245results about How to "Increase computing speed" patented technology

The invention provides a central air-conditioning system real-time fault prediction method and system based on digital twinning, and the method comprises the steps: deploying various types of sensors for multi-dimensional detection, and collecting the data of the sensors; edge computing nodes are deployed around the central air conditioning unit; a digital twinborn model based on the central air conditioning unit is constructed according to the preprocessed data, a dynamic view of key performance indexes is provided, and the state of the central air conditioning unit is mapped and simulated in real time; designing a fault prediction model, and predicting and identifying faults; deploying the fault prediction model at an edge computing node and operating the fault prediction model, and performing real-time fault monitoring and diagnosis; when a fault is found, the AI-drive maintains decision support, and automatically adjusts a fault prediction model parameter or arranges a maintenance strategy; and constructing a learning loop based on performance feedback, and optimizing a fault prediction model and a maintenance strategy. Quantum calculation, the digital twinning technology and edge calculation are fused, and the operation efficiency and reliability of the central air conditioning system are remarkably improved.

The invention relates to the technical field of knowledge questions and answers, in particular to a method and system for detecting and repairing large language model output illusion, which comprises the following steps of: dividing answer content into a plurality of sentences, calculating the confidence coefficient of each sentence, judging whether the confidence coefficient of each sentence exceeds a set threshold value or not, performing fact extraction on the sentences with the confidence coefficient lower than the set threshold value, obtaining triple data of each sentence; generating a subgraph A of each sentence based on the triple data of each sentence; extracting a sub-graph B from the local knowledge graph based on the subject in the triple data of each sentence; calculating the similarity between the sub-graph A and the sub-graph B, and if the similarity is smaller than a set threshold value, repairing the sub-graph A according to the sub-graph B to obtain a repaired sub-graph A; according to the triad data corresponding to the repaired subgraph A, obtaining a repaired sentence; and after all the sentences with the confidence lower than a set threshold value are repaired, final answer content is obtained. And the accuracy and reliability of model output are improved.

The invention discloses a structure parameter library construction method, a super-lens design method and an optical system. The super-structure grating primitive structure parameter library construction method comprises the steps of obtaining a first grating primitive structure and determining a first periodic distribution function according to the first grating primitive structure; according to the characteristics of the grating primitive structure, full-analytic Fourier transform expansion is carried out on the first periodic distribution function, and a first Fourier analytic function corresponding to the first grating primitive structure is obtained; performing strict coupling wave analysis on the first Fourier analytic function in a preset polarization direction to obtain first complex amplitude information; and according to the first complex amplitude information, determining first matrix information used for describing a polarization behavior of a first grating primitive structure, and associating the first matrix information and the first grating primitive structure as first design information in a parameter library. The structural parameter library construction method provided by the invention is suitable for an application scene for processing polarization characteristics and phase information, and avoids segmentation or discretization.

The invention discloses a stereo matching method based on context geometric features and distortion parallax optimization, which comprises the following steps of: firstly, shooting a calibrated left picture and a calibrated right picture through a binocular camera, extracting multi-scale context features of the two pictures, and calculating the similarity between feature points according to the feature points of the left feature graph and the right feature graph under different parallaxes; the matching cost is the matching cost, so that the constructed geometric cube contains rich geometric matching information. Meanwhile, context features are copied and expanded into context feature cubes with the same size according to parallax, and rich context information is contained. The geometric cube contains matching conditions of feature points of left and right views with different parallax errors, cost aggregation is guided through context features of the left view, and context feature information, feature point cost matching information under different parallax errors and geometric information of adjacent feature points are preliminarily fused to improve cost matching reliability. And generating a geometric attention weight cube. And filtering the contextual feature cube.

The invention discloses a water quality detection method and system based on artificial intelligence. The method comprises the steps of data collection, data preprocessing, water quality pre-classification, water quality trending analysis, pollution traceability prediction and water quality detection. The invention relates to the technical field of water quality detection, in particular to a water quality detection method and system based on artificial intelligence, and the scheme adopts an artificial intelligence water quality detection method including water quality pre-classification, trend analysis and pollution tracing; performing water quality pre-classification by adopting a deep neural network method combining a genetic algorithm and a random forest model; performing water quality pre-classification and water quality trend analysis by adopting a deep neural network method combining a genetic algorithm and a random forest model; according to the method, data are classified in advance through a water quality pre-classification task, unpolluted water sources are eliminated, and then the calculation speed and the calculation effect of pollution tracing are improved through a causal enlightenment operator and an improved graph neural network in combination with a causal enlightenment graph neural network.

The invention relates to the technical field of electric power systems, in particular to an electric power system inertia evaluation method, device, equipment and medium based on a gradual difference method, and the method comprises the steps: carrying out the calculation through employing the gradual difference method for solving a frequency change rate after disturbance in the electric power system inertia evaluation; on the basis of the frequency change rate, an inertia estimation value is calculated by using a physical model derived from a rotor motion equation, and the actual inertia of the system is calculated by using an inertia theoretical value formula; constructing a neural network, dividing a training set and a test set, taking an output result of the physical model as input of the neural network, and evaluating a difference value between an inertia calculation value and a theoretical value by using the trained neural network; and obtaining a final power system inertia evaluation result by using the evaluated difference value and an inertia estimation value calculated by a physical model. According to the method, the inertia of the power system can be quickly and effectively evaluated through a physical model and data model combined mode, and the problem that the evaluation precision of a physical model is not enough is solved.

The invention discloses a sewage treatment high-density pool coagulation dosing prediction method, which accurately and quickly predicts the optimal time and the optimal dosage of coagulation dosing by relying on a resampling technology and an AO algorithm on the basis of collecting real-time and non-real-time sewage treatment data and further utilizing a data processing algorithm to carry out dimension reduction on the data. Important reference information is provided for real-time management and control of a sewage treatment plant, for example, application services such as resource management, water quality monitoring and sustainable water treatment can be realized.

The invention provides a multi-stage structure topological optimization method based on a potential diffusion model, and relates to the technical field of engineering design and structure design optimization. Creating a structure topology optimization image data set by using a traditional SIMP method and performing preprocessing; mapping the structure topological optimization image to a low-dimensional potential space for representation so as to reduce the data dimension and improve the calculation efficiency; a forward diffusion process and a reverse diffusion process are carried out on the initial potential variable in the low-dimensional potential pair, a denoising network is added in the reverse diffusion process, and a cross attention mechanism and a condition mechanism are introduced, so that a topological structure is adjusted and designed according to physical and functional requirements, and the structural fidelity is ensured; after training is completed, potential variables of a low-dimensional space are mapped back to a high-dimensional space, a topological structure is generated, few-step SIMP direct optimization is carried out on the topological structure, the structure is rapidly refined, physical information is directly injected into the design so as to adapt to given boundary conditions, and generation of the optimal topological structure can be completed through few-step iteration.

The invention belongs to the technical field of storage and calculation integration, and particularly discloses a three-dimensional storage and calculation integration system and an operation method thereof, and the system comprises an input module which has an input end connected to an output end of a main processor and is used for converting to-be-processed data into an analog voltage signal; the input end of the memristor array is connected to the output end of the input module, and the memristor array is used for carrying out matrix vector multiplication or matching operation on pre-stored operation data and the analog voltage signal and outputting a first operation result of the data to be processed; the input end of the sensing module is connected to the output end of the memristor array, and the sensing module is used for converting the first operation result into a corresponding digital signal based on the signal form of the first operation result; and the main processor is used for analyzing the digital signal and determining a second operation result of the data to be processed. According to the invention, the storage area and power consumption of the system can be effectively reduced, and the operation speed and efficiency of the system are improved.

The invention provides a wind power output analysis method based on WOA-K-means. A K-means algorithm is used as a basis, volatility and output efficiency are used as characteristic indexes to carry out dimension reduction processing on a daily output curve, an initial clustering center is optimized through global search capability of a WOA algorithm, a comprehensive difference distance suitable for processing the curve is obtained through shape and dimension fluctuation difference and generating capacity difference, and the comprehensive difference distance is used for the K-means algorithm to measure similarity of different samples. And finally, the clustering result is evaluated through the improved contour coefficient. Therefore, the problems that the traditional K-means algorithm is sensitive to the initial clustering center and the intrinsic characteristics of the daily output curve are difficult to reflect through the Euclidean distance are solved. The method has the advantages that the clustering result is high in accuracy and stability, the efficiency of the clustering algorithm is improved, the calculation speed of the clustering algorithm is increased, a systematic clustering analysis process is formed, and scientific and efficient clustering analysis of the wind power daily output data is achieved.

The invention discloses a ghost imaging target reconstruction method based on FSD-GIRA, and relates to the technical field of digital image processing. According to the technical scheme, a fuzzy clustering algorithm is used for classifying feature values of an optical field intensity measurement matrix of an optical path detector, the feature values are reconstructed through singular value decomposition, dimensionality reduction processing of measurement data of a ghost imaging system is achieved, and the ghost imaging target reconstruction method based on FSD-GIRA is obtained. A WE-FOMP-CG algorithm is proposed, and recovery of sparse signals is optimized by combining generalized orthogonal matching pursuit, a conjugate gradient method and a multi-threshold wavelet noise reduction method; and selecting the most relevant sensing matrix array in each iteration, solving a linear equation set by using PCG, carrying out wavelet preprocessing and multi-threshold de-noising on a result, and updating and recovering estimation. The calculation efficiency and the convergence speed are improved, and a more accurate recovery result is provided in a high-noise environment. The invention also provides a WBDH algorithm, and the algorithm employs a dual denoising mechanism, including wavelet transform and a BM3D method, so as to guarantee that the restored image details and structures are reserved, and further improve the reconstruction quality of the ghost imaging target.

The invention relates to the technical field of image processing and intelligent reading, in particular to a logistic regression-based PDF (Portable Document Format) file cross-page table merging and extracting method and system. The method comprises the following steps: firstly, loading a PDF file, analyzing line information in each page one by one, and constructing a transverse line set and a vertical line set of each page; calculating coordinates of intersection points of all the intersected transverse lines and vertical lines in the same page, constructing a four-dimensional matrix of each intersection point, restoring a table structure, and marking a table region page number and a context book; the four-dimensional matrix comprises four elements 0 or 1 in one-to-one correspondence with the intersection points in the up-down and left-right directions; traversing all the table areas of the PDF file, searching the table areas needing to be merged, and merging the table areas; and updating the combined table areas to a large-size page, updating the page number, and deleting the two combined table areas. The method comprises the steps of firstly analyzing lines, then transversely and vertically grouping and adjusting the lines, then merging the lines, and finally extracting intersection points according to the intersection condition of the lines; extraction of the table intersection points is guaranteed, contradictory errors possibly caused by simultaneous extraction of the lines and the intersection points are avoided, and improvement of accuracy and efficiency of subsequent table structure analysis is facilitated.

The invention discloses an echo segmented pulse compression method under a high sampling rate based on an FPGA (Field Programmable Gate Array). According to the method, down-sampling preprocessing is firstly carried out on the signals sampled by the ADC with the sampling rate of 124 MHz, the sampling rate is reduced to 62 MHz on the premise that spectrum aliasing does not happen to the signals, then segmentation range pulse compression is carried out on the 1PRT echo signals, the total processing amount is reduced by 40%, the data calculation amount can be remarkably reduced, the operation speed and the data processing efficiency can be improved, and the method is suitable for large-scale popularization and application. And FPGA logic resources are saved. And short-time 1s echo pulse compression preprocessing is realized by adopting a method of synthesizing 1000 PRTs, so that the communication data rate is greatly reduced. And in cooperation with operation of other components of the surface scene deformation monitoring system, the system reliability is improved, and the scene deformation monitoring task can be smoothly carried out.

The invention provides a loan farmer repayment capability assessment method, device and equipment, a medium and a product, and relates to the field of financial science and technology. Firstly, agricultural product data and a first image set of a planting field of a loan farmer are acquired; wherein the agricultural product data comprises at least one of planting area, planting variety, growth environment, climate condition and historical yield; inputting the first image set into a pre-trained diffusion model, and generating a prediction frame of agricultural product fruit detection and a category label of the agricultural product fruit; performing detection and counting according to the prediction frame and the category label to obtain the agricultural product yield of the borrowing farmer in the current time period; according to the agricultural product yield and the agricultural product data in the current time period, the agricultural product yield of the borrowing farmer in the target time period is predicted; and determining the repayment capability of the borrowing farmer according to the agricultural product yield in the target time period.

The invention provides a thermal runaway monitoring and early warning method and a thermal runaway monitoring and early warning system for a sodium-ion battery for electric energy storage. The method comprises the following steps: acquiring sampling data of a battery cell; according to the sampling data, generating a change curve of parameters such as battery surface temperature, voltage and capacity along with time and a change curve of battery temperature along with time at different heating rates; internal characteristic parameters and external thermoelectric data of the sodium-ion battery in the thermal runaway process are extracted according to the two change curves, a depth feature extraction model based on a multi-scale convolutional network is established, and thermoelectric data feature vectors of the sodium-ion battery in different thermal runaway stages are extracted; and generating a thermoelectric database according to the thermoelectric data feature vector, and predicting a temperature rise safety boundary of the battery cell to realize early warning of thermal runaway of the battery cell. According to the invention, the technical problems that the early warning threshold lacks dynamic adjustment and the early warning precision is limited are solved.

The invention belongs to the technical field of neural network acceleration design, and provides a low-bit neural network training method and an accelerator. The bit neural network training method and the accelerator are mainly characterized in the following three aspects. Firstly, a periodic function is used in the training process to approximate the gradient of an evidence obtaining function; secondly, the low-bit computing array is adjusted when the computing parallelism of the dimensions of the input channel and the output channel can be operated; and thirdly, the low-bit neural network accelerator uses full addition convolution. According to the bit neural network training method and the accelerator, four-bit wide operation is realized, relatively good network precision is maintained, relatively high calculation parallelism and calculation speed are achieved, calculation complexity and resource requirements are reduced, and the method and the accelerator are suitable for deployment of a resource-limited platform.

The invention relates to the technical field of voltage optimization control of a power system, in particular to a multi-agent self-discipline collaborative optimization method and system for multi-region reactive voltage control, and provides an optimal reactive voltage optimization decision method for establishing a data interaction model, setting a data interaction mode and researching a power distribution network. By optimizing distribution of reactive power, the active loss of the power distribution network is reduced, the economical efficiency of the power system is improved, and the service life of equipment is prolonged; by using a deep reinforcement learning algorithm, effective adjustment of voltage is realized, and node voltage is stabilized; a deep reinforcement learning method is adopted, so that the influence of model errors is reduced, the calculation speed and the real-time response capability are improved, and the calculation cost is reduced; through continuous interaction with actual power grid data, the intelligent agent can cope with the uncertainty of distributed new energy, energy storage and flexible load power generation and power utilization, and the stability and reliability of the system under various complex conditions are improved.

The invention discloses a method and equipment for replacing sea-gas coupling parameterization in an atmospheric waveguide forecast model based on physical constraint deep learning, and a storage medium. The method comprises the following steps: step 1, preparing training data; step 2, increasing physical constraint to improve generalization ability; step 3, constructing a network for calculation; according to the method disclosed by the invention, the occupancy rate of computing resources can be reduced, the computing speed and the model forecasting performance are improved at the same time, a new idea is provided for application of artificial intelligence in the meteorological field, and a new technical development direction is provided for innovation in the meteorological field.

The invention relates to a distributed system for solving a large-scale mathematical optimization problem and an implementation method, and aims to improve the calculation efficiency and the solving capability so as to meet the requirements of enterprises on efficient operation and accurate decision-making under the background of digitization, globalization and double carbon. According to the system, a complex optimization problem is decomposed into a plurality of small sub-problems, and the sub-problems are processed in parallel on a plurality of computing nodes, so that the solving process is accelerated. The system also adopts an innovative synchronization and communication mechanism to ensure the data consistency and calculation accuracy among the nodes. In addition, the invention further provides a self-adaptive load balancing strategy so as to optimize resource allocation and further improve the overall calculation efficiency. According to the method, data analysis, knowledge discovery and interdisciplinary research innovation are promoted while high-performance computing power is provided, and the method has important significance on improvement of enterprise competitiveness and scientific research.

The invention provides a power system intelligent fault diagnosis method and system based on data fusion, and relates to the technical field of intelligent monitoring. The method comprises the following steps: acquiring vibration signals and electric signals of rotating equipment of different fault types in real time by using a sensor, and correspondingly obtaining a vibration matrix and an electric matrix; respectively carrying out weighted combination on the vibration matrix and the electric matrix to obtain a vibration element confidence coefficient and an electric element confidence coefficient; performing normalization processing and fusion processing on the vibration element confidence coefficient and the electric element confidence coefficient to obtain a fusion matrix; performing dimension reduction processing on the fusion matrix by using a principal component analysis method to obtain fusion features; and inputting the fusion features into a back propagation neural network for training to obtain a diagnosis model used for diagnosing the fault type of the rotating equipment. According to the invention, the problems of single characteristic parameter, low mechanical fault diagnosis identifiability and large error of the fault diagnosis method of the rotating equipment are solved.

The invention provides a hypergraph-based weighted gene co-expression hypernetwork module identification method and system, and the method comprises the steps: obtaining a gene expression profile matrix, and integrating an effective gene set of each sample to construct a weighted hypergraph; calculating a Laplacian matrix of the weighted hypergraph, and obtaining a topological overlapping matrix based on the Laplacian matrix; and based on the topological overlapping matrix, performing module identification on the gene by adopting a hierarchical clustering method, and outputting a gene co-expression module. According to the method, the calculation efficiency is effectively improved by optimizing hypergraph construction and utilizing the hypergraph Laplacian matrix to carry out network analysis. Compared with a traditional WGCNA method, the WGCHNA shows higher calculation speed and processing capacity when processing a large-scale gene data set, so that the gene co-expression network can be analyzed more quickly, and the requirements of multiple samples and complex networks are met.

The invention provides a public building cold load prediction method, system, equipment and medium, and the method comprises the following steps: collecting cold load related data, carrying out the preprocessing, employing a random forest method to process the processed related data, and obtaining a model input data set; optimizing the SSA model by adopting the good point set, and optimizing the LSTM grid parameters based on the optimized SSA model to obtain an RF-I SSA-LSTM mixed cold model; based on the RF-I SSA-LSTM mixed cold model and a model input data set, obtaining a cold load prediction result; according to the method, evaluation index model establishment and characteristic value extraction of the data set are carried out on the related influence variables of the large building cold load based on the random forest method, the variables with high correlation degree are selected as the model input data set, the RF-ISSA-LSTM mixed cold model has better global search ability, the optimization result is more accurate, and the optimization efficiency is improved. The technical problem that the cold load of the public building cannot be accurately predicted in the prior art is solved.

The invention discloses a storage, calculation and training integrated cloud computing server optimization method based on a human brain neural network. The method comprises the following steps: S1, designing a self-adaptive learning model based on the human brain neural network; s2, forming an integrated framework; s3, adopting a novel processor design; s4, optimizing an adaptive learning model architecture of the human brain neural network through a deep learning technology; s5, dynamically adjusting distribution of computing resources based on the real-time data flow and the computing requirements; s6, realizing data transmission path optimization by designing an adaptive data routing algorithm; s7, self-optimization is realized in the cloud computing server by using a synaptic plasticity mechanism of the adaptive learning model, and the adaptive learning model can autonomously adjust parameters and optimize performance according to historical data and a current state; and S8, integrating an intelligent fault detection and repair module in the adaptive learning model. The system state is monitored in real time, faults are detected and repaired, and the reliability and stability of the system are improved.

The invention discloses a three-dimensional multi-seismic-source mixed collection seismic data rapid separation method based on prior information constraint, and the method comprises the steps: dividing three-dimensional seismic data into a series of slices along an x axis or a y axis, carrying out the multi-scale and multi-direction two-dimensional curvelet transformation of the three-dimensional seismic data to obtain a curvelet domain coefficient, and introducing a conventional iterative shrinkage threshold algorithm for separation; then, according to the characteristic that curvelet coefficient support sets of two adjacent slices have a large intersection, a priori information constraint condition is established in a curvelet domain by using a priori support set provided by a previous slice of which aliasing noise is removed, the constraint condition is fused into an iterative shrinkage threshold algorithm, and an estimation precision factor formula is proposed; and a novel index threshold parameter formula is adopted to perform multi-seismic source mixed collection seismic data separation. According to the method, the signal-to-noise ratio of the separated seismic data is increased, the calculation speed is increased, the requirement for a computer memory is lowered, weak effective wave signals are recovered, and the single-shot reflected wave event is more continuous and clearer.

The invention discloses a handwritten numeral recognition method based on a quantum neural network, a product, a medium and equipment, and relates to the technical field of quantum computing, and the method comprises the steps: obtaining image data of handwritten numerals; inputting the image data into a quantum neural network, and outputting a handwritten numeral recognition result; the quantum neural network is determined through a quantum optimization algorithm with the purpose of minimizing a cross entropy loss function; the quantum neural network comprises an input layer, a quantum layer, an activation layer and an output layer which are connected in sequence; the input layer is provided with a multi-quantum bit controlled Ry gate; the input layer is used for converting image data into a quantum image to obtain an image information quantum bit sequence. According to the method, the quantum superposition characteristic and the parallel computing characteristic of the quantum neural network can be fully utilized, so that the efficiency and the accuracy of handwritten numeral recognition are effectively improved.

The invention discloses a model processing method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a target running state data set of a target vehicle; labeling the target operation state data set to obtain a target data set; matching a corresponding reference model from a preset model library according to the target task type, and selecting to-be-trained data from a target data set according to the target task type and the reference model; training the reference model through the to-be-trained data, comparing an output result with an evaluation result of a truth value system, and correcting the reference model according to a comparison result to obtain a target model. According to the method, the reference model is trained according to the multi-source data set of the target vehicle, the reference model is corrected through the truth value system, the target model is obtained, the problem that the weather condition and the driving road condition in the driving process of the target vehicle cannot be comprehensively considered in the prior art is solved, and the detection capacity of the target model is improved.

The invention discloses a three-dimensional path planning method for a cardiac interventional surgical robot and a computer program product, and the method comprises the steps: dynamically adjusting and updating a three-dimensional model in real time according to real-time environment detection and image information in an operation; calculating an optimal three-dimensional path of the surgical robot through an improved genetic algorithm according to the three-dimensional model and the real-time position of the surgical robot; a fitness function F preset by the genetic algorithm considers the path length, the obstacle avoidance capability and the operation safety, and in different stages of the operation, different optimization objectives are emphasized by adjusting values of consideration factor weights. According to the method, the optimal three-dimensional path of the surgical robot is calculated through the genetic algorithm considering the path length, the obstacle avoidance capacity and the surgical safety, intelligent and precise planning of the surgical path is achieved, the method has the intraoperative real-time feedback and dynamic adjustment capacity, and the surgical accuracy and safety are improved.

The invention discloses an excitation system accurate modeling parameter identification optimization method, which belongs to the technical field of power system operation and maintenance, and comprises the following steps of: dividing an iteration process of a cystoid colony algorithm into two stages, and adjusting a basis for updating positions of cystoid individuals in the iteration process by utilizing a random selection method; the optimal cystoid individual position is replaced by the random cystoid individual position, the problem that an algorithm is converged to a local optimal solution in a traditional method is avoided, the search capability and flexibility of the algorithm are improved, and therefore the accuracy and calculation speed of parameter identification of the generator excitation system are improved. Therefore, the method provided by the invention has good practical application value.

The invention discloses a sea area natural gas hydrate and underlying gas multi-gas commingling production numerical simulation method, which comprises the following steps of S1, selecting a target block, and enabling natural gas hydrate and underlying gas in the target block to be symbiotic; s2, establishing a multi-gas commingling production numerical simulation physical model of the natural gas hydrate and underlying gas in the target block, and determining simulation initial parameters; s3, mesh generation is carried out, and boundary conditions are set; s4, setting a shaft, perforation information and a production system; and S5, solving the established model and carrying out sensitivity analysis on a result. According to the numerical simulation method provided by the invention, the resource conditions of multi-gas coupling symbiosis of the sea area natural gas hydrate and underlying gas are comprehensively considered, the process is clear and simple, the operability is high, and the method also has universality in other natural gas hydrate development areas.

The present application relates to a kind of lake reservoir type chlorophyll concentration prediction method, device and medium, method includes the following steps: obtaining the water quality, water dynamics and weather of the online automatic monitoring data of lake reservoir including forecast point and upstream point;The online automatic monitoring data is preprocessed, and the data after processing is obtained;The data after processing is carried out feature extraction to obtain the water quality feature and water dynamics index feature of forecast point and upstream point, and construct cumulative illumination feature;Upstream water quality feature, water dynamics index feature and illumination feature are input into the fusion prediction model pre-trained, to obtain upstream transport chlorophyll prediction result and time series influence chlorophyll prediction result, using error reciprocal method upstream transport chlorophyll prediction result and time series influence chlorophyll prediction result are fused;The chlorophyll prediction result obtained by fusion is output.Compared with prior art, the present application has the advantages of high accuracy, strong stability and the like.