61 results about "Trend extraction" patented technology

The invention provides a resource scheduling control method and system for a big data server, and the method comprises the steps: constructing a multi-dimensional resource portrait module, collecting the CPU, memory, network, storage I / O load and task queue length of each node in real time, and predicting a resource demand trend through a time sequence algorithm; extracting characteristics such as calculation intensity, data dependence, memory requirements, network transmission quantity and the like; adjusting the weight coefficients of the resource utilization rate, the task completion time and the energy consumption efficiency according to the system load and the historical effect; establishing a bipartite graph model by taking a resource trend as a node feature and a task vector as an edge feature, and calculating a matching score through graph convolution and a multi-objective optimization function; the scheduling scheme is synchronized by adopting a consistency algorithm; automatic rollback and reallocation are carried out when resources are detected to be insufficient; and optimizing a weight coefficient and a network parameter through reinforcement learning. Through the method, the system resource utilization rate can be improved, the task execution efficiency is improved, the overall scheduling effect stability is improved, and the system fault recovery time is shortened.

The invention proposes a flood disaster dynamic prediction method based on multi-source remote sensing data and a knowledge graph, and relates to the field of data prediction, and the method comprises the specific steps: firstly, building a space-time disaster dynamic model through a physical drive text generation module, and describing the evolution process of a flood disaster; generating personalized flood disaster description in combination with the static characteristics and the dynamic remote sensing data; then, a multi-scale residual diffusion enhancement module improves sensitivity to dynamic change of disasters through multi-scale trend extraction and residual calculation, noise is removed, and useful information is reserved; then, the multi-modal fusion module enhances interdependence and information sharing among different modals by using adaptive modal mapping, a cross attention mechanism and a weighted fusion strategy; and finally, training through a regression model, dynamically adjusting the feature weight by using an adaptive feature weighting mechanism and an incremental learning mechanism, and finally obtaining a flood disaster prediction value through the prediction set.

The invention relates to the technical field of construction control, in particular to an underground super-long thick and large concrete structure construction control method and system.The method comprises the following steps that through vertical displacement and strain data, trend extraction nodes are analyzed, difference sections are divided, load recognition influence areas are positioned, and structural sections are sequenced according to fluctuation increments; and adjusting cooling, supporting and loading parameters to form an intervention arrangement, and performing year-on-year adjustment based on construction data to generate feedback to complete regulation and control. According to the method, load-deformation dynamic mapping is established through time sequence association of displacement and strain data, a difference section identification mechanism is constructed based on time difference comparison, natural deformation and load surge response are distinguished, fluctuation characteristics of a load surge section are extracted to form response sensitivity ranking, and cooling parameters and a loading sequence are dynamically adjusted in combination with increment comparison. Graded regulation and control are realized, space-time dimensions of construction records and monitoring data are coupled, the crack positioning precision is improved, the predictability of deformation control is enhanced, and the regulation and control redundancy cost is reduced.

The invention relates to the technical field of power system operation and control, and particularly discloses a time-sharing electric quantity prediction method based on a logarithmic load density growth curve, which comprises the following steps of: firstly, performing causal detection and dynamic time-delay optimization on historical load and multivariate external data through convergence cross mapping and mutual information technologies, and constructing a causal time-delay feature set; and the problems of multi-element coupling and time-delay effect quantization are solved. Secondly, fitting a load trend by using time-frequency decomposition in cooperation with a segmented logistic model, extracting dynamic parameters representing a growth rate and a saturation capacity, and endowing the model with a sensing ability for a load evolution stage; then, causal features, growth parameters and load components are deeply fused through cross-domain modulation and a gating mechanism, the nonlinear modulation effect of an external environment on a load mode is explicitly modeled, and finally, a probability interval is generated in combination with quantile regression and residual error correction. According to the scheme, accurate and probabilistic prediction of the time-sharing electric quantity in a complex scene is realized, and the scientificity of an agent electricity purchase decision is improved.

The invention relates to the technical field of learning path planning, in particular to a personalized learning path planning method and system based on LSTM fused Attention, and the method comprises the steps: building an answer feature time sequence based on the information of an answer record, a wrong question table and a knowledge point set, generating an answer expression feature in a hidden state through a time sequence correlation trend extraction model, and carrying out the prediction of the answer expression feature in the hidden state; wherein the time sequence association trend extraction model is constructed based on an LSTM architecture; calculating a dynamic attention weight of the answer performance characteristics through an Attention model of a time decay item, a continuous answer difficulty feedback item and a knowledge point association item, and adjusting iteration in a hidden state based on the dynamic attention weight; generating a predicted knowledge point mastering degree through a fusion mapping model, wherein the fusion mapping model is constructed based on a full connection layer and an activation function; and generating a personalized recommended learning path based on the predicted knowledge point mastery degree. According to the invention, personalized learning path planning meeting individual requirements of students is realized, and the intelligence of a college teaching system is improved.

The invention discloses an oil chromatography data trend classification method and system based on depth feature enhancement and an attention mechanism, and the method comprises the steps: carrying out numeralization conversion, deletion detection and grouping trend calculation on oil chromatography original gas component data, and generating a basic feature vector; executing multi-scale sliding statistics, change rate and subsequence feature enhancement, and calculating comprehensive similarity and attention weight based on a template library to generate a weighted similarity vector; splicing the enhanced feature and the weighted similarity vector into a time sequence input sequence, and outputting an oil chromatogram trend classification result after attention expansion and long and short term memory network processing. According to the method, structured processing and basic trend extraction of data are realized, adaptive matching and weighted aggregation of historical operation modes are realized, and a multi-dimensional dependency relationship and time sequence dynamic change are captured, so that accurate classification of oil chromatogram trends is realized.

The invention discloses an automatic advertisement creativity generation method and system based on natural language generation. According to the invention, through establishing a keyword and context construction module, a brand semantic modeling and control module, an industry trend extraction module, an advertisement generation engine, a diversity control module and a putting adaptation and post-processing module, automatic generation of advertisement originality is realized. The system firstly receives a keyword and a mood instruction input by an advertiser, and constructs an initial semantic context; and then, coding a brand sample text by utilizing a pre-training model, and generating a brand semantic center vector as a prompt embedding so as to guide the generated content to accord with brand tonality. Meanwhile, through a keyword reservation supervision mechanism, it is ensured that core keywords appear naturally and at high frequency in the process of generating the copywriting. In the generation process, the system also uses a sampling strategy through a diversity control module and uses a semantic vector to remove duplication, and finally outputs diversified and non-redundant high-quality advertisement copywriting.

The invention relates to the technical field of storage intelligence, and discloses an early warning and allocation system and method for intelligent inventory of inspection and quarantine reagent consumables, and the system comprises a consumption trend extraction module, a demand prediction module, a risk early warning module, an early warning trigger module, a supply feasibility evaluation module and an intelligent allocation module. Performing seasonal decomposition on the historical consumption record to obtain a consumption trend component; periodically superposing the consumption trend component to obtain a demand prediction result; based on the demand prediction result, performing inventory risk research and judgment on the current inventory to obtain an inventory early warning index; when the inventory early warning index exceeds a safety threshold value, inventory early warning information is generated; according to the consumable demand information, carrying out feasibility evaluation on inventory data and logistics constraint conditions of the supply point to obtain a feasible allocation point; generating a deployment scheme according to the safety inventory standard of the feasible deployment point; according to the invention, the perspectiveness and accuracy of early warning and allocation of the intelligent inventory of the inspection and quarantine reagent consumables can be improved.

The invention discloses a tunnel construction safety risk management and control method and system. The method comprises the following steps: collecting multi-source sensor data of a construction site, constructing a continuous time sequence monitoring data set, introducing a Hampel model to detect abrupt change and abnormity and identify sudden risk features, and introducing a CUSUM model to detect a slow change trend and extract evolutionary risk features. Time synchronization and vector combination are carried out on the two types of risk features, a joint risk feature matrix is constructed, dominant risk types are further judged, dynamic weighted fusion is implemented, a fusion risk index is generated, risk levels are divided according to the fusion risk index value, and the risk level is determined by combining the dominant risk types and the risk trend. And an informatization risk response result including a response level, a response strategy and trend prediction is generated, and accurate identification and intelligent management and control of tunnel construction risks are realized. The invention is suitable for dynamic risk early warning and management of a tunnel engineering construction site, and belongs to the technical field of tunnel engineering safety management.

The invention relates to the technical field of building structure safety monitoring, in particular to a stress deformation monitoring method and system for a rib truss floor support plate. The method comprises the following steps: establishing a digital twinborn model, mapping sensor data to grid nodes on a plate surface, calculating a local curvature, and ensuring data reliability through temperature compensation and confidence interval verification; an abnormal sensitivity index ASI is constructed based on multi-modal data, strain, displacement, frequency and curvature deviation are fused and dynamically weighted, and an abnormal area is positioned in combination with spatial clustering; the ASI evolution trend is analyzed through sliding window regression, slope and intercept parameters are extracted, and a health evolution index HEI is generated to achieve four-level health grading early warning; and establishing an abnormal dynamic model to simulate a local abnormal propagation path, predicting the residual life and the health risk index, and guiding a maintenance decision. According to the invention, monitoring-early warning-decision closed-loop management is realized.

The invention discloses an electric actuator fault diagnosis method and system, and belongs to the technical field of fault diagnosis, and the method specifically comprises the steps: collecting the current, voltage, displacement, temperature rise and electromagnetic noise signals of an electric actuator in the operation process, and forming a multi-dimensional operation data set, performing time-frequency domain fusion processing on the multi-dimensional operation data to obtain a feature vector, constructing a health state track of the electric actuator, introducing an electromagnetic noise coupling factor into the health state track, performing evolutionary analysis on the health state track, extracting trend information related to a fault, and when the trend information exceeds a set threshold value, determining that the electric actuator is in a fault state. Comparing with a pre-stored actuator fault evolution sample, and judging the fault type and severity; according to the method, advanced identification and classification discrimination of potential faults can be realized through trend extraction and adaptive comparison, so that the comprehensiveness and accuracy of diagnosis are improved.

The invention discloses a district construction quality defect identification and positioning method based on multivariate data fusion, and the method comprises the steps: collecting multi-source data of a district construction site, and carrying out the preprocessing of the data, and generating a standardized data set; performing trend extraction and noise reduction on the time sequence monitoring data, and constructing fusion feature representation; an abnormal sensitive subspace is generated in a self-adaptive mode, and normalized coding is executed; embedding the abnormal sensitive subspace into a Diffusion diffusion model, and constructing an improved diffusion modeling structure; diffusion reconstruction is carried out on the fusion features to obtain reconstruction errors; performing anomaly analysis and defect judgment based on the reconstruction error, the smooth value and the residual error sequence; and the defect position is mapped to a topological structure or geographic coordinates, so that visual positioning and pushing early warning are realized. According to the method, the abnormal sensitive subspace and the improved Diffusion diffusion model are introduced, so that high-precision identification and spatial precise positioning of the construction quality defects of the transformer area are realized.

The invention relates to the technical field of digital twinning, in particular to a drilling and workover equipment digital twinning monitoring system which comprises a deposition path reconstruction module, a trend section recognition module, a differential pressure offset positioning module, an oscillation linkage analysis module and a joint control sequence generation module. According to the method, the corresponding relation between a rock debris return path and a drilling depth time sequence is constructed, particle radial deformation and liquid flow channel positioning information are fused, a space mapping map of a deposition track is formed, a continuous evolution section is extracted and a trend distribution set is constructed in combination with the drilling speed amplitude variation trend of a track dense section, and the drilling speed of the track dense section is obtained. The method comprises the following steps: identifying an interlayer abnormal point location based on pressure sequence difference amplitude comparison change, further superimposing a consistency relationship between a drilling tool oscillation amplitude and a drilling speed change direction, screening a linkage feature group with relevance, extracting a key node according to a trigger frequency and an amplitude variation concentration ratio, generating a corresponding operation instruction, and storing the operation instruction. And driving multi-parameter collaborative identification and intelligent generation of a drilling and workover control path.

The invention relates to a track prediction method based on multi-resolution hierarchical reasoning. The method comprises the steps that firstly, historical track data are acquired and preprocessed, and a track prediction data set is obtained; then, a track prediction model based on multi-resolution hierarchical reasoning is constructed, and the track prediction model comprises a track encoder module oriented to trend extraction, a probability type key point predictor module based on a mixed density network, a dynamic sampling module, a linear interpolation module based on learnable residual errors and a Transformer decoder module; then, inputting the track prediction data set into the track prediction model based on multi-resolution hierarchical reasoning for model training, and optimizing model parameters in combination with an uncertainty loss function; and finally, pre-processing real-time track data, inputting the pre-processed real-time track data into the trained track prediction model based on multi-resolution hierarchical reasoning, and outputting to obtain a future track prediction result. And the prediction performance and precision of the flight path in a complex air traffic control scene are improved.

The invention discloses an error prediction method of an electric energy metering device, which solves the problems of insufficient non-stationarity processing and poor environmental adaptability in the traditional method by adaptively decomposing an error sequence and applying a physical constraint fusion prediction component and combining an elastic model updating mechanism. The method has the advantages of improving error prediction precision and enhancing model environment adaptability. Spectral residual transform and adaptive wavelet threshold decomposition are adopted for cooperative work, the defects of noise sensitivity and trend term extraction deviation of a traditional method are effectively overcome, the detection capacity of transient characteristics is enhanced through spectral residual transform, and the distortion problem of principal component analysis under strong interference is avoided; the adaptive wavelet threshold algorithm dynamically optimizes the decomposition granularity, and precisely separates the components of a trend term, a periodic term and a random term.

In the information processing methods implemented in conventional management support systems, the management issues and measures presented are based on management issues and measures that have proven successful at other companies, and do not necessarily apply accurately to the company in question. [Solution] In the management support system 10, the management device, which functions as an information processing device, has a trend analysis device analyze internal corporate information that belongs to a specified category from internal corporate information that is created in relation to corporate activities and classified into multiple categories based on predetermined criteria, extract corporate trend information that indicates trends in corporate activities, determine whether the extracted corporate trend information is new by referring to corporate trend extraction history information, and have a message generation device generate a message related to measures to carry out the future activity plan using corporate trend information that is determined to be new and future plan information related to the company's future activity plan.

The invention relates to the technical field of post-operation follow-up visit management, in particular to a post-operation follow-up visit management system based on artificial intelligence, which performs standardization and time alignment processing on information such as electronic medical records, vital signs, inspection and examination data, medication records and post-operation symptom texts through a data acquisition module to construct a unified initial data matrix; and the time sequence continuity of modeling is ensured. And introducing a medical natural language processing flow, extracting keywords of symptoms, parts and discomfort properties, and realizing structured expression. The symptom keywords and the related physiological indexes are dynamically matched through the feedforward neural network, the limitation of a fixed template is avoided, and the pertinence of feature extraction is improved. And finally, inputting the symptom associated data into a time sequence model comprising a gating circulation unit and an attention mechanism, modeling a characteristic evolution trend, extracting key time period changes, and effectively revealing potential anomalies. Intelligent analysis, index focusing and trend prediction of postoperative discomfort feedback are realized, and follow-up management efficiency and predecessibility are improved.

The invention provides a cellular data low-frequency trend self-adaptive extraction method, which is used for automatically extracting an evolution trend from nonlinear and strong-fluctuation cellular flow data. According to the method, firstly, missing values in an original sequence are restored through Akima spline interpolation, and continuity and smoothness of a data structure are restored; and then constructing a joint objective function to minimize the permutation entropy of the candidate trend and maximize the Pearson correlation coefficient of the candidate trend and the original sequence, and then guiding the adaptive confirmation of the order and modal quantity parameters of a B spline in TVFEMD decomposition, thereby extracting a low-frequency trend component with the highest representation force, and realizing unsupervised and automatic trend extraction. Finally, the provided method can perform adaptive modeling under various time scales such as day, week, month, year and the like, outputs trend components of corresponding time scales, and can be applied to application scenes such as capacity planning, holiday and festival load analysis, energy-saving scheduling and service prediction in a cellular network. The method has the advantages of generalization, full-automatic parameter adjustment and the like, the trend extraction efficiency and stability of the cellular data are remarkably improved, and reliable trend information support is provided for intelligent operation and maintenance and decision optimization of the cellular data.

The application discloses a kind of bridge monitoring data accurate multi-step prediction method based on space-time hypergraph neural network, the method comprises the following steps: one, collect bridge structure space-time monitoring two-dimensional data as original data set;Two, original data set is carried out missing data filling, trend extraction, data standardization preprocessing operation;Three, space domain data is expressed as different vertex on hypergraph, time domain data is expressed as one-dimensional time series on each vertex of hypergraph, define correlation matrix;Four, design space-time hypergraph neural network model to carry out space-time correlation modeling to bridge monitoring data;Five, use the monitoring data in the initial stage of bridge operation within half a year to train space-time hypergraph neural network model;Six, the trained space-time hypergraph neural network model is applied to monitoring data after half a year.The application solves the shortcoming that data-driven response prediction method is insufficient in the degree of space-time correlation of monitoring data, realizes the multi-step accurate prediction of monitoring data.

A business management system with integrated business intelligence and IT-supported optimization, consisting of: a computer device comprising at least one processor and a memory arrangement for storing executable instructions; a data acquisition and integration unit that is operationally connected to the at least one processor and is configured to receive heterogeneous enterprise data from a variety of source systems, including financial transaction systems, operational control systems, human resource information systems, supply chain systems, customer interaction systems, and IT infrastructure monitoring systems, wherein the data acquisition and integration unit is further configured to normalize syntactic formats, resolve temporal inconsistencies, and map source-specific attributes into a unified enterprise data representation stored in the storage system; a business intelligence processing unit that is operationally coupled with the data acquisition and integration unit and is configured to calculate business performance indicators through multidimensional aggregation, correlation analysis, and trend extraction over the unified business data representation, with the calculated business performance indicators being time-stamped and permanently stored; an optimization unit that is operationally coupled with the business intelligence processing unit and is configured to compare the calculated business performance indicators with stored business objectives and constraints and to determine optimized business control parameters that correspond to resource allocation, operational planning, cost efficiency or performance improvement; a control and orchestration unit that is operationally linked to the data acquisition and integration unit, the business intelligence processing unit, and the optimization unit, wherein the control and orchestration unit is configured to coordinate the execution sequence, forward the results of the business intelligence analysis to the optimization unit, and manage feedback by incorporating the effects of applied optimized business control parameters into subsequent data acquisition cycles; and a communication interface that is connected to at least one processor and configured to exchange data and control signals with external enterprise systems and user terminals.

The application relates to a charging pile intelligent operation and maintenance management method and device, computer equipment and a medium. The method comprises collecting battery state parameters and charging pile operation state parameters; trend extraction is performed on the time sequence change of the battery state parameters and the charging pile operation state parameters, and correlation analysis is performed to generate a multi-dimensional state index representing the current operation state of the charging object and the charging equipment; based on the multi-dimensional state index, a corresponding control strategy is selected from a charging control strategy set, and stage control parameters set for different residual capacity intervals in the control strategy are adaptively evaluated with the current state index; and according to the adaptivity evaluation result, a charging operation of the corresponding strategy is performed. The application has the effect of improving the charging control efficiency of the charging pile.

The present application relates to the technical field of image processing, and more particularly to an endoscope fault image feature extraction method based on artificial intelligence, which comprises: bad point detection to determine abnormal pixels and coordinates; density ratio and cluster analysis to determine spatial distribution patterns; identification of fault types to determine the timing evolution trend; extraction of fingerprint feature vectors; prediction of development trend to determine the maintenance time window; and positioning of the physical location of the fault. The present application locates abnormal pixels through bad point detection, determines whether the bad point distribution belongs to edge priority or linear arrangement through density ratio and cluster analysis, and further determines the fault type of sealing failure or cable breakage. Meanwhile, the feature vector is extracted through the fusion of CNN and Transformer, the development trend is predicted in combination with the timing evolution to determine the maintenance window, and finally the physical location is located through coordinate mapping when the maintenance conditions are met, thereby improving the efficiency and accuracy of endoscope fault diagnosis.

The application discloses a kind of blade fracture early warning method and device of wind turbine unit, and the early warning method is: according to characteristic variable selection method and trend extraction method, the historical operation data in the preset time period is extracted into the model training data that can be used for model training, according to the characteristics of characteristic variable, the structure of a regression model is given, the regression model is trained using model training data, according to the model obtained by training, the difference between model output data and real-time operation data is compared, early warning judgment is carried out according to the preset early warning judgment rule, and the early warning information obtained by judgment is used to prompt operation and maintenance personnel, and the health status of blade is checked.The blade fracture early warning method and device of wind turbine unit of the application solve the problem that the blade health check method of wind turbine unit in the prior art needs to invest a lot of manpower and material resources, and the cost is higher, and the health problem of blade can be found early.

The application discloses a transformer area construction quality defect identification and positioning method based on multi-element data fusion, comprising the following steps: collecting multi-source data of a transformer area construction site, and preprocessing to generate a standardized data set; extracting trends and reducing noise from time series monitoring data, and constructing a fusion feature representation; adaptively generating an abnormal sensitive subspace, and performing normalization coding; embedding the abnormal sensitive subspace into a Diffusion diffusion model to construct an improved diffusion modeling structure; diffusing and reconstructing the fusion features to obtain a reconstruction error; based on the reconstruction error, a smoothing value and a residual sequence, performing abnormal analysis and defect determination; mapping the defect position to a topological structure or geographical coordinates to realize visual positioning and early warning push. The application realizes high-precision identification and spatial accurate positioning of transformer area construction quality defects by introducing an abnormal sensitive subspace and an improved Diffusion diffusion model.

The invention relates to the technical field of intelligent design and simulation, in particular to an intelligent design and simulation system for engineering technology research and development, which comprises an evolution trend extraction module, a dependency structure construction module, an instruction offset identification module, a working condition variable section positioning module and a matching relation screening module. Extracting the variation amplitude, the rate and the span in the stage design record, grouping to generate a trend label, constructing a trend continuous path, identifying a behavior offset fragment, positioning a high-density disturbance section, screening a matching path with a consistent direction, and outputting a simulation operation result. According to the method, data processing of direction change, rate fluctuation and boundary span is introduced, behavior trend grouping and path construction are completed, included angle matching of a target direction and a path trend is combined, a structural offset fragment and a high-density disturbance section are identified, and a matching path with continuous direction and consistent response is extracted; and the mapping precision and continuity between design and simulation are improved.

The invention discloses a business demand analysis method and device, computer equipment and a storage medium. According to the invention, the purchase data, the transaction information and the rejection record in the hotel resource management system can be uniformly mapped to the labeled performance index set, and the system can accurately identify the dependency relationship between the purchase behavior and the transaction result based on the indexes. The abnormal transaction relationship is identified through the order rejection record, and the dependency relationship model is divided into a plurality of relationship model subgroups. Key operation trend characteristics are extracted through trend clustering and analysis of the multi-dimensional integrated intersection points, and the trend characteristics are mapped to all links of the resource operation process. Based on the operation demand results, the system can identify a key optimization path and generate an executable set of demand adjustment rules. On the whole, closed-loop management from data acquisition, dependency relationship identification, anomaly analysis, trend extraction to demand optimization rule generation is realized, and the intelligent level of resource operation is improved.

The invention discloses a WeChat applet operation trend prediction method based on digital twinning, and the method comprises the following steps: collecting WeChat applet multi-source operation data, and generating structured input data; constructing a digital twin structure map; injecting a strategy disturbance event into the digital twin structure map through a strategy disturbance simulation method, and generating a structure variant sequence and a behavior variant sequence; performing fusion coding on the structured input data and the variant sequence; using an improved Giraffe model to output an operation trend prediction result and a confidence interval; carrying out trend extraction and strategy influence analysis by adopting a sensitivity scoring method and an inflection point detection method; and selecting an optimization strategy parameter according to the strategy sensitivity score, updating the digital twin structure map through a strategy backfilling and prediction verification method, and performing re-prediction. According to the method, precise modeling and strategy optimization of the WeChat applet operation trend are realized, and the strategy decision efficiency and the operation prediction reliability are improved.

The invention relates to the technical field of self-adaptive temperature control of an SSD, and discloses a self-adaptive temperature control method and system based on the SSD.The method comprises the steps that internal data and external parameters of the SSD are collected, trend extraction is carried out, and a load change trend is obtained; according to the load change trend, identifying a heat accumulation risk point, carrying out heat dissipation adjustment, collecting temperature data, and generating a temperature distribution diagram; performing regional division on the temperature distribution diagram, and generating a load distribution scheme in combination with preset load adjustment parameters; screening tasks in the load distribution scheme, and determining an initial task list; if the continuous high-intensity read-write operation exists in the initial task list, redistributing the continuous high-intensity read-write operation to other partitions of the SSD to generate an optimized task list; and recording temperature and performance fluctuation conditions in the operation process of the optimization task list, and generating a temperature stable state record. According to the method, the temperature can be controlled, and the SSD performance is improved.

This application relates to the field of computer-aided process optimization technology, and particularly to a method and apparatus for optimizing and analyzing automotive bolt forming processes. The method includes: acquiring a dataset of process parameters for an automotive bolt forming task and optimization trend information for bolt forming; extracting trends from the fluctuation data of each parameter in the process parameter set to obtain parameter characteristic information of the process parameter set; determining parameter fluctuation information for each process parameter set based on the parameter characteristic information; performing optimization calculations based on the parameter fluctuation information and optimization requirements in the process parameter set to obtain a target process set; determining process influence information from the parameter information corresponding to the target process set; and calculating the optimal parameter combination for the bolt forming process based on the parameter adjustment range of the process influence information and optimization trend information. The method provided in this application can solve the problem of deviations in process optimization direction, which in turn affect product quality.

The invention provides an urban heat island effect space-time evolution mode mining method and system fusing time sequence clustering and trend extraction. The method comprises the following steps: acquiring remote sensing image data of a research region in a research year section; dividing urban areas in the research area based on the land coverage data; dividing a rural reference area based on the urban area in the research area, the land coverage data, the normalized vegetation index and the elevation data; calculating the surface urban heat island intensity of each pixel in the urban area in the research year section in combination with the surface high temperature data and the country reference area; performing clustering processing on each pixel in the urban area based on a calculation result of the surface urban heat island intensity by adopting a time sequence clustering algorithm to obtain a plurality of space partitions; and constructing an average earth surface urban heat island intensity time sequence of each space partition and carrying out trend analysis to obtain urban heat island effect space-time evolution mode characteristics, including earth surface urban heat island intensity evolution trends and significance levels of the earth surface urban heat island intensity evolution trends of each space partition.