158 results about "Similarity analysis" patented technology

The invention discloses a digital GIS equipment fault intelligent monitoring management system, and relates to the technical field of fault intelligent monitoring, and the system comprises an information retrieval module which takes attribute information as a constraint, and carries out the networking retrieval to obtain a sample fault event set; the event clustering module is used for obtaining a plurality of high-frequency fault event sets; the traceability analysis module is used for determining a high-frequency fault factor set and generating a high-frequency fault feature matrix; the fixed-point monitoring module is used for obtaining a real-time monitoring data set and constructing a monitoring data matrix; the similarity analysis module is used for performing similarity analysis on the high-frequency fault feature matrix and the monitoring data matrix to determine comprehensive similarity; and the fault early warning module is used for performing fault early warning on the target equipment. Through the method and the device, the technical problem that real-time and accurate monitoring and management are difficult due to the fact that the intelligent degree and the detection accuracy are insufficient in the prior art can be solved, and the technical effect of improving the automation degree and the detection accuracy is achieved.

The invention discloses a seismic activity parameter determination method and system based on incomplete seismic data, and relates to the technical field of engineering seismology, and the method comprises the steps: collecting seismic data, and constructing a historical directory; simulating a seismic directory according to the seismic activity parameters and the activity model; similarity comparison is carried out on the simulated seismic catalog and the historical catalog; and determining a seismic activity parameter combination according to a similarity result between the directories. According to the method, the seismic activity parameters are determined based on the incomplete seismic data, and the sample size is increased by coupling various seismic data, so that the defect of a historical seismic directory can be overcome, and the length of available data can be prolonged; a Monte Carlo method is adopted to simulate a seismic directory so as to obtain a characteristic index sample matrix, and Euclidean distance is adopted to carry out mapping similarity analysis on a historical directory and a simulated directory so as to give a parameter optimization result; the earthquake information is utilized as much as possible, and excessive constraints are avoided.

Methods, system, and non-transitory processor-readable storage medium for test case management system are provided herein. An example method includes identifying, by the test case management system, a namespace for inclusion of test features required to provide test coverage in a software test life cycle system. The test case management system creates a test case feature matrix data set for test cases in a test case suite. The test case management system performs a similarity analysis between the test features in the namespace and the test cases in the test case suite, using the test case feature matrix data set, to identify a subset of the test cases that provide at least a portion of the test coverage. The software test life cycle system executes the subset of the test cases on a test system to provide at least a portion of the test coverage.

The invention belongs to the technical field of data mining and analysis processing, and particularly discloses a real-time data stream processing method and system based on distributed computing, and the method comprises the steps: obtaining real-time and historical data streams of a server program window, employing a time sequence synchronization mode to rapidly position and start a window matching path and historical abnormal performance, and obtaining a real-time data stream of the server program window; the high real-time requirement is met; historical abnormal event statistics and real-time data flow similarity analysis are combined to dynamically construct a distributed tracking link, a depth-first search algorithm is used to predict a risk event, and an abnormal probability and a deviation degree are accurately quantified; different optimization strategies are configured according to different performance parameters, a multi-level defense mechanism is formed, and the self-adaptive capacity of the system is improved. According to the method, the interaction track and the performance parameter set are comprehensively analyzed, the abnormal performance link relation is marked, abnormal event correlation analysis is refined, the maintainability and expansibility of the system are enhanced, and the data processing efficiency is remarkably improved.

A learning device generates learned data to be used for anomaly indication detection. The learning device includes a preprocessing unit that subtracts, from a value at each of points in one cycle of normal data, an average of values at corresponding points in normal data from one to N cycles ago in computing difference values with respect to the preceding N cycles, the one cycle corresponding to a specified time length, N being an integer greater than or equal to 2, the normal data being data from a normal state; and a first waveform analysis unit that generates, through waveform similarity analysis using the difference values with respect to the preceding N cycles, normal waveforms and a normality determination threshold to be used in determining whether or not there is normality as the learned data.

The invention discloses a similarity and distance calculation system and method based on an R language, and relates to the technical field of data processing, and the key point of the technical scheme is that the system comprises the following modules: a data reading module, a data standardization module, a similarity calculation module and an error checking module. Through automatic data inspection, standardized processing and efficient similarity calculation, the user can easily perform complex similarity analysis. The method has a wide prospect in the application of big data analysis, machine learning and the like, a more convenient and more accurate similarity calculation scheme can be provided for a user, and the data processing and analysis efficiency is greatly improved.

The invention provides a wrong question book intelligent learning system and method based on OCR and similarity analysis. The method comprises the following steps: S1, carrying out wrong question picture preprocessing and feature extraction; s2, multi-dimensional similarity calculation is associated with knowledge points; s3, knowledge point error rate statistics and error pattern analysis; and S4, generating and pushing personalized exercises. According to the method, intelligence and individuation of error question management are realized through multi-technology fusion, the bottleneck of current error question management is broken through, and the learning efficiency and the knowledge mastering depth are improved.

The invention belongs to the technical field of bid and bid behavior monitoring, and particularly discloses a bid and bid behavior monitoring management platform based on artificial intelligence, and the bid and bid behavior monitoring management platform adopts a chart normalization preprocessing technology, carries out the multi-dimensional similarity analysis of text content, chart features and image-text matching degree, and combines the order of images and texts with a recombination comparison mode. Omnibearing detection of the bidding document is realized, and the recognition rate of chart tampering and content recombination type bidding behaviors is effectively improved; based on a dynamic weight distribution mechanism of a project type, accurate risk assessment of different importance contents is realized through intelligent segmentation and differential weight setting of bidding document unit bodies; the multi-dimensional risk fusion analysis relation is constructed, the risk index is generated through comprehensive evaluation, and graded early warning is triggered, so that targeted monitoring on different structural weight markers is ensured, the detection breadth and depth are remarkably improved, and the problem of stiffness of a multi-modal analysis evaluation standard in the prior art is solved.

The invention discloses a topology anti-error risk management and control method and system, and relates to the technical field of risk management and control, and the method comprises the following steps: obtaining a power grid topology structure chart, and employing a graph algorithm to code power grid nodes in the topology structure chart, and obtaining a node feature vector; constructing a truth-supplementing parameter group based on the measurement missing node identification result and the node level, and performing random disturbance on the truth-supplementing parameter group based on a preset first constraint condition to obtain a plurality of truth-supplementing schemes; repairing the node feature vectors of the measurement missing nodes by adopting a truth-supplementing scheme to obtain a node repairing feature vector set, and performing similarity analysis on the node repairing feature vector set and a preset risk scene; and calculating an optimal similarity threshold value corresponding to the authentic scheme based on a similarity analysis result, constructing an optimal pairing group, and applying the optimal pairing group to actual anti-error risk identification, so that the problem that the accuracy of power grid risk management and control is reduced due to the lack of a dynamic adaptation mechanism between the repaired node features and an actual risk scene in the face of node measurement deficiency is solved.

The invention relates to an electric power project demand similarity analysis method based on a rearrangement model and retrieval enhancement generation, and belongs to the technical field of computer science, artificial intelligence and natural language processing. The method comprises the following steps: constructing a multi-modal knowledge base; after user query is received, self-adaptive query expansion is carried out through a large language model, preliminary recall is carried out by adopting a mixed search strategy, secondary accurate scoring and reordering are carried out on candidate lists by utilizing a rearrangement model of a cross encoder framework, and a final result is generated in combination with an interpretability analysis technology. And finally, driving the large language model to generate a multi-dimensional intelligent analysis report through chained thinking prompt. According to the method, the problem of analysis deviation caused by low retrieval quality in a traditional RAG method is solved, the transparency of the system is enhanced through interpretability analysis, and the finally generated multi-dimensional analysis report can provide unprecedented decision support for project management.

The invention belongs to the technical field of product intelligent design, and particularly relates to a product design auxiliary system and method based on big data. According to the method, the potential design direction on the market is quickly identified through big data analysis and calculation, so that the design period is shortened, the design efficiency is improved, the risk of blind design is reduced based on market popularity data and similarity analysis, it is ensured that the designed product better meets the market demand, and the market competitiveness is improved. Through weight analysis of local features and overall features of a product, it is ensured that a design scheme is more comprehensive and reasonable, through support of big data, the designed product better meets the market trend and consumer demands, the market competitiveness of the product is enhanced, the design process is more scientific and data-driven by using the big data technology, and the design efficiency is improved. According to the method, the uncertainty caused by subjective judgment is reduced, the provided reference three-dimensional product data can provide valuable reference basis for subsequent detailed design and production, and the efficiency and quality of the whole product development process are improved.

The invention discloses a forestry disaster three-dimensional monitoring and early warning method and system, a terminal and a medium, and belongs to the technical field of forestry disaster monitoring. In order to solve the problems that existing forestry disaster monitoring is low in efficiency, insufficient in precision, poor in practicability and the like, the invention provides a solution integrating a high-resolution remote sensing image and an artificial intelligence technology. The method comprises the following steps: firstly, acquiring a multi-view and multi-level high-resolution image of a target forest by using a satellite and an unmanned aerial vehicle; then, through the steps of intelligent preprocessing, multi-dimensional feature extraction, refined similarity analysis and the like, forest abnormal areas are automatically recognized, and disaster types of the forest abnormal areas are judged; and finally, a disaster area distribution map is generated, and key disaster information is extracted to serve disaster early warning and forestry management decision. According to the method, the timeliness, accuracy and practicability of forestry disaster monitoring are remarkably improved, and a new thought and a new method are provided for precise and intelligent forestry disaster prevention and control.

The invention discloses a solid waste classification method based on a spectrometer, and relates to the technical field of solid waste analysis, and the method comprises the following steps: S1, preliminary classification: obtaining a sample of unknown solid waste, and observing the characteristics of the sample to obtain characteristic information; s2, selecting a spectrograph: selecting the spectrograph according to the feature information; analyzing the sample by using a spectrograph to obtain a spectrogram; the spectrogram comprises a spectrum curve; s3, spectrogram preprocessing: carrying out normalization processing on the data of the spectrum curve; s4, similarity analysis: for the spectrum curves of the multiple samples, randomly selecting two samples for comparison to obtain a similarity value; if the similarity value is within a preset threshold range, the two samples to be compared belong to the same kind of solid waste. The method is low in operation cost and low in professional degree requirement on workers, and can realize rapid and efficient analysis and classification of solids.

The invention discloses a hyperspectral remote sensing mineralization alteration information extraction method, device and equipment and a medium, and is applied to the technical field of remote sensing geological survey, the hyperspectral remote sensing mineralization alteration information extraction method comprises the following steps: obtaining sample laboratory spectral data and sample hyperspectral satellite remote sensing data of a rock and soil sample; calculating the sample laboratory spectral data and the sample hyperspectral satellite remote sensing data by adopting a direct standardization algorithm, and determining a transfer matrix required for converting the sample hyperspectral satellite remote sensing data into the sample laboratory spectral data; based on the transfer matrix, converting hyperspectral satellite remote sensing data of a research area to obtain laboratory-like spectral data of the research area; and performing similarity analysis on the laboratory-like spectral data and the standard spectral data of the altered mineral, and determining mineralization alteration information in the laboratory-like spectral data.

The invention belongs to the technical field of data analysis, and provides an abnormal data analysis method and system in a financial fast report scene based on artificial intelligence, and the method comprises the steps: comparing obtained target financial data with a pre-configured threshold value through employing a three-dimensional threshold value model, judging whether there is abnormal data or not according to a comparison result, and carrying out the analysis of the abnormal data in a financial fast report scene. The threshold value of the three-dimensional threshold value model is configured by integrating an industry financial benchmark, historical financial data of the enterprise and financial data of the enterprise in the current year; if the abnormal data exists, retrieving data associated with the abnormal data by using the updated knowledge graph, and identifying a business event having correlation with the abnormal data according to the retrieved data; and performing similarity analysis, statistical significance analysis and anomaly classification analysis on the abnormal data and the business event, and performing weighted fusion to obtain a final analysis result. The accuracy of financial abnormal data analysis in the prior art is improved.

The invention provides a microbial community analysis method based on statistical analysis, and belongs to the technical field of microbial community analysis. The microflora analysis method comprises the following steps: collecting a plurality of water samples, and carrying out pretreatment; sequencing the microflora of the water sample by adopting high-throughput sequencing to obtain a microflora sequencing result; preprocessing the microbial community sequencing result by adopting OTU clustering analysis and species annotation to obtain a data preprocessing result; and analyzing the data preprocessing result by adopting a statistical method. According to the method disclosed by the invention, various microorganisms in the water sample are sequenced by adopting high-throughput sequencing, the sequencing process is rapid, the result is accurate, and the microflora sequencing result is analyzed by combining various statistical analysis means such as difference analysis, similarity analysis and diversity analysis; and the structural composition and characteristics of the microbial community can be analyzed more comprehensively and accurately.

The invention belongs to the technical field of hydrological forecasting, and particularly provides a method for forecasting future session flood based on peak rainstorm distribution characteristics, and the method comprises the steps: collecting and reorganizing data, and processing the data into a time sequence with equal time intervals; obtaining space-time distribution of the flood peak formation rainstorm amount of each session of the research basin; calculating the rainfall process of each sub-basin of the flood to be forecasted, and analyzing the spatial and temporal distribution characteristics of peak rainstorm; performing similarity analysis on the spatial and temporal distribution characteristics of the peak storm of the to-be-forecasted flood and the historical session flood to obtain a session flood which is highly similar to the spatial and temporal distribution characteristics of the peak storm of the to-be-forecasted flood and is close to the peak storm amount, and taking the session flood as a to-be-calibrated session flood; establishing a multi-objective function; optimizing the objective function to obtain an optimal parameter of the hydrological model; and performing hydrological forecasting on the to-be-forecasted flood. According to the method, the parameters of the hydrological model for forecasting the flood in the future session with high accuracy can be obtained, so that a high-accuracy flood forecasting result is obtained.

The invention discloses a method, a device, equipment, a medium and a product for identifying partial optical fibers in the same cable. The method comprises the following steps: acquiring a first OTDR curve and a second OTDR curve of two optical fibers to be identified; extracting event characteristics of each event in the first OTDR curve and the second OTDR curve; identifying the same event in the two optical fibers to be identified according to the extracted event features; and performing similarity analysis on the OTDR curve of the cable section where the same event is located in the two optical fibers to be identified according to a preset similarity comparison model, and outputting a same-cable optical fiber identification result. According to the scheme, similarity analysis is carried out on the cable sections where the same events are located based on identification of the same events in the OTDR curves of the two to-be-identified optical fibers, blindness of identification of the same-cable optical fibers is reduced, identification of part of the same-cable optical fibers in a complex scene can be dealt with, and different parts of the same-cable optical fibers can be accurately and efficiently identified.

A method to perform a field operation based on similarity of wells in a field is disclosed. The method includes generating, based on well log data files, a geology related similarity score for each pair of wells, generating, based on production data files, a production related similarity score for said each pair of wells, generating, by combining the geology related similarity score and the production related similarity score, a combined similarity score of said each pair of wells, determining, by aggregating the combined similarity score of said each pair of wells, an aggregate similarity score of each well, and facilitating, based at least on the aggregate similarity score of each well, the field operation in the field.

The invention discloses a medium surface microstructure detection device and method based on a spectrum technology, and relates to the technical field of structure detection, and the device comprises a partition division module which is used for dividing a target medium surface to obtain a medium partition sequence, and collecting a reflection spectrum of a surface microstructure to obtain a reflection spectrum sequence; the quality identification module is used for carrying out quality identification on the target medium to obtain a quality coefficient sequence and configuring abnormal residue identification precision; the exception identification module is used for calling exception residue identification resources and carrying out exception residue identification on the reflection spectrum sequence to obtain an exception residue parameter sequence; and the result processing module is used for performing processing to obtain abnormal residual distribution, performing similarity analysis on the abnormal residual distribution and historical abnormal residual distribution, obtaining identification confidence, combining the quality coefficient sequence and the abnormal residual parameter sequence, and performing calculation to obtain a medium quality parameter as a detection result. The technical problem that the medium surface detection effect is poor in the prior art is solved.

A device may process data associated with a first entity and a second entity, using one or more machine learning techniques, to identify a set of trends associated with a set of documents. The device may receive new documents associated with the first entity and the second entity. The device may generate, based on the set of trends, a first set of exceptions indicating that a new document is associated with a first type of error. The device may generate, using a similarity analysis technique, a second set of exceptions indicating that a new document is associated with a second type of error. The device may communicate with one or more systems to perform one or more actions associated with correction or prevention of processing errors relating to the new document based a claim relating to the first set of exceptions or the second set of exceptions.

The invention relates to a multi-dimensional feature-based case series-parallel analysis method and device, computer equipment and a storage medium. The method comprises the following steps: performing feature extraction processing on denoised sample data of each case sample to obtain a case feature set of the corresponding case sample; performing vector construction processing according to the case feature set of each case sample to obtain a multi-dimensional feature vector of the corresponding case sample, and performing calculation according to the multi-dimensional feature vector of each case sample to obtain a reference multi-dimensional feature vector; performing similarity analysis according to the multi-dimensional feature vector of each case sample and the reference multi-dimensional feature vector to obtain case similarity of each case sample; wherein the case similarity is the similarity between the multi-dimensional feature vector of the corresponding case sample and the reference multi-dimensional feature vector; in response to the existence of the target case similarity, recording a case identity identification number of a case sample corresponding to the target case similarity to a case string, and analyzing a statistical result; by adopting the method, the efficiency and the accuracy can be improved.

The invention relates to the technical field of ecological environment monitoring, in particular to an environment assessment method and device based on carbon footprint and regional ecological data. The method comprises the following steps: acquiring carbon footprint data and regional ecological data of a target region; performing dimension alignment on the carbon footprint data and the regional ecological data to obtain an associated data set associated with a preset grid of the target region; and based on the associated data set, through at least one of a time sequence similarity analysis strategy, a spatial distribution overlapping degree analysis strategy and a causal relationship influence analysis strategy, determining an environment assessment result of the target area. Compared with the prior art, a manual evaluation mode is replaced, and the analysis efficiency is improved. And on the other hand, the carbon footprint data and the regional ecological data are aligned according to the preset grids of the region, so that the problem of non-uniform comparison dimensions and indexes is solved. And meanwhile, three analysis strategies are provided for reflecting the internal relation of the two types of data, so that the data analysis precision is improved.

The invention discloses a role copyright infringement detection method and system based on visual and semantic features, and the method comprises the steps: S1, role target separation and feature extraction: inputting to-be-detected content, separating a role target in the content, and extracting role features which comprise at least one of a visual feature, a semantic feature and a time sequence feature; s2, role feature correspondence and matching: performing correspondence and matching on the extracted role features and existing role features in a copyright content library; and S3, role feature similarity analysis: when the similarity between the extracted role and the matched role exceeds a preset threshold value, judging that the role is a potential infringement role. According to the method, the visual and semantic features of the role are taken as the core, and a full-process detection framework from target separation to feature matching to similarity analysis is constructed, so that the problem of low efficiency caused by dependence on a manual comparison method in the prior art can be effectively solved; an automatic and intelligent solution is provided for role copyright infringement affirmation of large-scale digital content, and the detection efficiency and the judgment scientificity are remarkably improved.

The invention relates to the technical field of network security, and discloses a program-level code similarity analysis method and device based on semantic slicing, and the method comprises the steps: constructing a program-level target function call graph based on a target source code; matching the target function call graph with a standard function call graph to obtain a plurality of isomorphic sub-graphs; performing semantic slicing on each function node in the first sub-graph and the second sub-graph to obtain a first slicing result of the first sub-graph and a second slicing result of the second sub-graph; and evaluating the slice similarity between each function in the first slice result and each function in the second slice result, and determining the similarity of the target source code according to the slice similarity. According to the method, the user-defined function call graph for eliminating system function interference is constructed, an isomorphic sub-graph matching algorithm is combined, multi-dimensional features are comprehensively considered, the accuracy of similarity calculation is improved, effective support is provided for practical application scenes such as code reconstruction and security audit, and the guarantee of code quality and security is improved.

The invention discloses an inquiry teaching method and system based on a large language model, and the method comprises the steps: randomly extracting to-be-recombined data from a pre-constructed patient play background library, a patient disease question and answer library and a disease picture library, recombining each piece of data to be recombined to generate a virtual digital patient object for interacting with the user; in the interrogation teaching process, performing similarity analysis on the interrogation question output by the user and the patient disease question and answer library, and screening and determining a matching result corresponding to the interrogation question from the patient disease question and answer library; and obtaining reference inquiry reply data according to a matching result, and inputting the reference inquiry reply data into the constructed big language inquiry teaching model to obtain a reply result which is output by the big language inquiry teaching model and meets the inquiry teaching standard so as to realize virtual inquiry teaching. Doctor-patient flexible interaction can be carried out, and the independent diagnosis ability of doctors and students is improved.

The application provides a tunnel positioning method, device, equipment, medium and product, the method comprises the following steps: obtaining a plurality of network parameters reported by a user terminal when the user terminal is at a to-be-positioned point in a tunnel, predicted position data of the to-be-positioned point output by an inertial navigation system, and tunnel position data; based on the plurality of network parameters and the tunnel position data, a plurality of first MR fingerprint data are matched from an MR fingerprint library; similarity analysis is performed based on the plurality of first MR fingerprint data and the predicted position data, and MR fingerprint positioning data of the to-be-positioned point is obtained; and position data correction is performed based on the MR fingerprint positioning data and the predicted position data, and actual positioning data of the to-be-positioned point is obtained. The tunnel positioning method provided by the application can accurately calculate the positioning data of a user in a tunnel environment through an algorithm without the need to separately deploy a positioning device in the tunnel environment and without the need to be limited by hardware devices, so that the flexibility is improved while the accuracy is ensured.

The invention relates to a method for identifying microbial core species in a river ecosystem, and belongs to the field of microbial ecology. Through OTU relative abundance data, a presence-absence matrix is constructed, and an OTU occurrence rate and an average relative abundance are calculated. And evaluating the OTU ecological importance, and identifying a potential core OTU. And analyzing the consistency of the OTU occurrence frequency and the time scale in combination with the time point location, and generating a ranking index Index for ranking. And performing Bray-Curis similarity analysis to quantify the structural diversity of the OTU community, and identifying the OTU which has the greatest influence on the community similarity. And determining a core OTU threshold value based on an inflection point method and a growth rate method, verifying a core classification group combination mechanism by using a Sloan neutral model, and finally determining the representativeness of the core OTU by comparing the actual occurrence frequency with model prediction. According to the method, rich information is provided for explaining the diversity pattern of the microflora level.

The invention discloses a science and technology project document duplicate checking method and device based on multi-level abstract generation and a medium, belongs to the technical field of document duplicate checking, and solves the problem of how to improve the reliability and accuracy of science and technology project document duplicate checking. The method comprises the following steps: firstly, carrying out structured analysis on a project document to be subjected to duplicate checking, introducing a fine-tuned abstract generation model to obtain a multi-level abstract of a project to be subjected to duplicate checking, and extracting a feature vector of the multi-level abstract through a fine-tuned and trained text feature extraction model; secondly, performing similarity search in a vector database based on feature vectors corresponding to the dimensions of the main abstract, further calculating cosine similarities based on the dimensions of the subordinate abstract, and sequencing the weighted similarities to obtain a similarity rank; and finally, according to the structured analysis information and the similarity ranking result, LLM similarity analysis is performed by using a large language model, a document duplicate checking report is formed, the originality of the science and technology project is ensured, repeated subsidization is avoided, and the method has important practical application value for science and technology project management.

The invention discloses an object recognition method and device, equipment and a storage medium, and relates to the technical field of machine learning, and the method comprises the steps: carrying out the feature similarity analysis of a target object feature of a to-be-recognized object and a class index feature group corresponding to a target object class, and obtaining target feature similarity data; the category index feature group is a clustering center feature corresponding to the positive sample object feature; the positive sample object is a sample object which is screened based on a feature density clustering result of a plurality of original sample objects and belongs to a target object category; based on the target feature similarity data and a preset feature similarity threshold, determining object category indication information of the to-be-recognized object; the preset feature similarity threshold value is obtained by performing threshold value adaptive adjustment based on a preset identification index threshold value and the sample feature similarity data; the sample feature similar data represents the feature similarity condition between the positive sample object feature of the positive sample object and the category index feature group. By using the scheme of the invention, accurate identification of a new category can be rapidly realized.