321 results about "Dynamic screening" patented technology

The invention provides a photovoltaic module fault detection system, method and device based on infrared image hot spot detection, and a storage medium, and belongs to the field of photovoltaic module fault detection. The system comprises a temperature difference boundary extraction module, an image sequence registration module, a hot spot track identification module, an abnormal dynamic screening module and a fault hot spot confirmation module. Pixel difference screening is carried out by setting a temperature difference threshold value, a boundary communication structure is established, frame-level displacement calculation between time sequence images is introduced to realize coordinate alignment, hot spot center points in continuous frames are extracted to form a motion path, abnormal point screening and time node labeling are carried out in combination with path displacement characteristics, and the time sequence image is obtained. According to the method, the boundary area growth rate and temperature change double factors are fused to screen fault areas, dynamic tracking and accurate detection of abnormal hot spots are achieved, the logic relevance between abnormal behavior recognition and fault judgment is enhanced, and the integrity of photovoltaic module fault information extraction and the accuracy of hot spot recognition are guaranteed.

The invention relates to an experimental data processing method and device, an AI analysis module and computer equipment, and belongs to the field of data processing.The method comprises the steps that multi-dimensional original data are partitioned according to types, and formats are unified; generating a similarity matrix based on time and space neighborhood information, and marking abnormal fluctuation points; effective signals are separated through a time-frequency feature matching noise library; extracting multi-layer features of basic statistics, time sequence correlation and trend change; and dynamically screening core features to update the tracking type experimental model. The matched AI analysis module integrates hardware circuits of data partitioning, similarity calculation, anomaly marking, noise matching, feature extraction and model updating, and whole-process acceleration is achieved. According to the method, through multi-dimensional data compatibility processing, accurate anomaly detection, multilayer feature fusion and model adaptive optimization, the experimental data processing efficiency and conclusion reliability are remarkably improved, and the method is suitable for real-time analysis of multiple scenes such as scientific research and industry.

The invention discloses a health degree evaluation system and method based on a photovoltaic string, and belongs to the technical field of photovoltaic operation and maintenance intellectualization, and the method comprises the steps: firstly collecting SCADA operation data of a plurality of strings of a photovoltaic power station, including voltage, current, assembly temperature, environment irradiance and power factors; secondly, constructing a whole-station feature reference model, and generating a state vector fusing environment and load characteristics through a nonlinear projection algorithm; aiming at the target group string, extracting a health reference track, calculating a disturbance propagation factor, and identifying a health abnormal state; when the deviation degree exceeds a threshold value, dynamically screening heterogeneous reference group strings, constructing a health score distribution model, and further calculating a confidence score and a prediction residual error; if the score is low and the residual error is unstable, determining that the string is in a sub-health or hidden fault state, and generating an intervention instruction; the method has high accuracy, strong adaptability and good interpretability, and can be widely applied to intelligent diagnosis and refined operation and maintenance management of the photovoltaic power station.

The invention provides a dynamic screening medical image target tracking method and device based on visual Transform, and relates to the technical field of computer vision, and the method comprises the steps: standardizing near-infrared or visible light fundus video frames into uniform resolution, constructing a template-search frame pair, and then jointly mapping the two frames of images into a Token sequence; a dynamic local interaction module is embedded in front of each pruning layer of the whole network, a local context is captured by using depth separable convolution and point convolution, a dynamic convolution kernel generator is driven, and a neighborhood Token is adaptively weighted and aggregated. Next, the Token screening and the compression mechanism TSC are operated in the same pruning layer, only the Top-K key Token is reserved, the redundant Token is cut off, and the original index is recorded; the objective of the invention is to improve the positioning stability and reasoning efficiency of a focus area (such as an optic disc) in a complex operation video.

The invention relates to the technical field of state monitoring, in particular to an inner package production data real-time monitoring and processing system which comprises a running state acquisition module, a performance deviation evaluation module, an abnormal behavior recognition module, a task scheduling optimization module and a load balancing optimization module. According to the invention, through a cooperative acquisition mode of operation data such as temperature fluctuation, pressure change and flow velocity stability, real-time perception of dynamic collection and high-frequency change of the operation state is realized, and by means of a cross discrimination strategy of distribution uniformity and response time, non-representative data segments are eliminated, and the accuracy of performance abnormity identification is improved. Through time coupling analysis of gradient tracks and offset, abnormal behavior fragments are locked in advance, forward recognition of trend instability is achieved, intervention windows are dynamically screened, actual effect and synchronization of response are ensured, a real-time matching mechanism based on loads and demands is adopted, task priority and processing channel distribution are optimized, and intervention efficiency and scheduling adaptability are improved.

The invention relates to the technical field of chemical raw material processing, and discloses a petroleum coke particle size grading and batching system which comprises a raw material pretreatment module, a multi-stage dynamic screening module, a particle size real-time monitoring module, an intelligent batching decision module and a closed-loop control execution module. The classification definiteness of materials with different particle sizes is ensured; meanwhile, through a particle size monitoring feedback mechanism, the particle size distribution abnormal state in the screening process is recognized in real time, the material purity within the target particle size range is guaranteed, the particle size grading accuracy is improved, a closed-loop matching decision module is arranged, particle matching weight parameters are dynamically optimized in combination with the electrolytic aluminum process requirement, and the particle size grading accuracy is improved. The deviation degree between the ingredient proportion and the target performance can be judged in real time; the speed ratio of the conveying belt and the opening degree of the stock bin are automatically corrected through the executing mechanism, it is guaranteed that the mixing proportion of coarse particles, medium particles and fine particles is always in the optimal interval matched with electrolytic aluminum anode production, and the quality stability of anode carbon blocks is improved.

The invention provides an irrigation water demand prediction method based on meteorological soil crop multi-source feature fusion, and relates to the field of data prediction, and the method specifically comprises the steps: firstly collecting farmland region multi-source feature data, and constructing an original data set; then, a nonlinear disturbance relation between farmland characteristics is extracted through a high-order disturbance encoder module, and a structure energy tensor is introduced to describe high-order structure relevance; thirdly, a dynamic gating characteristic generator module is introduced, a disturbance gain factor and a time coupling factor are combined to generate a gating signal, and dynamic screening of multi-source characteristics is achieved; then constructing a cross-variable interaction modeling module, and generating a feature embedding vector to model a complex coupling relationship among weather, soil and crops; and finally, outputting an irrigation water demand predicted value by adopting a multi-layer sensing network in combination with channel mapping and a cross memory mechanism, so that efficient dynamic prediction of the farmland water demand is realized, and the water resource utilization efficiency and the accurate regulation and control capability are improved.

The invention relates to the technical field of intelligent charging, in particular to an edge intelligent calculation enabling type peak-valley energy storage charging system which comprises a power grid frequency judgment module, an energy storage dispatching output module, a load trend recognition module, a multi-station matching sorting module and a load dynamic distribution module. According to the invention, through the continuous data analysis of the power grid frequency, the energy storage unit power and residual energy, and the load current, the system can accurately identify the linkage relationship of various parameters during the frequency abnormal response period, refine the condition judgment of peak-valley energy storage triggering and scheduling, optimize the energy storage output decision path, and improve the reliability of the system. Through dynamic screening of load trends and changing stations, the real-time performance and flexibility of energy allocation among multiple stations are enhanced, the adaptive capacity of the system to complex supply and demand changes is further improved in station matching and priority ranking, energy storage resources and loads are dynamically allocated, the electric energy utilization rate and the cooperative regulation and control level of the energy storage system are effectively improved, and the energy utilization rate and the cooperative regulation and control level of the energy storage system are improved. And intelligent upgrading of energy management is promoted.

The invention relates to the technical field of big data analysis, in particular to an operation management system and method based on financial marketing big data, and the system comprises a data standardization module, a customer portrait disambiguation module, a strategy matching module, a real-time transmission module and an operation strategy generation module. According to the method, a data table with a unified structure is constructed through combined judgment of field semantics and numerical rules, the data consistency is improved, customer information integration is executed based on field conflict time, credibility level and logic verification, the recognition accuracy is enhanced, and the matching relation is judged by combining the transaction amount, the product association degree and the interaction frequency with strategy attributes; according to the method, strategy dynamic screening is achieved, pushing content is screened according to the strategy emergency degree and the channel state, the transmission efficiency is improved, customer clicking, conversion and feedback participate in behavior trend judgment, strategy updating linkage is supported, an overall processing chain is of a closed-loop structure, and the fusion accuracy and the strategy response timeliness are improved under the conditions of data fragmentation and multi-source heterogeneous.

The invention provides a switch security protocol interaction method and device, equipment and a storage medium, and the method comprises the steps: carrying out the protocol type recognition of a received network data frame, and generating first intermediate data according to the recognized protocol type; according to the protocol type identifier of the first intermediate data, converting the format of the network data frame through a protocol mapping rule to generate second intermediate data; determining a security processing strategy corresponding to a preset strategy rule set according to the strategy identification field of the second intermediate data; reconstructing the original content of the second intermediate data into a target data frame conforming to a target protocol format according to a security processing strategy; and according to the security level field and the communication direction of the target data frame, performing path screening through the trusted path list, and determining a target interaction path. According to the scheme, the trusted interaction path can be dynamically screened based on the protocol type and the security level field of the target data frame, and controllable data transmission between switches under security protocol cooperation is realized.

The invention relates to a method for dynamically constructing a service quality evaluation system fused with a large language model, which comprises the following steps of: extracting themes, keywords and representative documents from a user evaluation text data set, constructing cue words and inputting the cue words into the large language model to generate candidate primary service dimensions divided by five service dimensions of an SERVQUAL model; the service quality candidate index item list is composed of candidate second-level topics and candidate third-level index items; constructing a multi-channel heterogeneous hypergraph set fusing sliding window co-occurrence and an external knowledge graph; graph structure information is extracted, joint prediction of emotion labels and emotion scores of user evaluation texts is carried out, and topic-level multi-class input factor spaces are constructed to be used for calculating final fusion weights; and performing comprehensive sorting and dynamic screening on the candidate three-level index items to form a multi-level service quality evaluation system index scale. According to the method, accurate modeling of the user perception quality is realized, and the emotional discrimination of the indexes of the service quality evaluation system can be improved.

The invention discloses an abnormal data monitoring method and device based on artificial intelligence, and the method comprises the steps: 1, dividing an original data stream through a sliding window, extracting statistics, time sequence and change rate features, and dynamically screening features adaptive to data distribution based on an SHAP value; 2, constructing a double-flow model, capturing a global isolated mode by adopting an improved isolated forest in a static flow, capturing time sequence dependence on the basis of LSTM-AE in a dynamic flow, and fusing two-flow scores through performance-driven dynamic weight distribution; 3, combining a density peak value algorithm with historical density attenuation weighting, and dynamically adjusting an abnormal threshold value; 4, realizing low-delay incremental learning through a double-trigger mechanism and experience playback; 5, multi-granularity interpretation is generated, manual annotation feedback is supported, feature engineering and model training are integrated, and a'detection-interpretation-feedback-optimization 'closed loop is formed; high-adaptability anomaly monitoring is realized through dynamic feature screening, double-flow fusion detection, threshold value self-adaption and man-machine collaborative optimization.

The invention relates to the technical field of intelligent manufacturing, in particular to a chain production data monitoring and optimizing system based on industrial internet of things, which comprises a stress load monitoring module, a shock wave propagation analysis module, a vibration feature extraction module, a load optimization control module and a fracture risk early warning module. According to the method, through combination of stress data acquisition and coordinate index and gradient calculation, a transient stress overload area of chain plate stamping and pin shaft heat treatment is positioned, local extreme points are dynamically screened to improve anomaly detection precision, an energy accumulation coefficient is quantified by shock wave propagation speed and attenuation rate, and a vibration anomaly source of a chain pin shaft and a chain plate is associated. The vibration signals are subjected to short-time Fourier transform to analyze riveting and assembling characteristic frequencies, high-resolution stress mode characteristic values are generated to support parameter optimization, operation data are simulated to construct a stress accumulation curve, test parameters are dynamically adjusted in combination with a short-term stress limit to optimize load uniformity, and an impact energy accumulation value is matched with a safety threshold to calibrate load distribution; the fracture risk is reduced.

The invention relates to the technical field of three-dimensional scene information transmission, and discloses a digital information transmission method and system in a three-dimensional level loading process, and the method comprises the steps: obtaining three-dimensional level scene information, and dividing the three-dimensional level scene information into a plurality of scene region units; executing multi-factor priority evaluation; executing secondary priority evaluation to generate a resource task queue; adopting a multi-stage asynchronous streaming mechanism to complete digital resource transmission based on the loading granularity; and dynamically screening resources in combination with the GPU rendering budget. Compared with the prior art, the problem that loading efficiency and real-time performance cannot be considered in a complex three-dimensional scene is solved, and especially the technical problem that loading and rendering collaborative optimization is difficult to realize when a user view angle is quickly switched or the granularity difference of scene resources is large is solved. Due to the fact that space partitioning, dynamic priority evaluation and frame budget scheduling mechanisms are introduced, the problems of loading delay and resource redundant transmission are avoided, and the real-time loading efficiency and rendering fluency under the large-scale three-dimensional level scene are improved.

The invention discloses a privacy protection truth value discovery method based on reputation in a block chain. The method provided by the invention aims at the problems of perception data quality reduction, participant privacy information leakage and the like caused by lack of a trusted participant screening mechanism and insufficient privacy protection in the crowd intelligence network. Therefore, participants with high creditworthiness are screened on the premise of privacy protection to complete a data perception task, so that high-quality data are obtained. According to the method, firstly, a double-chain collaborative architecture is constructed based on a block chain, and privacy protection is performed on creditworthiness of participants through exclusive or homomorphic encryption; in the subsequent task allocation stage, the high-reputation participants are preferentially selected to execute the data acquisition task in combination with multi-round encryption communication and a dynamic screening mechanism; and finally, on the basis of the data of the high-credibility participants, calculating the task scores of the participants with unknown reputation, dynamically updating the reputation of the participants, and ensuring the authenticity and accuracy of the data.

The invention discloses a visual question-answering system based on multi-modal knowledge autonomous learning and a construction method, and belongs to the technical field of artificial intelligence. The invention provides a visual question and answer model construction method fusing image information, natural language questions and external knowledge. A knowledge dynamic screening and replacement strategy is constructed by introducing an autonomous evolution mechanism, and whether knowledge is inserted or updated can be judged according to a current training state and knowledge similarity, so that the problem that a traditional static knowledge base cannot adapt to task changes is solved. According to the method, through pseudo data construction and multi-modal triple extraction, structured and high-association-degree'image-question-answer 'knowledge representation is established, so that knowledge has better semantic expression ability, and redundancy and noise are effectively reduced. According to the method, rapid retrieval and active calling of knowledge are achieved by means of the vector indexing technology, key support information can still be obtained in a complex scene, and the expression of the model in a visual question-answer task based on knowledge is remarkably improved.

The invention provides an NL2SQL database table dynamic screening method and system based on multi-strategy fusion. The method comprises the steps that a preliminary candidate set is generated, a semantic encoder is used for encoding a user natural language problem and table and column information in a database into vectors, the semantic similarity is calculated, a preset number of tables are screened out from a whole database table based on the similarity, and a preliminary candidate table set is formed; performing deep analysis and reordering, performing multi-dimensional analysis on the tables in the preliminary candidate table set, and calculating a graph relationship compactness score and a content correlation score of each table; and performing fusion decision, inputting the semantic similarity score, the graph relationship compactness score and the content correlation score of each table into a fusion model to obtain a final comprehensive score of each table, and screening out a final correlation table set based on the final comprehensive score. The method has high accuracy and high recall rate, the generation speed and response efficiency of the NL2SQL system are improved, meanwhile, the robustness is high, and the practicability and generalization ability of the system are improved.

The invention discloses a trajectory planning method based on a multi-stage optimization strategy and a mixed diffusion model. The method comprises the following steps: fusing multi-source sensor data and extracting features; screening and fusing intention anchor point tracks; and optimizing the fine-grained trajectory based on the Transform-Mamba mixed diffusion model. According to the method, an intention anchor point track screening mechanism is provided, the anchor point track matched with the scene is selected from the compact vocabulary through the dynamic screening module and fused with the static prior anchor points, the calculation complexity is reduced, the diversity of the initial track and the scene consistency are guaranteed, and the problem that a traditional fixed anchor point set is insufficient in flexibility is solved. According to the method, a mixed diffusion decoder is designed, space environment dependence is efficiently modeled through a cross attention mechanism, a bidirectional Mama module captures time sequence motion dependence with linear complexity, global perception ability and long sequence modeling efficiency are both considered, and safety and dynamics feasibility of generated tracks are ensured.

The invention belongs to the field of computational chemistry, and particularly relates to a multi-parameter collaborative dynamic screening method for adsorbed species on the surface of a catalyst. Comprising the following steps that an initial crystal structure is constructed and optimized through VESTA software, and a stable crystal structure model is obtained through density functional theory optimization; integrating working condition parameters such as a solvent, pH and potential, and drawing a Bayer diagram to label adsorption species and a mapping relation; and calculating a zero charge point and capacitance to obtain electrochemical parameters, and determining stable adsorption species under actual reaction through constant potential molecular dynamics simulation. According to the scheme, a deep structure-activity relationship between an active site structure and catalytic activity under a working condition is established through integration of all working condition parameters, labeling of adsorption species by a Boubye diagram, constant potential molecular dynamics simulation and the like; the problems that a systematic method combined with actual working condition parameters is lacked in prediction of the adsorbed substances on the surface of the catalyst, and real adsorbed species are difficult to accurately screen are solved, and accurate capture and efficient screening are achieved.

The invention relates to the technical field of service signaling, in particular to a voice call data analysis system based on cloud computing, which comprises a signaling acquisition module, a stability evaluation module, a dynamic compression module, an abnormal track clustering module and a cloud feature library module. According to the method, link multi-dimensional parameters are collected through distributed nodes and stored in a time slice mode, a continuous three-hop rate difference is calculated through a sliding difference value, path stable segmentation is accurately recognized, time delay in a mean value compression screening segment is generated to generate feature vectors to reduce redundancy, cloud K-means integrates state duration, timeout rate and retransmission times to detect trajectory offset, and abnormal clustering labels are constructed. Time sequence association storage and elastic architecture dynamic screening path segments reduce invalid data processing amount, compression mean reduces calculation complexity, multi-dimensional clustering improves detection precision, a time window optimizes a storage structure, a sliding difference enhances fluctuation capture capability, elastic storage improves mass data access efficiency, and an end-to-end quality analysis closed loop is formed.

The invention discloses a surface temperature downscaling method and system based on feature interaction optimization and a multi-model screening mechanism, and belongs to the technical field of surface temperature data processing, and the method comprises the following steps: screening spectral bands, remote sensing indexes and topographic features; resampling the topographic feature parameters to a resolution matched with the optical wave band and the remote sensing index; variables significantly related to the surface temperature are screened through correlation analysis, interaction features are constructed, and high-contribution features are dynamically screened based on an SHAP value; constructing a regression model based on double frameworks of a linear model and a machine learning model, and verifying the regression model; sHAP optimization features extracted from Sentinel-2 data are fused with SRTM topographic data, a verified regression model is input, and surface temperature data with the resolution of 10 meters are generated; according to the method, the high-order nonlinear relation is mined through combined feature interaction, the prediction RMSE in the uniform earth surface area is greatly reduced, the data scale effect is reserved, the prediction is more suitable for the physical process, and the space consistency is better.

The invention discloses a cross-platform three-dimensional rendering task allocation co-processing system and method.In the system, a cloud rendering task scheduling module constructs a rendering time consumption prediction model of a rendering task based on triangle throughput rate calibration and video memory bandwidth calibration, and predicts the rendering duration during local rendering; and a cloud rendering task is scheduled based on a frame-level preemptive formula. According to the method, a dynamic unloading strategy is adopted, load balancing between the rendering task of the mobile equipment and the rendering task of the edge node can be realized, the load pressure of the local rendering task is reduced, and the computing power of the edge node is saved to a certain extent; according to the method, a three-order assembly line and frame-level preemptive scheduling mode is adopted, so that the delay between ends is effectively reduced; and under the condition of network jitter, dynamic screening of cloud rendering tasks is realized, local rendering is taken as a main part, and the influence of delay fluctuation is reduced.

The invention discloses a scientific research theme evolution method based on text embedding vector clustering, and relates to a data processing technology, the method comprises the following steps: collecting scientific research literature data, preprocessing, identifying and extracting field keywords, generating text embedding vectors corresponding to literatures, and constructing a scientific research literature representation vector library; taking a preset time slice as a unit, performing density adaptive clustering on the text embedding vector, generating a scientific research subject cluster in the corresponding time slice, and dynamically extracting Top-N subject keywords of the scientific research subject cluster based on keyword weights and semantic vector center distances of literatures in the cluster; and constructing a dual-verification mechanism of vector space similarity and keyword semantic overlap ratio, dynamically screening a topic evolution relationship between scientific research topic clusters in adjacent time slices, introducing a keyword overlap ratio to filter associated noise, constructing a high-confidence topic evolution chain, and revealing development venation and technical migration rules in the field of scientific research.

The invention discloses a weld defect detection method and a computer program product, and relates to the technical field of image processing.The method comprises the steps that a weld image to be detected is input into a target detection model, the target detection model comprises a feature extraction layer and a feature fusion layer, the feature extraction layer is provided with a multi-scale edge feature optimization sub-model, and the feature fusion layer is provided with a multi-scale edge feature optimization sub-model; an advanced screening feature pyramid network is arranged in the feature fusion layer; performing feature enhancement on the to-be-detected weld seam image through the multi-scale edge feature optimization sub-model to obtain an enhanced feature map; performing feature fusion on the enhanced feature map through an advanced screening feature pyramid network to obtain target features; and performing weld defect detection on the to-be-detected weld image based on the target feature to obtain a weld defect category. Through layered optimization, local calculation replaces global calculation, redundant features are inhibited through dynamic screening, and on the premise that defect detection precision is guaranteed, model calculation resources are remarkably reduced.

The invention discloses a federated learning dynamic hierarchical aggregation method and system based on convolution kernel importance sampling, and belongs to the technical field of federated learning. The method is a dynamic layered aggregation method based on convolution kernel importance sampling. The method comprises the following steps: firstly, designing a layer contribution degree evaluation mechanism guided by a super network, dynamically layering and aggregating weights, and gradually mining and utilizing potential similarity relationships among clients; secondly, a dynamic screening method based on threshold judgment is designed, the L1 norm of the convolution kernel weight of each client is used as an importance measurement index, only high-quality clients are allowed to participate in parameter aggregation of a key layer, and the negative influence of low-quality or abnormal clients on a global model is inhibited; according to the method, the problems of model convergence difficulty and generalization performance reduction in a heterogeneous data scene are effectively relieved. Experimental results show that in a Cifar100 image classification task, compared with a reference method, the recognition accuracy of the method is improved by 28.94% at most.

The invention relates to the technical field of industrial electric control optimization, and discloses an intelligent industrial electric control optimization system based on a deep learning algorithm. The system comprises a multi-modal sensing module which integrates a temperature sensor, a flow sensor and a pressure sensor to collect multi-modal data flow; a feature extraction module extracts three types of features to construct a multi-modal industrial feature set; the state comparison module generates normal and abnormal operation state comparison characterization by means of a generative adversarial network; the contribution degree evaluation module takes the system load rate as a tool variable, and calculates the contribution degree score of each sensing channel to the system stability through a Bayesian network; the dynamic screening module fuses the two types of indexes, and adjusts threshold screening through a deep belief network to generate optimization target representation; the time sequence grouping module performs phase grouping and weighting according to an operation cycle; and the fusion decision module generates a regulation instruction through hierarchical attention mechanism fusion, so that the operation stability and efficiency of the industrial electronic control system are effectively improved.

The application discloses a computing power scheduling system and method of a computing power network, relates to the technical field of computing power distribution and scheduling, and comprises the following steps: acquiring all computing power nodes in the computing power network and constructing a computing power node interaction network model; acquiring a computing power task initiating node and computing power task attributes; determining a target attribute group cluster of the computing power task based on the computing power task attributes, and recording all computing power nodes in the target attribute group cluster as demand target nodes of the computing power task; determining an execution trust target node of the computing power task based on the computing power task initiating node; finding an intersection of the demand target nodes of the computing power task and the execution trust target node of the computing power task, and recording the intersection as a comprehensive target node; and determining an execution computing power node of the computing power task from the comprehensive target node in combination with balanced load. The application has the advantages that the interaction network model of the cooperative trust relationship and the attribute group cluster is constructed, multi-dimensional node dynamic screening and optimized matching are realized, and the efficiency and reliability of the computing power scheduling are significantly improved.

The invention relates to the technical field of construction risk early warning, in particular to a power grid construction risk early warning method based on multi-source data fusion, and the method comprises the steps: S1, obtaining transient electromagnetic field intensity and ground potential change data of a construction site, carrying out the synchronization processing, obtaining synchronized multi-source data, carrying out the credibility evaluation, and carrying out the early warning of the construction risk; forming a credible input data set with a credibility identifier; s2, according to the credibility identifier, performing dynamic screening or weighting processing on the multi-source data, constructing fusion features, inputting the fusion features into a risk analysis model, and generating a construction risk grade result; and S3, whether an early warning condition is met or not is judged according to the risk level result, when the early warning condition is met, a construction intervention instruction is generated, and the construction intervention instruction comprises switching of an anti-interference monitoring mode, adjustment of a construction process or pause of operation. According to the method, the robustness of multi-source data fusion and the accuracy of subsequent risk analysis in a complex electromagnetic interference scene are improved.

The invention relates to the technical field of powder preparation, and particularly discloses a preparation process method for grinding and screening powder, which comprises the following steps: removing impurities from blocky raw materials through a vibration screening device, and then removing metal foreign matters by using a magnetic separation device. Then, a grinding step is carried out, the blocky raw materials are ground through multi-stage grinding equipment, primary powder is formed, and the grinding mesh number is increased step by step; and then, in the dynamic screening stage, coarse powder, medium coarse powder and fine powder are separated from the primary powder through a powder selecting device, the coarse powder is collected through a gravity settling method, and the medium coarse powder is separated through centrifugal force and airflow. In the closed-loop recovery link, coarse particles are sent back to grinding equipment for treatment, medium coarse powder is sent to a medium coarse powder collecting area, and fine powder is conveyed to a fine powder collecting device. And finally, cleaning the fine powder by using a pulse bag-type dust collector, and temporarily storing the finished product powder into a finished product collecting bin. According to the process, efficient screening, grinding and recycling of the raw materials are achieved, and the purity and high quality of the powder are ensured.

According to the intelligent auxiliary learning method and system based on the model context protocol and the cognitive state modeling, decoupling of a model end and a tool end is achieved through the model context protocol, an error type-tool dependency topological graph is constructed through the cognitive state modeling, tool dynamic screening and pruning based on deterministic rule constraints are achieved, and the method and the system have the advantages that the method and the system are easy to implement. The Token consumption is reduced, and the accuracy of model reasoning is improved at the same time. In the aspect of safety control, semantic firewall middleware is introduced into the system, output streams are monitored in real time, illegal behaviors directly giving code answers are intercepted through text semantic analysis and code abstract syntax tree comparison, and a model is forced to turn to a thought guide mode. The system also includes hierarchical context compression to maintain long term memory, adaptive difficulty knowledge retrieval based on user cognitive states, and a mechanism to utilize code sandbox to assist verification of model reasoning logic correctness. According to the method, the behaviors of the large language model can be effectively regulated and controlled, and safe, efficient and personalized intelligent auxiliary learning is realized.