62 results about "Complexity index" patented technology

The invention discloses a method and device for determining the number of test cases and electronic equipment. The method comprises the following steps: generating an initial orthogonal table of a target software system; determining a target service operated by the target software system, obtaining a complexity index variable, and determining a service influence index variable of the target service; determining a complexity factor value according to the complexity index variable and the service influence index variable; acquiring historical operation information of the target software system, acquiring an error rate index variable from the historical operation information and the software code, and determining an error rate factor value of the target software system according to the error rate index variable; and adjusting the preset number according to the complexity factor value and the error rate factor value to obtain a target number of the test cases of the target software system. By means of the method and device, the problem that in the related technology, due to the fact that the accuracy of determining the number of supplementary cases of the orthogonal table is low, the accuracy of software testing according to the orthogonal table is low is solved.

The invention relates to the technical field of point cloud data processing, and provides a ground filtering method and system based on cloth simulation parameter dynamic optimization, and the method comprises the following steps: obtaining point cloud data of a to-be-processed region, carrying out the preprocessing, constructing a complexity index, and calculating a complexity index value based on the preprocessed data; clustering the obtained complexity indexes by adopting a clustering method fusing an elbow rule and a spatial neighborhood constraint to obtain a terrain complexity category; and selecting distribution parameters of a distribution algorithm based on the terrain complexity category and the dynamic mapping of the constructed complexity category-optimal parameter group, and further performing ground point filtering and ground extraction by adopting the distribution algorithm. According to the invention, distribution simulation parameter configuration is guided through terrain complexity analysis, accurate adaptation of different terrain areas is realized, and the accuracy and global consistency of ground point extraction are improved.

The invention relates to a deep learning cloud particle target detection method based on a dynamic detection head. According to the method, firstly, complexity evaluation is carried out on a cloud particle image data set, the weighted information entropy, the weighted texture complexity and the weighted local standard deviation of each image are calculated, and the comprehensive complexity is calculated based on the complexity indexes. Then, according to the overall complexity grade of the data set and the image data complexity, a detection head selection standard is formulated, and a proper dynamic detection head combination is selected according to the standard; and finally, performing target detection on the cloud particle image data set by using the selected dynamic detection head combination. Through the method, the most suitable detection head combination can be adaptively selected, the target detection precision and efficiency are effectively improved, and the method is particularly suitable for a large-scale cloud particle image data set with complexity difference.

The invention relates to the technical field of PLC (Programmable Logic Controller) control parameter setting, and discloses a PID (Proportion Integration Differentiation) parameter intelligent setting method and system based on a PLC control system. According to the method, a process variable signal, a set value signal and an actuator output signal are collected in real time through a PLC communication protocol, feature quantities are extracted after smooth filtering and noise elimination processing, and a standardized control data set is formed; querying matched setting strategy entries by using the set, calculating complexity indexes, evaluating parameter configuration compliance conditions, and generating setting state judgment of a single control loop; detecting common entities among the loops and calculating an interaction strength index by combining a plurality of standardized control data sets, and generating a multi-loop interaction relation graph; and based on the map fusion setting state judgment, calculating the overall quality score and forming a setting recommendation scheme, and after field verification and closed-loop control simulation test, outputting a final PID parameter setting instruction.

The invention discloses an automatic processing method and system for graphic labeling, and particularly relates to the field of image clustering and automatic labeling, and the method comprises the steps: calculating an entropy value and a gradient variance of a pixel region of an input image to generate a complexity index, adjusting the region division scale according to the complexity index, and obtaining a divided region; calculating a color histogram, a texture direction and an edge curvature in the divided region to form a region feature, and combining a complexity index with the region feature to generate a feature matrix; according to the method, the feature matrix is generated by calculating the region complexity index and extracting the multi-dimensional features, the clustering model is input to identify the boundary cluster and the hopping region, and multi-category and single-category annotations are generated in combination with the weight map and segmented weighting, so that the problems of semantic confusion, boundary instability and error accumulation are solved.

The invention relates to the technical field of digital recognition and intelligent classification, in particular to an intelligent recognition and classification method for non-missing patterns, which comprises the following steps of: constructing a multi-dimensional embroidery pattern knowledge base, calculating a disparity map and a height map to obtain a stereoscopic strength coefficient, and calculating a stitch complexity index as a physical vector by combining a histogram of oriented gradients; meanwhile, the pattern recognition model is used for outputting the category probability of each independent pattern part as a semantic vector, then an embroidery method is determined, graph nodes are created based on physical and semantic vectors, a spatial relation edge and a cultural relation edge are established, then a multi-modal heterogeneous graph is generated, the graph is input into a graph neural network for message passing and global pooling, and the multi-modal heterogeneous graph is obtained. According to the method, a global feature vector is obtained, an embroidery theme is matched through a theme classifier, finally, the comprehensive credibility is calculated, and through multi-modal data fusion and heterogeneous graph analysis, the accuracy and automation level of pattern recognition are effectively improved, and the method is particularly suitable for intelligent classification of complex non-missing embroidery.

The invention relates to the technical field of data processing, in particular to an outpatient service payment medical insurance data rationality analysis method and system based on clustering analysis, and the method comprises the steps: converting the multidimensional resource consumption of outpatient service records into standard comparable structure expression through constructing a unified resource structure vector, and providing unified input for subsequent analysis. On the basis, an improved clustering algorithm with resource sensitivity and an abnormal penalty term is introduced, different resource consumption modes are automatically recognized, resource cluster tags are generated, and it is guaranteed that a clustering result fits the actual features of medical insurance. Furthermore, a behavior path modeling mechanism is established in the cluster, and an operation type weight and a redundant path penalty function are introduced to quantify and form a complexity index reflecting structural rationality and behavior normalization. And finally, comparing the resource cluster with the medical insurance group, and proposing an intra-group structure consistency score and a payment deviation evaluation index.

The invention provides a cross-layer annotation correction and real-time rendering method and application thereof, and belongs to the technical field of digital pathological image processing. According to the method, the problems of labeling dislocation, editing distortion and large-point-set rendering lagging during switching of the multi-focal-plane slices are solved. The method is characterized by comprising the following steps: determining interlayer offset parameters of each focal plane, and carrying out associative storage with focal plane identifiers; anchoring an original focal plane and storing original geometric data when the label is created; when the target focal plane is switched, cross-layer mapping is carried out according to the relative offset parameters, and geometric semantic stability is kept for different labeling types; a rendering complexity index is calculated, hierarchical simplified rendering is executed in a continuous interaction stage, and high-precision display is recovered after interaction is stable; and after editing, reversely writing the target geometric data back to the original focal plane coordinate system. According to the method, cross-layer accurate correction and real-time smooth interaction of the labels are realized, and pathological diagnosis is effectively assisted.

The invention discloses a main network SVG graph model updating method, system and device based on an overlapping sliding window mechanism and dynamic correction and a medium, and the method comprises the steps: carrying out the sliding cutting of a main network SVG graph according to a preset window size and an overlapping rate, and generating a sub-graph set containing an overlapping region; electrical equipment primitives and coordinates in a sub-graph set are recognized through a target detection model, connecting lines between the equipment are extracted based on a straight line detection algorithm, the equipment primitives and the coordinates of the connecting lines are accurately positioned, and the problems of missed recognition and false recognition in a high-density graph are solved; the connection relation is dynamically corrected according to a power topology rule, reconnection operation is executed on the connecting lines crossing the bus to correct abnormal topology, a final to-be-rendered area is generated, and it is ensured that a graph model is consistent with an actual power grid structure; calculating a comprehensive complexity index of the region according to the attribute value of the to-be-rendered region; and on the basis of the comprehensive complexity index and the set threshold value judgment result, the judgment area is subjected to CIM model data rendering or updating operation, interface jamming is avoided, and the main network SVG graph model updating requirements of different complexities based on an overlapped sliding window mechanism and dynamic correction can be met.

The invention provides a course reinforcement learning method, device and equipment for low-resource dialect recognition, relates to the technical field of artificial intelligence, and aims to solve the problems that in a low-resource dialect speech recognition scene, existing reinforcement learning enables a model not to perform effective learning and performance improvement is limited. The method comprises the following steps: performing supervised fine tuning on an initial speech recognition model by adopting a low-resource dialect speech data set to obtain a preliminary optimization model; recognizing the voice data based on the preliminary optimization model, and calculating a complexity index for reflecting the recognition difficulty of the voice data according to a voice recognition result and the labeled reference answer; according to the complexity index, sorting the voice data from low to high according to the recognition difficulty to form a training data sequence of course learning; and applying reinforcement learning on the training data sequence, and optimizing, iteratively updating the preliminary optimization model through a dynamic reward function and a group relative strategy until a preset convergence condition is met, thereby obtaining a target dialect recognition model.

This invention discloses a computer-based method for evaluating the complexity of the working environment of unmanned ground vehicles (UGVs). First, it determines environmental complexity evaluation indicators and establishes an evaluation indicator system. Then, it acquires and normalizes multi-source environmental data using onboard sensors and a deep learning model. Next, it determines the weights of static environmental indicators using the Analytic Hierarchy Process (AHP) and calculates the static environmental complexity index using a nonlinear mapping function. Simultaneously, it establishes a potential energy function incorporating distance, speed, and predicted collision time, calculating the total environmental potential energy to output the dynamic environmental complexity index. For the state evolution complexity evaluation indicator, it constructs a state evolution complexity quantification model based on state change entropy, outputting the state evolution complexity index. Finally, it outputs the working environment complexity index by establishing a multi-dimensional coupled complexity model. This invention achieves real-time quantification and evaluation of working environment complexity, providing reliable data for autonomous decision-making, path planning, and safety control of UGVs.

The application relates to the technical field of PLC control parameter setting, and discloses a PID parameter intelligent setting method and system based on a PLC control system. The method collects process variable signals, set value signals and actuator output signals in real time through a PLC communication protocol, extracts characteristic quantities after smoothing filtering and noise elimination processing, and forms a standardized control data set; the set is used to query matched setting strategy entries, calculate a complexity index and evaluate parameter configuration compliance, and generate a setting state judgment of a single control loop; a plurality of standardized control data sets are combined to detect shared entities between loops and calculate an interaction intensity index, and a multi-loop interaction relationship graph is generated; based on the graph, the setting state judgment is fused, the overall quality score is calculated, and a setting recommendation scheme is formed; after field verification and closed-loop control simulation testing, a final PID parameter setting instruction is output.

The application relates to a motor intention detection method based on electroencephalogram microstate system automaton theory analysis, which comprises the following steps: acquiring multi-try and multi-category electroencephalogram signals based on a motor imagination experiment paradigm and converting the signals into discrete observation sequences; reconstructing an optimal prediction model for the discrete observation sequences based on a causal state splitting reconstruction algorithm; calculating multi-dimensional neural dynamics complexity indexes based on the optimal prediction model to form feature vectors for inter-group comparison; taking the feature vectors as inputs to train a classifier, evaluating the performance of the model in distinguishing different motor intention categories through cross-validation, and outputting the probability of an individual belonging to a certain motor intention category to realize the decoding of motor intention. The application combines formal automaton theory with neural time series analysis to provide a new way for realizing non-invasive and fine decoding of motor intention.

The invention belongs to the technical field of radar signal sorting, and particularly discloses an environment adaptive optimization method for radar signal sorting under a Bayesian framework, which comprises the following steps of: acquiring a plurality of characteristic parameters of a radar signal, grouping the characteristic parameters into a plurality of characteristic domains according to physical attributes of the characteristic parameters, and then calculating statistical dispersion of the characteristic parameters in each domain, obtaining a complexity index of each feature domain; performing cross-domain weighted fusion on the complexity indexes of the feature domains to obtain a comprehensive electromagnetic environment complexity quantitative score; and dynamically mapping the electromagnetic environment complexity quantization score into a probability parameter for controlling generation of a new cluster in the CRP model under the Bayesian framework, and realizing adaptive optimization of the CRP model parameter. According to the method, adaptive adjustment of the CRP model parameters can be realized, and the robustness and accuracy of a sorting algorithm in a dynamic electromagnetic environment are improved.

Systems and method for an AI-based calculation of a case complexity index, CCI. For training a neural network, NN, the method includes receiving training data comprising a medical image of a set of medical images and for example a related report for the medical image and a CCI for the medical image. The method may further include training the NN for providing a trained NN, that is configured for determining the CCI for a medical image by adjusting weights and biases of the NN such that a loss function is minimized.

The invention relates to the technical field of material informatics and data science, in particular to a material performance sensitivity quantification and grading method based on multi-dimensional statistical analysis, which comprises the following steps: inputting a standardized identifier containing a material chemical formula and at least one column of performance index data; a diversity index and an element complexity index are formed; and calculating a variable coefficient, a quantile range ratio, a skewness absolute value, a kurtosis absolute value and an abnormal value proportion for the performance index data column. According to the method, the problems of dependence on expert experience and high subjectivity are solved through multi-dimensional statistical feature fusion and a quantitative model for forming variability correction; the analysis accuracy is improved through five complementary statistical characteristics; different material systems are adapted by forming variability coefficients, evaluation deviation is eliminated, and universality is enhanced; automatic grading and knowledge base construction significantly improve the material design optimization efficiency, provide precise support for process control and quality assurance, and promote intelligent transformation of material research and development.

The application provides an adaptive Bayesian identification method for long-term prediction of industrial chain electric load, comprising: aggregating active power data into daily frozen power time series; performing discrete Fourier transform on the daily frozen power time series, obtaining amplitude spectrum, and calculating spectral complexity index; constructing an adaptive basis function library containing sine and cosine functions according to the screened dominant frequency; establishing a sparse regression model containing regularization, representing the daily frozen power time series as a linear combination of basis functions in the adaptive basis function library, and obtaining a sparse coefficient vector by solving an optimization problem; and using the identified sparse coefficient vector and adaptive basis function library, calculating the daily frozen power prediction value of the future time step through time index extrapolation. The method can plan the characteristics of upstream and downstream industrial chains, accurately capture multi-scale periodic structures, and has low calculation cost and high physical interpretability.

The application belongs to the technical field of data processing, and particularly relates to a large model question and answer method and system based on a knowledge base, which comprises the following steps: receiving a user question and extracting a plurality of basic linguistic features; constructing a two-dimensional query essence space and mapping the user question into a query point in the space; in the query essence space, nonlinearly calculating dynamic weights of the basic linguistic features based on geometric distances between the query point and a plurality of preset characteristic ideal anchor points; calculating a dynamic question complexity index according to the dynamic weights and the basic linguistic features, and determining a retrieval quantity of the knowledge base; retrieving knowledge fragments from the knowledge base according to the retrieval quantity, and generating an answer in combination with the user question. The application realizes accurate self-adaptation of a retrieval range by dynamically evaluating a question essence, can provide appropriate context information for a large language model, and thus improves the accuracy and reliability of large model question and answer.

The application relates to the technical field of intelligent unmanned aerial vehicle manufacturing, and discloses a method for constructing and dynamically evaluating an unmanned aerial vehicle cluster comprehensive task complexity index system, which comprises the following steps: a target state parameter and a task-equipment matching function are proposed and improved; the unmanned aerial vehicle cluster task allocation is converted into a multi-target optimization problem; an improved NSGA-II algorithm is used for task allocation under the condition of multi-target optimization; an index system for evaluating task complexity is established by comprehensively considering the index composition of the task target dimension and the task allocation dimension; and multi-dimensional task complexity comprehensive evaluation is carried out. According to the application, personnel can quickly and accurately understand the change of a task situation and make relatively correct judgments accordingly. The application considers the dynamic change of a target state and a task, and can provide guidance for unmanned aerial vehicle task execution.

The application discloses a kind of graphic annotation automation processing method and system, specifically related to image clustering and automatic annotation field, including the entropy value and gradient variance of the pixel region of input image are calculated to generate complexity index, according to complexity index adjustment region division scale, obtain division region;Color histogram, texture direction and edge curvature are calculated in division region, form region feature, and complexity index and region feature are combined to generate feature matrix;The application generates feature matrix by calculating region complexity index and extracting multi-dimensional features, inputs clustering model to identify boundary cluster and jump region, combined with weight map and segmented weighting generates multi-class and single-class annotation, to solve the problem of semantic confusion, unstable boundary and error accumulation.

The invention relates to the technical field of machine vision and operation evaluation, and discloses a size measurement scoring system and method based on machine vision. The method includes parsing out a pause event and associating the pause event with a predefined standard measurement phase by synchronously capturing a continuous spatial position stream and a timestamp stream during a measurement process, forming a sequence of steps to check a logical sequence and generate a sequence deviation factor. Meanwhile, preset image features are dynamically detected, feature capture moments of the features are recorded, position point sets in time windows before and after the moments are extracted, and local trajectory complexity indexes are calculated. And integrating the total time consumption, the sequence deviation factor and the local trajectory complexity index, calculating an original operation efficiency value, carrying out normalized fusion on the original operation efficiency value and the reference size value, and finally outputting a comprehensive measurement score. According to the invention, automatic comprehensive evaluation of the standardization of the measurement operation flow and the stability of the operation process is realized.

The application discloses a lightweight convolutional neural network deployment method and system for smart glasses, a dynamic reasoning strategy is embedded in a feature extraction backbone network, the strategy is a reasoning strategy dynamically selected according to a texture complexity index of a feature map of the current input feature extraction backbone network, the dynamic difference of the input feature is fully considered, a feature extraction backbone network embedded with the dynamic reasoning strategy is trained using knowledge distillation, a lightweight convolutional neural network is obtained, a gradient balance factor inversely proportional to the loss gradient modulus of the training sample is introduced into the distillation loss function, the gradient balance factor is used to effectively balance the contribution of different difficulty samples to parameter updating, the lightweight convolutional neural network is subjected to mixed precision quantization, the quantized lightweight convolutional neural network is mapped to a special instruction set of a smart glasses processor, a binary execution file is obtained, and the binary execution file is deployed to the smart glasses processor, so that the reasoning efficiency and precision of the smart glasses are improved, and the power consumption is reduced.

The application relates to the technical field of machine vision and operation evaluation, and discloses a size measurement scoring system and method based on machine vision. The method comprises the following steps: synchronously capturing a continuous spatial position stream and a time stamp stream in a measurement process, analyzing a pause event and associating the pause event with a pre-defined standard measurement stage, forming a step sequence to check a logical sequence and generating a sequence deviation factor. Meanwhile, preset image features are dynamically detected and their feature capture moments are recorded, a position point set in a time window before and after the moment is extracted, and a local trajectory complexity index is calculated. The original operation efficiency value is calculated by comprehensively considering the overall time consumption, the sequence deviation factor and the local trajectory complexity index, and is normalized and fused with a reference size value, and finally a comprehensive measurement score is output. The application realizes automatic comprehensive evaluation of the standardization of a measurement operation process and the stability of an operation process.

The invention discloses an electroplating bath production operation and maintenance big data analysis and optimization system and method, and the method comprises the steps: building an operation and maintenance state mapping table through electroplating bath process parameter data collection and key control node sequence determination; generating a process probability distribution tree by adopting a probability backtracking processing technology, obtaining a quality achievement probability value, determining a main process family and an alternative process family, and constructing a probability optimization chain; a stirring flow velocity distribution coefficient and a plating solution circulation complexity index are extracted from the probability optimization chain, mass transfer efficiency distribution is analyzed and predicted through an eddy current effect, and process optimization configuration is generated; identifying a high-risk process section and a stable process section by using a risk energy release analysis technology, and forming a mass balance process pair through hedging combination; hot spot distribution is determined by combining temperature field analysis, an area with an overlarge temperature gradient is identified, and a heat dissipation optimization factor is extracted; and finally, generating a dynamic production unit and an elastic production process by matching a beat control parameter with a probability optimization chain, so as to realize big data analysis and optimization of production operation and maintenance of the electroplating bath.

The present application relates to the field of material informatics and data science technology, in particular to a material performance sensitivity quantification and grading method based on multi-dimensional statistical analysis, comprising the following steps: inputting standardized identification containing material chemical formula and at least one column of performance index data; and composing diversity index and element complexity index; for the performance index data column, calculating coefficient of variation, quantile range ratio, absolute value of skewness, absolute value of kurtosis and proportion of outliers. The present application solves the problem of strong subjectivity and dependence on expert experience by using a multi-dimensional statistical feature fusion and a quantitative model of variation correction composition; improves analysis accuracy by five complementary statistical features; eliminates evaluation bias and enhances universality by adapting different material systems with the composition of the variation coefficient; automatic grading and knowledge base construction significantly improve the efficiency of material design optimization, provide accurate support for process control and quality assurance, and promote the intelligent transformation of material research and development.

This invention provides a multi-target tracking method and system for special steel production processes using edge-cloud collaboration, belonging to the field of industrial intelligent manufacturing. The method deploys a multi-target tracking model at the edge of the special steel production line and a multimodal inference model in the cloud. Real-time production data is collected and input into the multi-target tracking model, outputting initial tracking results. Based on the initial tracking results, a scenario complexity index is calculated, and then it is determined whether the scenario meets stability conditions. If it does, the initial tracking result is output as the edge tracking result; otherwise, the cloud-based multimodal inference model performs multimodal inference based on the data uploaded from the edge side to obtain the cloud tracking result. The edge side fuses the edge and cloud tracking results to obtain and maintain the multi-target tracking trajectory. The cloud uses knowledge distillation to train a copy of the multi-target tracking model and distributes it to the edge side to update the model. This invention improves the accuracy and precision of multi-target tracking in special steel production processes.

The present invention relates to an AI-based calculation of a case complexity index, CCI. For training a neural network, NN, the method (100) may comprise receiving (S102) training data, comprising: a medical image (I) of a set of medical images and optionally a related report (R) for the medical image; and a CCI for the medical image (I). The method (100) may further comprise training (S103) the NN for providing a trained NN, which is configured for determining the CCI for a medical image (I) by adjusting weights and biases of the NN such that a loss function is minimized.

The invention relates to the technical field of computers, and discloses an intelligent sensing and early warning method for the residual amount of printer carbon powder. According to the method, a vibration sensing unit, a capacitance sensing unit and an optical scattering sensing unit are arranged in a powder supply channel, mechanical vibration, dielectric characteristics and particle scattering signals are synchronously collected, current and rotating speed data of a powder supply motor are fused, and a four-dimensional synchronous time sequence flow is constructed; the energy entropy of each signal is extracted through wavelet packet decomposition to serve as a dynamic complexity index, and the dynamic complexity index is input into a pre-trained neural network model to generate a carbon powder fluidity health index; and when the index is smaller than a threshold value, triggering graded early warning and intervening the printing task. According to the invention, perception upgrading from existence of the carbon powder to availability of the carbon powder is realized, and the powder supply reliability and the equipment intelligence level are improved.

And AI-based calculation of case complexity indexes is carried out. The invention relates to AI-based calculation of case complexity index (CCI). To train a neural network, NN, a method (100) may include receiving (S102) training data including a medical image (I) of a set of medical images and optionally a correlation report (R) for the medical image; and a CCI for the medical image (I). The method (100) may further include training (S103) the NN for providing a trained NN configured for determining a CCI for the medical image (I) by adjusting the weight and bias of the NN such that the loss function is minimized.

The application discloses a shale gas reservoir dessert evaluation and analysis method, including: collecting seismic data, well logging curve data and geological data of strata of a historical shale gas reservoir to obtain standard characteristic variables; calculating a comprehensive index, a coupling index and a complexity index of the strata; calculating a reservoir dessert index Y; constructing a training data set; constructing a physical constraint neural network PCNN and adding a physical regularization term to output the trained physical constraint neural network PCNN; collecting seismic data, well logging curve data and geological data of strata of a target shale gas reservoir and inputting the trained physical constraint neural network PCNN to output a reservoir dessert index of the target shale gas reservoir, and then evaluating the target shale gas reservoir dessert. The application constructs an interpretable shale gas reservoir dessert intelligent evaluation method, and realizes accurate evaluation of advantages of a shale gas reservoir dessert through the predicted reservoir dessert index.