention discloses a spaceborne interference source Doppler observation strategy optimization method, which comprises the following steps: acquiring an original radio frequency signal and satellite orbit data, predicting mutation characteristics in Doppler data, and generating a self-adaptive observation scheme; according to the self-adaptive observation scheme, energy-aware resource scheduling is carried out, observation is executed, and enhanced Doppler observation data is obtained; and carrying out positioning calculation and performance evaluation based on the enhanced Doppler observation data, and generating parameters for iteratively optimizing an observation strategy. According to the invention, intelligent matching of the observation resource and the signal information content is realized, and the resource utilization efficiency, the key event capturing capability and the final positioning precision are improved.

The invention provides a different types of database visualization table management method and system, and relates to the technical field of computers.The method comprises the steps that a database general operation system comprising a general operation interface and a dialect abstract base class is constructed, and a query performance evaluation system is established based on performance data; and predicting an optimal database connection mode through deep reinforcement learning and generating a multi-stage execution plan. Meanwhile, an anomaly feature knowledge base based on a graph neural network is constructed, and anomaly prediction and preventive processing are carried out based on the anomaly feature knowledge base. A multi-stage execution plan is executed after a database operation request is subjected to grammar adaptation, and a distributed transaction processor dynamically adjusts a transaction isolation level and a concurrency control parameter. And finally, according to the terminal and the data volume, selecting a proper data display mode to carry out visual presentation.

The invention relates to the technical field of task scheduling, and discloses a distributed task scheduling method, device and equipment for OpenHarmony Internet of Things equipment, and the method comprises the steps: carrying out the hardware parameter collection and equipment classification of the Internet of Things equipment in an OpenHarmony distributed network, and obtaining an equipment capability data set; performing task analysis and dependency analysis on the application program to obtain a task directed acyclic graph; carrying out calculation and resource allocation prediction on the equipment load to obtain an equipment resource reservation table; constructing a nonlinear optimization model, and solving to obtain an initial task allocation scheme; performing two-stage distributed reinforcement learning optimization to obtain a target task scheduling scheme; the target task scheduling scheme is deployed to the Internet of Things equipment, data collection and performance evaluation are carried out, and a performance evaluation data set is obtained.The efficient communication and cooperative computing between the equipment are achieved based on the distributed soft bus, the overhead of task migration and data transmission is reduced, and the overall performance of the system is improved.

The invention discloses an integrated scheduling system for realizing a PCS, an EMS and a BMS, and relates to the technical field of power control, and the system comprises a multi-dimensional performance evaluation module which constructs a battery aging dynamic model, carries out the training, carries out the health state pre-judgment through the battery aging dynamic model based on a standardized state vector, and generates a multi-dimensional performance evaluation index; the multi-objective optimization module is used for generating a collaborative scheduling strategy set by combining a fuzzy analytic hierarchy process with a multi-objective optimization solver of an improved genetic algorithm based on the multi-dimensional performance evaluation indexes; the dynamic derating module is used for generating an executable instruction queue with security constraints by combining an industrial internet of things protocol stack with a dynamic derating coefficient algorithm based on the collaborative scheduling strategy set; according to the invention, through the physical driving characteristic layer and the dynamic parameter calibration layer, the nonlinear coupling modeling of the cyclic attenuation and calendar aging mechanism in the battery aging dynamic model is realized.

The invention relates to the technical field of defect detection, in particular to a cross-process defect root cause tracing method and system. According to the method, the data feature matrix covering multiple dimensions is formed by integrating the process parameters, the equipment state and the quality detection information, so that the performance evaluation of each process is more comprehensive, the interaction and influence paths among the processes can be clearly described by constructing the process relation graph, and the performance evaluation efficiency is improved. Meanwhile, basic data support is provided for quantifying the relation between the procedures through introduction of procedure attenuation factors, the shortest propagation path and the propagation probability of the defects can be accurately recognized by analyzing a procedure relation graph, root cause tracing of the cross-procedure defects becomes systematized in combination with construction of a knowledge graph, and the defect tracing efficiency is improved. The knowledge graph not only can effectively integrate and display data, but also is convenient for quickly positioning problems, and by utilizing an adaptive correlation analysis technology, the system can intelligently adjust an analysis model and a path and continuously optimize a defect detection and tracing process when facing new data.

The invention discloses a health risk assessment method and early warning system based on multi-source data analysis. The health risk assessment method comprises the following steps: S1, collecting and preprocessing multi-source health data through medical detection equipment; s2, extracting key health indexes, sequence features and statistical features based on the health data set; s3, adopting a recurrent neural network model to construct a health risk assessment model; s4, optimizing structural parameters of the health risk assessment model by adopting an improved dragonfly algorithm; s5, performing performance evaluation on the health risk evaluation model by using the optimal parameter set; s6, deploying the final health risk assessment model in a health risk assessment and early warning system; and S7, when the health risk assessment result exceeds a preset risk threshold, automatically generating early warning information. According to the method, the recurrent neural network model, the improved dragonfly algorithm and the multi-source health data fusion optimization technology are combined, and health risk assessment and early warning based on multi-source data analysis are realized.

The invention belongs to the field of data processing, and discloses a test method for pedestrian trajectory prediction and emergency braking in an automatic driving platform. Comprising the steps of segmenting a real road environment in advance, obtaining grades of small road segments according to calculation of segmentation coefficients, carrying out resource allocation on the small segments according to the grades, and collecting multi-source heterogeneous data; importance optimization is introduced into a Transform model, possible prediction trajectories of individuals are generated, graph data are constructed by using the possible prediction trajectories, an Autoencoder anomaly detection mechanism is introduced, high-risk trajectories are screened out, and high-risk scenes are output; combining the road geometric model with the high-risk scene to form a scene library, loading the scene in a simulation platform, carrying out automatic driving test, carrying out virtual-real data cross validation in combination with real vehicle test data, and obtaining performance evaluation of an algorithm; pedestrian track prediction and emergency braking in the automatic driving process are achieved.

The invention relates to the technical field of dredge pump performance analysis, and discloses a numerical simulation and model test-based dredge pump performance analysis method, which comprises the following steps of: planning an analysis process, and determining numerical simulation (dredge pump internal flow field simulation) and model test contents (actually measured performance data under different working conditions); determining working condition types according to design parameters and working conditions; generating a performance analysis process set and determining an execution sequence; generating a simulation parameter setting list and a test scheme list of each working condition; and associating the data to generate an overall scheme. The method further relates to working condition analysis boundary determination, working condition type subdivision, dynamic adjustment of the process, test rule optimization, database construction and the like. According to the method, comprehensive and accurate analysis of the dredge pump performance is achieved through multi-dimensional data integration and a dynamic mechanism, the analysis efficiency and reliability are improved, and the method is suitable for evaluation and optimization of the dredge pump performance.

The invention relates to an online multi-dimensional comprehensive test evaluation method for an automatic driving test. The method comprises the following steps: constructing a test scene library; constructing a test scene complexity model; building an intelligent driving joint simulation test platform, and embedding the intelligent driving algorithm into the intelligent driving joint simulation test platform; constructing an automatic driving test multi-dimensional evaluation index system for performing multi-dimensional performance evaluation on the automatic driving vehicle; an intelligent driving joint simulation test platform is utilized to obtain vehicle test data, an evaluation index system and a test scene complexity model are combined to calculate objective evaluation index weights, then comprehensive performance quantitative scores are calculated, and finally an automatic evaluation report is output and obtained. Compared with the prior art, the subjectivity of manual evaluation can be reduced, and the evaluation efficiency of the automatic driving vehicle is improved; comprehensive and objective quantitative scoring can be carried out on the multi-dimensional performance of the automatic driving vehicle, and the influence of different test scenes on the scoring difference of the automatic driving vehicle is reduced.

The present invention relates to the technical field of industrial boilers, and provides a prediction method based on adaptive boiler thermal energy. It includes data collection and preprocessing, model selection and training, adaptive adjustment, prediction and evaluation stages. Data collection covers multiple types of parameters, environmental data and historical data, and corrects abnormal values; model selection and training adopts a multi-model fusion strategy to build a hybrid model; adaptive adjustment uses a variety of algorithms to dynamically adjust model parameters and structures through real-time data monitoring and working condition identification; the prediction and evaluation stage combines the working condition optimization model fusion prediction, calculates the comprehensive performance evaluation index, and visualizes and analyzes the results. The present invention improves the prediction accuracy and stability, helps to optimize energy management, reduce costs, and ensure production safety.

The invention provides an LCM module aging test method, device and equipment and a storage medium, and the method comprises the steps: obtaining the initial state data and initial environment parameter combination of an LCM module through intelligent environment simulation and initialization processing, and carrying out the multi-mode alternate aging test and real-time monitoring processing of the module through an intelligent environment simulation system, and obtaining performance data in a dynamic environment, and carrying out surface periodic scanning and working sound recording to obtain microstructure data and acoustic data. Through intelligent fault detection and deep analysis processing, fault features and reasons of the module are identified, predictive analysis is performed on the fault in combination with a machine learning algorithm, and a predictive fault trend and an optimized environmental parameter combination are generated. And performing comprehensive performance evaluation on the module according to the fault features, the reason analysis, the prediction trend and the optimization parameters. According to the invention, through combination of intelligent environment simulation, real-time monitoring and deep analysis, accurate performance evaluation and fault prediction of the LCM are realized.

The invention relates to the technical field of bridge maintenance, in particular to a beam bridge dynamic anti-seismic performance evaluation method and system, and the method comprises the steps: model preparation, model establishment, boundary condition and load application, analysis and calculation, and result analysis and evaluation. According to the method, through detailed data collection and model preparation, a detailed finite element model of the bridge is accurately constructed, actual characteristics of a main beam, a pier, a support and other key components are fully considered, boundary conditions are reasonably set, seismic loads are applied, calculation is performed by adopting a proper anti-seismic analysis method, and the seismic response of the bridge is effectively calculated; the method has the advantages that the method is simple, the anti-seismic performance of the bridge under the earthquake action is evaluated, potential weak parts of the bridge under the earthquake action are accurately identified, reliable basis is provided for formulating targeted anti-seismic reinforcement measures, and the efficiency and reliability of anti-seismic analysis and performance evaluation of the bridge structure are remarkably improved.

The invention relates to the technical field of energy storage scheduling, and discloses a distributed energy storage power system optimization scheduling method and system. The method comprises the following steps: collecting system operation data, performing normalization processing and anomaly detection, and predicting 24-hour load demand and new energy output in combination with standardized data; performing cost calculation and constraint analysis according to the prediction data, and generating a day-ahead scheduling plan; the operation state is updated according to the 15-minute cycle, and the output data is adjusted; performing multi-time-scale hierarchical processing on the power fluctuation; based on this, the energy storage capacity is optimally configured and the life characteristic is evaluated; and parameter optimization and updating are realized through operation index monitoring and analysis. According to the invention, through multi-dimensional performance evaluation and parameter optimization, efficient utilization and service life prolonging of the energy storage system are realized.

The invention discloses a big language model evaluation system and method, and belongs to the technical field of big language model evaluation. The system comprises a data preparation module, a basic performance evaluation module, an efficiency evaluation module, a robustness and multi-mode evaluation module, a distributed task scheduling module and a feedback optimization module. Through the system, the technical effect of multi-dimensionally, efficiently and automatically evaluating the large language model is achieved.

The invention discloses a method and a device for evaluating the performance of an automatic and rapid unfolding and folding tent. The method comprises the following steps: acquiring a performance index test data set of the automatic and rapid unfolding and folding tent; the performance index test data set comprises an unfolding and folding type index test data set, an efficiency type index test data set and an environment adaptation type index test data set; each type of index test data set comprises a plurality of types of index test data sequences; preprocessing the performance index test data set to obtain a preprocessed data set; performing classification evaluation processing on each type of index test data set in the preprocessed data set to obtain a sub-index test value set; and performing fusion evaluation processing on the sub-index test value set to obtain a performance evaluation result value of the automatic rapid unfolding and folding tent.

The invention belongs to but is not limited to the technical field of software development, and particularly relates to a multi-round reasoning and feedback optimization method and system for industrial software codes, and the method comprises the steps: S1, vector code library establishment: the establishment of a vector code library is composed of three parts of feedback establishment, vector database establishment and format summarization; s2, code reasoning optimization: the code reasoning optimization mainly comprises four aspects: control flow graph analysis, multi-query generation and reciprocal ranking fusion retrieval, reasoning generation subtask and code chain submodule generation, and code combination completion; and S3, feedback optimization: the feedback optimization is composed of score feedback optimization and log feedback optimization. According to the method, an automatic verification mechanism is introduced, logic consistency check and performance evaluation are carried out on the generated and optimized codes, and the correctness and execution efficiency of the codes are ensured. The verification mechanism can find and correct errors in real time in each stage of generation, so that the cost of manual verification is reduced, and the development efficiency is improved.

The invention provides a log analysis adaptive optimization method and system based on swarm intelligence, and relates to the technical field of log analysis, and the method comprises the steps: obtaining a database log, and carrying out the feature extraction to generate a multi-dimensional feature matrix; constructing a hierarchical analysis network and deploying an agent analysis unit cluster with a self-learning capability; the agent analysis unit executes an analysis task through a feature recognition module, a parallel computing engine and a decision optimizer; constructing a multi-target evaluation model based on the performance evaluation indexes; and generating an optimization strategy set by adopting a self-adaptive evolution model and updating the optimization strategy set to the analysis network. According to the invention, the accuracy and efficiency of log analysis are improved, and the self-adaptive capability of the system is enhanced.

The invention relates to an aging test method and device and a medium, and relates to the technical field of aging tests.The method comprises the steps that based on a distributed data acquisition system architecture, performance parameters of a to-be-tested product are synchronously acquired at a plurality of test points, data acquired at all the test points are synchronously integrated through timestamp alignment, and integrated data are obtained; based on a preset data analysis model, performing time sequence analysis on the continuously collected performance parameters, fitting a performance degradation curve by using a regression analysis method, determining a degradation function, and predicting the performance failure time of the to-be-tested product according to the degradation function; and determining a plurality of accelerated aging test schemes based on the performance failure time and a combination of a plurality of environmental factors, and executing all the accelerated aging test schemes through a simulator to obtain a plurality of life prediction results. The effect of improving the accuracy of performance evaluation and life prediction of the electronic product is achieved.

The invention provides an unmanned aerial vehicle (UAV)-based flux-inductance integrated beam forming and trajectory optimization method, on one hand, an FP-AROS method is proposed based on fractional programming and semi-definite relaxation technologies in combination with approximate rank-one solution construction to carry out optimization design on ISAC beams, the Cramer-Rao bound of AoD is adopted as a sensing performance evaluation index, and the FP-AROS method is adopted to carry out optimization design on the ISAC beams; target estimation performance can be reflected more directly, so that a collaborative optimization effect of communication and sensing performance is realized; on the other hand, the invention provides an improved continuous convex approximation method based on a trust domain, and the method is used for optimization design of a UAV track. The ITRSCA method obtains a local trend approximate solution of an original objective function by calculating a second derivative, and introduces a dynamic trust domain mechanism to control approximate precision, so as to ensure global convergence of an output solution; in addition, the method is combined with a penalty function method to process non-convex constraints, and the limitation of a trust domain method in a constrained optimization problem is effectively overcome.

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 automatic driving performance optimization method, system and device, a storage medium and a product, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining a driving scene parameter and an automatic driving algorithm; constructing a simulation platform by using the driving scene parameters and an automatic driving algorithm, and performing a simulation test on the automatic driving system through the simulation platform to obtain a simulation test result; and adjusting the weight and the evaluation key point of the simulation test result through a pre-constructed multi-dimensional evaluation system to obtain an evaluation result of the automatic driving performance. According to the scheme, by introducing a multi-dimensional evaluation system, more accurate and more comprehensive performance evaluation of the automatic driving system can be realized, and one-sided evaluation caused by a single index in the past is avoided. Besides, by dynamically adjusting the weight of the simulation test result and the evaluation key point, more accurate and more comprehensive performance evaluation of the automatic driving system is realized, and the reliability and safety of the automatic driving system in a complex and changeable real driving environment are improved.

The invention discloses a watershed pollution prediction method based on multi-source data fusion, and belongs to the technical field of watershed pollution prediction. According to the method, water quality, runoff, land utilization and meteorological data sequences are firstly constructed, and then features of the sequences are extracted and spliced to form a fusion feature vector. And forming a training set by using a plurality of fusion feature vectors, inputting the training set into a random forest regression model for training, performing performance evaluation on the trained model by using a verification set, and finally using the model meeting evaluation conditions to predict the drainage basin pollution degree. According to the invention, through multi-source data fusion and scientific model training and evaluation processes, the accuracy of drainage basin pollution prediction is effectively improved.