24 results about "Score matrix" patented technology

The invention relates to a natural resource asset management and territorial space planning system fusion method and system. The method comprises the following steps: encoding territorial space planning vector data and natural resource asset registration data to generate a space-time binding code; constructing a spatial topological graph and a time sequence diagram based on the codes, respectively extracting planning constraint feature vectors and asset dynamic feature vectors, and fusing the vectors to generate a planning asset coupling scoring matrix; recognizing a conflict unit set based on the planning asset coupling scoring matrix; based on the conflict unit set and a preset business rule base, performing classification processing on each conflict unit in the conflict unit set to obtain a conflict type; and based on the conflict type, generating a service execution instruction set through a preset reinforcement learning decision model. According to the method, through space-time unified coding and feature fusion processing, the accuracy of data association and the scientificity of decision making are improved, so that collaborative governance of territorial space planning and natural resource asset management is realized.

The invention relates to an intelligent deep questioning method and system, and belongs to the technical field of natural language processing and intelligent evaluation. The method comprises the following steps: acquiring a candidate response, converting the candidate response into a standardized text, analyzing the text in combination with multi-dimensional monitoring data, positioning competency missing elements, generating a missing identification set, and marking a sorting question-chasing point; constructing a dynamic self-adaptive score matrix in which competency is linked with a questioning point, fusing monitoring data to calibrate scores and confidence, and generating a questioning technology intervention scheme; a virtual interviewer role library is preset, roles are dynamically matched, and the intervention logic and the missing identification set are combined to generate specialized questioning content; and when the core element capability element characterization is insufficient or the confidence coefficient does not reach the standard, generating a target element cognition questioning problem, and after element extraction and fusion, outputting a multi-dimensional competency score and a confidence coefficient report. According to the invention, the upgrading of the questioning from mechanical questioning to intelligent technical intervention is realized, the questioning targeting and the evaluation accuracy are improved, and reliable support is provided for accurate talent selection.

The invention discloses a depth sensor anomaly detection method and system, a storage medium and electronic equipment. The method comprises the steps that an absolute depth value collected by a depth sensor and a relative depth value output by a monocular depth estimation model are acquired; global sorting is carried out on the absolute depth value and the relative depth value, a sorting difference matrix between the absolute depth value and the relative depth value is further calculated, and a sorting inconsistency score vector is obtained; obtaining a first-stage detection threshold value by adopting a fused adaptive threshold value strategy based on the sorting inconsistency score vector, and forming an initial anomaly set; for each point in the non-abnormal point set, constructing a comprehensive similarity scoring matrix after fusion; and accumulating the comprehensive similarity scoring matrix according to rows to obtain a second-stage scoring vector, and determining a final refined abnormal point set. According to the invention, by introducing the relative depth information generated based on the monocular image, the robustness and environmental adaptability of the depth sensing system can be significantly improved.

This application relates to a life cycle impact assessment calculation method suitable for GIS-LCA, including the following steps: S01) Obtaining the process spatial location set S p S02) Obtain a list D suitable for GIS-LCA; S03) Obtain the set of spatial locations of characteristic influencing factors S c S04) Construct the spatial data matrix C of the characteristic influence factors; S05) Construct the process location set P of the characteristic influence factors. s ;S06) will P s The parameterization process is transformed into a characteristic influence factor location matrix P; S07) Calculate the score matrix H of the characteristic influence factors in spatial location; S08) Based on the score matrix H, perform environmental assessment and interpretation of the target product. This application changes the existing LCIA calculation method that mechanically adopts the flow name correspondence method. This application can utilize geographic information system technology to achieve automatic flow correspondence by means of spatial relationships, so as to meet the GIS-LCA calculation requirements for LCIA.

The invention discloses a real-time interactive learning method and system for recording and broadcasting course transformation based on AI guided questions and answers, and relates to the technical field of wisdom classs.A knowledge point confusion scoring matrix M is constructed based on preset knowledge points, corresponding interactive instructions are generated for the preset knowledge points, and AI guided questions and answers are started according to the interactive instructions. Analyzing text character strings of students, updating a knowledge point confusion scoring matrix M according to an analysis result, taking the knowledge point confusion scoring matrix M as input, performing data fusion on the knowledge point confusion scoring matrix M and a JSON-format file of a preset scene template, outputting prompt word character strings of an AI role system and the JSON-format file of a finite-state machine, constructing an AI dialogue engine, and performing interactive circulation. The data tuple of the interaction round is stored as an interaction log sequence, a three-dimensional performance evaluation vector is generated, the three-dimensional performance evaluation vector is matched with a preset logic set, an action instruction is generated, and the problems of one-way infusion, knowledge disjunction and difficult application of recording and broadcasting classes are solved.

The application discloses a kind of scene matching methods based on deep learning, first construct scene matching model, select 1 photograph and 4 different angle template images;Then the selected image is simultaneously input to full convolution twin neural network model;Next using the two branches of full convolution twin neural network model respectively extracts feature, generates five feature maps;Afterwards, the feature map is convolved, the similarity is measured, and a score matrix is generated;Then retrieve the maximum value coordinates in score matrix, obtain the position of matching target;Again, the matching result is compared, and the template image with the best matching effect is output as the matching result;Finally, the scene matching model is deployed to the unmanned aerial vehicle platform, and the visual pose estimation of the scene matching result is performed to realize the cross-view visual positioning of the unmanned aerial vehicle. The application improves the accuracy of unmanned aerial vehicle positioning, and lays a foundation for subsequent construction of a scene matching model with higher model accuracy and faster matching speed.

The application discloses a digital person video detection method based on facial semantic features, acquires real person voice visual video to constitute a training data set; constructs a digital person video detection model based on facial reality mode representation, respectively extracts facial reality mode representation features, mouth features and voice features, after multi-modal feature fusion, generates a matching score matrix according to the fused features; trains the digital person video detection model by using the training data set, generates a matching score matrix by using the trained digital person video detection model, and aggregates the matching score matrix to obtain the overall matching score of the to-be-detected video, so that the forgery detection is realized. The application proposes a unified audio and video forgery detection framework based on the facial reality mode, and improves the deep forgery detection performance in different scenes.

The invention provides a gene sequence alignment method and electronic equipment, the alignment process is divided into a seed search stage and a seed expansion stage, in the seed search stage, a plurality of seeds are determined for a read sequence to be aligned; acquiring a plurality of query sequences to be compared and corresponding reference sequences thereof based on the seeds in a seed expansion stage; expanding the length of each sequence by an integral multiple of the data width of the SIMD instruction, and initializing a score matrix; in the calculation process of each round of comparison score, the calculation range of the current round is determined in each score matrix, and then the comparison score is calculated by using an SIMD comparison instruction in the reverse diagonal direction of the score matrix; and after all rounds of calculation are completed, determining an optimal matching sequence according to the maximum comparison score. According to the scheme, the sequence length difference in the comparison process is effectively considered, a large amount of invalid calculation is reduced, the operation speed of the seed expansion stage is increased, and then the sequence comparison efficiency is improved.

The invention discloses a self-adaptive multi-source credit risk model fusion method and device, equipment and a medium, and relates to the field of artificial intelligence, and the method comprises the steps: selecting a sub-score with the optimal distinction degree as a main score, and carrying out the discretization binning processing of the main score; mapping the discretized main score level into an embedded vector to form an embedded matrix; inputting the embedded matrix into a preset neural network gate controller to obtain a gate control probability vector representing the activation probability of each sub-score; performing sparse processing on the gating probability vector to generate a sparse mask; splicing the main score and all the sub-scores into a score matrix, and performing adaptive selection on the score matrix by using a sparse mask to obtain a mask score matrix; and inputting the mask scoring matrix into a pre-trained neural network classifier to obtain a risk probability prediction result. According to the method, the model discrimination performance and the data use efficiency can be considered at the same time, so that the technical bottleneck faced by a traditional static linear fusion mode is broken through.

The invention relates to the technical field of natural language processing, and discloses an AI role-oriented multi-dimensional consistency test system, which comprises the following steps of: extracting core role characteristics of an AI role according to an application scene of the AI role so as to construct a dynamic role map of the AI role; quantifying the dimension weight of the role dimension corresponding to the dynamic role map; a basic test case library of the role dimension is established, and the basic test case library comprises a standardized problem set and a scene script, so that a dynamic derivative case of the role dimension is generated; a cross-dimension conflict use case of a dynamic derivative use case is introduced, a multi-layer test engine of role dimensions is established, and the multi-layer test engine comprises a surface language analysis layer, a logical reasoning verification layer and a value alignment evaluation layer; and calculating cognitive load indexes of the AI roles to establish a multi-dimensional scoring matrix of the AI roles, and calculating global consistency indexes of the AI roles through the multi-dimensional scoring matrix. According to the invention, the accuracy of the AI role multi-dimensional consistency test can be improved.

The application relates to the technical field of geological prospecting, and provides a method for comprehensively optimizing a prospecting target area. In the method, a grade interval boundary limit value of a target area metallogenic possibility is determined, a comment set score model corresponding to the grade interval is constructed, a score matrix of a geochemical exploration index of the prospecting target area and a score matrix of a geological index are generated; then, according to a corresponding weight matrix, a membership score matrix of the geochemical exploration index and the geological index of the prospecting target area in the grade interval is calculated respectively, so as to determine an index score comment of the prospecting target area in the grade interval; and according to the grade membership of the prospecting target area in the grade interval and the interval upper limit value of the grade interval in the index score comment, the comprehensive membership of the prospecting target area is calculated, and the optimized prospecting target area is output by sorting the comprehensive memberships of all the prospecting target areas. Therefore, the optimized prospecting target area is scientifically determined through theoretical calculation, and the deviation and interference of human factors are effectively avoided.

The application discloses a movie recommendation method based on triple auto-encoder combined with a knowledge graph, which comprises the following steps: 1) encoding the comment information between the user and the movie into an emotional classification as the input of an automatic encoder; 2) merging the rating of the movie, auxiliary information and generated comment representation into a semi-automatic encoder for reconstructing the output, learning the low-dimensional feature representation of the extended information through the semi-automatic encoder, fusing the obtained low-dimensional feature representation into the original feature space of the movie, and inputting the new feature as additional information into the semi-automatic encoder model; 3) designing a serial connection of the semi-automatic encoder and the automatic encoder, and comparing the output prediction score matrix of the third auto-encoder with the original score matrix. The application can expand the features of the movie information by using the interaction information between the user and the movie and the knowledge graph, and process the expanded features through the auto-encoder, so that the more accurate recommendation for the user is achieved.

The invention relates to the technical field of expert score exception screening, and particularly discloses an expert exception score screening method and system, and the method comprises the steps: obtaining expert scores of a single bidding and tendering activity, constructing a score matrix, calculating the score mean value and standard deviation of each expert for scoring different evaluated enterprises, and a standardized score corresponding to each score, and arranging the standardized scores of the scores corresponding to the experts from small to large, forming a corresponding data set, calculating a first quantile, a second quantile and a quantile distance, setting an abnormal value judgment boundary according to the quantile distance, and judging the score corresponding to the standardized score exceeding the abnormal value judgment boundary as an abnormal score. According to the method, a statistical discrete model principle is adopted, and an abnormal value deviating from the overall scoring trend is identified according to the scoring deviation degree of the same expert for different bidders, so that scoring supervision is changed from experience judgment to data support, and a quantitative basis for checking and reviewing processes is provided for supervision departments.

The application discloses a dynamic expansion fault detection method based on a regression relation and relates to the technical field of fault detection engineering. The method comprises the following steps: obtaining a predictable dynamic information matrix according to an input data matrix and an output data matrix of a standard missile; obtaining a main score matrix and a number of principal components related to output according to the predictable dynamic information matrix; obtaining an input load matrix according to the main score matrix and a dynamic input information matrix; obtaining an input residual matrix according to the main score matrix and the input load matrix; obtaining a number of principal components irrelevant to output according to the input residual matrix; obtaining an input control limit and an output control limit according to the number of principal components; obtaining a statistic quantity related to output and a statistic quantity irrelevant to output according to an input data matrix and the input load matrix of a to-be-detected missile; and determining a fault detection result according to the statistic quantity related to output, the input control limit, the statistic quantity irrelevant to output and the output control limit. The application can reduce a false alarm rate and improve detection precision.

The invention discloses a data set enhancement method and device based on low-rank matrix decomposition. A forward answer scoring matrix is decomposed into a potential semantic attribute matrix and a first potential scoring behavior matrix based on low-rank matrix decomposition; decomposing the negative answer scoring matrix into a potential semantic attribute matrix and a second potential scoring behavior matrix; meanwhile, a tagger weight matrix for representing the tagging credibility of each tagger is introduced; and performing unified modeling and structural fusion on the multi-answer annotation data according to the positive answer scoring matrix, the negative answer scoring matrix, the potential semantic attribute matrix, the first potential scoring behavior matrix, the second potential scoring behavior matrix and the annotator weight matrix to obtain a target function, and optimizing the target function to obtain the multi-answer annotation data. And the potential structure of the preference data is extracted, so that the processed preference tag is closer to the real human preference, and the quality of the preference data set is improved.

This invention discloses a deep learning-based intra-frame lossless compression method for video, specifically relating to the field of image compression technology. First, a multi-scale anomaly response map is constructed to characterize weakly predictable regions in an image. Then, a compression difficulty score matrix is ​​generated based on the response map, identifying high-complexity regions and inputting them into a weakly predictable region modeling network, outputting a high-order residual probability field. Further, the residual probability distribution guides an arithmetic encoder to perform adaptive intra-frame lossless compression on the original image, generating a compressed bitstream. The decoder, combined with the shared residual probability field, can losslessly restore the original image pixel by pixel. This invention effectively improves the compression efficiency and restoration accuracy of edge detail regions, and is suitable for video image compression tasks in scenarios such as remote sensing, security, and satellite communication.

The invention relates to a construction risk behavior identification method combining score clustering and a post structure. The method comprises the following steps: constructing a construction stage technical risk factor list; designing a questionnaire, converting a risk factor scoring result into a scoring matrix, setting a range of candidate clustering numbers, executing K-means clustering operation on each clustering number, and calculating a post structure consensus degree; selecting the clustering number corresponding to the maximum value of the post structure consensus degree, and finding out an employee list in each clustering cluster; and calculating the distance between each employee and the centroid of the cluster to which the employee belongs, marking the employee whose distance exceeds a preset distance as a potential cognitive deviator, and regarding the potential cognitive deviator as a key object of construction risk identification. According to the method, the post matching degree and the cognitive difference can be identified from the subjective scoring data of the employees, and a reliable basis is provided for identification and management of technical risks in the construction period of the construction project.

The present disclosure provides a user similarity calculation method, system, computer device and computer readable storage medium, the method comprising: obtaining a user-goods score matrix; calculating the global similarity of any two users in the user-goods score matrix by using a preset global similarity calculation method, and calculating the local similarity of any two users in the user-goods score matrix by using a preset local similarity calculation method; and calculating the overall similarity of any two users in the user-goods score matrix according to the global similarity and the local similarity of the two users. The technical scheme of the present disclosure makes full use of all the score information, so that the user similarity can be calculated even if the common score information between users is less, alleviating the limitations of data sparsity and cold start, making the similarity calculation result more in line with the actual situation, and making the commodity recommendation more accurate.

The application discloses a portable near-infrared spectrum quantitative model and unknown sample matching degree discrimination method, constructs a quantitative model by using a partial least square method, obtains a load matrix and a score matrix of PLS principal components, respectively calculates score thresholds of the first principal component and the second principal component, constructs an elliptical polar coordinate expression by using the two thresholds, maps collected unknown sample spectrum to the principal components to obtain score values, substitutes the score value of the first principal component into the elliptical polar coordinate expression to obtain two predicted score values of the second principal component to form a judgment interval, detects whether the score value of the second principal component of the unknown sample is in the judgment interval, and thus the matching degree of the unknown sample and the quantitative model is judged. Through the application, the matching degree of the unknown sample in a prediction process and the quantitative model is judged, abnormal samples are discriminated, the model can be updated in time, and the stability and accuracy of the spectrometer during long-time use are ensured.

The invention provides a three-stage image matching method and network, and the method comprises the steps: carrying out the multi-scale feature extraction of two input images through a residual network feature pyramid structure, and outputting two coarse-grained features and two fine-grained features; in the rough matching stage, generating an initial matching score matrix and a rough matching prediction set based on the two coarse granularity features; in an intermediate matching stage, optimizing a matching result based on the two coarse granularity features and the coarse matching prediction set to obtain an updated matching score matrix and an intermediate matching prediction set; and in the refined matching stage, a final matching score matrix and a refined matching prediction set are obtained based on the two fine-grained features and an intermediate matching prediction set optimization matching result in a manner of geometric consistency verification and a cross-attention mechanism of local features. An intermediate matching stage is introduced, global and local information is effectively fused, abrupt property in the matching process is overcome, and more accurate and smooth matching is realized.

The invention discloses a video intra-frame lossless compression method based on deep learning, and particularly relates to the technical field of image compression. The method comprises the following steps: firstly, constructing a multi-scale abnormal response graph for depicting a weak predictable area in an image; generating a compression difficulty scoring matrix based on the response diagram, identifying a high-complexity region, inputting the high-complexity region into a weak prediction region modeling network, and outputting a high-order residual probability field; further, an arithmetic encoder is guided by using residual probability distribution to carry out adaptive intra-frame lossless compression on the original image, and a compressed code stream is generated; the decoding end is combined with the shared residual probability field to realize pixel-by-pixel lossless restoration of the original image; the method can effectively improve the compression efficiency and recovery precision of the edge detail area, and is suitable for video image compression tasks in scenes such as remote sensing, security and satellite communication.

The invention discloses a training method of an entity generation model and an entity generation method and device, and relates to the field of computers, in particular to the field of artificial intelligence such as deep learning and large models. According to the specific implementation scheme, an input sequence corresponding to any main body in a target domain is obtained; wherein the input sequence comprises a sample text and a to-be-extracted field; encoding the input sequence by adopting a base model of a target domain to obtain an encoding vector; performing linear transformation on the coding vector to obtain a scoring matrix corresponding to the to-be-extracted field; wherein each element in the scoring matrix represents that any two characters in the input sequence are scores of the entity boundary of the to-be-extracted field; decoding the coding vector by adopting a base model to obtain an entity extraction result; and training the base model according to the scoring matrix and the entity extraction result to obtain a trained entity generation model corresponding to any subject.

The invention discloses a local sequence alignment algorithm and device for detecting the number of continuous similar rows of a file, and belongs to the technical field of code clone detection. The method comprises the following steps: carrying out preprocessing of blank removal, blank line removal and annotation removal on two files to obtain a line sequence; an improved Smith-Waterman algorithm is utilized, a'row 'is taken as a unit, difflib.Sequence Matcher > = 0.8 is taken as a similar threshold to construct a score matrix, and all continuous matching fragments with the length > = minlines are extracted through backtracking; and outputting starting and stopping line numbers and line-by-line contrast contents of each segment in a descending order according to the similar line number. According to the method, a language parser and time complexity O (n.m) are not needed, continuous similar regions of Type-1 to Type-3 clone can be positioned at one time, the result is visual, parameters can be configured, and the method is suitable for repeated code cleaning, plagiarism detection and reuse rate measurement of million row-level codes.

The application relates to a kind of information-based commodity recommendation method, comprising: obtaining historical data, using correlation level marking mode, early marking is carried out to historical data;The labeled grade data combination is used as training target, and a plurality of initial recommendation models are obtained by model training;Based on score matrix, the loss function corresponding to a plurality of initial recommendation models is calculated respectively, and the initial recommendation model with the minimum loss function value is screened out as a verification recommendation model;The verification recommendation model is verified correspondingly, and the final recommendation model is obtained;The actual information name and all commodity data are input into the final recommendation model, and the corresponding recommended commodity is output. Compared with the prior art, the application can effectively improve the labeling efficiency and the richness of the correlation, thereby ensuring the accuracy of the constructed model, i.e. ensuring the accuracy of subsequent commodity recommendation.