411 results about "Online analysis" patented technology

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 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 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 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 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 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 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 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.

A system for measuring greenhouse gases and automatic generation of carbon credits in forest areas, based on real-time data, which allows online analysis, generating automatic certification of carbon credits. The system presents a microprocessor unit (UP) that receives signals from sensors measurement of gases (Sg) analyzed in a chromatography unit High Efficiency Liquid (HPLC) and rainfall sensors (Sp). The data is processed in the local processing unit (UP) and transmitted to a remote processing unit (UPr) through the communication module (MC), using transmission protocols of data. The processed data generates credit certificates for authenticated and authorized by the UNFCCC (Convention-United Nations Framework on Climate Change), ensuring its internationally recognized validity.

The invention provides a gas sensor low-delay classification method based on a sliding window and confidence accumulation, and relates to the technical field of gas sensing and intelligent diagnosis. The invention provides an SWSD-CAD framework aiming at the problem of high detection delay caused by dependence on a complete periodic signal of an existing temperature modulation gas sensor. According to the framework, a small window sliding detection (SWSD) strategy is adopted, and high-frequency online analysis is carried out on real-time data streams; meanwhile, continuous prediction results are arbitrated through a confidence cumulative decision CAD logic, and a final decision is made only when the prediction results continuously meet a confidence threshold within a preset time, so that the reliability of the results is ensured. In order to realize the framework, the invention designs a ConvMama hybrid network model which is used for efficiently extracting discriminative features in a short signal window. According to the invention, on the premise of ensuring high classification precision, the detection delay is obviously reduced, and early, rapid and reliable identification of gas types is realized.

The invention discloses a large power station boiler inferior coal low-nitrogen combustion optimization control system and method based on machine learning and multi-objective optimization, and relates to the technical field of coal-fired power generation and clean combustion control. The control system is composed of a combustor subsystem, a detection subsystem and a control subsystem, the combustor subsystem constructs a subarea air supply structure with stable combustion at the bottom layer, low nitrogen at the middle and upper layers and burnout at the upper part through layered combustors and OFA nozzles, and the detection subsystem completes online monitoring of coal quality and online analysis of combustion state parameters; and the control subsystem comprises coal quality adaptive decision, multi-objective optimization and closed-loop feedback. According to the control method, inferior coal is recognized through machine learning, layered air distribution is activated in a self-adaptive mode, the optimal air volume and the rotational flow angle are optimized and solved online with efficiency and NOx as multiple targets, and finally precise execution and fine adjustment are conducted through closed-loop feedback. According to the invention, wide-load stable combustion, continuous low NOx level and burnout degree improvement can be realized, and the method is suitable for engineering application of combustion of inferior coal in a large-scale unit.

The invention discloses an online monitoring and regulating system for inhibiting nitration reaction by thiourea for sewage treatment, and relates to the technical field of sewage treatment. Comprising an on-line monitoring module which monitors and obtains nitrification parameters and environmental parameters in the nitrification activity process through an on-line analyzer and a sensor network, and adjusts parameter monitoring frequency according to the nitrification parameters; the intelligent control module is used for determining optimal parameters of a PID (Proportion Integration Differentiation) controller through a virtual reference model according to the nitrification parameters and the environmental parameters, and determining the addition amount of a thiourea solution according to the optimal parameters; and the thiourea adding unit is used for adding the thiourea solution at an adding point through adding equipment according to the adding amount of the thiourea solution. According to the method, excessive inhibition possibly caused by a traditional control model based on historical data is avoided, the risk that fragile nitrifying flora is thoroughly killed is effectively prevented, success of the bacterium cultivation process is ensured, and the system starting time is shortened.

The application discloses an AI sound online analysis and identification method and system based on optical fiber sensing, which comprises the following steps: capturing sound signals of a target area by a distributed optical fiber sensing unit and converting the sound signals into optical fiber transmission optical signals, and completing photoelectric signal conversion to form an acoustic electric signal stream through a sound signal conversion transmission unit. Hierarchical voiceprint feature extraction is sequentially performed through an optical fiber voiceprint feature self-encoding model, feature refinement and redundancy elimination are realized by using a voiceprint feature distillation optimization algorithm, time sequence feature correlation is constructed through a sound signal time sequence prediction identification model, and finally online analysis and category determination are completed by a sound signal AI real-time identification engine. The application fully adapts to the characteristics of optical fiber sensing signals, strengthens the synergy of feature processing and time sequence modeling, improves the identification response speed and accuracy, effectively solves the problems of insufficient feature synergy and time sequence adaptation in the prior art, and can be widely applied to online analysis of sound signals in complex scenes such as industrial monitoring and security warning.

The utility model relates to the technical field of cold box interlayer gas oxygen content analysis, in particular to a cold box interlayer gas oxygen content on-line analysis device which comprises a sampling pump, a three-way electromagnetic valve, a pretreatment box, an analysis instrument and a control system, wherein the sampling pump is used for pressurizing and introducing interlayer gas of the cold box into the analysis device; the analysis instrument is used for analyzing the oxygen content in the pretreated gas; the three-way electromagnetic valve is arranged between the sampling pump and the analysis instrument and is used for remotely switching and analyzing the waste nitrogen and the cold box interlayer gas; the pretreatment box is connected between the three-way electromagnetic valve and the sampling pump. Through remote switching of the three-way solenoid valve and automatic logic setting of the DCS, online intelligent analysis of the oxygen content of the interlayer gas of the cold box is achieved, and the analysis accuracy and efficiency are improved; and an operator can finish the analysis work of the waste nitrogen and the interlayer gas of the cold box without frequently and manually switching an analysis instrument, so that the operation burden is reduced.

This utility model discloses a purification system for adjustable fuel gas specific gravity and calorific value, comprising a mixing tank, a primary pressurization device, a harmless treatment tower, an adsorption purification device, and an online analysis unit. The mixing tank is connected to the primary pressurization device via a pipeline, the primary pressurization device is connected to the lower part of the harmless treatment tower via a pipeline, and the upper part of the harmless treatment tower is connected to the adsorption purification device via a pipeline. The adsorption purification device is electrically connected to the online analysis unit for real-time monitoring of petrochemical gas component data entering the primary pressurization device, transmitting the data to the adsorption purification device for adjusting process parameters. The purification system for adjustable fuel gas specific gravity and calorific value provided by this utility model allows the fuel gas specific gravity to be adjusted to any desired value, which is beneficial for generating sufficient calorific value during fuel gas combustion.

The application relates to the technical field of analytical chemistry detection, and specifically discloses a rare earth ion chromatographic online analysis and detection method and system, which adopts a chromatography-mass spectrometry combined system, realizes multi-scale time-frequency decomposition of signals by constructing an adaptive wavelet base function library, accurately identifies and classifies interference types in combination with a convolutional neural network and vacuum degree coupling analysis; a U-Net generator with an attention mechanism and a multi-scale discriminator are designed for signal repair aiming at repairable interference; a state transition model containing a mass-to-charge ratio database is established, empirical mode decomposition and adaptive filtering technology are used to realize dynamic calibration of a mass axis; quantitative accuracy is ensured through double internal standard correction and a triple verification mechanism; when unrepairable interference is detected, a hierarchical self-checking program is started, and fault diagnosis is carried out in combination with spectrum fingerprint analysis.

The application relates to an online analysis system applied to a high-performance resin production line, and relates to the technical field of resin production, which comprises a sampling module, a main flow path module, a rapid reflux module, an analysis and detection module and a tail gas recovery treatment module. The sampling module can be inserted into a resin gas generation flow device, and can convey resin gas to the analysis and detection module through the main flow path module. The analysis and detection module can detect and analyze the resin gas. The two ends of the rapid reflux module are connected with the main flow path module and the tail gas recovery treatment module, so that part of the resin gas in the main flow path can be branched to the tail gas recovery treatment module. The analysis and detection module is connected with the tail gas recovery treatment module, so that the resin gas after detection and analysis can be recovered and treated. The application has the effects of improving the gas flow rate, shortening the time for reaching an analyzer, reducing detection errors and improving the detection accuracy of the online analysis system.

The invention provides a performance testing method and device and a storage medium. The method comprises the steps that all executable programs of a software system submitted by a user and modified program files in the software system are received; based on the program file and all executable programs of the software system, performing a performance test to obtain a test report; and sending the test report to the user. According to the application, a user only needs to send all the executable programs of the software system and the currently modified program file to the server. And the server automatically completes the performance test of the software system and returns a test report to the user. Automatic testing of the whole process of the software system is achieved, the automation degree is high, and the testing efficiency is improved. And the test is performed only when the modified program file appears, so that the incremental performance test is realized, the performance test is more targeted, and the test efficiency and accuracy are improved. And tests in the aspects of off-line analysis, on-line analysis, integration test and the like can be carried out, and the performance test is more comprehensive, efficient and accurate.

The invention relates to the field of cement production equipment, in particular to an air volume intelligent control system and method for efficient intelligent pneumatic mixing equipment. The control system comprises a CaO content online analyzer which is integrated at the front section of a feeding point of a warehousing bucket elevator and is used for detecting the CaO content in a cement finished product in real time; the control unit is in communication connection with the sensing unit and is configured to calculate the total air volume requirement in real time based on the following parameters: real-time warehousing bucket lifting current I; the CaO content Wt is measured in real time; presetting a reference current I0, a CaO target value Wset, a current adjusting coefficient KI and a CaO content adjusting coefficient Kw; the air supply unit is controlled by the control unit and inputs mixing high-pressure air flow into the cavity of the multi-chamber mixing box, and the operation frequency and the air flow of the fan can be adjusted according to the total air volume requirement. The cement mixing device is used for the mixing procedure of all component cement finished products in the cement production process, efficient mixing of all component cement under different working conditions is guaranteed, and the use amount of clinker in cement is further reduced.

The present application relates to the technical field of oil and gas production, in particular to a kind of crude oil production water content online analysis and measuring device, including pipeline positioning mechanism, centering clamping mechanism and lifting mechanism, lifting mechanism is arranged on the side of oil and gas pipeline, pipeline positioning mechanism and centering clamping mechanism are arranged on the lifting mechanism, when installing water content online analyzer to oil and gas pipeline, the accurate control of the installation position of water content online analyzer is realized, the setting position and inclined posture of the sensing probe of water content online analyzer in oil and gas pipeline are guaranteed.

The invention provides a method, a device and a system for rapidly analyzing components of direct coal liquefaction oil based on a near infrared spectrogram, the method comprises the following steps: acquiring sample data of the direct coal liquefaction oil, the sample data of the direct coal liquefaction oil comprises near infrared spectrum data of the direct coal liquefaction oil and detailed composition data of sample oil; according to sample data of the direct coal liquefaction oil and a ResNet-34 neural network algorithm, the near infrared spectrum of the liquefied oil with known composition is subjected to statistical treatment, the relationship between the liquefied oil composition and a spectrogram is obtained, then the near infrared spectrum spectrogram of an unknown oil product is identified to predict the composition, and a direct coal liquefaction oil component prediction model is established. The prediction model is at least used for component prediction of the direct coal liquefaction oil; the direct coal liquefaction oil in the production process is analyzed according to the prediction model obtained through training, online analysis can be rapidly and accurately conducted on the direct coal liquefaction oil, and the method can solve the technical problems that in the direct coal liquefaction oil production process, analysis is difficult, and the analysis period is long relative to the production period.

The application discloses an oil blending component oil proportioning optimization control method, system, device and medium based on soft measurement. The application predicts the quality attribute of the finished oil in real time through a soft measurement module, obtains an attribute prediction value, determines a target gain with the original proportioning data, and corrects the model gain of a multivariable controller online by using the target gain, generates adjusted target proportioning data by the multivariable controller, and issues a formula control instruction to a control device, so that the component oil proportioning is accurately adjusted. When the component oil formula or attribute changes, the application can automatically and timely adjust the controller gain, effectively overcome the nonlinearity of the blending process, improve the control precision and system robustness, thereby guaranteeing the stability of the finished oil quality and avoiding the waste of high-value component oil. By introducing the soft measurement technology, the method can replace the traditional online analyzer to a certain extent, reduce the dependence on expensive instruments, and improve the economy and maintenance convenience of the system.