390 results about "Support vector machine algorithm" patented technology

A water-turbine generator set voiceprint monitoring and diagnosing method comprises the steps that a multi-channel voiceprint collecting system is adopted, a plurality of voiceprint sensors are arranged at key parts of a water-turbine generator set, and voiceprint signals at different positions are obtained; performing time-frequency analysis on the pre-processed voiceprint signal by using wavelet transform, and extracting voiceprint features to obtain a voiceprint feature vector; if the matching degree of the voiceprint feature vector and the features in the fault feature library exceeds a preset threshold value, determining the fault type and the fault degree; classifying the determined fault type and fault degree by adopting a support vector machine algorithm to obtain a fault diagnosis result; a fault diagnosis result is optimized through a deep learning network, the diagnosis precision is improved, and a final fault diagnosis report is obtained; performing association analysis on the fault diagnosis report and the equipment operation parameters to generate an equipment operation state evaluation report; according to the equipment operation state evaluation report, an equipment maintenance plan is made, and intelligent maintenance of the water-turbine generator set is achieved.

The invention relates to the technical field of intelligent fault diagnosis, and discloses a fault diagnosis method for new energy power generation equipment, and the method comprises the steps: obtaining a multi-physical-quantity time dynamic data set; outlier elimination is carried out on the data set, and a deviation degree sequence of each physical quantity is extracted; based on the deviation degree sequence, normalization processing is completed, and a correlation matrix among multiple physical quantities is constructed; generating a preliminary network structure through correlation screening and symmetry completion; calculating a path weight based on the initial network structure and performing topology reconstruction to obtain a final network topology structure; performing deviation propagation analysis according to the final network topology structure, and determining potential fault position distribution; based on the fault position distribution, performing fault grade classification by adopting a support vector machine algorithm, and outputting a fault risk grade label; and in combination with the fault level label and the network topology structure, node risk scoring and area identification are executed, and finally fault positioning is completed. According to the method, accurate positioning of the fault position in a complex system can be realized.

The invention discloses a geological disaster early warning method and system based on big data analysis, and particularly relates to the technical field of geological disaster early warning. Geological deformation time series data of a target area are obtained through satellite remote sensing monitoring, feature importance is evaluated through a random forest algorithm, and regional geological risk grades are divided; meanwhile, wavelet transformation and long-short-term memory network modeling are carried out on rainfall spatio-temporal data of the meteorological monitoring station, and the potential influence of rainfall is evaluated; high-risk areas are screened, an underground water level change rate and rock-soil body strength correlation model is constructed, and a support vector machine algorithm is adopted to evaluate the landslide probability; and finally, analyzing a geological risk grading result, the potential influence degree of rainfall on the target area and the landslide occurrence probability by adopting an analytic hierarchy process, calculating a comprehensive risk index, comparing the comprehensive risk index with a comprehensive risk threshold value, and judging whether to trigger grading early warning, thereby realizing data fusion and intelligent analysis, early warning in advance, and improving the early warning efficiency. And effective help is provided for disaster prevention and reduction.

The invention discloses an intelligent construction method based on data driving, and relates to the field of intelligent construction, and the method comprises the steps: collecting multi-source heterogeneous data of an intelligent construction project; performing feature selection on the acquired multi-source heterogeneous data by adopting a recursive feature elimination (RFE) algorithm to obtain a feature subset reflecting project progress, quality and safety; according to the feature subset, a multi-target model used for predicting project progress, quality and safety is constructed through a support vector machine (SVM) algorithm; a non-dominated sorting genetic algorithm NSGA-II is adopted to solve the multi-target model, a simulated annealing strategy is introduced in the iterative search process of the NSGA-II to jump out of a local optimal solution, and an optimized Pareto optimal solution set is output; and selecting an optimal parameter combination according to the Pareto optimal solution set, and adjusting the BIM model of the current intelligent construction project by using the optimal parameter combination. In view of low multi-objective optimization efficiency in intelligent construction project management in the prior art, the optimal model parameters considering different management objectives are obtained, and the efficiency is improved.

The invention discloses an abnormity early warning method and system based on artificial intelligence, and relates to the technical field of abnormity early warning. The method comprises the following components: S1, brain wave data acquisition and preprocessing, S2, emotional state recognition and feature extraction, S3, specific business data acquisition and arrangement, S4, data fusion and abnormal feature construction, and S5, abnormal early warning decision and notification. According to the method, the brain wave emotion features and the specific business data are fused, abnormal feature construction is carried out by adopting a fusion algorithm of multi-scale feature matching and a support vector machine algorithm based on quantum annealing optimization, the abnormal feature rule can be mined more accurately through the comprehensive data processing and analysis mode, and the accuracy of abnormal feature extraction is improved. Therefore, in the real-time data monitoring and analysis process, the accuracy of abnormal early warning is improved, meanwhile, the system can dynamically adjust the monitoring frequency according to the fluctuation characteristics of the data, the timeliness of early warning information is ensured, and related personnel can take countermeasures in time.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle battery endurance flight capability prediction system, which comprises a multi-dimensional data acquisition module, a feature mapping module, a prediction module, an optimization module and a feedback optimization module, and can be additionally provided with an early warning module. The multi-dimensional data acquisition module acquires battery data and cleans the battery data to generate standardized data; the feature mapping module maps the data to a feature space, and generates a feature sequence cluster containing a multi-dimensional association relationship by using a time sequence segmentation algorithm; the prediction module divides prediction intervals based on a support vector machine algorithm and extracts prediction indexes; the optimization module generates an endurance prediction strategy by predicting and optimizing the network model; and the feedback optimization module performs multi-source data fusion optimization and outputs a prediction instruction. The early warning module can associate the prediction instruction with the battery health degree, output a grading early warning signal and trigger a response mechanism.

The invention relates to the technical field of network security, in particular to a network security risk assessment method and device based on artificial intelligence and a medium, and the method comprises the following steps: S1, collecting network flow data, user behavior data and device state data, and generating a standardized data set; s2, extracting features related to network threats, and constructing a risk data set with labels; s3, dividing the risk data set with the label into a training set and a verification set, and training the risk assessment model by using a support vector machine algorithm; s4, outputting a risk score and a threat level, and identifying a high-risk event; and S5, performing backtracking analysis on the high-risk events identified in the step S4, and forming a risk assessment report. According to the invention, through multi-source data processing, intelligent feature extraction and support vector machine model training technologies, accurate identification and real-time evaluation of network threats and deep traceability analysis of high-risk events are realized, and the initiative of network security protection is improved.

The invention discloses a landslide displacement prediction method for reservoir bank inspection, and relates to the technical field of disaster prevention, and the method comprises the following steps: collecting original data of geological conditions, meteorological monitoring and bank landslide mass monitoring of reservoir bank inspection, and carrying out the preprocessing of the original data; extracting features related to the landslide deformation mode from the preprocessed original data, and integrating the features to obtain a feature set; and training the extracted features by adopting a support vector machine algorithm based on the related data of the feature set. According to the landslide displacement prediction method for reservoir bank inspection, by combining geological conditions, meteorological monitoring and reservoir bank landslide mass monitoring data, landslide deformation inducements can be analyzed more comprehensively, the influence of rainfall and earthquakes on landslides is considered, the possibility of landslide occurrence can be predicted more accurately, and the prediction accuracy is improved. And by setting a risk assessment threshold and assessing different landslide risk levels, early warning can be performed before the occurrence of the landslide, so that early recognition of the landslide risk is realized.

The invention discloses a goat milk product cold-chain logistics system risk evaluation method. The method comprises the steps of S1, collecting a plurality of key parameters in a goat milk product cold-chain logistics process in real time; s2, historical cold-chain logistics data are collected, risk event labeling is carried out, and a historical data set is generated; s3, preprocessing the real-time data set and the historical data set, and generating a feature vector for risk assessment by applying a feature engineering technology; s4, based on the feature vectors, a support vector machine algorithm is adopted to construct a multi-level risk assessment model through a multi-dimensional feature fusion technology; s5, performing dynamic risk assessment according to the real-time data set and the multi-level risk assessment model; and S6, according to a dynamic risk assessment result, by setting an intelligent threshold value, automatically identifying a potential risk and triggering an early warning mechanism, forming an early warning signal, and according to an early warning level, automatically starting an intervention measure. The method has the advantages of being accurate, real-time and dynamic in response, and the safety and reliability in the cold-chain logistics process are effectively improved.

The invention discloses a marine water environment abnormal state secondary and derivation probability modeling and estimating method, which belongs to the field of state modeling estimation, and comprises the following steps: acquiring real-time data flow of marine environment parameters, performing segmentation processing by adopting a sliding window mechanism, and reducing data dimension by utilizing principal component analysis. Then, constructing a graph structure model based on a time sequence, and capturing correlation and a space-time dependency relationship among parameters; and performing nonlinear mapping on the graph structure model by adopting a radial basis function core, and capturing complex nonlinear characteristics among the marine environment parameters. And finally, classifying the abnormal states by using a multi-class support vector machine algorithm, and outputting secondary and derivative probability estimation results of the abnormal states in real time. According to the method, abnormal events in the marine environment can be found in time, dynamic modeling support can be provided for marine environment monitoring, and the accuracy and real-time performance of marine environment monitoring are remarkably improved.

The invention provides an intelligent actual measurement and quantity management method and system for a building. The method comprises the following steps: capturing environmental parameters, material circulation paths and spatial geometric information of key parts of a building in a construction process in real time to obtain a measurement data set; a distributed ledger technology is combined with a homomorphic encryption algorithm, a graph theory analysis technology is applied to evaluate a dependency relationship among operation points of a construction site, and a data log is generated; according to the data log, using an augmented reality technology to superpose a BIM model, introducing a support vector machine algorithm to carry out automatic classification and risk assessment processing on deviation data between measured data and preset standard data, and generating a rectification suggestion report; and a closed-loop quality management process is formed by continuously collecting rectification effect feedback. According to the technical scheme, high transparency and efficient quality control in the construction process are ensured.

The invention relates to the technical field of surgical intervention, in particular to a catheter system and method for neurosurgery surgical intervention, in the invention, through accurate path prediction, real-time monitoring and dynamic adjustment, the accuracy of catheter operation is remarkably improved, and through analysis of historical surgical data, information such as path, speed and angle in the surgical process is accurately extracted, so that the accuracy of catheter operation is improved. A more accurate neural structure position can be provided by timely adjusting the movement direction of the catheter, spatial positioning and form calculation, the spatial deviation between the current position of the catheter and a target neural structure is calculated in real time through a support vector machine algorithm, a path is adjusted according to a set threshold value, historical operation data is learned through a convolutional neural network, and the operation accuracy is improved. The catheter operation strategy is optimized, operation data and deviation are analyzed in real time, the operation precision is automatically adjusted, catheter operation in the operation process is more accurate, patient wounds are smaller, the recovery time is shorter, the treatment effect is better, and the recovery speed of patients is increased.

The invention discloses a wind driven generator oil leakage fault early warning method, which comprises the following steps: arranging vibration, temperature and oil sensors at a monitoring part, collecting multi-dimensional data for comprehensive analysis, analyzing a vibration signal by using a frequency spectrum to judge a mechanical fault, and combining a temperature trend and an oil parameter abnormal condition to realize early warning of an oil leakage fault of a wind driven generator. And carrying out classified early warning on the oil leakage fault by adopting a support vector machine algorithm. Furthermore, a Bayesian network algorithm is used to carry out accurate reasoning on the oil leakage position. According to the invention, the early warning result and the oil leakage position are uploaded to the monitoring center, and the processing suggestion is generated, so that intelligent and precise early warning of the oil leakage fault of the wind driven generator is realized. According to the multi-sensor fusion method, the accuracy and timeliness of oil leakage fault diagnosis are remarkably improved, the equipment loss and the maintenance cost are effectively reduced, and the method has important significance for guaranteeing safe and stable operation of the wind driven generator.

The invention provides a sewage treatment progress monitoring method and system, and the method comprises the steps: collecting and processing a first water quality parameter value of a key node of a sewage treatment facility in a historical time period, constructing a dynamic model of a sewage treatment process through a deep neural network, and introducing a genetic algorithm to optimize the structure and parameters of the dynamic model. An optimized dynamic model is obtained, the optimized dynamic model is used for predicting the sewage treatment effect, and a prediction result and operation data of the sewage treatment facility are obtained; using a fuzzy logic control algorithm to adjust the operation parameter setting value of the sewage treatment system, and using a support vector machine algorithm to classify and identify the fault mode of the sewage treatment system to obtain a fault classification result; based on the prediction result, the operation data, the optimized operation parameter setting value and the fault classification result, a comprehensive monitoring management system based on the cloud platform is constructed; according to the invention, comprehensive monitoring and fault identification of the sewage treatment process are realized, and efficient, stable and reliable operation of sewage treatment is ensured.

The invention provides a power supply equipment temperature characteristic detection method and system. The method comprises the following steps: acquiring temperature data sets of power supply equipment under different working conditions; performing partial derivative calculation on the mathematical model, quantifying the contribution degree of each working condition to the temperature characteristic, and determining a main influence factor and a secondary influence factor; a time sequence analysis method is introduced, the temperature data set is analyzed, the dynamic change trend of the working conditions is captured, and nonlinear relation characteristics between the working conditions and temperature characteristics are identified; a self-adaptive filtering algorithm is adopted, changes of working conditions are tracked in real time, and parameters and structures in the mathematical model are dynamically adjusted, so that the mathematical model can adapt to the dynamic changes of the working conditions; and training and optimizing the adjusted mathematical model by using a support vector machine algorithm, thereby improving the prediction precision and generalization ability of the mathematical model.

The invention relates to the technical field of concrete detection, and discloses a concrete quality rating method and system based on a multilayer neural convolutional network, and the method comprises the steps: building a standardized performance index data set, training the standardized performance index data set through employing a support vector machine algorithm, and building a concrete material resistance evaluation model; environment parameters of a construction site are collected in real time through a wireless sensor network, and the influence of the current construction environment on concrete is judged; and inputting the construction environment parameters into the concrete material performance evaluation model to obtain an optimal concrete material ratio suggestion, uploading the concrete material performance parameters to a cloud server in the concrete use process, and uploading a concrete quality evaluation report to a concrete quality monitoring platform after concrete construction is completed. According to the invention, intelligent evaluation and optimization of the concrete material performance are realized, and the concrete quality and efficiency are improved.

The invention relates to the technical field of non-destructive testing, and particularly discloses an aero-engine blade non-destructive flaw detection method for a miniature unmanned aerial vehicle, which comprises the following steps: acquiring stress data of a blade in a use process in real time by using an MEMS sensor, analyzing the data, and identifying a stress concentration area; carrying out real-time monitoring on crack data of the stress concentration area, extracting crack propagation fluctuation characteristics by utilizing a wavelet transform algorithm, and calculating a crack propagation fluctuation index; fatigue strength data of the material is monitored, fatigue failure characteristics are extracted, and a fatigue failure index is generated through a support vector machine algorithm; comprehensively analyzing the crack propagation fluctuation index and the fatigue failure index, and judging whether the blade is in a healthy state or not; and finally, based on the blade health state, determining whether to carry out further detection or maintenance.

The invention discloses a network abnormal state identification method and device. According to the method, the aging degree quantized value of the network object is generated by combining time sequence analysis, a mining algorithm and a support vector machine algorithm, and the importance quantized value is generated based on the network attribute. And carrying out weighted fusion on the two to generate a comprehensive score, setting an abnormal threshold according to a real-time score distribution curve, and screening out an aged object set. And identifying an aging mode and generating aging description by using a deep learning model combining a long-short-term memory network and a graph neural network. The multi-modal features of the aging object are extracted from multiple dimensions, and a dynamic feature space is constructed. And finally, in combination with the aging description and the dynamic feature space, identifying an abnormal state in the network by adopting a multi-dimensional data association algorithm and a self-adaptive visualization method. According to the process, comprehensive, dynamic and real-time identification of the abnormal state of the network is realized, and the problem that the abnormal state of the network cannot be comprehensively and accurately identified in the prior art is effectively solved.

The invention discloses a continuous attention neural feedback training method and system based on a brain-computer interface, and relates to the technical field of neural feedback, and the method comprises the steps: collecting a multi-channel electroencephalogram signal of a user in visual task training in real time; extracting power spectral density characteristics of the multi-channel electroencephalogram signals in a beta frequency band, classifying the power spectral density characteristics by adopting a support vector machine algorithm, and outputting a judgment result of an alert or non-alert state; and according to a judgment result, dynamically adjusting an information fusion proportion alpha value in the visual task through a reward-punishment mechanism, updating image information feedback in the visual task in real time, and adjusting the attention state of the user through an image information feedback result. Neural feedback and a dynamic reward and punishment system are fused, real-time excitation feedback is obtained by autonomously adjusting electroencephalogram activity, the problem of insufficient training power caused by traditional static tasks or single positive feedback is solved, and the long-term training effect is enhanced.

The invention provides an ink quality online monitoring method and system based on multi-sensor fusion, and relates to the technical field of printers, and the method comprises the steps: obtaining the temperature, humidity, pH value, viscosity, particle thickness, flow velocity and other data collected by a plurality of sensors in the production process of water-based ink, and employing a multi-sensor data fusion algorithm of adaptive weight, calculating a sensing weight corresponding to the sensor data, and combining the sensor data to obtain fused water-based ink quality characteristic data; establishing a water-based ink quality prediction model by adopting a support vector machine algorithm, and inputting the fused water-based ink quality feature data into the trained water-based ink quality prediction model to obtain a water-based ink quality prediction result; and if the water-based ink quality prediction result does not meet the preset condition, judging that the water-based ink quality is unqualified, sending a feedback signal in a preset format to water-based ink production equipment, and adjusting water-based ink production parameters through a PID controller of the water-based ink production equipment. According to the method, the related parameters of the ink can be monitored and reflected in real time, and the possible problem of artificial false description is solved.

The invention discloses an elevator steel wire rope wear real-time monitoring system and equipment thereof, and relates to the technical field of safety monitoring, the elevator steel wire rope wear real-time monitoring system comprises an acquisition and extraction module which acquires real-time surface image data and vibration signal data of a steel wire rope through a sensor array, extracts surface damage features and vibration spectrum features by adopting an image processing algorithm, and sends the surface damage features and the vibration spectrum features to a server; fusing kernel function selection and hyperplane separation to process the preliminary wear feature set; the crack identification module is used for fusing tensile strength and corrosion resistance data in the material characteristic database according to the initial wear characteristic set, classifying potential fatigue crack types through support vector identification and crack morphological characteristics by adopting a support vector machine algorithm, and determining a fatigue crack distribution diagram; according to the elevator steel wire rope abrasion real-time monitoring system and equipment thereof, accurate abrasion evaluation and dynamic maintenance optimization are achieved, the safety of the steel wire rope is improved, and the service life of the steel wire rope is prolonged.

The invention provides a method for solving signal drift of an oxygen sensor in a high-temperature and high-pressure environment, which comprises the following steps: collecting acid-base gas concentration data and zirconium oxide material surface state information in the high-temperature and high-pressure environment, and classifying erosion sensitivity degrees by adopting a support vector machine algorithm to obtain an initial response characteristic deviation value; a neural network model is adopted to process the determined potential signal drift trend and oxygen content detection historical data, a simulation response characteristic curve is generated, and a quantitative mapping relation of the pH value change to erosion sensitivity is obtained; refining the correlation between the determined long-term monitoring reliability index and the pH value change by adopting a neural network model, generating a dynamic response characteristic prediction sequence, and obtaining a signal drift suppression threshold set; and through fusion of the obtained signal drift suppression threshold set and the oxygen content detection real-time signal, if it is judged that erosion sensitivity is aggravated in a prediction sequence, a compensation model is activated to update and circulate, and optimized sensor stable output is obtained.

The invention discloses a blockchain technology-based switch security management and data integrity method, which comprises the following steps of S1, deploying blockchain nodes in a network, and accessing each switch as a node to the blockchain network; s2, using a chain structure to store configuration data and operation log information of the switch, and generating a digital signature; s3, configuring an adaptive consensus mechanism; s4, training the threat prediction model by using a support vector machine algorithm, and detecting and identifying known abnormal behaviors and potential threats; s5, performing clustering analysis on the data in the switch network in combination with a density-based clustering algorithm and an improved isolated forest algorithm, identifying unknown abnormal behaviors and potential security threats, and triggering an early warning mechanism; s6, deploying a smart contract for switch authority management and configuration recovery operation; and S7, providing a user visual interface. According to the invention, a self-adaptive consensus mechanism and an intelligent contract are utilized, so that switch security management and data integrity are realized.

The invention relates to an intelligent integrated warehouse logistics management method based on an electronic tag, and the method comprises the steps: obtaining real-time humidity monitoring data of a warehouse environment, and generating an environment parameter matrix in combination with temperature and ventilation conditions; according to the environment parameter matrix, a mapping relation model of humidity and label performance is trained, and the influence weight of the humidity on the label performance is obtained; according to the label performance comprehensive score, adopting a support vector machine algorithm to judge whether the label performance is close to a failure threshold value, and generating a performance early warning signal; when the performance early warning signal is triggered, predicting a label performance change trend through time sequence analysis, and generating a performance degradation curve; according to the performance degradation curve, a Bayesian network model is adopted, label performance evaluation model parameters are updated, and model prediction precision is optimized; and applying the updated model parameters to the dynamic performance evaluation model, and outputting a label health state diagnosis result in real time in combination with the real-time environment parameter matrix.

The invention relates to the technical field of power distribution networks, in particular to a power distribution network resource self-adaptive scheduling method, system and device and a medium. Using a support vector machine algorithm to construct an agent model representing a nonlinear coupling relationship between the novel power factor real-time parameters and the initial data set of the power distribution network; decomposing a multivariable interaction effect in the agent model by using a global sensitivity analysis method to obtain a sensitivity index; adaptively correcting the sampling space of the agent model according to the sensitivity index to obtain an optimized agent model; and on the basis of the optimization agent model and the real-time monitoring data of the power distribution network, generating a power distribution network resource adaptive scheduling strategy by adopting a particle swarm optimization algorithm, thereby carrying out optimal configuration on the power distribution network resources in the novel power factor access scene. According to the method, efficient and accurate optimal configuration of the power distribution network resources in a novel power element access scene is realized, and the operation performance and stability of the power distribution network are remarkably improved.

The invention provides an oily water treatment equipment control method based on the Internet of Things, which comprises the following steps: feeding back data through an operation state, calling a multi-dimensional data analysis module, carrying out association calculation on energy consumption, treatment efficiency and adjustment parameters, predicting a subsequent operation trend by adopting a support vector machine algorithm, and obtaining a feasibility evaluation result of energy-saving mode switching; if the feasibility evaluation result of the energy-saving mode switching shows that the energy consumption optimization space is relatively large, starting an energy-saving mode switching module, reducing the power output of a non-key processing unit, maintaining the core separation efficiency, and determining new energy consumption balance point data; and through equipment state updating information, continuously monitoring an operation cost control module, comparing cleaned energy consumption data with historical records, calculating a cost saving range, and judging a potential improvement space for prolonging the service life of the equipment.

The invention discloses a safety construction management method based on digitization, and belongs to the field of construction management. The safety construction management method comprises the steps that disk saw operation data of a construction site are collected; preprocessing the collected disk saw operation data; according to the preprocessed disk saw operation data, the safe operation state of the disk saw is judged through a support vector machine (SVM) algorithm; according to the safe operation state, performing risk assessment by adopting a risk assessment method based on combination of an evidence theory and a fuzzy set theory to obtain a safety risk level; and according to the safe operation state and the safety risk level, carrying out safety management on the disk saw through a multi-level early warning control strategy based on reinforcement learning. In view of low risk assessment precision in disk saw construction in the prior art, the risk assessment precision is improved through risk assessment combining an evidence theory and a fuzzy set theory and by using a reinforcement learning early warning control strategy and the like.

The invention discloses a full-automatic feeding control method and system for multiple areas of cattle and sheep, and the method comprises the steps: obtaining basic information and ingestion behavior data of cattle and sheep in each area, carrying out the preprocessing, and carrying out the training through a support vector machine algorithm, and obtaining a cattle and sheep ingestion speed prediction model; a relation model of the feed physical parameters and the feeding speed is constructed, and the initial feeding speed of each area is obtained based on the relation model and the feed physical parameters of each area; acquiring real-time feeding behavior data and real-time satiation degree data of the cattle and the sheep in each area, and judging whether the average feeding speed of the cattle and the sheep meets the requirement or not; if the requirements are not met, based on the change condition of the feeding speed of the cattle and the sheep, a fuzzy control algorithm is adopted to generate a feeding speed regulation and control instruction; and obtaining feeding demand data of cattle and sheep in each region and current feed supply data, and calculating an optimal feed supply speed. Accurate feeding is achieved, the feeding efficiency is improved, and the problems that in traditional feeding, supply and demand are not matched, and the efficiency is low are effectively solved.

The invention relates to the technical field of electrode preparation, and discloses a dry-method electrode preparation process control method and system and a storage medium. The method comprises the steps that in the rolling forming process of a dry-method electrode material, pressure roller gap data and rotation angle data of a driving motor are monitored and collected in real time, and a support vector machine algorithm is used for recognizing the transmission ratio change trend of a pressure roller system; calculating a pitch precision offset caused by wear through Kalman filtering in combination with historical wear data; when the offset exceeds the limit, an adjustment coefficient is generated based on a simulation model, and the control precision is improved through feed-forward compensation and feedback optimization; when the adjustment precision meets the process requirement, a self-adaptive parameter set is determined through dynamic deviation residual analysis, and an actuator is driven to adjust the gap between the compression rollers; and monitoring the thickness uniformity index of the electrode plate produced after adjustment in real time, and finally forming quality closed-loop control by taking the thickness uniformity of the electrode plate as a verification standard. According to the invention, the quality precision of dry-method electrode production is improved.

The invention discloses an urban rail transit energy management method based on multi-source fusion, and the method comprises the steps: employing a support vector machine algorithm to analyze a correlation mode between train intensive operation and passenger flow surge according to an obtained energy demand fluctuation index, and determining a potential energy consumption peak value position; the determined adjustment parameters are obtained, a power supply system control instruction is updated in combination with real-time train track information, and dynamic power supply load configuration is obtained; whether the obtained dynamic power supply load configuration is matched with the current passenger flow surge data or not is judged, if yes, a mode switching signal is sent to an equipment controller, and energy use feedback data after execution is obtained; according to the obtained energy use feedback data, evaluating the response accuracy of the system integration effect to demand fluctuation by adopting a gradient boosting decision tree algorithm, and determining further trajectory optimization suggestions; and updating a train operation scheduling model through the determined trajectory optimization suggestion to obtain an integrated multi-source information linkage mechanism.