357 results about "Dynamic screening" patented technology

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

The invention belongs to the field of federated learning communication optimization, and provides a federated learning communication optimization method based on adaptive rarefaction and quantization, so as to reduce communication overhead and improve energy efficiency. The method comprises the following steps: a central server issues a global model, randomly selects part of clients for local training, calculates gradient update, dynamically screens important gradients for transmission by adopting a dynamic quantization strategy, and further compresses residual gradients by applying an adaptive rarefaction method; meanwhile, an error feedback mechanism is introduced, and quantization errors are accumulated to improve the convergence effect of the model. And the client uploads the compressed gradients to the server, the server aggregates and updates the gradients and then continues to distribute the gradients, loop iteration is carried out in this way, and finally an efficient convergence global model is obtained. According to the method, the communication cost in federated learning is remarkably reduced by combining adaptive rarefaction, dynamic quantization and error compensation, and convergence is accelerated on the premise of ensuring the model performance, so that the method is more suitable for a distributed learning environment with limited resources.

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 discloses a data processing method and system based on a bidding and tendering management platform. The method comprises the following steps: obtaining a bidding and tendering data set and constructing an initial supplier set; dynamically screening the initial supplier set based on a preset qualification verification rule to generate a compliance supplier set; performing multi-dimensional semantic analysis on bidding document contents in the compliance supplier set to generate a risk assessment report; and generating a recommended supplier sequence based on the risk assessment report and the bid invitation party demand information. The method has the following advantages and effects: semantic mapping and dynamic weight distribution among data are realized; based on dynamic rules and real-time data updating, response delay caused by manual intervention is reduced; through confidence scoring, a quotation rationality model and performance matching degree calculation, a quantifiable risk assessment report is generated, and subjective deviation is reduced; and the recommendation logic is dynamically adjusted in combination with the customized demand of the bid invitation party, and the intelligent level and the result credibility of the bid invitation and tendering process are improved.

The invention relates to the technical field of mining data processing, and particularly discloses a coal yard production operation real-time monitoring management system. Aiming at the problems of difficulty in dynamic matching of time series data caused by insufficient cache capacity of edge nodes and fault diagnosis delay caused by data flow breakpoints or redundancy, the system adopts a multi-stage cache architecture, and physical mapping and quick positioning of data are realized through dynamic resource allocation of a real-time processing layer and a batch buffer layer in combination with three-dimensional grid spatio-temporal indexing. A breakpoint compensation mechanism is utilized to trigger target area resampling and historical data prefetching, and data stream continuity is guaranteed; and dynamically screening the data based on the confidence coefficient weight, and inhibiting redundancy accumulation. The collaborative optimization engine establishes a parameter linkage rule of the collection frequency, the cache period and the fusion threshold value, and the resource priority is inclined during high-risk early warning. And through closed-loop feedback and edge-cloud collaborative learning, a data processing strategy is continuously optimized. The cache resource utilization rate and the diagnosis timeliness are improved, and the method is suitable for real-time safety monitoring of complex industrial scenes.

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 discloses an image feature matching optimization method based on intra-class space consistency in the technical field of computer vision and image processing. The method comprises the following steps: feature point extraction and preliminary matching: extracting feature points from a query image and a reference image and performing preliminary matching; initialization and transformation model estimation: initializing a matching point set and a residual error, and calculating an initial transformation model; error calculation and matching point set updating: calculating the error of the matching point pair, and updating the matching point set by adopting a dynamic screening method; performing residual optimization: judging whether the optimal condition is reached or not based on the residual, and deciding whether to continue iteration or not; performing intra-class space consistency clustering and isolated cluster elimination: performing clustering analysis after the optimal residual error is obtained, and eliminating isolated clusters based on an intra-class space consistency separation ion structure; and outputting a result: outputting a matching point set after the isolated clusters are removed. The method solves the problem that a traditional feature matching method is difficult to completely remove mismatching in a complex scene and is sensitive to noise.

The invention provides an intelligent English question setting method and system based on a large model, and the method comprises the steps: obtaining all kinds of information in a teaching material, and generating a knowledge graph; extracting a dynamic knowledge graph; and constructing a question generation constraint, and generating test questions in combination with the dynamic knowledge graph. Through hierarchical and classified management and a dynamic screening mechanism of the knowledge graph, contents related to a current teaching target can be rapidly extracted, coverage and reasonable application of knowledge points are ensured, meanwhile, a new target can be rapidly adapted according to change of teaching materials, topic generation is restrained through the dynamic knowledge graph, and the teaching efficiency is improved. The generated questions are limited in the knowledge range of the students, the difficulty is dynamically adjusted according to the layering of knowledge points, the questions better conform to the teaching outline and the learning level of the students, teaching objectives can be effectively served, and meanwhile, the questions are subjected to multi-dimensional optimization by using a context enhancement and automatic evaluation mechanism, so that the teaching efficiency is improved. The method ensures that the questions are standard in language and clear in logic, and interference items of the selected questions are confused, so that the thinking investigation effect of students is 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 discloses a Swin-YOLOv9 target detection method based on multi-stage dynamic pruning and feature compensation, and the method is characterized in that the method comprises the following steps: 1, carrying out the multi-stage dynamic pruning and feature compensation; the method comprises the following steps: 1, preprocessing an input target image; 2, designing a target detection network, wherein the target detection network is used for carrying out feature extraction on the preprocessed target image; 3, training the extracted features based on a multi-stage dynamic Token screening mechanism and a feature compensation mechanism of an attention mechanism, dynamically screening meaningful tokens, and performing feature compensation; and step 4, performing token structured pruning based on an attention mechanism on the token after feature compensation to realize target detection. On the premise of keeping or even improving the target detection performance, the pruning strategy is optimized, the redundant parameters and the calculation amount of the model are reduced, and therefore the training efficiency is improved, and the requirement for calculation resources is reduced.

The invention provides treatment equipment for preparing cable material particles by recycling waste plastic, and relates to the technical field of waste plastic recycling. A tearing box is mounted at the upper end of the recovery treatment box body, and the upper end of the tearing box is of a flaring structure convenient for waste plastics to enter; a smashing box is fixedly arranged at the butt joint position of the inner cavity of the recycling treatment box and the lower end of the tearing box. The equipment adopts a two-stage crushing structure of a tearing box (including a tearing roller) and a crushing box (including a crushing cutter), and through a stage treatment mode of preliminary tearing and spiral crushing, the energy consumption pressure for directly crushing large plastic blocks is remarkably reduced. The tearing roller pre-crushes the materials, so that the load of the crushing cutter is reduced, the spiral blade group of the crushing cutter synchronously pushes the materials to the middle part in the cutting process, and a cyclic crushing path is formed in combination with a dynamic screening mechanism of the screen, so that the crushing uniformity is ensured, and the energy waste caused by repeated treatment is avoided.

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 discloses a method for preparing activated carbon by using waste tire pyrolytic carbon black, which comprises the following steps of: selectively and dynamically screening and removing magnetic impurities, namely dynamically screening a pyrolytic product through an airflow grading device, separating out high-purity carbon black particles with the particle size of less than 10 microns, and removing metal impurities and residual steel wires by using a magnetic separation technology to obtain the activated carbon. And the purity of the carbon black reaches 95% or above. Therefore, the pore structure is optimized through combination of a wet method and high-temperature activation, the pore distribution is further maintained, regulated and controlled through freeze drying and solvent heat treatment, the specific surface area of the finally prepared activated carbon reaches 1500-2000 m < 2 > / g, and the pore volume retention rate reaches 90% or above; the technology not only effectively solves the problems of insufficient carbon black purity, unstable pore structure and high energy consumption in the prior art, but also remarkably improves the adsorption performance and chemical stability, so that the carbon black is widely applied to the fields of water treatment, gas adsorption, catalyst carriers and the like, and has the outstanding advantages of high efficiency, low consumption and environmental protection.

The invention relates to the technical field of transformer processing, in particular to drilling equipment for oil-immersed transformer processing, which comprises a base, a processing table and a separation box, wherein drilling equipment is installed on the machine base, the interior of the machine base is hollow, the top face of the machine base is in an opening shape, the machining table is arranged in the opening in the top face of the machine base, fixing rods are fixed to the two ends of the machining table, and the two fixing rods are rotationally connected with the machine base; the end, away from the machining table, of one fixing rod extends out of the machine base and is provided with a hand wheel, and a first opening and a second opening are formed in the side face of the machine base. According to the device, chippings can be independently collected, water flow can be recycled, traditional static screening is replaced with dynamic screening, the situation that the chippings block the screen is avoided, manual regular dredging is not needed, and the separation efficiency and use convenience are improved.

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.