1299 results about "Data cleansing" patented technology

The invention relates to a reservoir dam operation safety sky-land project intelligent sensing system and an operation method, and relates to the technical field of hydraulic engineering safety monitoring. The system is composed of a sky-land water conservancy project integrated monitoring and sensing system, a self-adaptive sampling module, a layered distributed architecture and a software and hardware integrated module, and multi-source data such as deformation, seepage, stress strain, vibration and environmental quantity are cooperatively collected through five dimensions of sky domain, airspace, territory, water domain and work domain. The monitoring frequency is dynamically adjusted by using an adaptive sampling strategy, and data cleaning, standardization, space-time registration and fusion processing are completed through a distributed architecture to generate a high-quality comprehensive data set. The system can realize total-factor and whole-process refined monitoring, effectively eliminates data islands, improves data quality and monitoring efficiency, has high reliability, real-time performance and expandability, and provides powerful data support and decision basis for dam safety assessment and intelligent early warning.

The invention provides an urban drainage pipe network monitoring data cleaning and intelligent prediction method, and the method comprises the steps: firstly obtaining pipe network monitoring data, and carrying out the classification tracking and repairing of missing values; adopting a dynamic IQR algorithm based on a sliding window to adaptively identify abnormal candidate points; secondly, introducing a pipe network topological relation, comparing upstream and downstream data change trends, eliminating non-physical anomalies caused by equipment faults, and reserving real hydraulic events; calculating the physical delay time between the nodes by using the cross correlation coefficient; and finally, constructing a random forest model, taking upstream historical data after delay alignment as feature input, and realizing accurate prediction of a future water level and quantification of a feature contribution degree. According to the method, a physical mechanism and machine learning are fused, the problems that data cleaning lacks adaptivity and a deep learning model lacks interpretability are effectively solved, and the accuracy of waterlogging early warning is improved.

The invention relates to the technical field of reservoir dam safety monitoring, and discloses a reservoir dam siltation dynamic monitoring and early warning system. Multi-dimensional sensing node arrays of the system are arranged at key positions of a dam body structure and a reservoir area, and sediment thickness distribution data, water flow velocity field data and sediment concentration gradient data are synchronously collected. And the edge computing node receives the original monitoring data, executes data cleaning and space-time alignment processing, and generates a standardized siltation feature data set. And the cloud analysis platform receives the data set, calculates a deposition evolution trend matrix through a space-time coupling prediction model, and outputs a reservoir area deposition risk level distribution map. And the dynamic visualization engine analyzes the risk level distribution map, generates a three-dimensional dynamic deposition situation model, and marks the space coordinates of the abnormal deposition area. And the early warning decision center generates a graded early warning instruction set according to the space coordinates of the abnormal region, and triggers a corresponding emergency response strategy.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data analysis method, which comprises the following steps: carrying out data cleaning and preprocessing on multi-modal data by adopting dynamic noise detection, cross-modal standardization and meta-learning driving methods; performing cross-modal feature fusion and analysis on the multi-modal data subjected to data cleaning and preprocessing through potential association between dynamic weight distribution and graph neural network mining modes to obtain a fusion feature vector with high feature association degree; obtaining a multi-modal large model through lightweight training and field fine tuning, and inputting the fusion feature vector into the multi-modal large model to obtain a multi-modal decision result; and displaying a multi-modal decision result, carrying out dynamic optimization and iteration on the multi-modal decision result, and adjusting and optimizing parameters of the multi-modal large model through a real-time evaluation and feedback mechanism. The method breaks through the efficiency and precision bottlenecks of existing multi-modal data analysis, and has the advantages of high efficiency, robustness and expandability.

Hierarchical modelling and advanced feature engineering discover abnormalities in time series data with irregular trends. Data is collected in real time to ensure temporal integrity in the invention. Extraction filters and isolates useful data. Data cleansing removes noise and extraneous data after preliminary analysis identifies patterns and abnormalities. Feature engineering organizes cleansed data for machine learning algorithms. Primary storage stores this data for fast retrieval and extensive trend analysis. Holidays and weekends provide unique patterns in trend analysis. These trends are used to cluster data and create hierarchical predictive models, starting with a first-order model for general trends and increasing in order to refine residuals. Serializing these models improves storage and retrieval. Trend clusters are created from new data points, and algorithms detect pattern deviations. Statistical tests and machine learning classifiers identify anomalies and create alerts and remedial measures. The system monitors and analyzes incoming data to detect anomalies.

The invention discloses an energy efficiency improving method and device for a slope type gravity energy storage system, and relates to the technical field of electric energy storage systems. The method and device for improving the energy efficiency of the slope type gravity energy storage system comprise the following steps that S1, energy storage operation data are obtained through multi-source sensor fusion, and data cleaning and normalization processing are conducted on the energy storage operation data; s2, extracting a multi-dimensional state factor, carrying out quantitative evaluation on the operation state of the energy storage unit, constructing a state grading mechanism based on an evaluation result, and generating an operation state label; s3, calling a running state label to screen candidate units, constructing a scheduling priority ranking index, and dynamically allocating tasks and adjusting an execution sequence according to the index; and S4, analyzing structural wear and energy consumption load characteristics, establishing a scheduling correction mechanism, and dynamically adjusting a scheduling priority ranking index. The problem that the scheduling self-adaption cannot be realized according to the load, loss and wear difference of each unit in the parallel operation process of a plurality of energy storage units is solved.

Disclosed in the present invention is an artificial-intelligence-based performance prediction processing method for a carbon-fiber carbonization process. The method comprises: preprocessing experimental data under test, so as to obtain said experimental data that has been subjected to data cleaning; then, using a sliding window processing method to slide on time series data, extracting data within a window at each position and using the extracted data as an input sample, and determining an input feature and an output variable feature of each input sample, so as to convert the time series data into a plurality of experimental data samples under test in the format of a target model input; performing random data set division on said plurality of experimental data samples, so as to obtain some training sets and some test sets; and constructing a target model, and inputting said experimental data samples into the target model. The target model can implement a relatively accurate mechanical-performance prediction for a carbon-fiber-precursor carbonization process, and the model has an optimal performance in all aspects and has a relatively good generalization capability.

The invention relates to the technical field of industrial digital twinning, in particular to a digital twinning-oriented real-time data synchronization and consistency verification method, which comprises the following steps of: S1, acquiring multi-source heterogeneous data from a physical entity and a service system, and performing timestamp alignment based on a unified time reference to generate a standardized data stream; and S2, based on the standardized data stream, carrying out data cleaning, aggregation and preprocessing, verifying the integrity and time sequence of the data stream through a transmission verification mechanism, and outputting high-fidelity credible data. According to the method, a bidirectional consistency verification mechanism between a physical entity and a digital twinborn model is constructed, real-time deviation is quantified into a loss function, an online learning engine is driven to adaptively adjust physical parameters in the model, and a complete closed loop from data acquisition to parameter optimization is formed; the digital twinborn model can continuously adapt to dynamic conditions such as material aging and working condition change of a physical entity, and the tedious process that a traditional system depends on manual regular calibration is avoided.

The present application provides a multi-terminal ecosystem fund allocation management system for an e-commerce enterprise, comprising: acquiring tax jurisdiction information of each business entity, comparing tax policies of different regions, and determining whether there is a difference between the tax policies; if there is a difference between the tax policies, acquiring from financial systems of each business entity fund distribution-related transaction data and tax information, and obtaining a unified-format tax information dataset by means of data cleaning and integration; using an association rule mining technology, finding an association mode between fund allocation and tax processing from the tax information dataset, so as to form a tax association rule library for fund allocation; on the basis of the tax association rule library for fund allocation, constructing a tax benefit evaluation model for fund allocation, and predicting a tax impact of a solution by means of inputting different fund allocation solution parameters.

The invention discloses a multi-dimensional enterprise qualification evaluation method and system, and the method comprises the steps: obtaining multi-dimensional original data, and obtaining a standardized enterprise multi-dimensional feature data set through employing a data cleaning and normalization preprocessing technology; aiming at a standardized enterprise multi-dimensional feature data set, performing grouping optimization and weight calculation on indexes by utilizing an improved WP-PVC algorithm, and constructing a multi-dimensional evaluation index system; based on the multi-dimensional evaluation index system, a double-standard WP-PVC algorithm is adopted to carry out inter-index correlation analysis and score calculation, and an enterprise qualification comprehensive score result is generated; according to an enterprise qualification comprehensive scoring result, in combination with a multi-logarithm algorithm of random online sorting, calculating a matching degree between the enterprise and various science and technology projects, and outputting a project matching recommendation list and application success rate prediction; and automatically generating an enterprise qualification diagnosis scoring report based on the enterprise qualification comprehensive scoring result and the project matching recommendation list. According to the invention, comprehensive evaluation of enterprise qualification, accurate project matching and scientific application guidance are realized.

The invention discloses a sleep staging method and system. The method comprises the steps that physiological feature original signals of a target object are obtained; performing signal state detection on the physiological feature original signal; if the user leaves the bed or the signal is invalid, performing data cleaning on the sleep staging data of the day to obtain a final sleep staging result, and if the user is in the bed state and the signal is valid, performing preprocessing operation on the physiological feature original signal, and performing separation to obtain at least two physiological parameter signals related to the sleep state; performing multi-dimensional feature extraction on the physiological parameter signals to construct a target physiological feature array; based on the target physiological feature array, the target object information array and the sleep state information array, a sleep staging result corresponding to the current moment is obtained through a preset sleep staging model. According to the invention, non-inductive home monitoring can be realized by relying on non-intrusive equipment such as an intelligent mattress and an intelligent pillow in an intelligent home scene, and the signal anti-interference capability and the sleep staging accuracy are effectively improved.

The invention relates to the technical field of environmental monitoring and atmospheric pollution early warning, in particular to a Yangtze River Delta composite extreme weather ozone pollution early warning model construction method, which comprises the following steps of S1, acquiring high-resolution meteorological data and pollutant concentration data of a Yangtze River Delta region to form an original data set; and S2, carrying out missing value interpolation and abnormal value elimination on the meteorological data and the pollutant data, and carrying out grid alignment according to time and space to generate a unified spatial-temporal characteristic matrix. According to the method, by collecting Yangtze Delta high-resolution weather and pollutant data, performing data cleaning, bimodal feature coding and joint representation modeling, predicting the ozone concentration and generating regional early warning through multi-layer Transform self-adaptive attention, the problems that traditional ozone early warning mostly depends on a single-modal prediction model, and the reliability of the ozone early warning is greatly improved are solved. And due to the lack of multi-modal space-time dependent capture, the problem of early warning information lag is caused.

The invention discloses a robot remote operation and maintenance task scheduling and optimization method based on artificial intelligence, and the method comprises the following steps: collecting data, executing data cleaning, formatting and feature extraction, and forming a standardized feature vector set; constructing a state space, an action space and a reward function; the method comprises the following steps of: inputting an improved Distributional SAC (Software Consensus Consensus) task scheduling model; constructing a task demand matrix and a robot state matrix; generating a plurality of subtask sets, and establishing a high-level scheduling strategy according to task priority weights; in the low-layer execution network, a subtask strategy network is trained, and an action instruction set is generated to control the robot to execute tasks; updating strategy parameters based on environment feedback; establishing a self-adaptive hierarchy switching mechanism; and a hierarchical parameter updating mechanism is adopted to complete task scheduling and optimization. The improved reinforcement learning model is adopted, intelligent scheduling of robot operation and maintenance tasks is achieved, and the method has the advantages of being efficient, self-adaptive and high in stability.

The invention relates to the technical field of intelligent vocational ability analysis, and discloses a dynamic planning method for a vocational ability genetic map based on multi-modal data and federal learning. The method comprises the steps that multi-source heterogeneous data in occupational activities is collected, and a multi-modal data set is formed through data cleaning and standardization processing; a graph neural network technology is utilized to construct a correlation topology between capability elements, spatio-temporal context information is fused, a dynamically evolved vocational capability gene map is established, and the accuracy and interpretability of vocational capability analysis are improved. Multi-node cooperative training is realized by adopting a federated learning framework, data privacy security is ensured in combination with a differential privacy technology, and the problem of data islands is solved. A capability evolution path is simulated based on a three-dimensional visualization and digital twinning technology, an occupational development scheme is generated through an intelligent recommendation algorithm, and a real-time feedback optimization mechanism is established. According to the method, dynamic, visual and personalized vocational ability analysis is realized, and accurate decision support is provided for vocational planning.

The invention relates to the technical field of data processing, in particular to an intelligent supervision method and system based on a data visualization platform, and the method comprises the steps: collecting multi-source water conservancy data through dual-channel redundancy check, converting the multi-source water conservancy data into a standardized space-time matrix, carrying out the data cleaning through a containerization adaptation and variational auto-encoder, and marking the confidence; hierarchically storing the data based on a confidence threshold, and generating a routing strategy through reinforcement learning to distribute data fragments; calculating a flood peak evolution prediction result by using space-time diagram convolution, calculating an output risk assessment value by combining a Bayesian network and long and short-term memory, and activating federal learning parameter updating when the confidence coefficient is insufficient; the digital twin model loads physical constraint parameters to deduce a flood control scene, and a scheduling instruction set and a rehearsal animation are generated through multi-objective optimization; and dynamically rendering to generate an interactive visual interface, associating a causal deduction path, and triggering federal model parameter updating by user feedback, thereby realizing dynamic collaboration of the forecasting and early warning rehearsal plan function chain.

The invention relates to the technical field of three-dimensional models, and discloses a GIS data-based low-altitude visual three-dimensional model automatic generation method, which comprises the following steps of: acquiring multi-source GIS vector data from an open geographic data source, performing standardized preprocessing through a data cleaning rule set, constructing a Cesium earth scene basic framework in a UE5 engine environment, and dynamically generating a city L2-level three-dimensional visualization model based on a preprocessed GIS data stream; when a low-altitude visual three-dimensional model is generated, a lightweight data format is adopted, data transmission efficiency and dynamic updating capability are improved, complex light environment changes can be adapted in real time, the problem of color distortion on the surface of the model is reduced, meanwhile, a visual angle deviation detection and correction mechanism is established, the geometric body edge distortion phenomenon is relieved, and the generation efficiency of the low-altitude visual three-dimensional model is improved. And the color rendition precision and the spatial positioning reliability of the low-altitude scene are improved.

The invention discloses a one-code interconnected city governance optimization method and system based on space-time identification. According to the method, city grids are dynamically divided based on city granularity requirements to obtain city networks, scene semantic annotation is performed based on space-time attitude parameters of the city grids and moving tracks of entities in the city grids, and digital identity codes are distributed to the entities based on scene semantic annotation results and time dimensions of the city grids; events in each city grid are dynamically processed based on a scene semantic annotation result, data cleaning is carried out based on abnormal screening parameters of historical city operation data, and early warning is carried out based on the cleaned historical city operation data and real-time city operation data; the corresponding entity information is updated in real time based on digital identity code scanning, the entity information is fused through the digital identity codes, a city operation holographic portrait is generated and output, and a novel data-driven, dynamically-optimized and holographic visual city governance mode is constructed.

The invention relates to an electric power marketing business abnormity real-time detection method and system based on stream-oriented computation, and belongs to the technical field of electric power system optimizing.The method comprises the steps that data snapshots are extracted from an electric power marketing business system, difference comparison is conducted on the data snapshots and historical snapshots of an intermediate library, and standardized increment events are generated and stored; capturing an incremental event in real time through a data change capturing tool and pushing the incremental event to a message queue; a streaming computation engine consumes the event stream, sequentially performs data cleaning, association with a static dimension table and sliding window statistical feature calculation, and constructs a feature vector; and performing parallel analysis and weighted fusion on the feature vectors based on a business rule base and an online machine learning model to generate a comprehensive risk score, and outputting an abnormal event when the score exceeds a threshold value. According to the method, the problems of exception identification lagging and complex work order process in a traditional batch processing mode are solved, the crossing of the business risk from hour-level detection to minute-level real-time perception is realized, and the timeliness and accuracy of power marketing risk management and control are improved.

The invention discloses a combined electric appliance health analysis method and system based on multi-source data fusion, and the method comprises the steps: cleaning text data, carrying out the semantic mapping of the text data to a preset state quantity index, extracting a basic feature value from image data, and generating a standardized evaluation index set; calculating a weight vector of each evaluation index by constructing a judgment matrix, and performing consistency verification; weibull distribution fitting is carried out based on historical state quantity data, and grading threshold values of the positive degradation index and the negative degradation index are calculated; dividing a state interval based on the normal cloud model and calculating a membership degree vector of each state level; the membership degree vector is converted into a basic probability distribution function, evidence fusion is carried out in combination with the weight vector, and a comprehensive state level is output; and identifying the high-conflict evidence according to the Pignistic probability distance, performing refusion after correction, and outputting a final state level. Deep fusion and intelligent evaluation of multi-source heterogeneous data are realized, and the accuracy and robustness of health state judgment of the combined electric appliance are improved.

The invention relates to the technical field of power distribution lines, in particular to a power distribution line abnormal line loss analysis and treatment method and system, and the method comprises the following steps: synchronously extracting real-time monitoring data and historical data based on a power distribution line network, carrying out the data alignment and data cleaning, screening features associated with current leakage and voltage drop, and carrying out the analysis and treatment of the abnormal line loss of the power distribution line. Comprising load change rate and temperature fluctuation. According to the invention, through application of real-time and historical data analysis, the detection and management efficiency of the abnormal line loss of the distribution line is improved, the accuracy of data processing is improved through synchronous extraction and data alignment, early recognition of abnormal modes is allowed, the possibility of energy loss and unplanned power failure is reduced, and through adoption of Fourier transform and wavelet transform, the detection efficiency of the abnormal line loss of the distribution line is improved. According to the method, abnormal signals are accurately recognized, the analysis capability of the signals is enhanced, the accuracy of abnormal detection is improved, fault positioning and maintenance plan making are effectively optimized through combination of the GIS system, and the pertinence and efficiency of maintenance work are remarkably improved.

The invention belongs to the field of near-inertia internal wave modeling, and particularly relates to a near-inertia internal wave modeling method based on a reverse echo observation array, which comprises the following steps: arranging a reverse echo measurement device array in an observation area, and acquiring echo signals from the seabed to the sea surface; performing data cleaning and band-pass filtering on the signals, and extracting near-inertia internal wave signals; calculating the depth and the relative vorticity of a mixed layer by combining historical temperature-salinity data; calculating a wind input energy flux by using the wind stress data; calculating a net level energy flux by adopting a boundary integration method; judging whether the boundary energy flux influence is smaller than a threshold value or not, and if not, adjusting array layout; and finally establishing a multivariable linear model containing wind energy input, mixed layer depth, relative vorticity and energy flux. According to the method, the temporal-spatial resolution and prediction precision of near-inertia internal wave modeling are effectively improved, the problems that in a traditional method, the boundary effect is remarkable, and multi-factor comprehensive modeling is insufficient are solved, and the method is suitable for long-term efficient monitoring of a large-range marine environment.

The invention relates to the technical field of environment management information systems, in particular to a method and a system for extracting an ecological environment access list rule of an intelligent agent. The method comprises the steps of collecting ecological environment policy and regulation data from a plurality of data sources, and performing data cleaning and labeling to obtain a standardized text data set; training and optimizing the data set to obtain an ecological environment professional knowledge large model; designing a prompt project template including input, instructions, examples and output requirements; inputting the cleaned new policy data into the large model, and automatically extracting a structured access list rule according to a prompt template; and finally outputting and storing the rule. According to the method, automatic conversion from an unstructured policy text to a structured access rule is realized, the problems of low efficiency, poor accuracy and difficulty in updating and maintenance of a traditional manual extraction mode are effectively solved, and the intelligent level and decision support capability of ecological environment partition management and control are remarkably improved.

The invention relates to the technical field of soil drainage and salt improvement, in particular to a coastal saline-alkali soil drainage and salt improvement intelligent regulation and control method and system based on artificial intelligence, and the method specifically comprises the following steps: dividing treatment units, arranging sensing equipment, and collecting multi-source heterogeneous data to form a data set; after data cleaning and time-space synchronization are conducted, standardized feature vectors are obtained through sectional physical perception standardization processing; constructing a graph neural network micro-area irrigation volume demand prediction model, and predicting recommended irrigation volume and soil salinity variation; calculating a loss function of the model and performing iterative training on the model; and performing periodic decision regulation and control circulation on the trained model, converting the output of the multi-source heterogeneous data collected at each decision moment through the model into an instruction, issuing the instruction, and synchronously performing drainage regulation and control. According to the invention, water-salt environment optimization regulation and control can be realized, and an intelligent treatment closed loop is formed.

The invention discloses a foundation pit early warning method based on multi-source monitoring data and dynamic evaluation, which relates to the technical field of foundation pit safety early warning and comprises the steps of S1, multi-element monitoring, S2, data processing and normalization, S3, dynamic safety level establishment, S4, dynamic weight and credibility calculation, S5, evidence fusion and safety level judgment and S6, dynamic risk evaluation and calibration. According to the method, all-dimensional data acquisition and high-quality data processing are formed through the steps S1 and S2, all-dimensional information capture of foundation pit engineering characteristics, geological conditions, surrounding environments and body states is achieved, data quality is guaranteed through data cleaning, normalization and validity verification, and the problems of monitoring data fragmentation and uneven quality are avoided; through the steps S3 and S5, a security level judgment system of dynamic probability allocation and multi-source evidence fusion is constructed, limitation of a static threshold method is prevented, and scientificity and accuracy of security level judgment are improved.

The invention relates to the technical field of motor detection, in particular to a generator set fault diagnosis and detection method based on deep learning, and the method comprises the steps: firstly, synchronously collecting three-phase voltage, current and rotating speed signals, and constructing a multi-dimensional time sequence matrix through data cleaning and sliding window segmentation; then, carrying out multi-scale decomposition on the matrix by adopting adaptive wavelet packet transformation, combining each frequency band reconstruction coefficient with an original signal channel, and constructing an enhanced feature tensor; then, a CNN-BiLSTM parallel network is constructed; and finally, dynamically fusing the features of the two branches through a self-adaptive weighted fusion strategy, and inputting a multi-layer full-connection classifier to output a fault type. According to the method, early weak fault features are effectively enhanced, bearing faults, rotor eccentricity, electrical imbalance and composite faults thereof can be accurately recognized, the intelligent level and accuracy of fault diagnosis of the generator set are remarkably improved, and the method can be widely applied to online monitoring and health management of power generation equipment of a power system.

The invention discloses a data cleaning scheme generation method and device and a readable storage medium, and the method comprises the steps: carrying out the grouping of pre-training task data through employing a clustering mode, and obtaining a plurality of data groups; extracting data characteristics of each data packet based on a large model; generating a data cleaning scheme for the corresponding data groups by using the pre-training model in combination with data characteristics, and executing the data cleaning scheme; calculating a data cleaning index, and storing the data characteristics, the data cleaning scheme and the data cleaning index as short-term memory into a short-term memory module; evaluating the overall cleaning quality evaluation score of the data cleaning scheme by using a reflection module to obtain a reflection result; the data characteristics, the data cleaning scheme, the overall cleaning quality evaluation score, whether data cleaning succeeds or not and the reflection result serve as long-term memory and are stored in a long-term memory module, the data cleaning process can have traceability, and the data cleaning scheme adjusting efficiency is improved.

The invention relates to the technical field of engineering construction intelligent management, and discloses a municipal engineering construction quality monitoring method and system based on a dual-carbon target, and the method comprises the following steps: collecting the carbon emission data and construction quality parameters of a municipal engineering construction site in real time through an Internet of Things sensor network; the carbon emission data comprises construction machinery energy consumption data, material transportation carbon emission data and on-site construction carbon emission data, and the construction quality parameters comprise structural strength data, material ratio data and construction process parameters; and the acquired carbon emission data and construction quality parameters are transmitted to a cloud data processing center for data cleaning and standardization processing. By constructing an Internet of Things real-time acquisition network and a carbon efficiency quality correlation evaluation model, the limitation that the Internet of Things real-time acquisition network and the carbon efficiency quality correlation evaluation model are mutually independent in traditional management is broken through, and on the premise of ensuring that core quality indexes such as structural strength and material performance reach the standard, a high-carbon emission link and a low-efficiency construction process can be identified, and an optimization decision scheme giving consideration to an emission reduction target is dynamically generated.

The invention discloses a content auditing abnormity monitoring and early warning method and system based on intelligent alarm suppression, and the method comprises the following steps: obtaining real-time content auditing data collected in multiple dimensions, determining multi-dimensional data, carrying out the data cleaning and standardization processing of the multi-dimensional data, and carrying out the monitoring and early warning of the content auditing abnormity. Storing the multi-dimensional data subjected to data cleaning and standardization processing into a database; and constructing a deep reinforcement learning model, determining data input of the deep reinforcement learning model based on the standardized multi-dimensional data, and driving the deep reinforcement learning model to optimize an alarm mode in a training process. According to the method, hidden violation and semantic evolution trends are recognized through semantic clustering, and the traditional detection bottleneck based on a numerical threshold value is broken through; according to the invention, by realizing cross-cycle trend evolution analysis, early signals before abnormal outbreak can be identified in advance; the method has the cross-event semantic comparison capability, and the false alarm rate and the redundant data volume are remarkably reduced.

The invention relates to the technical field of control adjustment, and discloses a humanoid robot control method based on image segmentation, and the method comprises the steps: carrying out the data cleaning of the original environment image data of a humanoid robot, and obtaining a standard environment data frame; performing pixel semantic segmentation on the standard environment data frame to obtain a semantic segmentation region mask; analyzing and identifying a passable area and an interactive object of the humanoid robot to obtain a two-dimensional path navigation route and interactive object information; performing parameter quantization on the two-dimensional path navigation route, and encoding quantized data into an executable leg instruction of the humanoid robot; performing action sequence instruction conversion on the joint rotation angle and the grabbing force of the humanoid robot to obtain an arm control instruction; performing association fusion on the executable leg instruction and the arm control instruction to obtain a comprehensive control instruction; according to the invention, the efficiency of humanoid robot control based on image segmentation can be improved.

The invention relates to the technical field of digital manufacturing and precision machining, and discloses a part machining method and system based on inspection planning and feature driving, and the method comprises the steps: reading a full three-dimensional model, analyzing a tolerance mark, instantiating the tolerance mark into a procedure inspection item node, and building a persistent reference; identifying geometric features and matching the geometric features with the inspection items to generate a to-be-tested feature set; measuring point distribution is calculated in a self-adaptive mode according to the feature types, and a collision-free measuring path is generated; converting the path into a numerical control code containing a radius compensation algorithm and embedding the numerical control code into a machining program; after the numerical control center executes measurement, abnormal data calculation deviation is eliminated, and a compensation value is written into a register to correct parameters. By establishing full three-dimensional model data association and applying curvature adaptive sampling and statistical data cleaning strategies, data faults of design and detection are eliminated, the problem of environmental noise interference is solved, and the precision control level of complex feature processing is improved.