29 results about "Negatively associated" patented technology

The application discloses a kind of based on radar chart's hole making quality evaluation method, system and storage medium, belong to the technical field of hole making quality evaluation, determine N features data of influencing hole making quality, and corresponding N-dimensional radar chart is drawn as radar chart evaluation model;Obtain the feature data of hole making, adopt box plot method to correct.Based on the feature data after correction, draw closed radar chart;The area of the radar chart drawn is calculated, and the quality of hole making is measured based on the size of area;If N feature data is all negatively correlated with the quality of hole making, then the smaller the area of the radar chart drawn, the higher the quality of hole making;If N feature data is all positively correlated with the quality of hole making, then the larger the area of the radar chart drawn, the higher the quality of hole making.The application realizes multidimensional feature correlation evaluation, avoids the difference caused by subjective evaluation, intuitively compares the quality of hole making by radar chart area, quickly locates the advantages and disadvantages of hole making quality, with good practicability.

The invention provides a product recommendation method and device, equipment, a medium and a product, and can be applied to the field of artificial intelligence. The product recommendation method comprises the steps of obtaining product resource data of preferred products of a target object and candidate resource data of candidate products, wherein candidate text data in the candidate resource data is used for describing product operation activities of the candidate products in a preset historical period; processing the product resource data and the candidate resource data based on a correlation algorithm to obtain a correlation degree; determining an intermediate product which does not meet a preset correlation degree threshold value from the candidate products based on the correlation degree; performing semantic understanding on the candidate text data by using a large language model to obtain a correlation influence relationship between the preference product and the intermediate product; and under the condition that the correlation influence relationship meets a preset negative correlation condition, determining a target product from the intermediate products, and pushing the target product and the preference product to the target object.

Provided is an information processing apparatus that performs processing for presenting a relationship between variables in multivariate analysis. The information processing apparatus includes a detection unit that detects a combination of two variables having a characteristic relationship in multivariate analysis, and a presentation unit that presents information regarding the characteristic relationship between the two variables. The detection unit detects whether or not there is a characteristic relationship including at least one of a positive correlation, a negative correlation, or a non-linear relationship as the entire variables on the basis of the relationship between the explanatory variable and the explained variable for each of the two consecutive categories of the explanatory variable, and further quantifies the relationship between the explanatory variable and the explained variable as the entire variables.

The invention relates to the technical field of cooperative control, in particular to a cooperative optimization control method and system for the expansion degree and strength of flow-state solidified soil, and the method comprises the steps: designing a sample according to all influence factors, measuring performance data, and forming test data; constructing a correlation matrix through preprocessing such as abnormal value elimination, variable standardization and virtual variable conversion; screening significant influence items through a variance analysis model and quantifying weights; constructing a dual-output prediction model based on a random forest algorithm, and performing training verification optimization; taking the prediction model as a constraint, and combining economy and convenience to screen an optimal parameter combination; on-site trial mixing verification is carried out, deviation reaches the standard, parameters are determined, and otherwise, data are complemented to update the model to resolve. According to the method, the single factor and the interaction effect are effectively removed, the problem of collaborative prediction under negative correlation of the expansion degree and the strength is solved, collaborative optimization of the expansion degree and the strength is realized, and the scientificity and efficiency of construction parameter determination are improved.

The invention discloses an abnormal data detection method applied to a battery system and a related device, and relates to the field of clustering algorithms, and the method comprises the steps: obtaining a plurality of attribute parameter sets of the battery system, and determining a plurality of original clustering centers from the attribute parameter sets; after the clusters corresponding to the multiple original clustering centers are obtained, the distance between each attribute parameter set and the corresponding original clustering center is obtained for each cluster, the weight is determined according to the distance, and due to the fact that the weight and the distance are in negative correlation, the contribution degree of the attribute parameter set with the large distance in subsequent clustering center updating is automatically attenuated; re-determining an updated clustering center based on a weighting mechanism, and continuously optimizing the updated clustering center corresponding to each cluster through repeated iteration; in the iteration process, if the standard function value is smaller than a preset threshold value, it is indicated that the interior of each cluster meets the iteration condition; at the moment, based on the optimized clusters, the attribute parameter set which cannot be absorbed by any cluster is determined as abnormal data located outside the multiple clusters, and therefore abnormal data identification is achieved.

The invention relates to a classification model training method and device, a classification method and device, electronic equipment and a program product. The method comprises the steps that training data are acquired, the training data comprise a plurality of training samples, and each training sample comprises a plurality of features and corresponding classification labels; the training data are input into a classification model based on preset target optimization logic for training, a trained classification model is obtained, the target optimization logic comprises a prediction error item, the prediction error item is determined based on a sample weight, and the sample weight is in negative correlation with the possibility that the sample is correctly classified. According to the scheme provided by the invention, the sample weight is negatively correlated with the possibility that the samples are correctly classified, so that the training process can give a higher focusing degree to the samples difficult to classify, and the classification effect of the classification model is improved.

The invention provides a product pushing method and device based on financial personality and electronic equipment, and the method comprises the steps: collecting a plurality of pieces of user feature data of a user under the condition of obtaining user authorization; a factor analysis method is adopted to determine scores corresponding to the user feature data in multiple dimensions, and multiple user feature scores are obtained; constructing a training data set by adopting a positive and negative correlation factor method based on the user feature score, and training a personality classification model according to the training data set; obtaining personality classification data, and processing the personality classification data by adopting the personality classification model to obtain a target financial personality; and determining a corresponding product pushing strategy according to the target financial personality, and performing product pushing according to the product pushing strategy. The method solves the problems that in the prior art, user positioning is carried out through a user portrait, analysis on user behavior psychology is lacked, positioning is inaccurate, and product pushing does not conform to user preferences.

This application relates to the field of data processing technology, and in particular to a method and system for anomaly detection of refrigeration valves. The method includes: acquiring preprocessed operating data of the refrigeration valve; for any data point in the operating data, calculating the sparsity of the data point based on its distribution in the local area; simultaneously, constructing a temporal neighbor reference set for the data points, and using the LOF algorithm to obtain the initial anomaly score for each data point in the neighbor reference set; constructing a trend anomaly degree based on the initial anomaly scores of the data points in the temporal neighbor reference set and the changes in data values; adjusting a preset initial anomaly threshold based on the trend anomaly degree and the sparsity to obtain a dynamic threshold, wherein the dynamic threshold is negatively correlated with the trend anomaly degree; and judging the operating data of the refrigeration valve based on the dynamic threshold. This application has the effect of improving the accuracy of anomaly detection in refrigeration valves.

This invention belongs to the field of pipeline leakage detection technology, and relates to a method for identifying leaks in water supply networks used in urban lifeline projects. The method involves collecting pressure fluctuation data and synchronous flow data; calculating the difference between the pressure fluctuation data and a benchmark fluctuation template point by point to obtain a fluctuation residual sequence; extracting features from the fluctuation residual sequence to obtain spatial distribution characteristics; combining the spatial distribution characteristics and synchronous flow data to calculate the negative correlation between pressure drop and flow increase, as well as the time difference between pressure drop and flow increase; and marking suspected leakage features based on the calculation results. Finally, the method performs correlation analysis on the suspected leakage features based on pipe aging parameters and historical maintenance records to identify leaks. This invention uses a benchmark fluctuation template to strip away normal fluctuations to highlight weak abnormal signals, and integrates pipe aging parameters and historical maintenance records for multi-factor correlation confirmation. It progressively separates minute leakage features from complex background noise, effectively improving the accuracy of leak identification and reducing the false alarm rate.

In this image, data is input to an AI model by adjusting the length of the bars extending horizontally. For example, if the data input to the AI ​​model is the Iris dataset, the length and width of the sepals and petals of iris flowers are parameters, and the magnitude of these values ​​can be changed by manipulating the bars. A histogram corresponds to a combination of bar lengths and visually represents the correlation between each parameter. For example, if there are many low-brightness areas in the histogram, there is a positive correlation, and if there are few low-brightness areas, there is a negative correlation.

Embodiments of the present application disclose a kind of energy consumption prediction method, device, electronic equipment and storage medium of electrical object, comprising: according to the preset period, the multiple historical energy consumptions of electrical object are collected;Set the clustering target function with weight to the historical energy consumption clustering is carried out to be granulated and obtain granulated data, in the clustering target function, the distance of historical energy consumption to cluster center is negatively correlated with the weight;The granulated data is degranulated, and the data after degranulation is obtained;Using the data after degranulation establishes fuzzy rule model to predict the energy consumption of the electrical object.The embodiments of the present application, since in the clustering target function, the distance of historical energy consumption to cluster center is negatively correlated with the weight, can be dynamically adjusted in the clustering process by weight to clustering target function, further refine the distance of different historical energy consumptions to each cluster center, improve the clustering effect of historical energy consumption, ultimately improve the accuracy of energy consumption prediction of electrical object.

The present application relates to the technical field of retrieval, more particularly, the present application relates to a kind of property information cross-modal retrieval method and system based on knowledge graph, comprising: the multi-modal attribute graph of static node and dynamic event node is constructed, each node in the graph is given source label according to data source, and text information and image information in the graph are processed into feature vector;The source entropy confidence of each node is calculated, and the source entropy confidence and the historical data dispersion of the corresponding source of the node in the preset time window are negatively correlated.The present application realizes the quantitative evaluation of the objectivity of different sources (such as IoT and artificial record) by constructing multi-modal attribute graph and calculating source entropy confidence, comprehensively considers historical dispersion, text and picture self-consistency and credibility factor, effectively suppresses subjective noise according to low-entropy high-power principle.

The present application relates to the technical field of fault early warning, more specifically, the present application relates to a kind of cutting fluid filling equipment's fault automatic early warning method and system, comprising: the acquisition cutting fluid filling loop's multiple-source flow variable data, include pressure, flow and fluid density data;Based on the pressure data and flow data in the multiple-source flow variable data.The present application constructs impedance factor and the discrete significance of statistics by combining the principle of fluid mechanics, quantifies the severity of failure, and establishes a negative correlation mapping between fault significance and algorithm calculation dimension, improves the extended isolation forest algorithm, automatically reduces the feature dimension to reduce the calculation redundancy when facing serious failure, maintains high-precision in weak failure, thereby solving the problem of slow response and wasted computing power of existing fixed dimension algorithm in high-speed filling scene, realizing real-time, low-latency and accurate early warning.

The invention provides a data table storage method and device and a data storage system.The method comprises the steps that related information of a data table is obtained, and the related information comprises one or more of the number of calling times, the urgency degree of service requirements, the proposing time of the service requirements and the data size; the comprehensive score is the score of the importance of the data table, the calling frequency and the comprehensive score are in positive correlation, the emergency degree of the service demand and the comprehensive score are in positive correlation, and the distance between the proposing time of the service demand and the current time and the comprehensive score are in negative correlation; the data volume and the comprehensive score are in negative correlation; sorting all the data tables according to the comprehensive scores to obtain sorted data tables; and storing all the sorted data tables into a database according to the sequence of the sorted data tables. According to the method, the problem that in the prior art, when data is stored in a database, due to the fact that the data size is large, the efficiency is poor in the whole database storage process is solved.

The embodiment of the invention provides a data set construction method, a model training method, an object recommendation method and a content recommendation method, and the data set construction method comprises the steps: obtaining a return visit behavior set of a target user, the return visit behavior recording a user identifier, an object identifier, a click tag for clicking an object or not, and an access timestamp; determining a return visit interval according to the access timestamp; respectively generating a positive correlation label and a negative correlation label according to the click label and the return visit interval; and based on the return visit behavior set, the positive correlation tag and the negative correlation tag, constructing a data set of a training object sorting model. Accurate interest preference modeling of the object sorting model in the cold start stage can be effectively supported, and the accuracy of object recommendation and the user activeness are improved.

A method for improving reduction of negative correlation noise (anti-correlation noise) artifacts in spectral CT imaging. A first set of embodiments employs operations performed in a pre-decomposed projection domain by first decomposing acquired projection data (optionally only with low resolution decomposition), a respective set of decomposed data values for each sinogram voxel is mapped to a known pre-calculated statistical distribution of anti-correlation noise associated with that set of decomposed data values, and then a smoothing operator is applied that is tuned to reduce all noise variance values below a set threshold. Another set of embodiments includes detecting photon detector counts (low count readings) below a particular threshold, and then determining a reliability index for each sinogram voxel with the set of detected low count readings. Smooth operators are utilized to operate on those readings in the sinogram that are below threshold reliability. This is based on a pre-calculated probability distribution of different possible sets of probe count readings that is based on a theoretical probability of each combination of the read counts of a hypothetical attenuation distribution for a given scan volume.

In this image, data is input to an AI model by adjusting the length of the bars extending horizontally. For example, if the data input to the AI ​​model is the Iris dataset, the length and width of the sepals and petals of iris flowers are parameters, and the magnitude of these values ​​can be changed by manipulating the bars. A histogram corresponds to a combination of bar lengths and visually represents the correlation between each parameter. For example, if there are many low-brightness areas in the histogram, there is a positive correlation, and if there are few low-brightness areas, there is a negative correlation.

The invention relates to the technical field of fault early warning, in particular to an automatic fault early warning method and system for cutting fluid filling equipment, and the method comprises the steps: collecting multi-source rheological data, including pressure, flow and fluid density data, of a cutting fluid filling loop; and based on the pressure data and the flow data in the multi-source flow change data. According to the method, the discrete saliency of an impedance factor and statistics is constructed in combination with the fluid mechanics principle, the fault severity is quantified, the negative correlation mapping of the fault saliency and the algorithm calculation dimension is established, the extended isolated forest algorithm is improved, the feature dimension is automatically reduced in the face of the serious fault so as to reduce the calculation redundancy, and the calculation efficiency is improved. And high-dimensional precision is maintained when a weak fault occurs, so that the problems of slow response and waste of calculation power of an existing fixed dimension algorithm in a high-speed filling scene are solved, and real-time and low-delay accurate early warning is realized.

The invention relates to the field of data processing, in particular to a financial statement abnormal data detection method. The method comprises the following steps: firstly, carrying out standardization processing on collected financial statement data, calculating a Spearman rank correlation coefficient, and generating an incidence matrix; counting all Spearman rank correlation coefficients in the incidence matrix, and dynamically setting positive and negative correlation thresholds; then, based on a Spearman rank correlation coefficient and positive and negative correlation thresholds, classifying the index relations; based on the standardized financial statement data and the index relationship, introducing a positive and negative and uncorrelated fluctuation feature extraction algorithm, and extracting fluctuation features; on the basis of the Spearman rank correlation coefficient and the fluctuation characteristics, an abnormal score is calculated in a segmentation mode; and finally, comparing the abnormal score with an abnormal threshold value, and judging whether the data is abnormal data or not. The technical problems that the influence of abnormal values on a correlation analysis result is too large, the dependency relationship between financial indexes is neglected, and fluctuation feature extraction is insufficient are solved.

The present application relates to the technical field of electric variable measurement, and especially relates to a detection method and a detection system for a starting power cell. The method comprises the following steps: obtaining a local abnormal trend of a data point according to the difference between the instantaneous amplitude of the data point and the average of the instantaneous amplitudes of the window of the data point, the standard deviation of the instantaneous amplitude, and the average of the window fitting slopes of the data point and its two adjacent data points, so as to obtain the proportion of the suspicious data point in the current time sequence, and constructing a slope value sequence based on the instantaneous amplitude slope values between the adjacent data points to obtain a global abnormal trend of the current time sequence; calculating the bin width of the current time sequence, and the bin width is negatively correlated with the global abnormal trend; using the bin width of the current time sequence in the HBOS algorithm to obtain the abnormal detection result of each data point, and effectively improving the detection result accuracy of the starting power cell.

This application proposes a data processing method and related apparatus for selecting the embedding size of input feature data based on the information-compressed embedding vector. The method includes: converting input feature data into an N-dimensional first embedding vector, where the input feature data includes user behavior data and / or item feature data; inferring from the N-dimensional first embedding vector using a neural network model to obtain an N-dimensional second embedding vector, where the information content of the i-th dimension in the N-dimensional second embedding vector is negatively correlated with the value of i; determining the target dimension of each input feature data based on a preset total dimension and the N-dimensional second embedding vector of each input feature data, where the preset total dimension is the sum of the vector dimensions allocated to each input feature data, and the target dimension is used to obtain a target embedding vector, which is the embedding vector of the input feature data converted to the target dimension. The target embedding vector is used by the recommendation system to predict the first prediction result to be recommended to the user.

A model training method based on distribution calibration and a computing device, the method comprising: acquiring a training set, a test set and a verification set, the training set, the test set and the verification set respectively comprising sample data of different data distributions; obtaining a weight parameter corresponding to each piece of sample data in the verification set, wherein the weight parameter is in positive correlation with a first probability that the input data in the sample data belongs to the test set, and is in negative correlation with a second probability that the input data belongs to the training set; model parameters of the first model are adjusted based on the training set, in the adjusting process, weighting performance indexes of the first model are evaluated based on the verification set and the weight parameters, the size of the weight parameters corresponding to the sample data is in positive correlation with the influence degree of the sample data on the weighting performance indexes, and optimization of the weighting performance indexes is taken as a target. Searching is carried out in a hyper-parameter space, a plurality of target hyper-parameters are obtained, and the hyper-parameter space comprises value spaces of the plurality of hyper-parameters; and training a second model based on the target hyper-parameter and the training set.

The embodiment of the invention discloses a data annotation method and device. The method comprises the steps that firstly, any target sample in a to-be-labeled first sample set is processed through n first large models, corresponding n labeling results are obtained, and each labeling result comprises a labeled category label and a corresponding labeling basis; and then, processing the n labeling results by using a second large model to obtain a sorting result of m candidate labels involved in the n labeling results. Then, m confidence coefficients corresponding to the m candidate tags are determined, and any ith confidence coefficient is positively correlated with the sorting weight of the ith candidate tag determined based on the sorting result and negatively correlated with the confusion degree when the second big model generates the ith candidate tag; and then, under the condition that the maximum value in the m confidence coefficients is greater than a preset threshold value, determining the candidate label corresponding to the maximum value as the final label of the target sample.

The invention relates to the technical field of retrieval, in particular to a property information cross-modal retrieval method and system based on a knowledge graph, and the method comprises the following steps: constructing a multi-modal attribute graph containing static nodes and dynamic event nodes, endowing each node in the graph with an information source label according to a data source, carrying out feature vectorization processing on the text information and the image information in the atlas; and calculating the information source entropy confidence of each node, wherein the information source entropy confidence is in negative correlation with the historical data dispersion of the information source corresponding to the node in the preset time window. According to the method, the multi-modal attribute map is constructed, the information source entropy confidence coefficient is calculated, the historical dispersion, the image-text self-consistency and the reputation factor are comprehensively considered, quantitative evaluation of objectivity of different information sources (such as IoT and manual recording) is achieved, and subjective noise is effectively restrained by following the low-entropy and high-weight principle.

The application relates to the technical field of piston life prediction, and specifically discloses a data-driven piston life prediction method, which comprises the following steps: establishing a piston life prediction model, periodically inputting real-time features into the piston life prediction model to obtain the residual life W of the piston; if the residual life W is smaller than a preset residual life threshold, the values of the features in a verification period are obtained and recorded as historical values; the historical values of a single type are sorted according to a time axis sequence to obtain a first sorting, and a verification rate is calculated; positive features and negative features are determined according to the positive correlation or negative correlation between the features and the residual life of the piston; whether the positive features and the negative features are abnormal features is determined based on the verification rate and the historical values; and whether to send a piston life warning is determined according to the total number of abnormal features. The application improves the accuracy of sending the piston life warning.

The application relates to the technical field of visual data processing, and discloses an underground pipe network defect visual identification method and system. The method is characterized in that: firstly, a pipe network defect type is preset, corresponding training image data is collected and expanded, and two deep learning models based on target detection are trained according to the training image data; secondly, an underground pipe network image is acquired, part of the image is split in proportion as training data, and the remaining image is preprocessed to realize defect feature shift, so that input data is obtained; the input data is input into a first model to obtain output data and mark the output data on the image; finally, the input data is input into a second model to obtain verification data according to a preset verification period, a consistency value and a consistency ratio of the two are calculated, and an image extraction ratio is adjusted according to a negative correlation of the ratio. Through the double-model collaborative verification and dynamic ratio adjustment, the accuracy and adaptability of underground pipe network defect identification are improved.

The application belongs to the technical field of data processing, and particularly relates to a data processing method and system of a laser thickness gauge, which comprises the following steps: acquiring a thickness measurement data sequence, setting a state vector and initializing a particle set carrying a weight and the state vector; predicting a particle state by using a Mahalanobis distance self-adaptive mixed weight Gaussian-Laplace mixed noise model; calculating a particle weight by using a Student t distribution with a negative correlation between a degree of freedom and a Mahalanobis distance as a likelihood function; performing quantum behavior particle swarm optimization updating by using a high weight subset kernel density peak value as a single global attractor; identifying a multimodal particle distribution based on a self-adaptive neighborhood radius density clustering, and extracting a thickness estimation value by using a weighted kernel density estimation of a dominant cluster. The application simultaneously improves the anti-interference ability, thickness mutation tracking ability and measurement stability of laser thickness data processing under complex industrial interference conditions.

This disclosure provides a data management method, electronic device, and computer program product. The method first scans data records to obtain multiple indicators, effective time, and corresponding business categories of the data records. The multiple indicators are used to represent the usage of the data records. Then, a first value representing the importance of the data records is determined through the multiple indicators. A second value representing the memory strength of the data records is determined through the effective time. A third value of the data records is determined through the number of data records contained in the business category corresponding to the data records. The second value is negatively correlated with the cumulative duration since the effective time, and the third value is negatively correlated with the number of data records. Then, the retention index of the data records is determined by combining the first, second, and third values. If the retention index is less than the index threshold, the data records are deleted from their storage locations.

The invention relates to the technical field of information retrieval and analysis driven by multivariable time series data, in particular to a time-varying dependent sensing time series data interpolation method oriented to information retrieval and analysis, which comprises the following steps of: constructing a global-local fusion diffusion model of time-varying dependent sensing; the method comprises the following steps: adaptively modeling variable dependence of time sequence change through a window segmentation module based on fast Fourier transform (FFT), and distinguishing positive and negative correlation in combination with a correlation feature extraction module; according to the information retrieval and analysis-oriented time-varying dependent sensing time sequence data interpolation method, experiments on AQI, TEP and Entry data sets show that the interpolation performance of the method is superior to that of a mainstream baseline model, and the method has the advantages that the method is simple in structure, convenient to operate and high in reliability. The global consistency and the local fidelity can be considered, and the data quality is improved to guarantee the information retrieval and analysis efficiency.