3881results about How to "Improve effectiveness" patented technology

The invention provides an intelligent task alarm rule self-learning method and system based on support priority, and relates to the technical field of intellectualization, and the method comprises the steps: constructing a bidirectional feature extraction network to collect task operation data, and combining historical alarm data to generate a task exception association model; and carrying out migration mapping on the model and a business scene knowledge base by utilizing a migration learning algorithm, generating a task priority quantitative index, and constructing a self-adaptive priority scoring system. And training an alarm rule generator based on the model and a scoring system, generating a multi-level alarm strategy tree, and realizing differential alarm. And the incremental learning engine monitors a task state in real time, dynamically adjusts an alarm response strategy and feeds back data to optimize a rule. A federal learning mechanism is adopted to cooperatively train a model, and the generalization ability and adaptability of alarm rules are improved, so that more accurate and efficient task alarm is realized, the false alarm rate is reduced, and the operation and maintenance 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 relates to the technical field of data processing, and discloses an industrial one-stop multi-source data acquisition and monitoring system and equipment, and the system comprises a data acquisition module which is used for obtaining multi-source industrial monitoring data and generating a data stream input node; the data preprocessing module is used for processing the input data in the data flow model to generate a standardized data flow; the data conversion module is used for dynamically converting data nodes in the standardized data stream according to different data protocols to generate a compatible data stream; the task scheduling module is used for optimizing the data flow and distributing the data flow to a target node according to the compatible data flow in combination with a node priority and a dynamic scheduling strategy in the data flow model; and the exception detection module is used for carrying out exception state analysis on the optimized data stream and judging whether an exception exists or not according to a preset rule. According to the technical scheme, the compatibility and reliability of the monitoring system can be improved.

The invention provides a wireless signal and thermal imaging multi-modal cooperative detection and directional interference method and system, and relates to the technical field of electronic countermeasures, and the method comprises the steps: carrying out the time domain synchronization and spatial domain registration of wireless signal data and infrared thermal imaging data, constructing a multi-modal feature fusion network, and carrying out the wireless signal and thermal imaging multi-modal cooperative detection and directional interference. And an attention mechanism and a recurrent neural network are adopted to extract time sequence features of the target, and position coordinates, motion states and behavior pattern information of the target are output. And then, based on a target state evaluation model and a reinforcement learning algorithm, an intelligent interference strategy is designed, and an interference beam is dynamically adjusted through an adaptive beam forming technology, so that accurate directional interference on the target is finally realized. The target detection precision and the interference efficiency can be effectively improved, the false alarm rate is reduced, and the target identification and interference capability in a complex environment is enhanced.

The invention discloses a geological disaster deformation monitoring method based on remote sensing data, and relates to the technical field of environment monitoring, and the method comprises the following steps: S1, data source selection and preprocessing; s2, region feature extraction; s3, differentiating the multi-temporal remote sensing images; s4, analyzing a time sequence; s5, performing three-dimensional reconstruction on the deformation area; s6, settlement and landslide monitoring; s7, performing fusion analysis on environment and meteorological data; s8, performing data anomaly detection and automatic early warning; and S9, risk assessment and influence analysis. According to the geological disaster deformation monitoring method based on the remote sensing data, SAR, optical images and LiDAR data are combined, the defect of dependence of a single data source in the prior art is overcome, dynamic data fusion and time sequence analysis are used, multi-dimensional real-time monitoring of earth surface deformation is achieved, the monitoring precision and timeliness are improved, and the geological disaster deformation monitoring method based on the remote sensing data has good application prospects. More comprehensive geological disaster early warning can be provided, and the effectiveness of disaster prevention and control is greatly enhanced.

The invention provides a space competition situation threat level evaluation method and system based on credibility weighting, and relates to the technical field of information, and the method comprises the steps: constructing a threat feature vector based on historical orbit data and an operation state, and obtaining an initial target threat probability vector through a deep learning model; then, a credibility weighting coefficient is calculated based on the multi-source situation awareness data, and an initial threat probability vector is corrected in combination with a feature weighting coefficient; and finally, classifying target threat levels by adopting an integrated learning method, determining a target priority in combination with the target orbit parameters and an ant colony algorithm, and outputting a threat target sequence and a situation threat assessment report. By fusing the multi-source situation awareness data and the credibility weighting mechanism, the threat level of the space target can be evaluated more accurately, and the reliability and efficiency of space situation awareness are effectively improved.

The invention discloses an image self-supervised learning method and system fusing contrast learning and feature mask modeling. An original image which is low in cost and does not need to be labeled is used as training data, a double-branch self-supervised learning network is constructed, one branch is a comparison learning branch based on a SimCLR framework, the other branch is a mask self-encoder branch based on a mask feature model, and the whole network comprises an image preprocessing module, an image feature extraction module and a shared weight encoder. And carrying out self-supervised training by combining the two branches with a loss function specially designed for the two branches, and finally obtaining a pre-training model capable of fully extracting image features. According to the method, the advantages of comparative learning and feature mask modeling are combined, information is effectively extracted from unlabeled image data, and image understanding is enhanced. The method has the main advantages that the robustness is high, the method is particularly suitable for limited data sets in the remote sensing and medical fields, the model precision is improved, and the data requirement and labeling cost are reduced.

The invention relates to the technical field of pile foundation engineering, in particular to a construction method of a long-pile wharf pile foundation lateral displacement prediction model. The method comprises the following steps that displacement, stress and load parameter monitoring is conducted on a pile foundation of the high-pile wharf, and real-time monitoring parameter data is generated; performing working condition identification on the pile foundation structure through the real-time monitoring parameter data to generate working condition data of the pile foundation structure; multi-source load characteristic analysis is conducted on the pile foundation structure working condition data, load coupling effect calculation is conducted, and load action data are generated; performing soil-pile interaction analysis according to the load action data to generate soil-pile response data; and performing material performance deterioration evaluation according to the soil-pile response data to generate material state data. The working condition identification method based on the multi-source monitoring data is established, comprehensive perception of the pile foundation structure state is achieved, and a reliable data basis is provided for subsequent displacement prediction.

The invention discloses a short video advertisement putting optimization evaluation method based on deep learning, and relates to the technical field of advertisement putting, and the method collects user behavior data in real time, constructs a dynamic user portrait, enables an advertisement system to adjust the advertisement content and putting strategy in time according to the interest change of a user, improves the correlation of advertisements, and improves the advertisement putting efficiency. Meanwhile, user behavior mode data and advertisement content labels are subjected to multi-dimensional matching, and the matching degree of the advertisements and user interests is calculated by combining cosine similarity with a weighting algorithm, so that advertisement recommendation is more personalized and accurate, the advertisement click rate and conversion rate are greatly improved, and the advertisement recommendation efficiency is improved. Meanwhile, the optimal opportunity of advertisement display is defined by analyzing the exit tendency of the user and the advertisement clicking tendency, it is guaranteed that the advertisement can be displayed at the moment when the user is most likely to interact and click, and the problem of low conversion rate caused by poor opportunity in a traditional method is solved.

The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster activity target monitoring method based on dynamic visual tracking, which comprises the following steps of: adjusting multiple shooting parameters of a camera according to illumination intensity difference in an environment based on surface image acquisition equipment, and recording time points and geographical location information of image shooting in real time; a sequence of geo-tagged images is generated. According to the method, camera parameters are adjusted according to real-time illumination conditions, the quality of surface images is optimized, the monitoring and recognition capability of surface cracks and deformation is improved, and data support is provided for prediction and type recognition of geological disasters through multi-time-point image analysis and calculation of displacement speed and sedimentation rate, so that early warning is more accurate and targeted, and the early warning efficiency is improved. According to the method, the monitoring data and the topographic features of the target area are combined, the disaster type is accurately recognized, the development trend is predicted, dynamic monitoring and pre-judgment of geological disaster activities are achieved, the effectiveness of prevention measures is enhanced, and potential threats to human life and property are reduced.

The invention discloses an ear-nose-throat nursing scheme intelligent recommendation system based on cloud computing, and relates to the technical field of big data analysis. The intelligent recommendation platform is in communication connection with a data acquisition and preprocessing module, a knowledge graph construction module, a patient portrait construction module, a nursing scheme recommendation module, an evaluation and adjustment module and a user interaction and feedback module. According to the intelligent recommendation system for the ear-nose-throat nursing scheme based on cloud computing, deep analysis and mining are carried out on real-time physiological data, ear-nose-throat related symptoms, wound healing conditions and function recovery progress of patients by utilizing the powerful data processing capability of a cloud computing platform, individual differences and rehabilitation requirements of the patients are accurately recognized, and the intelligent recommendation system for the ear-nose-throat nursing scheme based on cloud computing is provided for the patients. And personalized nursing schemes are customized for the patients, so that the pertinence and effectiveness of nursing are improved, and the prolonged rehabilitation time and the discomfort of the patients caused by the improper schemes are greatly reduced.

The invention discloses a medical field large model pre-training and fine tuning method and device based on a knowledge graph, and the method comprises the steps: training a reward model through employing collected medical related text data and a quality marking result, and screening out high-quality data from the newly obtained medical related text data through employing the trained reward model; coding the medical knowledge graph into a text format processed by a large language model, and alternately inputting the high-quality data and the coded medical knowledge graph into the large language model in different batches for model pre-training; and designing an instruction fine tuning data format comprising an instruction, a knowledge graph reasoning path and a generated answer, inputting a training sample constructed in the instruction fine tuning data format into the pre-trained large language model for model fine tuning, and outputting a final answer and reasoning path according to the instruction newly input by the user by utilizing the fine-tuned large language model. According to the method, the professionality of the large language model in the medical field can be effectively improved, and AI application in high-risk fields such as medical treatment is promoted.

The invention relates to the technical field of image classification, in particular to a fetal heart monitoring image classification method and system based on a neural network. The method comprises the following steps: acquiring a fetal heart monitoring image, generating a standard monitoring image through Gaussian noise elimination and spatial domain normalization, then performing multi-channel convolution on the standard image to extract image convolution features, obtaining standard fetal heart features through compression regular optimization, performing morphological reconstruction to generate a fetal heart dynamic model, and finally obtaining the fetal heart dynamic model. Baseline features are identified based on historical data, then periodic variation quantification and safe floating range derivation are performed on fusion features, and finally a multi-scale discrimination framework is constructed to realize intelligent classification of fetal heart monitoring images. The fetal heart monitoring image classification method is more stable and more accurate.

The invention relates to the technical field of electric power peak regulation of energy storage equipment, in particular to a dispatching energy management system for assisting electric power peak regulation, which comprises a load prediction unit, a dynamic adjustment unit and a model optimization unit. A dynamic adjustment unit monitors multiple parameters of an energy storage equipment power system through principal component analysis dimensionality reduction, a peak regulation threshold value of the energy storage equipment is dynamically adjusted according to working conditions and load prediction in combination with a fuzzy logic reasoning model, and a model optimization unit analyzes operation data, compares deviation, adjusts fuzzy rules and evaluates the peak regulation effect of the energy storage equipment. The method can flexibly adjust the threshold according to the system operation parameters and the load prediction, improves the peak regulation accuracy of the energy storage equipment, guarantees the stable operation of a power grid and the efficient utilization of energy, further optimizes the peak regulation effect of the energy storage equipment through the feedback improvement rule, and solves the problem of insufficient adaptability of the peak regulation strategy of the existing energy storage equipment.

The invention relates to a shared energy storage virtual inertia compensation control method for wind power cluster collection, and belongs to the technical field of power system control. In order to solve the problem of insufficient cooperative control of a wind power system and an energy storage system in the prior art, a dynamic model is established by obtaining operation parameters, and the dynamic model comprises a wind turbine generator active power control model and an energy storage system charging and discharging model; designing a virtual inertia control strategy, and introducing virtual inertia control to simulate inertia response characteristics of the synchronous generator; shared energy storage coordination control is carried out, and a strategy is designed by using an MPC method according to an inertia demand and an SOC state; establishing a frequency dynamic response model and calculating inertia response time as a virtual inertia control start-stop condition; and optimizing controller parameters and designing locking logic by adopting an intelligent algorithm. The method can improve the system performance and efficiency, enhance the frequency stability, improve the energy utilization efficiency of the energy storage system, optimize the system performance, and improve the system stability and reliability.

The invention relates to the technical field of artificial intelligence, and discloses an abnormal behavior real-time early warning method and system based on intelligent security, and the method comprises the steps: determining a monitoring environment feature set corresponding to a security monitoring region, and formulating a behavior early warning threshold value corresponding to security monitoring equipment; collecting dynamic behavior information of personnel in the security and protection monitoring area, analyzing the behavior modal analysis dimension corresponding to the security and protection monitoring equipment, and evaluating the area security situation corresponding to the security and protection monitoring area; analyzing physical feature attributes corresponding to the personnel in the security and protection monitoring area, analyzing action mode features corresponding to the personnel in the security and protection monitoring area, and calculating behavior risk entropies corresponding to the personnel in the security and protection monitoring area; analyzing the monitoring adaptation degree of the security and protection monitoring equipment in the security and protection monitoring area; and formulating an abnormal behavior early warning strategy corresponding to the security and protection monitoring area, and executing abnormal behavior real-time early warning in the security and protection monitoring area to obtain an early warning result. According to the invention, the real-time early warning efficiency of abnormal behaviors under intelligent security is improved.

The invention provides a landslide displacement space-time prediction method, device and system and a storage medium, and relates to the technical field of engineering geology, and the landslide displacement space-time prediction method comprises the steps: obtaining the historical landslide multi-field time sequence monitoring data of each monitoring point of a to-be-measured region, constructing a monitoring point bitmap based on the historical space related data, constructing a time sequence characteristic matrix according to the historical displacement time sequence, the historical landslide external disturbance factor time sequence and the monitoring point bitmap; inputting the time sequence characteristic matrix and the monitoring point bitmap into a trained landslide displacement space-time prediction model to obtain a landslide displacement space-time prediction result; the landslide displacement space-time prediction model is constructed based on an improved GCN network and an improved BiLSTM network; according to the method, the space-time prediction precision of landslide displacement can be remarkably improved by combining the space correlation data and the external disturbance factors and utilizing the capability of the deep learning model.

The invention discloses a risk early warning method and device constructed based on dynamic rules, equipment and a medium, and relates to the technical field of risk early warning, and the method comprises the steps: extracting a user static attribute and a dynamic behavior sequence, constructing an original data set, carrying out time sequence analysis, generating a behavior time sequence response feature, carrying out cross-modal feature fusion, and constructing a space-time perception behavior vector; constructing a dynamic rule graph structure according to the risk rule and the corresponding standard space-time perception behavior vector, iteratively updating a risk rule weight based on a graph convolutional network, constructing a wavelet domain risk quantification model, and generating a multi-scale risk value; and inputting the newly generated space-time perception behavior vector into the wavelet domain risk quantification model, generating a to-be-judged multi-scale risk value, and performing risk early warning when a threshold condition is not met. Through time sequence analysis and a multi-scale risk quantification model, the accuracy of risk identification is improved, and multi-dimensional and multi-scale risk analysis is supported, so that the accuracy and real-time performance of risk management are effectively improved.

The invention provides an advertisement recommendation method and system based on deep reinforcement learning. The method comprises the following steps: receiving a real-time interaction signal of a user, analyzing time sequence characteristics in the real-time interaction signal to extract behavior characteristics of the user, and identifying a contextual model of the user; modeling the interest preference of the user to generate a personalized interest dynamic graph; performing intelligent exploration and decision processing on the optimal advertisement content and the corresponding pushing opportunity to obtain an advertisement recommendation strategy; calculating the optimal pushing opportunity corresponding to the optimal advertisement content from the optimal advertisement content and the corresponding pushing opportunity, and pushing the optimal advertisement content to the user based on the optimal pushing opportunity; establishing a real-time feedback mechanism to obtain an adjusted advertisement recommendation strategy; and performing comprehensive evaluation processing on the advertisement effect of the adjusted advertisement recommendation strategy to generate an advertisement effect evaluation result. According to the technical scheme provided by the invention, the flexibility and efficiency of advertisement recommendation can be improved.

The invention discloses an operation and maintenance mode analysis model generation method and system, a medium and a program product, and relates to the field of electric digital data processing. Establishing an equipment basic life model based on the equipment fault time data and the non-fault working time data; calculating an environment correction coefficient according to the equipment operation environment parameter data and a preset environment influence factor, and combining the environment correction coefficient with the basic life model to obtain a corrected life prediction model; calculating the correlation degree of each equipment node based on the equipment topological structure, and constructing an equipment importance evaluation model; calculating equipment maintenance cost, replacement cost and shutdown loss according to a prediction result of the corrected life prediction model to obtain a cost loss model; and carrying out weighted fusion on an output result of the equipment importance evaluation model and a calculation result of the cost loss model to generate an operation and maintenance mode analysis model. The scientificity and accuracy of operation and maintenance mode analysis are improved.

The invention relates to the technical field of computer television image communication, in particular to a computer television all-in-one machine control method, system and device. The method comprises the following steps: through multi-view image acquisition and processing, generating a three-dimensional scene grid and carrying out scene element analysis to form a scene element set, then chasing limbs of a user, generating a user limb image set, restoring an environment basic tone, obtaining display content of the all-in-one machine and carrying out content semantic analysis, and finally obtaining a user limb image set. Calculating interaction intensity to obtain content interaction intensity data, speculating a user body language and performing user intention prediction in combination with the interaction intensity data, adjusting display content of the all-in-one machine based on the predicted user operation intention, generating simulation adjustment content, performing composition optimization through a scene element set, and generating updated display content. The method for controlling the computer and television all-in-one machine is more accurate and more efficient.

The invention belongs to the field of disaster early warning, and provides a disaster early warning method and system based on key target detection and image segmentation, and the method comprises the steps: obtaining the real-time image data of a site, and carrying out the preprocessing of an image; processing the preprocessed image through a YOLOv5 target detection algorithm, detecting a key target in a disaster site, and outputting a category label and a bounding box of the target; performing pixel-by-pixel segmentation processing on a bounding box region output by target detection by using a U-Net image segmentation algorithm to generate an accurate segmentation mask of the target; and inputting the image segmentation mask, historical disaster data, meteorological data and geographic information system data into an LSTM prediction model, carrying out multi-source data fusion and time sequence analysis, and outputting dynamic prediction information and early warning levels of disasters. According to the invention, real-time monitoring and early warning of disasters are realized, the response speed of the early warning system is obviously improved, more comprehensive disaster early warning information is provided, high precision of disaster prediction is ensured, and the phenomena of false alarm and missing alarm are reduced.

The invention provides a digital twinborn model and method for a coal mine disaster scene based on multi-modal data. The method comprises the following steps: acquiring original data in a mine; performing cleaning, normalization and de-noising preprocessing on the original data; fusing different types of preprocessed original data by using a data fusion algorithm, and constructing a comprehensive digital twinborn model; establishing a virtual mirror image of the mine by using a digital twinning technology, and mapping the mine in the real world into a digital twinning model; real-time mapping and simulation analysis are carried out based on the established digital twinborn model, and the disaster development trend is predicted by monitoring the internal environment and the equipment state of the mine; and according to a model analysis result, optimizing prevention and control measures of coal mine disasters, establishing a real-time monitoring and early warning system of the disasters, and taking measures in time when abnormal conditions are found. The system comprises a data acquisition module, a model construction module and a model analysis module. The pertinence and effectiveness of coal mine disaster prevention and control can be improved.

The invention discloses an information management method and system for a smart ward, a medium and a program product, and relates to the field of electrical digital data processing.The method comprises the steps that physiological index data and behavior characteristic data of a target patient are collected, and a characteristic data sequence is constructed; analyzing each piece of data in the feature data sequence, and determining a current characterization behavior and a behavior change sequence of the target patient; obtaining individual information of a patient, and inputting the individual information of the patient, the current characterization behavior and the behavior change sequence into a behavior evaluation model to obtain a risk characterization behavior and a behavior risk score; determining an early warning level and an early warning response time limit based on the behavior risk score; when the early warning level is higher than a preset early warning threshold value, generating a task execution list according to the early warning response time limit and the risk characterization behavior, and sending the task execution list to the medical care terminal; and updating the behavior risk score according to an early warning response result returned by the medical terminal. By implementing the application, patient state monitoring of the smart ward can be optimized, and potential safety hazards can be avoided to a greater extent.

The invention relates to a multi-modal implicit sentiment analysis method based on a multi-granularity attention mechanism, which belongs to the field of artificial intelligence, and comprises the following steps: S1, extracting visual features in image information by using a visual feature extraction strategy of multi-scale attention fusion; s2, generating a text description of the image information by adopting an image translation engine, splicing the text description with an original text, and extracting text features through a hierarchical attention mechanism; s3, carrying out normalization processing; s4, performing multi-modal fine-grained correlation fusion on the visual features and the text features by adopting a bidirectional cross attention mechanism, and extracting fine-grained features in the visual features and the text features through a multi-head attention mechanism and residual connection; and S5, fusing the fine-grained features by using an adaptive multi-modal classification module to complete sentiment classification. The multi-modal sentiment analysis method solves the problems that multi-modal sentiment analysis is insufficient in inter-modal correlation mining, insufficient in fine-grained information capture and large in noise interference.

The invention discloses a cloud computer end-to-end fault root cause analysis method and system, and relates to the technical field of operation and maintenance data analysis, and the method comprises the steps: deploying data collectors on a virtual resource pool, a multi-stage cloud management platform and a cloud computer terminal, and monitoring and collecting various operation and maintenance data in real time; a unified operation and maintenance management center is introduced to complete the following work: providing management and configuration services of the data collector; receiving and summarizing operation and maintenance data; various AI models are accessed, operation and maintenance data from different sources are distributed based on the types of the AI models, the AI models perform deep analysis and diagnosis on the operation and maintenance data, and potential fault modes or abnormal behaviors are identified; customizing an alarm strategy, an alarm processing flow and an alarm notification mode; and introducing an AIOps service to generate an alarm and fault analysis report according to the analysis and diagnosis results of the AI model. According to the method, the service quality of the end-to-end full link of the cloud computer is monitored in real time, so that faults can be quickly positioned, early warning can be performed in advance, and the operation and maintenance cost and the fault risk are reduced.

The invention discloses a method suitable for adding a control shunt under multiple scenes in a demand response execution process, and the method comprises the steps: obtaining real-time load data, historical load data, environment parameter data and user behavior data, and carrying out the processing through a tensor decomposition method and a support vector machine, and obtaining a scene feature vector and a scene type identifier; real-time load data and system constraint data are combined, a fuzzy comprehensive evaluation method and a graph neural network are adopted for processing, and a shunt response capability matrix and a control constraint matrix are obtained; processing by adopting a multi-objective optimization method to obtain an optimization objective function set, a weight coefficient matrix and a constraint condition set; and processing by adopting a genetic algorithm and a dynamic programming method to obtain a control strategy matrix and an execution time sequence vector. According to the invention, accurate control and dynamic optimization in the demand response process can be realized, and the execution efficiency and reliability of the control strategy are improved.

The invention relates to the technical field of genomics, in particular to a gene detection data cleaning method and system based on artificial intelligence. The method comprises the following steps: acquiring gene detection data, and performing preliminary filtering on the gene detection data based on removal of low-quality reads to obtain preliminary filtering data; performing linker sequence identification and removal on the preliminarily filtered data, and performing sequencing error correction to generate gene sequence data; performing GC content analysis and repetitive sequence identification on the gene sequence data to obtain sequence feature data; performing dynamic quality threshold adjustment according to the sequence feature data, and performing secondary filtering on the gene sequence data to generate high-quality sequence data; and performing sequence pattern recognition on the high-quality sequence data to obtain sequence pattern feature data. According to the method, deep analysis is carried out by utilizing sequence characteristics such as GC content and nucleotide distribution, and the recognition capability on sequence complexity and variation modes is improved.

The invention relates to the technical field of health monitoring, in particular to a health monitoring system based on wearable equipment, which comprises a data acquisition module, a health data analysis module, an emotion and sound analysis module and a health prediction feedback module. According to the health monitoring system, the prediction accuracy of the health monitoring system is effectively improved by combining time sequence analysis with a processing strategy of environmental factors, and more accurate health condition early warning, disease prevention and management strategy optimization and emotion and sound analysis integration are provided by comprehensively analyzing long-term health data and real-time environmental changes of the user, so that the user experience is improved. The emotion change is clarified through acoustic feature analysis, the understanding of the emotion state of the user is improved, the health monitoring dimension is enhanced, the participation degree and satisfaction degree of the user are improved through personalized health suggestion and real-time feedback, the practicability and effectiveness of the system are enhanced, and health management is more accurate and has interactivity.

The invention discloses a calculation method for multi-target well group precipitation optimization based on an intelligent algorithm. The calculation method comprises the following steps: constructing a foundation pit precipitation seepage-settlement mathematical model in a research area; performing parameter assignment and parameter correction on the model; establishing a target function; establishing a judgment matrix; determining each sub-target weight of the target function according to the judgment matrix to obtain a final target function, forming an optimization solution algorithm by the final target function and the genetic algorithm, and coupling the optimization solution algorithm with the established MODFLOW underground water foundation pit rainfall seepage-settlement mathematical model through Flopy to obtain a target function model. And the optimization solution algorithm continuously reads MODFLOW simulation results and performs iterative calculation until an optimization target is achieved. According to the method, the functions of opening and closing of the well, the single well water pumping amount and the like can be automatically optimized, a corresponding solution set is given, the influence of foundation pit dewatering construction on the environment is effectively reduced, the construction cost is reduced, and a new technical means is provided for modern foundation pit well group dewatering design.