202 results about "Classification treatment" patented technology

The invention discloses an expensive constrained multi-objective optimization method and system based on constrained hyper-volume expectation. The method comprises the following steps: firstly, initializing a population and training an agent model; then calculating a correlation coefficient between the solution with the best population convergence and the constraint violation degree of the solution, and performing classification processing; then, optimization is carried out on the agent model, and a new solution is selected by utilizing a filling criterion based on constraint hyper-volume expectation to carry out evaluation and is filled into a file; further selecting and screening individuals by using an improved environment to enter a next-generation population; and finally, judging whether a stop condition is reached or not, if so, outputting a final population, otherwise, turning to execute an iteration process. According to the method, the optimization efficiency is remarkably improved, local optimum is avoided, constraint conditions are precisely processed, and the population can be more effectively converged to a better feasible region.

To efficiently and effectively classify a large amount of document and text data by an LLM.SOLUTION: The present invention relates to a text classification system 1 which classifies documents accumulated in a document DB 14 by categories, and the text classification system has a classification processing part 12 which lets an LLM 2 proposes one or more categories based upon a classification policy specified by a user, a search processing part 13 which searches the document DB 14 for documents belonging to the respective proposed categories, and a UI processing part 11 which presents the respective categories and the numbers of documents belonging to the respective categories to the user.SELECTED DRAWING: Figure 1

The invention discloses an intelligent express parcel automatic sorting method based on deep learning, and the method comprises the steps: collecting a multi-view image, carrying out the standardization enhancement processing, carrying out the bidirectional space sequence coding through a Vision Mama structured state space model, obtaining a fusion visual feature map, inputting a preliminary classification network, and carrying out the primary classification processing, and according to the initial classification category, dynamically selecting expert sub-networks of the MoE-Mama hybrid expert network to complete deep feature reconstruction and fine classification, and finally, according to the final classification category, generating a control instruction to realize automatic guide positioning and accurate sorting. According to the method, the parcel sorting accuracy and efficiency are remarkably improved, and the method is suitable for intelligent logistics sorting operation in a complex environment.

The invention discloses a thesis generation method and device based on a large model. The method comprises the following steps: collecting original thesis data through network acquisition and official purchase channels; preprocessing the original paper data to obtain preprocessed data; based on a preset classification system and a metadata table, carrying out classification processing on the preprocessed data to obtain classification processing data; constructing a training sample based on the classification processing data, and outputting a corresponding bar content; a multi-task learning framework is adopted to carry out fine tuning on the pre-trained large language model, the multi-task learning framework comprises an outline generation task and a text generation task, and task types are distinguished through system prompt; and receiving a paper title and a keyword input by a user, sequentially generating a paper outline through the fine-tuned model, and generating text contents of each chapter in parallel based on the paper outline. According to the invention, the paper generation quality and efficiency can be improved, so that the user experience is improved.

The invention discloses a multi-granularity financial text noise open classification method based on a large model, and belongs to the technical field of text noise classification. Performing clustering processing on the semantic features to obtain a plurality of balls with different labels, and calculating attributes of the plurality of balls with different labels; according to the positions of the samples in the pellets and the attributes of the pellets, the samples in the pellets are classified into clean samples, internal noise is distributed, and external noise is distributed. A plurality of pellets with different labels are obtained through clustering processing, the attribute of each pellet is calculated, at the moment, each category is finally represented by a plurality of pellets with the same label but different attributes, the multi-granularity prototype and the distribution range of the category can be effectively reflected, and the representation learning effect is improved. Meanwhile, classification processing is carried out according to the positions of the samples in the pellets and the attributes of the pellets, and the method can be suitable for data environments of various distribution types.

The invention provides a large language model generation code detection method and system, which is applied to the technical field of artificial intelligence, and comprises the following steps: obtaining a to-be-detected code; a to-be-detected code is input to a trained shared encoder, a code feature vector is obtained, the shared encoder is obtained through multi-target joint training, and the multi-target joint training is used for optimizing classification loss, domain confrontation loss, comparison loss and difficult sample loss at the same time; l2 normalization is carried out on the code feature vector, and the code feature vector is mapped to a hyperspherical space to obtain spherical embedding; the sphere is embedded and input into a sphere category classifier based on sphere logistic regression for classification processing, a detection result of the to-be-detected code output by the sphere category classifier is obtained, the detection result comprises AI generation and human writing, and a decision boundary of the sphere category classifier is an intersection line of a hyperplane and a hypersphere for classification. According to the invention, the AI generation code and the human compiled code can be accurately distinguished.

The invention discloses an anti-unmanned aerial vehicle interference effect evaluation method and system, and belongs to the technical field of anti-unmanned aerial vehicle interference effect evaluation, and the method comprises the following steps: collecting two types of anti-unmanned aerial vehicle scheme cases, carrying out the standardization and classification processing of cases which are good in evaluation but failing in off-site simulation, and building a failure case library; cases which are both good in evaluation and off-site simulation are classified, then the economic benefit range of each type of evaluation method is determined, and a successful case library is established. According to the method, a failure case library containing structured data and a success case library with a clear economic benefit range are established, a to-be-evaluated scheme is evaluated, then three-dimensional data are extracted from the failure case library to construct a multi-dimensional association model, and an evaluation blind area is positioned; and finally, three evaluation methods are generated in combination with a success case library, economic benefits are calculated and sorted, the difference between evaluation and off-site simulation can be effectively reduced, the evaluation accuracy is improved, and off-site simulation failure conditions are reduced.

The invention relates to the technical field of natural language processing, and discloses a short text classification processing method utilizing a graph attention network, which comprises the following steps of: cleaning short text data, performing word segmentation and part-of-speech tagging, removing redundant words, converting each word into a vector representation with a fixed length by utilizing a word embedding technology, and extracting the vector representation by utilizing a graph attention network; obtaining a text vector matrix; constructing a text graph based on the text vector matrix, and constructing a tag graph according to a known tag relationship; a graph attention mechanism is applied to nodes in the text graph and the label graph, attention coefficients between the nodes are calculated, feature weighted summation is carried out, and updated node embedding representation is obtained. The short text classification processing method using the graph attention network aims at solving the problem that in an existing short text classification processing method, due to the fact that sparsity and characteristics of short texts are insufficient, labels are difficult to predict accurately through a traditional classification method.

The invention provides a text classification method and device, electronic equipment and a storage medium, and relates to the technical field of natural language process.The method comprises the steps that a training sample set is obtained, and the training sample set comprises sample texts and sample classification labels and sample reasoning reasons corresponding to the sample texts; performing fine tuning on a first pre-trained large language model through the training sample set to obtain a text classification model; in response to a text classification request, through the text classification model, based on a preset reasoning constraint parameter, only performing classification processing on a to-be-classified text to obtain a prediction classification label; wherein the preset reasoning constraint parameters comprise an output length limiting parameter and a logit probability intervention parameter. According to the method, the text classification efficiency can be improved while the accuracy and the reliability of a text classification result are improved.

The invention provides a work order processing method based on a large model, relates to the technical field of artificial intelligence, in particular to the technical fields of computer vision, deep learning, large models and the like, and can be applied to work order processing scenes. The method comprises the steps of obtaining a to-be-processed work order, wherein the to-be-processed work order comprises problem information; determining cue words of the to-be-processed work order, wherein the cue words comprise preset cue words or cue words obtained by updating the preset cue words according to the service category information; and performing work order classification processing on the cue word and the problem information by using the large model to obtain a work order category for the to-be-processed work order so as to process the to-be-processed work order according to the work order category. The invention further provides a work order processing device based on the large model, the intelligent agent, equipment and a medium.

The invention discloses a voice deception detection method based on double attention and multi-scale feature fusion, and belongs to the technical field of voice deception detection. The method comprises the steps that training corpora are acquired and preprocessed; inputting the preprocessed training corpus into a detection model for model training; adjusting hyper-parameters to minimize the target loss function to obtain a trained detection model; obtaining a test corpus and preprocessing the test corpus; inputting the preprocessed test corpus into the trained detection model, and outputting a voice deception detection result; the detection module comprises a Sinc module, a feature extraction module, a feature fusion module and a classification processing module; the Sinc module is used for extracting a preliminary feature X in an input voice segment, the feature extraction module further extracts a multi-scale feature Yi from the preliminary feature X, the feature fusion module aligns and splices the multi-scale feature Yi and then outputs a fusion feature Y, and the classification processing module processes the fusion feature to obtain a classification result.

The invention discloses a label classification method and device for a long text, equipment, a storage medium and a product, relates to the technical field of computers, and discloses a label classification method for the long text, which comprises the following steps: obtaining a to-be-classified text; and based on the to-be-classified text, performing label classification processing through a preset large language model to obtain a label classification result. According to the method, the to-be-classified texts are converted into the text sequence vectors by the large language model through position coding, and then correlation among the text sequence vectors is calculated in parallel by utilizing a multi-head self-attention mechanism, that is, the large language model can process the text sequence vectors in parallel, so that the video memory utilization rate of the large language model is improved, and the classification efficiency is improved. Due to the improvement of the utilization rate of the video memory, the large language model can process the long text with more characters, namely, the large language model does not need to segment the long text, so that semantic loss caused by segmentation is avoided, and the tag classification accuracy of the long text is improved.

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 discloses an intelligent classification processing system and method for building solid waste, and relates to the technical field of intelligent classification, and the method comprises the steps: traversing a target region, collecting multi-dimensional data of the building solid waste, and determining a to-be-classified data set; determining a plurality of classification labels to construct an intelligent classification model, and synchronizing the to-be-classified data set to the intelligent classification model to obtain a classification decision; the classification decision is simulated and executed to conduct classification processing monitoring on the building solid waste, and operation feedback data is generated; optimizing the intelligent classification model based on the operation feedback data to obtain an intelligent optimization classification model, and synchronizing the to-be-classified data set to the intelligent optimization classification model to obtain a classification optimization decision; and intelligent classification treatment is conducted on the building solid waste. The technical problems that in the prior art, when the building solid waste is classified and treated, efficiency is low, and accuracy is not high are solved, and the technical effect of improving the accuracy and efficiency of building solid waste classification is achieved.

The invention discloses a three-stage differential garbage classification treatment system, and relates to the technical field of energy conservation and environmental protection. A throwing opening is formed in the top of a shell, a classification mechanism is arranged in the shell, and the classification mechanism comprises a three-stage differential separation module, a throwing module and a visual recognition module; the three-stage differential separation module comprises a first conveying belt, a second conveying belt and a third conveying belt which are sequentially arranged from top to bottom, a dispersing roller is arranged below the throwing opening and is in transmission connection with the first conveying belt through a belt, a throwing module is arranged at one end of the third conveying belt, and a visual recognition module is arranged above the throwing module; more than two garbage cans are arranged at the bottom in the shell, and a compression mechanism is arranged in one garbage can. According to the three-stage differential garbage classification treatment system, the three-stage differential separation module is utilized, multiple kinds of garbage can be thrown at a time and accurately recognized and sorted, compared with manual throwing, the error rate is greatly reduced, and the recognition accuracy rate reaches up to 97.3%.

The invention relates to a Json file parallel analysis and data writing method and device based on DataX. The method comprises the following steps: acquiring a source end Json file set, and dividing the source end Json file set into small file subsets and large file subsets according to file sizes; the small file aggregation path is configured with a json reader of the DataX, and parallel reading and analysis are carried out; splitting a root array of the large file into a plurality of independent small Json files through a Python script, aggregating paths, and reading and analyzing by using a DataX parallel channel; and finally executing the DataX Job to write the data into a target end. Through a classification processing strategy, the large-scale Json data analysis writing efficiency is remarkably improved, the large file memory occupation risk is reduced, the file scale is automatically adapted, a mature DataX frame and a Python script are reused, high performance and low maintenance cost are both considered, multi-type target end storage is supported, and the problems that a traditional tool is weak in parallel, high in resource consumption, poor in flexibility and the like are effectively solved.

The invention discloses a charging pile operation and maintenance optimization method and system based on intelligent prediction and scheduling, and belongs to the technical field of charging pile operation and maintenance. The method comprises the steps of designing a deep learning model, dividing a data set for training, and outputting a fault emergency degree; dividing candidate task groups, setting radiation range constraints of operation and maintenance sites, and combining the candidate task groups to perform priority ranking and task combination to obtain task groups; in combination with the state and skill of the operation and maintenance personnel, distributing a corresponding task group; calculating the passing time of the operation and maintenance route, optimizing the route based on the task group to obtain an optimized route, and combining the optimized route with the task group to obtain an operation and maintenance task; environment safety detection is carried out on the fault charging pile, and visual verification is carried out on the equipment state; defining an execution condition, judging whether the execution condition of the operation and maintenance task is met or not, and executing the operation and maintenance task when the execution condition is met; and if not, running the classification processing rule, carrying out time window replanning, and executing a conflict resolution strategy to obtain a new operation and maintenance task.

The invention relates to the technical field of code classification processing, and discloses a waste code cleaning method and device based on reachability analysis, equipment and a medium, and the method comprises the steps: scanning static codes in a preset program, and collecting and analyzing a dependency relationship and a reference relationship between the static codes; analyzing the reachability of the dependency relationship and the reference relationship between the codes, and determining static codes without the reference relationship; marking the static code without the reference relationship as a waste code according to the reachability analysis result; according to the marked abandoned codes, clearing suggestions and clearing operation steps of the abandoned codes are made, output and displayed. The method can be applied to development of business systems such as financial science and technology, medical treatment, health and pension and the like, waste codes in projects are accurately recognized and cleared through combination of control flow analysis and data flow analysis, and code quality and maintenance efficiency are improved.

The invention provides a workshop wastewater treatment method, a workshop wastewater treatment device and a computer storage medium. The workshop wastewater treatment method comprises the following steps: acquiring first water quality sensor data and an equipment operation log; inputting the first water quality sensor data and the equipment operation log into a large language model decision engine to obtain a large language model decision instruction; and performing classification treatment on the workshop wastewater according to the big language model decision instruction. According to the workshop wastewater treatment method, the big language model decision instruction is introduced, the workshop wastewater is dynamically treated by using the autonomous decision ability of the big language model on the basis of automatic treatment, and compared with the formulation of expert experience or a preset decision mode, the waste of a large amount of human resources can be reduced, and the efficiency is improved. And the loss caused by personal error of decision making is effectively reduced.

The utility model relates to an in-situ remediation device for polluted soil and underground water in an in-situ industrial park, which comprises an integrated shell, a plurality of through holes are arranged on the shell, and a plurality of injection channels and a plurality of extraction channels matched with the through holes are arranged; an injection configuration unit is arranged in cooperation with the injection channel, and the injection configuration unit is connected to the injection channel through an output assembly; a classification processing unit used for classifying and processing different extracted objects is arranged in cooperation with the extraction channel, and the classification processing unit and the injection configuration unit are arranged in a matched mode. And a controller is matched with the device. The device disclosed by the utility model realizes sustainable low-cost remediation of the polluted soil and the underground water, and is particularly suitable for long-term treatment of the polluted soil and the underground water in industrial parks or large-scale in-production enterprises; various in-situ remediation functions can be realized; integration of a software operation platform and an online monitoring control end is facilitated; the construction safety is high; the use in a park is more convenient.

The invention discloses a plantar pressure automatic classification method and system based on a multi-task learning framework, and belongs to the field of plantar pressure data research. The method comprises the following steps: S1, collecting plantar pressure data; s2, preprocessing the plantar pressure data: a plantar pressure extraction algorithm: segmenting the plantar pressure data through a DBSCAN clustering algorithm and extracting to generate a footprint sequence; footprint image representation: accumulating and summing the extracted plantar pressure sequence to reduce the length, and performing noise suppression processing on the image through Gaussian filtering; carrying out data enhancement; s3, a channel and a space attention module are introduced into an Inception block of the GoogLeNet, an Inception-CBAM block is established, and a multi-task learning model is constructed; the multi-task learning model is used for carrying out classification processing on the footprint images; and S4, obtaining plantar pressure classification data. The method provided by the invention is not only suitable for barefoot data, but also suitable for complex footwear data in a pressure area, and lays a data preparation foundation for automatic gait analysis and recognition based on plantar pressure.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an emergency perception method containing semantic extraction, which comprises the following steps of: S1, constructing a historical word library about emergencies, and forming an association weight between a vocabulary group and a user through historical data training; s2, constructing an entity variant word expansion model based on a double-layer model to realize generation of a variant word bank, wherein the double-layer model comprises a proper noun expansion layer and a historical word bank expansion layer; s3, collecting text data on a network, performing classification processing on the text data based on a text classification rule, and preliminarily extracting entity words; s4, inputting the extracted entity words into the variant word expansion model to obtain a corresponding variant word group, and completing identification and extraction of the entity words; and S5, carrying out situation analysis based on the knowledge graph, and predicting the occurrence and development situation of the emergencies. According to the method, the variant word bank suitable for the whole network is mined on the basis of the incidence relation of different users to different similar words, and the reliability of vocabulary expansion is improved.

The invention provides a garbage classification treatment system and method based on a two-dimensional code, and the garbage classification two-dimensional code has garbage category characterization information, and is pasted on a garbage container, filled with garbage, of a user during use; the information processing platform binds the garbage classification two-dimensional code and the user identification information in advance; the two-dimensional code processing equipment is arranged at a garbage storage point, scans garbage classification two-dimensional codes on garbage containers to obtain two-dimensional code information, generates garbage storage data based on the two-dimensional code information and sends the garbage storage data to the information processing platform, and the garbage storage data comprises garbage categories; and the information processing platform stores the garbage storage data according to the user identification information. According to the method, while personal privacy is protected, garbage can be traced, implementation of a garbage classification system is promoted, a basis is provided for reward and punishment policies, and effective management of garbage classification is improved.

The invention discloses an auscultation sound data mining method and device, and the method comprises the steps: obtaining a training data set which comprises labeled training data and unlabeled training data; iterative training is carried out on the main model and the auxiliary model based on the labeled training data to obtain a model pool, and the model pool comprises the main model and the auxiliary model which are preliminarily trained; respectively inputting the unlabeled training data into the primary model and the auxiliary model which are preliminarily trained for classification processing to obtain a classification result; performing data screening on the classification result to obtain high-value data; after labeling the high-value data, combining the labeled high-value data with the labeled training data to serve as new labeled training data; and performing iterative training on the primary model and the auxiliary model which are subjected to preliminary training on the basis of the new annotation training data until a preset convergence condition is met. The invalid labeling is effectively reduced, the data labeling efficiency is improved, and the labeling cost in the aspects of manpower, time, funds and the like is reduced.

The invention discloses an intelligent treatment system for multi-stage microwave catalytic degradation of VOCs, and relates to the technical field of concrete crack detection, and the intelligent treatment system comprises the following specific steps: (1) waste pretreatment, (2) concerted catalytic degradation, and (3) tail gas monitoring and emission. According to the intelligent treatment system for multi-stage microwave catalytic degradation of VOCs, during waste gas pretreatment, waste gas is adjusted to the optimal reaction temperature and humidity, and subsequent waste gas reaction is facilitated; during synergistic catalytic degradation, synergistic treatment of plasma reaction and multi-stage microwave catalytic reaction is adopted, the reaction effect and degradation efficiency of waste gas are improved, complex VOCs waste gas is subjected to classified treatment through multi-stage labor division, a microwave catalytic reactor is divided into a first-stage treatment section, a middle treatment section and a tail-stage treatment section, and the treatment efficiency is improved. Meanwhile, the middle treatment section is further divided into a low-temperature chamber, a medium-high-temperature chamber and a high-temperature chamber, and the problem that complex VOCs are not thoroughly degraded through traditional single-section catalysis is conveniently solved through multi-stage catalytic degradation of the multiple treatment sections.

The invention relates to the technical field of power grid engineering review, in particular to an intelligent review method for power grid engineering design. The method comprises the steps of obtaining a design scheme of review, determining at least one review project corresponding to the design scheme for the design scheme, and performing classification processing on the review project by utilizing a decision tree model of a review field to obtain a category judgment result of a to-be-reviewed project; and for each review item, intelligently selecting a review expert corresponding to the review item to execute a review operation, and executing a matched review operation to obtain an item review result and a review opinion of the review item. According to the intelligent evaluation method for the power grid engineering design, the evaluation efficiency of the design scheme is effectively improved, and the reliability and accuracy of the evaluation result of the obtained evaluation scheme are improved.

To provide a technique for executing highly accurate class classification.SOLUTION: A method includes the steps of: (a) generating N pieces of input data from one target object; (b) inputting the input data to a machine learning model and obtaining M classification output values, one determination class, and a feature spectrum; (c) obtaining a similarity degree between a known feature spectrum group and the feature spectrum for the input data, and obtaining a reliability degree with respect to the determination class as a function of the similarity degree; and (d) executing a vote for the determination class, based on the reliability degree with respect to the determination class, and determining a class determination result of the target object, based on a result of the vote.SELECTED DRAWING: Figure 8

The invention belongs to the technical field of big data, and particularly relates to a data feature management method, a computer program product and electronic equipment. The data feature management method comprises the following steps: acquiring service feature data in a plurality of scenes; classifying the business feature data according to a classification model to obtain a classification result, the classification result comprising the type of each piece of business feature data; performing evaluation processing according to the business feature data of each type to obtain classification evaluation data of the classification model on the business feature data; performing optimization processing on the classification model according to the classification evaluation data to obtain an optimized classification model; and performing data feature management based on the optimized classification model. Therefore, the data management efficiency can be improved.

The invention provides an article classified placement method, a region setting method and a self-moving cleaning device, and the method comprises the steps: setting a plurality of placement regions in a map in advance, and enabling the placement regions to be used for the placement of different types of movable articles, and when the self-moving cleaning device recognizes the movable articles in a cleaning process, carrying out the recognition of the movable articles. The target placement area corresponding to the article category of the movable article can be determined, and the movable article is moved to the target placement area through the mechanical arm module. According to the self-moving cleaning equipment, classified placement of objects can be achieved, the labor intensity of manual classification of a user is reduced, the garbage classification treatment efficiency and accuracy are improved, secondary pollution cannot be caused, and the problem that in the prior art, a single storage area is used for storing different types of garbage is solved; the technical problems of low garbage classification treatment efficiency and secondary pollution exist in the prior art.

The present invention discloses an intelligent classification and retrieval method and system for archives. The method includes: according to the original data format of the archives, using a multi-modal data fusion algorithm to perform unified format conversion and feature extraction on text, image, and audio data to obtain a standardized archives feature data set; based on an adaptive classification model of deep learning, classifying the standardized archives feature data set to generate multi-level archives classification labels; through an enhanced semantic network construction technology, analyzing the semantic relationships between the archives classification labels to generate a semantic association graph, and optimizing the semantic association graph to obtain an optimized semantic association graph; according to the retrieval request input by the user, using the optimized semantic association graph for semantic matching and relevance ranking, and outputting the archive information most relevant to the retrieval request. By using the embodiments of the present invention, the relevance and accuracy of retrieval can be improved, and the modernization and intelligence of archive management can be promoted.