710 results about "Data filtering" patented technology

The invention relates to the technical field of agricultural internet of things and environment intelligent control, in particular to a greenhouse environment adaptive regulation and control system based on artificial intelligence, which comprises an environment acquisition module used for acquiring multi-dimensional environment data in real time through a distributed multi-source sensor; the central controller is used for generating an optimized regulation and control strategy; the regulation and control execution module is used for driving execution equipment to carry out regulation; the central controller comprises a multi-source data fusion unit, an AI decision-making unit and a dynamic optimization engine which are respectively responsible for data filtering and fusion, generating an initial regulation and control strategy based on a space-time joint AI model, and reconstructing and optimizing the initial regulation and control strategy through a multi-target optimization algorithm. According to the method, the response real-time performance is improved through multi-source sensing and data fusion, predictive regulation and control and multi-parameter cooperation are achieved through the AI model, balance of energy consumption, growth and carbon emission is achieved in combination with multi-target optimization, and long-term self-adaption and strategy iteration of the system are supported.

The invention relates to an alarm event root cause analysis method, device and equipment based on a large language model and business topology, and the method comprises the steps: based on an analysis engine, according to an alarm event sent by an alarm system, obtaining an association node set, collecting multi-source data, generating a preliminary context packet, carrying out the extraction of abnormal information according to the preliminary context packet, and carrying out the analysis of the abnormal information. And generating a refined context package by removing irrelevant node information, and generating an interpretable root cause analysis report through large language model constraint and reasoning based on the constructed large language cue word template. According to the method, the initial context packet covering the fault propagation link is constructed based on the service topological graph, rapid modeling of the context is achieved, the high-frequency error mode template is generated through clustering, irrelevant nodes are dynamically pruned to generate the refined context packet, accurate focusing and data filtering are achieved, and the fault propagation efficiency is improved. And embedding the refined evidence chain through a structured cue word template, and driving the large language model to output an interpretable root cause report.

The invention discloses a fault diagnosis and remote monitoring system and method for a solar power supply system, and relates to the technical field of fault diagnosis of a solar system, and the system comprises a heterogeneous multi-mode sensing module which collects the multi-dimensional information of an assembly through a plurality of sensors; the memristor storage and calculation integrated unit is used for realizing data filtering and feature extraction; the multi-scale causal diagnosis engine is used for diagnosing faults by fusing deep learning and causal diagrams; a self-adaptive topology communication network ensures data transmission; a digital twinborn monitoring platform and visual operation and maintenance are adopted, and in addition, an intelligent evolution decision and self-reconfiguration sensor network module is further arranged, so that the intelligence and reliability of the system are improved. Through cooperation of multiple modules, accurate fault diagnosis and positioning are realized, the diagnosis time is shortened, stable data transmission is ensured, self-repairing and autonomous learning capabilities are provided, the operation and maintenance cost can be reduced, the power generation efficiency can be improved, and the reliability and economic benefits of a solar power supply system can be enhanced.

The invention provides a recruitment patient determination method and device for a clinical test, a computer program product and electronic equipment, and relates to the technical field of medical big data, and the method comprises the steps: obtaining medical data of a plurality of patients, and converting the medical data into structured data; performing natural language analysis on the group entering standard information of the clinical test according to a large language model to generate a structured screening condition; performing data filtering on the structured data based on the structured screening condition through intelligent agents corresponding to a plurality of accommodating and arranging conditions of a clinical test to determine a recall result, matching the recall result based on the plurality of accommodating and arranging conditions, and determining an accommodating and arranging score of the medical data of each patient according to a plurality of matching results; and determining a target recruitment patient corresponding to the clinical test based on the recruitment score. According to the invention, the patient recruitment efficiency and accuracy can be improved.

The invention relates to the technical field of battery temperature control, and discloses a new energy automobile power battery temperature adjusting method, system and equipment, and the method comprises the following steps: synchronously collecting vehicle operation data, traffic information, charging pile state and battery aging parameters, and carrying out timestamp alignment and abnormal data filtering; converting the road test data into a vehicle coordinate system, predicting an aging trend in combination with LSTM, and generating a cross-domain feature vector through feature coding and tensor fusion; and generating a basic temperature control strategy based on reinforcement learning, predicting SOH deviation in combination with digital twinning, dynamically calculating an aging compensation coefficient, and synthesizing an anti-aging enhancement strategy. According to the invention, the maximum temperature is controlled within a safety threshold value by constructing a full-link cooperative temperature control system, so that the effectiveness of a thermal management strategy in a full life cycle is ensured.

The embodiment of the invention provides a multi-mode unmanned material checking method based on combination of RFID and machine vision, the method is applied to a mobile module and a camera module, an RFID read-write module is arranged on a mobile trolley, and the method comprises the following steps: in response to movement of the mobile module, the RFID read-write module scans a target material to obtain RFID identification data, and filters and de-duplicates the RFID identification data to obtain a target material; an RFID identification result is determined; dividing a shooting range of the camera module based on a scanning area of the RFID read-write module, and detecting a material box and a material surface sheet label in video image data when the video image data is shot; and identifying the material surface sheet label to obtain material data information, and matching the material data information with the RFID identification result to obtain a multi-modal material matching result.

The invention relates to the technical field of intelligent traffic systems, in particular to a linear intelligent monitoring system for a bridge fabrication machine, which comprises a multi-source sensing module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, and is characterized in that the multi-source sensing module comprises strain sensors, inclinometers, GPS positioning units and machine vision units; the edge computing node is internally provided with a self-adaptive data fusion algorithm and is used for denoising sensor data in real time; the cloud digital twin platform is used for synchronizing the BIM design model and generating a millimeter-level linear deviation early warning signal; the PLC control interface is used for receiving the early warning signal and automatically adjusting parameters of the template system and the walking mechanism; according to the method, environmental noise interference is effectively eliminated through multi-source sensor fusion and a self-adaptive data filtering technology, the monitoring precision is improved to a millimeter level, real-time comparison of construction data and a design model is realized based on a BIM and digital twinning dynamic interaction system, and the timeliness and accuracy of linear control are remarkably improved.

The invention provides a structure health early warning method and system based on space-time correlation characteristics and digital twinning, and relates to the technical field of data processing. The method comprises the following steps: acquiring multi-source heterogeneous data of a target structure; performing dynamic sampling alignment and wavelet packet decomposition on the multi-source heterogeneous data to extract energy features to obtain synchronous data, and performing abnormal data filtering on the synchronous data to obtain cleaned fusion data; performing wavelet decomposition on a high-frequency vibration signal in the fused data to obtain a damage impact feature, performing time sequence processing on low-frequency temperature data in the fused data to obtain a temperature time feature, and performing dynamic graph convolutional network processing on strain data in the fused data to obtain a spatial correlation feature; constructing an input vector; calculating a damage degree index; and according to the damage degree indexes, early warning grades are divided, and corresponding control instructions are triggered for different early warning grades. By implementing the technical scheme provided by the invention, the accuracy of structural health early warning can be improved.

The invention discloses a carbon emission prediction method and system, and belongs to the technical field of carbon emission prediction, and the method specifically comprises the steps: collecting multi-source data, carrying out the preprocessing of the multi-source data, constructing a hierarchical federated block chain architecture, dividing a target area into block chain nodes, and carrying out the prediction of the multi-source data, each node deploys a lightweight sub-chain, generates a hash abstract with a timestamp and carries out chaining storage, data intercommunication is carried out between the sub-chains through a cross-chain interaction mechanism, the confidence degree of multi-source data is dynamically evaluated based on a data quality scoring model, the confidence degree is screened by using an intelligent contract, and the data quality is evaluated. Training a carbon emission prediction model by each district node by using local data, predicting the carbon emission of the district nodes, and generating a regional carbon quota allocation scheme according to a prediction result; according to the invention, the data is screened twice, so that the data quality and the accuracy of carbon emission prediction are effectively improved.

Methods, apparatuses, and computer readable media are configured to perform operations comprising: obtaining, by a data management system (DMS), a first snapshot of a computing system, wherein the first snapshot comprises data associated with a set of files; determining, by the DMS, from among the set of files, a first subset of files or portions of files that comprise sensitive information; generating, by the DMS, one or more vectors based at least in part on data associated with a second subset of files or portions of files from among the set of files, the second subset of files or portions of files exclusive of files from the first subset of files or portions of files; and adding, by the DMS, the one or more vectors to a vector database along with metadata or a pointer to the metadata.

The invention discloses a data filtering method and system for a digital twin industrial control safety target range. The method comprises the following steps: firstly, marking an IP address of an industrial control system network in a segmented manner; feature extraction is carried out on the network behavior data of each IP segment, and splicing is carried out on the IP segment label to serve as a feature sample for constructing an industrial control network security identification model; a plurality of inverted residual modules with the same structure are adopted as core construction units, network behavior features in the feature samples are extracted and learned, and classification training is carried out; deploying the trained industrial control network security identification model to a pre-filtering module of a digital twin industrial control security target range, and carrying out classification, identification and filtering on network behavior data flowing in each IP segment in real time; and the misjudged sample is subjected to backtracking analysis, and the IP marking rule is updated. According to the technical scheme, the accuracy of network behavior data identification can be improved, so that the effectiveness of industrial control system simulation is improved.

The invention discloses an SLAM-oriented multi-sensor space-time synchronization and online calibration method, and relates to the technical field of computer vision and robot perception. Comprising the steps that S100, initialization is carried out, and an initial pose and an external parameter transformation matrix initial value are obtained through coarse alignment; s200, data acquisition and preprocessing: acquiring IMU data, LiDAR data and Odom data in real time, and performing data filtering, distortion removal, time sequence correction and point cloud downsampling processing on the acquired data; and S300, performing time synchronization processing, estimating the time offset of the LiDAR and Odom data in real time, and performing unified correction to generate a unified timestamp. According to the method, an online time offset estimation and compensation mechanism with an IMU as a main clock is established, accurate synchronization of sensor data of different frequencies is achieved, and the problem of data alignment caused by sampling frequency difference and clock drift is effectively solved; and meanwhile, tight coupling fusion is realized by using an IMU pre-integration factor, a LiDAR residual factor and an Odom constraint factor, so that the space-time consistency of multi-sensor data is remarkably improved.

The invention relates to the technical field of network security operation and maintenance, in particular to a multi-mode network security operation and maintenance method and system driven by big data analysis, and the method comprises the steps: obtaining an operation state and network flow data to generate a comprehensive trigger signal, dividing a behavior feature section set, and extracting a main behavior feature region; and constructing a multi-modal behavior vector set, executing clustering analysis, and outputting a classification result. According to the method, through the steps of double-feature screening, mutation point division, weighted fusion, similarity screening and the like, the data filtering precision and the boundary recognition capability are improved, salient region extraction and cross-modal consistency expression are enhanced, adaptive aggregation and accurate classification of threat attributes in a complex scene are realized, and the stability and the recognition rate of network security operation and maintenance are guaranteed.

The invention relates to the field of log burying point downloading, and discloses a front-end log classified downloading method and device and a storage medium. The method comprises the steps that a log collection system collects a DOM event triggered by a user behavior, and behavior JSON data is generated; the data processing coordinator performs data filtering processing on the behavior JSON data according to a preset filtering rule to generate filtered JSON data; based on a preset classification algorithm, performing label classification processing on the filtered JSON data to obtain classified JSON constraint data; the storage system performs hierarchical encryption of metadata and content on the classified JSON constraint data to obtain encrypted data; and the transmission system receives a log downloading request, queries corresponding log data from the persistent queue based on the log downloading request, and obtains requested log data. In the embodiment of the invention, log collection, data processing and storage are integrated at the front end, so that the conditions of network delay and data loss are reduced, and the classification efficiency of the log data is improved by utilizing the classification labels for marking.

An Artificial Intelligence (AI) based filter apparatus includes an input filter and an output filter protecting a generative AI model and preventing restricted content from being transmitted to user devices. When a user query is received, the input filter determines if the user query can be transmitted to the generative AI model by generating an input risk score for the received user query. If the user query is transmitted and a model query response is received from the generative AI model, the output filter determines an output risk score based on which the model query response may be transmitted to the user. The input filter and the output filter each include a pre-trained language model as a base with additional layers trained to estimate the corresponding risk scores.

The invention relates to an electric vehicle power battery capacity estimation and remaining service life prediction method, and belongs to the technical field of electric vehicle battery management. The method comprises the following steps: S1, data preprocessing and calibration capacity fragment extraction; s2, data screening and feature selection; s3, deep learning SOH model training is carried out; s4, capacity prediction and data correction; s5, semi-empirical physical model fusion and gridding: establishing a battery capacity semi-empirical physical model, and performing gridding processing on the mileage-capacity data in the step S4 in combination with RANSAC fitting parameters; s6, carrying out vehicle clustering analysis; s7, constructing a physical model and a residual LSTM (Long Short Term Memory) model for vehicles of each clustering category, and predicting the residual service life by fusing static physical model parameters and a dynamic capacity-mileage sequence; and S8, evaluating and optimizing the model. The method improves the accuracy and robustness of capacity estimation and life prediction, and is suitable for vehicle-mounted BMS or cloud battery health management.

The invention discloses a mold life cycle management method and system based on an RFID (Radio Frequency Identification) technology, relates to the field of mold management, and in particular relates to a method and system for realizing whole-process management of a mold life cycle based on data acquisition and analysis of RFID and PLC (Programmable Logic Controller) equipment collaboration. The method comprises the following management steps: step 1, initializing mold information and binding the mold information with an RFID (Radio Frequency Identification Device); step 2, RFID reader-writer deployment and station identification are carried out; 3, mold state sensing and data filtering; 4, collecting data used in the production process; 5, RFID and PLC data fusion verification is carried out; step 6, uploading the data to the cloud and a life cycle management platform; and 7, automatically judging the state of the mold and outputting management suggestions. The problems of low recognition efficiency, inaccurate state tracking, lack of life cycle management and the like in the existing mold management process are solved, and real-time sensing, automatic statistics, state judgment, visual display and intelligent decision support of the mold in the whole life cycle are realized, so that the efficiency, accuracy and intelligent level of mold management are improved.

The invention discloses an industrial network protocol integration platform realized based on JavaWeb. Comprising a protocol adaptation layer used for carrying out adaptive connection with each hardware interface, a service management layer used for carrying out configuration management and task scheduling optimization, an edge computing layer used for realizing data filtering and event triggering, and a cloud collaboration layer used for realizing big data storage and AI model reasoning prediction. The signal output end of the protocol adaptation layer is connected with the signal input end of the service management layer, the signal output end of the service management layer is connected with the signal input end of the edge computing layer, and the signal output end of the edge computing layer is connected with the signal input end of the cloud collaboration layer. Equipment instructions of different protocols are uniformly converted into a standard data format and packaged into a TCP / IP protocol for transmission, meanwhile, conversion of cross-platform protocols such as OPC UA is supported, and the multi-protocol compatibility problem of the industrial Internet of Things is solved.

The present invention relates to the field of real-time data filtering technology, and in particular to a real-time data filtering method and system with multi-dimensional feature fusion. The method comprises the following steps: obtaining data transmission logs and extracting original transmission data features, parsing data frame field structure information; identifying multi-dimensional abnormal coupling of data transmission based on field time gradient change rate and dynamic offset status; further detecting abnormal dilution of transmission data, predicting the data transmission complexity exceeding limit state, and detecting the overload condition of data filtering chip structure; then evaluating the attenuation trend of data real-time filtering efficiency, and optimizing the data real-time filtering path accordingly, completing real-time filtering processing of data transmission complexity exceeding limit condition, and outputting filtering results; the present invention achieves higher credibility of data filtering by filtering data in real time.

Some aspects relate to technologies for generating data filters from natural language queries and using the data filters to retrieve data from a structured dataset. In accordance with some aspects, a natural language query is received. A generative model generates an initial filter based on the natural language query, where the initial filter includes an initial attribute name and an initial attribute value. A valid attribute value corresponding to the initial attribute value is identified, where the valid attribute value comprises an attribute value in the structured dataset. Additionally, a valid attribute name corresponding to the initial attribute name is identified, where the valid attribute name comprises an attribute name in the structured dataset. A valid filter is generated using the valid attribute value and the valid attribute name, and data is retrieved from the structured dataset using the valid filter.

The invention discloses an abnormal water quality ion trend deviation early warning system based on an unsupervised confrontation anomaly detection model. The abnormal water quality ion trend deviation early warning system comprises a water quality ion data acquisition module which generates an ion concentration data sequence in real time; the data preprocessing module is used for carrying out abnormal data filtering, missing completion and smoothing processing on the sequence data; the feature extraction module extracts sequence trends and statistical features to generate multi-dimensional feature data; the USAD model construction module is used for training the normal working condition data to obtain an anomaly detection model; the real-time trend deviation analysis module reconstructs and analyzes the real-time characteristic data by using a USAD model to generate a trend evaluation result; the abnormity judgment module judges the trend deviation abnormity according to a preset threshold value; the early warning module automatically generates abnormal early warning information; and the output module visualizes the abnormal indexes in real time. According to the invention, real-time and high-confidence early warning of water quality ion trend deviation is realized, and the early monitoring precision and response speed of water quality abnormal events are obviously improved.

The invention discloses a financial data intelligent interaction system based on retrieval enhancement and multi-modal alignment, and relates to the crossing field of financial science and technology and artificial intelligence, and the financial data intelligent interaction system comprises a request analysis module which is configured to receive a user query request and analyze a semantic intention and a time range limiting condition to obtain query text information; the multi-modal vector generation module is configured to dynamically encode the query text based on the query text information, generate a plurality of feature semantic vectors and combine the feature semantic vectors to form a dynamic semantic vector library; the data filtering module is configured to start a two-way recall engine for parallel execution to obtain recall result information, the recall result information comprises multiple pieces of sub-data retrieval information, weighting calculation is conducted on the sub-data retrieval information to obtain a dynamic weight value S, sorting is conducted on the sub-data retrieval information on the basis of the dynamic weight value S, and the sorted sub-data retrieval information is obtained. And multi-modal data information is obtained through screening. The method has the effect of improving the efficiency of financial data intelligent interaction based on retrieval enhancement and multi-modal alignment.

A training scope of an instance of an Artificial Intelligence (AI) algorithm is determined based on a training corpus used to train the instance of the AI algorithm. AI input data is received. The AI input data is input data for the instance of the AI algorithm. A determination is made if some or all of the AI input data is not within the training scope of the instance of the AI algorithm. In response to determining that the some or all of the AI input data is not within the training scope of the instance of the AI algorithm, the some or all of the AI input data is filtered out. The filtered out some or all of the AI input data helps prevent the instance of the AI algorithm from having as many hallucinations.

The present disclosure provides a method, apparatus and system for monitoring ultra-high frequency partial discharge of a hydro-generator, and belongs to the technical field of hydro-generator partial discharge monitoring. The method includes: cleaning a partial discharge pulse sequence using a cleaning threshold to obtain a valid pulse sequence; performing redundant data filtering on each data unit divided from the valid pulse sequence to obtain a first target pulse sequence for short-period partial discharge monitoring; determining sub-sequences that are partial discharge events from the valid pulse sequence, forming a second target pulse sequence after associating an amplitude statistical feature of the sub-sequences, and storing the second target pulse sequence. The aforementioned method combines data cleaning, redundant data filtering, and partial discharge event identification, which enhances the real-time performance of partial discharge monitoring and records the long-period partial discharge change trend. The corresponding system adopts a multi-buffer zone and multi-processor architecture, thus further improving the real-time performance of partial discharge monitoring.

The invention discloses a PID parameter optimization system based on offline system identification, and the system comprises a data selection module which is used for screening modeling data in a historical database; the data preprocessing module is used for performing real data complementation and data filtering on the screened modeling data; the model system identification module is used for establishing a controlled object model by using the preprocessed data; and the PID parameter optimization module is used for performing off-line optimization of PID parameters on the controlled object model. The defects in the prior art can be overcome, any on-line closed-loop test is not needed, and the optimal PID parameter is output at a time.

The invention belongs to the technical field of data filtering query, and particularly relates to a dynamic SQL-based parameter data transmission filtering query mode for a low-code visual component, which comprises the following steps of: S1, constructing a low-code visual large-screen component and a background service API (Application Program Interface); s2, interactively filtering the linkage demand by the visualized large-screen component, and realizing dynamic parameter transmission of the SQL parameter transmission query request; s3, providing a visual operation interface by using the visual large screen component; s4, calling the data source identifier stored in the step S2 by the SQL parameter analysis constructor generation component; and S5, performing parameter extraction on the structured filtering condition data by the SQL parameter analysis constructor. According to the overall structure provided by the embodiment of the invention, the SQL parameters are transmitted during data query, and then the back-end service constructs the filtering and screening conditions meeting the conditions based on the dynamic SQL parameter transmission mode to query the SQL and query the database to obtain the data, so that accurate filtering and screening query of the data can be realized; the problem that a traditional large screen cannot perform database level filtering and screening is solved.

The invention relates to the technical field of power grid data privacy protection, in particular to a smart power grid data privacy protection system and method based on a homomorphic encryption technology. The system protects data privacy of an intelligent power grid based on a homomorphic encryption technology, and comprises eight modules: a data acquisition module is connected with an intelligent electric meter in a multi-protocol manner in a 15-minute period to adopt electric data; the data filtering and preprocessing module optimizes data quality; the homomorphic encryption module generates a ciphertext capable of being added and multiplied by using an FHE algorithm; the multi-level key management module manages multiple types of keys in the whole life cycle; the secure transmission module transmits the ciphertext by means of a hybrid protocol; the data analysis module decrypts and analyzes the ciphertext in the data center; the decryption module decrypts after authorization; and the auditing and monitoring module records operation and monitors abnormity. According to the invention, it is ensured that the power use data is kept in an encrypted state in the whole life cycle of collection, transmission and analysis, the data is available and invisible, and the data privacy protection level and quantum attack resistance of the smart grid are improved.

The invention provides a dynamic management method and system for an international transmission data stream based on an Agent agent, and relates to the technical field of data management and cross-border compliance. According to the method, an original data pool with a semantic index is constructed through a distributed crawler, a governance instruction is generated based on multi-modal perception and a joint decision model, operations such as data filtering and desensitization are executed to form a governance result, channel division is performed according to a compliance ontology model, and continuous self-adaptive updating is performed through a multi-target Bayesian optimization driving strategy. And dynamic management and optimization of cross-platform and multi-language data streams are realized.

The invention discloses a CO2 well flooding oil casing pressure intelligent control and anti-freezing and anti-blocking system, and relates to the technical field of oil and gas field development engineering.The CO2 well flooding oil casing pressure intelligent control and anti-freezing and anti-blocking system comprises the steps that a controller starts a self-checking program, loads a preset target pressure value, a preset temperature early warning threshold value and a preset ice blocking critical temperature parameter, completes initial configuration, and sends the initial configuration to the controller; zero calibration and precision verification are carried out on the front-end pressure sensor, the rear-end pressure sensor and the temperature sensor, front-end pressure data, rear-end pressure data and temperature data are collected, data filtering and abnormal value removing processing are carried out, and the real-time temperature data and CO2 physical property parameters are obtained. According to the invention, hardware self-inspection and sensor precision calibration are realized through the controller initialization module, the data acquisition module completes multi-source data synchronous acquisition and filtering processing, and the dynamic risk assessment module based on an improved Span-Wagner equation and a fuzzy logic algorithm can accurately calculate a CO2 phase change critical point and output a quantitative risk index. The problems of energy waste and untimely freezing and blocking prevention caused by a traditional fixed threshold value method are solved.