85 results about "Trend extraction" patented technology

The invention discloses a geological disaster networking monitoring and early warning method, and relates to the technical field of geological disaster monitoring and early warning, and the method comprises the following steps: S1, collecting the original data of multiple types of monitoring equipment in a monitoring region, extracting a high-amplitude sudden change region and a frequency drift factor according to the time and frequency distribution, constructing a high-frequency disturbance sensing matrix, and carrying out the recognition of the high-frequency disturbance sensing matrix; and generating a disturbance characteristic index map for representing the spatial distribution of the unnatural disturbance source. According to the method, active identification and modeling of non-natural interference are realized by constructing a high-frequency disturbance perception matrix and a disturbance index map, disturbance propagation analysis and residual difference are combined to strengthen precursor signal features, the risk level is accurately judged through trend identification and causal analysis, and finally, early warning model parameters are dynamically optimized based on response regulation factors, so that the early warning accuracy is improved. A closed-loop mechanism of interference identification, signal purification, trend extraction, risk judgment and strategy adjustment is formed, and the early warning stability, accuracy and practicability of the system in a high-interference environment are remarkably improved.

The invention discloses a data management method based on a distributed storage system, relates to the technical field of data management, and is used for solving the problem of storage management behavior strategy mismatch. On the basis of dynamic perception of data access behaviors in a distributed storage system, a behavior feature vector fusing an access mutation rate, periodicity and an access span is constructed, behavior pattern recognition and strategy structure generation are completed, optimal deployment and hierarchical storage of copies are achieved through node resource state perception and construction of a cost function, and the strategy structure generation efficiency is improved. And after compression and consistency configuration are executed and the strategy falls to the ground, the compressibility and the offset trend of the behavior path are analyzed, scheduling management information is extracted, and a stable execution or strategy adjustment signal is generated, so that the problem of strategy execution mismatching caused by behavior perception deficiency in the distributed storage system is reduced, and the strategy execution efficiency is improved. The strategy closed-loop control and the resource scheduling optimization under behavior driving are realized, so that the data management efficiency and the strategy adaptation capability of the distributed storage system are improved.

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 discloses a method for determining an electrical fire risk assessment weight index coefficient, and relates to the technical field of risk assessment, and the method comprises the steps: deploying a plurality of types of sensors to collect original environment data streams including historical fault data, environment parameter data and communication network data in real time, carrying out the preprocessing of the original environment data streams, and carrying out the calculation of the original environment data streams; forming a preprocessed feature data set; performing sparse optimization on the preprocessed feature data set by using an Elastic Net regression function, optimizing regularization parameters in the regression function through a cross validation method, and finally obtaining an optimized feature set and a preliminary weight vector; and processing the time evolution sequence of the preliminary weight vector by using a convolution mode to generate a convolution feature tensor, and carrying out nonlinear mapping on the convolution feature tensor by using a ReLU activation function to obtain a predicted weight sequence. Effective fusion of multi-scale features is realized through a dynamic segmentation strategy of an adjustable time window in combination with a high-frequency signal analysis and long-term trend extraction technology.

The invention relates to the technical field of deformation prediction, in particular to a lateral deformation prediction system and method based on a deep foundation pit wall body, and the system comprises a deformation data collection module which collects a ground surface settlement monitoring value and a lateral displacement monitoring value of a corresponding measuring point of the deep foundation pit wall body, carries out the time sequence alignment processing of data points, removes an abnormal monitoring value, and obtains a deformation data collection module; and establishing calibration deformation sequence data. According to the invention, through real-time acquisition and strict execution of data time sequence alignment processing, the consistency of ground surface settlement data and wall lateral displacement data is ensured, abnormal monitoring data is effectively eliminated, and the data accuracy and consistency are improved; in the spatial interpolation process, width measurement of a settlement area is introduced, the width characteristic of a settlement groove is determined, and the earth surface deformation trend is captured according to a deformation index; and extracting the maximum lateral displacement of each time section of the monitoring section, establishing a dynamic corresponding relation between the maximum displacement and the width of the settling tank, and identifying the regular influence of the change of the construction process on the lateral deformation.

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 relates to the technical field of satellite navigation and time synchronization, in particular to a satellite clock error prediction method and system. According to the method, a satellite clock error prediction model is constructed, multi-scale features of input data are extracted, sequence decomposition is performed on the input data, decomposed seasonal items are subjected to self-correlation processing through an encoder and a decoder, and the data are subjected to sequence decomposition in the encoder and the decoder; and the season item output by the decoder and the trend item after each time of sequence decomposition are subjected to dimension superposition and then converted into clock error prediction data. The method comprises multiple times of sequence decomposition, adopts a progressive decomposition architecture to peel noise interference step by step, purifies trend components in seasonal terms through time convolution operation, and optimizes a trend accumulation process through trend term superposition by utilizing a residual feedback mechanism, so that the purity of trend extraction is remarkably improved, and the method is suitable for large-scale popularization and application. And meanwhile, the phase integrity of the seasonal components is kept, so that the model can accurately model the multi-scale characteristics of the clock error data.

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 construction quality detection, in particular to a building construction quality automatic detection method and system.The building construction quality automatic detection method comprises the following steps that vertical projection plane coordinates of multiple components in a building fabricated structure are obtained, a horizontal edge vertex coordinate set is selected, and the geometric center of the components is obtained through calculation and averaging; and generating a component positioning center vector group according to the component number sequence connection points. According to the method, through extraction of vertical projection coordinates of the components, construction of a positioning center vector, establishment of sequence expression of the position relation between the components, judgment of the consistency of the offset difference change rate and the included angle, continuous offset trend extraction, extraction of the gravity center of the components in the section, calculation of the deviation degree of a connecting line to a theoretical axis, and reflection of the overall offset trend of the component section. And identifying the system characteristics of the error according to the offset consistency and the amplitude distribution. According to the process, the component position, direction and aggregation offset characteristics are integrated in a serialization, vectorization and gravity center weighting mode, and the capacity of trend recognition and cluster error judgment is improved.

The invention discloses a germanium single crystal growth intelligent optimization control system based on machine learning, which relates to the technical field of crystal growth control, and is characterized in that dimension conversion, timestamp synchronization and centralized indexing are carried out on multi-source sensor data to realize original data standardization; secondly, generating cleaned data with high credibility by adopting anomaly detection, drift correction and missing interpolation technologies, further fusing and storing the structured process parameters and the unstructured simulation data into a special database, and constructing a multi-dimensional joint index and version management mechanism; and finally, generating parameter adjustment suggestions by utilizing real-time trend extraction, online prediction and risk early warning in combination with an online optimization algorithm, and performing closed-loop feedback to realize intelligent monitoring and dynamic regulation and control on the germanium single crystal growth process. The scheme has the advantages of simplicity and convenience in implementation, high applicability, data interconnection and intercommunication and the like, the data consistency, fault early warning and process control precision are remarkably improved, and the quality of germanium single crystal products is effectively improved.

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.

The invention provides a blast furnace ironmaking process anomaly detection method based on score co-integration, which comprises the following steps: acquiring key process variables in a blast furnace ironmaking process, and forming a time sequence by the key process variables; carrying out stationarity analysis on the time sequence by adopting an augmented Dickey-Fuller test method, and identifying non-stationary variables in the data to obtain a non-stationary time sequence; extracting a non-stationary trend part of the non-stationary time sequence by adopting a trend extraction algorithm; modeling the trend part of the non-stationary variable by using the FCVAR, and constructing an anomaly detection model based on the FCVAR; through the control limit detection of the statistical magnitude, the abnormality detection of the blast furnace ironmaking process is realized, and if the statistical magnitude exceeds the control limit, the abnormality exists in the blast furnace ironmaking process. The method solves the problem of non-stability of variables in the ironmaking process, is particularly suitable for variables with long memory characteristics, and can accurately capture the long-term equilibrium relation between the variables, find tiny abnormal changes in time and ensure the safety and stability of production.

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.