104 results about "Adaptive learning algorithm" patented technology

The invention discloses a coal mine heat energy monitoring and cooling regulation and control method based on the Internet of Things, and relates to the technical field of coal mine heat energy monitoring and cooling regulation and control. The collected temperature data are transmitted to a central monitoring system in real time through an Internet of Things gateway; redundant temperature sensors are arranged at key positions, and data of the temperature sensors in the same area are regularly compared and verified. According to the invention, the monitoring robustness is improved through a redundant temperature sensor and a data fusion algorithm, a monitoring blind area caused by a single-point fault is avoided, and the accident risk is reduced; a self-adaptive learning algorithm is adopted to dynamically optimize a detection threshold value and a compensation strategy, so that the anomaly detection accuracy is improved, and false alarms and missing alarms are reduced; the temperature trend is pre-judged through a predictive control algorithm, cooling is conducted in advance, balance between safety and energy efficiency is achieved, overheating accidents are prevented, and energy consumption is reduced.

The invention discloses a geological disaster early warning method and a precise early warning system based on multi-source data fusion, and relates to the technical field of geological disaster early warning. According to the method, multi-source heterogeneous data such as remote sensing, meteorological and geological monitoring are fused, a standardized protocol is utilized to unify a data format and temporal-spatial resolution, a standardized data set is formed, key features are extracted by adopting principal component analysis and a recursive feature elimination algorithm, and a long-short-term memory network and a convolutional neural network model are combined, so that the real-time performance of the system is improved. According to the method, the disaster risk is accurately predicted, the space risk distribution diagram is generated, in addition, through application of the real-time stream processing framework and the self-adaptive learning algorithm, rapid distribution of early warning signals and dynamic optimization of model parameters are achieved, the accuracy and timeliness of an early warning system are remarkably improved, and the geological disaster risk is effectively reduced.

The invention relates to the technical field of data processing, and discloses a multi-protocol fusion Internet of Things equipment intelligent gateway data conversion method and system. The method comprises the following steps: collecting a multi-protocol equipment data packet, and extracting protocol features to construct a vector library; protocol types are identified based on the vector library, data are analyzed, and a data object set containing semantic tags is constructed; semantic correlation is analyzed through an adaptive learning algorithm, and a dynamic protocol semantic mapping matrix is established; converting the data into a standard format according to the mapping matrix and recording a matching degree to form a target data pool; and extracting fusion data from the data pool, recoding according to a target protocol format, and outputting a data frame. The problem that an existing multi-protocol fusion data conversion method lacks protocol semantic understanding and self-adaptive learning ability is solved, and semantic consistency and conversion quality of data conversion among multi-protocol equipment are improved.

The invention discloses a power enterprise intelligent collaborative office method and system based on artificial intelligence, and relates to the technical field of power informatization. According to the intelligent collaborative office method and system for the power enterprise based on artificial intelligence, request data is received from a user terminal through a deep learning model, a service demand type, an emergency degree and task complexity are identified, a service priority index is generated, classification and priority distribution are carried out, and based on the service priority, the user terminal is subjected to intelligent collaborative office. The method comprises the following steps of: matching associated tasks and resource requirements through a model, retrieving related historical data to optimize a resource configuration path, dynamically tracking task progress, execution state and resource consumption, identifying a bottleneck in an execution process and generating an optimization instruction, and finally, dynamically adjusting model parameters and resource configuration rules through an adaptive learning algorithm. And a closed-loop feedback mechanism is formed. Information flow and resource configuration are effectively integrated, the flexibility and response speed of collaborative office are improved, and the development requirements of smart grids and smart cities are met.

The invention relates to the technical field of water and soil conservation monitoring, and discloses a remote sensing image-based water and soil conservation monitoring method, which comprises the following steps of: 1, integrating optical, infrared and microwave remote sensing data, and constructing a soil information atlas; step 2, capturing soil humidity change in each season by using long-time-sequence data according to the soil information, and determining the influence of rainfall, snow melting and evaporation on the reflectivity; step 3, training data of each wave band through a deep neural network according to the influence information of the reflectivity; through an adaptive learning algorithm and a model parameter regular updating mechanism, the problem that in the prior art, characteristic spectrums of wet soil, vegetation and bare soil are overlapped due to seasonal changes is effectively solved, after the seasonal modulation factors are introduced, the system can accurately capture influences of natural factors such as rainfall, snow melting and evaporation on the soil reflectivity, and the soil reflectivity is improved. And model parameters are adaptively adjusted, so that the interference of reflectivity fluctuation on a monitoring result in wet and drought states is eliminated, and the stability of long-term monitoring is ensured.

The invention relates to the technical field of energy efficiency optimization, and discloses a lamp energy efficiency optimization method based on artificial intelligence, and the method comprises the steps: data collection: carrying out the all-around and multi-dimensional data collection of an environment where a lamp is located through a plurality of sensors. According to the lamp energy efficiency optimization method based on artificial intelligence, in order to make energy utilization efficient, the multi-dimensional environment and lamp operation data are obtained through the data acquisition step, optimization is carried out in combination with the adaptive learning algorithm, lamp energy efficiency control parameters can be adjusted in real time according to different environments, places, time periods and other situations, and the energy efficiency optimization efficiency is improved. The lamp can be kept in the optimal energy efficiency state in various complex and changeable environments, the energy utilization efficiency of the lamp is effectively improved, energy waste is reduced, in the scene-based energy efficiency optimization link, for different application scenes, differentiation strategies are implemented according to respective typical lighting requirements and energy-saving potentials, parameters such as lamp brightness are accurately adjusted, and the energy efficiency of the lamp is improved. And the energy utilization efficiency of the lamp in various scenes is improved.

The invention relates to the technical field of hydraulic ring surveying and mapping, in particular to a hydraulic ring surveying and mapping data acquisition method and system based on geographic space information, and the method comprises the following steps: S1, data acquisition and preliminary data set generation: generating an initial geographic space data set; s2, space-time alignment and coupling feature extraction: constructing a preliminary geographic space model; s3, dynamic verification and cross verification: generating a dynamic verification parameter set; s4, three-dimensional geologic modeling and parameter fusion: generating a comprehensive three-dimensional geologic model; s5, environment interference correction and data optimization: analyzing the offset of an environment interference factor to sensor data through an adaptive learning algorithm, and outputting corrected hydraulic ring surveying and mapping data; and S6, safety threshold comparison and early warning map generation: generating a visual early warning map. According to the method, deep support is provided for underground structure analysis, and the comprehensive capability of hydraulic ring surveying and mapping is improved.

The invention discloses a UWB-based multifunctional integrated vehicle-mounted safety control method and system, and the method comprises the following steps: collecting the dynamic environment data around and in a vehicle in real time based on a plurality of sensors, carrying out the path adjustment and driving mode optimization through generating a multi-level three-dimensional environment model, and carrying out the control of the driving mode. The system performs risk assessment and dynamic adjustment by using external data such as obstacles, road conditions, weather and the like collected by a sensor and combining a behavior mode of a driver and a health state of a passenger, and in a collision prediction and protection mechanism, the system gradually activates a protection system by analyzing factors such as vehicle speed and acceleration, so that the safety is guaranteed, and the safety is ensured. The system optimizes the energy distribution of a key system through an energy scheduling model, improves the energy efficiency, and preferentially guarantees the operation of a safety system in an emergency. According to the invention, a self-adaptive learning algorithm and a multi-scene optimized driving assistance function are integrated, and self-adaptive adjustment and safety control of the vehicle in different driving environments are realized.

The invention provides a rail transit equipment fault prediction method and system, and the system comprises a data perception and fusion layer which is used for collecting and fusing multi-source heterogeneous data; the digital twinning and intelligent prediction layer is used for realizing accurate prediction and early fault early warning of the health state of the equipment by constructing a lightweight digital twinning model of the equipment and combining a space-time diagram attention network and a self-adaptive learning algorithm; the interpretable analysis and decision support layer is used for analyzing the root cause of the prediction result and providing visual analysis; and the maintenance linkage and execution layer is used for automatically generating a maintenance strategy and scheduling resources according to a prediction result, relates to the technical field of rail transit operation and maintenance, and overcomes the defects of single data, static model, weak early warning capability, poor interpretability and disjunction between prediction and maintenance in the prior art. And the full-life-cycle, self-adaptive and intelligent management of the fault prediction of the rail transit equipment is realized.

The invention provides an efficient crack detection and intelligent risk assessment system, belongs to the technical field of structural health monitoring, and is particularly suitable for crack detection in buildings, bridges, tunnels and industrial equipment. According to the system, comprehensive detection on the surface and the interior of a crack is realized by combining an optical imaging sensor, a laser scanning sensor and an ultrasonic sensor through a multi-mode sensor fusion technology. The system comprises a data acquisition unit, a data processing unit, a multi-modal data fusion module, a three-dimensional modeling module and an intelligent decision support module. Through an adaptive learning algorithm and a deep learning technology, the system can dynamically optimize a detection model and provide a high-precision detection result. Meanwhile, the system supports three-dimensional reconstruction and visual display of the crack, carries out real-time assessment on the crack risk through an intelligent decision support module, automatically generates a health assessment report and gives out an early warning. Compared with traditional manual detection, the system has the advantages that the comprehensiveness, the automation degree and the efficiency of detection are greatly improved, and the system is suitable for structural health monitoring in various complex scenes.

The invention discloses a touch and talk pen interactive content generation method based on adaptive learning. The method comprises the following steps: S1, acquiring a learning behavior data set of a user; s2, based on the learning behavior data set, constructing a dynamic knowledge graph of the user through an adaptive learning algorithm; s3, analyzing the learning behavior data set by using a query committee algorithm; s4, triggering a content generation module according to a bifurcation calculation result, wherein the content generation module generates learning content adaptive to the learning demand of the user based on the dynamic knowledge graph and the learning behavior data set of the user; s5, performing multi-mode interactive presentation of the learning content through the touch and talk pen; s6, performing feedback based on a learning behavior data set of the user for the learning content; s7, outputting a learning feedback report, wherein the learning feedback report is analyzed and generated based on the learning behavior data set of the user; and S8, repeating the steps S1 to S7. The interactive experience of the user is remarkably improved, the touch and talk pen is converted into an intelligent learning assistant from a passive content presentation tool, and efficient learning support and feedback are provided for the user.

The invention discloses an environmental pollutant toxicity detection and evaluation method, which comprises the following steps: deploying a multi-parameter sensor array in a target monitoring environment, and obtaining initial monitoring data; performing deep mining and pollutant toxicity feature extraction on the initial monitoring data to generate a pollutant feature data set; forming a customized evaluation model framework according to the target environment monitoring requirement, the preset monitoring parameters and the configuration of the evaluation model; inputting the generated pollutant characteristic data set into a preset customized evaluation model, and performing toxicity evaluation on the environmental pollutants to obtain a preliminary evaluation result; and automatically adjusting parameters of the customized evaluation model based on the obtained preliminary evaluation result by adopting an adaptive learning algorithm to obtain an optimized evaluation model, carrying out toxicity evaluation again to obtain an optimized evaluation result, and the like. The method adapts to monitoring requirements under different environmental conditions, and comprehensively evaluates the toxicity risk of pollutants.

The invention relates to the technical field of distribution network planning, and provides a distribution network planning index prediction method based on artificial intelligence, which combines three models to establish a multi-level model, and significantly improves the accuracy and efficiency of distribution network planning index prediction. The main trend and influence factors of distribution network planning are identified by using a multi-layer sensor (MLP), and the nonlinear modeling capability is provided. The time dependence and periodic change of data are captured through a time sequence model ARIMA, and the sensitivity to short-term fluctuation is ensured. A long short-term memory (LSTM) network captures a complex non-linear relationship and long-term dependence, and the prediction precision is further improved. And finally, through a Blending technology, weighted summation is carried out on prediction results of the models, so that maximum output of model advantages is realized, and the accuracy of a fusion result is optimized. Model parameters are dynamically adjusted through a real-time data flow mechanism and an adaptive learning algorithm. Therefore, the prediction is real-time, the method can adapt to continuously changing market and environment conditions, and high prediction precision is kept.

The invention discloses a dynamic course planning method based on an adaptive learning algorithm. The method comprises the following steps: S1, constructing a multi-dimensional data model of a learner knowledge space; s2, forming a knowledge graph represented by fractal characteristics; s3, identifying weak areas and potential knowledge blind spots in the knowledge space of the learner; s4, recursively decomposing the knowledge graph by using a fractal recursive algorithm according to a real-time knowledge state analysis result of the learner in combination with a learning target; s5, optimizing recommendation content and performing dynamic adjustment according to knowledge mastering conditions, weak knowledge points and learning behavior preferences of the learners; and S6, according to the learning resources recommended in the step S5, by monitoring the performance data of the learner in the learning process in real time and combining a performance data analysis result, dynamically adjusting a learning path and resource recommendation content, optimizing learning suggestions and updating the knowledge state of the learner in real time until course planning is finished. According to the method, the personalized learning requirement can be better met, and the learning effect and the cross-scene adaptability of the system are improved.

The invention discloses a training method and system for an abnormal transaction recognition model of an e-commerce platform, and particularly relates to the field of e-commerce. According to the method, multi-source transaction data are integrated, the accuracy of labeling is judged through cross validation, data labeling is divided into accurate labeling and inaccurate labeling, and after the inaccurate labeling is recognized, the similarity between prediction and an actual result is calculated and the performance stability of a model is evaluated by analyzing the sample proportion fluctuation condition of the model influenced by labeling errors; according to an evaluation result, the models are divided into a stable performance type and an unstable performance type, and different training strategies are adopted: for the stable performance model, the labeled data set is continuously used for training; and for the model with unstable performance, training is carried out in stages, pre-training is carried out by using accurate annotation data, then inaccurate annotation data is introduced, and optimization adjustment is carried out through an adaptive learning algorithm, so that the negative influence of annotation deviation on the model is effectively reduced, and the accuracy of the model in practical application is improved.

The invention discloses a wolfberry quality stability evaluation method and system based on a neural network, and the method comprises the steps: collecting wolfberry samples from different producing areas, obtaining the growth environment parameters and harvesting and processing information of the wolfberry samples, and building a correlation database of the producing area environment and the wolfberry quality, analyzing the influence rule of the producing area factors on the quality stability of the Chinese wolfberry through a data mining algorithm to obtain a quality stability static prediction model of the Chinese wolfberry in different producing areas; a long-short-term memory neural network is adopted, a multi-level feature extraction structure is designed, network parameters are optimized through an adaptive learning algorithm, generalization and robustness constraints are introduced at the same time, a quality stability dynamic prediction model is obtained, and the prediction capacity and the anti-interference capacity of the model for new samples are enhanced. According to the method, the problems of unstable quality caused by large climate and soil difference in the wolfberry planting area and high subjectivity of wolfberry quality evaluation are solved, and accurate prediction of the wolfberry quality stability is realized.

The invention discloses a coal mine production management knowledge question-answering method based on natural language processing, and the method comprises the following steps: S1, collecting environment data and user behavior data, and constructing a data set; s2, performing real-time processing on the data set; s3, constructing a user portrait by using a density-based spatial clustering algorithm; s4, constructing a knowledge graph, constructing a cognitive model by adopting a graph convolutional network and an improved Transform, and performing reasoning in combination with logistic regression and an XGBoost algorithm; s5, constructing an environment simulation model through historical data analysis; s6, generating a personalized natural language answer according to the user portrait, the knowledge graph and the environment state prediction; s7, collecting user feedback data, and adjusting model parameters and an optimization strategy by using an adaptive learning algorithm; and S8, storing the plan template by using a relational database, and continuously optimizing the plan template through historical data analysis. Natural language processing and deep learning are utilized, coal mine production management intelligent question answering is achieved, and the method has the advantages of being efficient and high in individuation.

The present invention discloses an energy-saving optimization control method for an air-conditioning rotary wheel dehumidification system based on a mechanism model, belonging to the technical field of air-conditioning energy-saving control. Specifically, it includes: establishing and validating a mechanism model by collecting historical operation data; based on the validated model, using a model predictive control method to predict the system state, and applying an adaptive learning algorithm and a deep reinforcement learning algorithm to adaptively adjust the model and the predictive control method; using the adjusted model and predictive control method, combining the current state and load prediction, and calculating the optimal control input through an optimization algorithm; real-time monitoring the system operation data, and using an intelligent decision-making algorithm and a deep reinforcement learning algorithm to correct and optimize the model; applying the optimal control input to the system, implementing the control strategy, evaluating the effect and optimizing the control strategy to achieve efficient and intelligent dehumidification control.

The invention provides a blood sample automatic analysis system based on artificial intelligence. The blood sample automatic analysis system based on artificial intelligence comprises a sample collection module used for automatically collecting a blood sample of a patient, a data acquisition module comprising a sensor and an imaging device and used for acquiring an image of the blood sample or original data in other forms, and a data preprocessing module, and the noise filtering module is used for carrying out noise filtering, standardization and normalization processing on the original data. According to the blood sample automatic analysis system based on artificial intelligence, by introducing a dynamic adjustment mechanism and a self-adaptive learning algorithm, the system can adjust model parameters in real time, and especially under the condition that data is insufficient or a data set has deviation, the recognition accuracy of rare diseases is greatly improved. The dynamic model training method not only improves the generalization ability of the model, but also effectively relieves the limitation of relying on a large-scale annotation data set.

The invention provides a multi-device automatic switching method and system for an intelligent mouse, and the method comprises the steps: collecting the connection signal strength of a plurality of external computing devices through a built-in wireless signal receiver of the intelligent mouse, and constructing a dynamically updated signal strength matrix; calculating the optimal probability of switching the mouse to each device by combining the device feature vector, the spatial position relationship, the use scene similarity and the historical use frequency; constructing a switching cost function based on a dynamic programming algorithm, and solving an optimal equipment switching path; through a switching pre-connection mechanism, a self-adaptive learning algorithm, a switching rollback mechanism and a mistaken touch prevention mechanism, efficient, stable and low-delay automatic switching of the intelligent mouse is realized. According to the method, the connection strategy can be dynamically adjusted according to the real-time scene, the mouse parameter configuration is optimized, cross-device switching is more intelligent and seamlessly connected, and the user operation experience is improved.

The invention provides an employee health risk monitoring method and system based on big data, and relates to the technical field of health management and data analysis, and the method comprises the steps: S1, collecting the multi-modal health data of an employee, including but not limited to physiological data, behavior data, working environment data, social interaction data and genome data, and S2, fusing and analyzing the collected multi-modal health data by adopting an integrated neural network model and an adaptive learning algorithm. According to the big data-based staff health risk monitoring method and system, through the combination of the integrated neural network and the deep causal inference model, the multi-modal health data of the staff can be deeply analyzed, and the complex causal relationship between the health data can be effectively identified. Through the dynamic analysis of the historical health data, the current health state and the working environment of the employees, the model can achieve the precise health risk prediction, and provides personalized health intervention suggestions according to the individual differences of the employees.

The invention provides a material light guiding method. The method comprises the following steps that row indicator lights are installed on each row of goods shelves, and layer indicator lights are installed on each layer of goods shelves. All the indicating lamps are connected to the central control system through the wireless network controller; the central control system maintains a database and records position information of all goods and corresponding goods allocation numbers. After an operator inputs a query instruction through the tablet personal computer or the PC, the system searches the goods allocation number corresponding to the goods in the database according to the instruction, and sends a signal through the network controller to activate the row indicator light and the layer indicator light of the corresponding goods shelf; the system introduces an adaptive learning algorithm, and can automatically adjust the display mode of the indicator lamp according to historical operation data and real-time environment change. Through combined use of the row indicating lamps and the layer indicating lamps, the goods picking personnel can quickly locate the target goods allocation without assistance of handheld equipment, so that the goods picking speed is remarkably improved, the guiding accuracy is ensured, and errors caused by manual misoperation are reduced.

The invention relates to the technical field of coal wastewater treatment, in particular to an intelligent monitoring method for HPAM microbial degradation for coal slime water treatment. The method comprises the steps that index data in coal slime water are collected and preprocessed in real time, the preprocessed index data are intelligently processed and analyzed through a hybrid dynamics model and an adaptive optimization algorithm, and an analysis result is obtained; according to an analysis result, automatically adjusting reaction conditions and operation parameters through a multi-dimensional time sequence feedback and nonlinear adaptive learning algorithm; meanwhile, detecting the degradation process to obtain detection data; and performing anomaly analysis based on the detection data to obtain an anomaly analysis result, and performing anomaly diagnosis and alarm according to the anomaly analysis result. The problems that in the coal slime water treatment process, the analysis accuracy of the microbiological degradation process is low, and the self-adaptability is poor are solved.

The invention discloses a dynamic voltage control method considering voltage risk assessment and network reconstruction, and the method comprises the following steps: S1, data collection and preprocessing: data collection: monitoring the voltage, current and load of a power grid and the power generation data of a distributed power supply in real time through a high-precision sensor and an Internet of Things technology; data preprocessing: performing cleaning, normalization and feature extraction on the acquired data to form a data set for model training; s2, model training and optimization: model selection: selecting a suitable adaptive learning algorithm, and constructing a voltage risk prediction model; according to the invention, the voltage stability and the power supply reliability of the power grid can be effectively improved, and the method has obvious advantages especially when facing a complex power system and large-scale distributed power supply access; the adaptive learning algorithm can adjust the control strategy in real time and predict and prevent the voltage risk, and the intelligent scheduling algorithm can optimize the utilization of the distributed power supply and improve the overall efficiency of the power grid.

The invention relates to the technical field of power grid information system fault positioning, and particularly discloses a power grid information system fault positioning system based on adaptive learning, and the system comprises a data collection module which is used for collecting electrical quantity data and state quantity data in the operation process of a power grid; the data preprocessing module is connected with the data acquisition module and is used for carrying out preprocessing operations such as filtering, denoising and normalization on the acquired data and removing interference and invalid information in the data; and the fault feature extraction module is connected with the data preprocessing module and is used for extracting feature parameters related to the power grid fault based on the preprocessed data. According to the power grid information system fault positioning system based on adaptive learning, a dynamic multi-scale adaptive learning algorithm is fused with reinforcement learning and transfer learning mechanisms, model parameters and structures are dynamically adjusted according to the real-time operation state of a power grid, similar scene experience is migrated, and the fault positioning accuracy and adaptability are remarkably improved.

The invention relates to the technical field of power grid exception handling, and discloses a power grid exception event handling method and system based on a knowledge graph. The method comprises the following steps: collecting time sequence data flow and space coordinate information of a power grid abnormal event in real time through multi-source sensing equipment, and fusing equipment operation parameters to construct a dynamic event data pool; performing graph structure conversion on the dynamic event data pool based on a knowledge graph topological connection relationship, calculating event node propagation path intensity and generating an event propagation network model; key abnormal indexes are separated out from the model by adopting a multi-modal feature extraction technology, risk probability calculation is completed in combination with an adaptive learning algorithm, and an abnormal risk distribution diagram is output; and classifying event levels according to the graph, and automatically generating a processing instruction sequence. According to the method, omnibearing data perception and accurate analysis of the abnormal events of the power grid are realized, an event propagation rule is clearly presented, risk assessment is enabled to be closer to an actual operation state, and accurate guidance is provided for abnormal disposal.

The invention relates to the technical field of artificial intelligence, in particular to an energy AI agent decision-making method and system based on adaptive learning and loopback control. The method comprises the following specific steps: collecting energy data and environment data in real time, and performing data fusion processing; generating an original strategy of the energy AI intelligent agent according to the fused data based on an adaptive learning algorithm; correcting the original strategy in real time based on a preset loopback loss function, and outputting a preliminary strategy instruction; receiving the preliminary strategy instruction, and optimizing the preliminary strategy instruction to generate a final execution instruction under the condition of meeting physical constraints and security constraints of the system; and executing the final execution instruction. By adopting the method, the self-adaptive optimization capability can be realized, the execution safety can be ensured, and the method has the characteristic of supporting continuous optimization.

The invention relates to an intelligent interactive design-based reading ability improvement device for children with reading disabilities, which comprises an interactive interface unit, a display unit, a display unit, a display unit and a display unit, wherein the interactive interface unit displays a text on a milk color background through black fonts to provide maximum reading comfort; the single-person reading practice mode unit realizes text reading through a natural language processing technology and voice synthesis, and synchronously changes font colors to guide a user to follow contents; an AI voice assistant in the AI accompanying reading mode unit guides the user to continuously read a specified part in a mode of randomly stopping reading, analyzes the pronunciation and fluency of the user through an adaptive learning algorithm, and provides feedback and adjustment difficulty; the reading challenge mode unit enriches the reading training of the user through a three-level challenge mode, and improves the interesting reading experience of the user. According to the method, interface friendliness and personalized reading experience are achieved, and the requirements of developmental reading disorder children can be better met.

The invention discloses an intelligent power consumption management system for Internet of Things equipment, which relates to the technical field of intelligent power consumption management systems and comprises a behavior acquisition module, a dynamic adjustment module, an energy consumption optimization module, an energy prediction module, an energy supply switching module and a system feedback module. The behavior acquisition module is used for monitoring the running state of equipment to obtain equipment activity feature data; the dynamic adjusting module is used for processing the equipment activity characteristic data by adopting a self-adaptive learning algorithm to obtain a dynamic dormancy threshold parameter; the energy consumption optimization module is used for applying the dynamic dormancy threshold parameter by adopting a multi-objective optimization method to obtain a power consumption distribution scheme of each functional module; the energy prediction module is used for analyzing the power consumption distribution scheme based on a neural network model in combination with environmental parameters to obtain an energy supply prediction result; and the energy supply switching module is used for responding to the energy supply prediction result by adopting a priority strategy.

The invention discloses a full-life-cycle health management method for enteritis patients, a medium and equipment, and the method comprises the steps: extracting a key lever factor through a lever point recognition algorithm based on obtained full-life-cycle health data, and generating a lever point intervention priority sequence; generating an individualized toughness development path plan in combination with a calculation result of the toughness evaluation model, and designing a structured self-verification experiment scheme according to the individualized toughness development path plan; continuously updating the leverage point recognition algorithm and the toughness evaluation model by using an adaptive learning algorithm according to a scheme execution result until the optimal parameter configuration and the individualized health management strategy are locked; and when the sorting stability of the key lever factors exceeds the standard, starting a grading response mechanism to generate a precise intervention scheme, and finally outputting a full-life-cycle health management comprehensive scheme. According to the method, a dynamic optimization closed loop is constructed, so that a management strategy is converted from static matching to dynamic self-adaption, and the defects of an existing method in the aspects of intervention target spot recognition, adaptive capacity quantification and strategy continuous optimization are overcome.