3581 results about "Data sequences" patented technology

The invention relates to a meter state adaptive estimation method supporting data incomplete completion. The method comprises the following steps: acquiring continuous measurement data of a meter under a unified time reference to form an original measurement data sequence; generating a state parameter set for describing the current working state based on the original measurement data sequence; dividing an original measurement data sequence into a plurality of data windows, and predicting an expected measurement value in each window according to the variation amplitude and trend direction of collected data in adjacent time periods; comparing the predicted measurement value with an actual measurement value, and identifying positions with difference values exceeding a set threshold value to form an incomplete position index set; aiming at the incomplete position index set, in combination with the change trend and continuity of adjacent data, searching a complementation value in a preset reasonable numerical value interval, and generating a complemented measurement data sequence; according to the invention, the integrity and accuracy of the meter data under the condition of loss or abnormity are improved.

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 power adapter charging protocol identification and compatibility self-learning optimization method. After the adapter establishes physical connection with a target terminal, a voltage signal output by the target terminal is collected, a handshake data sequence is formed, feature extraction is performed on the handshake data sequence, and a first protocol feature vector is generated. And performing similarity matching on the feature vectors in a protocol feature library to determine a historical protocol category and extract a corresponding historical charging parameter template. And during voltage and current step-by-step adjustment, collecting target terminal load impedance change data, extracting impedance spectrum features, and fusing the impedance spectrum features with the first protocol feature vector to form an enhanced protocol feature vector. And based on the enhancement protocol feature vector and the historical parameter template, predicting negotiation parameters supported by the target terminal, generating a predicted negotiation parameter set, and applying the predicted negotiation parameter set to a charging negotiation process, thereby realizing stable charging output with the target terminal. According to the method, the compatibility and the self-adaptive capability of the power adapter to various fast charging protocols can be improved.

The invention discloses a method for intelligently regulating and controlling production parameters in a fruit concentrated juice production process, which comprises the following steps of: acquiring multi-dimensional process parameters such as temperature, pressure, flow, concentration, equipment state and the like in real time through a multi-channel sensor network, and forming a standardized data sequence after filtering, normalization and drift correction; extracting stage features by using technologies such as a sliding window and Fourier transform, and inputting the stage features into the lightweight classification model to realize production stage identification; in combination with an identification result, dynamically calling a corresponding multi-target optimization sub-model, and realizing nonlinear prediction and optimal solution selection of process parameter setting by adopting an LSTM and a multi-target genetic algorithm; on the basis of real-time feedback, the performance of the model is automatically evaluated, self-adaptive adjustment and optimization of the optimization algorithm are achieved through reinforcement learning and an incremental updating mechanism, multi-target collaborative optimization, self-adaptive adjustment and model switching in the production process can be achieved, and the consistency of production efficiency and product quality is improved.

The invention relates to the technical field of pile foundation monitoring, and discloses a pile foundation state real-time monitoring and diagnosis system based on a digital twinborn technology. The system comprises a multi-source data acquisition module, a data confidence evaluation module and an acoustic emission monitoring decision module. The multi-source data acquisition module comprises a plurality of sensor groups deployed at different depths of a pile foundation, each group comprises a strain sensor, an acceleration sensor, an acoustic emission sensor and a temperature sensor, and pile foundation data can be acquired in multiple dimensions; the data confidence evaluation module receives original data, generates a correction data sequence through time sequence noise separation and reconstruction, and calculates data confidence according to correction data distribution dispersion; the acoustic emission monitoring decision module judges whether acoustic emission monitoring is started or not according to the data confidence coefficient, and controls the acoustic emission sensor array at the top of the pile foundation to collect acoustic emission signals during starting. The system can comprehensively obtain pile foundation data, improve data accuracy, achieve early damage recognition and guarantee pile foundation safety.

The invention relates to a self-adaptive control rewinding machine tension and coiled material deviation collaborative optimization method in the field of intelligent manufacturing, and the method comprises the steps: deploying a distributed tension sensor network at a key position of a rewinding machine coiled material path, collecting the tension value of each measurement point in real time, and generating a multi-point tension distribution data matrix arranged according to a time sequence; processing the multi-point tension distribution data matrix by adopting a sliding window time sequence analysis algorithm, detecting tension fluctuation abnormity, and if a tension value exceeds a preset threshold range, recording a tension abrupt change timestamp and a change amplitude, and generating tension abrupt change data; based on the working condition state description, the rolling diameter real-time change data sequence and the tension sudden change data, a prediction model reflecting rolling diameter change and tension fluctuation is constructed in real time, and a predicted tension trend is obtained; and comparing the predicted tension trend with a preset ideal tension range through a model prediction control algorithm, and generating a multi-target optimization instruction which comprises a dynamic torque regulation and control quantity and a floating roller position set value.

The invention relates to a wind power prediction method and device based on a pre-trained large language model, and belongs to the field of wind power generation, and the method comprises the steps: dividing a wind power data sequence into a plurality of time sequence patches; dividing the time sequence patch into a plurality of equal-length subsequences; extracting data features of the equal-length subsequences; converting the data features into semantic cue words; adding a text instruction to the semantic prompt word to obtain a semantic instruction; splicing the time sequence patch and the corresponding position code, time code and semantic instruction to obtain a fusion sample corresponding to the time sequence patch; inputting the plurality of fusion samples into a large language model for pre-training, and performing parameter adjustment on the large language model to obtain a wind power prediction model; and inputting the fusion sample of the new wind power data into the wind power prediction model, and outputting a wind power prediction value. According to the method, under the condition that the number of historical operation data samples is small, the large language model is pre-trained through the fusion samples, and high-precision wind power prediction is achieved.

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 engineering construction supervision, and discloses a building engineering construction supervision system based on big data analysis, and the system comprises a multi-modal data collection module which is used for collecting multi-source data of a construction site, and the multi-source data comprises structure sensor data, environment monitoring data, video image data, construction log data and building information model (BIM) state data; performing standardization processing and time synchronization on the data to generate a construction state data sequence; and the construction event modeling module identifies key events in the construction process based on the construction state data sequence and constructs a construction event graph, and the construction event graph is composed of event nodes representing construction events and event edges representing event collaboration or time correlation. By introducing an event atlas construction mechanism based on multi-source construction data driving, structured expression and semantic association mapping of key behavior units of a construction site are realized, and the problem of insufficient non-structured information processing capability in construction monitoring is overcome.

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 neurology patient rehabilitation nursing method based on multi-modal data analysis, and relates to the technical field of medical health, and the method comprises the steps: extracting neural function features through a multi-modal data fusion algorithm, and carrying out the calculation through a weighted fusion and statistical analysis method, and obtaining a neuroplasticity index vector; combining the neuroplasticity index vector with the unified multi-modal feature representation, carrying out multi-modal abnormal mode recognition analysis, obtaining a rehabilitation risk early warning signal and a personalized intervention suggestion, and generating a personalized rehabilitation scheme; performing dynamic optimization and self-adaptive adjustment on the individualized rehabilitation scheme by using a feedback loop mechanism to generate an optimized individualized rehabilitation scheme; physiological signals in individualized rehabilitation training based on the optimized individualized rehabilitation scheme are collected in real time and preprocessed, and a standardized physiological response data sequence is generated. According to the invention, the scientificity, timeliness and individual adaptability of regulation and control are improved, so that nerve function remodeling is accelerated and the rehabilitation risk is reduced.

The invention relates to the technical field of data processing, in particular to a monitoring method and system for electrical equipment, and the method comprises the steps: enabling real-time data of any monitoring index of the electrical equipment and historical data in a historical time period to form a data sequence, and according to the change rule of each piece of data in the data sequence under different conditions, obtaining a data sequence; obtaining three abnormal characteristic values of each piece of data; for any abnormal characteristic value, obtaining similar historical data similar to any abnormal characteristic value of the real-time data, and obtaining an abnormal reaction degree of any abnormal characteristic value according to relevance between the real-time data and each similar historical data under other abnormal characteristic values; according to the abnormal reaction degree of each abnormal characteristic value and the three abnormal characteristic values of each piece of data in the data sequence, the abnormal degree of any monitoring index is obtained, abnormal early warning is carried out on the electrical equipment according to the abnormal degree of each monitoring index, and the accuracy of abnormal early warning on the electrical equipment is improved.

The invention is suitable for the technical field of data asset management, and provides a data asset management system based on a block chain and big data analysis, and the system comprises a first terminal which carries out the storage of the ownership information of data assets through a block chain network, and generates a storage data package; generating an evaluation result based on a preset technical index analysis model; writing the hash value of the evaluation result into transaction data of the main chain of the block chain, and generating an evidence storage voucher matched with the block chain transaction; an interaction data sequence is packaged for identity signature, encryption and compression to generate a target code stream, and the target code stream is sent to the second terminal; the second terminal decrypts the encrypted data in the target code stream by using a private key, decompresses the data by using a compression algorithm matched with the first terminal, and restores the data into an interactive data sequence; verifying the access authority and the transaction condition of the evidence storage data packet; checking the anchoring state of the evidence storage voucher on the main chain of the block chain; and the data transaction is completed, the data asset state report is generated, and the data element marketization configuration efficiency is improved.

The invention discloses a steel structure quality defect tracing and analysis method based on deep learning, and the method comprises the following steps: collecting image data, sensor data and construction log information of a steel structure member, and generating a tracing identifier; performing alignment based on the traceability identifier to generate an alignment data sequence; based on the aligned data sequence, outputting a defect segmentation result by using an SE (3) isovariant graph neural network; performing continuous coherence topology analysis on a defect segmentation result, and outputting topologically continuous defect areas and severity scores; extracting process parameters of the defect area, calculating statistical dependency by utilizing an independence criterion, and screening out a paired sample set; based on the sample set, performing stability screening on the causal edges to form a causal graph for output; calculating a causal contribution score output by the causal graph, and outputting a liability sorting list; and filing the responsibility sorting list, and visually outputting a defect traceability analysis atlas and report at the same time. According to the invention, steel structure quality defect tracing and analysis are realized.

The invention relates to the technical field of electrical testing, in particular to a battery testing method and system, and the method comprises the steps: obtaining a voltage data sequence and historical log data of each cell in a to-be-tested battery pack; determining a dynamic instability score of each battery cell according to the parameter volatility index of each battery cell and the spatial correlation weight between each battery cell and the adjacent battery cell; on the basis of the historical log data, analyzing a repeated cumulative effect of all historical damage events corresponding to each battery cell, and determining a historical damage degree of each battery cell; performing deep fusion on the dynamic instability score and the historical damage degree through a nonlinear function to obtain a comprehensive risk index of each battery cell; and based on the extreme values and distribution of the comprehensive risk indexes of all the cells, determining a fault degree capable of representing the overall health risk of the battery pack, and determining a health test result of the battery pack according to the fault degree. According to the method, the accuracy and reliability of battery pack testing 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 environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

The invention provides a network camera monitoring identification method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a monitoring data stream which is outputted by a network camera and comprises a video frame sequence and a corresponding time sequence metadata sequence, and then carrying out the spatial-temporal context coding processing of the monitoring data stream; the method comprises the following steps: generating a context feature cube containing spatial position information and time evolution information, then executing a normal behavior mode learning operation based on the context feature cube, and generating a reference feature library containing typical scene feature templates and feature evolution rule description; and dynamically matching and comparing the context feature cube of the current time period with the reference feature library, calculating a feature matching deviation value and generating an abnormal confidence score, and finally generating a monitoring early warning instruction containing abnormal occurrence time, a space coordinate range and a confidence level identifier according to the abnormal confidence score and corresponding space-time position information. And the accuracy and the early warning effect of monitoring and identification of the network camera are effectively improved.

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.

The invention relates to the technical field of wind power prediction, and particularly provides a generation power prediction method and system for a wind generating set, and the method comprises the steps: firstly obtaining a continuous operation data sequence of equipment state parameters and environment parameters containing timestamp marks, and then carrying out the time sequence feature analysis, generating an equipment state feature sequence and an environmental condition feature sequence, then performing correlation modeling on the two features through feature collaborative analysis operation to obtain a coupling feature sequence reflecting multi-factor collaborative influence, and inputting the coupling feature sequence into a pre-trained power prediction model to obtain a power prediction result; an initial power prediction sequence of a target time period is generated through time context learning and power value mapping operation, finally, an error calibration model is constructed based on historical data, the initial power prediction sequence is dynamically adjusted, and a calibration power prediction sequence is generated and output to a power dispatching system for power generation plan arrangement. And the accuracy of generating power prediction of the wind generating set is effectively improved.

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.

The invention relates to a low-delay edge reasoning deployment method for power terminal equipment. The method comprises the following steps: acquiring data under a unified time reference to form an operation data sequence; inputting the operation data sequence into a pre-trained reasoning model to generate a reasoning parameter set adaptive to the local operation capability; performing reasoning calculation based on the reasoning parameter set to obtain a load identification result and an electric quantity prediction result; determining a communication scheduling strategy according to the reasoning result, wherein the communication scheduling strategy comprises communication priority information, sending time sequence information and target receiving node information; a communication instruction is generated according to the communication scheduling strategy, and data content is sent to a target receiving node according to the communication instruction within a limited time, so that rapid data synchronization and dynamic response control based on load state change are realized; according to the invention, intelligent data processing and rapid communication scheduling can be locally realized at the power terminal equipment, the system response time delay is reduced, and the processing efficiency of a load change event is improved.

The invention relates to the technical field of fault monitoring, in particular to a concentrator intelligent fusion terminal based on multi-sensor monitoring and maintenance. Obtaining a suspected fault moment according to discrete features of data distribution in the state data sequence of any dimension; obtaining a target moment according to the data difference characteristics of the suspected fault moment and the adjacent moment; state characterization points in a multi-dimensional space are constructed according to the data of all dimensions at the target moment, clustering is carried out, and the fault characterization credibility of the state cluster is obtained according to the distribution characteristics of the state characterization points in the state cluster and the data discrete characteristics between the state characterization points. According to the method, the abnormal degree of the latest moment is obtained according to the distance feature between the state characterization point corresponding to the latest moment and the nearest state cluster, the range feature of the nearest state cluster and the fault characterization credibility; the operation state of the concentrator is monitored according to the abnormal degree, and the monitoring accuracy of the concentrator terminal is improved.

The invention provides an intelligent data alignment method and system based on time sequence dynamic multi-source embedding mapping. The method belongs to the technical field of multi-modal data fusion and spatio-temporal information processing. The method comprises the following steps: performing time sequence dynamic feature extraction on a multi-source heterogeneous data source to generate a heterogeneous data sequence containing a time dependency relationship; and constructing a time sequence dynamic multi-source embedded manifold space based on a manifold learning theory, mapping a heterogeneous data sequence to a unified evolution geometric structure representation space, and generating embedded manifold data. Through the method, heterogeneous data from various different data sources can be effectively processed, unified mapping is carried out through time sequence dynamic feature extraction and a manifold learning technology, cross-source alignment of the data is achieved, and the method is particularly suitable for a data scene needing to consider a time dependency relationship.

The invention discloses a sleep monitoring method and system based on a non-contact video data sequence. The method comprises the steps that S1, video data streams of human body sleep are collected through a camera system; utilizing a YOLOv11 network to obtain face video data and thoracoabdominal video data; s2, the RPPG signal extraction model extracts and obtains an RPPG signal by using the face video data; s3, the breathing signal extraction model extracts thoracic and abdominal micro-motion change characteristics in the thoracic and abdominal video data by using an optical flow method, and noise filtering processing is carried out to obtain thoracic and abdominal motion signals as breathing signals; s4, the oxyhemoglobin saturation extraction model detects and outputs an oxyhemoglobin saturation signal by using the RPPG signal; and S5, performing multi-modal fusion analysis on the multi-physiological index fusion recognition model according to time slice T1 division to obtain a long-time-sequence sleep stage staging result. According to the invention, the physiological index signals are extracted and recognized by adopting the non-contact video data sequence, and the sleep stage staging result with a long time sequence is obtained, so that high-precision sleep monitoring and sleep stage recognition are realized.

The invention provides a high-voltage circuit breaker voiceprint denoising method based on data enhancement and a storage medium, and the method comprises the steps: processing a collected original voiceprint data sequence, extracting stable and effective Mel-frequency cepstrum coefficient features, and constructing a two-dimensional feature matrix; a parallel mixed data enhancement strategy is adopted to generate a positive sample pair, and an encoder is trained in combination with a contrast learning mechanism, so that the representation robustness of the model under different voiceprint change conditions is improved. The method comprises the following steps: decomposing an original signal containing noise fringes into a plurality of modal components by using variational modal decomposition, extracting low-frequency effective components, introducing Gaussian white noise, constructing a corrosion target signal as a decoder training target, learning through a denoising automatic encoder, and finally outputting a denoised voiceprint feature signal. The method has the advantages of high robustness, high noise suppression capability, excellent feature expression capability and the like, is suitable for the field of online monitoring and intelligent diagnosis of the state of high-voltage circuit breaker equipment, and has good application prospect and engineering value.

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 provides a coal-bed gas well production dynamic analysis method and system based on AI time sequence prediction, and the method comprises the steps: firstly collecting a production dynamic data sequence of a coal-bed gas well, a corresponding well position geological condition data sequence and a surrounding environment data sequence, and constructing a ternary time sequence dynamic association network comprising node dynamic time sequence segments and a connection line real-time association relationship; and then calling a pre-trained AI time sequence coupling deviation evolution mining model to process the ternary time sequence dynamic association network to obtain a ternary time sequence coupling deviation result. And a deviation conduction closed-loop path is determined according to a ternary time sequence coupling deviation result in combination with forward conduction positioning and reverse tracing verification, and a deviation conduction analysis report is generated. And finally, generating a dynamic regulation strategy based on the deviation conduction analysis report in combination with a production dynamic prediction threshold value, outputting the dynamic regulation strategy to a production control terminal, and meanwhile, feeding back regulation data to update a network node association relationship, thereby realizing accurate analysis and regulation of the production dynamic state of the coal-bed gas well.

The invention belongs to the technical field of production monitoring systems, and discloses a numerical control machine tool dynamic fault diagnosis method and system. The method comprises the following steps: generating a global time reference signal through a main shaft encoder and a clock synchronization protocol; the method comprises the following steps: collecting vibration data of a main shaft bearing in each unit time, current data of an electric cabinet and process parameters, and generating a preprocessed data sequence through transmission delay compensation and multi-rate frequency raising processing; inputting the vibration data and the current data into a preset mechanical-electrical transfer function model, and calculating a time delay parameter; performing phase alignment on the preprocessed data sequence based on the time delay parameter to generate an aligned data sequence; inputting the aligned data sequence into a time sequence neural network, and outputting a fusion feature vector; calculating a cross correlation coefficient of the fusion feature vector, and generating a fault diagnosis result based on a preset cross correlation threshold value; the problem of failure of fault feature extraction caused by data asynchronization in the prior art is solved.