106results about How to "Accurate Data Base" 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 a power efficiency optimization method based on intelligent load management. The method comprises the following steps: S1, generating an initial load characteristic matrix; s2, decomposing the initial load characteristic matrix into a short-term behavior characteristic matrix and a long-term trend characteristic matrix through pseudo-symmetry decoupling; s3, multi-level load demand prediction is carried out through a fusion structure of the multi-scale convolution and the recurrent neural network; s4, coding a cognitive gene sequence, triggering gene mutation and recombination operation when significant change occurs, and generating a load distribution strategy; s5, a load distribution strategy is transmitted into a causal reasoning and self-supervision feedback control module, and if the deviation between feedback data and a prediction result exceeds a threshold value, a self-learning mechanism is triggered; s6, dynamically distributing the load priority, and generating a decision path of load management; and S7, performing adaptive optimization through a multi-layer feature feedback closed-loop optimization system. The intelligent load management method is adopted, and the power efficiency is optimized through multi-layer feature decoupling and closed loop.

The invention discloses an intelligent navigation system and method for a ship in the technical field of ship navigation, and the system comprises a sensing module which is used for collecting the surrounding environment information and the state information of the ship in real time; the data processing and fusing module is used for preprocessing, calibrating and fusing the surrounding environment information and the self state information of the sensing module; the route optimization module is used for formulating a navigation plan and a collision avoidance strategy; the navigation control module is used for autonomously controlling the ship according to the output of the ship route optimization module; and the communication monitoring module monitors the navigation state of the ship and the change of the surrounding environment through real-time communication between the ship and a shore-based control center, other ships and a maritime affair management department. According to the method, data of different sensing units are associated, and the associated data are fused, so that mutual verification and complementation can be realized, and the accuracy and reliability of target identification are improved.

The invention discloses a self-adaptive intestinal function adjusting system based on biological feedback, and relates to the technical field of intestinal function adjustment, the self-adaptive intestinal function adjusting system comprises a signal acquisition module, a data analysis module, an adjustment feedback module and a user interaction module; the signal acquisition module adopts a multi-sensor fusion algorithm and a multi-source sensor to acquire intestinal function state information; wherein the multi-source sensor comprises a pressure sensor, a peristaltic sensor and a nerve sensor. The intestinal function self-adaptive adjusting system based on biological feedback can comprehensively collect biological signals of intestinal tracts, including pressure, peristalsis, nervous system signals and chemical substance secretion signals; the multi-dimensional monitoring mode can reflect the real state of the intestinal function more accurately, the limitation of a single sensor is avoided, and a rich and accurate data basis is provided for subsequent analysis and adjustment.

The invention provides a dynamic target tracking method and system based on image return, and relates to the technical field of image data processing, and the dynamic target tracking method based on image return comprises the following steps: collecting image data of a target scene, recognizing a to-be-tracked target and position information of the to-be-tracked target by using a double-layer random forest recognition algorithm, and predicting a motion track of the to-be-tracked target; according to the predicted motion track of the to-be-tracked target and the position information of the unmanned aerial vehicle, and based on a target function of the distance between the unmanned aerial vehicle and the to-be-tracked target, using a cooperative control strategy to adjust flight parameters of the unmanned aerial vehicle; and the unmanned aerial vehicle continuously tracks the to-be-tracked target, and after tracking is finished, the cloud server stores the position information and the movement track of the to-be-tracked target in the tracking process. The system has the advantages of real-time performance, high efficiency, intelligence, data storage and subsequent analysis, expansibility and flexibility, safety improvement, risk reduction and the like.

The invention relates to the technical field of user personalized content pushing, in particular to a user personalized content pushing system based on big data and a promotion method. The method comprises the following steps: collecting user behavior and preference data through multiple channels; dynamically constructing user portraits, and enriching the user portraits in combination with multi-dimensional data; based on user portraits and content features, a recommendation model is constructed by using a deep neural network, and user preferences are accurately predicted through multiple hidden layers; through a push model and a multi-objective optimization method, the probability of pushed content is calculated in combination with indexes such as a click rate and a conversion rate, the push frequency and content selection are optimized, and the correlation of the pushed content and the user satisfaction are ensured; by monitoring user feedback in real time and adjusting a pushing strategy, intelligent decision and personalized recommendation of content pushing are realized, and long-term accurate pushing and improvement of user experience are ensured. According to the method, the accurate user portrait is constructed through multi-data source collection and processing, so that personalized content pushing is realized.

The invention relates to the technical field of data processing, and discloses a cell image recognition method and system based on model fine tuning, and the method comprises the steps: obtaining a to-be-recognized image comprising a cell image; inputting the to-be-recognized image into a trained cell definition recognition model to obtain a corresponding cell definition; inputting the to-be-recognized image into a trained cell existence region recognition model to obtain a corresponding cell existence region image; inputting the cell region image and the cell definition into a trained cell classification model to obtain a plurality of corresponding cell regions and corresponding cell types; the cell definition identification model, the cell existence area identification model and the cell classification model are obtained based on joint training and fine tuning. Therefore, the cell region and type in the image can be accurately identified by fully utilizing definition factors and prior region identification, and the cell identification precision and efficiency are improved.

The invention relates to the technical field of tire simulation design, in particular to a method and system for obtaining an actual material distribution diagram of a tire and a computer program product. According to the method, the outer contour and the material distribution diagram are accurately matched by scanning the outer contour of an actual tire, combining with the material distribution diagram of the section of the tire and adopting smooth processing and affine transformation technologies. The method comprises the specific steps of scanning the outer contour of a tire, drawing a material distribution diagram, smoothing the contour, selecting and equally dividing a mapping curve, calculating an affine transformation matrix, dividing the material distribution diagram into a plurality of small areas, and executing affine transformation on the small areas to generate an actual material distribution diagram. According to the method, the simulation precision is remarkably improved, the correction time is shortened, and the method is suitable for various tire models and complex structures. The material distribution map generated by the method can provide accurate data support for performance optimization of tire rolling resistance, stress analysis and the like, and has wide application value.

The invention discloses an online monitoring method for a capacitor voltage transformer, and the method relates to setting a host and a plurality of slaves which communicate with each other in a wireless or wired manner. And each slave comprises a slave MCU and an AD acquisition module and is used for synchronously acquiring fault signal data of the CVT transformer. Collected data are transmitted to the host computer through the slave computer MCU, and the host computer is responsible for reading and storing the data and carrying out comparative analysis. By comparing the data of the plurality of slaves at the same moment with the historical data of the same slave, the host can identify a fault point and send out corresponding alarm information. The method achieves the real-time monitoring of the CVT, and improves the safety and stability of a power system.

The invention provides a power quality disturbance detection method based on SWD and joint convolution. The method comprises the following steps: step 1, collecting electric energy quality disturbance data and marking a label, and carrying out overlapping sampling on each category in the electric energy quality disturbance data to generate an electric energy quality disturbance signal sample; 2, decomposing the electric energy quality disturbance signal sample through an SWD algorithm, and reconstructing decomposed sample components; 3, transforming the samples reconstructed in the step 2 to obtain a time-frequency graph; 4, constructing a joint neural convolutional network, and training the joint convolutional neural network by using the time-frequency graph to obtain a power quality disturbance recognition model; and 5, inputting the time-frequency diagram corresponding to the to-be-detected power quality disturbance data into the power quality disturbance identification model to obtain a classification result of power quality disturbance identification. According to the invention, disturbance signal processing and disturbance type identification can be realized, and the accuracy of power quality disturbance detection is improved.

The invention discloses a three-dimensional reconstruction method, system and device for an extra-high voltage power transmission and transformation line, and belongs to the technical field of point cloud reconstruction. The three-dimensional reconstruction method comprises the following steps: placing a laser radar along the extra-high voltage power transmission and transformation line to obtain point cloud data of the extra-high voltage power transmission and transformation line; rTK positioning software is placed at the laser radar and the selected reference point to obtain longitude, latitude and height coordinate data; correcting and filtering the obtained point cloud data and longitude and latitude coordinate data of the extra-high voltage power transmission and transformation line; splicing the filtered extra-high voltage power transmission and transformation line point cloud data, and constructing a preliminary reconstruction model; performing point cloud segmentation on the preliminary reconstruction model by using a region growing algorithm, and extracting incomplete power transmission tower point clouds in the preliminary reconstruction model; acquiring three-dimensional models of different types of required power transmission towers, and constructing a standard model library; and matching the incomplete point cloud of the power transmission tower with the constructed standard model library, and placing a matched three-dimensional model in a scene to realize complete point cloud reconstruction.

The invention belongs to the technical field of image analysis, and particularly relates to a mulberry growth period identification method and system based on image analysis, and the method comprises the steps: obtaining image data of each growth period of a mulberry, preprocessing the image data, and inputting the preprocessed image data into a feature extraction module; performing feature extraction on the image data in each growth period, and establishing a feature comparison table of the images in each growth period of the mulberry trees; mulberry image data of each mulberry planting area to be identified is collected in real time, and after preprocessing, the mulberry image data is input into the feature extraction module for real-time image feature extraction; inputting the real-time image features into a feature comparison module, calling a mulberry growth period image feature comparison table, and matching the real-time image features with the mulberry growth period image feature comparison table; obtaining the mulberry growth period of each mulberry planting area to be identified according to the matching result of the real-time image feature and the mulberry growth period image feature comparison table; a preset mulberry growth period management measure table is called, management measures of the mulberries in the mulberry planting areas are obtained, mulberry planting management is facilitated, and the economic benefits of mulberry planting are improved.

The invention discloses an agricultural product image quality enhancement method based on a visual large model. The method comprises the following steps: S1, acquiring an original agricultural product image data set of agricultural products during picking and transportation; s2, inputting the original agricultural product image data set of the agricultural product into a pre-trained large-scale visual model; s3, carrying out transfer learning fine tuning on the pre-trained large-scale visual model; s4, processing the original agricultural product image data set of the agricultural product by using the fine-tuned large-scale visual model; s5, using a super-resolution reconstruction algorithm to restore the detail part of the agricultural product image; s6, based on the multi-task processing capability of the large-scale visual model, executing a bureau partial region enhancement strategy on the agricultural product images of different agricultural product categories; and S7, outputting the finally enhanced agricultural product image. According to the method, surface details and texture information of agricultural products are clearer, and an accurate data basis is provided for quality detection and identification.

The invention discloses a service life detection method, medium and equipment for a contact mechanism of a molded case circuit breaker, and the method comprises the steps: obtaining a high-resolution spectrogram of the contact mechanism through solid nuclear magnetic resonance and magic angle rotation technologies, and carrying out the preprocessing of the high-resolution spectrogram to obtain reconstruction representation data; performing multi-step iterative prediction on the reconstructed data by using a multi-contact life decline prediction model so as to accurately obtain the life prediction condition of the contact mechanism; according to the method, reconstruction data are mapped to a high-dimensional feature space, an optimal separation hyperplane is constructed, an elastic net contact life SOH model is established based on the optimal separation hyperplane, and continuous scaling prediction is carried out. The method comprises the following steps: sorting and screening intrinsic characteristic variables of reconstructed data; and generating echelon life prediction of the contact mechanism by using a conditional generative adversarial network in combination with the reconstruction data and the SOH prediction condition. According to the method, service life detection of the contact mechanism is updated in real time, the detection precision is remarkably improved, prediction errors caused by parameter degradation are avoided, and a more accurate method is provided for service life prediction of the contact mechanism.

The invention discloses a clinical test data analysis system based on a large model, which comprises a data acquisition module, a preprocessing module, a training module, an analysis module and an output module, data acquisition is efficient and supports multiple modes, preprocessing adopts an intelligent algorithm, model training is advanced and has efficient indexes, analysis covers multiple aspects and has evaluation indexes, and result output is diversified and supports customization. The invention further comprises a corresponding data analysis method, related equipment and a medium, and each link is provided with an innovative technology and an evaluation mode to guarantee the performance and the quality; the system realizes efficient data acquisition and integration, intelligent processing and quality improvement, constructs a powerful analysis model, provides various visual result display and interpretation, has real-time updating and personalized service capabilities, can accurately evaluate the curative effect and safety, provides a scientific basis for clinical decisions, assists in improving the efficiency and accuracy of medical data analysis, and has a wide application prospect. Different user requirements are met, and development of the medical industry is promoted.

The invention relates to the technical field of tunnel deformation monitoring, in particular to a tunnel primary support surrounding rock deformation monitoring system and method, and the method comprises the steps: data collection, data transmission, data preprocessing, model training and verification, real-time analysis, feature extraction and early warning and response. Data acquisition equipment and an edge server are installed and deployed on the inner side wall of tunnel primary support surrounding rock, the data acquisition equipment acquires original data, it is ensured that data coverage is comprehensive and not excessive redundancy exists, the original data are timely transmitted to the edge server in a wireless mode of the low-delay Internet of Things, the data transmission process is combined with an MQTT protocol, and the data transmission efficiency is improved. The method comprises the following steps: preprocessing data, selecting a trained convolutional neural network model CNN, carrying out online analysis on the data transmitted in real time, predicting the deformation trend of the tunnel primary support surrounding rock, and immediately sending an early warning to a monitoring and early warning module when the deformation trend exceeds a set deformation threshold value, so that the system can monitor the deformation of the tunnel primary support surrounding rock in real time and ensure the accuracy and reliability of the data.

The invention belongs to the field of data center refrigeration, and provides a data center digital twin refrigeration method and system based on load sensing. The data center digital twin refrigeration method based on load sensing comprises the steps that the previous moment temperature and the current moment temperature of an air outlet of a server, the predicted power change condition of the server in a future set time period, a first temperature threshold value and a second temperature threshold value are obtained; wherein the second temperature threshold value is greater than the first temperature threshold value; the first temperature threshold value and the second temperature threshold value are both related to server attributes; based on the obtained information, a temperature control strategy is determined, and a control command is issued to the corresponding air conditioner so as to adjust the air outlet temperature of the corresponding air conditioner; the terminal air conditioner temperature control strategy can be obtained according to the current temperature of the air outlet of the server and the future power change condition, efficient and intelligent refrigeration is achieved, the refrigeration efficiency is improved, and local hot spots of the server are avoided.

The invention discloses a model construction method and device applied to bond data processing and electronic equipment, and the method comprises the steps: carrying out the recognition of target bond comment data based on a large model, obtaining a first data set, and enabling each piece of data in the first data set to comprise the bond comment data and a corresponding emotion label; and training the initial recognition model based on a bond dictionary in the bond field and the first data set until an output result of the initial recognition model meets a target condition, and determining the current initial recognition model as a target recognition model. According to the method, the model for performing emotion recognition on the bond comment data is constructed, and a large model is adopted for sample labeling, so that the cost of manual labeling is reduced, the accuracy of model recognition is improved, and an accurate data basis is provided for bond data processing.

The invention discloses a clock frequency calibration method and device for an HPLC + HRF dual-mode communication system. The method comprises the following steps: acquiring a real-time environment temperature value of a module crystal oscillator of the HPLC + HRF dual-mode communication system in a current detection period; if the real-time environment temperature value is greater than the preset reference temperature value, calculating a first frequency error value of the clock frequency of the module crystal oscillator based on a difference value between the real-time environment temperature value and the preset reference temperature value; if the real-time environment temperature value is smaller than the preset reference temperature value, calculating a second frequency error value of the clock frequency of the module crystal oscillator based on a difference value between the preset reference temperature value and the real-time environment temperature value; and calibrating the clock frequency of the module crystal oscillator in the next detection period based on the first frequency error value or the second frequency error value. The difference value between the real-time environment temperature and the preset reference temperature is considered, the frequency error values are calculated according to the situation that the temperature is higher than or lower than the preset value, the influence of temperature change on the clock frequency is accurately reflected, and an accurate data basis is provided for calibration.

The invention relates to the field of data testing and financial science and technology, and discloses a real-time data testing method, device and equipment and a medium, and the method comprises the steps: configuring a derivative environment for a Kafka cluster, and after the Kafka cluster is logically classified, creating a derivative task of the Kafka cluster based on the derivative environment; performing rationality evaluation on the derivative task, when an evaluation result meets a preset result, querying an execution resource quantity of a task execution system corresponding to the derivative task, and starting to execute the derivative task when the execution resource quantity meets a demand quantity of an execution number task; and during execution of the derivative task, reading theme message data of the Kafka cluster, performing data desensitization and data storage on the theme message data, obtaining desensitization data, transmitting the desensitization data to a data test environment, obtaining test data, performing data integrity test on the test data, obtaining a test result, and completing the derivative task when the test result is complete. According to the invention, the accuracy of real-time data testing can be improved.

The invention provides a crop phenotype data fusion method and device, electronic equipment and a storage medium, and relates to the technical field of electrical digital data processing, and the method comprises the steps: inputting target data corresponding to each piece of three-dimensional first to-be-fused crop phenotype data in each piece of to-be-fused crop phenotype data into a weight adjustment model, and obtaining a weight value corresponding to each piece of first to-be-fused crop phenotype data output by the weight adjustment model, and performing data fusion on each piece of to-be-fused crop phenotype data based on the weight value corresponding to each piece of first to-be-fused crop phenotype data and internal and external parameter data of a crop phenotype sensor for collecting each piece of to-be-fused crop phenotype data, the target data comprises environmental data when the crop phenotype data is collected, relative position information between the crop phenotype sensor and the crop, and a target parameter value of the crop phenotype data. According to the method, the dynamic weight is given to the crop phenotype data to be fused, so that the fusion effect, relevance, fusion efficiency and timeliness of data fusion can be improved.

The invention discloses an aluminum oxide water taking station equipment operation monitoring control method which comprises the following steps: acquiring operation parameters of key positions of water taking station equipment in real time through a sensor network, performing primary processing, uploading processed data to a data analysis control module, performing deep analysis on the received data by the data analysis control module, and transmitting the analyzed data to a server; when the running state of the equipment is abnormal, alarm information is generated immediately, a user is notified through a user interaction interface, meanwhile, the data analysis control module generates a control instruction according to an analysis result and sends the control instruction to the control execution module, and the control execution module intelligently controls the water taking station equipment according to the received control instruction. And stable operation of equipment is ensured. According to the invention, comprehensive real-time monitoring, intelligent fault early warning and diagnosis and automatic optimization adjustment can be realized, the operation efficiency of equipment is improved, the service life of the equipment is prolonged, and the maintenance cost is reduced.

The invention relates to the technical field of coal mining and rock mechanics numerical simulation, and discloses a stress field reconstruction method of a plane strain numerical simulation model for coal mining, and the method comprises the following steps: firstly, obtaining an actually measured vertical stress increment and an actually measured bearing pressure peak value position of a stope face; then, an initial plane strain numerical simulation model is constructed, the space error between the peak position of the simulated bearing pressure and the actual measurement position meets a preset threshold value by adjusting the excavation step pitch, and therefore the numerical simulation calculation state is locked to eliminate space-time dislocation; a simulation strain increment is extracted in a locked state, a theoretical vertical stress difference equation is constructed based on an elastic mechanics plane strain constitutive relation, and a parameter inversion equation set is established in combination with multi-measurement-point data; and solving the corrected elastic modulus and Poisson's ratio by using a least square optimization algorithm. According to the method, through physical state locking and incremental theory inversion, system errors are effectively eliminated, and the reconstruction precision and mechanical preciseness of the mining-induced stress field are improved.

The invention discloses a project achievement evaluation system combining literature measurement and a super network model. The system comprises a research basis acquisition module, a project element extraction module, a super network model construction module, a super network analysis and project evaluation module and the like. According to the system, all elements of science and technology projects are abstracted into network nodes, a hypernetwork model is used for carrying out correlation analysis on the project elements, and project achievements are comprehensively evaluated by combining methods such as literature metering, clustering analysis, balance score card evaluation and association rule mining. According to the method, the influence of project elements on project achievements is quantified, the accuracy and scientificity of project evaluation are improved, and the method has important significance in optimizing scientific and technological resource configuration and improving scientific and technological project management efficiency.

The invention discloses a distributed intelligent substation network monitoring system and a network time synchronization method. The monitoring system comprises sensors arranged in a plurality of substations, a data acquisition and processing module, a field monitoring terminal, an execution control mechanism, an industrial personal computer, a display screen, a network communication module, a remote management center and a time synchronization module. The method comprises the following steps: S1, constructing a multi-layer time synchronization network by introducing a plurality of time sources; the PTP time synchronization device is used for time synchronization, network time synchronization is completed by using a timestamp, and high-precision time synchronization is provided; and S2, when the PTP time synchronization device has a fault or the network is unstable. According to the invention, the environmental parameters of the transformer substation and the working parameters of the transformer can be monitored in real time, an accurate data basis is provided for intelligent control of the transformer substation, flexible selection can be realized according to actual conditions, accurate synchronization of system equipment is ensured, and the reliability and stability of system operation are improved.

The invention relates to the technical field of data processing, in particular to a human-computer interaction spoken language evaluation method and system based on speech recognition, and the method comprises the steps: obtaining a phoneme sequence of a speech signal of a target examinee, calculating the initial segmentation probability of a target moment in a time window of a target phoneme, and obtaining a speech signal of the target examinee; taking a time window between the target moment and the ending moment of the target phoneme as a first time period, taking a time window between the starting moment of the next phoneme and any moment of the next phoneme as a second time period, and calculating the similarity overlapping degree of the sound intensity of the first time period and the sound intensity of the second time period, and calculating a final segmentation probability of the target moment based on the initial segmentation probability and the similarity overlapping degree, and screening segmentation points to segment the phoneme sequence by using the final segmentation probability, so as to obtain a matching score of the segmented phoneme sequence and a pre-stored standard phoneme sequence based on a GOP algorithm. According to the invention, the accuracy of the obtained matching score can be improved, so that the accuracy of the spoken language evaluation result is ensured.

The invention discloses a parking lot management efficiency evaluation method based on parking frequency, and the method comprises the steps: collecting vehicle access time, license plate number, parking space number, vehicle type and parking lot real-time occupancy rate data through sensors, cameras and intelligent barrier gate systems which are disposed at an entrance, an exit and parking spaces of a parking lot; performing data preprocessing on the acquired data to form a standardized data set; key indexes are extracted from the data set; based on the historical data and the external influence factors, establishing a parking demand prediction model by adopting a time sequence analysis method, and predicting the parking demand in a specified time period in the future; based on the extracted key indexes, corresponding weight factors are set, a management efficiency evaluation model is constructed, and a parking lot management efficiency score is calculated; comparing the calculated efficiency score with historical data to generate an evaluation report; and according to the predicted parking demand and the evaluation report, implementing a dynamic pricing model, and optimizing the utilization rate of the parking lot.

The invention discloses an intensive culture precise feeding control method based on fuzzy-MPC control, and relates to the technical field of aquaculture intelligent control, and the method comprises the steps: employing a sensing system, collecting multi-source sensing data including fish activity images, healthy growth states and water quality environment data in real time, and forming a comprehensive state data set; and based on the acquired multi-source sensing data, constructing an MTL-LSTM-SAT multi-task learning model. According to the method, the multi-task learning model fusing the LSTM and the soft attention mechanism is constructed, the long-term dependency relationship and dynamic association among the water quality, the feeding and the fish state can be captured, the attention mechanism enables the model to be automatically focused on a key time step, the perception ability of the water quality fluctuation and the feeding influence stage is enhanced, and the accuracy of the water quality fluctuation and feeding influence stage is improved. Meanwhile, double-task collaborative prediction of the growth speed and the disease proportion is achieved through the shared feature layer, a prospective state sequence of the future 7 days is output, and a quantitative and reliable prediction basis is provided for feeding decision making.

The application discloses a kind of satellite radiation product-based remote sensing image geometric calibration method, electronic equipment and medium.The method can include: determining reference image, inputting image to be calibrated;For the same name point matching of image to be calibrated and reference image, obtain the same name point coordinates;The third polynomial distortion model about CCD probe element number is constructed;Based on the same name point coordinates, curve fitting is carried out on the distortion model, and the distortion coefficient is obtained;The resampling of image to be calibrated is carried out by distortion coefficient, and the correction of internal distortion of image is realized.The application can quickly and accurately carry out geometric calibration processing to satellite image, greatly improve the efficiency and quality of image processing.

The invention relates to the field of machine vision three-dimensional measurement, in particular to a self-adaptive high dynamic range measurement algorithm and device based on time domain stacking. According to the algorithm, through an adaptive parameter selection formula, the exposure time and the stacking frequency are automatically adjusted according to measurement scene requirements, the measurement precision is ensured, and exposure errors are avoided. The method comprises the following core steps: acquiring a phase shift fringe pattern of a measurement scene, and calculating an average white field pattern and a modulation degree and intensity proportionality coefficient thereof; selecting a region of interest according to the white field pattern, setting expected precision, and calculating optimal measurement parameters through a self-adaptive parameter selection method; and finally, performing three-dimensional reconstruction on the scene to obtain a measurement result conforming to expected precision. The invention further provides a corresponding measuring device which comprises a projection module, an image acquisition module and a data processing module, the three-dimensional morphology in a high dynamic range scene can be efficiently and accurately measured, the measuring efficiency and precision are remarkably improved, and meanwhile resource utilization is optimized.