2377 results about "Data sequences" patented technology

The invention relates to the technical field of exception prediction, in particular to a storage cabinet exception trend prediction system based on time series data analysis, which comprises a state monitoring module, an interval sensing module, a path reconstruction module, a symptom activation module and an evolution prediction module. According to the method, the state vectors including the temperature, the voltage, the current and the door lock state are constructed and combined with the timestamp information to form the time sequence data sequence, and the dynamic expression mode of state change is established; a jump characteristic is analyzed by using a ratio of a time interval to a state change amplitude, a short-time disturbance path and a trend evolution path are distinguished by combining a jump rate statistical index, and an evolution activation signal is identified based on trend maintenance and non-fallback characteristics. On the basis, a neural network structure with long-time dependent learning ability is introduced to capture an aperiodic thermal anomaly trend in a state sequence, and the accuracy and timeliness of anomaly recognition are improved through multi-dimensional parameter cooperative processing and path construction logic.

The invention relates to a multi-source data fusion-based urban physical examination evaluation index calculation method and system. The method comprises the steps of extracting a multi-source data sequence; identifying a data source of the urban physical examination index set, and extracting an independent time sequence data sequence; calculating the information entropy of the independent time sequence data sequence, and distributing a basic fusion weight; calculating a dynamic state evaluation value of the independent time sequence data sequence, and performing weighted fusion on the basic fusion weight and the dynamic state evaluation value to obtain a comprehensive state evaluation value; obtaining a distribution variance of the basic fusion weight, inputting the distribution variance into the uncertainty quantification model, and obtaining an index calculation result containing uncertainty measurement; the real-time performance of the evaluation result is enhanced through an aging attenuation mechanism, and the latest state of the city system is accurately reflected; the output uncertainty measurement index provides a quantitative basis of result credibility for a decision maker, and the decision risk caused by a data fusion error is reduced.

The invention relates to the technical field of safety early warning, and particularly discloses a vehicle driving safety early warning method fused with meteorological data, which comprises the following steps: acquiring real-time multi-modal data of meteorological, traffic flow and vehicle state of a target road area, performing exception handling, space-time alignment and standardization to form a standardized data sequence, then constructing a multi-modal fusion tensor, and finally performing data fusion on the multi-modal fusion tensor. Extracting each modal dynamic mode, fusing cross-modal features, outputting a joint feature vector, inputting the joint feature vector into a safety risk prediction model to calculate a dynamic safety risk value, combining digital twin simulation risk conduction, generating graded early warning according to a preset threshold value, and performing management and control through vehicle-road collaborative network publishing and high-risk scene linkage traffic facilities. And finally, collecting feedback data evaluation effects, associating decision data to generate hash records, recording the hash records in the block chain, and carrying out federated learning incremental training optimization model based on feedback. According to the invention, accurate early warning under multi-factor coupling can be realized, data privacy is guaranteed, closed-loop optimization is formed, and road traffic safety and stability are improved.

The invention discloses a crop disease diffusion prediction method and system based on multi-modal fusion, and the method comprises the following steps: S1, collecting and preprocessing an RGB image sequence and a sensor data sequence of a crop growth environment, and generating an RGB image time sequence difference result and a sensor difference result through time difference processing; s2, mapping the RGB image time sequence difference result and the sensor difference result to a shared time sequence space through a time alignment algorithm, and generating a sensor alignment result and an RGB alignment result; s3, an FD-ViT prediction model is constructed; inputting the sensor alignment result and the RGB alignment result into an FD-ViT prediction model for prediction, and generating a prediction result; and S4, generating a disease diffusion thermodynamic diagram and early warning information according to a prediction result. According to the method, RGB image data and sensor network data are fused, a Transform-based time sequence prediction model is constructed, and early recognition and diffusion trend prediction of crop diseases are realized.

The invention relates to the technical field of electrical digital data processing, and discloses a statistical entropy and multi-factor fused text similarity data processing method, which comprises the following steps that: a processor extracts substring sets which do not contain maximum common values of a first data sequence and a second data sequence, and calculates the quadratic sum of the lengths of substrings to generate local statistical entropy; traversing the maximum common substring set to obtain storage address indexes of the maximum common substring set in the first data sequence memory space and the second data sequence memory space, and constructing a topological mapping vector of a mapping structure displacement relationship; calculating the total number of inverted pairs of the topology mapping vector by using a merge sorting algorithm, and generating a normalized topology dissipation index; and by taking the local statistical entropy as an information carrier and taking the topological dissipation index as a structural damping factor, executing nonlinear damping modulation operation to obtain a final similarity score, and solving the technical problem that the block-level displacement cannot be identified by linear scanning logic by quantizing topological entropy increase of data distributed in a storage space.

A machine learning model may be configured for training using an associated learning technique. A model configured for end-to-end backpropagation may adapted for associated learning by introducing functions for projecting hidden vectors and labels to a shared representation space and for reconstructing labels from representation vectors. An associated learning loss may be calculated at each layer, with the resulting gradients backpropagated locally through that layer rather than all layers. A reconstruction loss may be calculated using each layer's output including the predicted label. Training by associated learning may be parallelized (e.g., layer by layer) to yield efficiency gains. In addition, associated learning training may be more robust to training label errors. The resulting model may be used to, for example, predict data sequences in an autoregressive manner in which subsequent portions of the output data sequence are predicted in part based on previous predicted portions of the output data sequence.

The invention discloses an intelligent supply chain management system and method based on artificial intelligence and big data, and belongs to the technical field of supply chain management and artificial intelligence, and the method comprises the steps: obtaining a state data sequence of a supply chain object, extracting abnormal features, and forming an abnormal feature data sequence, obtaining a supply chain environment and operation parameter time sequence aligned in time and space; and jointly inputting the abnormal feature data sequence and the supply chain environment and operation parameter time sequence into a pre-trained multi-modal deep learning model for fusion analysis, and outputting one or more key supply chain parameters causing the abnormal state and quantized abnormal fluctuation information thereof, accurately associating the key parameters with the specific physical position or visual form of the abnormal state on the supply chain object, and finally generating an association map; according to the invention, full-link closed loop from data perception, intelligent analysis to root cause visualization is realized, and the intelligent level and fault processing efficiency of supply chain management are improved.

Artificially intelligent systems and methods for financial coaching provide personalized, fiduciary-compliant financial guidance through advanced machine learning architectures with measurable performance criteria. The systems implement privacy-preserving processing pipelines that detect personally identifiable information using multi-layered pattern recognition including regular expressions for formatted data sequences, named entity recognition with confidence thresholds above 0.85, and contextual analysis algorithms. A multi-step artificial intelligence processing workflow includes automated language detection, emotional tone classification with confidence scoring, financial profile transformation using predefined templates, context-aware question rephrasing, and semantic similarity matching employing vector embeddings with financial domain vocabulary weighting applying multiplier values between 1.3-2.0. Specialized training methodologies expand datasets through mathematical transformation functions utilizing statistical standard deviations with incremental variations between 0.5-2.0. Mood-based escalation logic automatically transfers users to human advisors when emotional indicators exceed confidence thresholds above 0.8. The systems maintain response times below 5 seconds while providing regulatory compliance through curated content sources and predefined fiduciary instruction parameters.

The invention relates to the technical field of informatization management systems, and particularly discloses an AI-aid decision-making product full-life-cycle informatization management system which comprises a full-link data sensing module, a digital main line construction module, an AI decision-making center module, a service execution interface module and a man-machine collaborative interaction module. The method comprises the following steps: collecting multi-source heterogeneous data of each stage of a full life cycle of a product through a full-link data sensing module to form a full-amount data sequence with a space-time stamp; and in combination with a data fusion engine, an association graph construction unit and a twinborn model driving unit of the digital main line construction module, data cleaning, alignment, semantic fusion and cross-stage association are realized, a dynamically updated product digital twinborn model is constructed, real-time mapping of a physical entity and a virtual model is achieved, and the real-time mapping of the physical entity and the virtual model is realized. Unified and complete data support is provided for full-link decision making, and the problems of data fragmentation and prominent collaborative barriers of a traditional system are thoroughly solved.

The invention relates to the technical field of pipeline welding automation, and discloses a pipeline multi-layer and multi-pass welding track self-adaptive planning method. The method comprises the steps that weld joint track poses, welding electrical parameters and temperature information are collected in real time, and a sliding time window data sequence is constructed; establishing an interlayer constraint model and a trajectory prediction model based on the sequence, and generating a plurality of trajectory deviation predictions through short-term recursive prediction and long-term sequence prediction; a residual error reciprocal weighted fusion strategy is combined with a welding seam forming quality evaluation function, fusion track deviation is obtained, and uncertainty is evaluated; the fusion deviation is superposed to an original planned trajectory, and a self-adaptive correction trajectory is generated through a multi-objective optimization model; and the welding robot is controlled to execute track correction and real-time feedback updating. According to the method, through dual-time scale prediction and trajectory-process parameter collaborative optimization, the problem of insufficient welding seam forming precision caused by lack of dynamic correction in traditional static planning is effectively solved, and the welding quality and efficiency are remarkably improved.

The invention relates to the technical field of automobile electric control, in particular to a new energy automobile electric control steering control method and system, and the method comprises the steps: obtaining and preprocessing a four-wheel vibration data sequence, carrying out the segmentation and consistency evaluation of the vibration data sequence of each wheel, and selecting a current data sequence in an optimal segment to calculate a road condition influence coefficient. And matching the current data sequence with the historical vibration data sequence, calculating the similarity to determine the influence weight, adjusting the influence coefficient in combination with the steering angle change rate of the historical vibration data sequence, inputting the adjusted pavement condition influence coefficient into the ECU, dynamically adjusting the steering auxiliary torque, and realizing accurate steering control. According to the method, the optimal segment is selected through segment processing and consistency evaluation to accurately reflect the current road condition, the steering auxiliary torque is dynamically adjusted in combination with historical vibration data sequence matching and the steering angle change rate, and the driving safety and comfort are improved.

The invention discloses a short-term power load prediction method, system and device based on multi-intelligent-model fusion and a medium, and belongs to the technical field of short-term power load prediction, and the method comprises the steps: obtaining regional historical load data and meteorological data; performing data cleaning on the obtained load data and meteorological data; measuring linear and nonlinear correlation between the power load and the meteorological factors, and screening meteorological data with high load correlation; decomposing the load data into a time sequence by using an empirical mode decomposition method based on combination of multi-scale permutation entropy to obtain a multi-scale sub-data sequence; respectively predicting the multi-scale sub-data sequences to obtain prediction results; carrying out weighted fusion on the prediction result through a long short-term memory network model to obtain a load prediction result, and optimizing model parameters to obtain a trained multi-model prediction model; and predicting the test set data by using the trained model to obtain a final load prediction result. According to the invention, the precision and adaptability of load prediction are effectively improved.

The invention discloses a space-time adaptive power prediction system and method for distributed photovoltaic power generation in a mountainous area, and particularly relates to the technical field of fault prediction and health management. The method is used for solving the problem that the accuracy of power prediction and equipment health state evaluation is influenced by environment characteristic data reconstruction reference drift caused by equipment performance degradation in the prior art. A spatio-temporal data sequence is constructed by acquiring historical data of a photovoltaic unit, a spatio-temporal association network of generated power among equipment is constructed, performance stability of a reference equipment group is evaluated, and when the stability does not meet conditions, a performance attenuation spatio-temporal mode is analyzed to identify common features and personalized features. The propagation path of performance degradation on the space-time correlation network is analyzed based on the characteristics, a critical point is evaluated, reference correction is performed on the environment characteristic data by using the common characteristics and the critical point, and finally, the corrected environment characteristic data is used for executing power generation power prediction and equipment health state evaluation. Therefore, the prediction accuracy and the health management reliability are improved.

The embodiment of the invention provides a model training method, a carbon emission prediction method, a device and equipment. The method comprises the following steps: firstly, obtaining carbon emission sample sequence data; then, preprocessing the carbon emission sample data sequence to obtain preprocessed carbon emission sample sequence data; further, according to the preprocessed carbon emission analysis sample sequence data, determining a time sample feature and an interaction feature sample sequence; and then. Processing the sample time feature and the preprocessed power consumption sample sequence data to obtain a sample time feature component and a sample power consumption feature component; and finally, inputting the sample time characteristic component, the sample power consumption characteristic component, the interaction characteristic sample sequence and the preprocessed carbon emission sample sequence data into an initial Transform model for optimization training, and obtaining an improved target Transform model. In this way, the prediction precision of the prediction model and the generalization ability of the model are improved, and therefore accurate prediction of carbon emission data is achieved.

The invention discloses a mass data display method, device and equipment based on a browser client side and a medium, relates to the technical field of computers, is applied to a server side and comprises the steps that a data obtaining request sent by the browser client side is obtained; the data acquisition request carries a request time range and a target sampling frequency; obtaining original time sequence data in the request time range from a preset time sequence database, and determining the size of a sampling window based on the data frequency of the original time sequence data and the target sampling frequency; the data frequency is greater than the target sampling frequency; grouping the original time series data based on the size of the sampling window so as to obtain a plurality of data windows, and extracting a target feature point for representing data trend change in each data window; and merging the target feature points of the data windows to obtain a downsampled data sequence, and sending the data sequence to a browser client for display. According to the method and the device, effective visual display of mass data on the browser client can be realized.

The invention relates to the technical field of optical communication, and discloses an optical fiber loss evaluation method and system. The method comprises the steps that an optical fiber sensor collects real-time optical signal data and generates a dispersion velocity data sequence; extracting a fluctuation feature vector by adopting an adaptive filtering algorithm, and determining a dispersion velocity change trend; inputting the change trend and the transmission distance into a neural network model, predicting a loss fluctuation amplitude and generating a prediction loss sequence; calculating a coupling effect correlation coefficient, judging abnormal coupling and outputting an evaluation result; adjusting signal compensation parameters according to an evaluation result, optimizing nonlinear influence and determining a compensated dispersion velocity value; calculating a signal attenuation factor according to the compensated dispersion velocity value and the transmission distance, and generating an optimized transmission scheme; and updating optical communication network parameters according to the scheme, outputting stable signal quality and generating an optical fiber loss evaluation result. According to the invention, the problem of inaccurate evaluation caused by nonlinear fluctuation and signal coupling is solved, and the stability of optical fiber transmission and the signal quality are improved.

The invention discloses a water quality monitoring method and system based on large model data analysis, and the method comprises the steps: collecting multi-dimensional environment index real-time data through a remote sensing device and an underwater mobile sensor, completing the noise filtering and format standardization through an edge calculation node, and inputting the data into a machine learning model; the method comprises the following steps: automatically extracting space-time distribution characteristics and intelligently identifying potential abnormal modes, immediately starting multi-parameter correlation analysis when an abnormal degree exceeds a threshold value, constructing a deviation correlation map to accurately invert pollution source space coordinates, further performing time sequence prediction modeling in combination with a historical data sequence, and deducing a future diffusion path track of pollutants. And finally, deeply overlapping and fusing the predicted trajectory and the ecological sensitive area map, quantitatively calculating the comprehensive risk score distribution of each intersection area by using a risk assessment algorithm, automatically generating a visual environment monitoring report, and updating a historical sequence to form closed-loop learning at the same time. According to the invention, the emergency response speed, the traceability accuracy and the risk prevention and control capability of the sudden pollution event of the water environment are greatly improved.

The invention relates to the technical field of digital twinborn and drainage basin environment monitoring simulation, in particular to a drainage basin environment data simulation deduction system based on a digital twinborn technology, which comprises a digital twinborn management center used for calling multi-source heterogeneous monitoring data of a drainage basin environment to obtain an aligned environment data sequence; the causal coupling analysis unit is used for obtaining a steady-state logic signal or a dynamic decoupling signal; when a dynamic decoupling signal is generated, the topology adaptive reconstruction unit is used for performing structural mutation recognition feedback analysis on a calculation map of the digital twin model to obtain a reconstructed topology map; when the steady-state logic signal is generated, the simulation deduction execution unit is used for performing environment evolution deduction analysis on the aligned environment data sequence based on the current calculation map to obtain a simulation deduction result; according to the method, model calculation divergence or misleading output caused by sensor faults is effectively avoided, and the calculation problem caused by multi-source data heterogeneity is solved.

The invention discloses a distribution line abnormity monitoring and early warning method and system, and relates to the technical field of distribution line intelligent monitoring. The method comprises the following steps: acquiring operation data acquired by multiple types of sensors, and performing time alignment, normalization and fusion processing to form a comprehensive data set; inputting the comprehensive data set into a generative denoising model based on noise and abnormal signal distribution separability learning, and outputting a clean data sequence through noise suppression and abnormal feature fidelity joint optimization; identifying an abnormal evolution trend with a nonlinear amplification characteristic by using a Lyapunov index and a Hurst index, and generating a risk assessment result; and constructing and dynamically adjusting a self-adaptive early warning threshold set according to a risk assessment result, and outputting abnormal early warning information when a risk index exceeds a threshold. The method realizes parallel noise suppression and abnormal feature fidelity, has dynamic identification and self-learning capabilities, and significantly improves the accuracy of distribution line anomaly detection and the stability of an early warning system.

The invention discloses an urban intelligent water affair risk dynamic identification and early warning method based on deep learning, and the method comprises the following steps: S1, collecting the water pressure, flow, residual chlorine concentration, elevation, rainfall, valve state, pump station state and accident label of each node in a water supply network, and constructing a time alignment data sequence; s2, constructing a dynamic adjacency matrix according to the pipe network connection relation and the event state information; s3, inputting the data sequence and the dynamic adjacency matrix into an improved space-time diagram wavelet neural network to generate space-time feature representation; s4, multi-scale features are extracted and fused through the high-frequency branches and the low-frequency branches; s5, constructing a hyperedge set, executing graph structure propagation, and generating a risk representation tensor; s6, inputting the risk representation tensor into the risk prediction network, and outputting a node risk probability and a confidence interval; and S7, determining a risk level according to the risk probability and the confidence interval, and generating a corresponding early warning signal. According to the invention, fine modeling and dynamic early warning of urban water supply risks are realized.

The invention discloses a hydraulic power plant equipment full life cycle health management system and method based on the Internet of Things, and relates to the technical field of hydraulic power plant equipment management, and the method comprises the steps: collecting operation parameters through a sensor network disposed on various types of equipment of a hydraulic power plant, and forming a data sequence after time synchronization and analog-to-digital conversion; the data sequence is subjected to noise reduction processing at the edge computing node, whether an abnormal state exists or not is judged on the basis of the data analyzed and processed by the lightweight anomaly detection model, the data is graded and marked according to a judgment result, and only statistical characteristics of abnormal data or non-abnormal data marked as high priorities are uploaded to a cloud data center; and the cloud data center identifies the type of the equipment component according to the uploaded data, calls the corresponding physical degradation evolution model, dynamically calculates the degradation degree of each component in combination with the real-time operation state, determines the weight relationship of each degradation dimension according to the service stage of the equipment, and generates a comprehensive health index.

The invention relates to the technical field of moving target detection, and discloses a three-dimensional space moving target detection method, electronic equipment and a storage medium, and the method comprises the steps: obtaining a multi-modal sensor data sequence, carrying out the point cloud data fusion of the multi-modal sensor data sequence, and obtaining a fused point cloud data sequence; separating a moving target pixel region and a static background region from the fused point cloud data sequence, and performing time sequence optimization to obtain a dense point cloud sequence including a timestamp; performing motion target pose estimation according to the dense point cloud sequence including the timestamp to obtain a pose estimation sequence; and performing moving target matching according to the pose estimation sequence to obtain a tracking moving target. Through the implementation of the invention, the target deformation and the high-speed motion scene in the moving target identification can be effectively processed, and the dynamic characteristics of the target can be more comprehensively described, so that the moving target matching can be more accurately carried out, and the tracked moving target can be obtained.

The invention discloses a reinforcement learning-based spring steel wire drawing control method, which comprises the following steps of: acquiring real-time process parameters to form a process state data sequence; inputting the process state data sequence into the state space model, and constructing a virtual working condition sample; based on the virtual working condition sample, pre-training a reinforcement learning controller and outputting an initial control strategy; inputting the initial control strategy into a lower-layer strategy network, and outputting a wire drawing speed adjusting instruction; the current process state and the wire drawing speed adjusting instruction serve as synchronous input, and an implicit context vector is generated; extracting control strategy characteristics in the edge controller, performing compressed encoding and forming control strategy representation; uploading the control strategy representation to a cloud server, and outputting a unified global control strategy model; and carrying out anomaly detection on the current process state, and if a detected state reconstruction error exceeds an anomaly judgment threshold, triggering safety control logic. The spring steel wire drawing control device realizes spring steel wire drawing control.

The invention belongs to the technical field of forest disaster monitoring and early warning, and particularly discloses and provides a forest fire spreading prediction method based on multi-dimensional features, which comprises the following steps: dividing a target area into grid units, collecting an infrared thermal imaging data sequence through an unmanned aerial vehicle, dynamically evaluating fire intensity indexes of the grid units, and predicting the fire intensity indexes of the grid units. Identifying each fire spreading path, evaluating the spatial proximity of each grid unit relative to the path, marking a fire occurrence area, and determining the path intersection density in each grid unit; fusing the fire intensity index, the path space proximity and the path intersection density, calculating to obtain a comprehensive fire spreading risk index of each grid unit, and generating a forest fire spreading danger level zoning map based on the index; according to the method, the fire spreading risk of each grid unit is comprehensively evaluated by fusing the fire intensity index, the path space proximity and the path intersection density, and refined and three-dimensional evaluation of the risk of the unburned area is realized.

The invention discloses a high-resolution peak value positioning method based on multi-pulse superposition and offset clock cooperation, and belongs to the technical field of laser radar ranging. Aiming at the problems that a traditional laser radar depends on a high-sampling-rate ADC, so that hardware cost is high, power consumption is large, and sampling errors are likely to be caused by echo jitter, the method does not need to replace an existing ADC, and the technical breakthrough is achieved through the following core steps that firstly, the basic sampling rate X Hz and the target equivalent sampling rate improvement multiple N of the ADC are determined; laser pulses are emitted to a target object for N times, during Kth emission, ADC sampling is started by delaying (K-1) / (X * N) seconds, and N groups of echo data with time sequence offset are obtained; performing cross combination on the N groups of data, and constructing an N-time equivalent high-sampling-rate data sequence; repeating the sampling and combining process for S times, and performing alignment superposition and averaging on S groups of data to suppress echo jitter and noise; and estimating initial parameters (amplitude, mean value and standard deviation) of a Gaussian model based on the superposed data, optimizing the parameters through an LM nonlinear optimization algorithm, and finally taking the optimized mean value as a high-precision peak position of an echo signal to realize accurate measurement of the laser flight time. Under the condition of low-sampling-rate hardware, through the collaborative design of clock skew and multi-pulse superposition, the physical limitation of the sampling rate is broken through, low cost, low power consumption and high ranging precision are taken into account, and the method is adaptive to multi-scene application of automatic driving, industrial ranging and the like.

The invention discloses a CAN message priority dynamic arbitration method and system based on event triggering, and the method comprises the steps: obtaining a discrete sampling signal of a CAN node of an engineering vehicle and a state register value of a CAN controller, converting the discrete sampling signal into a working condition data sequence, and obtaining a bus load rate through the state register value; obtaining an event state sequence according to the working condition data sequence; all CAN messages to be sent are constructed into a message data record, and the message data record with event semantics and dynamic priority labels is obtained according to the protocol data unit and the event state sequence; and according to the bus load rate and the event state sequence, sending a message data record with event semantics and dynamic priority annotations by adopting a token bucket mechanism through a three-level priority queue linked with a three-state state machine. According to the invention, the security key message in the system can still be transmitted in time under high load, and the bandwidth occupation and delay jitter caused by the low-priority message are avoided at the same time.

The invention belongs to the technical field of fault positioning, and discloses a Beidou satellite time service-based power distribution network fault section positioning method, which comprises the following steps of: acquiring three-phase current, and performing phase synchronization alignment on the three-phase current by using a Beidou satellite to obtain a three-phase current synchronization data sequence; performing modulus transformation on the three-phase current synchronous data sequence to obtain a zero-mode current component and a line-mode current component; performing adaptive waveform decomposition on the zero mode current component and the line mode current component to obtain an intrinsic mode component, and performing signal reconstruction on a target component to obtain an enhanced fault traveling wave signal; performing waveform curvature sudden change detection on the enhanced fault traveling wave signal to obtain a wave head arrival time and a time difference observation sequence; performing geometric analysis on the topological structure based on the time difference observation sequence and the traveling wave propagation speed to obtain a suspected fault section; and comprehensively studying and judging the suspected fault section to obtain an actual fault section. The power distribution network fault positioning efficiency can be improved.

The invention discloses a method for evaluating piglet feed digestibility based on excrement indexes, and particularly relates to the field of piglet feed digestibility evaluation.The method comprises the steps that a piglet excrement sample under target feed intervention is obtained, and multiple biochemical index data in a preset time sequence are collected for the excrement sample; the biochemical index data comprises moisture content, ammonia nitrogen concentration and short-chain fatty acid concentration, and a corresponding excrement index original data sequence is formed according to the biochemical index data. Continuous aggregation of discontinuous sampling data is realized by constructing a sample index structure, and in combination with frequency domain feature extraction, interpolation reconstruction and a collaborative tensor modeling mechanism, an absorptivity inversion path having a robust adaptability to missing and asynchronous data is established. The key problems that in the prior art, a current evaluation method seriously depends on data integrity, and discontinuous information in a real sampling environment cannot be processed are solved.

The invention relates to the technical field of data processing, in particular to a rice field gate anti-clogging intelligent monitoring control method, which comprises the following steps of: acquiring multi-dimensional monitoring data of a gate at each monitoring moment in real time, and forming a multi-dimensional monitoring data sequence by the multi-dimensional monitoring data of the gate at each monitoring moment in each operation process; one gate operation process corresponds to one multi-dimensional monitoring data sequence; for any gate operation process, obtaining an optimized gate clogging coefficient of any gate operation process according to the multi-dimensional monitoring data sequence corresponding to any gate operation process; the method comprises the steps of obtaining an optimized gate clogging coefficient of each gate operation process, obtaining a gate clogging trend index by using the optimized gate clogging coefficient of each gate operation process, and carrying out gate clogging prevention intelligent monitoring control according to the optimized gate clogging coefficient and the gate clogging trend index of the current gate operation process. And early-stage, quantitative and trend monitoring of the clogging state of the gate is realized.

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.