527 results about "Online analysis" patented technology

The invention discloses a substation equipment state monitoring and intelligent fault early warning method based on deep learning. The method comprises the following steps: S1, obtaining a preprocessed multi-source state data set; s2, generating a high-dimensional equipment state feature matrix; s3, a fault sensitive deep belief network model is adopted to form a preliminary fault state recognition result; s4, obtaining an optimized sensitive depth belief network model; s5, performing online analysis on the multi-source state data acquired in real time by using the optimized sensitive deep belief network model, generating a real-time fault prediction result of the equipment state, and classifying and grading fault risks; and S6, according to a real-time fault prediction result, triggering a remote fault early warning mechanism, and sending fault early warning information including a fault risk level, an early warning signal and an emergency processing suggestion to a substation operation and maintenance center. According to the invention, intelligent alarm linkage and hierarchical control of the scheduling system are effectively supported.

The invention provides an intelligent online analysis and monitoring system and method for circuit board production, and the system comprises an image collection module, a multispectral imaging module, a three-dimensional scanning measurement module, a copper thickness test module, a data collection module, an intelligent analysis and recognition module, and a self-adaptive algorithm optimization module. The system is used for carrying out real-time image acquisition on each key station in the circuit board production process by adopting a high-resolution industrial camera. Through high-definition image acquisition, multispectral imaging, three-dimensional scanning and measurement, comprehensive data acquisition in the circuit board production process is realized, the problem of limited data acquisition range in the prior art is solved, accurate detection and classification of circuit board defects are realized by applying an intelligent analysis algorithm, and the production efficiency is improved. The accuracy and efficiency of defect detection are improved, automatic adjustment and optimization of algorithm parameters are achieved through the self-adaptive algorithm optimization module, it is ensured that the algorithm can continuously adapt to changes of the production environment, and the stability and reliability of the system are improved.

The invention relates to the technical field of quality detection, in particular to a gas atomization powder sphericity degree multi-dimensional online analysis system and a method thereof.The gas atomization powder sphericity degree multi-dimensional online analysis system comprises a dynamic dual-mode imaging module, a transverse wind field auxiliary detection unit and a multi-dimensional feature fusion analysis module; the transverse wind field auxiliary detection unit is used for applying controllable transverse wind power through an airflow nozzle orthogonal to the powder falling direction and measuring the deflection track of gas atomization powder ball particles in combination with a laser displacement sensor. The online calibration module is used for spraying standardized spherical particles to the powder flow according to a preset period; the traditional density detection depends on destructive sampling and off-line measurement, the efficiency is low, and the whole production line particles cannot be covered; through an orthogonal wind field trajectory inversion technology, a density value and an internal defect mark are synchronously output through non-contact dynamic measurement, 100% lossless online full inspection of a production line is realized, and sampling limitation and aging bottleneck of a traditional means are broken through.

Systems and methods for protecting electronic communications are described. A cyber security appliance may be configured to calculate a topic shift score for a communication by comparing a first lexical profile derived from the communication to a historical lexical profile established for an associated user. This analysis may be performed without using a large language model. The system may also parse communications to extract sensitive data and content from attachments, performing behavioral modeling on the extracted data. Based on the analysis, an autonomous response module may take a variety of mitigation actions. Furthermore, a security mailbox assistant module may perform a secondary, in-depth analysis on user-submitted communications and generate a deterministic report. For outbound communications, a data loss prevention architecture may divert messages for in-line analysis and may include a fail-safe timeout mechanism to ensure service continuity.

The invention provides an efficient quality management system for a cement production line, and the system comprises an online analysis device which is used for obtaining the chemical component data of a raw material and the technological parameters in the production process; the data processing unit is used for preprocessing the chemical component data and the process parameters to obtain standardized data; the mathematical model is obtained based on historical production data and product quality target training and used for predicting product quality according to the standardized data; the data acquisition module is used for continuously acquiring real-time data in the production process and forming a dynamic data set; the model optimization unit is used for carrying out iterative optimization on the mathematical model by utilizing the dynamic data set; the automatic equipment is used for executing raw material ratio adjustment and process parameter optimization according to the optimized control strategy; and the quality detection equipment is used for detecting the final product in real time, acquiring key index data and comparing the key index data with a preset quality target to form a closed-loop feedback mechanism.

The invention relates to the technical field of on-line analysis, in particular to a polyvinyl alcohol alcoholysis degree distribution on-line measurement method and system.The method comprises the following steps that spectral data of a polyvinyl alcohol solution is obtained, a characteristic absorption wave band in the ester group to hydroxyl group process is extracted, absorption intensity in the wave band is recorded to construct a sequence, peak positions are extracted, intensity difference is calculated, and the direction is marked; and identifying a fluctuation interval, calculating an angular velocity trend, and screening anomalies and generating an unstable section set in combination with a disturbance amplitude and a component coincidence rate. According to the method, quantitative analysis of alcoholysis degree conversion characteristics is realized through selection of characteristic absorption wavebands in spectral data and accurate identification of the change trend of the intensity difference values, the characterization precision of the alcoholysis degree of polyvinyl alcohol is improved, and more accurate trend fluctuation identification capability is provided through comparison of angular velocity difference values of absorption intensity change rates, so that the accuracy of the analysis of the alcoholysis degree of polyvinyl alcohol is improved. The analysis level of alcoholysis degree distribution details is remarkably improved, the abnormal fluctuation interval of alcoholysis degree component distribution is recognized, and the product quality difference caused by alcoholysis degree fluctuation is reduced.

The invention relates to an acetic acid production line on-line analysis system, and relates to the field of on-line gas analysis equipment.The acetic acid production line on-line analysis system comprises a pretreatment box and an analysis cabinet, the pretreatment box is connected with an acetic acid production line through a sample feeding pipe and connected with the analysis cabinet through a sample conveying pipe, and a pretreatment module is arranged in the pretreatment box; the pretreatment module comprises a sample pressure reduction unit and a heating unit, the sample pressure reduction unit is connected between the sample feeding pipe and the sample conveying pipe, and the heating unit is arranged adjacent to the sample pressure reduction unit and the sample conveying pipe and is connected with the analysis cabinet; a pretreatment module, an analysis module and a tail gas treatment module are arranged in the analysis cabinet, the pretreatment module comprises a bypass unit and a flow stabilizing unit, the bypass unit is arranged between the sample conveying pipe and the tail gas treatment module, the flow stabilizing unit is connected between the sample conveying pipe and the analysis module, and the tail gas treatment module is connected with the analysis module; the stability and the real-time performance of a sample detection result of an online analysis system can be improved.

The invention relates to the field of wastewater treatment, in particular to an online analysis method and system for ink-jet printing wastewater treatment data, and the method comprises the steps: obtaining the water quality, process and equipment state parameter data of ink-jet printing wastewater based on each sensor, and constructing an original data matrix through preprocessing; decomposing the matrix, selecting a preset number of principal components for reconstruction, and generating a reconstructed data matrix; a reconstruction error is calculated by analyzing the difference between an original matrix and a reconstruction matrix, a correlation distortion index is analyzed based on row vector similarity, and an objective function is constructed to determine the number of optimal principal components; and clustering is carried out by using reconstruction errors and distances, abnormal data points are screened, and process parameters are regulated and controlled. According to the method, by optimizing the number of principal components and improving clustering analysis, the noise interference problem under complex water quality changes is effectively solved, the anomaly detection precision and the system stability are remarkably improved, and the robustness and the adaptability of an ink-jet printing wastewater treatment system are enhanced.

The invention discloses a multi-modal AI-based contract online analysis optimization system and method, and relates to the field of contract analysis, and the method comprises the steps: obtaining a to-be-analyzed contract file, and carrying out the standardization processing of the to-be-analyzed contract file; dividing the content features of the contract file into text content features, visual layout features and logic structure features, and synchronously extracting the features; dynamically generating an analysis process path through a lightweight decision tree model, and selecting an optimal analysis engine combination; calling at least three analysis engines for parallel processing based on multi-engine collaborative analysis; correcting a conflict analysis result generated by the parallel processing analysis engine based on an attention mechanism to generate a final analysis result; and recording the accuracy rate and time consumption index of each analysis, and dynamically adjusting an analysis process generation strategy through reinforcement learning. The method has the advantages that the text, visual and logic features in the contract are extracted based on the multi-modal AI technology, and the accuracy and efficiency of contract analysis are improved through multi-engine collaborative analysis and attention mechanism correction.

The invention relates to a rapid VOCs source component spectrum construction and evaluation method. The method comprises the following steps of S1, performing field investigation and survey to determine a target VOCs emission source, a sampling port position and a sampling mode; s2, carrying out on-site collection on a target VOCs source sample by utilizing an air bag or a Suma tank; s3, the sample is sent to a vehicle-mounted VOCs online analysis laboratory staying near a sampling source within 1-2 hours after being collected, and analysis and determination are conducted through an analysis instrument; and S4, based on the measured data of the plurality of VOCs samples of the same type of emission sources, constructing and evaluating the VOCs source component spectrum of the source type. According to the present invention, the on-site sampling and analysis determination method is adopted, the rationality of the on-site sampling method and the dilution scheme of the high concentration VOCs source sample are improved so as to achieve the detection range of the analysis instrument, and the timeliness and the accuracy of the obtained data are improved.

The invention discloses an online analysis system based on hydrogen and methane expiration monitoring, and belongs to the technical field of respiration monitoring. Comprising a zero calibration module used for obtaining a first collection environment background carbon dioxide concentration; the original signal acquisition module is used for acquiring an expiration sample; the data processing module is used for processing the original signal data stream; the signal processing module is used for outputting the filtered concentration signal; the signal compensation module is used for obtaining the compensated hydrogen and methane concentration; the secondary zero calibration module is used for acquiring a reference carbon dioxide zero point; the baseline correction module is used for carrying out baseline correction; and the model building module is used for outputting trend judgment. By means of the online hydrogen and methane expiration analysis system, the technical effect that high-concentration precision is still kept under the expiration volume fluctuation and sensor nonlinear conditions is achieved, and the problem of measurement deviation caused by volume change, nonlinear response and carbon dioxide zero drift in the prior art is solved.

The invention relates to the technical field of power equipment monitoring, and discloses a low-voltage power distribution cabinet fault monitoring system and method. According to the system, multiple paths of original operation signals are collected and preprocessed in real time to form a standard time sequence data stream, frequency domain feature extraction, transient feature capture and load fluctuation analysis are carried out, and a dynamic multi-dimensional feature reference library is constructed; iteratively calculating a feature-fault association weight based on the feature library and a pre-stored fault case graph, and establishing a dynamic mapping relation network which can be corrected in real time; according to the fault probability distribution output by the mapping network, the sampling frequency, the feature priority and the diagnosis threshold are adaptively adjusted, and an optimal monitoring strategy is formed; and finally, generating a standardized diagnosis report containing fault types, positions and severity levels through online analysis. According to the invention, comprehensive perception of fault features, autonomous optimization of a diagnosis strategy and accurate output of a fault report are realized, and the intelligent level and reliability of fault monitoring of the low-voltage power distribution cabinet are significantly improved.

The invention relates to the field of online analysis of chemical components of materials, and discloses an online monitoring process for components of a lead frame electroplating solution, which comprises the following steps: conventionally monitoring the spectrum of the electroplating solution to calculate the concentration; calculating a spectral entropy index based on the deviation degree of the real-time spectrum and a pre-established statistical baseline model, and comparing the spectral entropy index with a preset failure trigger threshold; a dynamic calibration step is started only when the spectral entropy index exceeds the preset failure trigger threshold, the dynamic calibration step compares a physical true difference spectrum with a model virtual difference spectrum by injecting a standard pulse to generate a model error vector, and the model error vector is utilized to correct the chemometrics model, so that the stoichiometry accuracy is improved. According to the method, on-demand calibration is realized, and the consumption of the standard substance is reduced while the real-time accuracy of the model is guaranteed.

The invention discloses a battery material database construction method and system. The method comprises the following steps: manually inputting raw material and battery material information; automatically acquiring experimental data of the battery material; converting the acquired unstructured warehousing data into structured warehousing data; carrying out model training, testing and evaluation to obtain an optimized model; on the basis of the optimized model, online analysis is carried out on warehousing data, and the potential relation between the performance and the characteristics of the battery material is searched for actual prediction or classification tasks. According to the method and the system, the integrity and the richness of database information are ensured by collecting the relevant information of the whole life cycle of the battery material, a solid data foundation is laid for application, and by performing data mining and analysis on the warehousing data, the safety of the battery material is improved. And a scientific basis is provided for performance evaluation, life prediction and formulation of a personalized research and development scheme of the battery material.

The invention discloses an AI-based online data analysis method for medical molecular sieve oxygen production equipment. The method comprises the following steps: step 1, collecting multi-source operation monitoring data of the medical molecular sieve oxygen production equipment; 2, constructing a task prompt vector to obtain a normalized feature vector; 3, inputting the normalized feature vector into an improved TabPFN model to obtain a hidden space representation, and generating an enhanced representation in combination with a retrieval result of the prototype memory bank; 4, performing drift detection on the enhanced representation; 5, inputting the robust representation into a multi-task decoder; and 6, executing event label judgment, and outputting online analysis result data. According to the invention, real-time analysis of multi-source data and advanced identification of abnormal trends are realized, and the method is suitable for equipment state monitoring and risk early warning in hospital wards, rehabilitation centers and long-term oxygen supply scenes.

The invention provides an intelligent cloud testing system and method based on cloud and edge computing, and particularly relates to the technical field of automatic testing, and the system comprises a cloud service platform, an edge unit, an instrument discovery unit and message middleware. The cloud service platform manages test cases and test data in a centralized manner, provides a code-free programming tool, saves test records, and supports multiple report formats and data online analysis. The edge unit is deployed on a user site, automatically identifies test instruments and equipment, receives test cases, autonomously completes testing, pushes test data to the cloud service platform in real time, and supports offline testing. An instrument discovery unit automatically detects and configures an access instrument. And the message middleware improves the data throughput, realizes module decoupling, and is convenient for independent deployment and upgrade. The system solves the technical problems that an automatic test system is long in research and development period, poor in flexibility and difficult to deploy, and rapid construction, flexible adjustment and low-cost deployment of the automatic test system are achieved.

The invention discloses a high-proportion new energy power system frequency response modeling method and application. The method comprises the steps that all nodes in a power system are divided into active nodes and passive nodes; establishing a linear power flow model of the power system; reducing the linearized power flow model to obtain an equivalent active node power flow injection power expression; frequency-active response models of various power resources in the active nodes and the passive nodes are established respectively; combining an equivalent active node power flow injection power expression with a frequency-active response model, and constructing a unified linear state space model taking an active node phase angle and frequency as state variables; and calculating the frequency response of the power system based on the unified linearization state space model. The unified linear state space model constructed by the method can realize quicker and more accurate simulation and stability analysis of the frequency dynamic state of the high-proportion new energy power system, and is suitable for quick simulation and online analysis of a large-scale system.

The invention relates to the field of gates, and provides a gate vibration online analysis early warning method and system, and the method comprises the steps: training the historical monitoring data of a gate through a machine learning algorithm, and constructing a mapping relation between an independent variable and a dependent variable; upstream and downstream related independent variable information of the gate is obtained, and the gate vibration state is calculated online according to the mapping relation; analyzing and calculating the natural vibration frequency of the gate based on the modal analysis model of the gate; and evaluating the safety state of the gate according to the gate vibration state and the natural vibration frequency of the gate. When the system is used, the problems of data islanding, prediction lag, model distortion and low decision efficiency in the traditional technology are solved, high-precision and full-period intelligent guarantee is provided for hydraulic engineering safety, the resonance accident risk is remarkably reduced, and gate operation and maintenance management is promoted to be upgraded to the standard and unmanned direction.

The invention provides a building heating and ventilation intelligent monitoring and energy consumption final calculation method based on smart energy management and control, and relates to the field of intelligent monitoring. The intelligent building heating and ventilation monitoring system based on intelligent energy management and control comprises a data acquisition module, an intelligent analysis module and a digital twin engine module. The energy consumption final calculation module is used for obtaining calibrated equipment operation state time sequence data, carrying out itemized energy consumption calculation and multi-dimensional allocation final calculation and obtaining an energy consumption final result, and the intelligent decision module is used for carrying out online analysis on the energy consumption final result and deviation tolerance, carrying out multi-target optimization solution and obtaining an energy consumption final result. According to the invention, model updating is driven through real-time data, calibration is triggered in combination with a deviation threshold value, the problem of model drifting is solved, dynamic analysis is carried out in combination with an energy consumption final result, and the intelligent monitoring effect is remarkably improved.

The invention discloses an edge intelligent terminal device for robot welding process online monitoring and quality analysis, and relates to the technical field of industrial robot welding automation and intelligent manufacturing. The device adopts an edge intelligent calculation framework, integrates a self-developed multifunctional industrial data acquisition control board card, an edge AI calculation unit and an optimization control system, and specifically comprises the following steps: acquiring process parameters such as current, voltage, welding speed and gas flow of a welding robot and weld seam image data in real time through the self-developed control board card; on-line analysis and defect detection are carried out on the welding quality based on a deep learning algorithm, and a real-time quality evaluation result is generated; welding parameters are dynamically adjusted according to monitoring data and AI reasoning results, and closed-loop optimization control is achieved; localized data processing is carried out through the edge computing unit, the cloud transmission delay is reduced, and the response speed is improved. The device is equipped with an industrial control touch screen to realize man-machine interaction functions such as parameter display and real-time operation control. The device further integrates a model online learning mechanism, a predictive maintenance module and a self-adaptive regulation and control system, and has a continuous optimization capability and a fault early warning capability under complex working conditions. A domestic high-performance embedded chip is adopted as core hardware, low-power-consumption control and anti-interference design are combined, and it is ensured that the system stably operates in a harsh industrial environment. According to the device, the whole-process intelligent monitoring and self-adaptive optimization of the welding process are realized, and the accuracy of welding quality detection and the production efficiency are remarkably improved.

The invention relates to the technical field of in-situ electrics, and provides an in-situ electrical property real-time analysis system based on an environmental scanning electron microscope, comprising: an environmental atmosphere simulation module; an electron gun pole shoe is arranged on the top side of the electron microscope chamber, a low-temperature sample table is arranged in the electron microscope chamber, and the electron microscope chamber is communicated with the environment atmosphere simulation module; the low-temperature Dewar is connected with the low-temperature sample table; the electrical probe is arranged on the inner wall of the electron microscope chamber, and the source meter is used for monitoring electrical signals. An environment atmosphere is simulated through an environment atmosphere simulation module, an electron gun pole shoe is arranged on the top side of an electron microscope chamber and used for imaging, a low-temperature sample table is arranged in the electron microscope chamber and used for bearing a to-be-tested sample, a low-temperature Dewar provides a low-temperature environment for the low-temperature sample table, and an electrical probe in a resistance analysis module conducts electrical monitoring on the sample. And electrical signals are monitored through a source meter, so that in-situ real-time online analysis of electrical properties of a scanning electron microscope observation machine on the surface of the material under different environment atmospheres is realized.

The invention relates to a particle size visualization online analysis system for a spiral sorting machine, and belongs to the technical field of spiral sorting machines. Comprising a fixing frame, a spiral groove support is fixedly arranged on the fixing frame, a stirring mechanism is arranged in the fixing frame, a spiral groove is fixedly formed in the spiral groove support, a material intercepting mechanism is arranged between the spiral groove and the stirring mechanism, and the material intercepting mechanism comprises a material intercepting opening, a discharging pipe, an external sampler and a separating assembly. The material intercepting opening is formed in an outlet in the lower end of the spiral groove, n discharging pipes are arranged at the outlet in the lower end of the material intercepting opening, particle size analysis assemblies are arranged on the discharging pipes, separation assemblies are arranged at outlets in the lower ends of the discharging pipes, and output materials of the discharging pipes are controlled to flow into an external sampler or a stirring mechanism through the separation assemblies; a conveying mechanism is arranged at the lower end of the stirring mechanism and conveys materials stirred by the stirring mechanism into the spiral groove. The problem that the experimental result is inaccurate when the sorting effect of an existing spiral sorting machine is researched is solved.

The invention relates to the field of electric power system monitoring, in particular to an online analysis method and system of an electric power system based on big data, and instability prediction is carried out on the electric power system through a pre-constructed stability prediction model. And once the instability risk occurs, performing big data-based exception verification on the current operation parameters of the target power system. And abnormal operation parameters and abnormal power equipment are found. And finally, based on a pre-constructed association graph and the abnormal power equipment, determining the power equipment which may be affected. The method can be used as a supplement of an existing AI-based electric power stability analysis model, equipment related to instability is pre-judged when an instability risk occurs, and basic data is provided for maintenance personnel, so that the maintenance personnel can deal with the unstable equipment in advance.

The invention discloses an automatic detection method and system for the boundary dimension of a shield segment, and relates to the field of civil engineering, and the method comprises the steps: laying quantum dots on the surface of a to-be-detected shield segment, and exciting the quantum dots through ultraviolet laser to generate a fluorescence spectrum; the method comprises the following steps: receiving an optical signal penetrating through a medium gap in a to-be-detected shield segment through a single-pixel barrel detector to form a reflected light intensity sequence, and inputting the reflected light intensity sequence and a speckle pattern into a correlation reconstruction algorithm to generate a three-dimensional point cloud of a shielding area; and inputting the three-dimensional point cloud and the deformation compensation parameters into a deep learning network, generating a deviation thermodynamic diagram of the boundary dimension of the to-be-detected shield segment, automatically marking an area exceeding a tolerance threshold value based on the deviation thermodynamic diagram, and generating a detection result of the boundary dimension of the to-be-detected shield segment. According to the method, the internal medium gap is reconstructed through the single-pixel barrel detector and the basis tracking noise reduction algorithm, in a bolt hole oil stain shielding scene, compared with a traditional method, the reconstruction porosity error is reduced, and online analysis of the full-surface size error is achieved.

The application provides a multifunctional exhaled breath online sampling device and a method thereof. The device comprises a pressure measuring system, a flow measuring system, a mode switching system and a cleaning system. The method comprises the following steps: before sampling, the NC of a two-position three-way electromagnetic valve is closed, the NO is opened, and the gas in the exhaled breath online sampling device is exhausted through a normally open two-way electromagnetic valve; then the NO of the two-position three-way electromagnetic valve is closed, and the NC is opened; the exhaled breath is blown into the exhaled breath online sampling device through a blowing nozzle, the exhaled breath flows through a pressure sensor, the pressure of the exhaled breath is measured, and the saliva is collected into a saliva warehouse; the exhaled breath flows from a high-pressure detection port to a low-pressure detection port, and the flow of the exhaled breath is measured through a flow sensor; the exhaled breath flows from the low-pressure detection port to an electromagnetic valve frame, and then flows through a sampling port through a passage NO, and is collected by a sample container or analyzed online by an analysis instrument; and the pressure of the exhaled breath, the flow of the exhaled breath and the result of the online analysis are displayed through a screen and a control system.

The invention relates to the technical field of green high-performance concrete admixtures, and discloses a multi-element synergistic grafting and digital control starch-based concrete additive preparation method, which comprises: S1, pre-establishing a soft measurement model for online analysis of monomer concentration, and a kinetic model capable of predicting the dynamic change of a reaction process; s2, in the reaction process, collecting process information such as spectrums on line, and analyzing the current concentrations of various monomers in real time by using a soft measurement model; s3, taking the real-time concentration as an initial condition, and solving a control strategy taking the optimal comprehensive performance of a final product as a target on line by utilizing the kinetic model; and S4, according to the optimized control strategy, implementing dynamic closed-loop regulation and control on the reaction process by adjusting operation variables such as dropping speed and temperature. Through the soft measurement and model prediction control technology, online optimization of the reaction process facing terminal application performance is realized, and accurate regulation and control of product quality and batch stability are ensured.

The invention discloses a new energy box-type substation state abnormity diagnosis method and system, and relates to the technical field of power equipment monitoring. According to the method and the system, through event-triggered high sampling and cross-channel time base self-inspection, multi-source sequence synchronization is ensured, and a consistent time axis is provided for subsequent estimation, so that online analysis under the condition of no power outage is supported; introducing dual-path estimation of RL step fitting and voltage drop robust regression in a transient stage, cooperatively inhibiting a closing peak and outlier data, and obtaining a stable initial value of the contact resistance; in a steady-state stage, adopting Goertzel frequency selection to extract a fundamental wave phasor and performing ohmic purity judgment, and expanding to a resistance-inductance joint model when necessary to remove interference of parasitic inductive reactance on resistance estimation; and then mutual identification and residual constraint are carried out on the temperature and electric power coupling relationship by using a discrete heat RC model, and local write-back correction is carried out on the deviation, so that the consistent closed loop of the electric domain and the heat domain is realized.

The invention provides a bulk metal ore grade online analysis method based on high-voltage pulse discharge breakdown spectroscopy, which comprises the following steps: (a) placing bulk metal ore particles in a high-voltage discharge electrode system filled with a liquid insulating medium, and applying 30-250kV voltage through a high-voltage electric pulse generator to carry out pulse discharge, carrying out electric breakdown on the ore particles and generating plasmas; (b) synchronously acquiring a plasma characteristic spectrum signal and a voltage waveform signal and a current waveform signal of pulse discharge in the electric breakdown process; (c) preprocessing the characteristic spectral signal, determining element types according to the wavelength of a characteristic spectral line, and extracting the intensity of the characteristic spectral line; and (d) inputting the characteristic spectral line intensity, the pulse discharge waveform parameter, the discharge condition and the ore particle property into a pre-established quantitative analysis model, and outputting the metal element grade of the ore particles.

The invention belongs to the technical field of power system stability analysis, and discloses a power system anticipated disturbance frequency safety on-line analysis system, a training method and an application, through a local-global double-input channel feature extraction architecture, the spatial topology correlation features and global physical characteristics of a power system are captured at the same time, and the stability of the power system is improved. And more comprehensive and essential feature expression of the frequency dynamic process is realized. The frequency response knowledge of the power system is embedded into a frequency safety online analysis system based on a guided learning training mode, and the prediction precision and generalization performance of the model under frequent change of the operation condition of the power system are remarkably improved by combining the advantages of a physical mechanism and real data. A conditional distribution-based adaptive loss function is designed as a loss function of a second stage, so that the knowledge learned by the model in the first stage is prevented from being forgotten in the second stage. The online real-time analysis requirement can be met, so that the safety of a power system is improved, and the practicability is high.

The invention provides a full-spectrum photo-thermal synergistic and microwave gradient driven waste heterogeneous composite material interface dissociation and recovery method, which comprises the following steps of performing pretreatment and online analysis on a waste heterogeneous composite material to obtain a structured data set; preparing a full-spectrum photothermal conversion layer to obtain coated particles; collecting structured data and electromagnetic-optical attributes of the coated particles to obtain physical field data and an optimal process parameter set; carrying out integral preheating operation on the coated particles by utilizing a preheating system to obtain a photo-thermal activated intermediate; carrying out deep penetration microwave and synchronous photo-thermal coupling heating to obtain an interface weakening body; performing interface dissociation to obtain a dissociation product; and based on the dissociation product, airflow separation, corona-electrostatic separation and metal purification are carried out, and a multi-stream high-purity resource is obtained. According to the method, the interface dissociation efficiency, the purity of dissociation components and the resource output quality are greatly improved, and low-carbon, intelligent, accurate and efficient recycling of the waste heterogeneous composite materials is achieved.