286 results about "Parametric identification" patented technology

The invention relates to a lightweight component surface defect quantitative evaluation method, system and device, and the method comprises the steps: obtaining a multispectral surface image, laser three-dimensional data and ultrasonic detection data of a lightweight component, and carrying out the fusion registration based on a preset multi-sensor synchronization protocol, and obtaining a component joint data set; performing dynamic pruning analysis on the component joint data set to obtain geometric contour features, and performing internal feature extraction on the component joint data set to obtain internal abnormal response features; stress gradient distribution data and volume ratio parameters are carried out on the geometric contour features and the internal anomaly features; and obtaining an original working condition data set of the lightweight component, carrying out service working condition coupling analysis on the multi-dimensional prediction matrix, and carrying out multi-dimensional quality l parameter identification to obtain component comprehensive quality information. According to the method, the morphology mechanical coupling feature extraction precision of composite defects such as cracks and holes can be improved.

The invention relates to an intelligent inspection control system and method. The system comprises an environment sensing module, a situation recognition module, a task management and scheduling module, a path planning module, an execution control module and a communication linkage module. The system can collect various environmental parameters of an inspection area in real time, dynamically adjust the priority of an inspection task after identifying an abnormal state, and generate an optimal path to drive a robot to execute response operation; meanwhile, abnormal information, images, videos, sensor snapshots and other contents are uploaded to a background management platform through a communication linkage module, and sound-light alarm equipment, broadcast equipment and other equipment are linked to carry out field response. The control method has task preemption and path reconstruction capabilities, and supports distributed sensing deployment and whole-process execution state monitoring. Compared with the prior art, integrated fusion of environment situation awareness, task dynamic scheduling and response control is achieved, and the adaptive capacity, the response speed and the safety guarantee level of the system to complex scenes are improved.

The invention discloses a driver controller detection system, which comprises an acquisition module, an acquisition module, a processing module, an identification module, a judgment module, an analysis module and a generation module, the acquisition module captures contact information and electromagnetic interference data of contacts in real time by means of a high-dynamic voltage sensor and a broadband EMC probe; the acquisition module is triggered by a locomotive signal and records dynamic working condition data; the processing module aligns the data, performs denoising and extracts fault parameters; the identification module scans the voltage waveform and judges the transient contact failure; the judgment module monitors EMC data and compares an instruction position, and judges misoperation; the analysis module analyzes fault association, judges a composite type and triggers an alarm; the generation module generates a detection report and a log. According to the system, a multi-dimensional monitoring network is constructed under a dynamic working condition, data reliability is guaranteed, faults are accurately identified, a compound fault source is positioned, and the safety and stability of locomotive operation are improved through risk grading early warning and automatic reporting.

The invention provides a method and device for detecting vehicle falling into water, a vehicle and a storage medium, the method is applied to the technical field of vehicle falling into water detection, the method comprises the steps that M falling into water detection parameters are obtained through M collection devices of different types in the vehicle, the falling into water detection parameters are used for representing the falling into water state of the vehicle, and M is a positive integer larger than 1; determining M basic probability distribution values corresponding to the M collection devices according to the M water falling detection parameters; according to the M basic probability distribution values, the falling-into-water confidence coefficient of the vehicle is determined; and when the falling-into-water confidence is greater than or equal to the preset confidence, determining that the vehicle is in a falling-into-water state. According to the method, whether the vehicle falls into water or not can be identified by fusing the falling-into-water detection parameters collected by multiple sensors, the reliability and robustness of vehicle falling-into-water detection can be effectively improved, and misjudgment caused by identification of single equipment is avoided.

The invention belongs to the technical field of parameter identification, and provides a thermal power generating unit electro-hydraulic servo system PID parameter identification method and system, and the method comprises the steps: obtaining the historical operation data of a steam turbine, carrying out the preliminary screening of the data, and constructing a parameter identification data set; according to the mean square error between the actual value and the calculated value of the opening degree of the valve, an objective function of PID parameter identification of the DEH electro-hydraulic servo system is established; setting population initial parameters, and generating a chaotic initial population; based on a parameter identification data set and a target function, introducing an initial population value of the generated chaotic initial population into an improved Bayesian optimization model, and obtaining a preliminary optimization result through Gaussian modeling and kernel function prediction; and taking the preliminary optimization result as input, and utilizing a whale optimization algorithm introducing a Levy flight disturbance mechanism to carry out iterative search for multiple times to obtain a final optimization result. According to the invention, the parameter identification precision and robustness are improved.

The invention relates to a bridge structure modal parameter identification method, which is based on a bridge structure tiny vibration video acquired by a camera. VMD (variational mode decomposition), PBVM (phase-based video motion amplification), Gabor filtering-based phase extraction method GBP (Gabor filtering), an improved clustering algorithm ICA (independent clustering algorithm) and an integrated covariance-driven random subspace recognition (SSI-Cov) and FDD (frequency domain decomposition) algorithm are fused to construct a set of complete non-contact structural modal parameter recognition system. According to the method, effective decoupling of multiple vibration modes of a bridge structure, frequency domain feature extraction of small-amplitude vibration and intelligent and automatic modal parameter extraction are achieved under the conditions that the target frequency bandwidth does not need to be known in advance, environmental noise interference exists and no obvious feature target exists, and the method belongs to the technical field of bridge structure health monitoring.

The invention belongs to the field of power distribution network parameter identification, and discloses a power distribution network line parameter identification method and system based on a graph neural network, and the method comprises the steps: employing a unified projection and attention weighting mechanism, obtaining the point features of a power distribution network line, and carrying out the self-adaption integration of multi-source heterogeneous observation data; initial edge features are explicitly constructed in neural network propagation of each layer of graph, node and edge representation is alternately updated, and the line parameter prediction precision is improved; power grid core laws such as Ohm's law and node power injection constraint are converted into a loss function to constrain a training process, consistency constraint of a parameter prediction result and a power system physical law is realized, and interpretability and credibility of the prediction result are improved; and inputting the power grid data into the graph neural network optimization model, and outputting a power distribution network line parameter identification result, thereby solving the problems that in the prior art, dynamic information interaction between nodes and edges is neglected, so that parameter identification is sensitive to network structure change, and physically incredible parameter output is easy to generate.

The invention provides a rubber bushing dynamic parameter identification method and device, and the method comprises the following steps: obtaining the test data of a driving end and a driven end of a rubber bushing through a test, and calculating the test dynamic stiffness and the test loss angle according to the test data; establishing a simulation model containing at least one dynamic characteristic parameter to be identified, and generating a result of the same caliber in the model according to a calculation caliber which is the same as that of the test so as to extract simulation dynamic stiffness and a simulation loss angle; aggregating differences between the test dynamic stiffness and the test loss angle and differences between the simulation dynamic stiffness and the simulation loss angle on a preset frequency domain evaluation set, and constructing a target function; and optimizing the dynamic characteristic parameters to minimize the target function to obtain the dynamic characteristic parameters of the rubber bushing. According to the invention, the accuracy and stability of parameter identification in the whole vehicle state are effectively improved.

The invention discloses a power distribution network short-circuit parameter real-time measurement method, system and device based on PMU cooperation and a medium, and belongs to the technical field of power systems and automation, and the method comprises the steps: collecting PMU real-time measurement data, loading preset parameters, and constructing a PMU cooperation measurement network; carrying out a synchronous sampling algorithm, carrying out comprehensive preprocessing on real-time measurement data, and carrying out state reconstruction and inhibiting the influence of abnormal data according to a state estimator; establishing a short-circuit parameter identification model, and executing short-circuit parameter identification; performing multi-dimensional evaluation on the identification result, and evaluating the real-time performance of the algorithm; and based on the evaluation result, updating the key parameters, and executing adaptive adjustment of the system parameters. According to the invention, through high-precision synchronous acquisition, adaptive signal processing and closed-loop optimization mechanisms, comprehensive improvement of the real-time measurement precision, reliability and adaptability of the short-circuit parameters of the power distribution network is realized.

The invention discloses an aviation food truck docking identification method and system based on feature fusion, and the method comprises the steps: obtaining multi-sensor data of an aviation food truck, extracting cabin door positioning information, and carrying out the feature fusion processing to form a docking-pose feature matrix; performing pattern recognition based on the pose feature matrix to obtain track feature parameters, recognizing a cabin door geometric feature spectrum, generating a cabin door inversion feature library through inversion processing, and performing differential comparison to determine space focus features; identifying a cabin door center position and an obstacle interference position by using a space focus feature, constructing a docking failure preset condition to generate a reverse verification factor, and forming a composite space weight map; acquiring a butt joint gradient information identification position deviation area based on the weight map, and converting the butt joint gradient information identification position deviation area into a path optimization trigger area through deviation conversion parameters; adjusting pose information is formed through the trigger area, a recognition coordinate system is constructed for collision conflict detection, a butt joint recognition result is generated, and safe and accurate butt joint of the aviation food truck and the aircraft cabin door is achieved.

The invention relates to the technical field of electric power generation equipment monitoring, in particular to an intelligent multi-point monitoring method and system for a generator set. The method comprises the following steps: acquiring data of a generator set, and dividing the generator set into a primary monitoring sub-region and a secondary monitoring sub-region; deploying multiple types of sensors according to monitoring requirements of key components of each monitored sub-region, and collecting multi-dimensional parameters of the sub-regions in real time; the multi-dimensional parameters of the sub-regions are analyzed in real time according to the multi-dimensional parameters, power generation grid connection abnormal conditions are identified, and multi-level warning and abnormal positioning are carried out; the system comprises a multi-point region division module, a multi-dimensional data monitoring module and a power generation grid-connected abnormity warning module. By means of the mode, the abnormal recognition efficiency and adaptability are improved, and the operation safety and grid connection reliability of the generator set are improved.

The invention discloses a recommendation system-oriented privacy sensitive parameter identification and accurate deletion method, and relates to the technical field of recommendation system model optimization and privacy protection. In order to solve the problems that privacy sensitive parameters in an existing recommendation system model are difficult to accurately position and the model performance loss is too large after deletion, according to the evaluation thought of parameter importance and data feature relevance, importance scores of model parameters on two types of data sets are calculated respectively by dividing a forgetting set and a reservation set, and the privacy sensitive parameters are obtained. The privacy sensitive parameters with the forgetting set importance higher than the reserved set importance are screened out, and mask zero setting processing is carried out. According to the method, the model does not need to be retrained or subjected to weight updating, sensitive parameter recognition and deletion can be completed only through single-time forward propagation, and the original recommendation performance of the model is reserved to the maximum extent while the privacy security of a recommendation system is guaranteed. According to the method, accurate identification of privacy sensitive parameters is realized on a mainstream recommendation model, the reduction range of model recommendation accuracy after deletion is controlled within an acceptable range, and the privacy leakage risk is remarkably reduced.

The invention discloses a power transmission line external damage target Internet of Things information sensing detection method and system, and the method comprises the steps: analyzing whether an external damage target exists along a power transmission line or not based on the detection data of a monitor group; when the external damage target does not exist, the detection range of the monitor group is optimized; when the external damage target exists, tracking and early warning are carried out on the external damage target; the method can accurately grasp weak links of monitoring coverage by actively acquiring environment data and combining monitor parameters to identify blind areas, and can specifically reduce or eliminate the monitoring blind areas by dynamically adjusting detection parameters of monitors adjacent to the blind areas according to blind area maps, thereby improving the monitoring efficiency. Therefore, the overall monitoring coverage effectiveness along the power transmission line is improved, the capability of sensing the external damage target is remarkably improved, the risk of report failure caused by hidden activities of the external damage target in a blind area is reduced, and the adaptability and reliability of the power transmission line safety monitoring system are enhanced.

The invention relates to the technical field of construction management, and discloses a construction progress recognition method and system based on image recognition. The method comprises the steps that real-time image data of a construction scene are obtained, a construction progress feature monitoring database mapped with the real-time image data is established, and database monitoring indexes comprise image definition, feature matching degree and the like; configuring a directional recognition target, including key node recognition, construction stage division and the like, and then determining a target classification identifier; key identification parameters are selected through the main identification target and the auxiliary identification target, directional identification target influence analysis is carried out, parameter directional identification is executed after parameter identification constraints are established, and construction progress identification is completed. When parameter directional recognition is executed, a time efficiency and accuracy evaluation function is also established, and a balance adaptation function is constructed to screen recognition schemes. The system comprises a data acquisition module, a target configuration module and the like and can implement the method. Through multi-dimensional analysis and optimization, efficient and accurate recognition of the construction progress is achieved, and the construction progress management requirement is met.

The invention discloses a vertical superfinishing machine intelligent servo control method based on dynamic parameter identification, and relates to the technical field of servo control. Comprising the steps that first sensing equipment is arranged on a key motion axis of target equipment and used for obtaining vibration data of the motion axis, and vibration information items are obtained; and second sensing equipment is arranged on the oilstone unit, is pressure and temperature integrated sensing equipment and is used for acquiring real-time pressure data and temperature data of the oilstone to obtain a pressure information item and a temperature information item. According to the method, damage sorting is combined, low-damage combinations with different oscillation frequencies and feeding speeds are preferentially selected to serve as equipment execution, updating information of vibration information items, pressure information items and temperature information items is deeply obtained, secondary adjustment is conducted on initial information items, and the accuracy of the equipment is improved. Therefore, oilstone driving control based on dynamic parameter identification of vibration data, temperature data and pressure data combination is realized.

The invention discloses a hydroelectric generating set data acquisition and analysis method, system, equipment and medium, and belongs to the technical field of data acquisition and analysis, and the method comprises the steps: obtaining various original data of the operation of a hydroelectric generating set, and achieving the information collection of the set and the operation environment; performing multi-level structured modeling and node feature extraction on the acquired data by adopting a mode of combining a knowledge graph and a graph convolutional neural network, and analyzing an operation state and a future trend of a unit; according to a data analysis result, comparing a historical working condition with a scheduling task, judging whether the operation of the unit is in a reasonable interval, and triggering a corresponding regulation and control measure when the operation is abnormal; by monitoring the state and operation data of key equipment in real time, actual and reference parameters are compared, potential fault risks are identified, early warning information is generated, and data archiving and statistical analysis are carried out; and carrying out visual display on data acquisition, analysis, scheduling and early warning results. Collected data are effectively utilized, and scheduling decision and early warning maintenance are powerfully supported.

The invention provides a PINN-based bridge parameter identification method and device, equipment and a medium, and belongs to the technical field of bridge data analysis, and the method comprises the steps: obtaining vehicle vertical dynamic response data and bridge vertical dynamic response data when a vehicle passes through a bridge at a preset speed; constructing a motion differential equation of the axle coupled vibration system based on a preset pseudo bridge parameter, a vehicle basic parameter, a bridge basic parameter and an axle contact parameter, writing the differential equation as physical information into the initial physical information neural network model, and embedding the physical information into a loss function of the model to obtain a target loss function; and training the initial physical information neural network model based on the vehicle vertical dynamic response data and the bridge vertical dynamic response data, stopping training until the target loss reaches a preset loss threshold, obtaining a target physical information neural network model, and extracting target bridge parameters from the target physical information neural network model. According to the invention, accurate and efficient identification of medium and small span bridge parameters can be realized.

The invention discloses a DC-DC converter parameter identification method based on digital twinning, and the method comprises the following steps: firstly building a Buck converter simulation model, collecting needed data from the simulation model, building a mathematical model according to a Buck converter circuit structure, KCL and KVL, and carrying out the calculation of the Buck converter simulation model. And solving a differential equation of the mathematical model by using a Runge-Kutta method to obtain calculation data, substituting the collected simulation data and calculation data into a target function, performing iterative optimization on the target function value by using a Kirchhoff's law optimization algorithm, and outputting an optimal solution of a to-be-identified parameter, namely an identification value of the circuit parameter. According to the method, the parameter identification error of the DC-DC converter can be reduced, and the accuracy is improved.

The invention discloses an aircraft wake flow identification method, system and device based on a wind lidar and a storage medium, and belongs to the technical field of radar detection. The method comprises the following steps: acquiring low-resolution laser radar detection data; calculating the central position of an aircraft trailing vortex pair according to the low-resolution laser radar detection data, and dividing the low-resolution laser radar detection data based on the central position of the aircraft trailing vortex pair to obtain an input region; and inputting the input region into a pre-trained aircraft wake flow super-resolution model to obtain a high-resolution reconstructed aircraft wake flow field. According to the invention, the problems of fuzzy wake flow structure, large vortex core position deviation and underestimation of vortex circulation caused by insufficient range resolution of a wind measurement laser radar in the prior art are solved, and the detection precision and parameter identification capability of the laser radar on aircraft wake flow are effectively improved.

The invention relates to the field of drilling automation, and discloses a rotary drilling pile machine drilling method and device suitable for complex stratums.The method comprises the following steps that S1, working condition feedback parameters are obtained in real time; s2, recognizing the real-time geological working condition of the drill bit; s3, optimal drilling operation parameters are determined in combination with a preset intelligent decision model; and S4, according to the optimal drilling operation parameters, an execution mechanism of the rotary drilling pile machine is automatically controlled, self-adaptive drilling is carried out, and the device comprises a data acquisition module, a working condition recognition module, a decision optimization module, a closed-loop control module and a drilling tool type selection and recommendation module. According to the method, depth data mining is carried out on key parameters such as rock-soil body specific energy, so that real-time working conditions are accurately classified into specific types such as soft clay and pebble beds, and the technical problems that in the prior art, the geological misjudgment rate is high, the recognition precision is low, and complex stratums cannot be effectively distinguished are solved.

The invention belongs to the technical field of unmanned aerial vehicle aircraft design, and particularly relates to a longitudinal aerodynamic parameter identification calculation method in a closed-loop state and a large angle-of-attack range, and the method comprises the steps: building a flight simulation model; performing aerodynamic characteristic simulation based on a flight simulation model to obtain an aerodynamic load value, comparing the aerodynamic load value with data tested in an actual flight experiment, and calculating and minimizing an error sum of squares; dividing the whole angle-of-attack range into a plurality of sub-intervals, respectively obtaining the optimal aerodynamic parameters of each sub-interval based on the minimum sum of squares of errors, inputting each optimal aerodynamic parameter into the flight simulation model, and carrying out model accuracy verification; and drawing an aerodynamic characteristic curve according to each optimal aerodynamic parameter, and verifying the applicability of the flight simulation model. The method has universality and is suitable for longitudinal aerodynamic coefficient parameter identification of different types of unmanned aerial vehicles, the parameter identification efficiency is improved, and meanwhile the applicability of the model under the complex flight working condition is enhanced.

The invention belongs to the technical field of material processing and manufacturing, and particularly relates to a multi-process complex loading process material deformation and performance unified prediction method and application thereof. Aiming at the technical defects of an existing material constitutive model in the aspects of cross-process applicability, multi-process historical effect characterization and formation-structure-performance whole-process unified evaluation, a unified internal variable system is constructed; by combining a multi-process unified constitutive theory, a multi-process composite loading path calibration method and a multi-data source collaborative parameter identification and simulation integration technology, unified prediction of deformation behaviors, structure evolution and post-forming performance of materials under different forming processes, different loading modes and complex historical paths is realized. Compared with the prior art, the method of the invention makes significant technical progress in the aspects of model versatility, prediction precision and engineering implementation convenience.

The invention relates to the technical field of motor rotor dynamics modeling, in particular to a motor rotor assembly modal modeling and parameter identification method and device and a medium. The method comprises the following steps: firstly, acquiring rotor assembly data, performing simplification and mesh generation, and constructing an equivalent transition layer model to accurately simulate connection boundary characteristics by combining finite element analysis with component connection relation definition; material parameters are calculated based on the model, the inherent frequency and the vibration mode are extracted, and actual modal parameters are identified through modal analysis and experimental verification; performing data arrangement and parameter extraction on an identification result, and obtaining an optimization model by adopting modular processing and optimization calculation; and finally, performing experimental verification and error analysis on the optimization model to generate a final report. By introducing the equivalent transition layer and modular processing, the modal simulation precision and optimization efficiency of the rotor assembly are remarkably improved, and a reliable basis is provided for dynamic performance design and optimization of a rotor system.

The invention relates to the field of tool wear analysis, in particular to a tool wear prediction method and device, equipment and a storage medium. The method comprises the following steps: collecting a cutting area thermal image sequence in a milling process, performing chip falling depth visual identification, and extracting chip falling distribution data; performing high-speed micro-contour scanning on the cutting edge of the tool, and calculating the wear rate of the tool based on the chip falling distribution data to generate an instantaneous wear rate parameter; the machining planning log is recognized, and a multi-stage standard cutting parameter set is obtained; wear numerical simulation is conducted based on the instantaneous wear rate parameters and the multi-stage standard cutting parameter set, and tool wear simulation data of multiple stages are generated; and performing time sequence wear situation analysis and tool real-time life calculation on the tool wear simulation data to generate a real-time tool life curve. According to the method, accurate cutter wear state analysis and prediction in the milling process are improved, and the stability and safety of the machining process are improved.

The invention discloses an automatic detection method for dynamic balance parameters of an automobile hub, and the method comprises the steps: employing a piezoelectric ceramic array self-sensing main shaft technology, enabling detection reference establishment and signal collection to be integrated in a main shaft body, and carrying out the fusion of a hub installation process and a detection reference establishment process; synchronous acquisition of multi-dimensional vibration signals is carried out; an original mixed signal is decomposed into modal components in a self-adaptive mode, and environmental vibration interference is eliminated; a FastICA-RLS hybrid algorithm is adopted to separate an independent source signal from the preliminarily purified signal, and non-stationary interference is tracked and compensated in real time; constructing a flexible rotor model based on modal parameter identification, and calculating an unbalance amount; self-adaptive Kalman filtering error compensation and prediction are carried out, and the amount of unbalance after compensation is output; and an optimal counterweight scheme is automatically calculated based on the compensated unbalance amount, and visual display and guidance are performed through a human-computer interface, so that the detection precision and the detection efficiency are improved.

The invention discloses a structure parameter-load input joint identification method based on a Gaussian potential force model, and the method comprises the steps: obtaining a mass matrix, a stiffness matrix and a damping matrix, and constructing a state-space equation; modeling unknown load input into a zero-mean stationary Gaussian process with a specific covariance function to fuse prior information of the covariance function, and converting into an equivalent state-space equation; obtaining sparse dynamic response characteristics, and establishing a state space measurement equation; a structure state and load input are augmented into a state equation, the state equation and a measurement equation set are synthesized into a complete Gaussian process potential force model, unknown structure parameters are regarded as hyper-parameters, kernel function hyper-parameter optimization and structure parameter identification are achieved with the criterion of a maximized negative likelihood function, sequence reasoning is conducted on the Gaussian process potential force model, and the Gaussian process potential force model is obtained. Estimation of load input is obtained. The method has the advantages of being stable in parameter recognition result, high in precision, stable and reliable in load estimation, higher in robustness to model errors and measurement noise and the like.

The application relates to an improved random subspace identification method based on MCKF and DBSCAN, and belongs to the technical field of structural health monitoring and signal processing, which comprises an MCKF denoising pretreatment module, a COV-SSI identification module and a DBSCAN clustering post-processing module, and comprises the following steps: S1, directly analyzing a measurement response signal by using the COV-SSI identification module to estimate an initial system state matrix F and a measurement matrix H of MCKF; S2, setting parameters of MCKF in the MCKF denoising pretreatment module, and realizing the reduction of measurement noise in the measurement response signal in step S1 by using the MCKF denoising pretreatment module; S3, identifying structural parameters by using the COV-SSI identification module, inputting the measurement response signal pretreated in step S2, and recalculating the system state matrix and the measurement matrix according to step S1; and S4, adopting the DBSCAN clustering post-processing module to cluster the result identified in step S3; the method can effectively reduce measurement noise, improve data quality and signal quality, and improve the robustness and accuracy of modal parameter identification.

The invention provides an online model updating hybrid test method based on a dual adaptive UKF algorithm, and the method comprises the steps: carrying out the initialization of a to-be-recognized constitutive model parameter, determining a parameter initial value seismic oscillation according to a genetic algorithm, inputting the seismic oscillation into a step-by-step integration algorithm, and obtaining the displacement response dk of an overall structure; obtaining the boundary displacement of the test substructure based on the displacement response dk and the constitutive model parameter estimation value of the previous step, carrying out real loading according to the boundary displacement, obtaining the real counterforce and real displacement of the test substructure, and carrying out parameter estimation based on a dual adaptive UKF algorithm; and obtaining a new constitutive model parameter value according to the new constitutive model parameter value and the displacement response dk of the overall structure, obtaining the counter-force Rk of the overall structure on each dynamic degree of freedom, realizing online identification and model updating of the constitutive model parameter, and performing loop iteration to the process of ending the test, thereby improving the precision and stability of parameter identification, and improving the test efficiency. The problem that the loading boundary of the test substructure is inaccurate is effectively solved.

The invention discloses a method for identifying logging interpretation electrical standard parameters, and solves the problems of inaccurate difference and low efficiency caused by the influence of subjective factors on the manual identification result of the logging interpretation electrical standard parameters in the prior art. The method comprises the following steps: S1, establishing a benchmarking array for a well section to be identified; s2, matching the target well data on the apparent resistivity curve A based on the established benchmarking array; obtaining optimal matching data; s3, determining a reference layer identified by the electrical standard; s4, identifying a microsphere standard, a sound wave calcium judgment standard and a deep three lateral standard; s5, identifying a microelectrode standard; and S6, identifying a micro-gradient calcium judgment standard. The device for realizing the automatic identification of the logging interpretation electrical standard parameters comprises an establishing unit; a matching unit; a determination unit; a first identification unit; a second identification unit; and a third identification unit. According to the method, logging interpretation electrical standard parameters can be rapidly identified, and quality improvement and efficiency improvement of logging interpretation reservoir thickness division work are assisted.

The invention provides a method and a device for processing a rock mass mechanical parameter identification model based on monitoring while drilling, which are used for realizing deep fusion and analysis of data by applying a Bayesian optimization machine learning model on the basis of integrating monitoring while drilling data, drilling optical image data and drilling in-situ penetration strength data, so that the accuracy of the rock mass mechanical parameter identification model is improved. According to the method, high-precision inversion of the rock mass mechanical parameters is achieved, compared with a traditional method, the accuracy and reliability of the inversion work of the rock mass mechanical parameters are greatly improved, meanwhile, a series of matching optimization is conducted from the detail aspect, and therefore the method can be widely applied to rock mass exploration and stability analysis of underground engineering such as deep underground spaces, tunnels and coal mines. Scientific basis can be provided for tunnel support design, coal mining planning and the like, engineering safety and quality are guaranteed, and the method has good application value.