594 results about "Reference model" patented technology

The invention discloses a network security situation early warning method and system based on a knowledge graph, and relates to the technical field of network security, the method establishes a normal behavior reference model of a network node through the combination of the knowledge graph and a deep learning algorithm, and enhances the accurate identification and real-time early warning capability of abnormal behaviors. Through an unsupervised learning algorithm, historical data of each node is learned and modeled, and a normal behavior reference value is formed. In the real-time monitoring process, deviation calculation is carried out on the current access behavior of each node and the reference value of the current access behavior, a deviation degree value is obtained and compared with a set deviation threshold value, and abnormal nodes deviating from normal behaviors are identified and marked. The deviation degree model combines key factors such as a transmission protocol and data transmission duration, and can sensitively detect tiny deviation of the node in communication frequency or authority, so as to quickly adapt to a dynamic network environment.

The invention discloses an energy acquisition and monitoring system based on big data, particularly relates to the field of energy monitoring, is used for solving the problem of energy consumption abnormity identification and management optimization in steel production, and is characterized in that energy parameters are acquired in a production process, and an energy signature reference model of multi-dimensional energy consumption characteristics is constructed based on historical data and equipment operation characteristics; an accurate reference is provided for dynamic comparison and anomaly detection; potential anomalies are accurately identified by utilizing comprehensive analysis of energy consumption deviation distribution and load characteristics, and key influence factors and root causes are deeply mined through a multivariable machine learning algorithm; in combination with a predictive scheduling engine and an online optimization algorithm, process parameters, equipment running states and task schedules are dynamically adjusted, abnormities are effectively eliminated, and energy consumption balance is recovered; therefore, the real-time monitoring and intelligent anomaly detection of the process energy consumption are realized, the refinement level of energy management is improved, the energy utilization efficiency and production stability of iron and steel enterprises are improved, and the potential production risk is reduced at the same time.

The invention discloses a plug-in inductor winding process control method based on production parameter dynamic compensation, and provides a closed-loop compensation control strategy taking a physical field evolution model as a core aiming at the problems of unstable sensing quantity and non-uniform structure in the traditional process. The method comprises the following steps: firstly, constructing an ideal physical field reference model according to target inductance parameters and a structure, and describing an evolution law of characteristics such as a magnetic field, an electric field or impedance in an ideal state; in the actual winding process, a physical field signal is collected through a sensor, production parameters such as wire tension and main shaft rotating speed are obtained, and a physical field distribution snapshot of the current winding state is generated. And the system compares the extracted actual features with the reference model, identifies a dominant disturbance source causing deviation in combination with real-time production parameters, and outputs a disturbance type label carrying causal information. And according to a label and a production parameter change trend, a compensation instruction is dynamically generated and executed, fine adjustment of tension, a winding displacement track or density is realized, and consistency and precision of an inductor finished product are ensured.

The invention relates to the technical field of material physicochemical property monitoring of a multi-source sensing network, in particular to a multi-source sensing storage environment collaborative monitoring and early warning method, which comprises the following steps of: dividing grid units according to material storage types, binding sensor node coordinates, constructing a grid region reference model, and constructing a grid region reference model; integrating the volatilization characteristic parameters analyzed by the laboratory, historical monitoring data and an environment threshold value to generate a substance and environment relation matrix; the method comprises the following steps of: calling model parameters to invert a theoretical gas concentration value, and triggering finite element physical field simulation through residual analysis to generate a three-dimensional simulation field data set containing a temperature gradient and a diffusion path. And the dynamic correlation analysis precision and the early warning reliability of storage environment monitoring are improved.

The invention relates to an injection mold cavity state monitoring method, system and device and a medium, and belongs to the technical field of mold state monitoring. The monitoring method comprises the steps that cavity state data of a to-be-detected injection mold is received, and clock synchronization and preprocessing are conducted; dynamically segmenting the time sequence data according to the injection molding process cycle, generating a sliding window for each process stage of the injection molding process cycle, and marking a key point in each sliding window; extracting statistical features and difference features between cavities, calculating trend features of adjacent data key points in each sliding window, combining to obtain high-dimensional feature vectors, inputting the high-dimensional feature vectors into a normal process reference model for comparison, and calculating to obtain a feature difference degree of each feature; performing anomaly detection according to the feature difference degree, positioning an anomaly time period and an anomaly space region, and obtaining an anomaly detection result; and generating maintenance prompt information according to the anomaly detection result and sending the maintenance prompt information to the management terminal. According to the invention, timely early warning of the abnormal state of the mold can be realized.

The invention relates to the technical field of cable production automation control, and provides a crosslinked cable production control method based on an improved PID (Proportion Integration Differentiation) control algorithm, which comprises the step of constructing an optimal reference model by establishing a cooperative control framework of temperature, pressure and traction speed and taking the temperature of a vulcanization pipe as a main control variable and the pressure and the traction speed of an extruder as slave control variables. A main controller tracks a temperature set value in real time, PID parameters are dynamically set according to technological parameters such as the thickness deviation of an insulating layer and the crosslinking degree of a material, and precise control is achieved through a three-ring cooperative coupling improved PID controller. A temperature control ring enables the steady-state error of the actual temperature and the set value to be converged to zero. The pressure and speed control loop converges the fluctuation amplitude to a process allowable range. The temperature stability, the pressure uniformity and the traction synchronism of crosslinked cable production can be improved, the process fluctuation and the defect rate are reduced, and the product quality and the production efficiency are improved.

The invention relates to the technical field of network security and data protection, and discloses a data leakage prevention method and system based on user behavior perception, and the method comprises the steps: obtaining historical operation data, and constructing a personalized behavior reference library; monitoring a current access behavior in real time by sliding a time window, calculating a deviation degree and triggering anomaly detection; performing multi-level feature analysis on the abnormal behavior and calculating a comprehensive abnormal score; dynamically adjusting the access authority according to the score, recording an abnormal behavior and carrying out relevance matching; optimizing the reference model through feedback learning; and evaluating the credibility and the risk level based on an accurate modeling result, and adaptively adjusting permission configuration. According to the method, the user behavior rule can be accurately captured, the data leakage risk can be efficiently identified, the access permission can be dynamically adjusted, the false alarm rate can be reduced, adaptive protection can be realized, and data security and business smoothness can be guaranteed.

Systems and techniques are disclosed for generating a three-dimensional (3D) model. For example, a process can include estimating a plurality of features associated with at least a portion of images; inverse warping the plurality of features into reference pose features having a reference pose; generating filtered reference pose features by selecting features from the reference pose features based on a distance of the selected features from corresponding features from the reference pose; generating modified reference pose features by modifying the filtered reference pose features based on a feature grid associated with a reference model associated with the reference pose; projecting the filtered reference pose features into one or more two dimensional (2D) planes; identifying first features associated with the person from the one or more 2D planes; and generating a 3D model of the person having a pose using the first features, the modified reference pose features, and pose information.

The invention relates to the technical field of nondestructive testing, and particularly discloses an automatic detection system and method based on a titanium plate welding part, and the method comprises the steps: obtaining an original physical field signal of a to-be-detected part under the excitation of a single energy field, and extracting a space energy attenuation gradient and time phase lag distribution through wavelet packet decomposition and Hilbert transform; constructing a dynamic propagation model for describing a signal propagation path evolution rule, and performing space-time registration and vector difference calculation with a preset ideal reference model to generate a difference evolution graph; high-dimensional topological feature mapping, density clustering and multi-scale persistence analysis are carried out on the atlas, and a stable abnormal mode caused by defects is identified and confirmed; and backtracking a dynamic evolution path of an abnormal mode, extracting defect core parameters, and completing three-dimensional positioning, type classification and security level evaluation in combination with process information.

The invention discloses a chemical process fault diagnosis method and system, and relates to the technical field of chemical process fault diagnos.The scheme aims at the diagnosis bottleneck of catalyst inactivation type progressive faults, fine parameter offset is captured in real time through a dynamic reference model, weak signals are accumulated and amplified in combination with an attenuation weighting mechanism, and the fault diagnosis accuracy is improved. The problem that a traditional method is not sensitive to slow drifting, and consequently report omission is caused is solved, and a fault recognition window is remarkably advanced. The dynamic threshold value is updated in real time based on mobile statistics, and raw material fluctuation and sensor noise can be self-adapted; during working condition switching, the model is automatically reset and the threshold value is relaxed, so that false alarm triggered by parameter mutation is avoided, and the stability of production scheduling is guaranteed; the design that fault half-life period weight and moving window length are associated with an inactivation period is introduced, so that the model autonomously adapts to different catalyst characteristics.

The invention relates to a power distribution network distributed energy bearing capacity assessment method and device. The method comprises the following steps: carrying out topology analysis on a network structure of a power distribution network to obtain an initial network topology model; obtaining a node dynamic feature data set based on the initial network topology model and the distributed energy access point data of the power distribution network; wherein the node dynamic characteristic data set comprises operation parameters of each node of the power distribution network in different load scenes; generating a parameter incidence matrix according to the node dynamic characteristic data set, and obtaining a bearing capacity reference model of the power distribution network according to the parameter incidence matrix and real-time data of the power distribution network in an operation state; wherein the parameter incidence matrix is used for quantifying the coupling degree between the operation parameters; and obtaining a risk distribution mapping graph according to the bearing capacity reference model, and identifying a potential overload area of the power distribution network based on the risk distribution mapping graph. According to the invention, power distribution network operation risk assessment can be accurately realized.

The invention discloses a high-current accurate measurement method, which relates to the technical field of current measurement, and comprises the following steps: obtaining multi-dimensional measurement parameters, carrying out preprocessing, constructing an error model to obtain a comprehensive error, and carrying out calculation based on the comprehensive error to obtain a comprehensive feedback value; based on the comprehensive feedback value, utilizing a closed-loop feedback mechanism to dynamically optimize a compensation parameter, and further utilizing the compensation parameter and the comprehensive feedback value to carry out error correction on the measurement current; real-time measurement parameters are obtained through a built-in sensor and a factory calibration reference model, an error model is constructed, the dynamic closed-loop self-calibration function is achieved, dependence on external calibration equipment is not needed, the problems that a traditional measurement method is high in complexity and high in cost are solved, and the method is suitable for integrated application of a high-precision electric energy meter and industrial measurement equipment and has wide application prospects. And meanwhile, the real-time performance and the convenience of measurement are remarkably improved.

The invention relates to the technical field of wind power generation. The invention provides a wind field unit comparison type fault diagnosis method, system and device and a storage medium. The method comprises the following steps: synchronously acquiring operation and corresponding environment data of units of the same type in the same wind field, and establishing a multi-dimensional data set; building a normal working condition multi-dimensional parameter reference model by using clustering analysis and updating in real time; performing multi-scale comparison on a target and a reference unit, and extracting feature fusion to generate a comprehensive health index; establishing a double-layer diagnosis model, constructing a virtual unit based on digital twinning in the first layer, comparing residual errors and positioning a potential fault source, detecting outliers by using an improved isolated forest algorithm in the second layer, determining a fault propagation path in combination with an association rule, and establishing a fault mode knowledge base; and triggering an early warning mechanism according to the comprehensive health index and the fault mode classification. The problems that a traditional operation and maintenance mode is difficult in fault prediction, high in cost and lack of a reliable evaluation system, state monitoring is limited to a single parameter, multi-unit comparative analysis means are insufficient, and fault diagnosis is lagged are solved.

The invention discloses an entity relationship extraction method fusing semantic information and syntactic information, and belongs to the technical field of language processing. According to the method, the information in the sentences is learned, so that the fusion of semantic and syntactic information is realized, and the accuracy of relationship classification between entities is improved. According to the method, an external syntax device and word vector information are combined, the influence of noise in a text is reduced by using a graph convolutional neural network of a self-attention pooling layer and a residual shrinkage network, and long-distance dependence between entities is improved. Meanwhile, loss is calculated in an interpolation prediction mode, and interaction between the BERT and the graph neural network is perfected. Experiments show that semantic vectors and syntactic structure information are fully fused, so that the relation extraction effect is remarkably improved compared with other reference models, and the problems that the recognition rate of the long-distance relation between entities in sentences is low and noise exists are solved.

The invention relates to the technical field of building curtain wall keel structure design, and discloses a curtain wall keel structure intelligent optimization design method, which comprises the following steps: firstly, obtaining geometric parameters, load distribution data and material performance indexes, and verifying the geometric parameters, the load distribution data and the material performance indexes; analyzing the verified data to generate multi-dimensional structure characteristic parameters, and constructing a structure optimization reference model; keel structure data are verified in real time according to the model, and abnormal matching of node displacement and stress distribution is quantitatively evaluated; according to a verification result, generating a structural anomaly index to judge a design defect; and when defects exist, optimization design schemes including keel section adjustment, node reinforcement and the like are automatically generated and sequenced. According to the method, multiple factors are comprehensively considered, multi-objective optimization is achieved, the curtain wall keel structure design quality and efficiency can be improved, the structure safety is guaranteed, and the cost is reduced.

The invention provides a steel surface defect detection method and device, equipment and a storage medium. Relates to the technical field of image recognition. The method comprises the following steps: collecting steel surface defect images, and performing data enhancement processing on the steel surface defect images to obtain a data set after data enhancement; a backbone and neck network of a reference model YOLOv11s network is improved, an ECS-YOLOv11s model is constructed by adopting a multi-scale feature fusion mode, a data set after data enhancement is input into the ECS-YOLOv11s model to train and shoot and collect steel surface images in real time, the steel surface images are input into the trained ECS-YOLOv11s model, and a steel surface defect detection result is obtained. According to the method, the steel surface defects can be accurately and efficiently recognized, meanwhile, the needed calculation amount and parameters are reduced, and the requirement for deployment on mobile equipment can be met.

The invention discloses a curve tunnel deformation monitoring data fusion processing method based on a multi-station robot, and belongs to the technical field of tunnel measurement, and the method comprises the steps: obtaining tunnel original point cloud data collected by a plurality of mobile robots and corresponding initial pose data, and constructing a multi-station dynamic unified coordinate system; performing preliminary registration based on a tunnel structure feature point set in the extracted tunnel original point cloud data to generate a preliminary fusion point cloud; performing weighted fusion on the preliminary fusion point cloud according to the fusion error distribution parameter to generate a final fusion point cloud; performing geometric modeling on the final fusion point cloud to generate a deformation monitoring reference model; performing comparative analysis on the deformation monitoring reference model and the tunnel design model to obtain a deformation distribution diagram; and generating a deformation trend prediction report. A unified coordinate system is constructed, multi-source point clouds are subjected to weighted fusion, and time sequence prediction is carried out, so that high-precision three-dimensional modeling and dynamic deformation trend early warning of the curve tunnel can be realized.

The invention discloses an activated carbon forming defect online detection method and device, and belongs to the technical field of material defect detection.The method comprises the steps that technological parameters are collected in real time and preprocessed, a parameter sequence is generated, self-sensing recognition of a new technology is conducted, lagged starting of a quality control vacuum period is avoided, and once the new technology is recognized, the quality control vacuum period is started. An unknown defect pre-recognition mechanism is triggered immediately, novel anomalies which do not conform to known defects are recognized, and an unknown defect candidate set is generated; constructing a multi-stage hierarchical detection network, performing adaptive detection on the unknown defect candidate set, establishing a reference model, screening samples, updating the reference model, and forming a defect identification model adaptive to a new process, so that the influence of insufficient samples on the detection accuracy is effectively relieved; and setting three-dimensional quantitative indexes, judging a transition period, automatically switching to a full-model detection mode when the indexes meet preset requirements, and objectively evaluating the recognition capability of the detection system on new process defects and the process suitability through the quantitative indexes.

The invention discloses a roadside guardrail deformation detection method based on image and point cloud fusion and related equipment, and the method comprises the steps: collecting a guardrail region image through a high-definition camera carried by an unmanned plane, and synchronously collecting the point cloud data of a guardrail region through a laser radar; then, a joint calibration and iterative nearest algorithm is used for carrying out space-time registration on the guardrail area image and the point cloud data which are synchronously collected, and a multi-modal data set is generated, so that the problem of space-time dislocation caused by sensor movement is solved; an improved double-branch deep learning model is adopted to respectively extract image texture features and point cloud geometric features from a multi-modal data set, and the two features are fused to overcome a single-modal defect; and extracting the contour of the guardrail beam plate from the fusion feature map, aligning the contour of the guardrail beam plate with the reference model, and carrying out quantitative calculation to quantify the deformation degree of the guardrail and realize quantitative detection of the deformation of the guardrail.

The invention relates to a power transmission line multi-defect detection method and system based on improved YOLOv8s, and the method comprises the steps: collecting insulator defects and bird nest images on a power transmission line, and obtaining an initial data set; adopting an SAM large model to preprocess the initial data set to obtain a target data set; dividing the target data set into a training set, a verification set and a test set; taking the YOLOv8s network model as a reference model to construct an improved YOLOv8s network model as a power transmission line multi-defect detection network model; inputting the training set into a model for model training; inputting a verification set into the trained power transmission line multi-defect detection network model for model evaluation; and inputting the test set into the model to obtain a power transmission line defect detection result, and evaluating the model. According to the improved YOLOv8s network model, the problems of missing detection and false detection of defects can be avoided, the detection precision of the model is improved, and the light weight of the model is considered at the same time.

The invention discloses an assembly type composite floor slab cast-in-place section span beam joint design method based on digital twinning, and relates to the technical field of intelligent construction, the method comprises the following steps: adopting a least square optimization algorithm to obtain a material performance correction parameter and a geometric compensation parameter; based on the historical material performance correction parameters and the geometric compensation parameters, training the structure model through a genetic algorithm to generate a digital twin reference model; inputting the material performance correction parameters and the geometric compensation parameters into the digital twin reference model, performing multi-scale finite element analysis, and outputting a multi-scale node performance evaluation report; identifying a multi-scale node performance influence factor and a sensitive area by adopting a response surface method, outputting a design parameter sensitivity analysis result, and converting the multi-scale node performance influence factor into a preliminary node design scheme through a design parameter conversion algorithm; according to the method, the real-time accuracy of node design is remarkably improved through least square dynamic parameter correction.

The invention discloses a tunnel intelligent primary support construction wet spraying over-consumption analysis method and system, and the method comprises the steps: obtaining spraying track space distribution data through a three-dimensional motion capturing system installed on a spraying unit, and combining a material flow fluctuation curve generated by a dynamic weighing system, constructing a three-dimensional feature tensor comprising a material flow state feature, an injection process feature and an environment interference feature; a theoretical volume of a primary support structure is extracted based on a geological BIM model, surrounding rock back break data obtained by point cloud scanning are fused, a dynamic use amount reference model considering rebound loss is established through fluid mechanics simulation, and a theoretical use amount interval value with a confidence interval is generated. According to the method, the three-dimensional characteristic spectrum and the closed-loop optimization system are constructed by fusing multi-physical-field dynamic data, the problems that dynamic adaptation of geological conditions is not accurate and a multi-factor coupling super-consumption mode cannot be analyzed in wet spraying construction are solved, and dynamic accurate control over the material consumption and explainable diagnosis of super-consumption causes are achieved.

The invention provides a bucket wheel machine safety monitoring method and system, and relates to the technical field of bucket wheel machine safety monitoring, and the method comprises the steps: obtaining a historical data set; performing feature extraction processing according to each period in the historical data set to obtain a high-dimensional feature vector set; performing graph construction processing according to the high-dimensional feature vector set to obtain a feature association graph set; performing embedding processing on the feature association graph set based on a preset graph neural network to generate a fusion feature vector set; modeling processing is carried out according to the fusion feature vector set, and an anomaly detection reference model is constructed; and inputting the real-time data into the anomaly detection reference model for comparison to obtain an anomaly detection result, the anomaly detection result including whether an anomaly exists or not and an anomaly classification result. Based on vibration, rotating speed, electrical signals and environmental parameters, the operation state of the bucket wheel machine is comprehensively described by combining time domain, frequency domain and multi-mode interaction characteristics, and the limitation of traditional single signal monitoring is avoided.

The invention discloses a method for generating a three-dimensional building GLTF file based on index compression. The method comprises the following steps: S1, collecting three-dimensional coordinate data of a building; s2, grouping and classifying the data according to floors to form a classification index structure based on a hash table; s3, selecting a reference model and recording Mesh data and vertex coordinates of the reference model; s4, calculating a translation matrix T of the polyhedral structure, and performing coordinate transformation; s5, generating Mesh data of other polyhedrons based on index compression, and reducing redundant surface data by adopting a topology preserving surface optimization algorithm; s6, independently processing the geometric data of each floor, and generating corresponding Mesh data and Node nodes; and S7, combining all floor data to generate a complete GLTF file, and performing compression by using a dynamic Huffman coding algorithm. Through the improved index compression and dynamic Huffman coding technology, the file size is remarkably reduced, the transmission efficiency is improved, and the geometric and topological integrity of the model is ensured.

The invention relates to an irregular workpiece processing and positioning method and system combined with machine vision, and the method comprises the steps: firstly obtaining a multi-view image data set which completely covers different areas of the surface of a to-be-processed workpiece, and providing rich and complete original materials for subsequent processing; three-dimensional reconstruction processing is carried out on the multi-view image data set, and generated three-dimensional model data can visually and accurately present the three-dimensional form of the workpiece to be machined; then a spatial feature set is extracted from three-dimensional model data, the spatial features of the workpiece can be deeply described from multiple dimensions, and a comprehensive and meticulous basis is provided for accurate positioning; and finally, the spatial feature set and a preset workpiece machining reference model are subjected to feature matching processing, generated positioning information can effectively drive machining equipment to execute workpiece machining path adjustment operation, the actual form of the workpiece can be dynamically adapted, the machining path is optimized, the machining precision and efficiency of the machining equipment are improved, and the machining efficiency is improved. And high-quality completion of workpiece machining is guaranteed.

The invention relates to the technical field of backhaul monitoring, in particular to a backhaul link monitoring method and system applied to a 5G base station, and the method comprises the steps: collecting physical signal statistical data, backhaul service data and network topology data of a backhaul link in real time; according to physical signal statistical data, a physical layer entropy value is calculated through a Shannon entropy formula, and recessive degradation characteristics of a physical layer are quantified; a pulse neural network space-time coding mechanism is used to generate business layer anomaly prediction parameters, and cross-layer anomaly prediction is realized; according to service types and priorities, constructing a time-varying service awareness reference model by adopting a chaos kinetic equation to serve as a dynamic judgment reference; when the return state is abnormal, combining the physical layer entropy and the network topology data to construct a feedback correction loop, and generating a self-adaptive protection strategy; according to the method, advanced prediction and accurate positioning of recessive degradation of the backhaul link are realized, and the self-healing capability and the service quality guarantee level of the backhaul link in a multi-service scene are improved.

The invention relates to the technical field of refrigeration house energy-saving control and digital twinning, in particular to a refrigeration house fan energy-saving operation method and system, and the method achieves the whole-field temperature deduction of a sensor-free area by constructing a CFD reference model and simulating and predicting the three-dimensional transient airflow and temperature distribution in a refrigeration house. Model parameters are calibrated through measured data, and the order of the model is reduced by adopting intrinsic orthogonal decomposition and Galerkin projection, so that the calculation amount is remarkably reduced, and online optimization is supported. And in combination with a data assimilation algorithm, the prediction result of the reduced-order model is continuously corrected, and the adaptability to environment change and system aging is improved. The optimization control stage takes minimization of the total energy consumption of a fan as a target, solves an optimal fan control sequence under the hard constraint that the whole-field temperature does not exceed the cargo safety upper limit and does not exceed the cargo safety lower limit, and adopts a rolling time domain mode for execution to realize collaborative optimization of safety and energy conservation.

The invention provides a multi-model fusion asynchronous motor fault diagnosis method. The method comprises the following steps: converting a collected asynchronous motor time domain signal into a frequency domain signal by adopting fast Fourier transform; extracting a plurality of characteristic values related to the motor fault from the frequency domain signal; the current working mode of the motor is determined by calculating the power frequency and the load rate of motor operation; using all normal samples to train a main model as a reference model; and for each working mode, training the sub-model by using the corresponding sample data. According to the working mode of the to-be-predicted sample, the corresponding sub-model is preferentially selected for calculation, and if the matched sub-model cannot be found, the main model is used for calculation. And calculating the fault probability of each component of the motor according to the abnormal probability of the characteristic value predicted and calculated by the model. The fault diagnosis method can effectively meet the fault diagnosis requirements in different working modes, is particularly suitable for fault diagnosis of the variable frequency motor under complex working conditions, and improves the accuracy and adaptability of diagnosis.

The invention discloses a temperature calibration method and system for a temperature sensor, and belongs to the technical field of temperature testing, and the method specifically comprises the steps: reading temperature data in real time through an intelligent temperature sensor, and transmitting the temperature data to a processing system; the processing system verifies and decodes the received data and then constructs a self-adaptive temperature reference model in combination with historical temperature data and current environment parameters; temperature deviation is identified, deviation reasons are analyzed through a machine learning algorithm, the temperature change trend is predicted, and correction parameters are automatically generated; the correction parameters are automatically distributed to the equipment end through the intelligent contract, so that instant adjustment and effect feedback of equipment setting are realized; the processing system collects feedback data, evaluates an adjustment effect, optimizes a self-adaptive temperature reference model and corrects a parameter generation strategy according to the adjustment effect, and forms a closed-loop optimization mechanism; according to the invention, the calibration efficiency and precision are improved, the environmental adaptability is enhanced, and the method is especially suitable for high-precision measurement requirements under extreme temperature conditions.

The invention relates to a method and system for detecting a target grabbed by a mechanical arm, and the method comprises the steps: collecting a target material image, and obtaining an initial data set; preprocessing the initial data set by adopting a data enhancement mode to obtain a target data set; taking the generative residual convolutional neural network model as a reference model to construct an improved generative residual convolutional neural network model as a mechanical arm grabbing target detection network model; calculating each output loss and total loss by using an improved loss calculation method, obtaining a difference between the output loss and the total loss and practical feasible capture information, and obtaining a training weight; and updating the weight of the model by using a loss calculation result to obtain an optimal training weight, and inputting a test set into the trained mechanical arm grabbing target detection network model to obtain a mechanical arm grabbing target detection result. According to the improved mechanical arm grabbing target detection network model, the grabbing target detection precision is improved, and the grabbing target detection speed is increased.