122 results about "Classification treatment" patented technology

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 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 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 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 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 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 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 discloses a project defect code determination method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a current defect report and code change information of a current project, carrying out the word embedding coding processing of the current defect report and the code change information, and obtaining a defect report input vector and at least one code block input vector; performing language coding processing on the defect report input vector and each code block input vector by utilizing a pre-trained defect positioning model to obtain a defect report feature vector and at least one code block feature vector; and performing defect positioning classification processing on the defect report feature vector and each code block feature vector by using a pre-trained defect positioning model to obtain at least one association probability, and determining a defect code of the current defect report according to each association probability. According to the method, the current defect report can be automatically analyzed, and the defect code of the current defect report is accurately and efficiently determined according to the association probability of the current defect report and each code block.

Embodiments of the invention disclose an object architecture restoration method and apparatus, an electronic device and a storage medium. The method comprises the steps of determining a current preset time range; obtaining target code submission log data of the object in the current preset time range; performing structured processing on the target code submission log data to obtain structured target code submission log data; performing classification processing on the structured target code submission log data to obtain a modular classification result of the target code submission log data; based on the modular classification result, generating a target modular log file corresponding to the target code submission log data; and obtaining a target architecture in the current preset time range according to the target modular log file. According to the architecture restoration method provided by the invention, the modular log file is generated by performing module classification processing on the log data, so that the architecture is restored, the evolution process of the software architecture is understood, and the quality of the software architecture is improved.

This invention discloses an automatic event extraction method for construction quality design specifications, comprising: acquiring and preprocessing the original specification dataset; inputting the data into a pre-trained language model for processing to obtain the event trigger word recognition results of the preprocessed dataset; employing single-trigger word and multi-trigger word classification processing methods, utilizing rule matching methods and deep learning models to jointly process multi-trigger word cases, processing the multi-trigger word recognition results of the preprocessed dataset sequentially according to colon, semicolon, and comma order, and obtaining a complete meta-event dataset using deep learning methods and similarity methods; mining the association between measurement units and measurement terms based on clustering methods, and further supplementing event semantics using similarity methods to improve event expressiveness. This invention, through a combination of grammatical matching and deep learning, completes the generation of specification inspection data, contributing to increasing the number of publicly available Chinese construction datasets.

The invention discloses a control method and equipment of a knowledge distillation system and a storage medium, and belongs to the technical field of data processing. The method comprises the steps of obtaining a condition vector of a middle layer of a to-be-learned model based on a classification processing action of the to-be-learned model on sampling sub-graphs; carrying out noise addition on the sampling sub-graph to obtain a noise-added sub-graph; through the first teacher model, performing a denoising action on the noise-added sub-graph according to the condition vector to obtain a reconstructed sub-graph, and calculating reconstruction loss of the reconstructed sub-graph and the sampling sub-graph; calculating the model loss of the to-be-learned model according to the reconstruction loss and the fitting loss of the to-be-learned model and a second teacher model; and iterating the to-be-learned model according to the model loss to obtain a student model. Through the double-teacher model, in the student model fitting learning process, the student model is guided to construct an understanding mechanism for the graph result, so that the essential performance is improved.

The application discloses a low-gas mine gas emission abnormal source precision detection and treatment method, which is used for solving the problem of low-gas mine gas abnormal emission. First, the monitoring points and the monitoring point positions are determined, and all gas monitoring data are obtained through a mine monitoring system; data analysis is carried out based on SPC control chart theory, and a gas emission data anomaly criterion is established; the gas emission state is judged through the SPC control chart method, the gas emission abnormal area position is determined, and early warning is carried out; according to the determined gas emission abnormal area, the isotopic analysis method is used to detect the gas emission source and composition; finally, the gas emission abnormal source analysis result is tracked, targeted gas source classification treatment measures are formulated, and then the treatment effect test is carried out. The application can effectively control the low-gas mine gas emission abnormal risk, thereby improving the safety of coal mining.

The application provides a source network load storage virtual power plant-oriented internal microgrid balance optimization method and system, relates to the virtual power plant field, and the method comprises the following steps: obtaining first target power information from a power system, and performing cleaning, standardization, clustering and hierarchical classification processing to obtain second target power information; constructing a load model and designing a time sequence network according to the second target power information to construct a time sequence model; determining adjustable variables and adjustable capacity ranges to obtain boundary constraint conditions; building a double-layer optimization architecture facing user demand and user equipment end response scheduling, and constructing a multi-task learning model; solving the multi-task learning model, and outputting a target solution to adjust the internal microgrid balance of the virtual power plant. The target solution obtained by solving the multi-task learning model can represent the resource allocation instructions required by each device under the optimization target, and then the response strategy is determined to flexibly adjust the user load, so that the multi-objective optimization of source network load storage resources is realized.

The embodiment of the invention provides a policy text classification method and device, equipment and a storage medium, and relates to the technical field of large language models. The method comprises the steps of obtaining a policy text set, data information of at least one dimension of an enterprise and preset classification description data information, and inputting the policy text set, the data information of at least one dimension of the enterprise and the preset classification description data information into a first preset large language model; based on a first preset large language model, processing the policy text set according to the cue words to obtain multiple pieces of key element information; and based on a second preset large language model, determining a preset number of effective key element information from the multiple pieces of key element information, and performing classification processing on the policy text to be classified according to the preset number of effective key element information to obtain the category of the policy text to be classified. By adopting the technical scheme, the importance of the policies can be accurately distinguished, and classification is carried out according to the importance of the policies.

The application relates to the technical field of automatic driving application software development, and provides a processing method for optimizing a development system resource, which comprises the following steps: acquiring the running characteristics of a development system; performing classification processing on the modules of the development system according to the running characteristics to generate initial classification modules; presetting corresponding optimization rules according to the initial classification modules, and performing optimization processing on the initial classification modules to generate target classification modules; and collecting all the target classification modules to generate an optimized development system. The development system is optimized by performing optimization processing on the modules of the development system respectively, so that the efficiency of optimizing the development system resource is improved.

The invention discloses a disease treatment method for old airport lanes in a high heat area. The method comprises the following steps: S1, disease evaluation and classification treatment; s2, planning a processing scheme; s3, high-temperature-resistant composite cover surface treatment; and S4, interface reinforcement and drainage optimization. According to the method, the pavement disease type and severity are determined by evaluating and classifying the diseases, the treatment scheme is determined according to the pavement disease type and severity, local structure repair is performed according to the treatment scheme, and compared with an existing cement concrete plate replacement repair mode, the method has the advantages of being short in construction period and low in cost.

The application relates to the technical field of agricultural intelligence, and discloses a working area accurate calculation method and device based on classification-clustering bidirectional feedback, which comprises the following steps: determining target track data generated by a farm machine during working; performing working state classification processing on the target track data by using a target classification model to determine a target working data set of the farm machine; wherein the target classification model is determined by iteratively training an initial classification model by using suspicious track data in the target track data; and performing area calculation on effective plot units in the target working data set to obtain a target working area of the farm machine. By iteratively training the initial classification model, the target classification model can continuously learn and correct classification errors, compared with a single classification model, the target classification model can effectively reduce misjudgment and missed judgment, significantly improves the accuracy of the final working area calculation result, and can meet high-standard application requirements.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence-based question anti-asking method, device and equipment and a storage medium, and the method comprises the steps: receiving a user question; performing classification processing on the user question according to the first prompt word through a large language model to obtain a target insurance category involved in the user question; according to the target insurance category, matching a target to-be-asked decision tag of the user question from a tag library; through a large language model, according to the second prompt word, the user question and the target to-be-asked decision label, whether the user question is asked reversely is judged; screening out a key to-be-asked decision tag from the target to-be-asked decision tag under the condition that the user question is determined to be asked inversely; and generating an anti-question sentence of the user question according to the third prompt word and the key to-be-asked decision label through a large language model. The method can be applied to a dialogue recommendation system in a financial insurance product recommendation scene, and the understanding depth and the anti-question ability of the dialogue recommendation system on user questions can be improved.

Systems and methods are provided for learning a classifier for annotating documents with predicted labels under an extreme classification where there are more than one million labels. The learning includes receiving a joint graph comprising documents and labels as nodes. A multi-dimensional vector representation of the documents (i.e., document representations) is generated based on graph convolution of the joint graph. Each document representation varies in dependence on adjacent nodes to accommodate context. The document representations are feature transformed using a residual layer. A per-label document representation is generated from the transformed document representations based on adjacent label attention. The classifier is trained for each of the more than one million labels based on joint learning using training data and the per-label document representations. The trained classifier performs highly efficiently compared to other classifiers trained using disjoint graphs of documents and labels.

The invention discloses an information feedback file classification method, system and equipment based on a large model and a medium, belongs to the technical field of large models, and aims to solve the technical problem of how to efficiently and quickly classify the content of feedback files, realize quick identification and accurate classification of the feedback files and improve the classification efficiency of the feedback files. According to the technical scheme, the method comprises the steps of information feedback file collection and data aggregation, wherein an information feedback file is obtained through a service related party, and an excel data aggregation file is formed in a set format; data desensitization: obtaining original description and category classification of the information feedback file according to the excel data aggregation file, and carrying out sensitive information replacement processing on the original description through a Deepseek-V3 large model to obtain desensitized original description data; model fine tuning: performing fine tuning on the Qwen2.5-14B model by adopting a multi-round dialogue format in a Lora mode, inputting original description into the Qwen2.5-14B model, and outputting a classification result by the Qwen2.5-14B model; model deployment: performing model deployment reasoning through an artificial intelligence processor chip; and generating a classification result.

The invention discloses an enterprise internal problem classification method and device, electronic equipment and a storage medium, and the method and device are applied to the electronic equipment and are used for classifying problem description texts collected in an enterprise. The method specifically comprises the following steps: classifying the problem description text based on a pre-constructed inspection problem classification model to obtain a main category; and further classifying the problem description text based on a pre-constructed inspection problem-label similarity matching model to obtain an additional category. The examination question classification model is more suitable for question description texts, and case vocabulary level information is easier to learn, so that the question classification accuracy can be improved. Moreover, based on the inspection problem-label similarity matching model, the expression ability of semantic features can be further improved, and the model classification effect is improved.

This invention discloses a wastewater classification and separation system based on industrial wastewater recycling. The invention relates to the field of industrial wastewater treatment and resource recycling technology. The system includes a wastewater acquisition and pretreatment module, a multi-parameter real-time water quality detection module, a comprehensive pollution classification and determination module, a separation and control module, a classification storage and buffer module, a classification treatment module, a recycling intelligent scheduling module, and a data acquisition and intelligent management module. This invention utilizes a collaborative working mechanism of multi-parameter real-time water quality detection and comprehensive pollution classification and determination. It employs a piecewise linear dimensionless scoring function to uniformly map five water quality indicators to a scoring range of 0–10 and then performs a weighted summation. This achieves multi-dimensional, accurate, and real-time determination of the pollution level of industrial wastewater, overcoming the shortcomings of existing technologies where single-indicator judgments are inaccurate and multi-indicator dimension inconsistencies prevent comprehensive evaluation. The targetedness and effectiveness of wastewater classification and treatment are significantly improved.

The application discloses a car insurance fraud identification method, device and equipment based on a novel feature optimization algorithm, and the method comprises the following steps: acquiring a to-be-tested data set of car insurance claim fraud; performing classification processing on the to-be-tested data set based on a classification model to obtain a classification result; wherein the classification model is trained through a training set, and the training set is a data set that is balanced through a comprehensive sampling algorithm SMOTEEN and optimized through a feature selection algorithm EFS_BR proposed by the application; and the classification result is used for reflecting whether there is a car insurance claim fraud behavior. The application can achieve the effects of eliminating useless features, reducing the model calculation complexity and improving the model identification rate.