3819 results about "Cluster analysis" patented technology

The invention provides a BIM-based building energy consumption simulation analysis optimization method and system, and relates to the technical field of building energy consumption, and the method comprises the steps: building a building energy consumption digital twinborn model through integrating a building information model (BIM) and real-time operation data, such as indoor and outdoor temperature, equipment operation, electrical load and personnel density, collected by an Internet of Things sensor. An LSTM and GNN mixed deep learning algorithm is used to predict building energy consumption, and clustering analysis is used to identify an energy consumption abnormal mode and a peak period. And finally, based on a reinforcement learning algorithm, constructing an energy consumption optimization model taking total energy consumption minimization as a target and taking indoor thermal comfort and air quality as constraints, outputting equipment optimization operation parameters such as air conditioner temperature, fresh air volume, illumination brightness and the like, and realizing intelligent optimization control of building energy consumption, so that the building energy consumption is reduced, and the building operation efficiency is improved.

The invention discloses a data exception traceability tracking and positioning method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of insufficient data anomaly detection accuracy, low traceability positioning efficiency, incomplete knowledge association and the like in analysis and application links of power marketing data are solved. The method comprises the following steps: carrying out preprocessing and feature extraction on power marketing data, constructing a data association relationship network through a clustering analysis and statistical method, analyzing the difference of data features, analyzing the propagation path of an abnormal point identified in a data stream by adopting a path reasoning technology, and constructing a data abnormity tracing positioning model. A semi-supervised learning method is adopted to improve the accuracy of the model, result optimization and hyper-parameter adjustment are carried out, and multi-condition factors influencing business data information are introduced into the model as hyper-parameters. According to the technical scheme, the anomaly detection and traceability positioning capability of the power marketing data is greatly improved.

The invention provides an artificial intelligence enhanced distributed denial of service attack defense method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a data packet through a network flow collection module, extracting basic feature information, and obtaining a behavior feature vector through a deep learning feature extraction model; inputting the behavior feature vector into a pre-trained multilayer attention mechanism neural network model, calculating an attack probability score and marking potential attack traffic; performing clustering analysis on the potential attack traffic to calculate a hazard level, and generating a defense strategy according to the hazard level; and executing the defense strategy through the defense equipment, and dynamically optimizing the defense strategy by using a reinforcement learning algorithm based on defense effect feedback. According to the method, the attack traffic can be accurately identified, the defense strategy is adaptively optimized, and the accuracy and efficiency of distributed denial of service attack defense are effectively improved.

The invention relates to an underground equipment fault early warning and diagnosis method based on big data analysis. The method is suitable for equipment operation state monitoring and intelligent diagnosis in underground operation scenes such as mines. The method comprises the following steps: collecting multi-source data such as an equipment running state, environment parameters and operation behaviors and preprocessing the multi-source data; multiple signal features are extracted and fused to construct a unified feature vector; performing health modeling by using the residual self-encoder model to generate a health index; an early warning threshold value is dynamically set through clustering analysis and Bayesian reasoning, and anomaly recognition is achieved; after early warning is triggered, fault type identification is carried out by adopting the fusion discrimination model; performing causal reasoning and maintenance suggestion generation based on the equipment fault knowledge graph; and continuously optimizing the model in combination with operation and maintenance feedback information, and constructing a closed-loop diagnosis mechanism. The method has the characteristics of high recognition precision, high response speed, explainable result and sustainable optimization of the model.

The invention provides a self-adaptive brightness adjustment method for an LED backlight source, and the method comprises the steps: obtaining the intensity and spectral distribution information of downward natural light and an indoor lamp light source in real time through a light sensor, carrying out the light source superposition, calculating the total light intensity and spectral distribution, judging whether the self-adaptive adjustment condition is met or not, and if yes, adjusting the brightness of the LED backlight source. If yes, triggering an LED backlight brightness adjusting process; detecting the actual brightness of each to-be-adjusted unit in real time, comparing the actual brightness with the optimized target brightness value, calculating a brightness error, introducing a machine learning algorithm to learn and predict error data in real time, and dynamically adjusting the PWM duty ratio of the to-be-adjusted unit; and continuously obtaining evaluation feedback data of the user on the display image quality, and performing clustering analysis on the evaluation feedback data through an optimized unsupervised learning algorithm to generate a brightness adjustment curve adapted to different user preferences.

The invention relates to the technical field of geological monitoring, in particular to a method for intelligently monitoring geological disasters based on satellite remote sensing, which comprises the following steps of: obtaining corresponding earth surface coverage information through image registration and ortho-rectification processing; identifying terrain boundaries, geological structure features and geological attribute information through the land surface coverage information; recognizing surrounding hidden danger points through a topographic feature extraction method; an image fusion enhancement method is matched with a permanent scatterer interference method, a stable scatterer is extracted, a deformation monitoring network is constructed, and a phase difference value in a high-risk area is obtained through calculation; carrying out unwrapping processing through the phase difference value, and carrying out inversion to obtain earth surface three-dimensional deformation field information in the monitoring area; establishing a geological disaster risk assessment model through space-time clustering analysis; by establishing a risk assessment model, the geological disaster risk in the monitoring area is quantitatively assessed. The beneficial effect of the invention is that comprehensive monitoring and accurate early warning of geological hazard hidden dangers around the transformer substation are realized.

The invention discloses an intelligent electric meter abnormal electricity utilization detection method and system based on data analysis, and belongs to the technical field of electricity utilization abnormal detection, and the method specifically comprises the steps: obtaining the historical electricity utilization data of a user according to an intelligent electric meter, and carrying out the preprocessing of the obtained historical electricity utilization data of the user, extracting features of the preprocessed historical power consumption data of the user, carrying out three-layer clustering analysis on the extracted features, analyzing historical power consumption behaviors of the user, identifying abnormal behaviors obviously deviating from a normal power consumption mode according to the historical power consumption behaviors of the user, and adaptively adjusting model parameters through a feedback mechanism. The abnormal electricity utilization of the intelligent electric meter is monitored in real time, the method can adapt to the electricity utilization mode change of the user, the dynamic threshold value adjustment can be adaptively adjusted according to the electricity utilization behavior change of the user, and therefore more accurate anomaly detection is achieved.

The invention provides an intelligent post matching and employee growth path planning system and method and a storage medium. The system comprises a data acquisition and processing module, an employee portrait construction module, an intelligent post matching module and an occupational path planning module. The data acquisition and processing module collects employee performance data, education background, work experience and other information from internal and external data sources. The employee portrait construction module constructs a comprehensive portrait of each employee by using a clustering analysis and factor analysis technology; the intelligent post matching module performs intelligent matching by applying a recommendation system algorithm according to post demands and employee portraits, and finds the posts most suitable for the current capabilities and development desires of the employees; the occupational path planning module analyzes the industry development trend and future skill requirements based on the prediction model, and provides personalized occupational development paths and training suggestions for employees. According to the invention, historical performance evaluation and industry trend analysis of employees can be integrated, and comprehensive post matching and occupational planning solutions are provided.

The invention relates to the technical field of information, and discloses a ciphertext execution exception handling method and system for a privacy computing algorithm, which quantifies the data query sensitivity of each tenant by collecting differential privacy query parameters of a plurality of tenants, and performs time sequence analysis on the sensitivity quantification result by using an ARIMA model to predict the future trend of the tenant, so as to improve the accuracy of the ciphertext execution exception handling of the privacy computing algorithm. The residual error between the actual sensitivity and the predicted sensitivity is calculated to identify abnormal tenants; a sliding window method is adopted to extract statistical features of the abnormal tenants to construct a feature set, and clustering analysis is carried out on the feature set to identify abnormal modes and abnormal fingerprint feature clusters corresponding to the abnormal tenants; and training a model based on the positive and abnormal fingerprint feature clusters of the abnormal tenants, and quantifying the influence degree of each abnormal mode on the privacy protection level of the abnormal tenants by using the model, so as to adjust and execute the privacy budget allocation proportion of each abnormal tenant according to the obtained quantitative analysis result. And therefore, the exception in the ciphertext execution of the privacy calculation algorithm can be effectively processed.

The invention discloses a hydraulic engineering operation and maintenance monitoring system based on digital twinning, and the system comprises the steps: S1, a data collection module which is used for obtaining multi-modal actual detection data from a plurality of monitoring points of a hydraulic engineering; s2, a data generation module which simulates and generates multi-dimensional virtual monitoring data and an abnormal sample library related to hydraulic engineering operation and maintenance; s3, a data fusion module which fuses the multi-dimensional virtual monitoring data and the multi-modal actual detection data; s4, a digital twinborn model construction module which dynamically constructs a multi-dimensional digital twinborn model of the water conservancy project based on the fused multi-modal data set; s5, an anomaly detection module which detects potential anomaly and sudden faults through an anomaly sample library generated by the generative adversarial network in combination with a multi-dimensional spatial-temporal trajectory clustering analysis method; and S6, an operation and maintenance monitoring module which is used for providing real-time operation and maintenance monitoring service for the multi-dimensional digital twin model. The method has the advantages of being high in dynamic adaptive capacity, high in monitoring accuracy and high in abnormal response speed.

The invention provides a remote fault diagnosis method for photovoltaic power generation equipment, and relates to the technical field of data processing, and the method comprises the steps: carrying out the clustering analysis of distributed photovoltaic equipment according to an environment change feature set, an equipment model set and a performance parameter set, and determining a plurality of equipment sets; performing abnormal power generation identification based on the plurality of power generation capacity sets, and determining an abnormal equipment set; and matching and determining the associated fault feature library and the associated fault prediction model according to the equipment model, the performance parameters and the environment monitoring data of the abnormal equipment, performing fault prediction, and determining a predicted fault type. According to the invention, the technical problem that the operation efficiency, stability and safety of a conventional distributed photovoltaic power generation system are poor due to the fact that the number of photovoltaic power generation devices is large and the photovoltaic power generation devices are dispersed, and the abnormal working state of a single device cannot be timely and accurately found can be solved; the convenience and the accuracy of identifying the abnormal working state of the equipment can be improved, and the intelligence and the automation of equipment fault diagnosis are realized.

The invention relates to the technical field of software development management, in particular to an internet software development management system and method. The method comprises the following steps: collecting an original function demand set and carrying out function clustering analysis; extracting and classifying demand function features and generating independent demand units; carrying out dependency analysis on the independent demand units; constructing a dependency relationship set and a demand logic structure; the method comprises the following steps: generating a demand framework matrix, carrying out organization network simulation to obtain a demand connection network, carrying out code reverse evolution according to the demand connection network, carrying out basic parameter matching to obtain evolution basic parameters, carrying out a pressure simulation test on the evolution basic parameters to generate a pressure test result, and capturing resource pressure defects based on the pressure test result. And optimizing and adjusting the evolution basic parameters to generate optimized development parameters. According to the invention, a more efficient Internet software development management method is realized.

The invention discloses a risk assessment and early warning method and system applied to water conservancy project construction, and belongs to the technical field of water conservancy project construction safety. Multi-source data of a construction area is collected, and a multi-dimensional construction information flow set divided according to time, procedures and work points is constructed; constructing a space-time risk causal map based on the construction units, and expressing time sequence association and risk propagation paths among the construction units; mapping the information flow to a graph structure, training a multi-task graph neural network model, and obtaining a risk score and a future risk evolution trend of each construction unit; performing clustering analysis on the risk state, identifying a high-risk work point set, and identifying a risk linkage chain based on a causal map; when the risk score exceeds a dynamic threshold value or a closed propagation structure exists, a grading early warning signal is triggered; according to the method, dynamic identification, trend prediction and intelligent early warning of complex construction risks are realized, and the intelligent level of construction safety management is improved.

An early warning method for battery health degree and residual life of a medium-and-large-sized electrochemical energy storage power station belongs to the field of battery health assessment, and comprises the following steps: firstly, establishing a battery health state grading system, determining threshold ranges of key parameters such as voltage, internal resistance and charge and discharge capacity, training historical data through various algorithms such as a support vector machine to construct a discrimination model, and calculating the battery health degree and residual life of the medium-and-large-sized electrochemical energy storage power station; and the health level is accurately evaluated. The health detection system monitors data in real time, an evaluation model is constructed through preprocessing and correlation analysis, and the health level is judged and a report is generated according to the evaluation model. And for a sub-health battery unit, determining an isolation priority according to fuzzy comprehensive evaluation, unloading and predicting a deterioration trend to formulate a maintenance plan. And during discharging, the battery is selected to discharge and power is adjusted according to the health level. According to the method, historical data are clustered and analyzed by using a machine learning algorithm to construct an evaluation model to predict a new data level, and an energy storage system operation strategy is dynamically adjusted based on the health level, so that economy and reliability are improved.

The invention discloses an archive management system and method based on data analysis, and particularly relates to the technical field of archive management, and the method comprises the following steps: obtaining document attribute data of archives, generating document attribute vectors, carrying out clustering analysis, finding a similar group set containing similar attributes with a current archive from all archives, and carrying out clustering analysis on the similar group set; then whether an information exposure risk investigation mechanism and a transverse association risk level are triggered or not is judged; respectively carrying out content analysis and access analysis on the current archive, then generating a dynamic desensitization level of the current archive in combination with a transverse association risk level judgment result, and applying the dynamic desensitization level to the current archive in real time; according to the method, the behavior entropy access risk index is obtained by calculating the content sensitivity, dynamically evaluating the file security level and calculating the confusion degree of the user access behavior, the risk perception capability is improved, and the desensitization level of the file is automatically adjusted according to the content sensitivity, the access risk and the overall risk level, so that the data protection is more accurate.

The invention relates to the technical field of program exception positioning, in particular to an AI-based data interface program exception fault pre-positioning algorithm, which comprises the following steps: S1, data stream pre-analysis; s2, capturing an abnormal signal; s3, performing abnormal clustering and feature extraction; s4, interface behavior modeling is carried out; s5, abnormal traceability positioning; s6, generating a self-adaptive repair suggestion; s7, performing feedback circulation and continuous learning; and S8, performing visualization and report generation. Through the steps of data stream classification, key field extraction, behavior modeling and the like, the system can accurately capture abnormal signals in mass data, meanwhile, an abnormal mode is visually displayed through clustering analysis and a dimension reduction technology, a reliable foundation is laid for subsequent positioning and repairing, and in addition, the method has the advantages of being high in practicability and easy to popularize. The abnormal source is quickly positioned by adopting methods such as topological sorting and causal network reasoning, the false alarm rate and the missing report rate in the positioning process are effectively reduced by combining a sliding window and time sequence analysis, and the problem solving efficiency is greatly improved.

The invention relates to a network attack tracing method, system and device based on a user portrait, and a medium. The attack tracing method comprises the following steps: collecting interactive behavior data of a user in a Web page; performing compression and serialization processing on the interaction behavior data to generate a structured interaction log file; collecting fingerprint data of the user terminal and performing standardization processing to generate a fingerprint feature vector; based on the structured interaction log file and the fingerprint feature vector, generating a user unique identifier containing a risk level tag; performing clustering analysis on the interaction behavior data according to the unique identifier of the user, identifying an abnormal behavior mode of the abnormal user and marking an attack type; acquiring a public network IP address of an abnormal user, and positioning a geographic position in combination with an IP database; and constructing an attack traceability report based on the user unique identifier of the abnormal user, the abnormal behavior mode, the public network IP address and the geographic position. According to the invention, the user portrait can be accurately constructed, and the network attack behavior can be effectively identified and traced.

The invention discloses a control method and system for remote monitoring of the Internet of Things, and relates to the technical field of intelligent monitoring of the Internet of Things, and the method comprises the steps: inputting an operation data set of Internet of Things equipment into a space-time diagram convolution model, carrying out the local space-time feature extraction of an edge layer, carrying out the cross-equipment cooperation mode analysis of a cloud layer, and generating an abnormal propagation path; the method comprises the following steps: mapping three-dimensional space coordinate parameters in an operation data set of Internet of Things equipment into nodes of a topological structure, mapping interaction data between the equipment into edges of the topological structure, constructing a dynamic knowledge graph, injecting an abnormal propagation path into the dynamic knowledge graph, and updating a fault influence weight between the nodes by applying an improved graph convolution fusion algorithm. Acquiring propagation risk nodes, and performing dynamic sorting and community clustering analysis on the propagation risk nodes. According to the invention, through the improved graph convolution fusion algorithm and the space-time graph convolution model, the capability of identifying the fault behavior in the Internet of Things equipment is enhanced, and the efficiency of edge and cloud collaborative analysis is improved at the same time.

The invention relates to the technical field of power systems, in particular to a power grid operation risk level visualization intelligent management tool system. The system obtains power grid operation data through an Internet of Things sensor network, wherein the power grid operation data comprises current, voltage, equipment operation state and other multi-dimensional information; performing real-time analysis on the acquired data by adopting multi-level clustering analysis, a predictive maintenance algorithm and a deep learning model to realize abnormal behavior identification and risk prediction; a dynamic risk map is constructed based on a GIS technology, and accurate positioning and real-time display of risk levels are realized in combination with regional feature recognition and personalized threshold setting. According to the invention, multi-source data acquisition, intelligent analysis and visual presentation are creatively and organically integrated to form a closed-loop risk management system, so that the system has adaptive learning ability. Practical application shows that the system can early warn potential risks 30 minutes in advance, the operation and maintenance response time is shortened to 3 minutes, and the power grid operation risk monitoring and management efficiency is remarkably improved.

The invention discloses an intelligent detection method for product defects on an automatic production line and a detection system based on machine vision, and relates to the technical field of industrial product quality detection. According to the method, a multi-waveband imaging technology is combined with temperature and chemical component information to generate a multi-dimensional feature data set, and an unsupervised learning algorithm is utilized to perform clustering analysis, so that the limitation of traditional single-waveband imaging is broken through, product features are comprehensively captured, the defect detection range and accuracy are remarkably improved, and a foundation is laid for subsequent analysis and classification; further utilizing an attention mechanism and a deep learning technology to accurately position and classify defects, and triggering deep scanning through a priority index to improve the detection precision and the system adaptability; and finally, combining with a random forest algorithm to analyze defect influence, and feeding back and optimizing production parameters through a dynamic adjustment mechanism, thereby realizing production optimization closed-loop management, and improving production efficiency and product quality detection.

The invention discloses a large-scale unmanned aerial vehicle marshalling method for an emergency fire rescue task, and the method comprises the steps: carrying out the clustering analysis of task points of a rescue region through an FCM algorithm, determining the membership degree of a flight task, and generating a task region; on the basis of real-time environment change of a fire scene, a dynamic membership updating task allocation state is realized through a visual detection algorithm of fusing a convolutional neural network with an FCM; meanwhile, through 3DT route planning optimization based on the laser radar technology, a 3D model recombination map model is established, linear optimization is carried out by introducing time parameters, and optimal path planning is provided for the unmanned aerial vehicle in real time. In the rescue process, data are automatically transmitted in a segmented mode through the slicing technology of the 5G network, an autonomous Mesh centerless network topology structure is implemented when signals are intense, real-time signal optimization is conducted through a repeater of the high-altitude unmanned aerial vehicle, and high efficiency and flexibility of rescue are ensured.

The invention relates to the field of voice signal processing, and discloses a multi-level labeling method and device in a dialogue scene. Through the endpoint detection and automatic speech recognition method of the dialogue audio data, noise and mute in real speech can be removed, and effective speech segments can be obtained. And carrying out clustering analysis based on the effective voice segments, obtaining target voice segments and text data of the corresponding speakers, and carrying out emotion feature extraction through multi-modal emotion analysis. And finally, realizing automatic annotation of the inquiry strategy or intention understanding of the corresponding speaker by utilizing a large language model. According to the method, the recorded conversation voice can be subjected to multi-level labeling, an efficient and accurate information management and analysis tool is provided for a conversation scene, and the arrangement efficiency of conversation records can be greatly improved. The annotation information can be used for training and improving an artificial intelligence system, so that the artificial intelligence system plays a greater role in future psychological consultation service, and the overall quality and efficiency of psychological health service are improved.

The invention relates to the technical field of brand promotion, in particular to a brand promotion method and system based on user positioning, and the method comprises the following steps: collecting user browsing history, purchase records and social media interaction data, carrying out the statistical analysis of the data, recognizing the field in which a user is interested, and constructing and generating a user interest recognition result. In the invention, through detailed analysis and real-time updating of user data, a dynamic user portrait can be constructed, promotion content can be accurately customized, advertisement content is ensured to be highly related to current interest of a user, a user behavior mode is revealed through clustering analysis, key features are extracted, an advertisement system is allowed to adaptively adjust content and putting time, and user experience is improved. According to the method, the personalized degree and effect of advertisements are remarkably improved, dynamic advertisement configuration is automatically optimized according to real-time changes of user behaviors, promotion activities can accurately aim at potential key user groups, the conversion rate and the user interaction rate are increased, and finally more effective market penetration is achieved.

The invention discloses a lithium ion battery state of health (SOH) estimation method based on feature optimization, and the method comprises the following specific steps: obtaining the experimental data of a lithium ion battery, extracting initial health features reflecting the degradation of the battery, and carrying out the normalization processing of the initial health features; performing screening and dimension reduction on the preliminary health features by adopting a Spearman correlation coefficient and a grey correlation analysis method, and selecting six features with the highest SOH correlation degree; using an improved fully adaptive noise set empirical mode decomposition and sample entropy method to decompose and reconstruct health features, adopting t-SNE to perform transformation and dimension reduction on sample entropy features, using k-means + + clustering analysis, optimizing feature data and dividing the feature data into a training set and a test set; establishing a mapping relation between the optimized health features and the SOH through a bidirectional long short-term memory network (BiLSTM); a sparrow search algorithm (SCSSA) fusing sine, cosine and Cauchy variation is adopted to optimize BiLSTM parameters; and constructing an SOH estimation model based on SCSSA-BiLSTM, and obtaining a battery health state estimation value.

The invention relates to the technical field of image processing, and discloses a printed matter detection method and device. The method comprises the following steps: acquiring multispectral image data of a printed matter to be detected in different spectral bands; performing spectral feature extraction and multi-level fusion on the multispectral image data to obtain a comprehensive spectral feature map; acquiring sample spectral feature maps of a plurality of sample printed matters to perform frequent feature mode extraction, generating a spectral fingerprint database, and performing region division and matching degree search to obtain a reference spectral fingerprint map; carrying out multi-scale decomposition on the comprehensive spectrum feature map and the reference spectrum fingerprint map to obtain a plurality of scale layers, and carrying out local spectrum similarity calculation and adaptive threshold segmentation to obtain a binary mask map of a defect region; performing spectral feature clustering analysis to obtain defect categories; and performing defect root cause analysis and process adjustment on the printing process parameters to generate a process adjustment scheme, thereby improving the printing quality detection accuracy of the printed matter.

The invention discloses an AI-based cultural tourism content personalized recommendation system, which relates to the technical field of tourism planning and comprises a user information acquisition module, a cultural tourism content database information storage module, a recommendation engine module, an emotion analysis module, a real-time recommendation updating module and a multi-channel display module. According to the method, basic information, tourism preference, historical behaviors, geographic positions and social media data of users are collected, personalized portraits are generated and accurately recommended, sentiment analysis, real-time positions and dynamic behaviors are combined, the recommended content is continuously optimized by the system to adapt to the changing requirements of the users, and the user experience is improved. Through multi-channel display, personalized labels and clustering analysis, the system provides customized tourism suggestions, supports collaborative filtering and content recommendation strategies, real-time feedback and a self-optimization mechanism to improve the precision, adapts to intelligent equipment, recommends tourism contents according to environmental changes, and improves the user experience.

The invention relates to the technical field of cable asset management and fault prediction, and discloses a cable fault positioning method based on deep learning clustering analysis test waveform characteristics, and the method comprises the steps: collecting waveform and environment data in a cable operation period, and constructing a historical feature library comprising waveform, environment and position features; a self-adaptive detection model is adopted, and parameters are dynamically adjusted to adapt to different working conditions; multi-dimensional feature fusion and matching analysis are combined; a fault point distance is calculated through a signal propagation model and a time difference positioning algorithm, precise positioning is realized by fusing environment compensation and multi-point cross validation, and a three-dimensional geographic coordinate is generated by combining a laying path; and after multiple verifications, a structured report containing a fault type, a risk level, a prediction position, confidence and operation and maintenance suggestions is generated. According to the system, intelligent monitoring, fault risk prediction, asset optimization management and operation and maintenance decision support of a cable operation state are realized, and scientificity and economy of cable management in a complex environment are improved.

The invention provides a lower limb and gravity center dynamic balance posture early warning method based on a body measurement all-in-one machine, and the method comprises the steps: obtaining a pelvis forward inclination angle, an arch height and a gravity center position under a standard standing posture, taking the pelvis forward inclination angle, the arch height and the gravity center position as individualized baseline data, and carrying out the statistical analysis of the baseline data, and generating a dynamic balance reference value reflecting the normal balance capability of an individual; on the basis of the dynamic balance reference value, in combination with a gravity center forward trend and a lower limb muscle compensation mode, a balance offset index in standing posture change is calculated through a time sequence analysis algorithm, and a dynamic balance risk assessment result is obtained; and if a dynamic balance risk assessment result exceeds a danger threshold, clustering analysis is carried out on associated parameters of the pelvic forward inclination angle and the arch height, grouping processing is carried out on the associated parameters, and potential reasons causing balance capability degradation are identified.

The invention discloses a marketing data link abnormity diagnosis method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of multi-source data format difference, lack of real-time anomaly detection mechanism and low data management efficiency exist in anomaly diagnosis. The method comprises the steps that multi-source heterogeneous data of a marketing data link is collected in real time, and data cleaning, missing value filling and abnormal value processing are completed; extracting statistical characteristics of the link data, performing clustering analysis on the data, and generating a self-supervision label; constructing a support vector machine classification model, and detecting normal and abnormal data in the link; optimizing the anomaly detection model through a deep neural network; and realizing interpretability analysis and decision support of anomaly diagnosis by using a multi-classification result generated by the LSTM model and a self-supervised learning strategy. According to the invention, data link abnormity diagnosis is efficiently and intelligently realized, and data quality management, abnormity detection precision and service decision support capability are improved.

The invention discloses an intelligent online learning system and method for multi-device energy consumption optimization, particularly relates to the field of multi-device energy consumption optimization, and is used for solving the problems of energy consumption fluctuation and low operation efficiency caused by non-optimization of a feedback path. Extracting feedback path weights and action chain features, and analyzing a dynamic interaction relationship between devices; clustering analysis and reinforcement learning are combined to optimize classification boundaries, a high-risk feedback mode is marked, an optimization template is generated, and a basis is provided for path adjustment and strategy optimization; by dynamically adjusting the feedback path weight, generating an equipment adjustment instruction and combining with a feedback optimization template, realizing efficient control of equipment cooperative operation; therefore, the negative influence of a high-risk feedback path on operation is effectively inhibited, the energy consumption cumulative effect caused by feedback enhancement between equipment is reduced, the equipment cooperation relation is optimized, and the energy consumption distribution efficiency and the operation stability of the system in a dynamic load environment are improved.