414 results about "Continuous data" patented technology

The present invention relates to the field of power distribution network fault detection, and in particular to a power distribution network fault detection method applicable to a distributed power supply. Potential fault modes and rules are extracted from a large amount of real-time monitoring data by using multiple data models, achieving accurate detection and diagnosis of various fault types; data collected in real time is processed and analyzed, so that fault signals can be found in a timely manner and corresponding alarm and notification mechanisms can be triggered, improving the response speed and processing efficiency of faults; continuous data iteration and model updating are performed, so that the change of a system and the condition of different types of faults can be adapted, improving the robustness and accuracy of detection; and faults are detected and diagnosed in a timely manner, so that a power grid operation strategy is effectively optimized, the energy utilization efficiency is improved, and the energy consumption and the operation and maintenance costs are reduced.

The invention discloses a multi-dimensional carbon flux monitoring method, and particularly relates to the technical field of carbon flux monitoring, and the method comprises the following steps: collecting multi-source heterogeneous sensor data, and carrying out spatial resolution unification, time synchronization alignment and data format standardization preprocessing to ensure data consistency; executing fusion validity detection, and correcting time synchronization deviation and data redundancy conflicts; according to an abnormal detection result, dynamically adjusting a fusion weight or determining a weight based on historical clustering, and combining spatial adjacency interpolation and time interpolation to realize data fusion; and finally, inputting a carbon flux inversion model, and outputting a high-precision carbon flux monitoring value to realize dynamic monitoring and prediction. According to the method, a dynamic fusion weight adjustment mechanism is adopted, the fusion weight of each data source is dynamically optimized, adaptive fusion of multi-source observation data is realized, the problems of data discontinuity and insufficient data coverage in the prior art are solved, and a multi-dimensional, full-coverage and high-resolution carbon flux fusion data set is ensured to be formed.

The invention relates to the technical field of ecological monitoring, in particular to an agricultural ecological environment monitoring method and system based on digital twinning, and the method comprises the following steps: obtaining monitoring data in a farmland, constructing a mapping relation, carrying out time sequence arrangement, merging multi-source data, calling a virtual grid to update a three-dimensional model, generating a mapping result, and analyzing a crop growth image. And tracking environmental changes, comparing ecological stability intervals, extracting risk trends, calling the three-dimensional model, and generating a risk early warning interface layer. According to the method, the continuous data of the agricultural ecological environment is acquired, and the mapping relation is constructed to perform time sequence arrangement on the information, so that the response speed and the processing precision of the change of the agricultural ecological environment are improved, the monitoring and management of the crop growth environment are optimized, and the environment change can be visually displayed through the expression of the real-time updated three-dimensional environment model; by comparing humidity and temperature changes and screening space segments, careful monitoring and accurate early warning of environmental changes are enhanced.

The invention discloses a variable working condition rolling bearing fault diagnosis method and system, and the method comprises the steps: firstly obtaining bearing vibration signal data, temperature data and current spectrum data of a motor under different working conditions, carrying out the adaptive decomposition through employing an EEMD algorithm, and extracting a stable feature subset; then, constructing a cross-working-condition diagnosis model by utilizing transfer learning, and introducing an HMM (Hidden Markov Model) time sequence model to capture signal dynamic characteristics; during diagnosis, the working condition of the signal is firstly judged, and the corresponding model is selected for fault diagnosis. And if the diagnosis confidence is low, triggering an online updating mechanism, and performing incremental learning by using new data. Through continuous data collection and model updating, a dynamic evolution system capable of automatically adjusting the diagnosis strategy according to the working condition change is constructed, the problem that the diagnosis performance is reduced due to the working condition change in bearing fault diagnosis is effectively solved, and high-accuracy self-adaptive diagnosis is achieved.

The invention relates to the technical field of environment monitoring, in particular to an intelligent environment monitoring system based on the Internet of Things, which comprises a data quality checking module, a data exception correction module, a data association analysis module, an exception monitoring marking module and an environment early warning transmitting module. According to the method, the checking of data equipment numbers and time labels and the filtering of abnormal or discontinuous data fragments are emphasized, so that the integration of the data is improved, the calling and mode analysis of historical data are carried out, and the abnormity is identified and corrected, and the data better meet the requirements of a time sequence mode; according to the method, key monitoring item combinations can be accurately identified and recorded in data correlation analysis, so that the real-time analysis and response capability of monitoring data is remarkably improved, the adaptive capacity to environmental changes and the early warning accuracy are greatly enhanced through timely marking of abnormal states and rapid release of early warning information, and the early warning efficiency is improved. And the efficiency of environment monitoring and decision support is optimized.

The invention relates to the technical field of geological disaster early warning, in particular to a geological disaster early warning method and a geological disaster early warning system, and the method comprises the following steps: S1, obtaining geological data of a target area; s2, performing quality control and interpolation processing on the geological data obtained in the S1 to obtain standardized space-time continuous data; s3, based on the standardized space-time continuous data, calculating a geologic body stress state of the target area; s4, calculating a potential instability point in the region and the probability of occurrence of the potential instability point; s5, training a disaster evolution prediction model, and outputting a disaster occurrence probability and regional distribution thereof; and S6, generating graded early warning information. According to the invention, through application of multi-source data fusion and the random forest algorithm, the occurrence probability and evolution trend of geological disasters are accurately predicted, and the accuracy and timeliness of the early warning system are significantly improved.

The invention relates to a wind resource prediction method based on quality control assimilation of multi-source observation data, and the method comprises the steps: integrating the multi-source observation data, including a multi-band radar reflectivity factor, a radial speed, a ground observation station, and wind profile radar data; quality control processing such as mottle removal, data filling, speed folding removal and discontinuous data removal is carried out, so that the accuracy and continuity of the data are improved; then, through lattice point interpolation and three-dimensional variation assimilation technologies, the processed observation data are fused into an initial background field, and an analysis field closer to the actual atmospheric state is generated, so that the reliability of severe convective weather risk assessment is remarkably improved, and the accuracy of wind energy prediction is also enhanced; and more comprehensive, detailed and stable information support is provided for weather forecast and energy management.

The invention belongs to the technical field of Internet of Things prediction, and discloses a soil moisture content prediction method and system based on a BiLSTM-Transform dynamic weight hybrid architecture, and the method comprises the steps: collecting an observation data set of soil moisture content influence factors of an irrigation region, the observation data set comprising a meteorological element sequence and a soil parameter sequence; the method comprises the following steps: collecting time series data of soil moisture content influence factors, carrying out noise reduction processing on the time series data by adopting a wavelet noise reduction method to obtain noise reduction data, and filling missing values of the noise reduction data by adopting a linear interpolation method to obtain a continuous data set. According to the method, a feature fusion strategy is adaptively adjusted through a dynamic weight mechanism, the time sequence modeling capability of soil moisture content prediction is remarkably improved, parameter quantities are compressed while the prediction precision is kept, the feasibility of deployment on edge computing equipment is achieved, and the constructed framework has the advantages of improving the stability of a prediction result and improving the prediction efficiency. And the prediction result error of the soil moisture content can be effectively reduced.

An AI and machine learning-based system for automating employee management and work information processing within an organizational structure, the system comprising: a central processing module configured to aggregate and pre-process data from multiple sources, including attendance records, performance logs, task management systems, and communication channels, with the central processing module normalizing and filtering the data to ensure consistency and accuracy in real-time analysis; a machine learning-based analysis unit operatively connected to the central processing module, the analysis unit comprising a natural language processing (NLP) sub-module, a sentiment analysis sub-module, and a pattern recognition sub-module, and configured to extract, analyze, and interpret both structured and unstructured data for insights into employee behavior and performance assessment, and further configured to adapt and refine models based on continuous data inputs from the central processing module; a predictive task scheduling component comprising a reinforcement learning-based model that leverages employee skill profiles, historical task completion rates, and workload patterns to dynamically distribute tasks, with the predictive task scheduling component further configured to self-optimise based on real-time feedback regarding task completion efficiency, priority changes, and schedule adjustments within the organization; a performance tracking unit configured to receive inputs from the central processing module and the machine learning-based analytics unit, wherein the performance tracking unit continuously monitors employee performance, challenges, and areas for improvement and stores these insights in individualized, encrypted employee profiles that can be accessed in real time, thus supporting data-driven performance reviews and improvement plans; an input / output interface for user interaction, the interface providing managers with interactive access to review employee metrics, task assignments, and performance feedback, and enabling employees to securely view individual performance metrics, feedback, and task details, the interface further being configured with user-level access controls based on historiographical organizational roles; a secure data storage component configured to securely store employee data, task logs, and performance metrics, where the data storage component supports both local and cloud-based storage solutions, uses encryption protocols for secure data retention, and provides access control mechanisms for authorized retrieval of stored data; a communication interface module operatively connected to the central processing module and configured with multi-protocol communication capabilities, including Wi-Fi, Ethernet, and Bluetooth, wherein the communication interface module facilitates real-time data synchronization between remote and local devices and is integrated with an AI-based anomaly detection system that flags irregularities or potential security threats in data transmissions; and an adaptive learning module operatively connected to the machine learning-based analytics unit and configured to continuously retrain machine learning models based on real-time data inputs, where the adaptive learning module uses reinforcement learning and unsupervised learning techniques to refine task recommendations, adjust performance metrics, and optimize task assignment rules based on the evolving needs of the organization.

The invention discloses a tunnel structure full life cycle damage detection and safety evaluation method, and belongs to the technical field of damage detection and safety evaluation. The method comprises the following steps: completing installation configuration of a mobile detection platform along the axial direction of a tunnel; generating a moving track and a detection point location distribution scheme of the mobile detection platform; controlling the mobile detection platform to sequentially move to each detection section position according to the detection path, and synchronously starting each detection module to perform continuous data acquisition in the moving process; generating a tunnel three-dimensional mathematical model, a lining structure thickness distribution diagram, a heat radiation anomaly distribution diagram and a crack damage distribution diagram; equally dividing the tunnel three-dimensional model into N detection segments according to a preset interval, and counting the lining thickness mean value, the number of heat radiation abnormal points, the crack length density and the damage level of each segment as safety evaluation indexes; statistical analysis and trend judgment are conducted on the safety indexes of all the segments, abnormal segments exceeding a safety threshold value are recognized, and the risk level of the abnormal segments is determined.

The invention discloses a data aggregation method based on multi-modal features, which comprises the following steps: collecting multi-modal data, preprocessing the multi-modal data, extracting features according to modals, dividing the features into high-dimensional data, medium-dimensional data and low-dimensional data, and positioning neighbor points by using a ball tree algorithm for the high-dimensional data; the neighborhood range of the medium-dimensional data is dynamically adjusted based on the distribution density; a neighborhood of low-dimensional data is calculated through Euclidean distance, then the low-dimensional data is mapped to a low-dimensional space by means of local linear embedding, then the low-dimensional data is traversed, discrete data value frequency and continuous data probability density are counted, marginal probability is calculated in combination with information entropy, data aggregation weight is determined accordingly, and finally features after dimension reduction are spliced according to a high-level sequence, a middle-level sequence and a low-level sequence. And probability normalization is carried out in each hierarchical block to generate aggregated comprehensive features. According to the method, through multi-dimensional differentiation processing and weight calculation based on data distribution, aggregation of multi-modal features is realized, and feature complementarity and accuracy are improved.

The invention discloses an urban construction intelligent monitoring system integrating geographic information and the Internet of Things technology, and belongs to the technical field of urban construction. In order to solve the problems that response to urban problems is lagged and development imbalance among urban areas is difficult to effectively reveal and solve, environment monitoring and infrastructure state evaluation are fused, the intelligent degree, real-time monitoring and quick response of urban management are remarkably improved, the processing speed of the urban problems is effectively improved, and the urban management system is suitable for urban management. The urban area is comprehensively evaluated through the evaluation index system, meanwhile, policy making is more refined through the area grading function, data support is provided for resource allocation, the timeliness of information is kept through continuous data updating, and the evaluation result is combined with a G IS system, so that the system is more efficient and reliable. The problem area and the dominant area are visually displayed, the balanced development of a city is promoted, and the overall living environment quality is improved.

The invention provides a power system intrusion detection and defense method based on artificial intelligence, and belongs to the technical field of power system intelligent control, and the method comprises the steps: 1, collecting data based on a multi-dimensional data collection layer, and forming an original data flow containing a time-space synchronization mark; 2, dynamically segmenting the original data stream by adopting a self-adaptive sliding window mechanism, automatically adjusting the size of a window according to the arrival frequency of a data packet, and converting the continuous data stream into a final triple sequence; 3, extracting and analyzing the features of the final triple sequence, and determining an equipment behavior mode and sequential logic of power operation; 4, performing deviation analysis on the equipment behavior mode and the normal behavior mode, and generating a primary alarm signal when the deviation degree exceeds a dynamic threshold value; and 5, starting multi-stage interlocking verification based on the primary alarm signal to determine an intrusion type, and matching a preset response rule base according to the intrusion type. Intrusion behaviors in the power system are effectively detected and defended in real time.

The invention relates to the technical field of data acquisition, in particular to a large-scale node-oriented spectrum acquisition compression system, which is characterized in that the system acquires three types of sensor spectrum data characteristics, presets four types of alarm threshold generation configuration tables, and determines sampling periods of different load intervals according to the configuration tables and node real-time loads to generate scheduling schemes. According to the scheme, a sensor is controlled to collect data, compare a threshold, mark an alarm level and generate a data set, data which does not exceed the threshold is extracted, an amplitude difference is calculated, redundancy is judged, and different data are marked according to a redundancy result to generate a standardized compression format table. According to the method, multiple types of alarm thresholds are preset by obtaining spectrum data characteristics of multiple sensors, sampling periods of different load intervals are determined by combining real-time load data of monitoring nodes, data are collected according to a scheduling period, alarm levels are marked, continuous data which do not exceed the thresholds are extracted, amplitude difference values are calculated, redundancy is judged, and the reliability of the system is improved. Corresponding identifiers are added for different data to generate a standardized compression format, invalid sampling is reduced, redundancy is accurately judged, and the data transmission amount is reduced.

The invention discloses a mine pressure monitoring data optimization and feature extraction method, which comprises the following steps of: firstly, establishing a multi-channel and high-frequency original time sequence data set through continuous acquisition of underground multi-type sensors, and providing an input basis for subsequent fluctuation detection; and then, calculating the adjacent ratio of the data sequence point by point, and adaptively determining a threshold value in combination with a sliding window quantile statistical result, thereby realizing dynamic identification of high and low fluctuation points and providing a marking basis for an optimization strategy. And then, automatically matching a corresponding optimization strategy according to the distribution form of the fluctuation points, performing targeted smoothing and recovery on the abnormal data by means of point pair correlation correction, isolated point backtracking correction, continuous section batch correction, bottom backfilling and the like, and outputting an optimization sequence. And finally, taking the corrected data as new input, performing loop iterative calculation, and continuously optimizing parameters and strategies through correlation, smoothness and information entropy evaluation until indexes converge to obtain a final mine pressure monitoring data result with trend fidelity and noise suppression.

The invention relates to the technical field of forest fire early warning, in particular to a meteorological-assisted forest fire efficient early warning and scheduling system which comprises a meteorological data analysis module, a rescue resource scheduling module, a real-time path planning module and a fire behavior mode prediction module. According to the invention, through the application of real-time monitoring and data analysis, the response speed and accuracy of forest fire early warning are greatly improved, the real-time measurement and analysis of meteorological variables enable fire early warning not to only depend on a traditional probability model, but can dynamically adjust the early warning threshold based on real-time data, so that early warning is more timely and highly targeted, and the early warning efficiency is improved. The problem of data updating delay is solved, accurate risk assessment is provided, instant fire alarm issuing and real-time optimization of a rescue path are ensured, rescue resources can be ensured to quickly respond through the shortest path, the success rate and efficiency of rescue actions are directly influenced, and a fire behavior prediction model utilizes continuous data training to improve the rescue efficiency. And the adaptability and prediction capability to the fire change are further enhanced.

A behavioral analysis platform is a comprehensive system designed to enhance the accuracy of polls and surveys. It utilizes a online survey optimized through payload management and employs machine and deep learning models to analyze behavioral, haptic, or biometric data. The platform captures user behavior, including mouse movements, response times, and other haptic data, using custom APIs. It features a payload manager subsystem for campaign planning and execution, optimizing survey elements for behavioral analytics. The system establishes a behavioral baseline for each respondent, enabling precise analysis of survey responses in terms of conviction, veracity, and sentiment. Real-time adjustments to survey elements and continuous data capture contribute to improved predictive capabilities.

The invention relates to the field of data processing, and discloses a real-time data processing system and method, and the method comprises the steps: carrying out the real-time capturing of a continuous data flow from a multi-source collection end, and generating an initial data sequence with an emergency weight; carrying out cross-source interleaving processing on the initial data sequence, and forming a composite feature set with a time sequence hierarchy characteristic by combining an offset track of a historical data period, buffer areas of adjacent data channels and a conflict frequency of multi-source input; on the basis of the composite feature set, utilizing a partition aggregation deduction algorithm to extract a dynamic occupation path of the data event in a processing cycle, and marking potential abnormal candidate segments; performing re-grading labeling on the abnormal candidate segments, and constructing a local association network with a weight gradient; and according to the local association network, detecting and identifying the data processing units with resource mismatch in the target period, and deducing a corrected data processing sequence. The method has the advantage of improving the real-time performance of data processing.

The invention relates to a multi-scale ecological system carbon metering method, a metering model establishing method and system, equipment and a medium, and belongs to the technical field of ecological environment monitoring. The establishment method comprises the following steps: collecting continuous data of the carbon flux of the net ecosystem with different scales, and corresponding environmental factors and spatial heterogeneity parameters; performing data preprocessing on the continuous data of the carbon flux of the net ecosystem with different scales; performing feature construction on the continuous data of the carbon flux of the net ecosystem with different scales after data processing, the corresponding environmental factors and the spatial heterogeneity parameters; performing training data set construction on the data after feature construction; and based on the constructed training data set and the integrated learning model, completing the establishment of the multi-scale ecological system carbon measurement model. According to the invention, fusion of continuous data of the carbon flux of the net ecosystem with different scales is realized, and refined monitoring of the carbon of the ecosystem at different spatial positions is realized.

The invention relates to the technical field of medical data mining, in particular to a postoperative pulmonary infection prediction method and system.The postoperative pulmonary infection prediction method comprises the following steps that a continuous data set is formed by collecting breathing parameters and conducting alignment completion, tidal volume and blood oxygen response delay characteristics are extracted through sliding analysis, and a functional abnormal pulmonary segment is located through local anomaly recognition; and fusing physiological and anatomical features to establish a spatial probability map, extracting a respiratory variation coefficient and a risk feature regression analysis confidence index, and outputting an infection risk early warning list with anatomical positioning. According to the method, data faults are eliminated through timestamp alignment to improve the reliability of time sequence association, the breathing compensation capability is quantified through multi-scale window analysis dynamic coupling, lesion avoidance feature masking is accurately positioned through local anomaly detection, and the result interpretability is enhanced through combination of multi-modal feature fusion and space mapping. The three-stage progressive mechanism improves the prediction precision and timeliness, and provides a decision basis with both time sensitivity and anatomical orientation for clinic.

The invention relates to a transformer temperature and fault prediction method based on an LSTM technology, and the method comprises the steps: carrying out the continuous data collection of a temperature sensor at a monitoring point of a power distribution area, and carrying out the analysis of the temperature change data related to the time, the transformer temperature prediction model of the LSTM with a time sequence as correlation is established for data analysis and prediction, the transformer temperature prediction model of the LSTM comprises an input layer, a hidden layer, an output layer and model training, and the input layer is used for reading and preprocessing a data set and dividing a training test data set; the hidden layer is used for constructing an LSTM hidden layer and a full-connection neural layer; the output layer is used for verification processing of a prediction result; the model training is used for being responsible for loss function calculation, Adam algorithm optimization and iterative calculation of the model training.

The invention discloses a method and system for measuring and identifying a high crack of a house wall, and belongs to the technical field of buildings, and the method comprises the steps: collecting complete wall crack point cloud data, carrying out the geometric feature extraction of the wall crack point cloud data, and dynamically adjusting the deployment density of an environment and a strain sensor. And completing the prediction of the future width of the crack through the prediction model, completing the classification of the crack risk level based on the predicted value and the structure standard, and generating a final maintenance strategy according to the crack dynamic expansion condition, the risk level and the construction resource configuration rule. According to the scheme, by introducing dynamic data fusion and prediction analysis, through continuous data collection and environment change analysis, pre-judgment and intelligent decision making of the future development trend of the crack are achieved, the damage level of the crack is evaluated through an automatic algorithm, the system can generate personalized repair plans under different crack conditions, and the repair efficiency of the crack is improved. The repairing efficiency is improved.

The invention discloses an exercise health monitoring method and system of a smart watch, and relates to the technical field of smart watches, and the method comprises the steps: carrying out the time synchronization of continuous data, and enabling all monitoring indexes to be aligned on the same time axis; carrying out real-time analysis and anomaly detection on the heart rate and blood oxygen saturation data by utilizing a machine learning algorithm; the heart rate variability index of the user is evaluated, the blood oxygen saturation data and the body surface temperature data are integrated, and a pressure evaluation model is constructed; in combination with height and weight information input by the user, constructing a deep learning model to identify a motion mode of the user; the heart rate and blood oxygen health indexes of the user are tracked and analyzed by utilizing a time sequence analysis technology, the future health trend is predicted, and potential health problems are explored. By arranging the data prediction module, the heart rate, blood oxygen and other health indexes of the user can be tracked and analyzed, the future health trend is predicted, and potential health problems are explored.

The invention relates to the technical field of industrial equipment control, and discloses an industrial equipment autonomous decision-making control method based on reinforcement learning, and the method comprises the steps: obtaining the time sequence data of a vibration sensor, the continuous data of a temperature transmitter and the discrete data of a pressure instrument of industrial equipment through a multi-mode sensing module; multi-modal data alignment is achieved through a feature space mapping algorithm, the thermal drift compensation amount is calculated in combination with a thermodynamic state model, self-adaptive threshold segmentation processing is conducted on pressure data, the processed data are input into a reinforcement learning decision model to generate a fusion decision result, exploration-utilization balance parameters are adjusted through a strategy updating mechanism, and the fusion decision result is obtained. And outputting the autonomous decision control scheme. The method solves the problems of poor adaptability, low decision-making efficiency and the like of a traditional control method, can improve the autonomous decision-making capability, the control precision and the operation stability of the equipment, reduces manual intervention, reduces the cost, and is suitable for intelligent control of the industrial equipment.

The invention relates to the technical field of medical equipment monitoring, in particular to an intelligent anesthesia atomization equipment state monitoring method and system. The method comprises the following steps: firstly, collecting atomization flow, particle size distribution, motor vibration frequency, liquid medicine temperature and pipeline pressure, and constructing an equipment state feature set; synchronously collecting electroencephalogram signals of a patient and constructing electroencephalogram state vectors; inputting the feature set into a support vector machine model to identify the running state of the equipment, and judging whether abnormity exists or not; when the equipment state is normal, an atomization efficiency attenuation equation is constructed based on continuous data, and the future efficiency trend is predicted; the prediction result and the electroencephalogram state are input into an anesthesia depth index dynamic balance model, and the adaptability of atomization output to the sedation reaction of the patient is evaluated; and when the efficiency attenuation rate is too high or the dynamic error exceeds a threshold value, generating a prompt signal and uploading the prompt signal to a remote platform. According to the invention, equipment state monitoring, efficiency prediction and neural feedback linkage control are realized, and intraoperative safety and postoperative evaluation precision are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology, medical health and the like, and discloses an abnormity early warning method, device, equipment and medium based on stage penetration, and the method comprises the steps: collecting interaction whole-process behaviors to form a continuous data chain, and constructing a multi-dimensional dynamic portrait; establishing a stage penetration model, and defining progressive migration of three stages of service interaction, service use and exception occurrence; calculating the overlap ratio of the interactive and used feature identifiers at the first level, and calculating the difference degree of the used and abnormal feature identifiers at the second level; training an early warning model in combination with historical data; inputting the current state of the portrait, and outputting an abnormal level; and triggering an early warning signal according to the grade. According to the method, portraits and historical statistics are converted into calculable coincidence degree and difference degree indexes through staged modeling and hierarchical penetration, and the early warning model is formed by weights and threshold values, so that the abnormal level is judged in advance, the recognition accuracy and the response timeliness are improved, and preferential disposal of resources is facilitated.

A behavioral analysis platform is a comprehensive system designed to enhance the accuracy of polls and surveys. It utilizes an online or offline survey optimized through payload management and employs machines and deep learning models to analyze behavioral, haptic, or biometric data. The platform captures user behavior, including mouse movements, response times, and other haptic data, using custom APIs. It features a payload manager subsystem for campaign planning and execution, optimizing survey elements for behavioral analytics. The system establishes a behavioral baseline for each respondent, enabling precise analysis of survey responses in terms of conviction, veracity, and sentiment. Real-time adjustments to survey elements and continuous data capture contribute to improved predictive capabilities.

The invention discloses a seasonal frozen region salinization roadbed deformation monitoring and early warning method based on a neural network. Comprising the steps of static attribute data acquisition, dynamic monitoring data acquisition, action process data acquisition, data gridding fusion, construction and training of a first neural network model, construction and training of a second neural network model, regular monitoring and intelligent diagnosis and intelligent prediction and beforehand early warning. Data fusion is performed on spatial continuous data, investigation data, traffic load, freeze-thaw cycle and other action process data of a vertical deformation field and a salt concentration field of a roadbed, and then a serial neural network model is utilized to predict the disease type and severity of the roadbed under current and future working conditions. Full-chain intelligent analysis from multi-source data fusion, deformation prediction to disease identification is realized, the problems of monitoring fragmentation and early warning lag of a traditional method are solved, and the perception ability and the beforehand early warning level of the health state of the salinized roadbed in the seasonal frozen region are remarkably improved.

The invention provides a data processing method and device for a NOR memory, and relates to the field of data processing, in particular to the field of semiconductor devices, data transmission and data storage. According to the implementation scheme, input data is obtained, the input data comprises parallel write data provided for a NOR memory and a gating signal associated with the parallel write data, the parallel write data is in the form of a continuous data sequence comprising a plurality of data parts, and the parallel write data comprises a first data part needing to be written into the NOR memory and a second data part not needing to be written into the NOR memory, the first data portion and the second data portion are indicated by a strobe signal; encoding a second data part indicated by the strobe signal into a predetermined data form which is not written by default of the NOR memory to obtain target parallel write-in data, so that the target parallel write-in data is maintained in a continuous data sequence form; and performing parallel-serial conversion on the target parallel write-in data to obtain serial write-in data used for being written into the NOR memory.

The invention provides a cross-platform data secure storage method and system based on a block chain. The method comprises the following steps: acquiring full-life-cycle operation trace data generated in a cross-platform data migration process, and constructing a life cycle event atlas based on time sequence characteristics of an operation timestamp; synchronously acquiring temperature monitoring data, and marking an operation timestamp corresponding to the physical tampering behavior and the temperature data as an abnormal event data set; performing joint coding with the life cycle event atlas according to an operation timestamp sequence to generate a space-time association evidence chain; and after the evidence chain is segmented into continuous data segments and the association marks of the adjacent segments are embedded, cross-platform storage is performed on the data segments and the association marks through the distributed characteristics of the block chain. According to the technical scheme provided by the invention, the efficiency and precision of cross-platform data secure storage can be improved.