792 results about "Data calibration" patented technology

The invention provides a calibration method and system for measurement data of a pressure sensor, and the method comprises the steps: obtaining an original output signal of the pressure sensor and synchronous collection data of environment parameters, and carrying out the preprocessing, and obtaining multi-source sensing data and dynamic environment characteristics; based on multi-source sensing data and dynamic environment characteristics, a multi-error coupled comprehensive error model is constructed, and a dynamic compensation coefficient is generated through online parameter identification; according to the dynamic compensation coefficient and a preset confidence evaluation strategy, dynamic compensation calibration is carried out on the sensing data, a high-precision pressure value is generated, and the high-precision pressure value is used for representing a real environment pressure measurement value. By adopting the method, the accuracy and the reliability of the tester in the calibration of the pressure sensor in a multi-error coexistence and complex environment can be improved, and the precision and the stability of the measurement data calibration of the pressure sensor are improved.

The invention provides an intelligent traffic vehicle behavior modeling method and system based on collaborative learning, and relates to the technical field of unmanned driving, and the method comprises the steps: obtaining traffic data in a target region; performing data calibration processing according to the traffic data to obtain a collaborative perception data set; a vehicle space-time trajectory function is generated according to the collaborative perception data set, and a conflict domain space-time distribution diagram is obtained; according to the vehicle space-time trajectory function and the conflict domain space-time distribution diagram, probability distribution characteristics of the vehicle driving intention and a multi-workshop competition cooperation relation matrix are extracted, and a multi-dimensional interaction characteristic vector is obtained; and behavior modeling is carried out according to the multi-dimensional interaction feature vectors, and a vehicle behavior model representing interaction behavior rules and decision logic between vehicles is obtained. According to the method, through multi-vehicle cooperative sensing data calibration and dynamic fusion of road geometric constraints, the problems of single-vehicle sensor shielding and cross-vehicle data heterogeneity are effectively solved, and the global consistency of vehicle position and motion state estimation is remarkably improved.

The invention relates to the technical field of pumped storage power station multi-sensor monitoring network time-space synchronous measurement, in particular to a pumped storage power station multi-sensor safety monitoring system, which comprises a sensor data acquisition module, a time-space synchronous network module, a data calibration module, a dynamic association model construction module, a multi-dimensional data fusion module and a graded early warning module. The dynamic correlation model building module simulates dynamic response of seepage pressure and surrounding rock strain through a fluid-structure interaction equation and a discrete element digital twinborn model, updates fracture permeability boundary conditions in real time, and extracts cross-sensor space-time correlation characteristics by adopting a convolutional long-short-term memory network improved by a multi-head attention mechanism. According to the method, the real-time early warning precision of abnormal events and the reliability of structural health assessment in the complex hydraulic and mechanical coupling environment are improved.

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 the technical field of intelligent warehousing and logistics automation, in particular to a forklift dynamic path planning method based on deep reinforcement learning, and the method comprises the steps: deploying multi-view vision, geomagnetism and other multi-source sensors, achieving data calibration and fusion through employing an insect compound eye-imitating vision model, and constructing a high-dimensional state space vector; heuristic models such as migrant bird navigation and biological stress response are adopted, deep reinforcement learning is combined, decision instructions are generated from the three aspects of path planning, dynamic obstacle avoidance and energy efficiency management, actions are executed through a control system, and deviation is fed back; a reward function is used for evaluating the decision effect, rewards are formed by weighting path efficiency, obstacle avoidance success and energy consumption penalty, the weight can be updated in a self-adaptive mode, and therefore the deep reinforcement learning model is optimized. According to the method, the accuracy, safety and efficiency of forklift path planning are effectively improved, the method can adapt to complex dynamic environments, and the requirements of intelligent logistics and industrial automation for forklift intelligent operation are met.

The invention relates to the technical field of anomaly detection, in particular to a measurement abnormal data detection method and system based on a rule engine, and the method comprises the following steps: extracting a transmission path and a timestamp sequence based on a source point number, a transmission link number and a real-time state code of a measurement data packet, comparing a data flow direction path with a preset abnormal flow direction, and determining a data flow direction; and identifying an abnormal mode of which the risk path fitting degree exceeds a preset threshold value, and obtaining an abnormal flow direction feature set. According to the method, abnormal point locations in the data can be effectively identified and corrected by monitoring the change of the submission times and the activeness of the real-time measurement data of the power grid and the data synchronism, and the dynamic abnormal threshold setting and real-time data analysis further enhance the capability of capturing the abnormal trend, can adjust the processing strategy in time, and improve the accuracy of data processing. The method has the advantages that accumulation and propagation of error data are avoided, data redundancy and error repetition are effectively reduced due to generation of a data calibration set and application of data fingerprints, and data quality is guaranteed.

The invention provides a water pump residual life prediction method based on a large model, and the method comprises the following steps: S1, collecting the multi-source heterogeneous data of the operation of a water pump in real time through a vibration sensor, a temperature sensor, a pressure sensor and a monitoring unit, the temperature sensor monitors temperature gradient changes of the bearing and the sealing cavity, the pressure sensor records inlet and outlet pressure fluctuation characteristics, and the monitoring unit extracts three-phase current harmonic components of the motor; s2, carrying out lightweight preprocessing on the multi-source heterogeneous original sensing data at an edge computing node, wherein the lightweight preprocessing comprises vibration signal noise reduction processing based on wavelet transform, temperature and pressure data calibration normalization of load segments according to working conditions, and transient abnormal data flow filtering through a sliding time window; and S3, inputting the preprocessed data stream into a cloud large model platform, and analyzing the long-period dependency relationship of the vibration signals through a Transform encoder in a time sequence feature extraction module.

The invention discloses a dual-electric brake boosting system for a heavy-duty vehicle and a coordinated control method of the dual-electric brake boosting system, and relates to the technical field of vehicle brake control. The invention discloses a dual-electric brake power-assisted system for a heavy-duty vehicle and a coordinated control method thereof. The coordinated control method comprises the following steps that S1, a multi-source sensor synchronously collects and preprocesses wheel speed, current, suspension and pedal signals; s2, estimating the load in real time through Kalman filtering, and outputting a dynamic load coefficient to guide distribution; s3, triggering interruption when the pedal force change rate exceeds the threshold value, and directly outputting the maximum braking force; s4, dynamically distributing the braking force, predicting the temperature rise, and adjusting the output weights of the double boosters; s5, outputting a digital twinborn verification theory, and starting a compensation algorithm after deviation overrun; s6, encrypting an instruction by a TSN protocol, and ensuring low-delay transmission and communication security; s7, a closed-loop optimization link is formed based on the actual braking data calibration model; the method has the beneficial effect that the real-time performance, safety and reliability of the braking system are comprehensively improved.

The invention discloses a method for testing the dynamic response characteristic of a power supply by simulating the load change of the power supply, and relates to the technical field of power supply testing, and the method comprises the steps: generating a simulation load change sequence based on a preset load change mode, and enabling each load value to correspond to a specific time point; inputting the simulated load change sequence into the power supply to be tested, and controlling the power supply to be tested to work according to the simulated load change sequence by using the multi-channel synchronous test system so as to simulate the load change in the actual use process; a high-precision data acquisition module is used for acquiring and recording output voltage values, output current values and corresponding timestamps of the to-be-tested power supply under different load conditions at a sampling rate not lower than a first set value, and a dynamic response data set is formed; and processing the dynamic response data set by using a preset data calibration algorithm, and calculating dynamic response parameters of the to-be-tested power supply at each load change point. The motor power supply testing device has the effect of improving motor power supply testing precision.

Provided in the present invention is a ship hatch data management method based on point cloud technology. The method is implemented by means of a constructed ship hatch data management system, and the system comprises a ship scanning module, an automatic hatch identification and calculation module, a ship attitude data compensation module, and a hatch data calibration and accumulation module. In the present invention, by means of processing data of a scanner and analyzing point cloud data, a point cloud model of a ship to be subjected to operation is generated in combination with mechanism parameters of a ship loader, the position of a ship hatch is automatically identified, the spatial coordinates of the ship hatch in a rectangular coordinate system of wharf space are automatically calculated, hatch identification data for current berthing is calibrated on the basis of historical hatch data records from the same hull number after berthing in a database, and the ship loader is guided to realize unmanned and automated operation modes by means of the calculated hatch position, thereby enhancing the level of port automation.

The invention provides a 3D + 2D high-dimensional feature fusion-based material tracking multi-target identification method and system. The method comprises the steps of performing target identification on a to-be-tracked material by using a YOLO algorithm; performing multi-target real-time tracking on the material based on a Deep SORT algorithm; generating three-dimensional point cloud data of a target by adopting a multi-view data calibration and alignment algorithm, and carrying out noise reduction, sampling and data enhancement preprocessing operation; performing three-dimensional reconstruction on the preprocessed three-dimensional point cloud data by using a Gaussian Splitting method, and introducing time sequence information at the same time; segmenting a dynamic three-dimensional target and a static background through a Mask R-CNN technology; aligning the position of the target in the two-dimensional image with the coordinates in the three-dimensional space by using a feature fusion method; a PNP algorithm is adopted to estimate attitude and depth information of a target in a three-dimensional space, accurate positioning and state restoration of materials are realized, and efficient and reliable technical support is provided for material management and tracking in an industrial environment.

The invention discloses a sewage waste heat recovery heating control method and system based on a digital twinning heat pump. The method comprises the steps that a digital twinning platform of a sewage waste heat recovery heating system is constructed; based on the operation parameters of the entity equipment of the sewage waste heat recovery heating system, establishing a digital twinborn model of the sewage waste heat recovery heating system; deploying the established digital twinborn model of the sewage waste heat recovery heating system in the established digital twinborn platform of the sewage waste heat recovery heating system; based on the physical equipment operation parameters of the sewage waste heat recovery heating system and the virtual data of the digital twinborn model, an optimization target model of the sewage waste heat recovery heating system is constructed, and an optimal control strategy is obtained; dynamic optimization control is carried out on the heating system entity equipment according to the optimal control strategy; the prediction precision is improved through real-time data calibration, model prediction control is fused, second-level response and multivariable cooperative regulation and control are achieved, and the problems that a traditional sewage waste heat recovery heating system is low in energy efficiency, slow in response and high in maintenance cost are effectively solved.

The invention discloses a current sensor data calibration method for an industrial environment, and the method comprises the steps: collecting the original current sensor data of target equipment in the industrial environment in real time, and carrying out the dynamic preprocessing of the original current sensor data, and generating a first calibration data set; based on the first calibration data set, time domain features and frequency domain features are extracted, and a second feature vector set is constructed; iteratively calculating an optimal calibration parameter set through a self-adaptive optimization algorithm in combination with historical calibration data and the second feature vector set; and dynamically correcting the current data of the current sensor according to the optimal calibration parameter set, and outputting and updating the calibrated current value. According to the invention, through dynamic preprocessing, comprehensive time domain and frequency domain feature extraction and combination of historical calibration data, parameter adaptive optimization updating is carried out, and the problem that current sensor data calibration in an industrial environment is difficult to accurately adapt to dynamic change environment conditions in real time is effectively solved.

The invention relates to the technical field of high-precision pressure sensing, and discloses a pressure response data calibration method and device and computer equipment, and the method comprises the steps: collecting the output response data of a pressure sensor array, and constructing a spatial feature matrix, a time feature matrix and an environment feature matrix based on the output response data; performing trilinear least square separation to obtain dynamic response data and time characteristic data; performing symmetric geometric mode decomposition to obtain linear response component data, periodic fluctuation component data and nonlinear characteristic component data; inputting a multi-branch gating circulation unit network model to carry out dynamic calibration strategy prediction to obtain a first dynamic calibration strategy; performing particle swarm iterative optimization to obtain a second dynamic calibration strategy, and generating a progressive calibration parameter set; static and dynamic pressure sensing calibration is performed on the pressure sensor array to obtain a comprehensive pressure sensing calibration result, so that the accuracy of pressure sensing calibration is improved.

The invention provides an electroencephalogram eye tracker based on a brain-computer interface, a calibration method and device of the electroencephalogram eye tracker, computer equipment and a storage medium. According to the scheme, a stable stimulation source is provided for data acquisition by orderly displaying the calibration points, the eye movement data and the electroencephalogram signals are synchronously acquired, and the user state information is acquired from multiple dimensions. The attention index is quantified according to the electroencephalogram signal, the data quality is preliminarily controlled, the target calibration point is displayed again, the data is updated, and errors caused by inattention of the user are corrected. And finally, high-quality eye movement data is utilized to calibrate and compensate eye tracker system errors, individual differences and environmental influences, and the calibration robustness is greatly improved, so that the calibrated electroencephalogram eye tracker can more accurately detect the watching position of the user, and reliable data support is provided for subsequent research and application based on eye movement tracking.

The invention relates to a data calibration method suitable for an atmospheric pollutant sensor deployed in an embedded terminal. The method comprises the following steps: acquiring original response data of different sensors with different concentrations in different atmospheric environments after temperature compensation, and corresponding temperature, humidity, air pressure data and sampling time; collecting real concentration values of detection objects corresponding to different sensors at corresponding moments given by a national control station or a provincial control station; preprocessing the obtained data to obtain a final data set; a random forest algorithm is used as a teacher model, a gradient elevator algorithm is used as a student model, and hyper-parameters of the gradient elevator algorithm and the random forest algorithm are optimized by using a hedera helix algorithm; substituting a hyper-parameter optimal solution found by optimization into a corresponding algorithm, and retraining the teacher model and the student model by using hyper-parameter combinations; and deploying, updating and displaying the model in the embedded equipment. And the model size is reduced to realize the deployment of the embedded end on the premise of ensuring the accuracy.

The invention relates to the technical field of building environment temperature control, and discloses a temperature control equipment intelligent adjusting method and system based on machine learning, and the method comprises the steps: building a space thermal field sensing network, obtaining multi-dimensional temperature field data, and generating a temperature gradient distribution matrix; and the positions of cold and hot spots are calibrated by combining infrared temperature measurement data, an airflow path is predicted, and air supply parameters are optimized. Meanwhile, building heat capacity characteristics are analyzed, and a thermal response dynamic model is constructed. And the air supply direction is adjusted for the local supercooling area. And based on the temperature change rate and the air supply parameters, the equipment start-stop time is optimized. Through real-time temperature sampling, continuous updating of temperature gradient distribution, cyclic execution of cold and hot spot calibration, airflow prediction and air supply optimization, accurate control of space temperature is realized. According to the invention, the accuracy and energy efficiency of building space temperature adjustment can be obviously improved, and an innovative solution is provided for an intelligent temperature control system.

The invention relates to the technical field of geological surveying and mapping, in particular to a multi-source data fusion geological surveying and mapping system which comprises a multi-dimensional data collection module, a data calibration and synchronization module, a geological image enhancement module, a data fusion and model generation module and a visualization and interaction module. The data calibration and synchronization module carries out timestamp calibration and geographic position processing on data, the geological image enhancement module enhances geological images, the data fusion and model generation module fuses the processed data and constructs a geological model by using a modeling technology, and the visualization and interaction module provides a multi-dimensional data interaction platform. Through the geological texture recovery sub-module, visual quality of images is improved, reliability and accuracy during geological data and resource evaluation are enhanced, polarized light data are collected through the polarized image processing sub-module, key information such as surface material roughness is revealed, and surface and underground geological processes can be better understood and explained.

The invention relates to the technical field of data calibration, in particular to a photovoltaic panel adaptive tracking data calibration system which comprises a data acquisition preprocessing module, a sun position tracking module, a calibration module, a data fusion module, a management support module, an energy optimization module, a tracking optimization module and an environment enhancement module. The data acquisition and preprocessing module is used for data acquisition and data preprocessing. According to the invention, data acquisition and preprocessing, sun position prediction and tracking strategy optimization, real-time calibration and adaptive control, data fusion and intelligent evaluation, intelligent management and decision support, energy management and optimization, tracking efficiency optimization and feedback regulation, and cooperative work of environmental adaptability enhancement and intelligent maintenance modules are carried out; adaptive tracking data calibration of tracking precision and power generation efficiency is realized, environmental adaptability and an intelligent maintenance mechanism are integrated, and support is provided for development of photovoltaic intelligent data management.

The invention relates to the technical field of wind resource assessment and wind power plant optimization, and discloses a multi-source information fusion measurement system combining laser radar data and wind resource assessment, and the system comprises a data collection module which is used for collecting laser radar data, synthetic aperture radar data, numerical weather forecast data and ground weather station data; the data preprocessing module is used for carrying out noise filtering, time alignment, space alignment and data calibration on the collected data; the information fusion module is used for dynamically fusing the preprocessed multi-source data and outputting high-precision wind speed and turbulence characteristic distribution; and the wind field modeling and prediction module is used for constructing a dynamic three-dimensional wind field based on the fusion data and performing short-time wind speed prediction. Through multi-source data deep fusion, dynamic wind field modeling and intelligent optimization modules, the comprehensive effects of high-precision wind field evaluation, nonlinear wind speed prediction and fan layout global optimization are achieved.

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 discloses a humanoid robot intelligent decoration construction system and method based on multi-modal sensing, and the system obtains the geometric data of a construction surface in real time through multi-modal sensing, obtains the posture data of a robot through an IMU inertial measurement unit, facilitates the data calibration, guarantees the modeling precision of a building model, and improves the construction efficiency. Real-time feedback of construction progress and construction defects is carried out; the intelligent decision-making module is combined to dynamically adjust the movement and construction operation track of the robot; by means of an end effector of the flexible mechanical arm, high-precision multi-mode building construction operation is achieved; and the collaborative operation module can realize multi-robot construction operation task scheduling and progress management, so that the building comprehensive construction efficiency is improved. Compared with a traditional decoration construction mode, the construction efficiency, the construction precision and the robot operation and moving stability are remarkably improved, autonomous operation of a complex curved surface is supported, and the construction consistency and the continuity of multi-mode construction operation are improved.

The invention discloses a bearing defect intelligent detection method and system based on deep learning, and relates to the field of bearing defect detection. Multi-modal data such as bearing vibration, acoustics, thermal imaging and the like are acquired by using various sensors, and a four-dimensional feature tensor is constructed through preprocessing such as noise reduction and feature extraction; features are fused through a reconfigurable multi-branch convolutional neural network, and defects are identified and a development trend is predicted in combination with a meta-learning twin network; a decision threshold is optimized by adopting a quantum heuristic algorithm, and multi-level early warning is realized; and continuous evolution of the model is completed through edge-cloud collaboration and federated learning, the functions of data calibration compensation, model dynamic optimization and the like are achieved, and efficient and accurate detection of bearing defects is achieved. The detection time is remarkably shortened, and the positioning precision is high; the novel defect response speed is high, and faults can be predicted in advance; system energy consumption is reduced, model updating is improved, stable operation of equipment is effectively guaranteed, and cost reduction and efficiency improvement of industrial intelligent operation and maintenance are facilitated.

The invention discloses an elevator digital twinning virtual and physical sensor data calibration method. The method comprises the steps that real-time data of multiple types of physical sensors in an elevator system and real-time data of virtual sensors in an elevator digital twinning model in a reference environment at the same moment are obtained; preprocessing the data, and comparing the acquired data of the physical sensor and the virtual sensor of the same category according to a distance function to obtain an error between the physical sensor and the virtual sensor at the moment t; comparing whether the error exceeds a set error threshold value or not; and if the error exceeds the threshold value, correcting the physical sensor data through linear regression calibration and an offset correction function, and updating the virtual sensor data at the next moment in the digital twin. The actual measurement data of the physical sensor and the measurement data of the virtual sensor of the digital twin are combined, the sensor deviation is calculated and calibrated in real time, and the problem of single sensor error can be effectively solved.

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 discloses an ultrasonic water meter detection device and method, and relates to the technical field of ultrasonic flow metering, the device comprises an ultrasonic detection unit, a block chain evidence storage unit, a zero-knowledge proof module, a master control MCU and a low-power-consumption communication unit, the ultrasonic detection unit collects downstream and countercurrent ultrasonic propagation time difference delta t through symmetrically installed transducers, a time difference measurement module (precision + / -1ns) is combined with water temperature data to calibrate a delta t value, the block chain evidence storage unit constructs a Merkel tree for a delta t sequence according to a time window, and root hash is written into a private chain by improving a PBFT consensus mechanism (threshold 4 / 5 node confirmation). The method covers the whole process of data collection, evidence storage, verification and auditing, solves the problems that traditional water meter data is easy to tamper, privacy is leaked and installation is limited through a hash chain and zero-knowledge proof technology, and is suitable for an intelligent water affair scene.

The invention provides a method of a seabed mining plume diffusion monitoring device based on an acoustic-optical-electric technology, and multi-dimensional and high-precision monitoring of a deep sea mining plume diffusion process is realized by combining multiple monitoring technologies of acoustics, optics and electricity. The specific target comprises multi-source data fusion: acoustic, optical and electrical data are effectively fused, and the accuracy and comprehensiveness of plume diffusion monitoring are improved. And the concentration conversion precision is improved: through multi-source data calibration, high-precision conversion from acoustic data to particle concentration data is realized. And real-time monitoring capability: providing real-time monitoring and data analysis capability, and reflecting the dynamic change of plume diffusion in time. And environment impact assessment: providing reliable data support for deep-sea mining environment impact assessment, and promoting sustainable development.

The invention relates to the technical field of intelligent transportation, in particular to a data calibration method and device, electronic equipment and a storage medium, and is used for improving the accuracy and efficiency of data calibration of radar and video. The method comprises the following steps: performing target detection on each frame of radar data and video data of a specified scene to obtain position information of a target appearing in each frame; for each target, obtaining a first target track and a second target track of the target under the radar data and the video data according to an error between the position information of the target in each historical frame and the estimated position information and the position information of each target in the current frame; first feature points selected from first target tracks of different targets form a first feature point group, and then the first feature point group and second feature points on corresponding second target tracks form feature point pairs; and optimizing the calibration parameters to be solved according to the position mapping relation of each feature point pair to obtain calibration parameters of the radar data and the video data.

The invention relates to a servo-mechanism-free defocusing amount automatic compensation control system for an aerial camera, and belongs to the technical field of photoelectric instruments. According to different influences of the height, temperature and air pressure of a camera on an imaging focal plane, a temperature balance point is controlled through data calibration and temperature adjustment, the temperature balance point is slightly higher than a temperature point changing along with the height, and therefore automatic compensation of the comprehensive defocusing amount caused by the height, temperature and air pressure factors is completed. The system does not need any servo mechanism, only needs software to control the working temperature and keep the temperature balance point, can realize automatic focusing of the imaging focal plane along with the application environment, can adapt to different environmental conditions and different flight heights, and is good in thermodynamic property.