51results about How to "Realize quantitative analysis" patented technology

The invention relates to the technical field of intelligent sensing detection, in particular to an underground water detection system for hydraulic ring geological engineering based on data acquisition. The specific implementation process comprises the following steps: receiving a water quality sensing signal, and inhibiting a background interference domain and calculating a water quality physicochemical characteristic vector by utilizing water multiphase flow interference separation logic and comparing a physical scattering spectrum with a polarization drift spectrum; inputting the water quality physicochemical characteristic vector into a water quality physicochemical dynamic response model, executing concentration gradient variation modulus analysis, inverting a performance kinetic energy index, comparing the performance kinetic energy index with a water chemical equilibrium critical threshold, and regulating and controlling the sampling frequency; when the sudden change event is judged, generating a transient capture signal to execute locking sampling, and when the threshold is not broken through, generating a baseline water quality inspection signal to execute steady state monitoring. According to the method, the detection frequency is automatically adjusted by calculating the evolution kinetic energy index, the physical background interference is inhibited by utilizing the cross-correlation analysis between spectrums, the deviation degree of chemical equilibrium of the water body is quantified, and the fidelity of underground water monitoring data is improved.

The invention belongs to the technical field of industrial automation and intelligent manufacturing, and particularly discloses a steel analysis laboratory intelligent monitoring management system based on multi-source data fusion, which comprises a parameter monitoring module, a process judgment module, a path decision module, a deviation analysis module, a deviation propagation judgment module and a health assessment module. According to the method, the equipment operation state parameters and the material quality parameters of the steel sample in each process are collected in real time, and the preset steel quality detection standard is combined to judge whether the current process is qualified or not, so that the problem that equipment monitoring and quality detection are mutually disjointed is solved; meanwhile, the state deviation degree of the previous procedure and the real-time monitoring parameters of the subsequent procedure are subjected to fusion analysis, cross-procedure quality influence evaluation is established, then the propagation path of the quality deviation between the procedures is dynamically tracked, the process set value of the subsequent procedure is accurately adjusted based on the deviation of the previous procedure, and the quality of the subsequent procedure is improved. And beforehand intervention on the quality risk is realized.

The utility model relates to the technical field of rice processing, in particular to an internal pressure measuring and controlling device of a rice polishing machine, which comprises a polishing roll, a roll screen is arranged outside the polishing roll, a plurality of groups of support ribs are distributed on the outer wall of the roll screen, and the support ribs are fixedly welded with the surface of the roll screen. A supporting frame is arranged between every two adjacent supporting rib pieces, the two ends of each supporting frame are fixed to the corresponding supporting rib pieces through screws, a plurality of cushion blocks are distributed on the inner side of each supporting frame, strain gauge carriers are arranged on the sides, close to the roller screen, of the cushion blocks, and the supporting frames, the cushion blocks and the strain gauge carriers are fixed through screws. The construction of the internal pressure detection system of the polishing machine is completed by designing a multi-point strain gauge measurement sensor and a multi-path acquisition module; based on measurement data of an internal pressure detection system, quantitative analysis on the influence of factors such as the position of a pressing weight, the rotating speed and feeding control on the polishing pressure is achieved.

The invention provides a detection method and application of amphoteric and cationic perfluorinated and polyfluoroalkyl compounds, the method optimizes an extraction agent, can more comprehensively detect, qualitatively and quantitatively analyze amphoteric and cationic PFAS in solid samples such as biological solids, soil and the like, has high accuracy and detection precision, and can be widely applied to detection of amphoteric and cationic perfluorinated and polyfluoroalkyl compounds. The amphoteric and cationic PFAS with more unknown structures can be identified and analyzed, the blank of the prior art in the aspect of extracting and detecting amphoteric and cationic perfluorinated and polyfluoroalkyl compounds is filled, the occurrence level and fate-to-transition behavior of the amphoteric and cationic PFAS in the environment can be accurately analyzed, traditional and novel amphoteric and cationic PFAS in the environment can be identified, and the detection accuracy of the amphoteric and cationic perfluorinated and polyfluoroalkyl compounds is improved. And a scientific basis is provided for new pollutant treatment in China.

This invention relates to the field of semiconductor material testing technology, specifically a single-crystal silicon wafer slip line detection system and method, comprising: a light source module emitting light signals of different wavelengths and polarization angles to scan the silicon wafer; a silicon wafer scanning module mounting the silicon wafer under test, adjusting the position of the silicon wafer under test, and executing a scanning trajectory to scan the silicon wafer with light signals; a signal acquisition and processing module receiving the light signals transmitted through the silicon wafer under test, performing preprocessing to obtain DU values ​​at different wavelengths and polarization angles, and sending them to an industrial control computer; the industrial control computer reconstructing the strain field based on the DU values ​​using a strain field reconstruction algorithm to generate a strain field distribution map; and identifying slip lines based on the strain field distribution map using a convolutional neural network model. This solution enables non-contact, non-destructive, and high-resolution slip line detection, thereby improving detection accuracy and efficiency.

This invention discloses a method and system for identifying urban decline spaces, belonging to the field of urban planning technology. The method includes: S1. Acquiring population grid data and building area grid data of a target area for multiple consecutive years within a time interval; S2. Performing spatial alignment and standardization processing on the population grid data and building area grid data to form a standardized annual spatial database; S3. Removing grid units in the annual spatial database that do not possess effective urban activity characteristics to obtain a set of effective urban spatial grids; S4. Performing time series analysis on the effective urban spatial grid set to determine urban decline space grids; S5. Using a spatial clustering algorithm to cluster the determined urban decline space grids, dividing the urban decline space into continuous urban decline space and scattered urban decline space. This invention achieves automatic identification and classification of continuous and scattered urban decline spaces by analyzing the population and building area change trends over multiple consecutive years.

The invention belongs to the field of petroleum geological exploration, particularly relates to a method for determining the reservoir forming time of a deep concave area of a continental facies fault lake basin, and aims to solve the problems that the reservoir forming time of the deep concave area of the continental facies fault lake basin can only be obtained in a semi-quantitative manner and the reservoir forming time of the deep concave area of the continental facies fault lake basin is easily misjudged in the prior art. The method comprises the following steps: determining an oil-containing well section, selecting sandstone with developed calcite cement as a sample, and making the sandstone into slices; determining the type of a fluid inclusion, and further determining the type of an oil inclusion; obtaining the rare earth element partition mode similarity of the slices, and confirming the cementation period of the slices; selecting slices which contain the native inclusion and are in the same cementation period, and making the slices into a circular target sample; and performing U and Pb element scanning on the circular target sample, and performing U-Pb age calculation through a graphical method to obtain the reservoir forming age of the deep concave area of the continental disruption lake basin. According to the method, quantitative analysis of the reservoir forming time of the deep concave area of the continental fault lake basin is realized, and the correctness of reservoir forming time judgment is improved.

The application provides a method for determining residual guanidium gel content in guanidium gel breaking fluid. The method comprises the following steps: sample pretreatment; preparation of guanidium gel breaking fluid series samples with different guanidium gel contents; taking the content of guanidium gel standard in the guanidium gel breaking fluid series samples with different guanidium gel contents as a response value, taking the characteristic fluorescence peak intensity and viscosity of the guanidium gel breaking fluid series samples with different guanidium gel contents after blank deduction as variables, performing regression analysis to obtain a standard curve equation of residual guanidium gel; and substituting the characteristic fluorescence peak intensity and viscosity of the pretreated guanidium gel breaking fluid sample after blank deduction into the standard curve equation of residual guanidium gel to obtain the content of residual guanidium gel in the guanidium gel breaking fluid sample to be determined. The determination method is scientific and reasonable, has high repeatability and accuracy, realizes qualitative and quantitative analysis of residual guanidium gel in guanidium gel breaking fluid, and can support efficient resource treatment of oil and gas field fracturing flowback fluid.

A method for determining the association and continuity combination of top-side-rock structural surfaces based on unsupervised learning is proposed. This method involves investigating the structural surfaces of the roof and side-rock masses in underground engineering projects to obtain geometric and statistical information. A unified encoding is performed to construct a joint feature set of roof-side-rock structural surfaces. An unsupervised density-based clustering algorithm is used for initial cluster analysis of the joint feature set. A swarm intelligence-based Lüperfox optimization algorithm is introduced to optimize key parameters of the DBSCAN clustering algorithm. A Latin hypercube sampling method is used to improve the population initialization process of the Lüperfox optimization algorithm, resulting in better DBSCAN hyperparameters and optimized structural surface clustering results. The method determines the structural surface system affiliation of structural surfaces in different locations, quantitatively calculates the spatial continuity and combination relationship of structural surfaces, and outputs the spatial association determination results and continuity levels of the roof-side-rock structural surfaces. This method enables intelligent identification and quantitative analysis of the spatial relationship between roof and side-rock rock structural surfaces.

This invention belongs to the field of intelligent manufacturing technology and discloses a real-time data-driven intelligent digital twin system for the docking of aerospace components. First, a standardized communication interface is established between multi-source sensors and various modules of the system to collect and transmit sensor data in real time. Second, based on the real-time multi-axis displacement data of the positioner, the real-time motion trajectory of the component is derived through kinematic modeling, completing the spatial pose parameter calculation of the component. Finally, the component pose parameters and multi-dimensional force sensing data are fused to construct a dynamic and interactive three-dimensional digital twin interface, realizing twin monitoring of the component's motion state and internal forces during the docking process. This system, based on the Unity3D engine, achieves real-time synchronous mapping of multi-dimensional force information and motion attitude parameters. Through a closed-loop processing flow of data acquisition, analysis, and visualization, it realizes quality assessment and quantitative analysis of the aerospace component assembly process, significantly improving the controllability of the docking process.

The embodiment of the application provides a kind of soft package battery's multiple field in-situ strain characterization method and device, with shear thickening electrolyte, soft package battery speckle mark surface is upwards, it is placed in the recess of in-situ observation platform, and positive and negative lug is connected to hard plate;Using intelligent temperature control device, the ambient temperature is adjusted to experimental temperature, infrared thermal imager is opened, and it is aligned with soft package battery;Fixed mechanical loading device is below the impact window of in-situ observation platform, and the adjustable power supply outside explosion-proof cover is connected with point device;Soft package battery is connected with battery test system and carries out charge-discharge test;CCD camera is connected with computer and is aligned with in-situ observation platform observation window, and starts shooting;Adjust the adjustable power supply parameter outside explosion-proof cover, and start mechanical loading device;Output the first mechanical behavior information and the second mechanical behavior information corresponding to the first time and the second time of soft package battery;According to the first mechanical behavior information and the second mechanical behavior information, the surface strain of soft package battery is obtained.

This invention discloses a method for evaluating the bus voltage stability domain of a cascaded system under small current disturbances, relating to the field of power supply and distribution system technology. The method includes the following steps: constructing an impedance ratio test circuit; introducing a non-intrusive current signal disturbance into the bus voltage of the cascaded system using an impedance analyzer to excite the operation of the cascaded system feedback loop; simplifying the Middlebrook stability criterion to obtain an improved stability criterion; integrating the amplitude-frequency Bode plot data corresponding to each load operating point obtained from the impedance ratio test circuit and fitting the amplitude-frequency Bode plot data of each load operating point using Matlab to obtain a fitted surface based on the test results; comparing the fitted surface with the judgment region determined based on the stability criterion to determine the stable operating boundary of the cascaded system over a wide load range. This invention solves the bus voltage stability problem of black-box cascaded converter systems under a wide load range and proposes a test method that does not require intrusion into the bus line.

This invention relates to a method and system for monitoring the operating status of a transformer. The steps include: real-time monitoring and acquisition of key operating data of the transformer; preprocessing the key operating data; extracting key features from the preprocessed key operating data; constructing multivariate time series data; reconstructing the phase space using time delay embedding technology; constructing a chaotic attractor from the time series data; and conducting in-depth analysis of the morphology and dynamic evolution of the chaotic attractor through topological data analysis to identify abnormal states of the transformer. This invention, by introducing chaotic attractors and topological analysis, not only deepens the understanding of the complex behavior of transformers but also enables quantitative analysis of these behaviors to accurately identify abnormal states and provide early warnings before faults occur, thereby ensuring the safety and reliability of transformer operation.

The invention discloses a knee joint prosthesis evaluation method and system, and the method comprises the steps: carrying out the multi-modal data collection according to the biomechanical performance test data of a knee joint prosthesis, the medical image data and the postoperative motion capture data of a patient, and carrying out the data fusion through a multi-modal alignment network based on deep learning, a fused multi-modal data set is obtained; inputting the multi-modal data set into an adaptive convolutional neural network, and extracting mechanical characteristics of the knee joint prosthesis under a dynamic load to obtain a dynamic mechanical characteristic matrix; and inputting the dynamic mechanical characteristic matrix into a performance evaluation model based on a multi-scale attention mechanism, performing prosthesis performance quantitative analysis, and generating a performance evaluation report. By utilizing the embodiment of the invention, comprehensive quantitative analysis of the prosthesis performance can be realized, and scientificity and accuracy of prosthesis design are improved.

The invention relates to the technical field of online car hailing, and discloses a transport capacity management method and device and computer equipment, and the method comprises the steps: a server side receives an original position point reported by a user side in a preset range, updates the original transport capacity distribution information of a geographic grid corresponding to the preset range in a server map according to the original position point, and stores the original transport capacity distribution information; generating a corresponding transport capacity scheduling strategy; and the user side sends an original position point of the user side to the server side, receives a push data packet returned by the server side based on the original position point, analyzes the scheduling suggestion information and the transport capacity feedback information in the push data packet, executes an updating operation of a user side map according to the transport capacity feedback information, and executes a reminding operation of the scheduling suggestion information at the same time. Through interactive cooperation of the server side and the user side, dynamic updating and accurate scheduling of transport capacity distribution are realized, and the utilization efficiency of transport capacity resources is effectively improved. According to the invention, the problem of unbalanced supply and demand of transport capacity caused by asymmetry of driver and passenger information in the prior art is solved.

The embodiment of the application relates to the field of rock physical experiments, in particular to a method for identifying rock pore types, an electronic device and a storage medium, the method comprising the following steps: obtaining ultrasonic longitudinal wave speed and / or ultrasonic transverse wave speed of a target rock sample after ultrasonic longitudinal wave and transverse wave testing under different temperature and pressure conditions; establishing a temperature-pressure and speed covariant fitting equation based on the ultrasonic longitudinal wave speed and / or ultrasonic transverse wave speed under different temperature and pressure conditions; determining a pore type of the target rock sample according to a fitting coefficient in the temperature-pressure and speed covariant fitting equation based on a preset mapping relationship between pore types and fitting coefficients. The application establishes a temperature-pressure and speed covariant fitting equation, extracts a fitting coefficient, and realizes the differentiation of rocks with different pore types based on the fitting coefficient, thereby improving the identification efficiency of pore types.

The application discloses a kind of based on knowledge graph and big model fusion's fishery science and technology project innovativeness evaluation method, the method is as follows: S1: from English literature database and patent database automatically collects fishery science and technology related paper and patent data, constructs multi-source heterogeneous dataset;S2: to multi-source data is de-duplication, cleaning and standardization processing, through word segmentation, word class restoration and feature extraction form unified structured data representation;S3: based on structured data carries out entity, relationship and attribute extraction, combines cross-language alignment and expert knowledge verification constructs fishery science and technology field knowledge graph;S4: to project research content and existing technology carries out semantic correlation matching and difference analysis;S5: constructs and includes novelty and frontiers innovativeness evaluation model, carries out quantitative evaluation to project and exports evaluation result.The method not only improves the accuracy and systematization level of innovation point identification, but also guarantees the traceability and comparability of evaluation result.

The present application relates to remote sensing technology and ecological environment restoration evaluation technical field, especially in kind of based on multi-source remote sensing data's mine ecological restoration project achievement evaluation method.The method includes the following steps: to collect mine restoration area carries out multi-source remote sensing data cooperation collection and pretreatment, obtains the noise reduction data set;The multi-dimensional cooperative sensing processing is carried out to the noise reduction data set, and the multi-dimensional cooperative sensing data set is obtained;The soil spectrum feature separation extraction is carried out to the multi-dimensional cooperative sensing data set, and the soil spectrum feature data are obtained;The ground temperature distribution map is calculated according to the soil spectrum feature data;The surface soil moisture content inversion is carried out according to the soil spectrum feature data and the ground temperature distribution map, and the surface moisture content distribution map is obtained;The deep soil moisture content estimation is carried out according to the surface moisture content distribution map, and the soil moisture profile data are obtained.The present application improves the restoration input benefit by quantitative inversion of soil physical parameters and dynamic analysis of rainfall response process.

The invention relates to the technical field of artificial intelligence, can be applied to medical auxiliary diagnosis, financial risk analysis and other scenes, and provides a multi-agent cognitive strategy training and evaluation method, system and device and a medium, and the method comprises the steps: constructing a teacher agent and a plurality of learner agents with different learning behavior characteristics; running a simulation teaching cycle, controlling the teacher agent to publish learning stage information based on a preset scene problem, and controlling each learner agent to execute the information to generate an interaction response containing process behavior data and learning result data; recording process behavior data and learning result data of each agent, and generating a cognitive strategy evaluation report of each agent based on the process behavior data and the learning result data; and combining the plurality of learner agents into a multi-agent cluster capable of cooperatively solving the preset scene problem according to strategy features indicated by the evaluation report. According to the invention, dynamic simulation and evaluation of the agent cognitive strategy are realized, and a cooperative solution cluster oriented to an actual scene can be constructed according to the dynamic simulation and evaluation.

The application discloses a clustering division method and system suitable for magnetic resonance images and a magnetic resonance image information processing device. The method is realized by fusing magnetic resonance image features by using a multi-view algorithm. The method comprises the following steps: acquiring at least two feature similarity matrices according to magnetic resonance image information; fusing all the acquired feature similarity matrices by using a multi-view algorithm to obtain a fusion matrix, wherein the multi-view algorithm is realized by using a similarity network fusion method; and determining a clustering result of the magnetic resonance image information according to the obtained fusion matrix. The clustering division of the magnetic resonance image information is realized by extracting and fusing the magnetic resonance image features, and the clustering result is more accurate and more comprehensive.

This invention belongs to the field of marine acoustic detection and remote sensing technology, and discloses a method and system for inverting the abundance of deep-sea mixed sediments based on bottom echo data. The method involves preprocessing the bottom echo waveforms of multibeam bathymetry data to obtain a bottom echo sequence; extracting features from the bottom echo sequence waveforms and concatenating these features to the corresponding bottom echo waveforms to form a new waveform sequence; fusing the enhanced waveform sequences to construct an observation matrix Y; decomposing the observation matrix Y into an endmember waveform matrix H′ and an abundance distribution matrix W′, and introducing a minimum volume constraint during the decomposition process; adding a spectral reconstruction error term based on dynamic time warping to the loss function; iteratively optimizing H′ and W′ using gradient descent or multiplicative update rules until convergence; and obtaining the endmember waveform H′, which represents the acoustic response waveform of typical sediment types, and the abundance matrix W′, which reflects the mixing ratio of each endmember at different sampling points, demonstrating high classification accuracy.

The invention relates to the technical field of diagnosis and early warning, and discloses an intelligent diagnosis and early warning method and system based on a digital-intelligent integrated platform, and the method comprises the steps: obtaining a plurality of groups of health state data of a monitored object at all collection moments within a preset duration before a current moment, and carrying out the discrimination, and obtaining a plurality of health state data sets; performing multi-source analysis on each health state data set, and calculating a health state metric value of the monitored object; calculating a health state diagnosis coefficient of the monitored object according to the health state metric value corresponding to each health state data set; according to the relation between the health state diagnosis coefficient and the preset health state diagnosis coefficient, whether the monitored object has a fault is judged, if yes, fault early warning reminding is sent out, quantitative analysis of overall distribution and abnormal fluctuation conditions of multi-source data is achieved, the accuracy and early warning capacity of fault diagnosis are remarkably improved, and the fault diagnosis efficiency is improved. And the intelligent operation and maintenance level of the digital-intelligent integrated platform is improved.

The invention relates to the technical field of intelligent medical treatment, in particular to an artificial intelligence-based acupoint stimulation system, which is characterized in that strain values generated by contact between acupoint electrodes and skin are acquired through a data extraction module, each acupoint electrode is controlled to be in a pre-output state through a stimulated evaluation module, and stimulated acupoints are screened in the pre-output state; the stimulated condition is judged through an intelligent analysis module according to the analysis result of the stimulated performance; and judging whether the first stimulation parameter is qualified or not based on the stimulated condition through a positioning adjustment module, or determining a second output parameter. Furthermore, a dynamic parameter adjustment mechanism is generated based on human physiological signals and muscle response characteristics, and the precision and intelligence of the acupoint stimulation process are improved according to physiological association among different acupoints in a multi-acupoint collaborative stimulation scene.

The invention discloses a training video generation method for reconstructing a traffic accident scene, belongs to the technical field of intelligent traffic, and can solve the problem that the pertinence and effectiveness of driving training are reduced because the conventional scene reconstruction method is difficult to reflect typical characteristics of a real traffic accident scene. The method comprises the following steps: S1, generating a plurality of accident samples according to a plurality of traffic accident video data, and extracting a plurality of scene features of each accident sample; s2, performing scene classification on all accident samples according to scene features to obtain a plurality of scene categories and corresponding scene sample sets; s3, determining difference significant features from the scene features according to the scene sample set, and determining key influence features of each scene category from sub-features of the difference significant features; and S4, forming a scene reconstruction data set by using all the key influence characteristics of each scene category, and generating a training video of the corresponding scene category according to the scene reconstruction data set. The method is used for reconstructing the traffic accident scene.

The invention belongs to the field of simulation experiment systems, and discloses a deep-sea mining simulation experiment system which comprises a mining system model assembly, a buoyancy traction assembly, an identification piece and a visual positioning assembly. The mining system model assembly comprises a hull model, a mining vehicle model and a flexible connecting assembly, the buoyancy traction assemblies are sequentially distributed in the length direction of the flexible connecting assembly, and the buoyancy traction assemblies can adjust the space bending form of the flexible connecting assembly under different working conditions so as to simulate the dynamic deformation process of a hose under the action of ocean current. The multiple marking pieces are sequentially connected to the outer surface of the flexible connecting assembly at preset intervals, and the visual positioning assemblies are arranged in multiple directions. The real-time positions of all the identification pieces in the three-dimensional space are shot, the relative displacement and angle changes of the identification pieces are calculated through a vision measurement algorithm, then the overall space pose parameters of the flexible connection assembly are obtained through inversion, and quantitative data support is provided for analyzing the coupling motion relation between a mining vehicle model and the flexible connection assembly.

The invention relates to the technical field of concrete filled steel tube pumping and pouring, in particular to an in-tube concrete bubble escape test device and method, and the device comprises a test tube, a support frame, an air injection device and a gas collection device; the test tube is used for simulating an actual steel tube, is provided with an exhaust hole and is hermetically connected with the gas collecting device; the support frame is used for bearing and fixing the test tube and simulating different erection states of the steel tube by angle adjustment; and the air injection device is used for quantitatively injecting air into the concrete in the pipe and simulating a field bubble generation scene. The device can accurately simulate on-site steel pipe erection and concrete pouring working conditions, real-time observation and multi-index quantification of bubble escape are achieved, a scientific basis is efficiently provided for construction parameter optimization, and the device is simple in structure and capable of being repeatedly used and has high practical value and industrial popularization prospects.

The invention provides a dam micro crack detection and dynamic trend prediction method. According to the method, high-resolution images of the dam surface are shot through unmanned aerial vehicle cruising, and a two-dimensional data set is constructed; an improved YOLOvll small target detection model is adopted to extract spatial positions and scale parameters of the cracks, a crack time sequence matrix is established, and spatio-temporal evolution of the cracks is recorded; the time sequence matrix is input into a pre-training mask auto-encoder for masking and reconstruction, and the future evolution trend of the crack is predicted; and according to a prediction result, extracting length, width and area changes of the crack, calculating a risk index through a weighted risk scoring model, and finally outputting a risk level and a spatial distribution diagram of the crack. According to the method, crack identification and time sequence modeling are combined, an integrated solution is provided, the crack detection precision and stability are improved, the crack evolution trend prediction capability is enhanced, and technical support is provided for dam safety monitoring and management.

The application provides a data analysis method and system applied to industrial think tank assisted decision-making, relates to the technical field of industrial data analysis, and first acquires an initial industrial operation state record set uploaded on a time sequence collection link in an industrial field, then carries out industrial state thinking disassembly and hierarchical processing on the initial industrial operation state record set, generates an industrial thinking disassembly transmission chain with a hierarchical causal transmission path, inputs the industrial thinking disassembly transmission chain into a pre-constructed industrial thinking chain distillation migration network, obtains an industrial thinking transmission deduction track record, then determines an industrial regulation and control decision element combination according to the industrial thinking transmission deduction track record, and finally generates an industrial think tank assisted regulation and control instruction sequence according to the industrial regulation and control decision element combination, which is used for driving an industrial field regulation and control execution terminal to change an operation parameter configuration state. The application can accurately mine the causal relationship of industrial data, provide effective decision-making, and improve the industrial production efficiency.

This invention discloses an intelligent inspection grid positioning and data management method for large-area continuous engineering facades, relating to the field of engineering safety monitoring and inspection technology. It is particularly applicable to engineering facades with large areas, continuous surfaces, and lacking significant feature points, such as dam faces and engineering slope surfaces. The method includes: firstly, unified management of positioning and image data collected by intelligent inspection equipment, mapping the collected data onto an orthophoto map of the large-area continuous engineering facade to facilitate accurate location and tracking of defects during inspection; secondly, a grid-based representation method for surface defect data of large-area continuous engineering facades is proposed, establishing a grid-based defect data representation model for large-area continuous engineering facades to achieve standardized recording and efficient management of defect information.

The application discloses a kind of digital twin driven carbon fiber spinning oil intelligent regulation and control method and system, belong to artificial intelligence and intelligent manufacturing field.The method is by real-time acquisition nozzle multi-source sensing data and high-speed spray image, drive high-fidelity CFD digital twin model simulation internal flow field, and combine image processing extracted external spray feature, utilize pre-trained neural network model to predict the risk index of blockage.System according to risk level automatically triggers different cleaning procedures, including preventive ultrasonic cleaning and enhanced thermal-mechanical synergistic cleaning, and after cleaning, by reacquiring data to verify effect and calibrate model, form the closed loop of perception-simulation-decision-execution-verification.The application realizes early warning, accurate evaluation and active intervention to nozzle blockage, effectively guarantees oil agent spraying uniformity and the continuous stability of carbon fiber production.