50 results about "Dynaset" patented technology

The invention discloses a big language model-driven RAG knowledge base quality evaluation method and system. The method comprises the following steps of: constructing QAR triples of different problem types by utilizing a big language model; performing review optimization on the triple by utilizing a large language model; inputting the constructed questions into an RAG knowledge base question-answering system, and recording replies of the question-answering system, retrieved reference text segments and reordering model scoring for each question; according to a recording result, automatically calculating scores of various preset indexes; and according to the question type, weighting and calculating a comprehensive score. According to the method, the external information source can be flexibly adjusted according to user requirements, and the dynamic QAR set containing a plurality of problem types is automatically created, so that the flexibility and individuation of an evaluation scene are realized. Besides, a set of complete evaluation indexes and weighting rules is established to comprehensively consider the retrieval accuracy, the generation quality, the user preference and the requirements in practical application, so that the evaluation result can comprehensively and objectively reflect the performance of the RAG knowledge base question-answering system.

The invention belongs to the technical field of federated learning, and particularly relates to a federated learning method and device based on feature comparison optimization and classifier dynamic integration. The method comprises the steps that a global model is initialized through a server and issued to # imgabs0 clients participating in federated learning, the clients conduct local model updating based on the received global model and train the updated local model, then feature extraction is conducted on a local private data set through a trained local feature extractor, and a local private data set is obtained; the method comprises the following steps: constructing a local model to construct a local feature prototype set, uploading the local model, the local feature prototype set and the total sample size of a client to a server, using a contrast learning technology to improve the quality of a global feature prototype when the server globally aggregates, and finally issuing a global prototype and a global feature prototype set which are obtained by aggregation to each client. And executing the next round of learning until the local model converges or reaches the set communication round.

Example embodiments of the present disclosure provide for an example method including generating a baseline set of domains by comparing domains of interest to a group of existing registered domains. The domains of interest are generated using a permutation engine based on a first domain. The example method includes periodically generating a dynamic set of domains by comparing the domains of interest to an updated group of existing registered domains. The updated group is obtained in real-time. The example method includes determining a potentially malicious domain based on comparing the baseline set and dynamic set. The example method includes for each respective potentially malicious domain: obtaining an IP address associated with the potentially malicious domain and determining an IP address risk score. The example method includes determining a potentially malicious domain is a malicious domain based on the IP address risk score of the potentially malicious domain.

The invention relates to the technical field of enterprise data management, in particular to a lightweight-based enterprise data dynamic integrated management system, which is characterized in that an acquisition module is used for deploying edge computing nodes, receiving multi-source heterogeneous information streams from manufacturing execution systems and equipment logs, dynamically analyzing and standardizing the information streams, and adding metadata tags; uploading is carried out in a batch processing mode; the map construction module is used for constructing a semiconductor blood relationship map by taking the standardized key information as a blood relationship clue; a graph database is used for efficient storage, and a RESTful API interface is configured to support batch import, so that data storage and relevance expression are more flexible and efficient; the prediction module performs reasoning on the atlas by adopting a graph neural network to generate predictive risk distribution, and a correlation analysis set generated by the prediction module is stored back to the atlas in a structured manner; by introducing a multi-thread concurrent write-in and lock mechanism, the atlas supports complex combination query based on a Cypher query language, and supports multi-level and traceability query.

The invention provides an electric power information multi-modal data dynamic integration method and system, relates to the technical field of electric power information data integration, and realizes multi-modal data fusion through a technical chain of asynchronous receiving, feature extraction, elastic alignment, dynamic verification and intelligent compensation. The method comprises the following steps: acquiring a sensor numerical value, an infrared image and an environment audio heterogeneous data stream in real time; extracting each modal feature and calculating a historical deviation coefficient; sensor data is used as a reference axis, and a multi-modal maximum feature coincidence interval is determined through sliding window matching; performing cross-modal feature cross validation in the extended dynamic interaction interval, and marking credible fusion features; redundant feature compensation is triggered for the modals which do not pass verification, and an integrated feature vector is generated; and if continuous failure occurs, marking the data source as a low-confidence data source. Through elastic time alignment and a dynamic verification mechanism, the problem of native asynchronization of power multi-modal data is solved, the feature fusion reliability is improved, and accurate fault information can be provided.

An automated method and system for modeling intrusion detection on an Internet of Things network preprocess received raw data packets. The raw data packets are converted to a packet capture file, which is converted to a comma separated value (CSV) file. The CSV file is filtered to create normal and abnormal CSV files. An attack is identified in the abnormal CSV file. Features associated with the attack are labeled and classified as an attack type. The abnormal and normal CSV files are merged. Redundant data is removed from the merged file, which is added to a concatenated file with other merged files. Features are extracted from the concatenated file and a machine language (ML) model is built using the extracted features. ML classifiers are determined for each merged file for the ML model to build an ensemble ML model. A dynamic ensemble of ML classifiers detects evolving attack types.

The invention provides a multi-modal ecological environment monitoring element plug-in integration system and method based on unified objectification modeling. Firstly, an object model and an inheritance system of monitoring equipment, data and an algorithm are constructed; on the basis, a unified logic storage and metadata cataloguing mechanism oriented to heterogeneous databases such as relational databases, non-relational databases and file object databases is designed and realized; the plug-and-play and full-life-cycle management of equipment, data and algorithm models is further realized through a dynamic registration module; common APIs (Application Program Interface) such as object-level addition, deletion, modification and query, space-time association query, concurrent task scheduling and aggregation and the like are externally provided by relying on a unified service bus. And the hybrid storage engine cooperatively manages structured and unstructured data and maintains a topological relation between objects. According to the scheme, object dynamic integration and expansion are completed on the premise that operation is not interrupted, the multi-source heterogeneous data management and cross-modal analysis efficiency is remarkably improved, and the method is suitable for various ecological environment monitoring scenes such as natural reserve, drainage basin pollution supervision and disaster emergency response.

The invention provides an enterprise full-data dynamic integration method based on an intelligent algorithm, and relates to the technical field of data processing. Historical enterprise data and department architecture of a target enterprise are obtained, and a data dynamic integration network is established; the output end of the data cross-department verification window is connected with an enterprise data uploading interface of the data dynamic integration network, data integration reliability verification is conducted on the newly-added enterprise data to obtain a verification result, if the verification result is passed, the newly-added enterprise data is synchronized to the data dynamic integration network to execute data dynamic integration, and otherwise, the newly-added enterprise data is synchronized to the data dynamic integration network to execute data dynamic integration. And according to a verification result, extracting abnormal enterprise data in a data dynamic integration network in a backtracking manner, and marking an abnormal department. The technical problems that in the prior art, enterprise data is not effectively managed, so that the data quality is low, and the working efficiency and the decision-making quality are reduced are solved, and the technical effects of more efficient data integrated management, data quality ensuring, data utilization rate improving, decision-making quality improving and potential risk reducing are achieved.

The invention discloses an intelligent load balancing method based on a zero-sample hybrid expert model, and the method comprises the steps: creating a task spectrum mapping relation through zero-sample learning, generating a virtual label, refining the demands of a new task into a resource demand feature spectrum through multi-dimensional feature cross projection, and carrying out the optimization of the resource demand feature spectrum. Predicting a multi-dimensional resource demand of the new load balancing task; establishing a multi-view hybrid expert dynamic integration mechanism, configuring a multi-view hybrid collaborative expert for a task, applying a plurality of expert model combinations to different demand dimensions, creating a virtual image sample of a new task, simulating a resource demand and a load effect thereof in a virtual environment, and generating an initial resource allocation scheme; for dynamic load fluctuation, task cells are distributed to nodes with sufficient resources; according to the method, the robustness and efficiency of the system are improved, the implementation process is simplified, and the method has wide application prospects and practical value.

The invention provides a heterogeneous database query method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out security verification processing on a plurality of SQL execution requests sent by a user side, and carrying out SPI mechanism scanning processing on a plurality of database plug-ins in a heterogeneous database plug-in directory based on a high-performance database connection pool after the security verification processing is passed, determining a target database plug-in package corresponding to each SQL execution request; and performing metadata query processing on the SQL execution request in the target data source based on a database operation client interface and a metadata service interface under the target database plug-in package, and determining a structured tree result. According to the method, an extensible and dynamically integrated heterogeneous database plug-in mechanism is realized, unified access of various databases is supported, the platform maintenance cost is remarkably reduced, hierarchical metadata of any type of databases is obtained through a consistent metadata service interface, and the user experience and the cross-database query efficiency are improved.

The invention discloses an SDK (Software Development Kit) dynamic integration method, device and equipment and a readable storage medium, and relates to the technical field of software development, and the method comprises the following steps: after receiving an SDK loading instruction, analyzing the SDK loading instruction to obtain a first analysis result, and determining a class file storage path corresponding to a target loading SDK according to the first analysis result; loading a target SDK class file corresponding to the class file storage path according to a preset loading process template; instantiating the loaded target SDK file through a JAVA reflection mechanism to obtain a first instance corresponding to the target SDK file; and obtaining an identification code corresponding to the target loading SDK, obtaining metadata information in the target SDK class file, and registering the identification code corresponding to the target loading SDK, the first instance and the metadata information in a preset registry. According to the method, the SDK integration efficiency can be improved, and integration errors are reduced.

The invention relates to the technical field of cloud computing, and provides a cloud resource workload prediction method based on multi-scale self-adaption in a computing network environment, and the method comprises the steps: carrying out the data collection of a workload sequence of a cloud data center through employing a sample division method based on a sliding window, and obtaining the cloud workload data of different time scales; carrying out noise reduction processing on the cloud working load data by adopting an adaptive threshold noise reduction method based on wavelet multi-scale decomposition, and carrying out Fourier transform to extract frequency domain features; carrying out sample division on the processed cloud working load data by adopting an adaptive mode clustering algorithm, and outputting a mode data set; adopting a Mixup data enhancement method based on mode driving to generate a mixed sample, adding the mixed sample into the mode data set, and constructing an enhanced mode data set; and obtaining a cloud resource workload prediction result by adopting a similarity alignment dynamic integrated prediction method. Finally, an ablation experiment proves that the method has higher prediction precision.

The invention relates to the technical field of smart energy management, in particular to an intelligent industrial heritage energy exhibition hall energy efficiency transformation method, which comprises the steps of data acquisition, digital twinborn model construction, energy efficiency diagnosis and energy consumption regulation and control strategy design. According to the method, information such as historical energy consumption, a building structure, equipment layout and environmental parameters is acquired in real time and dynamically integrated, a digital twin model is constructed, an mRMR-HDC method is designed, and precise recognition of a complex energy consumption scene is realized through combination of minimum redundancy and maximum correlation feature selection and a high-dimensional super-vector model; the invention further designs a rotational flow optimization algorithm, constructs a multi-target optimization model for energy efficiency regulation and control, and assists the realization of energy conservation and emission reduction targets.

The present invention discloses an intelligent load balancing method based on a zero-sample hybrid expert model, comprising the steps of: creating a task spectrum mapping relationship through zero-sample learning, generating virtual labels, refining the requirements of new tasks into resource requirement feature spectra through multi-dimensional feature cross-projection, and predicting the multi-dimensional resource requirements of new load balancing tasks; establishing a multi-perspective hybrid expert dynamic integration mechanism, configuring multi-perspective hybrid collaborative experts for tasks, applying a combination of several expert models to different demand dimensions, creating virtual image samples of new tasks, simulating resource requirements and their load effects in a virtual environment, and generating an initial resource allocation plan; in the face of dynamic load fluctuations, distributing task cells to nodes with sufficient resources; the method improves the robustness and efficiency of the system, simplifies the implementation process, and has broad application prospects and practical value.

The embodiment of the invention discloses a task conversion method and device, electronic equipment, a storage medium and a product. The method comprises the following steps: analyzing an OpenAPI standard online document provided by a target service providing end, and extracting API metadata information from the OpenAPI standard online document; based on a preset MCP protocol conversion strategy, the API metadata information is converted into target metadata conforming to an MCP protocol, and an MCP proxy service is generated based on the target metadata and service URL information provided by a target service providing end; and in response to an MCP service calling request sent by the intelligent agent, obtaining MCP service information matched with the MCP service calling request from the target service providing end based on the MCP proxy service, and feeding back the MCP service information to the intelligent agent. According to the scheme, the compatibility of the external service and the intelligent agent is intelligently realized, and the efficiency and the accuracy of dynamic integration of the intelligent agent on massive external services are improved.

The application belongs to the technical field of information analysis, and particularly discloses a sudden event distortion information identification method and system, which comprises the following steps: constructing a keyword table of a sudden event according to keywords of the sudden event; screening a sudden event distortion information data set to construct a sudden event distortion information training data set and a test data set; building a first-stage verification large model through a sample embedding prompt strategy and building a second-stage review large model through a chain thinking prompt strategy; constructing an XLNet-BiLSTM hybrid model to identify sudden event distortion information; constructing a model dynamic integration rule considering the consequences of illusion relief and specific distortion scores, and obtaining the distortion information identification results of the two-stage large model and the XLNet-BiLSTM hybrid model. The application ensures the consistency and understandability of the identification results and the generated explanation, and highlights the transferability of information identification in different types of sudden events.

The invention provides a heterogeneous resource integration method and device. The method comprises the following steps: pre-configuring and storing heterogeneous resource attribute information; and initiating a resource query request or a resource operation request, querying a corresponding resource based on query condition configuration information in the heterogeneous resource attribute information, selecting a resource provider route based on basic configuration information in the heterogeneous resource attribute information, calling a resource provider interface based on interface configuration information in the heterogeneous resource attribute information, and sending the resource provider route to the resource provider interface. Obtaining resource data; processing the resource data based on resource operation configuration information and resource batch operation configuration information in the heterogeneous resource attribute information to obtain processed resource data; and filling the processed resource data into a preset structure, and rendering and displaying the page according to the filled data. According to the method, various heterogeneous resources can be dynamically integrated as required, and the problems of repeated workload and bloated codes in heterogeneous resource integration in the prior art are solved.

The invention discloses an open intelligent algorithm adaptive combination method and system based on meta learning and a dynamic pipeline. According to the method, by introducing an algorithm directed acyclic graph construction mechanism of a type system and an adapter, dynamic integration of heterogeneous components in an open algorithm library is realized, and the limitation that a traditional AutoML system algorithm combination space is closed and depends on a preset process is overcome. Environment perception and a performance monitoring closed loop during operation are integrated into a decision process, and a dynamic adjustment mechanism based on deviation triggering is designed, so that the system has the capability of coping with concept drift and resource fluctuation after deployment. By constructing and continuously updating the knowledge base recording the task-environment-performance mapping relation, the system has the ability of making decisions based on historical experience. According to the meta-learning mechanism, when the system processes similar tasks, repeated search overhead can be reduced, continuous evolution of performance is achieved, and a feasible technical path is provided for solving the continuous learning problem of a universal intelligent system.

The present invention relates to the field of data transmission, and particularly to a human-computer interaction method and system for legal consultation. The method includes: obtaining interaction data and counting word frequency information, calculating the distribution uniformity of words according to the index positions of the words in the interaction data, partitioning each word into sets based on the distribution uniformity and a preset uniformity threshold, obtaining the importance of words based on the words in the dynamic set according to the change in the occurrence frequency of the words during the interaction process, adjusting the Huffman tree nodes corresponding to the words in the dynamic set according to the importance of the words to optimize the coding strategy, and compressing and transmitting the interaction data according to the adjusted Huffman tree. By monitoring the change in word frequency in real time, the present invention inserts new words into the last node of the Huffman tree and dynamically adjusts their positions closer to the root node when the new words appear frequently, thereby optimizing the coding strategy and improving the compression efficiency.

The invention relates to the technical field of information, and discloses a dynamic integration system and method for an electric power large model platform and heterogeneous systems, and the dynamic integration system comprises a protocol adaptation layer which is used for processing original heterogeneous data of a plurality of heterogeneous systems through a three-layer conversion engine, obtaining a standardized data stream, and dynamically adapting a transmission protocol of the standardized data stream; the service perception layer is used for generating a real-time routing rule by adopting a three-layer linkage mechanism based on the standardized data flow to control on-demand routing of the standardized data flow so as to obtain a routed data flow; the data pipeline layer is used for acquiring a binary log event of each heterogeneous system in real time, processing the routed data flow through a three-layer guarantee mechanism, generating training data consistent with the binary log event, and outputting the training data to each heterogeneous system; in complex cross-platform data interaction, high consistency and high reliability of data are realized.

Embodiments of the present invention relate to the field of information technology, and disclose a method for expanding and contracting a database cluster, a service system, and a storage medium. In the present invention, detection values of operation metrics of each database in the database cluster are obtained; according to the detection values of the operation metrics of each database, the resource utilization rate of the database cluster is obtained; if the resource utilization rate of the database cluster meets a preset expansion and contraction condition, the database cluster is expanded and contracted. By comprehensively analyzing the detection values of the operation metrics, the analysis result is more accurate, and the dynamic expansion and contraction of the database cluster can be automatically realized, saving human resources and achieving the purpose of intelligence and automation.

The invention belongs to the technical field of information analysis, and particularly discloses an emergency distortion information identification method and system. Comprising the following steps: constructing a keyword table of the emergencies according to keywords of the emergencies; screening the emergency distortion information data set so as to construct an emergency distortion information training data set and a test data set; building a first-stage check large model through a prompt strategy of sample embedding, and building a second-stage review large model through a prompt strategy of chain thinking; an XLNet-BiLSTM hybrid model is constructed to identify the distortion information of the emergency event; and constructing a model dynamic integration rule considering the illusion relieving consequence and the specific distortion score, and thus obtaining a distortion information identification result of the two-stage large model and the XLNet-BiLSTM hybrid model. According to the method, the consistency and the understandability of the identification result and the generation explanation are ensured, and the mobility of information identification in different types of emergencies is highlighted.

The invention relates to the technical field of cloud work load prediction, and particularly provides a Kubernetes-based cloud work elastic telescopic capacity expansion method, a PM-SGRUs-DTW model is applied to a Kubernetes platform, the PM-SGRUs-DTW model comprises a dynamic integrated prediction key design of multi-scale time sequence decomposition and feature extraction, adaptive mode clustering and data enhancement and similarity alignment, and the dynamic integrated prediction key design of the multi-scale time sequence decomposition and feature extraction, adaptive mode clustering and data enhancement and similarity alignment is applied to the Kubernetes platform. And finally, performing a prediction experiment on the CPU utilization rate and the memory utilization rate based on the real data sets of Aliaba and Google. According to the method, the prediction result of the PM-SGRUs-DTW model is combined with the original HPA mechanism of Kubernetes, the capacity expansion and contraction decision in the initial stage of load fluctuation is realized by accurately predicting the load trend and pre-judging the resource demand, the response delay of an elastic expansion and contraction mechanism is reduced, and the resource utilization efficiency and the service quality are improved.

The invention provides a man-machine collaborative text data coding method. The method specifically comprises the following steps of S1, formulating a code book; s2, constructing a coding model; and S3, verifying the coding saturation. According to the method, the manual coding workload is effectively reduced, and the coding sample representativeness is improved. Meanwhile, a multi-agent data enhancement method is provided, and the key problems of label imbalance, sample finiteness, sample singleness and the like are solved. Besides, the dynamic integrated attention voting system provided based on the simulation thought of the real coding and discussion process focuses on complementarity between models, not only is the training deviation of a single high-performance model weakened, but also the actual effect of the model can be further improved. Finally, the invention provides a complete verification standard of the coding saturation, and the problem breakthrough of quality interpretation-quantitative verification is realized.

The invention discloses an electric power inspection work order trend prediction method and system based on a large language model. The method comprises the following steps: collecting text data of an electric power inspection work order and time sequence data of work orders with similar problems, and carrying out data preprocessing; extracting multi-dimensional time sequence features from the time sequence data; semantic features of the text data are extracted; carrying out feature alignment and fusion on the time sequence features and the semantic features by adopting a self-adaptive gating fusion mechanism; after time sequence decomposition and semantic binding are carried out, multi-resolution time imaging and seasonal component and trend component decoupling are carried out; and carrying out multi-scale and multi-resolution mixing on the seasonal image and the trend image to obtain a final feature of each scale, and predicting a work order quantity variation trend of the same kind of problems by adopting an attention-based dynamic integration algorithm. According to the invention, accurate prediction of the work order change trend of similar problems can be realized.

The application provides a large language model training and reasoning method based on mixed field entropy dynamic alignment. The training method comprises the following steps: acquiring a general field data set and a target field data set; generating multiple reasoning tracks corresponding to each general field sample in the general field data set and calculating corresponding reasoning uncertainty and exploration diversity; obtaining a comprehensive value score of each general field sample according to the reasoning uncertainty and the exploration diversity, and selecting the top k general field samples with the highest scores to obtain a selected general field data set; in each iteration training, a batch of data is sampled from the mixed data set for track generation and entropy dynamic extraction to obtain an entropy dynamic set of the target field sample and an entropy dynamic set of the general field sample, and an entropy dynamic alignment reward is calculated to update the parameters of the strategy model until the preset training condition is met to obtain a trained large language model; and the accuracy of the reasoning result is improved.

The embodiment of the invention provides a failover method and device for a read-write separation database cluster, equipment and a medium, and the method comprises the steps: building a connection pool group according to the node information of a plurality of nodes, the connection pool group comprises a plurality of independent connection pools, and each independent connection pool corresponds to one node; periodically acquiring node state information of each node and connection state information of the physical connection in each independent connection pool; updating an independent connection pool corresponding to each node according to the node state information and the connection state information; and when it is detected that a target node fault exists, determining a target connection pool according to the load state information of the new independent connection pool, the target connection pool being used for responding to the service request. Through a connection pool group architecture, a state timer, idle connection management and a load balancing strategy, rapid fault response, efficient resource utilization, development complexity reduction and dynamic cluster adaptation are realized, so that the stability and service continuity of a database cluster are remarkably improved.

The application discloses an open intelligent algorithm adaptive combination method and system based on meta-learning and a dynamic pipeline. By introducing a type system and an adapter algorithm directed acyclic graph construction mechanism, the application realizes dynamic integration of heterogeneous components in an open algorithm library, and overcomes the limitations of a closed algorithm combination space and a preset process of a traditional AutoML system. By integrating environment perception and runtime performance monitoring into a closed loop decision-making process, and designing a dynamic adjustment mechanism based on bias triggering, the system has the ability to cope with concept drift and resource fluctuations after deployment. By constructing and continuously updating a knowledge base recording the mapping relationship between tasks, environments and performance, the system has the ability to make decisions based on historical experience. This meta-learning mechanism enables the system to reduce repeated search overhead when processing similar tasks, realize continuous evolution of performance, and provide a feasible technical path for solving the continuous learning problem of a general intelligent system.

The invention provides a system for detecting and correcting misleading information of a large language model, which is characterized in that a state machine processing engine is integrated and configured to be a scheduling processing flow based on a deterministic finite state automaton, the state transition is driven by the attribute of an input problem and a knowledge state signal, and a dynamic knowledge updating unit is designed; knowledge is continuously grabbed from an external authoritative source through a lightweight knowledge crawler, knowledge freshness score is calculated to realize driving, and meanwhile, the timeliness of a large language model knowledge base is maintained through an incremental knowledge injection mechanism; and sequentially scheduling question detection, question error correction and question answering stages based on a state machine processing engine and a dynamic knowledge updating unit, and iteratively updating until a final accurate response is generated. The method has theoretical preciseness and knowledge real-time performance, correctness of model behaviors can be ensured mathematically, newest knowledge can be dynamically integrated, and therefore continuous and reliable services can be provided in complex and changeable real scenes.

The invention relates to the field of data transmission, in particular to a man-machine interaction method and system for legal consultation, and the method comprises the steps: obtaining interaction data and carrying out the statistics of word frequency information, calculating the distribution uniformity of words according to the index positions of the words in the interaction data, and carrying out the division and collection of all words based on the distribution uniformity and a preset uniform threshold value, based on the words in the dynamic set, the importance of the words is obtained according to the occurrence frequency change condition of the words in the interaction process, according to the importance of the words, Hoffman tree nodes corresponding to the words in the dynamic set are adjusted, the coding strategy is optimized, and interaction data are compressed and transmitted according to the adjusted Hoffman tree. According to the method, the change of word frequency is monitored in real time, the new word is inserted into the last node of the Huffman tree, and the position of the new word is dynamically adjusted to be closer to the root node when the new word frequently appears, so that the coding strategy is optimized, and the compression efficiency is improved.