124results about How to "Enhanced description ability" patented technology

The invention discloses a large container crane intelligent early warning system based on personnel behavior analysis, and the system comprises the following steps: collecting operator behaviors, equipment operation states and environment parameters in real time through a multi-mode sensor network, constructing a dynamic data set, extracting behavior characteristics through a time difference embedding algorithm, and carrying out the early warning of the operator behaviors, the equipment operation states and the environment parameters; a multi-level spatial-temporal characteristic matrix is generated in combination with dynamic scene influence factors, regional risk factors are dynamically calculated by using a Bayesian inference model, comprehensive risk values are generated through an adaptive multi-objective optimization algorithm, risk levels are divided into low, medium and high classes, and a system triggers graded early warning responses according to the risk levels. And an optimized behavior adjustment suggestion is generated in combination with a quantum probability behavior prediction algorithm, and a feature extraction rule and risk assessment logic are dynamically optimized through a progressive learning algorithm to form a closed-loop optimization process. According to the invention, intelligent analysis and real-time early warning of personnel behaviors are realized, and the safety and adaptability of the crane in an operation environment are improved.

The invention provides a remote acquisition system for operation data of gas equipment, and relates to the technical field of data processing, and the remote acquisition system comprises the steps: capturing the time-varying rule of the state of the gas equipment through learning the long-term dependency relationship in a sequence, and generating key features containing time sequence information; fusing the time sequence characteristic representation output by the long short-term memory network with the equipment attributes and relationships in the knowledge graph to form a characteristic vector reflecting the comprehensive state of the gas equipment; comprehensively analyzing the feature vector to obtain a hidden mode related to the risk of the gas equipment; and based on the hidden mode related to the gas equipment risk, adopting a multi-classification support vector machine to predict the risk level so as to obtain a final risk level prediction result. According to the invention, risk assessment of the operation state of the gas equipment can be realized, so that the safety and the operation efficiency of the equipment are improved.

The invention provides a motor stator vibration suppression method and system based on an electromagnetic pad and deep learning, and relates to the field of electrical technology, and the method comprises the steps: collecting a vibration signal of a motor stator, and carrying out the preprocessing of the vibration signal; inputting the preprocessed signal into a deep learning model, and training and optimizing the deep learning model; fusing the features through a full connection layer to obtain a comprehensive feature vector; predicting the future vibration state of the motor stator by using the comprehensive feature vector; establishing an electromagnetic force model, and designing an adaptive control law based on the predicted future vibration state and the electromagnetic force model; electromagnetic force required by vibration suppression is calculated by using a self-adaptive control law and is converted into a control signal to adjust the electromagnetic pad; a multi-objective optimization algorithm is used to consider the vibration suppression effect and energy consumption at the same time, an adaptive control law is adjusted, and closed-loop feedback control is formed to continuously optimize the vibration suppression effect.

The invention belongs to the field of power system cascade fault prediction, and relates to a graph neural network power grid cascade fault prediction method based on physical information guidance, and the method comprises the steps: obtaining the operation data of a cascade fault, inputting the operation data of the cascade fault into a trained power grid cascade fault prediction model, and obtaining a prediction result; the power grid cascade fault prediction model comprises a common embedded graph neural network and a structure coding module; according to the invention, data enhancement is carried out by using a multi-step random walk matrix considering power grid line attributes and structure coding represented by node degrees, so that the capability of capturing topological information is improved; according to the invention, the interaction between the power grid nodes and the power grid lines is promoted by using the co-embedded graph neural network, and the accuracy of power grid cascade fault prediction is improved; according to the method, the node-edge interaction aggregation function of the co-embedded graph neural network is changed through the power transmission distribution factor of the power grid, the description capability of the actual complex interaction relation of the power grid is improved, and the accuracy of power grid cascade fault prediction is improved.

The invention relates to the technical field of pipe network monitoring, in particular to a water supply pipe network pipe burst alarm positioning system based on real-time pressure monitoring data, and the system comprises a pressure data collection module which collects pressure data from a plurality of pressure monitoring points of a water supply pipe network; the pressure drop threshold calculation module is used for determining an alarm pressure drop threshold of each pressure monitoring point based on historical pressure data; the voltage drop alarm module is used for calculating a current time window voltage drop value and comparing the current time window voltage drop value with a voltage drop threshold value; the association alarm module is used for clustering alarm information of adjacent pressure monitoring points and combining a plurality of alarm points which are close in space into a single alarm event; the alarm positioning module is used for matching a pipe explosion node corresponding to the current alarm information by utilizing a mode recognition algorithm and quickly positioning the pipe explosion node; according to the method, the system calculates the aggregation characteristics, the description capability and the credibility of the alarm event are enhanced, and the identification and positioning efficiency of the system on the pipe explosion event in the complex pipe network environment is improved.

The invention relates to the field of computer vision and artificial intelligence, in particular to a real-time multi-person motion analysis system based on motion perception and semantic understanding, which comprises a motion perception module, a motion tracking module, a motion semantic understanding module and a motion performance analysis module. The motion area segmentation unit adopts a background modeling or inter-frame difference method to identify a motion area and generate a motion mask; the target detection unit is connected with the central processing unit through a data bus and used for processing video frame data and detecting the position and the bounding box of a moving target. The motion area segmentation unit is connected with the target detection unit and is used for generating a motion mask. By integrating a plurality of modules, the motion data can be comprehensively captured and analyzed, and the accuracy and real-time performance of motion analysis are improved. Through combination of the target detection unit and the motion area segmentation unit, the system can accurately identify a motion target and a motion area, and a solid foundation is laid for subsequent tracking and analysis.

The invention discloses a word document-based knowledge graph generation method and apparatus, and an electronic device, and relates to the field of data processing. The method comprises the following steps of: analyzing a target document to obtain a corresponding xml (Extensible Markup Language) file; the xml file is analyzed to obtain a first data structure, and the first data structure comprises a plurality of elements; in the first data structure, sorting the plurality of elements according to a preset sorting rule to obtain a second data structure; segmenting the second data structure by adopting a preset recursive function to obtain a plurality of entities and a relationship among the plurality of entities; and taking the plurality of entities as nodes of a preset knowledge graph, and taking the relationship among the plurality of entities as edges of the preset knowledge graph to obtain a target knowledge graph. According to the technical scheme provided by the invention, the document content is converted into the XML format for recognition and extraction, so that the accuracy of converting the document into the knowledge graph is improved.

The invention relates to the technical field of logistics management, in particular to an in-transit supervision method for large-size transportation on an expressway. The method comprises the following steps: acquiring transportation speed data and transportation vibration data; carrying out transportation speed feature extraction on the transportation speed data to obtain transportation speed feature data, and carrying out transportation vibration feature extraction on the transportation vibration data to obtain transportation vibration feature data; performing feature map construction on the transportation speed feature data and the transportation vibration feature data to obtain transportation speed vibration feature map data; abrupt change feature collection and change degree extraction are carried out according to the transportation speed vibration feature map data, and transportation abrupt change feature data and transportation change degree data are obtained so as to carry out large piece transportation anomaly detection operation. According to the invention, the safety and the management level of the large-piece transportation process are effectively improved.

The invention provides a vibration analysis method and system for an intermediate bearing in a double-rotor system. The method comprises the following steps: determining a rolling body entering a defect area at the current moment and a rolling body with the maximum contact deformation based on bearing parameters, an operation state, defect information, contact rigidity and damping parameters; respectively calculating the time-varying displacement and impact force of the rolling body entering the defect area and the rolling body with the maximum contact deformation, and the angle difference between the time-varying displacement and the impact force; the impact force is corrected through the angle difference between the rolling body entering the defect area and the rolling body with the largest contact deformation and serves as external force input of a nonlinear kinetic equation of an intermediate bearing system of the double-rotor system; and calculating a nonlinear bearing force in the nonlinear kinetic equation based on the time-varying displacement of the rolling body entering the defect area and the rolling body with the maximum contact deformation. And solving the nonlinear kinetic equation. According to the method, the vibration analysis precision under the condition of the defect of the intermediate bearing in the double-rotor system is improved.

The invention relates to the technical field of image generation, and discloses an arc-shaped screen multi-view 3D image generation method and device, and the method comprises the steps: collecting a view parameter set of an arc-shaped screen, and carrying out the residual feature processing of an original image sequence, and obtaining a multi-dimensional space feature map; performing tensor product operation on the multi-dimensional spatial feature map and the view angle parameter set to obtain view angle conversion delay data; performing graph convolution operation to obtain space-time dependence features; performing multi-head self-attention calculation on the space-time dependency features to obtain delay compensation features, and performing comparative learning on the view angle features based on the delay compensation features to obtain view angle mapping parameters; the view angle mapping parameters, the delay compensation features and the original image sequence are input into a U-Net network for 3D image reconstruction, and a 3D image sequence with continuous view angles is output, the feature consistency in the view angle conversion process is improved, image distortion during view angle switching is reduced, and therefore the visual quality of 3D images is improved.

The invention relates to an ionized layer TEC determination method and system based on satellite-ground active measurement and SAR image joint inversion. The method comprises the steps that S1, a mapping relation database of ionized layer scattering characteristics measured by a dual-frequency SAR satellite, ionized layer penetration characteristics measured by satellite-ground active measurement and ionized layer TEC measured by a GPS receiving station is established; s2, establishing and training a TEC inversion deep learning model by using a multi-source amplitude-phase parameter neural network according to the mapping relation database, and outputting a corresponding TEC value by the TEC inversion deep learning model by taking an ionized layer amplitude-phase parameter as an input; the ionosphere amplitude-phase parameters are ionosphere scattering signal amplitude-phase parameters and / or ionosphere transmission signal amplitude-phase parameters; and S3, performing on-orbit intelligent rapid inversion on the ionized layer TEC over the globe according to the TEC inversion deep learning model. According to the method, the TEC inversion model based on SAR satellite measurement data is established, the method has the advantages of high precision and high timeliness, and high-precision inversion of any space-time TEC can be realized.

The invention discloses a phishing webpage detection method fusing URL (Uniform Resource Locator) characters and HTML (Hypertext Markup Language) content semantics, which comprises the following steps of: finely extracting URL character-level local features based on a multi-scale convolution strategy, processing URL character strings in parallel by using CNNs (Convolutional Neural Neural Neural Neural Neural Neural Neural Neural Neural Neural Neural Neural Neural Neural Neural Neural Neural Neural Neural Neural And carrying out HTML text word-level feature extraction based on a cascade dense connection CNN architecture. A plurality of convolution layers are stacked, dense connection is established between convolution blocks, and deeper and fine-grained semantic features are extracted; the URL feature characters and the HTML features are fused through a full connection layer, and the corresponding probability is calculated to judge whether the webpage is a phishing webpage or not. According to the method, the advantages of the deep learning algorithm are fully utilized, the characteristics of the URL characters and the HTML content of the phishing webpage are integrated, more comprehensive semantic representation is formed, and the recognition rate of phishing webpage detection is greatly improved.

The invention discloses a new energy power generation scene generation method and system considering uncertainty, and relates to the technical field of new energy power scene generation, and the method comprises the steps: carrying out the modeling through combining the error fluctuation of Bayesian optimization and kernel density estimation; modeling is carried out based on the error time sequence of the Markov chain; and based on a new energy prediction error modeling process, generating a new energy power sequence scene, and analyzing prediction error characteristics. According to the new energy power generation scene generation method considering uncertainty provided by the invention, the output interval of each prediction box is determined through the Bayesian optimization method, the utilization efficiency of data in the prediction boxes is improved, the prediction error in each prediction box is fitted through the kernel density estimation method, the adaptability of the model is enhanced, and the generation efficiency of the new energy power generation scene is improved. The time sequence correlation of the prediction error is modeled through the Markov chain, the time sequence consistency of the generated scene is enhanced, and the method achieves better effects in the aspects of utilization efficiency, adaptability and time sequence consistency.

This invention discloses a knowledge- and data-driven time series forecasting method and system, belonging to the field of industrial time series forecasting technology. To address the technical problems of existing technologies when processing strongly non-stationary data, such as key signal attention dilution, systematic underestimation of peak amplitude, incomplete capture of multi-scale dynamic patterns, and difficulty in embedding physical domain knowledge, this invention constructs standardized multi-dimensional time series data, performs heterogeneous feature grouping and parallel encoding to obtain multiple sets of feature representations, then extracts and integrates features from multiple sources to obtain intermediate state features, and finally performs multi-branch prediction and adaptive weighted fusion based on the intermediate state features to obtain the final time series forecast result. This invention can achieve active focusing on abrupt events in industrial sequences and accurate characterization of extreme value amplitudes, significantly improving prediction accuracy, physical consistency, and robustness to extreme conditions.

The application provides a data analysis system and method for vehicle electrical system fault diagnosis, which extracts multi-dimensional feature indexes representing the running state of the electrical system from the running data of the electrical system under different working conditions; performs credibility evaluation on the corresponding multi-dimensional feature indexes under each working condition, and determines the credible evidence thereof, and then determines the credibility difference of the multi-dimensional feature indexes under different working conditions from all the credible evidence; performs difference analysis on the data collected under different working conditions according to the distribution correlation of the multi-dimensional feature indexes in each running data, and obtains the consistency difference of the multi-dimensional feature indexes under different working conditions; and performs feature fusion on all the collected running data according to the credibility difference and the consistency difference, and generates a fusion feature parameter capable of reflecting the running state of the vehicle electrical system. The application scheme can realize the fusion of the electrical system state features through the credibility difference and the consistency difference of the features under multiple working conditions, thereby improving the robustness of the electrical system fault diagnosis.

The application discloses an emotion recognition method based on online cross-modal knowledge distillation, comprising the following steps: acquiring electroencephalogram and electrocardiogram original signals and windowing and cutting; constructing electroencephalogram and electrocardiogram student models, extracting intermediate features from each modal data segment through an encoder, and obtaining non-normalized prediction output through a classifier; constructing a teacher probability distribution through a joint encoder fusion; introducing adaptive contrast loss to align the cross-modal intermediate features, introducing distillation loss to constrain the prediction probability distribution of each modal to align with the teacher probability distribution; synchronously optimizing new student model parameters through online collaborative training; and performing actual inference prediction based on the student model after training. The application combines double modal signals to make up for the defects of single modal information, excavates the complementarity of modes, realizes dynamic generation of teacher supervision signals and real-time collaborative learning of modes through online distillation, does not increase test calculation overhead, effectively improves the recognition accuracy, model robustness and generalization ability, and has good application prospect.

The application provides a drought propagation analysis method and system considering multi-dimensional characteristics of drought events. The method comprises the following steps: calculating a drought duration propagation index based on a meteorological drought total duration and an agricultural drought total duration; calculating a drought intensity propagation index based on meteorological drought intensities of previous meteorological drought events and agricultural drought intensities of previous agricultural drought events; calculating a drought intensity trend propagation index based on meteorological drought intensity trends of previous meteorological drought events and agricultural drought intensity trends of previous agricultural drought events; calculating a drought fluctuation propagation index based on meteorological drought index fluctuation rates of previous meteorological drought events and agricultural drought index fluctuation rates of previous agricultural drought events; calculating a drought probability propagation index based on a meteorological drought occurrence probability and an agricultural drought occurrence probability of a target region within a given time period; and constructing a comprehensive drought propagation index by using a CRITIC objective weighting method. The application can accurately describe the propagation process between meteorological drought and agricultural drought.

The invention relates to the technical field of virtual power plant operation and optimization control, in particular to a virtual power plant-oriented multi-data-source fusion multi-energy load prediction method, which comprises the following steps of: constructing aggregated multi-source heterogeneous data of a virtual power plant; the data comprises historical multi-energy load data, calendar and market information and external environment auxiliary data in the jurisdiction of a virtual power plant, preprocessing the multi-source heterogeneous data, performing feature screening on the time sequence feature matrix based on correlation analysis, constructing a model input set, constructing a load prediction model, and predicting the load of the power plant. The model comprises an input layer, a plurality of stacked space-time fusion layers and an output layer which are connected in sequence; the space-time fusion layer comprises a multi-scale time convolution module and a graph convolution module which are parallel, and is used for model training and parameter optimization, load prediction and result output; and combined prediction of the electric load, the thermal load and the cold load is realized under a unified multi-scale space-time diagram neural network framework.

The invention belongs to the field of network social group analysis, and provides a multi-expert collaborative network social group description method based on modal dynamic fusion. The method comprises the following steps: firstly, inputting multi-modal original data of a network social group, and respectively capturing multi-modal private feature representations through a modal representation adaptive extraction module; then, deep fusion of cross-modal information is realized by utilizing a bidirectional state space model through a modal complementary information fusion module, and unified group multi-modal fusion representation is generated; thirdly, a KAN multi-expert network architecture is introduced through a multi-expert collaborative prediction module, different experts are adapted to diversified group characteristic modes such as mainstream, small crowds and temporary groups, and dynamic weights are calculated through a gating network to achieve accurate fusion of expert output; and finally, in combination with long-tail boundary perception loss function optimization model training, outputting a multi-dimensional feature description result of the group. According to the method, more comprehensive and accurate group feature representation can be obtained, and the depicting performance of the network social group in a complex scene is improved.

The application relates to the technical field of battery health management, and discloses a lithium battery SOH prediction method and system based on a Koopman gated residual neural network, which comprises the following steps: acquiring electrochemical impedance spectrum data of a lithium battery; based on an equivalent circuit model, first physical characteristics are fitted and extracted from the electrochemical impedance spectrum data, and a characteristic frequency point is determined; an electrochemical impedance imaginary part value at the characteristic frequency point is extracted as a second frequency domain characteristic; based on the first physical characteristics, an initial prior estimation value of a battery health state is generated, and the initial prior estimation value is dynamically trend-corrected to obtain a corrected prior estimation value; the first physical characteristics, the second frequency domain characteristic and the corrected prior estimation value are input into a pre-trained Koopman gated residual neural network model, and an adaptive gated fusion mechanism of the model is used to output a final prediction value of the battery health state. The application realizes relatively accurate and high-robustness online prediction of the battery health state.

This invention provides an artificial intelligence-based internet property rights transaction risk assessment and decision-making system, relating to the field of data processing technology. The system includes: a data acquisition and preprocessing module for real-time acquisition of raw transaction data, followed by cleaning and standardization; a multi-dimensional feature construction module for modeling by time windows and constructing a multi-level interaction relationship graph to obtain time-series feature data and graph-related feature data; and for normalizing and weighting the features; a risk identification module for extracting suspicious transfer behavior chains and tracing the flow of benefits, performing composite risk discrimination; and an intelligent decision-making module for performing hierarchical adaptive discrimination and case migration comparison, summarizing and generating risk assessment results and pushing early warnings; and receiving feedback from the management terminal, which is then used to optimize identification and decision-making parameters. This invention improves the autonomy and accuracy of property rights transaction risk assessment and decision-making.

The invention discloses an electrochemical device residual life prediction method based on a fusion model. The method comprises the following steps: S1, acquiring historical operation data of an electrochemical device, performing correlation analysis on the historical operation data, and screening out parameters strongly related to life attenuation as performance degradation indexes; s2, preprocessing the data of the performance degradation index; s3, predicting a degradation track of the preprocessed performance degradation index through a data-driven prediction model, and dynamically correcting the degradation track by fusing a physical experience correction model to obtain a degradation track correction fusion model as shown in a formula (1-1); and S4, determining the residual service life of the electrochemical device based on the corrected degradation track and a preset failure threshold value. According to the method, signal processing, machine learning optimization and an empirical model are fused, so that the residual service life of the electrochemical device is quickly and accurately predicted.

This invention discloses a video summarization method based on two-layer routed sparse attention and spatial pixel recalibration, belonging to the field of computer vision. The method includes: reading the input video and extracting frame-level feature vectors; constructing a video summarization generation model, performing channel enhancement on the frame-level feature vectors to obtain enhanced features; performing two-layer routed sparse attention and spatial pixel recalibration on the enhanced features, and fusing them to obtain fused features; inputting the fused features into an importance scoring regression network, outputting frame importance scores, and selecting frames to generate video summaries. This invention effectively fuses global dependencies and local details by combining two-layer routed sparse attention and spatial pixel recalibration, accurately identifying key segments in the video while reducing computational complexity. Experimental results based on the SumMe and TVSum benchmark datasets demonstrate that the method of this invention exhibits good performance in video summarization tasks.

The invention discloses an emotion recognition method based on online cross-modal knowledge distillation. The emotion recognition method comprises the steps that electroencephalogram and electrocardio original signals are obtained and subjected to window segmentation; constructing an electroencephalogram and electrocardio student model, extracting intermediate features from each modal data segment through an encoder, and obtaining non-normalized prediction output through a classifier; teacher probability distribution is constructed through joint encoder fusion; self-adaptive comparison loss alignment cross-modal intermediate features are introduced, and distillation loss is introduced to constrain prediction probability distribution of each modal to align to teacher probability distribution; new student model parameters are optimized synchronously through online cooperative training; and performing actual reasoning prediction based on a student model after training. According to the method, bimodal signals are combined to make up single-modal information defects, modal complementarity is mined, teacher supervision signal dynamic generation and modal real-time collaborative learning are realized through online distillation, test calculation overhead is not increased, identification precision, model robustness and generalization ability are effectively improved, and the application prospect is good.

The invention relates to the technical field of model training optimization, and discloses a new energy truck carbon emission model optimization method and system based on a neural network, and the method comprises the steps: collecting the carbon emission influence characteristics of a new energy truck and the historical record of the carbon emission; performing response trend analysis on each carbon emission influence characteristic, and identifying reversible influence characteristics in the carbon emission influence characteristics; constructing a carbon emission prediction model and defining structural parameters; searching a group of reference structure parameters for the carbon emission prediction model based on an EPO algorithm; and carrying out iterative training and verification tuning on a carbon emission prediction model based on the reference structure parameters. The new energy truck carbon emission prediction model is improved in the aspects of input feature compression, structural parameter optimization and abnormal state self-adaption, and the prediction accuracy of carbon emission is enhanced.

This invention discloses an IoT-based Bluetooth verification system for slow-moving traffic vehicles, comprising: a Bluetooth acquisition module for collecting vehicle Bluetooth information; an association and processing module for generating associated vehicle Bluetooth information and acquiring reported vehicle information; a data filtering module for generating filtered vehicle Bluetooth information; a behavior generation module for inputting the filtered vehicle Bluetooth information into an improved SNN model, introducing a spatiotemporal impulse latent trajectory generation mechanism to generate vehicle Bluetooth behavior information; an information comparison module for generating vehicle status; a violation identification module for using the LOF algorithm and introducing a spatiotemporal neighborhood dispersion calibration mechanism to generate violation identification results; and a work order processing module for updating and outputting vehicle status. This invention achieves continuous behavior modeling and accurate violation identification of slow-moving traffic vehicles, improving the accuracy and processing efficiency of vehicle status determination.

The present application provides an asphalt pavement apparent disease evolution deduction method, system, device and medium, which belongs to the field of road engineering, transportation infrastructure operation and maintenance and intelligent detection technology, and comprises the following steps: constructing a unified space-time reference system, aligning and normalizing multi-source data in space-time; forming section-level disease quantification characteristics based on the recognition results of the inspection images and the structural indexes; event coding the maintenance measures of the road surface and pre-processing the section-level disease quantification characteristics corresponding to the time of the maintenance measures; constructing the correlation, time lag effect and spatial consistency between the disease types, disease characteristics and structural indexes to generate the collaborative evolution characteristics of the coupling relationship between diseases; constructing a deep time series prediction model to obtain the disease development trend at a specified time scale in the future, and forming the section-level disease evolution prediction result. The present application can depict the disease development law at different road sections and different time scales, and predict the future disease expansion.

The present invention discloses a twin network target tracking algorithm with enhanced positioning quality information, including: determining the target, obtaining the first frame image, determining the annotation box using known information, cropping the target area, and obtaining the target template; similarity comparison, obtaining the current frame, using the feature extraction subnetwork to obtain the feature map of the target and the search area and perform similarity comparison to obtain a similarity matrix; target positioning, the fusion classification branch performs the classification task based on the similarity matrix, and outputs the similarity prediction score; boundary prediction, the regression branch predicts the target boundary based on the similarity matrix, and selects the best prediction box in combination with the similarity prediction score; outputs the tracking result, outputs the target boundary box, and performs target tracking for the next frame. The present invention can improve the classification and boundary prediction process, introduces the CNN network to make the model more fully utilize the semantic information in the feature map, and the prediction result has a higher credibility.

The invention belongs to the technical field of compound microorganism anaerobic digestion process simulation, and particularly relates to a Logo-based compound microorganism anaerobic digestion process simulation method and a Logo-based compound microorganism anaerobic digestion process simulation system, each microorganism in compound microorganisms is regarded as Turbles in Logo, and nutrient substances are regarded as Patches in Logo; setting initial parameters of microorganisms and nutrient substances; performing an anaerobic digestion process by the compound microorganisms according to a self-adaptive interaction rule; counting and displaying the change of microbial biomass, the change of nutrient substances and the change of metabolites in the anaerobic digestion process of the compound microorganisms; according to different display results, parameters are adjusted, and simulation is repeated; and optimal initial parameters of microorganisms and nutrient substances are selected to guide an actual compound microorganism anaerobic digestion experiment. According to the invention, part of limitations of a heat production model in a compound bacterium pretreatment process in simulation of complex system characteristics of microorganisms in the prior art can be overcome.

The application provides a kind of source load power prediction method, device and electronic equipment, the method comprises: obtaining source load power data and multiple covariant data, source load power data is fused with multiple covariant data, and the multiple source time series data set after fusion is obtained;Determine the optimal lag time that each meteorological factor in the multiple source time series data set after fusion influences source load power, and determine the input time window length according to the optimal lag time;According to the input time window length, input sequence is extracted from the multiple source time series data set after fusion, and the input sequence is input into source load power prediction model to obtain the source load power prediction result of future time period.The application can solve the problem that the existing source load power prediction scheme is insufficient in describing the space-time coupling relationship between source load power and multiple covariants, and it is difficult to accurately extract key information, resulting in poor prediction stability.The application directly outputs the source load power prediction result of future time period through source load power prediction model, without relying on error correction, thereby improving the stability and reliability of prediction.