496 results about "Data calibration" patented technology

The invention relates to the technical field of data processing, in particular to an induction cooker data acquisition and processing system which comprises a power jump identification module, a response section correction module, a trend sampling analysis module, a temperature control abnormity judgment module and a working condition balance data calibration module. According to the method, the rate change value between the power sampling points in the operation period is calculated, the sections with obvious abrupt change characteristics are screened, and the power lag response is corrected in combination with the voltage change slope, so that the time offset between the control response and the electric signal change is effectively corrected; the temperature fluctuation trend and the temperature control deviation direction are analyzed on the basis of the correction sequence, a temperature control abnormal state label set is formed by matching current waveform symmetry characteristics, a basis is provided for output power adjustment according to the temperature rise rate, the adaptation capacity of data analysis to actual working conditions and the execution precision of a control strategy are improved, and the accuracy of data analysis is improved. And the stable operation capability of the electromagnetic heating equipment under the complex thermal load change is enhanced.

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 semi-aviation transient electromagnetic acquisition device and method for calibrating data drift in real time, and relates to the technical field of data calibration, and the device comprises an electromagnetic data acquisition unit which receives a secondary induction signal generated by a ground transmitting end through a transient electromagnetic hollow induction coil disposed in a coil storage cabin of an unmanned aerial vehicle, and transmits the secondary induction signal to an unmanned aerial vehicle; self-adaptive gain control is carried out according to the signal intensity to obtain real-time electromagnetic data; the flight control unit monitors the swing amplitude and frequency of the coil in real time according to the inclination angle and acceleration data fed back by the sensor in the coil storage cabin, and carries out flight attitude adjustment and control; the drift calibration unit adopts a static reference mode for calibration at the beginning stage of flight; and switching to a dynamic reference mode in the flight process, and carrying out self-adaptive calibration on the real-time data stream based on a sliding window. According to the invention, drift calibration of traditional software post-processing is migrated to a hardware acquisition stage, so that synchronous completion of data acquisition and drift compensation is realized.

The invention relates to the technical field of unmanned aerial vehicle supervision, and discloses a multi-band multi-channel unmanned aerial vehicle RID signal analysis system. According to the system, RID signals of 2.4 GHz, 5.2 GHz and 5.8 GHz frequency bands are collected in parallel through a multi-frequency-band antenna group and a multi-channel radio frequency receiving module, and real-time analysis and algorithm scheduling of the RID signals are achieved through an FPGA + ARM heterogeneous processing unit. Four modules of multi-sensor space-time alignment and cross validation, lightweight national secret encryption and data integrity verification, self-adaptive scheduling based on reinforcement learning and unmanned aerial vehicle behavior digital twinning and intrusion early warning are integrated in algorithm, and a closed-loop processing flow from RID signal receiving, data calibration and secure transmission to intelligent research and judgment is formed. The method effectively solves the problems of incomplete RID signal coverage, low data reliability, weak safety protection, insufficient early warning capability and the like in the prior art, has the advantages of high analysis precision, good safety, high resource utilization rate, accurate early warning and the like, and is suitable for low-altitude supervision scenes such as urban security and protection, border management and control and the like.

The invention provides a temperature sensor data calibration method and system, and relates to the technical field of sensor calibration, and the method comprises the steps: collecting a time sequence voltage sequence of a to-be-calibrated temperature sensor under the excitation of a temperature step; calculating a gradient change rate of the time sequence voltage sequence to obtain a dynamic response characteristic of the to-be-calibrated temperature sensor; based on the gradient change rate, the dynamic response asymmetry degree of the temperature sensor to be calibrated under temperature step excitation is calculated; establishing a thermal inertia evolution equation of the temperature sensor to be calibrated according to the dynamic response asymmetry degree; the theoretical lag amplitude of the temperature sensor to be calibrated is corrected with the trend of the thermal inertia evolution equation to be stable as the target, and the actual lag amplitude is obtained; obtaining the original measurement temperature of the target temperature by the temperature sensor to be calibrated; and correcting the original measurement temperature in combination with the actual lag amplitude and the dynamic response asymmetry degree to complete data calibration of the temperature sensor to be calibrated. And the measurement precision of the temperature sensor in a complex temperature change scene is improved.

The invention discloses a slope monitoring data calibration and three-dimensional point cloud matching method, belongs to the field of monitoring data calibration, and aims to improve orientation errors caused by the fact that global satellite positioning and navigation system measurement endpoint errors and equipment length problems are not fully considered in existing slope radar measurement. The method solves the problem of position deviation of a monitoring result caused by the fact that the position of a deformation area is monitored, breaks through the limitation of insufficient precision when a traditional radar image is converted into geographic space coordinates, improves the accuracy and reliability of data, remarkably improves the position precision of slope radar monitoring data, and enables the position monitoring of the deformation area to be more accurate. The high-precision requirement of slope stability monitoring is met, reliable data support is provided for fine monitoring of slope stability, potential safety hazards can be found in time, and corresponding measures can be taken.

The invention provides a carbon emission accounting method, system, equipment and medium in a port and aviation container transportation process, and belongs to the technical field of logistics carbon management and intelligent algorithm crossing. The method comprises the following steps: collecting transportation and energy consumption data of roads, waterways, railways and logistics storage links in a classified manner, and preprocessing the transportation and energy consumption data to form a standardized data set; a carbon emission factor parameter library is created in combination with industrial standards, different transportation modes and cargo types, and dynamic updating is performed through a machine learning algorithm. And special accounting sub-models are constructed for different transportation modes, and carbon emission accounting is carried out by using corresponding formulas and parameters. And inputting the standardized data into the nuclear operator model, calculating the carbon emission and screening an optimal path. And model parameters are calibrated by using historical data to ensure accuracy. And finally, collecting data in real time, inputting the data into the accounting sub-model, generating a decision support report comprising implementation steps, resource demands and expected effects, and assisting in realizing low-carbon and efficient management of the transportation process.

The invention relates to the technical field of agricultural intellectualization, and discloses a fruit tree growth monitoring system based on multi-sensor fusion, which comprises a multi-mode sensor array, an integrated soil humidity sensor, a three-dimensional ultrasonic ranging sensor, a hyperspectral imaging sensor and a microenvironment meteorological station, and realizes multi-parameter synchronous acquisition; a nonlinear data calibration model adopts a layered architecture, and cross interference between sensors is eliminated; a dynamic weight distribution algorithm is built in the edge computing node, multi-source heterogeneous data are fused in real time, a fruit tree growth situation map is generated, and a low-power-consumption processor is adopted to support lightweight AI model deployment; the self-adaptive power supply module adopts a photovoltaic-temperature difference composite power generation structure to realize cable-free deployment; the wireless ad hoc network communication unit constructs an inter-tree relay network based on a LoRa protocol, supports low-power-consumption remote data return, and integrates a relay node health degree evaluation mechanism. The real-time performance of fruit tree growth monitoring, the data reliability and the situation analysis accuracy are improved.

The invention discloses a data calibration compensation method for an off-axis magnetic encoder, and the method comprises the following steps: S1, calibration initialization: carrying out the initialization of used peripherals, calibration parameters and an encoder chip, and making preparations for calibration; s2, forcibly dragging the motor to align: forcibly dragging a motor rotor to rotate to a position 0 of a nearest electrical angle by adopting a non-inductive control mode, and recording a calibration initial position; s3, calculating calibration data by rotating the motor: recording a data compensation result of the encoder by taking the original data of the encoder as an index; the calibration method does not need other equipment to drive the motor to coaxially rotate for calibration, the stator magnetic field is directly utilized to calibrate the motor, system errors caused by external equipment are avoided, the accurate relative position relation between the electron magnetic field and the rotor magnetic field can be obtained, the driving efficiency is higher, the driving dead zone is smaller, and the calibration precision is higher. And various coaxiality and roundness error data of the off-axis magnetic encoder can be improved.

The invention provides a metering data automatic calibration method and system, and the method comprises the steps: obtaining an actual measurement output value of a target intelligent sensor for a measured physical quantity in a normal operation process of metering equipment, accessing a standard reference signal outputted by an external standard device subjected to the traceability of the quantity value, reading a pre-stored standard reference value and a standard threshold value, and carrying out the calibration of the measured physical quantity. According to the actually measured output value, the standard reference value and the standard reference signal, obtaining a standardized output value through a fitting mode; when the standardized output value exceeds a standard threshold value, switching to a calibration mode, providing forward and reverse standard input quantities by an external standard device, acquiring a response signal, and calculating a nonlinear compensation coefficient and a hysteresis correction coefficient; and updating the output correction parameter according to the compensation coefficient. By adopting the scheme of the invention, the cost constraint of different error attention in measurement data calibration can be realized, and the calibration robustness is improved.

The invention provides a ground-air combined road slope collapse measurement system and method, and relates to the field of road slope inspection. According to the system, all-weather non-contact acquisition of millimeter-level displacement data of side slope key points is realized through a scientific-level CMOS camera of the ground detection module and a digital image correlation algorithm, and a high and steep side slope is covered; the aerial detection module carries a synthetic aperture radar and a multispectral sensor through an unmanned aerial vehicle to obtain a slope overall three-dimensional deformation field and soil humidity data. Carrying out high-precision space-time registration on the data layer by adopting a dual-function mark point and a least square method; calibrating aerial deformation data on the feature layer through ground displacement data to realize association of macroscopic and microscopic features; and constructing an improved U-Net network and a hybrid prediction model in the model layer to realize accurate tracking of the crack and prediction of the collapse probability. Visual display is carried out through a digital twin platform, and graded early warning linked with a traffic management system is triggered. According to the invention, the limitation of a single detection mode is overcome, and accurate perception, intelligent prediction and dynamic response of road slope safety are realized.

The invention relates to the field of air tightness testers, and discloses an automatic data calibration method and system for an air tightness tester, and the method comprises the steps: obtaining a temperature sensor value T, a pressure sensor value P and a humidity sensor value H from the air tightness tester; performing three-dimensional coordinate positioning in the three-dimensional coordinate space to obtain a coordinate position C1; mapping the coordinate position C1 to a distribution model for coordinate mapping by using a coordinate transformation matrix M; obtaining a compensation parameter set S1 based on the error characteristic value E and the compensation parameter combination S; adjusting the compensation weight of each environment parameter according to the compensation parameter set S1; fusing the adjusted compensation weight with the error characteristic value E and the compensation parameter combination S to obtain a comprehensive compensation coefficient Kc; and performing correction processing on the original measurement data by using the comprehensive compensation coefficient Kc. The method has the advantages that automatic extraction and dynamic adjustment of calibration parameters are achieved when the test environment changes.

The invention relates to the field of animal husbandry industry environment regulation and control system optimization, in particular to a lightweight digital twinborn breeding environment monitoring method and system, and the method comprises the following steps: S1, carrying out the construction of a computational fluid dynamics (CFD) model for a breeding farm environment; s2, environment data are collected; s3, comparing model prediction with real-time data, and monitoring a breeding environment according to a comparison result; and S4, data presentation. According to the invention, through cooperative work of the flow field modeling unit, the environment data acquisition unit, the model prediction and data calibration unit and the visualization unit, light-weight, full-range and high-precision breeding environment monitoring and regulation are realized.

The invention relates to a double-robot automatic calibration method based on self-adaptive point location planning in a restricted space, and belongs to the technical field of double-robot calibration, and the method comprises the steps: building a three-dimensional simulation model of a double-robot operation space on an Rviz platform based on an ROS architecture; a collision detection module is used for randomly generating a calibration point composed of collision-free double-robot joint angle vectors; when the robot is driven to move, data of the motion capture camera and positive kinematics Cartesian coordinates of the double robots are synchronously collected, a calibration result is obtained through optimization of a least square method by adopting a hand-eye calibration algorithm, and the effectiveness of the calibration result is judged through the average value of data calibration precision until calibration of the double robots is completed. Simulation modeling, collision detection and a hand-eye calibration technology are fused, a complex calibration problem is converted into a virtual environment parameter optimization process through a self-adaptive sampling strategy and a data synchronous acquisition mechanism, calibration robustness and precision are remarkably improved, and guarantee is provided for high-precision cooperative control of double robots.

The invention provides a method and a system for evaluating the flatness of a welding seam based on deep learning. The method comprises the following steps of: acquiring marking data of a center line of the welding seam through image processing and edge data calibration; fusing the marking data of the center line of the welding seam and the geometric characteristic parameters of the welding seam to construct and obtain structured characteristic data; inputting the structured feature data to a deep learning model to calculate and obtain welding seam straightness deviation data; and comparing the flatness deviation data with an industrial standard, and generating a welding seam flatness evaluation report. According to the method, the center line of the welding seam is accurately positioned through image processing and edge calibration, structural data are constructed by fusing geometric features, the straightness deviation is calculated by adopting a deep learning model, and a quality report is generated, so that the problems of low efficiency and poor precision of a traditional method are solved, non-contact automatic detection is realized, the measurement precision and efficiency are remarkably improved, and the detection efficiency is improved. And the system has strong environment adaptability and system docking capability.

The invention provides a full-link data tracing processing method and system for a cross-border home supply chain, and relates to the technical field of supply chain management, and the method comprises the steps: 3, dividing a plurality of data unit sets which are correlated with each other based on the process data of the supply chain, determining a reference data unit set, and obtaining two groups of different data correlation rules; analyzing an interaction relationship between the two groups of data association rules, establishing a data state identification range, and selecting two data verification reference points in the data state identification range; and according to the evolution relationship of the data verification reference point on the time sequence, the evolution span is quantified by calculating the comprehensive distance between two points on the time and data feature dimensions, a data state evolution path is constructed to generate a data calibration coefficient, and a data link track comprising a timestamp and link information is formed. According to the invention, full-link data tracing of the cross-border home supply chain is realized, and the collaboration efficiency of each link of the supply chain is improved.

The invention provides an intelligent power grid multi-sensor fusion line loss analysis system and method, and relates to the technical field of intelligent power grids, and the system comprises a line loss analysis matrix drawing module which draws a line loss analysis matrix and sets a line loss deviation value; the dynamic analysis module is used for determining line loss abnormal offset, and when the line loss abnormal offset exceeds a normal line loss preset threshold range corresponding to the power transmission line, a dynamic analysis mechanism is triggered: differential data calibration is carried out based on a line loss analysis matrix and the line loss offset; the dynamic distribution module is used for dynamically distributing line loss regulation gain factors of each monitoring section; and the line loss calibration module is used for performing line loss precision enhancement control on the intelligent power grid through each edge calibration execution unit deployed in each monitoring section. According to the method and the device, the technical problem of inaccurate line loss analysis caused by difficulty in accurately positioning line loss abnormal sections and causes in the prior art is solved, and the line loss problem of the power grid is accurately identified through multi-sensor monitoring and data fusion.

The invention discloses an expressway traffic event video detection method, which relates to the technical field of video analysis, and comprises the following steps: acquiring a plurality of types of traffic event samples of an expressway, performing data calibration on an initial expressway traffic event training original data set according to multi-modal preprocessing, and obtaining an expressway traffic event training original data set; generating a highway traffic event multi-mode original training data set; performing spatio-temporal joint feature extraction on the multi-modal original training data set of the highway traffic events to obtain multi-modal spatio-temporal joint feature data, establishing a highway traffic event detection classification model, and evaluating probability distribution vectors of abnormal scores and event categories of the highway traffic events; and verifying the occurrence probability of the traffic event type under the given video stream and radar point cloud by using the probability distribution vector based on the abnormal score of the highway traffic event and the event type. The method has the advantages that the detection speed of the highway traffic incident is increased, and the detection accuracy of the traffic incident is improved.

The invention provides a road target identification method and system based on Leiyu fusion. The method belongs to the field of road target recognition, and comprises the following steps: constructing mapping from a radar coordinate system to an image coordinate system according to parameter information of optical equipment and radar equipment and a data acquisition timestamp so as to carry out data calibration; dividing the optical image data and the radar image data after data calibration into preset scales so as to adjust the resolution; inputting the optical image data and the radar image data before and after division into a trained multi-scale double-flow fusion road target recognition model for feature extraction; performing double-flow feature fusion on the multi-scale optical image features and the radar features obtained by feature extraction; and inputting the completed double-flow feature fused image features into a feature pyramid for feature enhancement, inputting the enhanced image features into a detection head for detection, and outputting a detection result. The precision of road target recognition can be improved.

The invention belongs to the technical field of high voltage and insulation, and relates to an XLPE breakdown field strength evaluation method and system based on gel content and preparation process, and the method comprises the following steps: S1, sampling, weighing, shearing and bagging; s2, extracting, weighing and calculating the gel content; s3, constructing an evaluation model; s4, predicting the breakdown field intensity; according to the technical scheme, the physical model fusing the cross-linking enhancement effect and the thermal damage effect is constructed, the breakdown field strength is predicted based on the physical model, accurate and rapid evaluation of the XLPE insulation performance under different process conditions can be achieved only through a small amount of breakdown test data calibration model, dependence on a traditional destructive electrical test is remarkably reduced, and the reliability of the XLPE insulation performance is improved. The test cost is greatly reduced, the research and development period is greatly shortened, and an effective tool is provided for cable insulation material performance analysis and production process optimization.

The invention belongs to the field of rubber, and relates to a rubber sealing ring multi-field coupling intelligent simulation optimization system which comprises a working condition and rubber material performance coupling module, a virtual assembly model construction module, a local finite element model generation module, a dynamic sealing performance simulation module, a simulation and test data calibration module and an intelligent optimization design module. According to the system, basic parameters are provided through coupling modeling of working conditions and rubber material performance, a simulation carrier is formed through virtual assembly and local finite element model construction, core performance analysis is achieved through dynamic sealing performance simulation, parameter precision is corrected by combining simulation and test calibration, and finally an optimal scheme is output through intelligent optimization design. And the whole process integration of simulation, calibration and optimization of the rubber sealing ring is realized.

The invention discloses a composite material fan blade layering design method in the technical field of aero-engine fan blade design, and the method comprises the following steps: S1, constructing a blade model, and carrying out optimization based on a model structure; s2, prepreg is selected, the curing thickness of a single layer of prepreg is obtained through a laminated plate curing process, a laying layer profile diagram is extracted according to the blade optimization model, and laying layers are grouped according to the height; s3, paving layers are uniformly arranged according to a height marshalling method under the paving layer design criterion condition, paving layer angles are given, whether the requirements are met or not is judged, and a preliminary blanking drawing is generated; and S4, carrying out cutting design on the prepreg according to the blanking drawing. According to the method, through parametric modeling and actual measurement data calibration, tenon extension design and layering angle distribution and sorting design are optimized, fiber continuity is ensured, process accumulative errors are eliminated, then, 90-degree layering is locally introduced to enhance out-of-plane rigidity, stress concentration is dispersed in combination with step transition treatment, and therefore the method has the advantages of being simple in structure and convenient to operate. And the peak value of the interlayer shear stress of a tenon-blade body transition area is reduced, and the impact resistance is improved.

The invention provides a low-power-consumption cooperative sewer water body integrated monitoring method. The method comprises the following steps: carrying out model selection and deployment on sensing node hardware of an adaptive scene; a low-power-consumption internet of things network topology and power supply design; a time-sharing triggering acquisition strategy of the multi-modal data is carried out; node-level environment self-adaptive data calibration is carried out; node-level flow velocity-stink association anomaly recognition is carried out; carrying out network-level data fusion and validity verification; collaborative traceability, diffusion simulation and linkage control are carried out. According to the invention, the problems of data distortion, difficulty in source tracing of pollution sources, short equipment endurance and response lag caused by independent collection of flow and stink and environmental interference in traditional sewer monitoring are solved; the integrated effect of ultrasonic Doppler and electronic nose cooperative monitoring, environment self-adaption accurate calibration, odor source rapid positioning and diffusion simulation and low-power-consumption and long-endurance linkage control is achieved.

The invention discloses an online calibration method and system for multi-degree-of-freedom parameters of a high-dynamic deflection measuring instrument, and belongs to the technical field of vehicle-mounted data calibration. Data acquisition and preprocessing are performed in a driving scene; and performing error correction and noise processing on the data. And multi-parameter decoupling and in-situ working condition self-adaptive calibration are carried out. Performing feature extraction and normalization processing based on the decoupled parameters to obtain working condition features; calculating a matching weight of the working condition characteristics and a standard working condition library based on a Gaussian kernel function; and based on the matching weight and a standard value of a corresponding working condition in a standard working condition library, carrying out weighted fusion to generate an adaptive calibration value adaptive to the current real driving working condition. And finally, carrying out quantity value traceability verification. Through technical chain cooperation of dynamic sensing, parameter decoupling, decoupling support compensation and traceability optimization, the core problems of dynamic adaptation, precise separation, anti-interference stability and credible traceability of multi-parameter multi-degree-of-freedom calibration in a normal driving scene are solved.

The invention discloses a state management system for a single-phase ammeter, and relates to the technical field of electrical variable measurement, the state management system comprises an acquisition module, a monitoring module and an early warning decision module, the acquisition module acquires initial data of the single-phase ammeter, transmits the initial data to the monitoring module, carries out distributed processing on the initial data through a monitoring unit, obtains a comparison result, and sends the comparison result to the early warning decision module; the method comprises the following steps: comparing a plurality of data, transmitting the data to an early warning decision module, carrying out distributed collaborative decision according to a comparison result, generating an early warning signal and a control instruction, realizing data local processing and distributed storage through multi-node edge calculation, and reducing the false alarm and missing report rate by relying on a dynamic differentiation threshold and historical data calibration. Parameter linkage risks are captured by means of time sequence correlation analysis and an abnormal path atlas, alarm storm is avoided through hierarchical decision-making and hierarchical early warning, data management is optimized by combining Hash fragmentation storage and combination key retrieval, traditional bottlenecks are comprehensively broken through, the monitoring reliability, accuracy and intelligence of an electric meter are improved, and novel electricity utilization ecological requirements are met.

The invention provides an intelligent operation and maintenance data processing system based on an Internet of Things matrix, and relates to the technical field of operation and maintenance management, and the system comprises a collection module which is used for collecting equipment real-time operation data through the Internet of Things matrix, and constructing a standardized feature data set; the calibration module is used for performing data relation topology analysis on the standardized feature data set, identifying sparse regions between core data nodes and constructing two groups of data association paths; and determining a feature analysis domain based on the feature distribution difference of the two groups of data association paths, respectively selecting verification nodes inside and outside the feature analysis domain, constructing a data verification ring according to the evolution relationship of the verification nodes on the time sequence, and generating data calibration parameters. According to the invention, real-time analysis, fault location and dynamic resource allocation of operation and maintenance data are realized, and the operation and maintenance efficiency and reliability are improved.

The invention relates to the technical field of unmanned aerial vehicle nondestructive testing, and discloses an unmanned aerial vehicle precision part rapid detection system based on multi-sensor fusion. Low-frequency harmonic vibration excitation is applied through a micro vibration excitation module, and a phase offset auxiliary harmonic signal is generated in combination with a multi-harmonic phase synchronous modulation module; and the defect response of the precision part is amplified. The multi-sensor array synchronously collects vibration, optical and thermal response data, and data calibration is carried out through the sensor array self-calibration network. And the defect pattern library pre-matching engine matches the current detection data with the historical defect pattern. And the fusion processing unit performs weighted fusion on the multi-modal detection data, adjusts detection parameters according to a detection result through a closed-loop feedback loop, and triggers iterative detection. According to the invention, low-frequency harmonic vibration excitation is applied through the micro vibration excitation module, and the multi-harmonic phase synchronous modulation module is combined to superpose auxiliary harmonic signals of phase deviation, so that the identification capability of micro defects is improved.

The invention relates to the technical field of zero-point positioning mother board pressure compensation, and discloses a pneumatic type zero-point positioning mother board pressure adaptive compensation system which performs multi-source fusion processing on initial pressure field data and covers a compensation sequence of static reference features, a feedback sequence of dynamic deformation features and a calibration sequence of axial offset features. And the pressure compensation module carries out dynamic fluctuation suppression on the real-time deformation data flow, realizes pressure field balance identification through a multi-field coupling layer and generates a compensation instruction. The pressure standardization module performs space grid division, anomaly filtering and data calibration, and a pressure field analysis layer, a zero point self-adaption layer and a compensation trigger layer of the field intensity reconstruction module sequentially complete feature fusion, dynamic correlation modeling and multi-dimensional field intensity fusion. According to the system, through algorithms such as deformation segmentation, virtual interpolation and dynamic clustering, the pressure field balancing capability and positioning precision of the mother board under complex working conditions are improved, and the system is suitable for high-precision positioning requirements in the field of industrial automation.

The invention provides a full-wave-band reflectivity dynamic calibration analysis method and system, and belongs to the technical field of data calibration, and the method comprises the steps: obtaining discrete reflectivity data at a plurality of moments and full-wave-band reflectivity data at a plurality of moments; aiming at the discrete reflectivity data at each moment, carrying out the following steps: dividing a full wave band at the same moment based on the discrete reflectivity data at the moment to obtain a plurality of wave band intervals corresponding to the moment; determining a Gaussian kernel function of a wavelength corresponding to each quantum dot in the discrete reflectivity data at the moment; determining a weight corresponding to each wavelength based on the Gaussian kernel function of each wavelength; determining the stretched reflectivity data based on the weight corresponding to each wavelength; optimizing a width parameter in the Gaussian kernel function corresponding to each wavelength to obtain a target width parameter; and re-determining the stretched reflectivity data based on the target width parameter to obtain target reflectivity data. According to the invention, the full-wave-band reflectivity spectrum can be calibrated.

The invention provides an enterprise knowledge graph construction and intelligent training method and system based on a large model, and belongs to the technical field of information retrieval and database structures. The construction method comprises the following steps: loading a current enterprise knowledge database based on a first data identification large model, and dividing the enterprise knowledge database into a static knowledge sub-database and a dynamic knowledge sub-database; calibrating the static knowledge sub-database as a standard static knowledge base based on the second data calibration large model; based on the standard static knowledge base and the dynamic knowledge sub-database, an enterprise knowledge graph is constructed, and the enterprise knowledge graph comprises static knowledge nodes and dynamic knowledge nodes; when the enterprise knowledge database is updated, updating content is uploaded to the dynamic knowledge sub-database; and updating the enterprise knowledge graph based on the updated dynamic knowledge sub-database. According to the technical scheme, the efficiency of enterprise knowledge graph construction and intelligent training can be improved.