2966results about How to "Improve science" patented technology

The invention, which relates to the technical field of medicine inventory management, discloses an intelligent medicine inventory management and optimization system comprising a demand prediction module, an inventory monitoring module, a purchase decision module and a medicine supply module. The demand prediction module is used for performing drug demand prediction according to historical sales data, seasonal factors and market trends; and analyzing and predicting by adopting a machine learning algorithm. According to the intelligent medicine inventory management and optimization system, based on a multi-objective optimization algorithm, in combination with the inventory turnover rate, the purchase cost and the medicine validity period, a scientific and reasonable purchase replenishment strategy is generated, including purchase time, quantity and supplier selection, and the scientificity and the accuracy of a purchase decision are improved; by integrating historical sales data, seasonal factors and market trends, demand prediction is performed by using a machine learning algorithm, and an accurate drug future demand trend prediction result can be generated.

The invention relates to the technical field of data processing, and discloses a coal and gas outburst dynamic monitoring and intelligent early warning method and device. The method comprises the following steps: collecting and preprocessing multi-source sensor data arranged in a target area to obtain a multi-dimensional data set; performing multivariable time sequence analysis on the multi-dimensional data set to obtain coal seam gas outburst risk prediction data; based on coal seam gas outburst risk prediction data, performing dynamic risk assessment and spatial interpolation processing on the multi-dimensional data set to obtain real-time risk distribution data of the target area; performing multi-dimensional threshold analysis on the real-time risk distribution data to obtain preliminary early warning data; performing multi-modal information fusion on the preliminary early warning data to obtain target early warning data; and performing intelligent decision tree analysis on the target early warning data to obtain a target early warning scheme. According to the invention, the accuracy and reliability of dynamic monitoring and intelligent early warning of coal and gas outburst are improved.

The invention belongs to the field of energy efficiency optimization in industrial production, and discloses a dynamic energy efficiency optimization method and system based on artificial intelligence and machine learning, and the method comprises the steps: collecting energy consumption data and environment parameters in real time through a sensor network and Internet of Things equipment; cleaning, screening and standardizing the collected data; constructing an energy efficiency prediction model and an optimization model based on a machine learning algorithm; training the prediction model and the optimization model by using historical data, and adjusting model parameters; and the trained model is utilized to analyze and predict data acquired in real time, and production parameters are dynamically adjusted to realize optimization of energy efficiency. According to the method, key parameter changes in the production process can be accurately predicted and optimized, dynamic adjustment of production parameters is achieved, an optimal production adjustment strategy can be automatically made, scientificity and efficiency of production decision making are improved, and the method has remarkable technical and economic effects in the field of industrial production.

The invention discloses a live-action three-dimensional model construction method, system and device based on a digital city, and the method comprises the steps: obtaining the multi-source sensing data of a city, and carrying out the data preprocessing, and the multi-source sensing data comprises city image data, point cloud data and GIS data; converting the preprocessed multi-source sensing data in different formats into a preset standard format, and performing registration and fusion of the multi-source sensing data to obtain fused multi-source data; performing data optimization on the fused multi-source data, and constructing a city-level three-dimensional real scene model according to the optimized fused multi-source data and a preset three-dimensional space analysis tool; the preset three-dimensional space analysis tool provides space analysis and three-dimensional space visualization functions; according to the invention, efficient and accurate real-scene three-dimensional data of a city is provided, and three-dimensional and visual space information is provided.

The invention provides a seedling cultivation growth condition monitoring method, which comprises the steps of performing data processing on a time series data set of hydrological conditions, obtaining a dynamic association rule among different hydrological parameters, constructing an association model of the hydrological parameters, and quantitatively describing mutual influence and restriction relationships among water temperature, dissolved oxygen and salinity parameters by the association model; performing data processing on the time sequence data set of seedling growth, establishing a seedling growth process model, determining key parameters of a seedling growth curve by analyzing growth rate change rules of seedlings in different growth stages, and correcting the model according to initial quality and stocking density factors of the seedlings; a seedling growth prediction model is combined with an intelligent optimization algorithm, key control parameters in the breeding process are dynamically adjusted, and according to the seedling growth trend predicted by the model, hydrological conditions and feeding strategies are adaptively optimized, so that intelligent management of the breeding process is realized.

The invention relates to the technical field of environmental protection, in particular to a big data monitoring and intelligent early warning platform and method for environmental protection. Comprising a multi-source data acquisition module, a data integration and preprocessing module, an environmental risk factor identification module, a real-time monitoring and anomaly detection module, a prediction model and early warning module, a self-adaptive early warning and feedback module and a decision support and visualization module. The data integration and preprocessing module preprocesses multi-source data, the environmental risk factor identification module identifies key risk factors by using an XGBoost algorithm, and carries out accurate prediction and early warning on risks through a random forest and long and short term memory network combination model, and an adaptive feedback mechanism dynamically adjusts an early warning threshold value, so that the early warning efficiency is improved. According to the invention, the intelligent level and the scientific level of environmental protection are obviously improved.

The invention discloses a battery life evaluation system and method based on different decline modes of a battery, and belongs to the technical field of battery management evaluation. Through combination of the data acquisition module and the information management module, battery manufacturing, operation and historical information is comprehensively acquired, accurate management of the whole life cycle of the battery is realized, the data analysis module and the performance evaluation module deeply analyze battery operation data, classify and identify recession modes and key influence factors, and the reliability of the battery is improved. The current performance and the remaining life of the battery are dynamically evaluated by combining a prediction model, decision making is simplified through health scoring, the battery management efficiency is improved, the adaptive performance optimization module adjusts battery parameters and temperature control strategies according to operation scenes, the performance and the life are balanced, active optimization and safety improvement are achieved, and the safety is improved. The carbon footprint evaluation module covers all stages of battery manufacturing, using and recycling, checks full-life-cycle carbon emission, traces the source of high emission links, provides low-carbon optimization suggestions, and assists enterprises in achieving the goal of green development.

The invention discloses a nursing decision scheme optimization analysis system based on artificial intelligence, and relates to the technical field of data processing. The system comprises a nursing knowledge base module, a data acquisition and preprocessing module, a nursing scheme matching degree analysis module, a nursing demand prediction module, a nursing scheme optimization module and a nursing scheme monitoring feedback module. According to the nursing decision scheme optimization analysis system based on artificial intelligence, the medical history, the diagnosis result, the physiological parameter data and the real-time nursing record multi-source information of the patient are integrated, comprehensive and dynamic evaluation of the health condition of the patient is achieved, deep mining and analysis are conducted on the data, and therefore more accurate nursing decision suggestions are obtained; individual nursing schemes are provided according to individual differences of patients, pertinence and effectiveness of nursing measures are ensured, in addition, execution conditions of the nursing schemes are monitored in real time, deviation is found and corrected in time, and scientificity and accuracy of nursing decision making are further improved.

The invention discloses an e-commerce intelligent operation monitoring and collaborative decision-making method based on big data analysis, and the method comprises the following steps: S1, collecting multi-source heterogeneous data, and constructing an original data set; s2, performing data cleaning and characterization processing to generate a feature data set; s3, constructing a knowledge graph and dynamically updating the knowledge graph by using the graph embedding model; s4, generating potential causal paths and weights in combination with the neural relationship reasoning model and the diffusion model, and introducing a microknowledge enhancement layer to generate causal enhancement features; s5, identifying abnormal behaviors in combination with a multi-level anomaly detection mechanism; s6, designing a dynamic game optimization model, determining a collaborative optimal solution under a multi-target conflict through Nash equilibrium, and processing a dominant and subordinate relationship in combination with a Stackelberg game to generate a collaborative optimization strategy; and S7, displaying the monitoring state and the collaborative optimization strategy through a visual interface. According to the invention, big data analysis, a knowledge graph and a dynamic game technology are combined, so that e-commerce intelligent operation monitoring and collaborative decision-making are realized.

The invention provides a DCS system autonomous controllable upgrading and system fault prediction method and system, and relates to the technical field of fault prediction, and the method comprises the steps: obtaining historical operation data and real-time operation data of a DCS system, carrying out the preprocessing, and generating an autonomous controllable upgrading scheme through a deep reinforcement learning algorithm; the method comprises the following steps of: setting specified time for upgrading time, carrying out consistent backup on the state of the DCS, executing software updating, hardware configuration adjustment and system parameter optimization according to upgrading steps, analyzing indexes of the DCS in real time in the process of executing the autonomous controllable upgrading scheme, and recovering the DCS to the state before upgrading when an abnormal condition is detected. And verifying and optimizing the real-time fault prediction result based on a knowledge graph, and generating an explanatory report including a fault prediction and upgrade decision process according to the optimized fault prediction result and the autonomous controllable upgrade scheme.

The invention provides an intelligent operation and maintenance method and system for an air conditioning system based on the Internet of Things technology, and the method comprises the steps: processing operation data through employing an intelligent sensor and an edge calculation node, and generating a comprehensive operation information packet through combining with environment parameters; next, simulation analysis is carried out through a cloud digital twin model, performance prediction and fault point evaluation are generated, the fault probability is evaluated by using a Bayesian network algorithm, and personalized operation and maintenance suggestions are generated; secondly, optimizing operation parameters by adopting a genetic algorithm, and smoothly adjusting the setting through a self-adaptive control theory; and when an abnormality is detected, the fault severity is judged by using a multi-level threshold value and fuzzy logic, an early warning mechanism is triggered in time, and an emergency response scheme is pushed. And finally, recording a maintenance process and user feedback, updating the digital twin model, forming a closed-loop operation and maintenance system, and ensuring efficient and stable operation and service quality improvement.

The invention relates to the technical field of data analysis, in particular to a business abnormal data analysis method based on a financial management system, and the method comprises the following steps: S1, obtaining various business data in an enterprise financial management system in real time; s2, processing the data acquired in the step S1; s3, extracting features for reflecting business exception; s4, establishing an anomaly detection model; s5, comprehensively analyzing the source and influence of the abnormal data to form a multi-dimensional abnormal data analysis report; s6, deeply mining reasons behind the abnormal data, and identifying business processes and operations causing the abnormity; and S7, providing a corresponding decision support suggestion based on an analysis result in S6. According to the invention, through integration of data mining and machine learning technologies, the business abnormity identification, analysis and processing capabilities in an enterprise financial management system are effectively improved, the decision support of an enterprise for coping with a complex business environment is significantly enhanced, and the operation and financial risks are reduced.

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention discloses a factor multiple optimization-based flood risk dynamic zoning method and system, and the method comprises the steps: constructing an initial flood risk assessment index set, carrying out the screening of the initial flood risk assessment index set, and calculating the frequency value of each index; constructing a comprehensive discrimination index value, and further screening to obtain a key flood risk assessment index set; constructing a risk assessment model and a key flood risk assessment index weight optimization model, and adopting a genetic-local search hybrid algorithm of simulated annealing optimization to calculate and obtain an optimal weight combination scheme; and calculating the similarity between the scene of each time point of the research area and a historical typical flood event, selecting an optimal weight combination scheme, and inputting the optimal weight combination scheme into the risk assessment model for calculation to obtain a flood risk dynamic division of the research area. According to the method, scientific and reasonable dynamic risk assessment can be provided, effective decision support can be provided for related decision makers, and the flood control toughness and the extreme environment coping capacity of the drainage basin area can be improved.

The invention relates to the technical field of expressway unmanned aerial vehicle route planning, in particular to an intelligent route planning method and system for an expressway unmanned aerial vehicle, and the method specifically comprises the steps: planning unmanned aerial vehicle waypoints on an expressway based on a target three-dimensional model, and obtaining a waypoint set of the expressway; historical route data of the expressway are obtained, a deep learning algorithm is used for analyzing the historical route data, safe flight path features are extracted, a route safety evaluation model is constructed, and the route safety evaluation model is used for evaluating the route safety level of the route of the unmanned aerial vehicle; and determining route planning parameters according to the unmanned aerial vehicle task type, and determining an unmanned aerial vehicle route through the waypoint set, the route safety evaluation model and the waypoint planning parameters based on a multi-objective optimization algorithm. According to the invention, the safety and efficiency of the unmanned aerial vehicle in highway inspection and monitoring tasks are improved, and automatic planning and dynamic adjustment of the route of the unmanned aerial vehicle based on real-time data analysis are realized.

The invention relates to the technical field of machine learning, in particular to a supervised time sequence water level data generation method and system and a storage medium. The method comprises the following steps: acquiring multi-source hydrological monitoring data; performing abnormal value identification and data preprocessing according to the hydrological monitoring data to generate standardized hydrological basic data; constructing a data generation model of the conditional generative adversarial network based on the standardized hydrological basic data; hydrological variable driven condition feature regulation and control are carried out on the data generation model, parameter optimization of the generation network is carried out, and hydrological adaptability enhanced condition input feature data are generated; and injecting the condition input characteristic data into the data generation model to generate initial water level time sequence data. According to the method, the rare extreme hydrological events are oversampled by using a hierarchical importance sampling and self-service method (Bootstrap), so that the performance of the generative model in rare event prediction is remarkably enhanced.

The invention provides a modular power supply system configuration method and system for a micro-module machine room, and relates to the technical field of electric power, and the method comprises the steps: collecting power utilization state data and load distribution data of functional equipment in the micro-module machine room, building a power supply network topology model, dividing a power supply system into a first-stage load, a second-stage load and a third-stage load, an independent power supply loop and a data acquisition unit are respectively configured in the first-stage load, the second-stage load and the third-stage load; a power supply network topology model is updated according to the power utilization data acquired by the data acquisition unit, a reinforcement learning algorithm is adopted to generate an adaptive power distribution strategy, and a modular energy storage unit is arranged to provide a standby power supply for a primary load and a secondary load; and constructing a machine learning fault prediction model based on the power consumption data, and dynamically adjusting a power supply path and a power distribution scheme of a power supply loop.

The invention relates to the technical field of clothing data processing, in particular to a digital design method of three-dimensional prototype clothing. The method comprises the following steps: obtaining human body three-dimensional scanning data and carrying out feature point labeling, reconstructing a three-dimensional human body model as basic data of costume design, carrying out contour looseness calculation on the human body model, generating a looseness data set, carrying out sample plate plane analysis to obtain a plane sample plate data set, and after collecting costume fabric data, carrying out data processing on the plane sample plate data set. The method comprises the following steps: performing physical characteristic mapping on a human body model and a planar template, reconstructing a three-dimensional garment model conforming to fabric characteristics, performing spatial positioning on the template, performing virtual sewing simulation, generating a shape data set of the garment, performing dynamic motion analysis on the shape data of the garment according to the human body three-dimensional model, and evaluating the comfort level of the garment. The garment form is locally adjusted to reconstruct a planar template. According to the invention, a more comprehensive and more reliable digital design method of the three-dimensional prototype clothing is realized.

The invention relates to the technical field of process data optimization, and discloses a traditional Chinese medicine extraction process optimization method and system based on dynamic countercurrent extraction and a medium. The method comprises the following steps: carrying out online monitoring data processing on a traditional Chinese medicinal material digital feature data set and a dynamic countercurrent extraction parameter initial data set to obtain a countercurrent extraction process real-time monitoring data stream and parameter associated data, and carrying out multi-dimensional parameter optimization processing; a dynamic countercurrent extraction process optimization parameter group is obtained and is analyzed and processed in real time, and an automatic parameter regulation and control instruction sequence and process abnormity early warning data are obtained; and performing quality characteristic data processing on the extract after execution of the automatic parameter regulation and control instruction sequence to obtain a process quality evaluation data set and a process tracing data set, and performing association analysis processing to obtain a dynamic countercurrent extraction process parameter optimization rule base. According to the invention, intelligent monitoring and automatic parameter optimization of the extraction process are realized, so that the accuracy and controllability of the traditional Chinese medicine extraction process are improved.

The invention discloses an ecological garden intelligent management method and system, and belongs to the technical field of landscaping management, and the method specifically comprises the steps: monitoring environmental parameters in real time through multiple sensors, and uploading the environmental parameters to a cloud server; at the cloud end, a dynamic data analysis model is constructed by utilizing historical irrigation data, a plant growth model and real-time monitoring parameters, the water demand of plants is predicted, and an irrigation decision is made; an irrigation threshold value is set according to the decision, and an irrigation program is automatically started and executed through remote control equipment; after irrigation, detailed information is recorded and fed back to the data analysis model, the irrigation effect is evaluated by adopting a data analysis method, and an irrigation decision is finely adjusted accordingly; intelligent and precise irrigation management is realized, and the utilization efficiency of water resources and the growth quality of plants are improved.

The invention is suitable for the technical field of recruitment portraits, and provides a recruitment talent portrait generation method and system based on multi-dimensional information, and the method comprises the following steps: collecting the resume information of candidates; performing data cleaning and data conversion on the resume information; performing feature extraction, analyzing candidate online behavior data based on a natural language processing technology, and extracting behavior patterns and interest points; performing model training and evaluation based on historical resume data to obtain a talent portrait model; the resume information, the behavior modes and the interest points of the candidates are input into a talent portrait model, candidate portraits are obtained, and the candidate portraits comprise skill ability evaluation, occupational development potential and culture adaptation degree; and providing personalized vocational development suggestions and post matching recommendations according to the candidate portraits. According to the method, through feature extraction and machine learning model training, a detailed talent portrait is generated, the scientificity of recruitment decision making is improved, suitable candidates are rapidly identified, and vocational suggestions are provided for the candidates.

The invention provides a river section water quality prediction method based on a combined machine learning model, and the method comprises the steps: building river monitoring section long-time sequence water quality big data, standardizing and supplementing water quality index data, preparing a water quality index data set and a water quality evaluation data set, and selecting and processing water quality big data features. Developing a water quality prediction model based on a combined machine learning algorithm, evaluating the performance of the water quality prediction model, and testing the generalization ability of the water quality prediction model. The correlation of river section historical data in a time sequence is fully utilized, the model not only improves the accuracy and real-time performance of water quality prediction, but also enhances the response capability to sudden water quality events, the monitoring and early warning cost is remarkably reduced, the efficiency and scientificity of water environment management are improved, and the method is suitable for popularization and application. The self-learning capability and the high-frequency data processing capability of the system ensure timely updating and long-term effectiveness of a prediction result, and provide solid technical support for water pollution prevention and control and water quality management.

The invention relates to the technical field of road maintenance and management, in particular to a pavement internal disease analysis method and system based on artificial intelligence. Five core processes of multi-layer sensor network real-time data acquisition, multi-modal data fusion and deep learning feature extraction, dynamic environment and use mode combined disease prediction and analysis, intelligent prediction and decision support system, and real-time disease condition evaluation and report generation are carried out. Through the technical means, comprehensiveness and timeliness of data acquisition, accuracy of disease identification and prediction, scientificity of a maintenance strategy and optimization of resource allocation are realized; the finally generated disease current situation report provides a detailed disease distribution diagram, disease type analysis and disease severity evaluation, comprehensive, accurate and timely disease information is provided for maintenance personnel, and the efficiency and effect of pavement maintenance and management are remarkably improved.

The invention discloses an event analysis method, system and device based on task generation and multiple modes, which are used for quickly and accurately mastering the development trend of an event. The method comprises the steps that structured event information is generated through multi-modal data, and an event relation is obtained; constructing an event graph; extracting a multi-modal feature, and obtaining a multi-modal emotion relationship vector by using a cross-modal attention mechanism; based on a preset graph neural network, combining the event graph to obtain an emotion feature vector; calculating a comparative learning loss value, and obtaining a target emotion feature based on the comparative learning loss value; generating a target event atlas by using a cross-modal attention mechanism; extracting relation characteristics, and combining the attention weight and a preset time sequence model to obtain an event prediction map; extracting a causal chain and reasoning, and obtaining a trigger condition and an emotion factor after reasoning; and combining the event prediction map and the target event map with the node feature sequence, the trigger condition and the emotion factor to obtain an event analysis result.

The invention relates to the technical field of real-time prediction of three-dimensional forms, in particular to a real-time dynamic prediction system and method for the three-dimensional form of a high-pressure jet grouting pile. The method comprises the following steps: carrying out stratum characteristic identification on a construction site to obtain stratum distribution data; key monitoring point positions are determined according to the stratum distribution data, and a multi-source sensor monitoring network is established; carrying out anti-interference optimization processing on the multi-source sensor monitoring network, and carrying out real-time acquisition on high-pressure jet grouting parameters to obtain real-time high-pressure jet grouting data; and spatio-temporal evolution processing based on injection pressure, lifting speed and rotating speed is conducted on the real-time high-pressure jet grouting data, and parameter change characteristic data are generated. According to the method, the construction stratum distribution and the high-pressure jet grouting parameters are obtained and analyzed in real time, the parameter-stratum response relation model is established, grout diffusion and pile body forming dynamic prediction are conducted on different stratum characteristics in a targeted mode, and the precise construction capacity under the complex stratum condition is greatly improved.

The invention discloses an intelligent monitoring method and a monitoring system for an IDC (Internet Data Center) machine room environment, particularly relates to the field of IDC machine room environment monitoring, is used for solving the problems of multi-parameter relevance and real-time early warning, and aims at solving the problems of multi-parameter relevance and real-time early warning by constructing a high-dimensional topological structure, a Bayesian network model and a dynamic threshold adjustment mechanism of the IDC machine room environment. The relevance and the causal relationship among the multi-dimensional parameters are effectively captured, and the analysis and prediction capability on the complex environment data is remarkably improved. And by utilizing a persistent coherence algorithm and random forest regression, nonlinear correlation of parameters is accurately identified, and the limitation of a traditional method in multi-parameter analysis is overcome. Through Granger causal relationship test, a parameter causal sequence is determined, and a basis is provided for accurate prediction of environmental changes. A dynamic threshold adjustment mechanism flexibly adapts to environment changes, false alarm and missing alarm are reduced, it is ensured that the system rapidly identifies abnormities and triggers alarms in a complex environment, and the intelligence and stability of the system are remarkably enhanced.

The invention provides a multi-sealing method and system for a high-vacuum die-casting die based on topological optimization, and relates to the technical field of die structures.The multi-sealing method comprises the steps that a sealing structure parameterized model is established, geometric parameters and boundary conditions of a cavity wall face are obtained, an initial design domain is constructed, and grid units are divided; carrying out topological optimization calculation based on a multi-objective function considering contact stress distribution, and iteratively optimizing the relative density of grid cells by adopting an SIMP method; and according to the obtained topological configuration of the sealing structure, multiple sealing structure design is carried out, a three-dimensional entity model is constructed, and the sealing performance is verified through finite element analysis.

The invention relates to the technical field of building engineering, in particular to a steel structure construction quality monitoring system based on an unmanned aerial vehicle and three-dimensional scanning, which comprises a data acquisition module, a data preprocessing module, a construction defect identification and risk assessment module and a quality assessment module, the data acquisition module acquires quality monitoring data of a construction site in real time; the data preprocessing module is used for preprocessing the acquired quality monitoring data; the construction defect identification and risk assessment module identifies steel structure defects in the construction process and assesses the severity of the steel structure defects; and the quality evaluation module performs comprehensive quality evaluation on the preprocessed quality monitoring data and provides corresponding repair suggestions. According to the method, key problem areas can be quickly identified, a construction resource allocation strategy is optimized, and comprehensive and accurate data support is provided for steel structure quality evaluation in a complex construction environment.

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 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.