216 results about "Domain analysis" patented technology

The invention discloses a data processing method and system for an industrial-grade data acquisition instrument, and relates to the technical field of electric digital data processing. The system comprises an acquisition module, an analysis module and a management module, the acquisition module acquires operation parameters and production environment data of industrial equipment and transmits the operation parameters and the production environment data to the analysis module; performing single-domain analysis and association processing on the operation parameters and the production environment data to generate a cross-domain association analysis result, generating a threshold range through dynamic threshold adjustment, re-evaluating the operation parameters and the production environment data, generating an analysis result after threshold optimization, transmitting the analysis result to a management module, and managing the operation parameters and the production environment data on the basis of the analysis result after threshold optimization. And generating an optimal scheme and automatically synchronizing the optimal scheme to a workshop system, presetting edge side primary early warning and edge side multi-level early warning mechanisms, mining parameter causality through cross-domain association analysis, dynamically adjusting a threshold value in combination with working conditions and environmental factors, generating an optimal scheme through edge cloud cooperation and multiple algorithms, and enabling industrial intelligent fine management.

The invention is suitable for the technical field of smart beam fields, and provides a design optimization method and system for a highway smart beam field based on big data, and the method comprises the steps: collecting the related data of transportation equipment through an Internet of Things and a real-time bidirectional data interaction channel, and constructing and dynamically updating a site three-dimensional digital twin model; fusing the model and adjacent node broadcast information, and outputting an optimal real-time driving path by using an improved pheromone diffusion algorithm; setting a hierarchical processing protocol for tasks with different priorities, and recalculating an optimal path sequence and adjusting task allocation when resource conflicts or path deadlocks are detected; identifying potential risks in combination with frequency domain analysis, and dynamically adjusting paths and equipment parameters; the algorithm is continuously optimized through offline training and online learning of a historical database. The system comprises a model building and updating module, a path selection module, a conflict detection module, a path adjustment module and an operation optimization module, efficient scheduling, risk early warning and continuous optimization of the intelligent beam field are achieved, and the operation efficiency and safety are improved.

The invention relates to the technical field of power electronic device diagnosis, and discloses a reverse conducting IGBT intelligent power module fault automatic diagnosis method and system. The method comprises the following steps: acquiring multi-source monitoring data including a grid voltage waveform, a collector current waveform and a shell temperature change curve when the power module operates; then establishing a dynamic feature extraction model, performing time domain and frequency domain conjoint analysis on the multi-source monitoring data, and generating a feature parameter set; then constructing a fault feature space, and mapping the feature parameter set to a high-dimensional space to form a feature vector distribution diagram; carrying out regional division on the feature vector distribution map by adopting a self-adaptive clustering algorithm, and identifying an abnormal feature aggregation region; and finally, comparing the abnormal feature gathering area with a preset fault feature library through a mode matching engine, and outputting a fault type identification result. According to the method, multi-source data can be integrated to realize dynamic feature extraction and adaptive fault identification, and the real-time performance and accuracy of fault diagnosis are improved.

The invention provides a multi-order segmentation-based structural member linetype rapid identification method and system, and relates to the technical field of engineering structure intelligent detection, and the method comprises the steps: obtaining an original image of a structural member; performing coarse segmentation on the original image to generate an initial binary mask of the structural member; automatically screening out a target mask region containing a foreground prompt point through a connected domain analysis mode; calculating bounding box coordinate information of a bounding rectangle of the target mask region through a contour detection mode; extracting a corresponding target area sub-image from the original image; adopting a super-resolution reconstruction model to enhance the sub-image of the target area; performing sub-image division on the enhanced target area sub-image; fine segmentation is carried out on the sub-images, and a high-precision mask is determined; mapping the high-precision mask to an original image through coordinate mapping; and combining an edge detection algorithm to extract an outer contour line of the structural member to complete linetype identification of the structural member.

The invention provides an automatic equipment anomaly monitoring method and system based on image processing, and relates to the technical field of automatic equipment anomaly monitoring, and the method comprises the steps: converting an equipment operation video into a multi-dimensional physical field feature: in a motion field dimension, based on optical flow field analysis, extracting a full-period displacement statistical histogram and a space thermodynamic diagram, the motion instability characteristic of the mechanical transmission system is accurately quantified; in a vibration field dimension, an energy spectrum and a vibration thermodynamic diagram are generated innovatively through time-frequency transformation of a displacement signal of a frequency spectrum monitoring point, and frequency domain feature visualization of a hidden vibration fault is achieved; in the dimension of a structure field, edge gradient analysis and texture feature extraction technologies are fused, a time sequence structure thermodynamic diagram sequence is constructed to capture a progressive damage evolution rule, a three-field abnormal index dynamic weighting fusion mechanism overcomes the limitation of traditional single-point monitoring, connected domain analysis of a fused thermodynamic diagram is combined with an LBP texture and morphological feature decision tree, and the defect of the prior art is overcome. Automatic equipment abnormity monitoring based on image processing is realized.

The invention discloses a handwritten element automatic segmentation and extraction method for a complex layout, relates to the technical field of handwritten element automatic segmentation and extraction, and aims to solve the technical problem that the recognition and separation precision of handwritten contents in a mixed image-text layout is insufficient. S201, a dynamic threshold segmentation algorithm is carried out; s202, context sensing connected domain analysis is carried out; s203, judging whether the elements are handwritten elements or not; s204, if the judgment result is yes, the handwriting region candidate is reserved; and S205, if not, filtering and eliminating. According to the method, the dynamic threshold segmentation algorithm and the context sensing connected domain analysis technology are cooperated, the segmentation threshold can be adaptively adjusted according to the pixel mean value and the standard deviation of the image local window through dynamic threshold segmentation, and the context sensing connected domain analysis is combined with the context information of the document to perform semantic analysis on the connected domain. The problem that the recognition and separation precision of the handwritten content in the mixed image-text layout is insufficient is solved.

The invention provides a low-voltage flexible DC system fault detection method based on complex domain analysis, and the method comprises the steps: obtaining and sampling a transient current signal in a low-voltage flexible DC system, fitting the transient current signal into a linear combination of a group of exponential functions, and obtaining a fitting exponential function; solving the fitting exponential function in a Z domain based on a Pade approximation method to obtain a complex index of the fitting exponential function; determining a fault threshold frequency according to the operation parameters of the low-voltage flexible DC system; the complex index is used as an analysis object, and the natural oscillation frequency of the low-voltage flexible direct-current system is obtained through formula calculation; and taking the fault threshold frequency as a state circle radius, constructing a fault discrimination model based on a complex plane, comparing the natural oscillation frequency with the fault threshold frequency, and outputting a fault detection result of the low-voltage flexible DC system in combination with a real part of a complex index. According to the method, the sensitivity to noise can be reduced, and meanwhile, the requirement of fault detection for speed is met.

The invention discloses an energy internet-oriented harmonic prediction data quality enhancement method, relates to the technical field of harmonic prediction, and is used for solving the problem of cross-order consistency misleading prediction of single harmonic order short-time peak misinformation damage. The method comprises the following steps: collecting photovoltaic grid-connected data to construct a power distribution network topology, identifying a neighborhood analysis subgraph and disassembling a node-order harmonic sequential sequence; extracting abnormal features, triggering cleaning through a multi-index weighted fusion triggering model, and generating a damaged order candidate set and a mask; calculating peak suspiciousness and a cross-order consistency sudden drop index to determine a damaged order; constructing a cross-order distance matrix cluster to obtain an initial anchor order candidate set, and screening and allocating weights; and on the basis of double-constraint optimization cleaning, the quality is evaluated, and the weight is optimized, so that the harmonic data quality is improved, and accurate prediction is supported.

The invention provides an image fuzzy detection method based on frequency domain analysis and deep learning fusion. The method comprises the following steps: S1, frequency domain feature extraction and quantification; s2, spatial domain feature extraction and modeling; s3, carrying out multi-modal feature fusion; s4, joint optimization and post-treatment are carried out; s5, outputting and verifying; through complementarity design of frequency domain and deep learning, complex fuzzy detection requirements of static images and video streams are covered, high efficiency and reliability are verified in industrial quality inspection, video conferences and other scenes, energy attenuation characteristics caused by global blur are accurately captured through frequency domain analysis, motion blur and out-of-focus blur are effectively distinguished, and the method is suitable for large-scale popularization and application. According to the method, local texture degradation of deep learning network modeling, dynamic track abnormity analysis of an optical flow network and complex scenes covering static images and video streams are realized through a bidirectional feature fusion mechanism, the mAP of mixed fuzzy detection is effectively improved compared with a single-mode method, and dynamic fuzzy and static out-of-focus fuzzy are effectively distinguished.

The invention discloses a distribution hidden danger assessment and prediction method based on a natural time domain and a fuzzy rough set, and belongs to the technical field of operation and maintenance of power grid equipment. According to the method, based on collected multi-source monitoring signals of leakage current, induction current, temperature and the like, adaptive signal preprocessing is carried out by adopting a method of combining empirical mode decomposition (EMD) and sample entropy, effective mode components are effectively extracted, and noise interference is suppressed; natural time domain analysis is introduced on the basis of a traditional time domain, an event sequence is constructed, dynamic features are extracted, and a hidden danger feature data set with time sequence evolution information is formed; further performing unsupervised attribute reduction on the high-dimensional features by using a fuzzy rough set theory, removing redundant information, and retaining key discrimination features; and finally, identification and trend prediction of wiring hidden danger types are realized through a support vector machine (SVM) classifier. According to the method, the accuracy and robustness of hidden danger identification are improved, and effective technical support is provided for intelligent operation and maintenance of power distribution.

This invention discloses a transition hydraulic support control method, electronic equipment, and control system. The transition hydraulic support control method includes a top coal caving process, which involves acquiring a top coal caving signal; adjusting the coal caving device of the transition hydraulic support to retract to a preset posture to form a coal caving window and caving coal; collecting the acoustic characteristic spectrum of the top coal collapse; performing time-domain analysis of the vibration signal based on the acoustic characteristic spectrum to obtain data information; determining whether the gangue content in the top coal exceeds a preset value based on the data information; if so, and the duration exceeds a first preset time, issuing a termination command for coal caving; and controlling the coal caving device to extend and close the coal caving window. This invention, based on the frequency and amplitude differences of the impact vibrations generated on the transition hydraulic support by falling coal and gangue, combined with algorithm analysis, can dynamically distinguish the state of coal and gangue, thereby accurately determining the timing of coal caving termination, preventing excessive gangue caving, and effectively improving the extraction rate and coal quality.

The invention relates to the technical field of industrial automation control, and discloses a process parameter remote collaborative optimization method and system. The method comprises the following steps: firstly, acquiring a parameter deviation value and transmission delay duration in a process parameter remote collaboration process, extracting a fluctuation period through time domain analysis, and constructing a collaboration degree and a delay influence degree; combining the relevance and the average state of the coordination degree and the delay influence degree of each period to obtain a current remote coordination difference degree; analyzing a parameter deviation descending trend and a transmission delay ascending trend at adjacent sampling moments, constructing an adjustment trend degree, determining a parameter optimization priority in combination with a collaborative difference degree, and further obtaining a feedback adjustment amount; and finally, optimizing a process parameter remote collaboration process through a collaboration controller by utilizing the feedback adjustment amount and the actual adjustment amount. According to the method, the accuracy and adaptability of remote collaboration can be improved.

The invention discloses a data security sharing and management method and system, and belongs to the field of data security management, and the method comprises the steps: extracting a shared data field name and format characteristics, marking legal sensitive information, forming dominant genes and risk values, calculating recessive and mutation risk values, and matching a belonging sensitive layer domain; analyzing and determining sensitive field weights, generating dominant, implicit and variant fingerprint segments, formulating a hierarchical verification rule, and binding the hierarchical verification rule with a data ID for storage; the user submits a calling application, the encrypted token and the fingerprint segment are obtained through auditing, data are fed back according to a layer domain after verification is passed, and otherwise, abnormity is intercepted and recorded; according to the method, full-dimension dynamic risk assessment of the data is realized, a protection mechanism adaptive to the sensitive hierarchy is constructed, the controllability and traceability of the whole data access process are ensured, and the safety and the sharing efficiency are balanced.

The invention discloses a multi-mode arc welding real-time quality evaluation system and method, and belongs to the technical field of intelligent welding quality control. The system comprises a distributed sensing array, an edge computing node and a dynamic quality mapping engine, wherein the distributed sensing array realizes multi-modal data synchronous acquisition through a high-frequency electric signal acquisition module, a molten pool thermal imaging module and an acoustic emission sensor array; gPU acceleration parallel processing is adopted by the edge computing nodes, an electric signal variable coefficient CV is extracted through a pulse cluster segmentation algorithm, and the main resonant frequency f0 of a molten pool is obtained by combining FFT frequency domain analysis; a dynamic quality mapping engine constructs a deep learning model containing a material-process knowledge graph, and real-time classification of defects such as pores, incomplete fusion and cracks is achieved. According to the method, the defect recognition accuracy is improved to 92.7%, the evaluation delay is compressed to be within 200 ms, the evaluation stability is high, and the method is suitable for precision welding quality monitoring in the fields of aerospace, new energy automobiles and the like.

The invention provides a medical image text generation method and system oriented to a visual language model, and belongs to the technical field of medical image processing. The method comprises the following steps: acquiring a labeled image of a medical image; performing connected domain analysis on the annotated image, and detecting and counting the number of tumor areas in the annotated image; for each tumor area, extracting morphological features including size, shape, position, cavity features and edge shape; according to a preset text template, the extracted morphological features are converted into structured natural language description, and final medical image text description is output. It is ensured that the generated medical image description has high consistency and specialty, and subjectivity and difference of manual writing are avoided. It can be ensured that key morphological characteristics (such as tumor size, shape and position) are accurately and completely described, and reliable supervision signals are provided for subsequent model training.

The invention relates to the technical field of image processing, and discloses a parallelization connected domain analysis method based on neighborhood labeling, computer equipment and a storage medium, through high parallelization design, the steps of father node, root node and area junction point searching, mapping establishing, re-numbering and the like are all designed into an independent parallel computing mode, and the parallel computing mode is designed into a parallel computing mode. According to the method, the acceleration capability of parallel computing hardware such as a GPU can be fully utilized, the processing speed is remarkably improved, the method is easy to expand, large-scale high-resolution images can be efficiently processed, the area of each connected domain is counted by adopting a Hash optimization method of grouping local reduction and global merging, atomic conflicts are effectively reduced, the statistical efficiency is improved, and the calculation efficiency is improved. The robustness of the algorithm in a parallel computing environment is further enhanced, so that the efficiency of the connected domain analysis algorithm is greatly improved, rapid processing of high-resolution images is realized, and powerful support is provided for real-time performance and large-scale application in the field of computer vision and image processing.

The invention belongs to the technical field of a strength analysis method of a lattice structure, discloses an efficient finite element modeling and analysis integrated method for static analysis of the lattice structure, and establishes an efficient and high-precision finite element modeling and analysis integrated method for the lattice structure. Aiming at the limitations that in traditional finite element analysis, high-quality hexahedron mesh generation is difficult and units need to ensure welt processing, the method adopts structured meshes to quickly divide the meshes, improves a self-adaptive integral strategy in a traditional finite cell element method, and further improves simulation efficiency. Meanwhile, a penalty function method is adopted to weakly apply displacement boundary conditions. In addition, in post-processing, a result calculated by a finite cell method is mapped to a smoother visual model. Through example comparison, it is verified that the method can perform strength check domain analysis on the complex lattice structure in a stable and efficient mode, and a feasible scheme is provided for efficient analysis of a large-scale complex engineering structure.

The invention discloses a semi-supervised medical image segmentation method based on momentum prototype alignment and adaptive uncertainty estimation, and aims to solve the problems of unstable feature alignment and false label noise accumulation caused by a batch effect in an existing semi-supervised learning method in a scene of scarcity of medical image annotation data. The method comprises the following steps: firstly, constructing a dual-network model based on a mean teacher architecture; secondly, designing a mixed strong and weak disturbance strategy, respectively applying weak data disturbance and strong data disturbance to the unlabeled image, respectively inputting the weak data disturbance and the strong data disturbance into a teacher and student segmentation network, and forcing the model to learn local and global robust features; secondly, a self-adaptive uncertainty estimation mechanism is introduced to dynamically screen false labels, and isolated noise is eliminated in combination with maximum connected domain analysis; meanwhile, the prototype of the current batch is calculated through uncertainty weighting, and a global category prototype library is updated and maintained through momentum. According to the method, the segmentation precision and the boundary integrity are remarkably improved under a small amount of annotated data.

According to the preparation method of the low-signal-loss copper-clad plate for the AI server, a microwave resonant cavity is integrated between heating plates of a laminating machine, on-line non-intrusive detection on dielectric response of a base material is achieved, the curing degree and stress distribution of resin of each layer are inversed in real time in combination with frequency domain analysis and material dielectric modeling, and the low-signal-loss copper-clad plate for the AI server is obtained. A pre-trained physical information neural network is used to map the three-dimensional pressure-deformation relation; according to the method, model prediction control is adopted to dynamically generate a pressure instruction, closed-loop feedback adjustment and optimization are carried out, technological parameter self-tuning and multi-objective optimization are matched, a knowledge base is established, and strategy autonomous recommendation is realized, so that the flexibility and precision of pressure distribution regulation and control and the consistency of finished products are improved, and continuous iterative optimization of technological experience is supported.

The invention discloses a line laser anti-welding noise center point extraction and weld joint point identification method, which is applied to the field of welding quality detection and machine vision measurement. The problems of low extraction precision, poor robustness, difficulty in welding seam point positioning and the like are solved for welding spatter, light reflection and background noise influences. According to the technical scheme, the method comprises the following steps: carrying out Otsu adaptive threshold segmentation, connected domain analysis, morphological opening operation and Gaussian filtering denoising; positioning an interference abnormal line and extracting a preliminary central point by a normal centroid method, and further smoothly extracting a detailed central point in an abnormal region by fitting straight lines of 10 front and back central points to assist judgment; and by calculating a second-order difference value, fitting front and back 50 points at the maximum difference value to identify welding seam points, and optimizing a track. The method can be applied to welding seam positioning and contour detection, the accuracy and stability in a complex noise environment are improved, the welding seam point recognition error is smaller than or equal to 0.2 mm, and the actual requirement is met.

The invention relates to the technical field of data processing, in particular to a multi-source data standardization management method and system.The multi-source data standardization management method comprises the following steps that S1, multi-source data are obtained and heterogeneous into a basic database; s2, performing data standardization on the basic database according to the data type and the use way; s3, carrying out quality inspection on the standardized basic database based on the use type and the service object of the data, and treating a data problem by combining a quality inspection result with reference data; and S4, performing data reinspection on the governed achievement data to guarantee the quality of the achievement data, and finally forming a basic achievement database. According to the method, through whole-process standardized management, the integration and quality problems of multi-source data are solved, the data application value is improved through visualization, dynamic management and cross-domain analysis, and powerful support is provided for natural resource space management and control and fine management and scientific decision in related fields.

The invention discloses a real-time well drilling lithology identification method fusing double attention and wavelet transform. The method comprises the steps that firstly, drilling parameters are collected and preprocessed, and a depth domain analysis matrix is formed through a sliding window; then carrying out wavelet transform, and extracting and filtering amplitude and phase characteristics; secondly, constructing a double-attention model, capturing time domain dependence by using self-attention, and fusing time-frequency features by using cross attention; after the fusion features are subjected to linear projection and layer normalization, a lithology classification result is output through a multi-layer perceptron; according to the method, through fusion of double attention and wavelet transformation, collaborative modeling of time domain and frequency domain features is achieved, the problems that long-range dependence modeling is weak, cross-domain fusion is difficult and the like in a traditional method are solved, lithology can be accurately recognized in real time, and the method is suitable for being used for a large-scale popularization and application. The method does not need to depend on expensive logging-while-drilling equipment, and has high practical value in petroleum drilling engineering.

The invention belongs to the field of artificial intelligence, particularly relates to a liquid crystal display screen defect automatic classification method based on deep learning, and aims to solve the problems that a traditional method is low in fine defect recognition rate, high in false alarm rate and poor in production line adaptability. The method comprises the following steps: collecting a high-resolution correction image, constructing a multi-scale feature extraction network of an encoder-decoder structure, embedding a space-channel joint attention module to strengthen key defect area response, adopting focus loss and boundary perception loss weighted optimization to relieve category imbalance, and obtaining a high-resolution correction image. And finally, 8 types of defect probabilities are output through a full-connection classification head, and the confidence coefficient is calibrated. In the post-processing stage, a structured report is generated in combination with morphological operation and connected domain analysis, and meanwhile, the small sample generalization ability is improved by introducing physical synthesis enhancement and transfer learning. According to the scheme, the classification accuracy is greater than or equal to 98.5%, and the omission factor 0.3%, and the single-screen detection period is lt; the daily productivity exceeds ten thousand pieces in one second, and the automation efficiency of a production line and the defect recognition robustness are remarkably improved.

The application relates to a complex texture textile defect recognition method and system based on feature decoupling, and relates to the field of computer technology, which comprises the following steps: obtaining a multi-scale feature set of an original textile image to obtain a complex texture fusion feature; projecting the complex texture fusion feature to two mutually orthogonal hidden subspaces to obtain a reference texture component and an abnormal disturbance component; performing manifold alignment processing on the reference texture component based on a preset texture library to obtain a texture reconstruction feature; performing feature fusion on the texture reconstruction feature and the abnormal disturbance component to obtain a texture residual feature; performing adaptive threshold segmentation on the texture residual feature to obtain a defect feature map of the original textile; and performing connected domain analysis processing on the defect feature map to output a defect detection result. The application has the effects of improving the robustness of textile detection, meeting the needs of real-time detection in a pipeline, and reducing the computing resources and labor costs.

The invention discloses a broadband random harmonic compression analysis method based on LSTM and multi-feature fusion, and belongs to the technical field of electric digital data processing. Comprising the following steps: collecting a broadband random harmonic signal for time domain analysis, and extracting a period; judging whether each period signal can be compressed or retained or not by using LSTM (Long Short Term Memory); carrying out down-sampling and coding compression on the compressible periodic section signal; decoding and reconstructing the signal through an LSTM decoder; the decoded and reconstructed signals are preprocessed, multi-dimensional features of the signals are extracted, and evaluation indexes are normalized; calculating the compression ratio of the dynamic fusion index and the signal; and dynamically displaying the evaluation index and the dynamic fusion index. According to the method, self-adaptive decision of compression or not is realized, the compression ratio is improved, and meanwhile, the spectrum fidelity and phase consistency are also kept; and parts which do not need to be compressed are reserved as original samples, so that real-time performance and error control are both considered.

The present application relates to the field of signal processing, deep learning and radar target detection, and particularly relates to a radar target detection method based on shallow and deep feature fusion. The present application solves the problem of poor detection generalization caused by relying on shallow features in the prior art by fusing shallow multi-domain features and deep learning features. The shallow features are subjected to multi-domain analysis, weighting and normalization processing, enhancing the ability to distinguish target echoes and sea clutter. The deep features are extracted by 1D-ResNet50 and spliced with the shallow features, combining physical meaning and expression ability, and improving the feature representation ability of the model. Combined with the constant false alarm rate algorithm, the detection threshold is dynamically adjusted to ensure stability and robustness in complex marine environments. The present application significantly improves the accuracy, generalization and practicality of target detection, and has high engineering application value.

The invention discloses an AI-based Jetson equipment system state dynamic atlas diagnosis system and method, and relates to the technical field of Jetson equipment state analysis, and the analysis system comprises a system call collection module which carries out the structural processing of a system call track; the calling sequence extraction module is used for carrying out continuous calling identification on the execution process recording path and extracting a calling sequence fragment with a specific mode; the state atlas construction module is used for establishing a system state change atlas for the screened calling sequence fragments in a graph structure splicing manner; the AI intelligent diagnosis module is used for analyzing the system state change atlas by using a pre-trained artificial intelligence model; according to the AI-based Jetson equipment system state dynamic graph diagnosis system and method, the system state change graph is established through graph structure splicing, so that a dynamic behavior has a static structure expression capability, the running state of equipment can be analyzed more accurately, and the fault detection precision and reliability are improved.

The invention provides a method and system for calculating the topological connectivity of a two-dimensional discrete fracture network containing n-type nodes, and the method comprises the steps: carrying out the binarization processing of an original fracture image of a rock surface, and generating a fracture binary image; extracting each crack region from the crack binary image based on connected domain analysis; extracting an outer contour pixel point set and a central axis point set of any crack region; an intersection point cluster is extracted from the central axis point set, and an effective extension point, located on the central axis, of each intersection point is obtained; determining the node type of each intersection point according to the number of the effective extension points of each intersection point; and calculating connectivity parameters of any crack region according to the node type of each intersection point of any crack region. Through the method, the node types in the two-dimensional discrete fracture network can be automatically identified, accurate quantization of connectivity is realized in combination with graph theory parameters, and the analysis precision and efficiency of the complex fracture network are remarkably improved.

The invention relates to the technical field of electrical engineering, and discloses a DC power flow controller transient oscillation law analysis method, system, device and medium, and the method comprises the steps: obtaining the inertia characteristics of a sending end converter station, providing precise basic data for model building, and guaranteeing the accuracy and reliability of a model; a direct current power flow controller time domain model is established based on inertia characteristics, actual system dynamic behaviors can be truly reflected, and support is provided for frequency domain analysis; the establishment of the frequency domain model enables the transient characteristic analysis of the DC power flow controller to be more intuitive and convenient, and facilitates understanding and application; the transient characteristics can be accurately obtained by solving the frequency domain model, and a basis is provided for optimizing controller parameters and improving system stability. According to the method, the accuracy and efficiency of transient oscillation law analysis of the direct current power flow controller are improved, and guarantee is provided for stable operation and optimal control of a power system. The transient characteristics are deeply analyzed to facilitate understanding of the working principle and behavior characteristics, and scientific basis is provided for design and operation of a power system.

The present invention relates to the field of 3D printing technology, and discloses a quality monitoring method, system, equipment and medium for direct writing molding circuits. The method comprises: determining a captured image of a discharge area according to a needle position; performing binarization processing and connected domain analysis on the captured image and a preset template image, respectively, to determine the discharge area of ​​the captured image and the discharge area of ​​the preset template image; performing a difference image subtraction between the captured binary image and the template binary image, and performing connected domain analysis on the difference image to obtain a difference analysis result; calculating the discharge area ratio of the captured image and the preset template image, and monitoring the quality of the direct writing molding circuit according to the discharge area ratio difference analysis result. The present invention analyzes the image of the discharge area, makes the judgment of material breakage more accurate, and can also perform material breakage detection on materials in various states. By calculating the discharge area, an early warning can be given before the material is completely exhausted, thereby ensuring the effect of direct writing molding.