74results about How to "Fully portrayed" patented technology

The invention relates to a method and system for establishing a multilayer oil pool inter-well connectivity model. The multilayer oil pool inter-well connectivity model capable of simulating oil and water dynamic states is established oriented to the limitation that by means of a current connectivity model, only the dynamic change of production liquid can be predicted, the dynamic states of the oil phase and the water phase cannot be calculated respectively, and connectivity analysis cannot be carried out layer by layer. According to the model, an oil pool system is layered and scattered into a series of inter-well communication units, matter balance equation calculation is carried out with the inter-well communication units as simulating objects, pressure solving and inter-well flow calculation are achieved, an inter-well saturation degree tracking calculating method is established with the combination of the lead edge boosting theory, and the output dynamic states of oil and water of all layers at well points are obtained; a model parameter inversion method is established in a fitting mode through dynamic states based on a random disturbance approximation method, a projection gradient method and the like. Layered inter-well flow distribution coefficients, single well longitudinal production liquid and production oil splitting coefficients and other information can be obtained in real time, the oil pool plane, the longitudinal oil and water flow relation and the production measure change can be more accurately reflected to guide the practical production of an oil field.

The invention provides a large-scale network security situation evaluation method based on an index system. The method comprises the following steps: step one, determining dimensions that are needed to be analyzed for a network security situation; step two, determining network security factors according all the dimensions; step three, determining concrete indexes according to all the network security factors, wherein the dimensions, the security factors, and the concrete indexes are all-level nodes of a hierarchical evaluation model; and the step four, carrying out security situation evaluation on the hierarchical evaluation model to obtain a network security value. According to the method provided in the invention, efficiency of large-scale network security situation evaluation can be improved.

The invention discloses a multidimensional field key knowledge-based recruitment information matching method. Keywords are extracted, direct matching between resume keywords and a position is conducted and correlation matching between a resume and position keywords is conducted to realize multidimensional precise matching between the resume and the position. The method comprises the steps of: extracting the keywords in position information and resume information, and expressing the position information and the resume information with a vector; carrying out the direct matching to obtain a direct-matching-obtained text similarity score; carrying out the correlation matching to obtain a correlation-matching-obtained text similarity score; acquiring a measurement index for predicting difference; and performing weighting calculation to obtain a final resume and position text similarity score which is used as an information matching result to complete recruitment information matching. The method can comprehensively show the information and knowledge between the keywords and the information and knowledge which are expressed by the keyword combination, effectively improves the matching precision of the position and the resume and reduces the human cost of HR (Human Resource) in recruitment.

The invention discloses an industrial control abnormal behavior detection method based on multiple machine learning algorithms. The method comprises the following steps of firstly, collecting and establishing flow data of a power generation distributed control system; respectively constructing a training sample set and a test sample set by adopting the calibrated flow data; carrying out multi-dimensional feature extraction and vectorization processing on the sample; then establishing a classification model based on multiple machine classification learning methods, finally collecting real-timeflow data of the power generation distributed control system and inputting the real-time flow data into the classification model, if an output classification result is malicious flow, judging that anindustrial control abnormal behavior occurs, and otherwise, judging that the industrial control abnormal behavior does not occur. According to the method, samples can be effectively classified and detected, the abnormal behavior problem existing in the industrial control system can be rapidly detected, and potential malicious and abnormal behaviors can be accurately identified.

The invention provides a financial information processing method. The financial information processing method comprises steps that the financial data is acquired and stored, and the acquired financial data is extracted; the acquired financial data is pre-processed, including cleansing, association and classification; the pre-processed financial data is processed, including desensitization protection, grading processing and packaging storage, risk analysis on the financial data respectively after pre-processing and after processing is carried out; a risk analysis result is outputted and displayed. The invention further provides a financial information processing system. Through the method and the system, repayment capability, repayment willingness and post-loan risk of a loan user can be accurately determined.

The invention relates to a cone beam CT (Captive Test) iterative reconstruction algorithm and especially relates to a creating method of cone beam CT iterative reconstruction algorithm projection matrix. The invention provides a projection matrix characterization method based on a finite element model and a Radon operator for the high-precision characterization problem of the cone beam CT iterative reconstruction algorithm projection matrix. The invention provides a new projection matrix characterization method by combining the respective characteristics of a ray coverage model and a basis function model and fully considering the mathematic characterization of various physical processes of the projection according to the ray projection rule beginning from the mathematic characterization of a continuous three-dimensional natural image, so that the projection process is fully characterized. As shown in a test result, the method disclosed by the invention effectively improves the characterization precision and reconstruction quality of the model.

The invention provides a robot casting grinding self-adaption method based on a vision system. The robot casting grinding self-adaption method includes the following steps that casting surface morphology characteristics are extracted through machine vision; by means of a method for depicting the casting surface roughness, the first-moment characteristic, the second-moment characteristic, the third-moment characteristic and the fourth-moment characteristic of the casting height represent the casting surface average deviation degree, the standard deviation, the twisting degree and the kurtosis correspondingly; according to the casting surface roughness, by combining the vision measuring and positioning method, the kinematics analysis method, the force control method and the position control servo method, vision self-adaption robot grinding parameters are generated and a grinding execution element is controlled to conduct grinding; and in the grinding process, the workpiece surface real-time dynamic morphology characteristic parameter is compared with target morphology characteristic parameter in real time till the workpiece surface roughness reaches the target morphology characteristic parameter. According to the robot casting grinding self-adaption method, casting surface morphology characteristics can be more comprehensively and more finely depicted through the workpiece surface quality obtained by depicting the casting surface morphology characteristics through moments.

The invention discloses a remote sensing image variation detecting method on the basis of weighted Gabor wavelet characteristics and two-stage clusters. The processed objects include optical remote sensing images and SAR (synthetic aperture radar) images and the remote sensing image variation detecting method includes (1) generating difference images according to remote sensing image types; (2) subjecting the difference images to Gabor wavelet transform; (3) extracting multiscale and multidirectional characteristics of the Gabor wavelet transform of the difference images; (4) designing the weighting coefficient and acquiring the weighted Gabor wavelet characteristics; (5) clustering the weight Gabor wavelet characteristics by means of the two-stage cluster strategy; (6) acquiring variation detecting results. By the remote sensing image variation detecting method, loss of marginal information is reduced, stronger, weak and slight varying areas can be detected at the same time, the total mistake pixel number is decreased, more detail information is reserved and variation results can be effectively extracted.

The invention discloses a remote sensing image recommendation method based on multi-attribute fusion, and belongs to the technical field of intelligent recommendation and distribution of remote sensing images. The method comprises the following steps: firstly, carrying out multi-dimensional knowledge graph modeling on responsibility information and ordering information of a remote sensing user andattribute information of a remote sensing image, and respectively carrying out embedded representation on the knowledge graph to obtain a user responsibility embedding vector, a user ordering embedding vector, an image order embedding vector and an image attribute embedding vector; performing semantic information extraction such as target detection and surface feature classification on the remotesensing image to obtain an image surface feature proportion vector to solve a problem of an information representation method of a user and an image in a recommendation process in the remote sensingfield effectively; and then, with a collaborative filtering recommendation network based on multi-attribute fusion, realizing fusion of multi-dimensional input by means of high-dimensional space modeling capability of a neural network, and embedding various input vectors in different spaces into a unified vector space to realize a better pairing recommendation effect.

The invention provides an image voxel and priori brain atlas division fused brain network construction method to overcome defects of node selection methods in the conventional brain network construction method. The method comprises the following steps: preprocessing functional magnetic resonance imaging data; constructing an initial brain network based on image voxels; and constructing a final brain network based on a priori brain atlas on the basis of the initial brain network. Two node selection methods in the prior art are fused, nodes with high degree values are searched on the basis of the image voxel, the nodes are screened by a Talairach encephalic region locating software on the basis of the priori brain atlas, the screened node is taken as a center of a circle to draw a sphere with the radius of 6 millimeters, the sphere is taken as a core node of the brain network, and the final brain network is determined according to the core node and sides of the obtained brain network. By the method, a brain functional network can be comprehensively and delicately described, the core node of the network is visualized in a brain space, and a function of observing a connecting mode between encephalic regions clearly is realized.

A polarized SAR image classification method based on superpixels and a topic model comprises steps of: inputting a polarized SAR image to be classified, and subjecting the same to refined Lee filtering; acquiring the sample points of the image processed the step 1 by using uniform sampling to obtain a sample point set; extracting three types of features from sample point set and normalizing the three types of features separately to obtain feature sets F1, F2 and F3; clustering the feature sets F1, F2 and F3 separately to form visual dictionaries V1 and V2 , V3, and merging the visual dictionaries V1 and V2 , V3 to form a multi-feature visual dictionary V; performing over-segmentation on the image processed in the step 1 to obtain a plurality of superpixels, and performing sparse coding on each of the superpixels according to the dictionary V; performing feature learning on the sparse coding of the superpixels by using the topic model, and performing classification by using a SVM classification method to obtain a final classification result. The polarized SAR image classification method can represent the heterogeneous region of the polarized SAR image.

The invention provides a user portrait constructing method and device as well as a recommending method and device. The user portrait constructing method comprises the following steps: acquiring a user's characteristic data, and determining the type of the characteristic data; constructing a data portrait according to the characteristic data and the type of the characteristic data; and constructing a user portrait according to the data portrait. The technical scheme of the invention realizes that the user owned personalized information is described by using sentence vectors and article vectors, the insufficiency in portraying the user by text labels is compensated, and implicated information at the semantic level can be expressed, so the portrait has semantic information, and the user is more comprehensively and accurately portrayed.

The invention provides a method and a system for predicting a function label of an App (Application). The method comprises the steps of acquiring a target description text for describing a target App; carrying out word segmentation on the target description text to acquire prediction data; and generating probability distribution of the target App in a plurality of function labels according to the prediction data and a target theme model, wherein the plurality of function labels are a plurality of themes of the target theme model. According to the method for predicting the function label of the App, word segmentation is carried out on the target description text of the App to acquire the prediction data, the prediction data are processed through the theme model, the probability distribution of the App in the plurality of function labels can be obtained, thereby classifying the multifunctional App objectively and comprehensively describing the function of the App. Furthermore, because the App is automatically labeled by use of a computer in the method, the complicated process for manually labeling the App is avoided.

The invention discloses an oil production data frequent pattern mining method based on weak wildcard, and relates to the field of data mining. The oil production data frequent pattern mining method based on the weak wildcard comprises the steps that 1, oil field daily fluid production time series data is converted into a coding sequence; 2, the coding sequence is mined to obtain different types of frequent sequence patterns, and an oil well is comprehensively depicted from various angles. The method has the advantages that the mining problem of three frequent sequence patterns is defined, the different types of frequent patterns can be mined from actual oil well production data, and the oil well is comprehensively depicted from the different angles; an algorithm is simple and efficient, a pruning algorithm is achieved, the time complexity is low, and the method is conveniently achieved in real time; a pattern filter technique is proposed, the different types of frequent patterns such as the powerful pattern, the special pattern and the popular pattern can be obtained through filtration for the different requirements; the method can be widely applied to various time series data.

The invention provides a multi-dimensional feature construction method and device, an electronic device and a storage medium. The multi-dimensional feature construction method comprises the followingsteps: when a feature construction instruction is received, acquiring historical access data of the target application program, detecting current access data of the target application program, calculating the similarity between the historical access data and the current access data by adopting a K-L divergence algorithm; obtaining first feature data, obtaining current time and determining a targettime slice to which the current time belongs, calling a target activity degree corresponding to the target time slice from pre-stored activity degree data, processing the target activity degree; acquiring second feature data, further acquiring third feature data acquired by a configuration sensor, performing normalization processing on the first feature data, the second feature data and the thirdfeature data, and performing data analysis to obtain multi-dimensional features, so that the description of user features is more comprehensive, and the flexibility is higher.

The invention provides a river course three-dimensional modeling method based on multi-attribute supervoxel and graph cutting, belonging to the field of geological body three-dimensional model construction. Aiming at the shortcomings of single seismic attribute describing river course, the invention provides a multi-attribute fusion method based on improved local linear embedding, the preferred attributes are fused into new attributes by ISOLLE algorithm, Considering the non-linear relationship between seismic attribute data, a non-linear fusion method is adopted, the fusion attribute is better than the previous attribute, and the accuracy of river edge and region description is improved, which lays a good foundation for the next segmentation and reconstruction. The invention is based on the river course segmentation method of supervoxel and graph cutting, and generates three-dimensional supervoxel through simple linear iterative algorithm, and the generated supervoxel is well close tothe edge of the river course, and has good homogeneity, and then obtains the final segmentation result by combining the graph cutting frame, and obtains the three-dimensional model of the river course surface in the way of extracting the isosurface.

The invention provides a method and a system for displaying archive catalogue information on a map. The system comprises a geographical information picture gallery, an archive catalogue information database and a geographical coordinate surveying tool. The method comprises the following steps of firstly, reading line information of the engineering from the archive catalogue information database by the geographical coordinate surveying tool, and drawing the line layer of the line in proportion; then, combining a geographical information coordinate database of the archive catalogue information database, and embedding the line layer onto the map; reading the coordinate information of an archive keeping unit in the archive catalogue information database, and drawing the position of the archive keeping unit; and finally, reversely correlating and searching the archive catalogue information through the coordinates of the line on the line layer. The cross-regional line project sections, bypass convertor stations, substations and the like are modeled and drawn on the map by the geographical coordinate technique, and the original disperse archive information distributed in each unit is drawn on the map in a general, accurate and lively way.

The invention discloses a cross-modal retrieval method for a sketch retrieval three-dimensional model based on spatiotemporal feature information, which comprises the following steps: firstly, selecting data, then constructing a sketch three-dimensional model image sequence and a spatiotemporal feature information extraction network, extracting spatiotemporal feature information of a sketch and athree-dimensional model by using the spatiotemporal feature information extraction network, combining the spatial and temporal feature information of the sketch with the spatial and temporal feature information of the three-dimensional model by using deep metric learning; and finally, calculating the similarity according to the Euclidean distance between the spatial and temporal feature information of the sketch and the spatial and temporal feature information of the three-dimensional model in the spatial and temporal feature information combination. The method is outstanding in retrieval performance, capable of effectively completing cross-modal retrieval of the sketch retrieval three-dimensional model, better in accuracy, easy to operate and high in practicability.

The embodiment of the invention discloses a data source quality evaluation method in a sensing cloud environment. The method comprises the following steps: acquiring current and historical monitoringdata of a sensing cloud storage data source, the sensing cloud being a combination of cloud computing and a wireless sensor network and used for collecting and processing monitoring data from a plurality of sensor nodes or sensor sub-networks; integrating the monitoring data of the data source based on the spatial correlation and the time correlation, and determining a data true value; generatingan initial quality evaluation vector of a data source based on the data true value, and adjusting the initial quality evaluation vector of the data source according to a quality rule; and calculatinga final quality evaluation result of the data source according to the adjusted initial quality evaluation vector of the data source. By adopting the method and the device, the data source quality canbe described from multiple angles, and the description of the data source quality is more comprehensive.

The invention discloses a multi-dimensional portrait construction method and recommendation method for researchers in the tobacco field. The method comprises the following steps: 1) obtaining tobaccoscientific research data, establishing associated information of the researchers participating in scientific research achievements, and marking the rank of the researchers in the participated scientific research achievements; 2) fusing repeated data in scientific research personnel and scientific research achievements; 3) establishing basic attribute dimensions of scientific research personnel based on the tobacco scientific research data processed in the step 2); 4) establishing scientific researcher academic influence dimensions based on scientific researchers and related achievement data thereof; 5) establishing a scientific research relationship dimension of scientific research personnel based on the tobacco scientific research data processed in the step 2); 6) classifying the scientific research achievements of each researcher, and establishing scientific research achievement dimensions of the researchers; 7) for each scientific research achievement of the researcher, establishinga scientific research interest dimension of the researcher; and 8) visualizing the researchers according to the dimension information to obtain multi-dimensional portraits of the researchers.

The invention discloses a combat-formation visualization method based on a visual perceptional topological relationship model. By using the method, internal association features of combat formation and visual range features of the combat formation can be expressed, and the combat formation can be more comprehensively characterized visually. According to the method, for a three-dimensional world coordinate set of each individual of the combat formation, a minimum-spanning-tree algorithm is adopted to generate a skeleton inside the combat formation, and the same is closer to a formation shape ofthe combat formation; at the same time, for a two-dimensional latitude and longitude coordinate set of each individual, a minimum-convex-hull method is adopted to generate a contour of the combat formation and distribution on a space sphere surface, and integrity of the combat formation is reflected; and then the two algorithms are superimposed, the internal association features of the combat formation can be expressed, the visual range features of the combat formation can also be expressed, the combat formation can be more comprehensively characterized visually, and connection lines have good visualization features of less intersection, formation fitting and the like.

The invention discloses a label prediction model training method, a label prediction method and a device, and is applied to the field of artificial intelligence. The method comprises the steps of: acquiring to-be-trained game data from a game video sample; acquiring image features according to the to-be-trained game data; acquiring in-game information features according to the to-be-trained game data; according to the to-be-trained game data, acquiring M overall-view labels, wherein M is an integer greater than or equal to 2; and according to the image features, the in-game information features and the M overall-view labels, training an overall-view label prediction model. According to the method, corresponding overall-view labels can be extracted from multiple categories for a same frameof training data, and the overall-view labels of multiple categories are combined to train the model, so that a single macroscopic intention is avoided, and the real intention of a player is describedmore perfectly. And the training data can be automatically labeled through a program, so that the labor cost of expert labeling is saved, and the labeling convenience is improved.

The invention discloses a credit score value determination method and device, electronic equipment and a storage medium, and relates to the field of machine learning, and the determination method comprises the steps: obtaining a plurality of account features of a target account based on an account identifier of the target account, obtaining a feature set, inputting the feature set to a pre-trained federal learning model, and obtaining a credit score value of the target account; the method comprises the steps of obtaining a parameter value of each account feature, performing weight coding on each account feature to obtain an evidence weight value of a feature box to which the account feature belongs, and determining a credit score value of a target account in a current scoring mechanism based on the parameter value of each account feature and the evidence weight value of the feature box to which the account feature belongs. According to the method and the device, the technical problem that the security of customer data privacy cannot be ensured in a mode of improving scoring accuracy by acquiring data of a plurality of institutions to jointly train a customer credit scoring model in related technologies is solved.

A medium and long-term load curve generation method for satisfying an electricity quantity constraint comprises the steps of reading an annual maximum load, an annual total electricity quantity, typical daily load curves in summer and winter, monthly load characteristics and 8,760 hours of historical load data into a program, and organizing the 8,760 hours of historical load data by the month; calculating a daily maximum load in a prediction year with the obtained 8,760 hours of historical load data organized by the month as original data, and then obtaining a time-sequence load curve of the prediction year according to a conservation of charge as a principle; and finally, calculating to form an accurate continuous load curve and a sectional continuous load curve on a 168-hour cycle. According to the medium and long-term load curve generation method for satisfying the electricity quantity constraint, typical daily load curve characteristics and monthly load characteristics are fully considered in the switching of the time sequence load curve and the continuous load curve, shape characteristics of the load curves are guaranteed to satisfy the requirement on the premise of the electricity quantity constraint; and the load tendency and the change characteristics can be described and depicted more comprehensively, and a foundation is laid for establishment of an optimized planningmodel of an electric power system.

The embodiment of the invention provides a sound processing method, device and equipment. The method comprises the following steps that at least two feature vectors of an audio frame are obtained, wherein the audio frame comprises voice signals and noise signals, and acoustic characteristic type of the at least two feature vectors are different; fusion processing is conducted to the at least two feature vectors to obtain a fusion feature vector; the label value of the audio frame is obtained based on the fusion feature vector, wherein the label value is used for indicating the power ratio of the voice signals to the noise signals in the audio frame; and processing results of the audio frame are determined based on the label value of the audio frame and the noise signal power of the audio frame. The sound processing method, device and equipment are used for improving the effect of noise suppression.

The invention proposes a method for determining unsteady flow bottom hole pressure of a fractured well in a dense oil reservoir. The method comprises the following steps of hydraulically fracturing areservoir to form a vertical well with multiple fractures; collecting fracture parameters of the multiple fractures and basic information of the oil reservoir, wherein the fracture parameters includefracture orientation and permeability; establishing a multi-fracture physical model for the fracturing vertical well; in the multi-fracture physical model, determining a dimensionless bottom hole pressure calculation equation based on the permeability and fracture orientation for a single fracture; determining a change value of the bottom hole pressure of a single fracture along with the time in combination with the fracture parameters and the basic information of the oil reservoir according to the dimensionless bottom hole pressure calculation equation; and repeating the above steps to calculate change values of the bottom hole pressure of each fracture in the multiple fractures along with the time. The method provided by the invention has the advantages of comprehensively considering thedense oil reservoir geology and the static parameters of the oil reservoir itself, considering the influence of asymmetric distribution of the fractures on the wellbore at the same time, and perfecting the description of a bottom hole fluid flow model of the fractured well.

The invention provides a day-ahead low-carbon scheduling decision-making method with the consideration to wind power integration based on the theory of ordinal utility. The method employs a multi-scene method for describing the uncertainty of wind power output, and enables a power scheduling decision-making problem to be constructed into a two-stage coordinated optimization model, wherein the model can achieve the depicting the day-ahead clearing and real-time balance process of scheduling decision making, and enables the scheduling decision making result to be higher in adaptability under theuncertain wind power integration. Meanwhile, the method employs the theory of ordinal utility for the consideration of the economical performance and carbon emission target, and achieves the more complete depicting of the preference information of a decision maker for each target.

The invention provides a rapid flood forecasting method and device based on rainfall similarity and model parameter intelligent adaptation, and the method comprises the steps: 1, building a rainfall flood conversion model, and recognizing the rainfall flood features; step 2, calculating the similarity of flood-causing factors: starting from the magnitude of flood-causing rainstorm, the rainfall pattern and the size of each component influencing rainfall in the early stage, calculating a similarity index vector based on a similarity theory, and quantifying the similarity among the flood-causing factors; step 3, transferring parameters, and constructing adaptation mapping among rainfall and flood samples: mutually transferring the parameters among the rainfall and flood samples, creating a model precision matrix after parameter transfer, establishing mapping between similarity index vectors and the precision matrix, and determining the importance degree among similarity indexes; and step 4, intelligent parameter adaptation: based on similarity calculation, clustering analysis and collaborative filtering theories, the reference model uses each set of parameters to forecast the precision of historical similar flood, the parameter with the highest precision is adapted to the model, and flood forecasting is carried out.

The invention discloses a Shutao self-service screening service system, relates to the technical field of information management and information monitoring, and solves the problems that an operation mode of an existing system is not transparent in target data, is poor in flexibility, long in waiting time of a demand proposer and high in vigor consumption of informatization personnel. A Shutao monitoring module is used for monitoring a total quantity of models and an attention ranking of each model in real time and tracking the data volume in real time; a model management module is used for showing related data information of each model; moreover, copying, screening, editing and newly added operation can be carried out on the models; a self-service screening module establishes a client group and the like on the basis of newly added models or original models in the model management module; and a flow list module is used for viewing and monitoring work order information and generating related data details after the work order information is examined and approved for a user to download or for pushing the related data details to a peripheral system. According to the Shutao self-service screening service system disclosed by the invention, tedious and complex background support is converted into a simple pulling, drawing and dragging self-service mode of the foreground, and meanwhile, learning cost is reduced.

The embodiment of the invention discloses a method, a device, an electronic device and a storage medium for acquiring a user portrait. The method comprises the following steps of: acquiring the facialfeature information of the current user in each application system; Acquiring a current user portrait of the current user in each application system according to the face feature information of the current user in each application system; A target us portrait of that current user is determine based on the current user portrait of the current user in each application system. The user portrait canbe obtained more comprehensively, and the user characteristics can be described more accurately.