356 results about "Differential analysis" patented technology

The invention relates to the technical field of financial data processing, in particular to a multi-source heterogeneous fund data processing method and system, and the method comprises the steps: recording source information through building a data source registry, and marking a unique identifier for data; performing differential analysis on the fund data in different formats to generate standardized column type storage data; traversing column type storage data to extract statistical characteristics, performing field classification through metadata analysis and financial dictionary matching, analyzing business connotations of fields difficult to classify in combination with a localized large language model, and mapping the business connotations to a header fusion knowledge graph; generating a mapping rule from the source field to the enterprise-level data model by applying a rule engine template on the basis of field classification and semantic recognition results; converting the data structure according to the mapping rule and executing standardization processing; the quality is further optimized through data cleaning; and finally, the data are verified, standardized fund data supporting data traceability are output, and the strict requirements of financial supervision application are met.

The invention relates to the field of electric power monitoring, and discloses an electric power monitoring system and method fusing image recognition and data analysis, and the method comprises the steps: carrying out the visual angle coverage modeling of a target equipment group through a multi-type visual collection unit disposed at a transformer substation and a power distribution terminal; performing cross-frame fine-grained texture differential analysis on the equipment state image sequence, and constructing an image event time window in combination with synchronous disturbance characteristics of multi-source monitoring parameters; based on the high-vigilance candidate frame set, fusing the image structure variability index and the operation data multi-dimensional deviation vector by using a feature encoder, and constructing a multi-modal state coupling feature tensor; map mapping is carried out on the potential fault evolution trend, and semantic association is established between structural nodes with abnormal attributes in the image and frequently fluctuating parameter indexes in the monitoring data; and combining a node interference path in the local fault association subgraph with fault precursor distribution induced in a historical accident sample. The method has the advantage that the operation safety is improved.

The invention relates to a method and a device for optimizing a digital twin model of power equipment. The method comprises the following steps: acquiring operation monitoring data and a digital twinborn simulation result of target power equipment to perform difference analysis, and determining a deviation between a digital twinborn model of the target power equipment and an actual operation state of the equipment; a prediction error matrix updated in real time is obtained based on the deviation, parameters of the digital twin model are dynamically adjusted according to the prediction error matrix, and a model after parameter correction is obtained; simulating dynamic characteristics of the target power equipment under different fault conditions by adopting the model after parameter correction, and correcting according to the dynamic characteristics to obtain a fault scene adaptation model; and performing parameter distribution optimization and model uncertainty correction on the fault scene adaptation model through model iteration processing to obtain an optimized digital twin model of the target power equipment. By adopting the method, dynamic correction and optimization of the digital twinborn body of the power equipment under complex working conditions can be realized, and the precision and reliability of the digital twinborn model are effectively improved.

The invention relates to the technical field of map data acquisition, in particular to a surveying and mapping geographic information map data acquisition method and system, and the method comprises the steps: building a space-time reference network: generating an underground-earth surface space-time reference network for connecting the underground with the earth surface through acousto-optic joint calibration; generating a multi-source correction parameter: fusing the underground inertial navigation data, the surface atmospheric refraction parameter and the equipment temperature drift coefficient, and calculating a multi-source correction parameter set for coordinate system conversion and error compensation; space-time evolution element collection: calibrating a mobile collection device in real time by using the multi-source correction parameter set, synchronously obtaining underground pipeline point cloud, an earth surface building image and a construction machinery track, and generating a space-time evolution element body with a space-time version label; and dynamic topology evolution modeling: analyzing an element change sequence in the spatio-temporal evolution element body, and constructing a dynamic evolution topology model. According to the method, differential analysis and a conflict backtracking strategy are combined, so that the adaptive updating and decision support capability of the map data in a construction disturbance scene is improved.

The invention discloses a low-altitude atmosphere three-dimensional wind field inversion method based on wind profile radar vertical observation, relates to the technical field of three-dimensional wind field inversion, and aims to solve the problem of poor inversion effect caused by inaccurate model parameters in the wind field inversion process. The difference between simulation data and actual observation data is evaluated from multiple angles, a comprehensive optimization basis is provided, parameter calculation optimization is performed by adopting a particle swarm optimization method, the method has the characteristics of high convergence speed and high global search capability, is suitable for optimization of complex problems, and is used for confirming model requirements according to a wind field inversion rule. The model is selected by combining the evaluation result of the model library, it is ensured that the selected model has high pertinence and applicability, the prediction precision and reliability of the model are improved, model initialization is conducted through wind field fusion data, data information of multiple sources is fully fused, and the comprehensiveness and accuracy of the data are improved.

The invention discloses a river sediment multi-parameter sensing self-adaptive sampling method and system, and relates to the technical field of environment monitoring, and the method comprises the following steps: S1, synchronously collecting the pH value, electrochemical potential, heavy metal binding state proportion, water body flow velocity and hydrodynamic parameters of sediment through a sensing device, and establishing a multi-parameter response baseline; and S2, based on the multi-parameter response baseline, collecting monitoring data in real time and executing differential analysis, and identifying a high-risk time period when the pH value change rate exceeds a threshold value and the heavy metal binding state proportion fluctuates. By constructing a multi-parameter response baseline and a linkage recognition mechanism, early sensing and accurate sampling of heavy metal release are realized, a closed-loop response system is constructed by combining chemical form analysis, self-correction and data feedback optimization recognition model, and the accuracy and intelligent level of pollution monitoring are improved.

The invention relates to the technical field of bridge safety monitoring, and comprises a bridge safety monitoring analysis system based on big data, and the system comprises a data collection processing module, a manifold feature dimension reduction module, an entropy change partition evaluation module, a health state dynamic analysis module, and a bridge safety early warning module. According to the method, a bridge measuring point topological relation matrix is established, measuring point spatial distribution characteristics are analyzed, spatial relevance of a bridge structure is reflected, measuring point local curvatures are calculated, a local curvature matrix is established, a neighborhood similarity matrix is combined, low-dimensional projection transformation is executed, local topological consistency of data is kept in the dimensionality reduction process, an entropy change partition matrix is established, and the spatial relevance of the bridge structure is reflected. The health states of different structural parts of the bridge are subjected to differential analysis based on mechanical characteristics, the entropy change trend and damage probability of a measuring point are calculated, and the overall safety state level of the bridge is judged through a damage risk threshold, so that the local damage severity of the bridge can be comprehensively considered in safety assessment, and the stability and adaptability of safety state judgment are improved.

The invention discloses a conductor galloping and sag monitoring method and system based on Beidou differential analysis. The method comprises the following steps: monitoring a lead position, a conductivity change rate, a surface temperature and electric field intensity data of different heights, and calculating an electric field intensity gradient sequence between adjacent heights; and in the incremental time interval, determining a signal drift coefficient according to the incremental wire position data and the incremental electric field intensity data. When the conductivity change rate is greater than a preset threshold value and the surface temperature of the wire is lower than the dew point temperature, multiplying the signal drift coefficient by the ratio of the conductivity change rate to a preset reference rate to obtain a conductive film correction coefficient, and taking the product of the correction coefficient and the real-time electric field intensity as a correction amount; and deducting from the real-time wire position data to obtain correction data. By implementing the technical scheme provided by the invention, the accuracy of the lead position monitoring data in the salt water mist environment is improved.

The invention relates to the technical field of traditional Chinese medicine drying process automatic control, and discloses a traditional Chinese medicine low-temperature belt type drying control system and method. A closed-loop control strategy integrating multi-point moisture sampling, trend analysis, target and fluctuation control, temperature feedforward, belt speed feedback, vacuum collaboration, stability criterion freezing and tail detection archiving is provided. A plurality of sampling points are arranged along the conveying belt, moisture is monitored in real time, and time sequence differential analysis is combined, so that static moisture content deviation is captured, the drying rate and trend are sensed dynamically, and multi-channel coordinated adjustment of temperature, belt speed and vacuum is achieved. The system is provided with a safety boundary and a minimum resolution, and it is guaranteed that control operation can be executed and is accurate. The stable interval criterion effectively avoids frequent invalid adjustment, and the tail multi-point detection and whole-course data archiving provide a solid basis for quality tracing.

The invention provides a photovoltaic panel intelligent installation robot path planning method based on vision, and relates to the field of image data processing. Comprising the following steps: collecting photovoltaic panel storage and installation area data in real time through a phase sensitive camera, and generating a holographic data stream; a continuous phase field is constructed by adopting a bidirectional space-time grid discretization algorithm based on a wave equation solver, pre-compensation parameters are generated in combination with time reverse light field prediction, a holographic phase field model is formed after fusion, and a digital elevation model is constructed; topographic relief and obstacles are avoided in global optimal path planning through a phase gradient tracking algorithm, and the path curvature and the movement speed are dynamically optimized; the phase fracture risk is detected in real time through time reverse light field prediction, and a microsecond-level correction instruction is generated to adjust the track of the robot; after installation, phase deviation is verified through differential analysis, time reverse light field prediction and reinforcement learning iterative optimization model parameters are triggered, and high-precision adaptive path planning and obstacle avoidance control are achieved.

The invention discloses a fire-fighting pipe network pressure dynamic monitoring method and system combined with the Internet of Things and a medium, and relates to the technical field related to pressure monitoring, and the method comprises the steps: deploying a pressure sensor and an auxiliary sensor at a fire-fighting pipe network monitoring node, and building a topology sensing structure through wireless Internet of Things communication; carrying out adaptive sampling adjustment and then establishing a node sampling data set; transmitting the data to a multi-time window pressure evolution model to execute sliding differential analysis, and calculating an abnormal trend index; configuring a neighborhood broadcast request and performing broadcast consistency verification; abnormal trend index enhancement correction is carried out, and group resonance detection is carried out to establish linkage cooperative early warning; and a fire-fighting pipe network pressure abnormity early warning signal is reported. The technical problems that in the prior art, dynamic abnormal data of the fire-fighting pipe network are difficult to sense in real time, cooperation is lacked, response lags, and pressure monitoring accuracy and reliability are poor are solved, and the technical effects that real-time and accurate dynamic monitoring is achieved, and the pressure monitoring accuracy and reliability of the fire-fighting pipe network are improved are achieved.

The invention provides a wind-light-heat-storage large-scale new energy base frequency coordination control method, and belongs to the field of power system active power control, and the method comprises the steps: carrying out the simulation analysis based on a wind-light-heat-storage large-scale new energy base DC sending-out system through a photo-thermal power generation system dynamic model, and obtaining a wind-light-heat-storage large-scale new energy base frequency coordination control model; analyzing the frequency modulation energy of the network-following type energy storage and the network-constructing type energy storage, constructing a wind-light-heat storage frequency response model, performing difference analysis on the response speed, and performing off-line solution to obtain a frequency response characteristic table; frequency modulation resources under different operation conditions are researched, and a coordinated output relation is obtained; deviation calculation is carried out to obtain a real-time deviation result, strategy matching is carried out by utilizing a frequency response characteristic table, a frequency modulation strategy is obtained and optimized in combination with a coordinated output relation, and a completely distributed frequency control strategy is obtained; according to the invention, reliable interaction of heterogeneous resource operation states in the primary frequency modulation scale and reasonable distribution of power output are realized.

The invention discloses a prediction control system for wrinkling of an automobile bearing stamping part, and relates to the technical field of model prediction, and the system comprises a data collection unit which is composed of a fluctuation collection matrix and a variable collection matrix, obtains the fluctuation change data of a plate in the stamping process through the fluctuation collection matrix, and transmits the fluctuation change data to a control unit; variable data of the plate in the stamping process is obtained through the variable collection matrix; and the stamping model is established based on historical stamping data, fluctuation change data are obtained for differential analysis, abnormal data segments of the historical stamping data are determined, abnormal sound wave and vibration wave characteristics of the plate are determined, and the corrugation of the stamping part is predicted through a corrugation prediction algorithm in combination with variable data, so that a prediction result is generated. In the plate stamping process, the state change of the plate in the mold is determined by monitoring the vibration wave and sound wave change of the plate, so that the purpose of predicting wrinkling is achieved, and the stamping quality of the plate is further improved.

The invention provides a soil pollutant detection system and method, and the method comprises the steps: carrying out the continuous spectrum compensation of a target compensation region of a soil Raman spectrum signal of a to-be-detected soil sample, and obtaining soil Raman spectrum compensation data; identifying a background baseline response interval in the soil Raman spectrum compensation data through iterative differential analysis, and segmenting the soil Raman spectrum compensation data into a plurality of peak-containing spectrum segments according to the background baseline response interval; determining a signal fidelity constraint of the soil Raman spectrum signal during smooth iteration; recognizing a local background baseline corresponding to each peak-containing spectrum section based on an incremental smooth window in combination with signal fidelity constraint, and determining a clean characteristic spectrum according to all background baseline response intervals and the local background baselines; and identifying according to the clean characteristic spectrum to obtain the pollutant concentration of the to-be-detected soil sample. According to the technical scheme provided by the invention, adaptive fidelity extraction can be carried out on the pollutant characteristics under the condition of the violently changing background base line.

The invention provides a zero-sample large model text detection method and system based on comparison prompt differential analysis, and the method comprises the steps: S1, coding a to-be-detected text into a lexical element sequence through a word segmentation device, and defining a human style prompt and a large model style prompt; s2, conditional probability calculation: respectively calculating the conditional probability of each lexical element position under two prompt contexts; s3, correction probability difference calculation: calculating a corrected logarithmic probability difference value of the lexical element at each position; s4, summarizing a difference sequence, and recording the difference values of all the lexical elements in the difference sequence in sequence; s5, parameter fitting and score generation: fitting negative exponential function parameters through a difference sequence; and S6, threshold classification judgment: comparing the fitted parameters with a preset threshold, and judging that the text is a text generated by a large language model or written by human. Based on the technical scheme of the invention, the detection can be completed without any training or fine tuning; the interpretability is high, and the word-level visualization capability is achieved; and the robustness is good and the fault tolerance is high.

The invention discloses a switch cabinet electric arc light detection method and system based on multispectral fusion, and relates to the technical field of electric power system intelligent detection, and the method comprises the following steps: S1, building a cross-spectrum unified time reference, and carrying out the synchronous comparison of an infrared light signal, a visible light signal and an ultraviolet light signal, extracting a temperature transition trigger feature and a strong light disturbance spectrum feature, and generating a time synchronization deviation vector; and S2, constructing an anti-fact spectrum playback model based on the time synchronization deviation vector, generating a shadow spectrum sequence, positioning an infrared response threshold lifting reason through energy difference analysis, calculating a spectrum overlapping risk interval and duration, and outputting a threshold disturbance distribution diagram. According to the method, infrared anomalies are identified through cross-spectrum time alignment, anti-fact playback and differential analysis, channel recovery is guided in combination with springback prediction and time sequence correction, and a spectrum broken chain window is constructed through dislocation sampling and phase regulation and control, so that the accuracy and the anti-interference capability of arc identification are effectively improved.

The invention provides a river sediment physical habitat evolution identification method, device and system and a medium. The method comprises the following steps: acquiring a multi-temporal remote sensing image and hydrological data (flow Q, water level Zw and slope S), screening and other hydrological condition image pairs; extracting a water surface range, and calculating the average width B of the main groove; deducing the average water depth h by using a Manning formula, and calculating the riverbed elevation zbed by combining the Zw; the difference value delta zbed of zbed in different periods is calculated, and the substrate state is judged according to the difference value delta zbed and a threshold value epsilon; and performing segment processing along the center line to output a spatial distribution result. On-site measurement is not needed by relying on conventional data, and consumption is reduced; water level interference is eliminated through equal hydrological condition matching, and comparability is ensured; water depth inversion and differential analysis directly reflect scour deposition and quantitatively reveal rules; long-distance automatic processing improves space-time coverage capability and precision, and provides a basis for river regulation, ecological restoration and drainage basin management.

The invention discloses an unplanned secondary operation decision support method and system based on EMR analysis. According to the method, firstly, multi-source EMR data are collected in real time through an HL7 FHIR standardized interface, differential analysis is conducted through a time sequence analysis algorithm and a BioBERT model fused with a medical knowledge graph, and standardized clinical indexes are generated; secondly, the indexes are matched with an unplanned secondary surgery thematic knowledge graph in real time, multi-path probabilistic reasoning is carried out based on a Bayesian network, and a risk conclusion with confidence and a reasoning chain are output; and generating a structured report including risk early warning, core basis, possible reasons and processing suggestions, and pushing the structured report in multiple channels. And finally, collecting clinical feedback, optimizing the knowledge graph and the analytical model, and forming a closed loop. According to the invention, dependence on experience of doctors is reduced, and accuracy and timeliness of risk research and judgment are improved.

The invention provides an analysis and management system for telecontrol information data, and relates to the technical field of electric power and data analysis, and the system comprises a configuration inspection module which carries out the differential analysis of a master station telecontrol information point table and a substation telecontrol information point table, and generates a telecontrol configuration file; the data acquisition module acquires test telecontrol information data and a telecontrol configuration file, and performs communication link matching to obtain a communication link check code; the data preprocessing module preprocesses the test telecontrol information data to obtain test telecontrol information preprocessing data; the data verification and analysis module performs exception verification and analysis on the test telecontrol information preprocessing data to obtain an integrity index; the abnormal data prediction module constructs a telecontrol information data abnormal prediction model and performs abnormal prediction on test telecontrol information data collected in real time; and the real-time management module manages the test telecontrol information data in real time according to the prediction result. According to the scheme, the operation efficiency of the system and the accuracy of telecontrol information data analysis can be improved.

The invention relates to the field of metal heat treatment and industrial automation control, in particular to a normalizing air cooling system of a metal continuous heat treatment furnace, and particularly relates to an ideal field reconstruction module which is used for obtaining steel plate parameters and environmental parameters, introducing a thermodynamic phase change dynamics principle to calculate a phase change latent heat release process and constructing an ideal pure temperature flow field matrix; the fault simulation generation module is used for calling a fault mechanism model to convert an equipment fault into a parameterized interference coefficient matrix, and superposing the parameterized interference coefficient matrix to an ideal matrix to generate a theoretical damaged state matrix; the double-track differential analysis module is used for collecting real-time data to construct an observation matrix, and respectively calculating a first difference between the observation matrix and the ideal matrix and a second difference between the theoretical damaged matrix and the ideal matrix to obtain a real and theoretical difference matrix; the feature coupling judgment module is used for extracting the difference matrix features, calculating the coupling degree, judging that the abnormality belongs to a real fault or environmental noise, and generating an air cooling regulation and control instruction; according to the method, non-linear fluctuation misjudgment is eliminated through phase change latent heat calculation, dual-track differential stripping environment common-mode interference is utilized, and micro equipment abnormity is accurately captured in a noisy environment.

The invention relates to a radiation report auxiliary writing technology, and discloses a radiation report auxiliary writing method and system based on intelligent follow-up visit, and the method comprises the steps: constructing a multi-modal training data set D, carrying out the preprocessing, and forming the input of a multi-modal model MF; building and training a multi-modal model (MF); obtaining difference text analysis; generating difference text analysis through a multi-modal model (MF); and the text generation model G combines the difference analysis text and a historical report to generate a follow-up report text. According to the method, the spatio-temporal change characteristics and the text semantic information of the images are effectively integrated, follow-up image data can be efficiently analyzed, focus changes in the follow-up period can be accurately captured, the workload of a doctor in writing a follow-up radiation report is reduced, the follow-up report writing efficiency is remarkably improved, and meanwhile intelligent support is provided for generation of the radiation follow-up report.

The invention relates to the field of data decision analysis, in particular to a unit internal control management decision method and system based on multi-modal data analysis. The method comprises the following steps: collecting heterogeneous original data streams of a plurality of heterogeneous data sources; performing space-time dimension entropy change differential analysis and multi-dimensional entropy change characteristic mining, performing multi-level semantic analysis on the heterogeneous original data stream, performing layer-by-layer abnormal mode semantic deconstruction, and constructing a precise semantic feature portrait of each abnormal mode; performing multi-target coordination request analysis and dynamic target priority evaluation on the cognitive entropy change reference characteristic spectrum to obtain a coordination request priority; and carrying out maximum decision capacity calculation on the cognitive entropy change reference feature map, and carrying out dynamic optimal decision boundary calculation based on the coordination request priority and the accurate semantic feature portrait so as to generate a dynamic tradeoff constraint range. Through multi-target collaborative decision analysis, the decision target conflict is smoothed, and the decision demand execution efficiency of the user is improved.

The invention provides a multi-dimensional power distribution optimization method based on ammeter big data and edge calculation, and relates to the technical field of intelligent power distribution, and the method comprises the steps: receiving user distribution topological information of a transformer area; extracting a first ammeter user type and a first ammeter user scale until an Nth ammeter user type and an Nth ammeter user scale; obtaining the predicted running capacity of the first ammeter in a preset time zone; obtaining the predicted running capacity of the Nth ammeter in a preset time zone; predicting the power consumption of the court to obtain the power consumption of the court in a preset time zone; and according to the preset time zone district power consumption, carrying out district power distribution management. According to the application, the technical problem that the power consumption prediction is not fine enough and the power distribution refinement degree is further influenced due to the lack of individual difference analysis of the power consumption of the transformer area users in the prior art can be solved, and the purposes of improving the power consumption prediction and power distribution refinement degree of the transformer area and improving the power distribution refinement degree are achieved. And the power distribution management efficiency and accuracy of the whole transformer area are improved.

The invention belongs to the technical field of electronic search, and relates to a radio reconnaissance method and system based on multichannel parallel processing and intelligent clustering, and the method comprises the steps: carrying out the multi-phase filtering digital channelization processing of a broadband radio signal, so as to obtain adjacent channel sequences with overlapped frequency bands; performing square law detection and constant false alarm detection on each channel sequence to generate an initial pulse description word; establishing a candidate corresponding relationship in adjacent channels based on arrival time similarity and noise consistency, and determining a cross-channel merging relationship according to frequency range continuity and frequency-time relationship slope consistency to generate a complete pulse description word; performing hierarchical clustering sorting based on the arrival angle and the pulse width; and performing multi-stage differential analysis on a clustering result to extract a repetition frequency sequence and determine a repetition frequency type. According to the technical scheme, the stability and consistency of cross-channel pulse merging and sorting results can be kept under the broadband high-density condition, and the radio search processing precision under the complex electromagnetic environment is improved.

The invention relates to the field of quality detection of mechanical parts, in particular to an intelligent recognition system for surface defects of mechanical parts based on deep learning. Comprising a data acquisition module, a differential geometric feature extraction module, a small sample learning module, an automatic labeling module, a data reinforcement learning module, a neural network training module, a defect identification module and a defect quantitative evaluation module. Extracting geometric features including curvature features and differential invariant features; small sample learning is adopted to solve the problem of sample scarcity; the efficiency is improved by utilizing automatic labeling; simultaneously capturing microcosmic details and macroscopic morphology through a multi-scale differential analysis framework; organic fusion of surface image information and depth geometric information is realized; according to the method, the problems of insufficient samples, difficulty in labeling, inaccurate defect evaluation and the like in industrial production are effectively solved.

The invention relates to a true triaxial fracturing test magnetofluid monitoring system and method, and belongs to the field of coal bed gas exploration and development. The system comprises a magnetic fluid mixing device, an injection device, a 4 * 4 magnetic induction sensor array, a data acquisition device, a high-pressure water pump device and a true triaxial experiment device. The method comprises the following steps: mixing magnetic fluid and adjusting viscosity; injecting a test piece; applying three-dimensional stress; monitoring magnetic field change in real time; millimeter-level positioning and three-dimensional visualization of crack expansion are achieved through magnetofluid dynamic response and data collected by a magnetic induction sensor array in combination with differential analysis, edge detection and a finite element inversion algorithm, and the magnetofluid recovery rate is larger than 95%. The method solves the problems that traditional monitoring is low in precision and poor in real-time performance, and is suitable for dynamic analysis of coal bed gas and shale gas fracturing cracks.

The invention discloses a real-time alarming method for thickness abnormity of a PVC (polyvinyl chloride) pipeline, which comprises the following steps: acquiring thickness data, temperature parameters and pressure parameters of the PVC pipeline, and constructing multi-dimensional monitoring data; processing the multi-dimensional monitoring data to form purified monitoring data; executing multivariate nonlinear regression analysis according to the purification monitoring data, and establishing a coupling relation expression; based on the coupling relation expression, calculating a deviation sequence between a thickness predicted value and a measured value by adopting a sliding window technology, and extracting a deviation trend index reflecting an abnormal change mode; performing differential analysis according to the deviation trend index, and extracting duration, strength grade and service attribute set; calculating a credibility value of the alarm event according to the strength grade, the duration time and the service attribute set; based on the credibility value, a preset alarm threshold value is adjusted, parameter configuration in the coupling relation expression is iteratively corrected, and the method is used for continuous detection and self-adaptive adjustment of the thickness abnormity of the PVC pipeline. According to the method, accurate identification of early-stage thickness abnormity and high-reliability alarm generation can be realized.

The present invention discloses a non-invasive blood glucose testing method based on a group big data model, which relates to the technical field of blood glucose testing, and includes the following steps: using a near-infrared spectrometer to detect spectral data in skin and tissues, and obtaining the absorption peak of glucose molecules in the near-infrared range; performing preliminary feature extraction on the obtained absorption peak, and after performing abnormal analysis on the extracted features, preliminarily judging whether the absorption peak is abnormal, and screening out the abnormal absorption peak. The present invention uses a machine learning model to screen out abnormal absorption peaks through feature extraction and abnormal analysis, ensures the use of normal data, improves the accuracy of blood glucose prediction, reduces the risk of overestimation or underestimation, provides reliable monitoring for diabetic patients, adjusts drugs in time, and reduces the risk of complications. At the same time, differential analysis and standardized deviation calculation screen out abnormal prediction values, optimize data processing, improve system robustness and final prediction accuracy, and provide accurate non-invasive blood glucose monitoring solutions.

The invention discloses a monitoring video image enhancement method for traffic construction road obstacle detection, and relates to the technical field of image enhancement, and the method comprises the steps: firstly obtaining each frame of image in a traffic monitoring video, and carrying out the illumination correction of the image, so as to obtain a current image after illumination correction; performing differentiation analysis on each pixel point in the current image subjected to illumination correction to generate a region mask, the region mask being used for identifying abnormal regions, including a shadow region and a strong light reflection region, in the current image subjected to illumination correction; next, recovering the shadow area and the strong light reflection area to obtain a processed image, then performing edge detection on the processed image to form two boundary pixel sets, establishing a matching relationship according to the two boundary pixel sets, and verifying the established matching relationship to generate verification result information; the verification result information is used for reflecting the obstacle fracture part.

The application discloses a kind of active fault borehole stratum comparison methods combined with high-resolution logging data, it is related to stratum comparison technical field, comprising: first respectively obtaining test fault region and test bottom layer region;Real-time data acquisition is carried out based on electrical imaging logging, and differential analysis model is established;The differential analysis model is optimized, and the ground fault comparison model is obtained;Active fault borehole stratum comparison method is obtained based on the data in ground fault comparison model;The application is used to solve the problem of low precision and efficiency in the existing active fault borehole stratum comparison method.