794 results about "Data vector" patented technology

The invention relates to the technical field of data processing, in particular to a whole-process data monitoring and visual management method and system for an air separation process, and the method comprises the steps: collecting key parameter data in the air separation process, and input data vectors and output data vectors among equipment; constructing a parameter correlation model to analyze the input data vector and the output data vector to obtain data deviation; if the data deviation exceeds a dynamic threshold value, triggering a multi-level alarm mechanism, and generating a block chain log record; generating a hash value for the key parameter data, and marking an abnormal link; a bimodal anomaly detection model is constructed to analyze an abnormal link, an equipment performance attenuation coefficient and an inter-equipment anomaly influence relation coefficient are obtained, and a comprehensive anomaly score is generated through a fusion technology; when the comprehensive abnormal score is greater than a preset threshold value, constructing a fault tree model to reversely trace an abnormal data link for the abnormal link, and positioning a fault source; and full-process data monitoring and management are realized through a visual interface.

The invention discloses a construction method and device of a digital power system based on a multifunctional agent. Respectively extracting feature production element data and control production element data from the power data of the power plant side and the power grid side; constructing a target data vector based on the power function demand, and determining key control production element data associated with the power function demand; learning by using an artificial intelligence algorithm to obtain a mapping function between the target data vector and the key control production element data, constructing a functional operator taking the mapping function as a kernel, and constructing an intelligent agent based on the functional operator; and adding the intelligent agents corresponding to the different electric power function requirements into the digital electric power system to serve as electric power function implementation units. According to the system, diversified and digital electric power functions based on data driving can be realized, the relation between data can be captured more accurately, and high-value data assets in the electric power system are formed.

The invention provides an intelligent cold chain transportation information management method, system and device and a storage medium, and the method comprises the steps: recognizing the type of transported goods and environment demands through a task recognition module, and determining and configuring a target sensor of a cold chain vehicle through a sensor configuration module; the target sensor collects environment data, the vehicle-mounted GPS module collects position information and a driving state, the RFID tag records a cargo loading state, and all data are sent to the central management platform. The platform constructs a multi-dimensional feature vector, the multi-dimensional feature vector is compressed into a low-dimensional feature representation through an encoder, a reconstruction data vector is obtained through reconstruction of a decoder, and a reconstruction error is calculated. And by analyzing the reconstruction error, the platform generates an analysis result, and the environment control equipment in the vehicle is automatically adjusted according to the result, so that the stability of cargo transportation is ensured.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention belongs to the technical field of text data processing, and particularly discloses an LLM-based contract element extraction and auditing method and device, and the method comprises the steps: analyzing a target contract to obtain text block data, carrying out the vectorization processing of the text block data to obtain a first multi-dimensional vector, and constructing a text vector database; extracting feature information in the to-be-processed text and taking the feature information as a contract index, generating a label of the contract index according to a category to which the feature information belongs, and vectorizing the feature information and the corresponding label to obtain a second multi-dimensional vector; searching first multi-dimensional vectors similar to the second multi-dimensional vectors in a text vector database, and sorting all the second multi-dimensional vectors to generate a vector sequence; generating a to-be-checked contract according to the vector sequence and the text block data, and labeling the to-be-checked contract through the difference between the first multi-dimensional vector and the second multi-dimensional vector; the method has the advantages that the indexes can be automatically extracted, the verification result containing explanation can be given, and the working efficiency of project management personnel can be remarkably improved.

The invention discloses a text travel sentiment analysis response method and system based on multi-modal fusion and incremental learning, and the method comprises the steps: obtaining a text, an image, an audio or a video input by a user, carrying out the query in a text and multi-modal knowledge base through employing a multi-modal retrieval technology, and optimizing a retrieval result through combining sentiment analysis, thereby achieving the purpose of improving the user experience. And finally generating a personalized tourism information response. The system integrates a large language model, text and multi-modal knowledge base construction, and a data vectorization processing and sentiment analysis technology, supports multi-modal input and output, can dynamically adjust retrieval and answer contents, and realizes personalized recommendation according to user feedback. The system also has the capabilities of multi-modal data expansion, dynamic knowledge base updating and voice interaction, can significantly improve the response speed and accuracy of tourism information service, is widely applicable to intelligent and personalized tourism information service scenes, and has relatively high innovativeness and practical value.

The invention provides a medical auxiliary diagnosis method and system based on time sequence and semantic weighting, and belongs to the technical field of medical information processing. Constructing the preprocessed current and historical medical record information of the patient into patient medical record data, and inputting the patient medical record data into a pre-trained medical language model to generate a preliminary diagnosis result; the comprehensive weight of the patient medical record data vector sequence is calculated through a time decay function and the content correlation weight, a medical record fusion vector is obtained through fusion, and the medical record fusion vector and the preliminary diagnosis result are spliced into a query vector; searching candidate fragments in a clinical guide knowledge base based on the query vector, calculating semantic evidence scores and coverage scores based on patient medical record data and tokens of the candidate fragments, obtaining sorting probabilities of the candidate fragments in combination with metadata prior scores of the candidate fragments, and screening out target guide fragments; and the preliminary diagnosis result and the target guide fragment are fused to generate final auxiliary diagnosis and treatment information, so that auxiliary diagnosis and treatment suggestions with traceability, verifiability and authoritative basis are provided.

The invention provides a wind tunnel health monitoring data mining method, medium and system, and belongs to the technical field of wind tunnel health monitoring data mining and processing, and the method comprises the steps: collecting a plurality of sensing signals to construct a monitoring data stream, and carrying out the filtering and noise reduction processing to obtain preprocessed data and an abnormal data set; and starting a GPU and CPU processing stream to receive the preprocessed data and the abnormal data set, performing component decomposition to obtain a steady-state data matrix, a dynamic data matrix, an abnormal feature matrix, a parameter data vector and an abnormal parameter vector, establishing a data mining feature equation set, and calculating a wind tunnel operation state evaluation result by using the wind tunnel operation state evaluation equation set. And generating a wind tunnel operation characteristic matrix and a wind tunnel state early warning index, and outputting an early warning signal according to a comparison result of the early warning index and a preset health threshold value. The technical problem that potential fault symptoms cannot be better found due to the fact that wind tunnel health detection data cannot be rapidly and efficiently mined and analyzed from the two aspects of steady state and dynamic state at present is solved.

The invention discloses a special large language model for a solid waste cementing material and an innovative hypothesis generation method thereof, which realize accurate understanding and reasoning of information such as material composition, process parameters, mechanical properties and the like by constructing a special knowledge graph in the field of materials, combining experimental data vector retrieval and fusing a mixed retrieval enhanced generation technology, so that the innovative hypothesis generation of the solid waste cementing material is realized. And a scientific reasoning module is used for deducing a material design rule to generate a potential innovation hypothesis. The method effectively breaks through the problems of long development period and low innovation efficiency of traditional materials, and is suitable for the fields of low-carbon building material development and solid waste high-valued utilization.

The invention relates to a construction method and system of an intelligent question-answering system based on a planning knowledge graph library. The method comprises the following steps: acquiring multi-source space planning data in real time; carrying out fusion on the planning data; vectorizing the fused planning data; a plurality of preset retrieval enhancement models are modularized, and interaction of vectorized planning data among the modules is realized through a message queue; through a natural language processing method and a message queue method, constructing a plurality of knowledge maps which can be accessed by the preset retrieval enhancement model; training a pre-training model through the knowledge graph; in response to a user question, retrieving an enhancement model through dynamic routing selection, and retrieving entities and relationships in the knowledge graph; and based on the retrieval result, generating a questioning result through a pre-training model. According to the method, intelligent question answering, scheme generation and evaluation of space planning are realized through the knowledge graph and the pre-training model constructed by the multi-source data, the working efficiency of the space planning is improved, and the manual workload and the error rate are reduced.

The invention discloses an equipment anomaly detection method based on deep learning, and relates to the field of equipment anomaly detection. The method comprises the following steps: acquiring and preprocessing original sensor data, and generating an equipment data vector; constructing a self-adaptive multi-dimensional time sequence self-error correction convolutional network, and carrying out equipment anomaly detection based on the equipment data vector; in the adaptive multi-dimensional time sequence self-error correction convolutional network, generating a comprehensive state vector by adopting an adaptive time window adjustment mechanism and a time weighted fusion mechanism; performing feature extraction on the comprehensive state vector to obtain a final feature, further calculating an initial abnormal score, and correcting the initial abnormal score by adopting a self-error correction mechanism in a self-adaptive multi-dimensional time sequence self-error correction convolutional network to obtain a final abnormal score; and performing anomaly judgment based on the final anomaly score. The problems that a traditional equipment anomaly detection method cannot effectively cope with the dynamic change of the equipment state and is easily influenced by equipment data fluctuation are solved.

The invention relates to a structured text analysis method and system based on title recognition and hierarchical abstracts, and the method comprises the steps: receiving a text, and obtaining sample data of a training language model; the vector database can perform vector dimension adjustment on the data of the stored text. The vector database takes vectors as basic storage units, and converts unstructured data into high-dimensional vectors through an embedding technology. And calling the data storage type information of the title text data set to determine the data storage type of the title text data set matched with the text storage mode. And obtaining a structure type of sample data of each text by utilizing a semantic segmentation script. And segmenting the document according to the identified chapter titles, and subdividing each chapter into a plurality of logic paragraphs to form structured text block units. The multi-level abstract generation model matched with the target model can fully mine level information and semantic association in the document. Therefore, the effect of analyzing the structured text by the intelligent question-answering system is improved.

The invention relates to the technical field of Internet of Vehicles communication, and discloses an Internet of Vehicles channel prediction method based on multi-modal fusion and related equipment. The method comprises the following steps: constructing an urban road scene of an Internet of Vehicles channel, and obtaining known time frame data and unknown to-be-predicted time frame data in the urban road scene in combination with a weather scene; carrying out data enhancement, data vector representation and position information processing to obtain a multi-modal vector, and carrying out feature extraction and splicing processing on the multi-modal vector to obtain a multi-modal feature vector; performing inter-modal deep fusion on the multi-modal feature vector to obtain a joint feature vector; and performing nonlinear mapping on the joint feature vector through a multi-layer perceptron module, outputting to obtain channel index probability distribution, and predicting the Internet of Vehicles channel according to the channel index probability distribution. The technical problems that in the prior art, the single-mode sensing capacity is limited, the original data quality is poor, and the robustness is insufficient in a complex scene are solved.

The invention discloses an RAG-based Deepseek-R1 dialogue question-answering method, which is characterized by comprising the following steps: carrying out knowledge base preprocessing, collecting information from a data source, converting the information into a uniform format, carrying out text analysis, partitioning, information extraction and labeling, vectorizing text data and storing the vectorized text data in a vector database; storing the preprocessed text data and the vectorized representation thereof by adopting a distributed vector database; receiving a question input by a user, understanding and rewriting the question, performing retrieval from the knowledge base by utilizing vectorization coding and similarity calculation, and optimizing a retrieval result through a reordering model; and based on the retrieved background knowledge, a Deepseek-R1 model is used to generate a natural language answer, and the natural language answer is output to the user after optimization and adjustment.

A method, apparatus and computer program product for homomorphic computation enables secure computation of determinants of a matrix under Fully Homomorphic Encryption (FHE). According to this disclosure, encrypted data that contains the values of a matrix is received at a server. The matrix is separated into at least a first portion, and a second portion. Each portion is configured as a square. A first data vector of ciphertext is computed for the first portion, and a second data vector of ciphertext is computed for the second portion. Under FHE, determinants of the first and second data vectors are computed as Single Instruction Multiple Data (SIMD) operations to generate a set of results. The set of results are then used to compute a determinant of the matrix. The determinant may then be used for FHE-based analytics.

The invention provides a full-automatic air compressor system integrating intelligent monitoring and fault diagnosis, and relates to the technical field of air compressor diagnosis. The management main system comprises an AI intelligent interaction module, an AR visual presentation module, a multi-source data fusion module, an intelligent diagnosis decision module, a fault prediction and prevention module, a user feedback learning module, an equipment linkage coordination module and a system core regulation and control unit. Operating parameters and microstructure change data of the air compressor are collected, and a high-precision comprehensive data vector is generated in cooperation with an algorithm based on tensor decomposition and generative adversarial network fusion. Compared with a traditional remote monitoring mode which depends on a single traditional sensor, the system can sense micro fault characteristics of a molecular level, and in cluster operation and maintenance of the air compressors in a factory, micro cracks of the air inlet valve are detected in advance, so that the problem of insufficient air pressure caused by delayed sensing is avoided.

The invention provides an urban block form optimization design decision-making method and system based on generation of a large model. The method comprises the steps that multi-mode green performance data influencing local heat island intensity and wind environment performance under the city block scale are collected and preprocessed; converting the preprocessed data into RDF triples according to entities, relationships and events, and constructing a multi-modal knowledge graph; performing modular clustering on knowledge in the multi-modal knowledge graph, obtaining a structured sub-graph, improving the knowledge retrieval efficiency based on the structured sub-graph, and obtaining a building green performance data vector database for establishing an external knowledge base enhanced large language model; and providing guidance and receiving feedback for a machine learning model for executing urban block form optimization design through a knowledge representation enhanced large language model, and forming a large and small model collaborative end-cloud collaborative architecture. According to the method and the system, the multi-modal data processing capability of a traditional single machine learning method can be improved, and the efficiency and the precision of optimization design decision making are improved.

The invention provides a software testing method and a software testing system. The software testing method comprises the following steps. And the processor obtains the test case. When the test case has an incremental code, the processor generates a supplementary test case according to the plurality of feature vectors in the vector database and the test case based on a coverage rate of the incremental code. The step of generating the supplementary test case further comprises the following steps. The processor extracts key information of a test case to obtain a plurality of first data vector units. The processor performs a similarity search according to the plurality of first data vector units and the plurality of feature vectors to obtain a plurality of second data vector units. The processor combines the plurality of second data vector units to generate a supplementary test case, thereby improving the performance of the test operation based on the incremental code.

The invention discloses a music stave sentiment classification method and system based on multi-level distillation, and belongs to the technical field of music information processing. The method comprises the steps of firstly collecting music stave data and converting the data into stave data vectors, then performing feature extraction by using a long short-term memory network, then constructing a teacher network and a student network for knowledge distillation, and realizing multi-level knowledge transmission through temperature scaling, KL divergence loss and mask feature distillation. And finally, training a lightweight classification model to complete sentiment classification. The knowledge distillation technology is creatively applied to staff sentiment classification, the classification accuracy is effectively improved through an online multi-level distillation mode, and the technical problems that a traditional method lacks semantic information and a self-supervised model is not suitable for sentiment tasks are solved. The method has the main advantages of high classification precision, light model weight, capability of effectively capturing music emotion features and the like.

The invention discloses a digital twin intelligent test run system based on a marine medium-speed diesel engine and a monitoring method, relates to the technical field of monitoring, and is used for solving the problems of degeneration identification lag and low emission early warning precision of an oil injector. The system comprises a multi-source heterogeneous data preprocessing module and a running state intelligent diagnosis module. The multi-dimensional data acquisition module is used for acquiring in-cylinder pressure, crankshaft torsional vibration and transient air-fuel ratio signals to construct multi-dimensional data vectors, and the multi-dimensional data acquisition module is used for analyzing combustion fluctuation characteristics based on an attention mechanism LSTM (Long Short Term Memory) network, identifying early deterioration of an oil injector and outputting an oil injection consistency coefficient and a fault type identifier. The method comprises the steps that according to an oil injection consistency coefficient, electromagnetic valve response delay and combustion parameter correlation are tracked, a degradation trend is predicted, and emission early warning is generated; and according to prediction and early warning results, fuel injection compensation parameters are dynamically adjusted in the digital twinborn model, a recursive least square method is adopted to identify combustion parameters and feed the combustion parameters back to an electric control unit, and self-adaptive optimization and closed-loop control in the test run stage are achieved.

An apparatus is described having processing circuitry to perform vector processing operations, a set of vector registers, and an instruction decoder to decode vector instructions to control the processing circuitry to perform the required operations. The instruction decoder is responsive to a given vector memory access instruction specifying a plurality of memory access operations, where each memory access operation is to be performed to access an associated data element, to determine, from a data vector indication field of the given vector memory access instruction, at least one vector register in the set of vector registers associated with a plurality of data elements, and to determine, from at least one capability vector indication field of the given vector memory access instruction, a plurality of vector registers in the set of vector registers containing a plurality of capabilities. Each capability is associated with one of the data elements in the plurality of data elements and provides an address indication and constraining information constraining use of that address indication when accessing memory. The number of vector registers determined from the at least one capability vector indication field is greater than the number of vector registers determined from the data vector indication field. The instruction decoder controls the processing circuitry: to determine, for each given data element in the plurality of data elements, a memory address based on the address indication provided by the associated capability, and to determine whether the memory access operation to be used to access the given data element is allowed in respect of that determined memory address having regard to the constraining information of the associated capability; and to enable performance of the memory access operation for each data element for which the memory access operation is allowed.

The invention relates to the technical field of airport safety guarantee, and particularly discloses an airport bird situation monitoring and early warning bird repelling equipment linkage control method comprising the following steps: obtaining bird situation data vectors collected by sensors, calculating fused bird situation data vectors, and obtaining bird flock information; constructing a bird situation risk assessment model based on machine learning, combining the fused bird situation data with an aircraft take-off and landing plan, a runway use state and airport surrounding environment information to form an input feature vector, calculating a risk value through a risk assessment function, and dividing risk levels according to the risk value; according to a risk grade result of the risk assessment module, corresponding bird repelling equipment is controlled to work according to a hierarchical linkage bird repelling strategy; and in the bird repelling process, the risk level is re-evaluated according to the new bird situation data, the parameters of the bird repelling equipment are adjusted, and the bird repelling strategy is optimized by using a reinforcement learning algorithm. According to the invention, the defects of monitoring range and precision of a single sensor are effectively overcome, and the reliability of bird monitoring is greatly improved.

The invention discloses an intelligent Internet of Things home energy management system, and relates to the technical field of intelligent homes, the system comprises a multi-dimensional data sensing module which uses a sensor and an intelligent equipment interface to collect human physiological data, obtains environment and external data through an environment sensor and a weather API, and sends the environment and external data to a cloud server; historical physiological records, environmental regulation logs and user feedback data are called, timestamps and space coordinate information are added to all kinds of data at the same time, and the data are converted into data vectors and uploaded to a system database; through the multi-dimensional data sensing module, human body physiological data, environmental data and external data can be comprehensively collected, and a physiological state prediction model and a health risk identification model exclusive to a user are constructed in combination with historical records and user feedback, so that the personalized level of health management is improved, and the health risk of the user is improved. And through a chaos embedding biological rhythm prediction formula and a dynamic Bayesian health risk assessment formula, accurate prediction of the physiological state of the user and identification of the health risk are realized.

The invention relates to a code diagnosis method, system and equipment, and the method comprises the steps: carrying out the preprocessing of code data, and generating structured data; vectorizing the structured data to construct a vector database; based on a user diagnosis request, a retrieval enhancement generation technology is adopted to retrieve related context fragments; screening out target context fragments, combining the target context fragments into context information, and outputting the context information to a large language model; the large language model identifies potential problems in the code data and generates repair suggestions. According to the method, efficient retrieval can be performed in a large-scale code library on the basis of the large language model by combining and applying a retrieval enhancement generation technology, necessary and accurate context information related to a user diagnosis request is provided for the large language model, so that the large language model can identify potential problems in codes more accurately; and a targeted repair suggestion is automatically generated.

The invention relates to the field of data processing, in particular to a data vectorization label processing method and device based on search self-feedback, and the method comprises the steps: extracting text clues through meta-file attribute extraction, optical character recognition, automatic voice recognition or subtitle analysis technologies; generating a content abstract by using the language model and the text clues; performing analysis processing on the content abstract by using an NER model based on a BERT architecture, and extracting features to obtain a tag set; processing the label set through a FastText algorithm, and performing dimension reduction to obtain candidate label vectors; matching the candidate tag vector and the basic vector through a tag system, and determining a search result corresponding to the candidate tag vector based on a search result set of the basic vector; and collecting user behavior data, calculating a user interest weight, and simulating and marking error cases by using the generative adversarial network to optimize the label system. In this way, for heterogeneous data, the accuracy rate of label marking can be greatly improved.

The invention provides a cooling and heating equipment energy consumption positioning and fault prediction method under an EMS energy consumption management system, and the method comprises the steps: S1, collecting state original data of target equipment to construct a comprehensive evaluation data set, employing a source data extraction function to enable indexes to be matched to a fuzzy set, forming a fuzzy vector, and carrying out the clustering, sorting and recognition of the important features, thereby achieving the energy consumption positioning and fault prediction of the target equipment; generating device-level time series data; s2, training a health score prediction model and a fault classification model based on the time sequence data vector; and S3, performing health scoring and fault classification prediction on the real-time data by using the training model, and generating early warning information. By defining the fuzzy set and the source data extraction function and extracting key features through clustering analysis, important features are further screened by using the random forest algorithm, so that the accuracy of target equipment performance evaluation is improved, a more accurate basis is provided for subsequent fault diagnosis, and the purposes of simplifying the model, improving the analysis efficiency and reducing the cost are achieved. And remarkable economic benefits are brought to enterprise partners.

This gas leakage monitoring device comprises: an image data acquisition unit that acquires, as original image data, image data generated by capturing images at different times; a vector generation unit that selects two pieces of original image data in chronological order from the original image data, and performs predetermined image processing for calculating the movement direction and movement speed of an object included in the images, for the selected two pieces of original image data, to generate a vector indicating the movement direction and movement speed for each pixel included in the original image data; and a frequency distribution generation unit that classifies the vectors generated by the vector generation unit into sections of predetermined vector lengths, and generates a frequency distribution for gas leakage monitoring.

The invention belongs to the technical field of fire safety, and particularly relates to a control method of a fire detection alarm. Firstly, a distributed sensor network is deployed to collect temperature and smoke data, self-calibration is carried out, and abnormal data is eliminated. Then, through three-level progressive anomaly screening, namely, first-level preliminary screening monitoring mutation features and second-level feature verification, a space-time matrix is constructed, CNN analysis is used for the space-time matrix, third-level scene adaptation is combined with historical data to calculate the probability, and an anomaly screening signal is output. A visual camera is used for collecting images to generate a visual evidence matrix, a sensor data vector and an abnormal screening signal are combined to calculate a comprehensive confidence coefficient, and an alarm signal is triggered if the comprehensive confidence coefficient is larger than a threshold value; and finally, through time window sliding verification taking one minute as a period, determining an alarm when an alarm signal is greater than or equal to three periods. According to the method, the data accuracy is improved, fire hazards are accurately identified, the false alarm rate is reduced, and the alarm timeliness and accuracy are guaranteed.

The invention relates to the technical field of artificial intelligence, in particular to a vehicle condition data exception handling system based on clustering analysis, which comprises a data vector module for processing according to collected historical normal sample data and calculating a first data vector; the vector processing module comprises a data acquisition unit, a running state vector unit and a judgment unit; the data acquisition unit constructs a multi-dynamic sensor network and acquires real-time data of the motorcycle in accordance with the first data vector; the operation state vector unit processes frequency spectrum information of the data in the real-time data through Fourier transform, and calculates a multi-feature information fusion vector; the judgment unit establishes a clustering analysis model to process the obtained multi-feature information fusion vector to obtain a first clustering result and a second clustering result; and the controller obtains a second clustering result and performs automatic adjustment according to a reinforcement learning algorithm.