274 results about "Data vector" patented technology

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.

A method for calculating domain relevance scores for responses generated by LLMs is disclosed. The method includes receiving a response generated by LLM corresponding to user query. The user query is associated with a domain. The method further includes splitting the response into a plurality of response chunks using a splitting technique. The method further includes generating a plurality of response vector embeddings based on the plurality of response chunks using at least one sentence transformer. The method further includes computing a plurality of cosine distances between the plurality of response vector embeddings and a corresponding plurality of training data vector embeddings, wherein the plurality of training data vector embeddings corresponds to domain-specific training data of the LLM. The method further includes calculating a domain relevance score corresponding to the response, based on a sum of the plurality of cosine distances and a number of the plurality of chunks.

The invention discloses a data processing device, a data processing method, a chip and electronic equipment, and relates to the technical field of data processing. The data processing apparatus includes: a storage module configured to store data; the calculation module is coupled to the storage module and is configured to access an original data vector stored in the storage module, and the original data vector comprises m data elements; splitting the original data vector based on the target k value to obtain a plurality of first data vectors; performing odd-even merging sorting on the data elements in the first data vectors to obtain a plurality of first ordered data vectors; and merging and sorting the data elements in the plurality of first ordered data vectors to obtain top-k data elements in the m data elements. According to the data processing device, the data processing method, the chip and the electronic equipment provided by the invention, the top-k data elements can be obtained without a special hardware sorting network.

The invention relates to the technical field of education big data analysis, in particular to a student information intelligent management system based on big data, comprising a data convergence module used for converging multi-source heterogeneous behavior data streams of target students in campus Internet of Things and interaction logs of the target students in a digital learning platform in real time to obtain an original time sequence data vector set; and the feature generation module is used for carrying out cross-modal time domain alignment and wavelet coherent transformation on the original time sequence data vector set so as to generate a multi-scale spatial-temporal feature map representing the individual learning and living states of the students. According to the invention, global network time protocol synchronization timestamp synchronization and Kalman filtering interpolation are introduced through the data aggregation module, and the feature generation module adopts remoley wavelet transform and wavelet coherence spectrum analysis, so that the phase locking relationship of online interaction and offline behaviors of students on a time-frequency domain can be quantified; therefore, the learning and living states of the students can be more comprehensively described.

The invention relates to the field of Internet of Things data processing, in particular to a hydropower station IoT real-time data acquisition and uplink method based on edge computing, the core technology comprises data acquisition and preliminary screening processing of received data, and the screening processing method comprises a threshold detection algorithm and a moving average anomaly detection algorithm. Cleaning the data through a quartile distance judgment algorithm; judging and calculating the state of the equipment, and calculating the credibility of the data vector to obtain the running state of the equipment; data packaging and uploading: generating a data uploading block chain collected by a digital fingerprint combination through dual hash; the oracle machine establishes a contract rule to monitor the uploaded data in real time, and pushes abnormal information to the early warning platform when the data is abnormal, so that real-time early warning is realized; through the three-level architecture of edge calculation, the block chain and the oracle machine, on the premise of ensuring data credibility, the decision-making precision and the response speed are improved, and the three pain points of uncredibility, high delay and many false alarms in the industrial monitoring field are solved.

The invention provides a vehicle-mounted process optimization method and device, electronic equipment and a computer storage medium, relates to the technical field of automobiles, and is applied to a vehicle control system. The method comprises the following steps: determining a vehicle panoramic data vector based on multi-dimensional vehicle running state panoramic data; inputting the vehicle panoramic data vector into an intelligent scene recognition model, outputting a vehicle operation activation scene and querying a corresponding target vehicle-mounted resource allocation strategy to schedule vehicle operation resources and process operation modes; by fusing multi-dimensional vehicle running state panoramic data and utilizing an intelligent model to actively identify a driving scene, resource scheduling is converted from static and universal to dynamic and accurate; according to the method, the system resources are finely distributed according to the real-time scene, the real-time performance and the fluency of the key tasks are effectively guaranteed, the processing capacity of the system in the multi-task concurrent scene is optimized, and therefore the response speed, the resource utilization efficiency and the overall user experience of the vehicle-mounted system are remarkably improved.

The invention discloses a color consistency control method in a bamboo kitchen ware carbonization process, and belongs to the technical field of bamboo processing. The method comprises the following steps: acquiring a multi-dimensional spectral data vector of the surface of the bamboo kitchen ware; determining a multi-dimensional change rate vector of the spectral data; respectively generating a first candidate control signal and a second candidate control signal based on the spectral data and the change rate vector; determining a system dynamic state index based on the change rate vector; generating a dynamic weighting coefficient according to the dynamic state index of the system; and performing weighted fusion on the first candidate control signal and the second candidate control signal by using a dynamic weighting coefficient to generate a final heat regulation control signal. According to the invention, the method achieves the self-adaptive optimal control of all working conditions through the construction of a dual-mode decision fusion composite control system, guarantees the quick response in a dynamic stage, guarantees the smooth convergence in a steady-state stage, and fundamentally improves the thermal inertia problem of the system.

The invention provides a marketing service method and system based on multi-modal generation type artificial intelligence. The method comprises the following steps: firstly, collecting multi-modal data related to a marketing target and carrying out vectorization; then constructing a joint embedding space containing marketing concept anchor points, aligning data vectors with related concept anchor points through a calibration mechanism, and dynamically updating the anchor points according to a new data track; related integrated vector clusters are identified in the space according to marketing targets, and an optimal commodity combination meeting budget constraints is searched in the clusters by adopting an optimization algorithm; and finally, inputting the commodity combination and the semantic core of the vector cluster into a language model as a context, and generating a marketing scheme matched with the target customer group. According to the method, the vector representation of the fused multi-modal data is realized, and the fused representation can be subjected to structured alignment with predefined marketing domain knowledge.

The invention relates to the technical field of robot visual guidance, and discloses a visual guidance robot material box grabbing module, which comprises a mechanical coarse positioning and state sensing unit for physically clamping a material box and collecting physical state data; a prediction type pre-positioning and dynamic reference generation unit receives the data, calls a historical deviation database, and generates a prediction target position and a temporary standard reference in parallel; the nested visual search and calibration unit moves to a prediction position and searches to obtain a complete feature image; the accurate deviation calculation and fine adjustment unit compares the image with a reference, calculates a final residual deviation and generates an accurate grabbing position; and the closed-loop learning and data feedback unit executes capturing and feeds back the final residual deviation to the historical deviation database. According to the method, a predicted target position and a dynamic temporary reference are established by using a physical state data vector and a historical deviation database, and parallel compensation of system errors and mechanical errors is realized.

The invention relates to the technical field of industrial process online monitoring, and discloses a digital twinning online quality control monitoring system for a stainless steel fastener production line, which comprises a data acquisition module for acquiring a high-dimensional process data vector, a feature projection module for generating a feature coordinate and a projection residual error based on a preset feature base vector, and a data processing module for processing the feature coordinate and the projection residual error; the state judgment module judges the catastrophic failure state or the quality drift state according to the residual priority, and the safety area self-adaption module updates the golden area center on the slow time scale based on qualified product coordinate statistics. The system can check the form of high-dimensional data in real time with low calculation cost, distinguish different failure modes, adaptively adjust the monitoring reference through slow time scale statistics, and avoid inherent contradictions among monitoring depth, real-time performance and long-term stability.

This invention provides a method and system for predicting diabetes by integrating knowledge graphs and deep learning. The method constructs an entity vector library of physical examination data, and obtains vector representations of patient physical examination data based on this library, resulting in physical examination data vectors. It then constructs a diabetes domain knowledge graph; based on this knowledge graph, it extracts entities and relationships from patient symptom descriptions and represents them as vectors, obtaining symptom description vectors, and thus a matrix representing the patient's condition; finally, it integrates a self-attention mechanism and a convolutional neural network to construct a classification prediction model, which yields the prediction results. Compared with existing methods, this invention, by simultaneously considering patient physical examination data and symptom descriptions and based on a dual-driven approach of knowledge and data, achieves higher accuracy and interpretability.

The invention relates to the technical field of data processing, in particular to a user and entity behavior modeling anomaly detection system, which comprises a timestamp generation module for receiving user power consumption data, equipment monitoring data and power grid monitoring data to generate data vectors; the dynamic association module interpolates the low-frequency data in the same time window to generate a virtual sampling point, and outputs a multi-dimensional association tensor; the behavior model generation module generates a joint behavior model and a confidence probability; the detection decision-making module adopts four parallel optimization channels to output three-dimensional decision-making indexes including abnormal probabilities; the simulation optimization module drives multi-algorithm simulation in the digital twin environment and writes back optimal parameters; and the feedback correction module generates a time reversal data flow reconstruction behavior model based on the simulation error, and outputs an equipment binding early warning signal to a power grid safety system. According to the method, the problem of dynamic association missing caused by multi-source data time sequence asynchronization is solved, and efficient collaborative diagnosis of composite anomalies such as electricity stealing is realized.

Provided is an information processing method, etc. that assists a user in interpreting behavior of a generated machine learning model. In the information processing method, a computer executes processing of recording a plurality of sets of an explanatory data vector xn input to an existing machine learning model (21) and an objective data vector yn output from the machine learning model (21) in association with each other, calculating an interpretation matrix A_dagger which is a vector product of an explanatory matrix X in which a plurality of sets of the explanatory data vector xn is arranged and a generalized inverse matrix of an objective matrix Y in which the objective data vector yn is arranged in an order corresponding to the explanatory data vector X, and outputting a chart (41, 42, and 43) related to the interpretation matrix A_dagger.

A method of training a student neural network is provided. The method includes feeding a data set including a plurality of input vectors into a teacher neural network to generate a plurality of output values, and converting two of the plurality of output values from the teacher neural network for two corresponding input vectors into two corresponding soft labels. The method further includes combining the two corresponding input vectors to form a synthesized data vector, and forming a masked soft label vector from the two corresponding soft labels. The method further includes feeding the synthesized data vector into the student neural network, using the masked soft label vector to determine an error for modifying weights of the student neural network, and modifying the weights of the student neural network.

The invention provides a construction site safety management and control method and system based on enhanced retrieval generation, and belongs to the technical field of data processing. Comprising the following steps: constructing a construction safety knowledge base, and vectorizing text data in the knowledge base; field self-adaptive fine tuning is carried out on the visual language large model through a low-rank self-adaptive method, and perception, cognition and decision-making processes of construction site safety management and control are executed: in a perception stage, construction safety hidden dangers are dynamically identified and positioned, and violation descriptions containing hidden danger types are generated; in the cognition stage, an enhanced retrieval generation process is started, and construction specification information associated with hidden dangers is obtained through multi-level semantic matching and retrieval; in a decision-making stage, based on violation description and construction specification information, a visual language large model is driven to automatically generate a structured security log through a preset structured cue word template. According to the invention, unsafe behaviors of the construction site can be timely and accurately warned, and effective data support is provided for the safety of the construction site.

The invention discloses a power plant cable channel intelligent robot inspection and adjustment method, system, equipment and medium, and belongs to the technical field of robot control inspection systems, and the method comprises the steps: collecting environment data, carrying out data fusion, outputting a fused environment data vector, and constructing an inspection control system; based on input data, vibration and microphone array sound wave analysis arranged by the inspection network control system are utilized to carry out environment initial simulation, a three-dimensional map with abnormal point marks is generated to carry out path planning, the inspection network control system carries out torque control on an execution mechanism, the inspection network control system issues an execution mechanism torque command, and the three-dimensional map is subjected to path planning. Routing inspection terminal track planning and hybrid control are carried out, a routing inspection network control system issues execution motion, and communication of multiple routing inspection control systems is established for routing inspection cooperation. According to the method, a stable and safe inspection sequence is generated, flexible and stable movement in a narrow space is achieved, the adaptability and energy efficiency of complex terrains are improved, and abnormal collaborative response and dynamic task allocation are achieved.

The invention relates to the technical field of occupational health safety monitoring and artificial intelligence data processing, in particular to an occupational health comprehensive risk management evaluation system based on reinforcement learning, which comprises a multi-source data acquisition step: acquiring multi-source heterogeneous monitoring data of a target operation scene; an ideal benchmark construction and simulation step: constructing an ideal benchmark model based on a standard operation program, and generating a virtual risk simulation data vector by using a reinforcement learning agent; a double-track difference calculation step: respectively calculating a real residual feature vector and a theoretical residual feature vector based on the ideal reference model; a similarity analysis and optimization step: performing feature space similarity analysis on the two to generate a risk assessment result and a strategy update reward signal; the problem that cumulative occupational hazards and transient environment fluctuation are difficult to distinguish is solved, and generalization ability and risk identification precision in a complex industrial environment are improved.

The invention discloses a vortex numerical analysis method based on transport equation decomposition. The method comprises the following steps: 1) constructing a three-dimensional geometric model; 2) exporting a computational domain established by the model into ICEM CFD to establish a model grid, and simulating a vortex generation condition in analogue simulation software; 3) extracting fluid data of each grid unit; 4) Liutex vector acquisition and expression: calculating the Liutex vector of each unit based on a given velocity field; 5) selecting a proper vortex kernel threshold value; (6) calculation of Liutex transport equation decomposition is carried out; 7) extracting a result after decomposition calculation of each grid; according to the method, the transport equation of the Liutex vector is constructed, decomposition and quantification of different physical effects in the evolution process of the Liutex vector are completed, and mechanism analysis is achieved.

Method and system for processing encrypted data comprising evaluating a client's encrypted data vector by at least one decision tree provided by a server. This method comprises, for evaluating a client's encrypted data vector by at least one decision tree (22) provided by a server (6), in a comparison phase: by a first execution environment, implementing a homomorphic encryption scheme, calculation (52, 54) of an evaluation vector, comprising encrypted components representing a difference between a component of the encrypted data vector and a decision threshold value associated with a corresponding node of said at least one decision tree, and providing the evaluation vector to a second execution environment which is a secure execution environment, executed by said server but controlled by said client.and by the second secure execution environment: - decryption (56) of said evaluation vector by applying a private key of said client, determination (58) of the sign of each component of the decrypted evaluation vector and provision of an encrypted sign vector to the first execution environment. Figure for the abstract: Figure 4,

The present application discloses a method and device for constructing a digital power system based on multifunctional intelligent agents. The method comprises: respectively extracting feature production element data and control production element data from power data of a power plant side and power data of a power grid side; constructing a target data vector on the basis of a power function requirement, and determining key control production element data associated with the power function requirement; using an artificial intelligence algorithm to learn a mapping function between the target data vector and the key control production element data, constructing a functional operator using the mapping function as a core, and constructing an intelligent agent on the basis of the functional operator; and adding intelligent agents corresponding to a plurality of different power function requirements into a digital power system as power function implementation units. The system can realize data-driven diversified and digital power functions, and implement more accurate capturing of the relationship between data, thereby forming high-value data assets in the power system.

The invention discloses a legal text auditing method and device and a medium. The method comprises the steps of obtaining a to-be-audited legal text; extracting risk terms in the legal text based on a semantic analysis model; the risk terms are input into a legal knowledge graph for retrieval to obtain a first retrieval result, the risk terms are input into a vectorization case semantic library for retrieval to obtain a second retrieval result, and the legal knowledge graph comprises structural data of a plurality of legal provisions and relationships among the legal provisions; the vectorized case semantic library comprises vector data of a plurality of unstructured legal cases; and inputting the risk terms, the first retrieval result and the second retrieval result into a large legal text auditing model, so that the large legal text auditing model outputs an auditing result of the legal text. The method can dynamically adapt to the legal text, and can improve the accuracy of legal text risk identification and the text editing efficiency.

A kind of detection method and device of fake APP, terminal, the method includes: determining training data;Keyword extraction is carried out to the brief introduction of each pair of target APP and the brief introduction of suspected APP, and the data vector of each keyword is determined;Determine the weight value of each keyword, and the data vector of each keyword in the target APP is weighted based on the weight value Operation is carried out to obtain target APP data vector, and obtain suspected APP data vector;The vector comparison result of target APP data vector and suspected APP data vector is calculated, the absolute value of the vector comparison result is determined as first data vector, and the square of the vector comparison result is determined as second data vector;Obtain the training text vector of the pair of target APP and suspected APP;Training fake detection model.The present application can enhance the association between keywords in model training phase, reduce the misjudgment rate.

This invention relates to the field of artificial intelligence education technology, specifically to a method and system for reviewing and providing feedback on physics, chemistry, and biology lab reports based on a multimodal large-scale model. The method includes: acquiring lab report question data, student answer data, and scoring standard data; vectorizing the question data and retrieving scoring criteria from a scoring knowledge base; selecting either a text-based or image-based scoring process based on the answer type, and calling a fine-tuned multimodal large-scale language model for scoring reasoning; calling an error identification module and a teaching suggestion generation module based on the scoring results; summarizing the complete review results and calculating the joint confidence level; and determining whether to push the results for manual review based on the confidence level. This invention can improve the accuracy, fine-grained understanding, and quality of teaching feedback in lab report review, enabling the self-evolution of the review system.

The application discloses a bank intelligent operation knowledge base implementation method and system based on a large language model and RAG. The method first extracts business terms from structured, semi-structured and unstructured multi-source heterogeneous multi-modal data of a bank, and then constructs a business dictionary and an ontology tree containing term similarity data. Then, the release agency weight, time effectiveness coefficient and penalty correlation degree of the regulatory provisions are quantitatively calculated to obtain an authoritative value and sort. Then, the pre-trained large language model is fine-tuned as a vector representation model using a double-constraint loss function, and the business data is vectorized and stored in a vector database. Finally, for user queries, the vector database is retrieved and combined with the regulatory provision sorting result for rearrangement, and a business response is generated after compliance verification. The method can improve the unstructured knowledge processing capability and retrieval accuracy of the bank, and realize efficient intelligent operation.

The invention relates to the technical field of surface water quality evaluation, in particular to a water quality sampling and evaluating method and device, electronic equipment and a storage medium. Secondly, for each section, constructing a detection data matrix by using a plurality of corresponding detection data vectors according to a time node sequence, and performing dimension transformation on the detection data matrix to obtain section vectors; finding a plurality of similar vectors from the plurality of water quality classes according to each section vector, and constructing the plurality of similar vectors into a reference vector group; and finally, determining the water quality grade of each section according to the reference vector group, and determining the water quality grade of the water body according to the water quality grades of the plurality of sections. According to the method, detection data of a plurality of detection items, a plurality of time nodes and a plurality of sections of the water body are integrated, the water quality of the water body is reflected objectively, few samples are needed, the construction process is simple, and the output result is more accurate and reliable.

The invention provides a credit risk assessment method and device, electronic equipment and a readable storage medium, and belongs to the technical field of data processing, and the method comprises the steps: obtaining credit data corresponding to a plurality of credit indexes of a pension institution; inputting the credit data corresponding to each credit index into a pre-trained embedding model to obtain a credit data vector corresponding to each credit index; based on the large model and a pre-constructed vector knowledge base, performing semantic analysis on the credit data vector corresponding to each credit index to obtain an evaluation value corresponding to each credit index; and determining a credit risk assessment result of the pension institution based on the assessment value corresponding to each credit index. According to the credit risk assessment method and device, the electronic equipment and the readable storage medium provided by the invention, the accuracy of credit risk assessment can be improved.

The application provides an image processing method, device and equipment and a storage medium. In the focusing process, a first prediction result of a current image and a second prediction result of a previous frame image are output by a trained convolutional neural network, and an incremental data vector is constructed based on the first prediction result and the second prediction result. The incremental data vector is input into the trained recurrent neural network, so that the recurrent neural network can combine the changes of multiple frames of images in time sequence in the process of determining a predicted focusing distance, the focusing accuracy is improved, the incremental data vector is input into the recurrent neural network in real time after each frame of image is captured, the predicted focusing distance is dynamically updated, the shooting is dynamically terminated in combination with credibility and the number of shooting times, and complete multiple frames of images are not needed, so that redundant shooting is reduced.

The application relates to a database service processing method and device, computer equipment and a storage medium. The method comprises the following steps: when a service request initiated by a client is received, determining request information corresponding to the service request, a target function and a service execution mode; when the service execution mode is a vectorization execution mode, a table data vector corresponding to a query keyword is pulled in a loop according to the query keyword in the request information in the disk, the table data vector comprises a preset number of table data, that is, a plurality of table data is pulled out from the disk, the target function is called to calculate and process each table data vector, that is, one table data vector is calculated and processed each time, compared with the prior art, the data amount of single calculation is improved, the number of repetitive executions is reduced, and the calling overhead of the function is reduced.

The present disclosure relates to a method for judging invalid work diagram based on point data, comprising: calling work diagram data, splitting into upstroke and downstroke displacement load data set, determining whether there is load stagger, if there is, judging as invalid work diagram and ending the judgment process, otherwise, continuing to calculate whether there is displacement reversal, if there is, judging as invalid work diagram and ending the judgment process, otherwise, continuing to combine with static data of pumping unit well, determining whether there is load anomaly, if there is, judging as invalid work diagram and ending the judgment process, otherwise, continuing to calculate whether there is stroke anomaly, if there is, judging as invalid work diagram, otherwise, as qualified work diagram, ending the judgment process; the present disclosure analyzes abnormal data characteristics of work diagram shape, load, stroke and stroke frequency, calculates abnormal characteristics by using work diagram coordinate data vector, so as to realize automatic identification, diagnosis and push of various abnormal invalid work diagrams, thereby replacing manual analysis and judgment of invalid work diagram work and improving work efficiency.