118results about How to "Realize evaluation" patented technology

The application discloses an intelligent scheduling control method for an automobile part automatic assembly line, and relates to the technical field of automatic control, which comprises the following steps: obtaining the task instruction response time length value and the node execution action state change value of each execution unit in a current scheduling period, and calculating the response delay value and the state fluctuation value of the corresponding node; generating a response efficiency value according to the response delay value and the state fluctuation value of each node, and generating a feedback control level label in combination with the node task attribute information; judging whether to perform compression processing based on the feedback control level label and the available idle time value of each node in the current scheduling period, and if the compression condition is met, encoding the response delay value and the state fluctuation value into a compressed feedback data frame; and completing the relay transmission of the compressed feedback data frame in a time period that meets the scheduling idle condition, so that the scheduling delay pressure control, task state accurate maintenance and instruction issuing sequence dynamic optimization of the automobile part automatic assembly line are realized.

This application relates to the field of simulation technology and discloses a method, apparatus, electronic device, and storage medium for simulation result analysis and evaluation. The method includes: in response to receiving any simulation result, determining whether the simulation result meets a performance target; when the simulation result meets the performance target, at least extracting the model diagram of the simulation model to which the simulation result belongs and at least one directly related scheme parameter, and obtaining at least one non-directly related scheme parameter related to the performance target and / or the simulation result, to determine the evaluation value and influence coefficient corresponding to each non-directly related scheme parameter; calculating a simulation evaluation score based at least on the evaluation values ​​and influence coefficients corresponding to all non-directly related scheme parameters, and then outputting an overall simulation evaluation; calling a simulation report template, and generating simulation optimization suggestions and an evaluation report based on the template, at least according to the overall simulation evaluation, the model diagram, and at least one directly related scheme parameter. This application facilitates the automated optimization and evaluation of simulation results.

The application discloses a kind of based on space alignment and timing correction multi-intersection adaptive signal control method, belong to traffic signal control field, including: through the traffic detection sensor deployed in intersection, real-time traffic state data are collected and sent to signal lamp controller;According to road network topology, send cooperative data to downstream intersection;Traffic flow and candidate phase set are constructed;The traffic data associated with candidate phase is aggregated as demand value and executes ordering transformation to obtain combination state;To combination state, execute spatial feature alignment processing and map as state feature vector;State feature vector is input into near-end strategy optimization controller, and output target phase and basic phase duration;The traffic flow information of last phase period on upstream intersection is combined to carry out timing correction to basic phase duration;Drive signal lamp hardware to execute and generate reward feedback and update near-end strategy optimization controller parameter.The application realizes the cooperative optimization of multi-intersection traffic signal, improves the traffic efficiency and adaptive capacity.

This invention relates to the field of intelligent management technology, and more particularly to a smart power management system for communication base stations. The system includes a data acquisition module, a data processing module, a fluctuation analysis module, a power supply analysis module, and a power management module. By determining the shading coefficient, stain attenuation coefficient, and temperature influence coefficient, it calculates power fluctuation characterization values, identifies abnormal power generation tendencies, and for power supplies without abnormal power generation tendencies, calculates power supply characteristic values ​​based on cloud cover coefficient, temperature coupling coefficient, and power fluctuation characterization values ​​to classify power supply stability levels. For weakly stable power supply levels, it locates key abnormal factors and adjusts power generation parameters. This invention analyzes the power supply status within communication base stations by comprehensively analyzing static and dynamic influencing factors, and determines adjustment parameters to improve power generation stability and communication quality.

The application discloses a handwriting standard monitoring method and system based on image enhancement, relates to the technical field of image processing and intelligent evaluation, and comprises the following steps: obtaining a handwriting character image through adaptive light compensation of multiple light sources; after image preprocessing such as geometric correction and adaptive binarization, a convolutional neural network is used to extract character skeleton topological structure features for structural standard analysis; meanwhile, a pen tip motion trajectory is extracted based on an optical flow method and time sequence standard analysis is performed; after the structural and time sequence bimodal features are fused, a scoring model constructed based on a style transfer network is input to calculate a standard score; and a hierarchical error correction report is automatically generated according to the score grade and deviation type. The technical problems of incomplete stroke extraction, insufficient structural analysis precision, lack of dynamic writing feature modeling and lack of hierarchical error correction feedback under the complex illumination and shielding conditions in the prior art are solved.

This application provides a temperature control method and system for vacuum wetting indium in an array field emitter, relating to the field of vacuum electronic device manufacturing technology. The method includes: obtaining the three-dimensional temperature field distribution of the surface of the field emitter using a deep convolutional neural network based on spectral radiation energy; calculating the transverse and longitudinal temperature gradients between the emitter pins based on this distribution data, and inputting this data into an evaluation model to dynamically assess the wetting uniformity; and generating multi-temperature zone heating power control commands based on the uniformity results using a proportional-integral-derivative controller, and adjusting the power output of each temperature zone to ensure the array temperature field meets the requirements for promoting uniform indium wetting. This application improves the control accuracy and reliability of indium wetting uniformity in complex three-dimensional field emitters under vacuum and high-temperature conditions.

The invention relates to the field of hoisting, in particular to a multi-device collaborative hoisting safety scheduling system based on the Internet of Things. Surface point cloud data and wind direction data of a hoisted object are acquired through a data acquisition module; the cutting construction module constructs a three-dimensional model based on the point cloud data and constructs a fluid control group in combination with a hoisting movement velocity vector and a current wind direction, and the fluid analysis module constructs a plurality of morphological observation surfaces to cut local parts to form contours and extracts fluid interference characteristics to obtain interference characteristic values so as to analyze interference tendency; and the scheduling control module analyzes the hoisting path section in a strong interference tendency section by section to identify a risk local path section and controls the hoisting equipment to adopt a deceleration scheduling strategy. According to the method, quantitative evaluation and adaptive adjustment of the dynamic influence of the outdoor complex wind environment on the hoisted object in the hoisting operation are realized, and the safety and scheduling accuracy of multi-equipment cooperative hoisting are improved.

The application provides a fraud identification method and system based on voice data, and belongs to the technical field of voice processing, and specifically comprises the following steps: performing fraud identification processing on voice communication by using an identification processing strategy, performing update processing on the sound characteristics of fraudulent calls by using an identification processing result, determining an optimized monitoring target in a monitoring target based on the identification processing data of the voice communication, and combining the coincidence of the associated monitoring target and the associated monitoring target of different monitoring targets in the monitoring target, determining the optimized monitoring target based on the update result of the optimized monitoring target, and combining the update processing result of the sound characteristics of the fraudulent calls to determine the fraud identification method of the voice communication of the optimized monitoring target, thereby improving the accuracy of the identification processing of the fraudulent calls.

This application relates to the field of biomedical technology, and in particular to a method for evaluating the effectiveness of autism intervention and its application. The method includes the following steps: collecting a test sample and a control sample; the test sample is a urine sample from an autism patient, and the control sample is a urine sample from a healthy volunteer; processing the test sample and the control sample separately to obtain NMR samples; testing the NMR samples using an NMR spectrometer to obtain NMR spectra; performing multivariate statistical analysis on the NMR spectra to construct a urinary metabolite model; and using the urinary metabolite model to screen and evaluate autism patients. This application provides a method for evaluating the effectiveness of autism intervention and its application, constructing a urinary metabolite model through NMR metabolomics technology, providing practical evidence for establishing methods for early screening and treatment process evaluation of autism.

The invention relates to the technical field of investment casting, in particular to a casting liquid fluidity experiment device and method. The casting liquid fluidity experiment device comprises a pouring cup, a main pouring gate, a bent pouring gate, a bottom pouring transverse pouring gate and a bottom pouring test plate. The first end part of the main pouring gate is communicated with the pouring cup; one end of the bent pouring gate is communicated with the bypass position of the main pouring gate; one end of the bottom pouring cross gate is communicated with the second end part of the main pouring gate; the interior of the bottom pouring test plate is hollow; one end, far away from the main pouring gate, of the bottom pouring cross gate is communicated with the bottom pouring test plate; the extension direction of the bottom pouring test plate is intersected with the extension direction of the bottom pouring cross gate; the bottom pouring test plate and the bent pouring gate are positioned on the same side of the bottom pouring cross gate; the cross section of the bottom pouring test plate is rectangular; the extension length of the bent pouring gate is greater than that of the bottom pouring test plate; in this way, the problems that in the prior art, casting liquid fluidity testing is single, and evaluation is not comprehensive enough are solved.

This application discloses a training method for a source evaluation model, a source evaluation method, and related products. Text sample sequences and frequency sample sequences are input into the evaluation model to be trained. The model encodes the text sample sequences and frequency sample sequences to obtain text sample vectors corresponding to the text sample sequences and frequency sample vectors corresponding to the frequency sample sequences. The evaluation model then performs prediction and evaluation processing on the text sample vectors and frequency sample vectors to obtain source evaluation prediction results. Based on the difference between the source evaluation result labels and the source evaluation prediction results, the parameters of the evaluation model are adjusted until the adjusted model meets the model training cutoff condition, and training ends to obtain the source evaluation model. Thus, this application can construct a source evaluation model based on the source sample name and the title, keywords, and publication frequency of the published sample text as features, thereby achieving the evaluation of the source.

The invention discloses a sports injury risk real-time assessment and early warning system. The system comprises an intelligent wearable device for collecting multi-dimensional physiological data and a remote analysis server for deep analysis; the server performs real-time risk assessment and graded early warning according to a personalized threshold value by fusing and analyzing the heart load, the body fluid balance state and muscle and joint stability characteristics extracted from kinematics parameters, and issues a specific intervention instruction; according to the invention, real-time perception of multiple risk factors of sports injury is realized through an end-cloud collaborative architecture, professional laboratory-level analysis ability is integrated into daily wearable equipment, the contradiction between single early warning dimension of existing equipment and incapability of real-time convenient application of a professional system is solved, accurate and prospective injury prevention guidance can be provided for a user, and the user experience is improved. And the safety of movement is greatly improved.

The present application relates to the field of data processing, and provides a rationality evaluation method, device and equipment for hydraulic support selection, the method comprising: obtaining multiple monitoring indexes of the hydraulic support, and determining original indexes according to the monitoring indexes; the monitoring indexes include initial support force, step sequence final resistance, safety valve opening pressure, safety valve opening time, column subsidence, roof beam deflection angle and impact acceleration; the original indexes are normalized, the obtained standardized data is input into a trained neural network model, and a rationality prediction result is output; the rationality prediction result is scored based on a target function to obtain a score result, and the rationality grade of the hydraulic support selection is determined according to the score result. The present application solves the problem in the prior art that the evaluation of hydraulic support selection relies on a single static index and lacks dynamic multi-dimensional comprehensive analysis, realizes intelligent comprehensive evaluation based on multi-source monitoring data, and improves the rationality and accuracy of selection.

The present application belongs to the technical field of geotechnical engineering seepage failure prevention and control, and particularly relates to a discontinuous graded sand soil latent erosion simulation and prevention and control method based on CFD-DEM coupling. The method breaks through the limitation of traditional models, adopts a micro calcium carbonate particle filling method, gives linear contact models and parallel bonding models to the three types of contacts of sand particle-sand particle, sand particle-calcium carbonate and calcium carbonate-calcium carbonate, and truly reproduces the double reinforcement mechanism of MICP "filling + cementation". A multi-dimensional dynamic evaluation system covering fine particle erosion rate, average coordination number, porosity and force chain network is constructed, and macroscopic and microscopic collaborative analysis is realized. Through systematic comparison, the three major prevention and control mechanisms of MICP filling, cementation and structure strengthening are quantitatively revealed. Further simulation of the latent erosion process under different fine particle contents, discontinuous ratios and calcium carbonate deposition amounts summarizes clear prevention and control rules, provides reliable numerical basis for differentiated and economically efficient MICP reinforcement schemes, and has important engineering application value.

This invention discloses an intelligent monitoring and analysis system for standardized operation processes in restaurant chains, belonging to the field of data processing technology. It includes: a multi-dimensional data perception module that collects video streams, order data, and material weight data; a spatiotemporal semantic association module that synchronizes the time deviation of each device to within 10ms based on the IEEE 1588 protocol and extracts corresponding video slices according to order timestamps; an operation behavior recognition module that extracts human skeleton points and identifies atomic action sequences such as feeding and stirring; a material flow tracking module that triggers pressure sensors in response to feeding actions to record weight changes at a sampling frequency of no less than 100Hz, calculating the measured feeding amount when the standard deviation is less than 0.3g; and a process compliance evaluation module that compares the atomic action sequences and measured feeding amounts with standard operating procedures to generate a quantitative score. This invention solves the pain points of logical disconnect between video and order data and difficulty in tracing material losses, achieving refined closed-loop monitoring of kitchen operations.

The application relates to a detection method in the field of mechanical processing, in particular to a pin type tenon blade tip measurement clamp and method, which comprises a base, a positioning pin, a smooth surface pressing block, a positioning block and a sliding block, the positioning block is fixed on the base, the positioning pin is arranged on the positioning block and is used for installing a positioning hole of a pin type blade tenon; the smooth surface pressing block and the sliding block are slidingly arranged on the base, and the sliding block is coaxially arranged opposite to the smooth surface pressing block; a circular hole is arranged on the positioning block, the axis of the circular hole is parallel to the surface of the base, the smooth surface pressing block and the sliding block are oppositely arranged on two sides of the axis of the circular hole, and the smooth surface pressing block and the sliding block are matched to press and position the position of the blade tenon. The pin type tenon rotor blade measurement clamp is used for positioning the blade, the process of establishing a coordinate system is simplified, the measurement efficiency is improved, different measurement methods can be selected according to different measurement conditions, and the measurement and evaluation of the blade tip height and angle are realized.

The invention discloses a multi-modal large language model evaluation method and a related device, and relates to the technical field of large language models.The method comprises the steps that firstly, a user behavior signal corresponding to a to-be-evaluated multi-modal large language model is acquired; then, based on the user behavior signal, determining a user behavior mode; then, determining an evaluation dimension index basic weight corresponding to the user behavior mode; the evaluation dimension indexes corresponding to the user behavior mode are a plurality of evaluation dimension indexes in preset multi-modal evaluation dimension indexes; adjusting an evaluation dimension index basic weight corresponding to the user behavior mode based on the user behavior signal to obtain an evaluation dimension index new weight corresponding to the user behavior mode; and finally, the to-be-evaluated multi-modal large language model is evaluated based on the new weight of the evaluation dimension index corresponding to the user behavior pattern, an evaluation score of the to-be-evaluated multi-modal large language model is obtained, and evaluation of the multi-modal large language model can be realized based on the scheme.

This application discloses a drilling risk early warning method, apparatus, equipment, and medium, relating to the field of shale gas drilling. The method includes: establishing a drilling parameter prediction model based on historical drilling parameters, and determining corresponding drilling risk impact parameters based on the drilling parameter prediction model; constructing a downhole risk identification model based on historical downhole risk data in a preset drilling risk data warehouse and the drilling risk impact parameters; acquiring current drilling parameters and current downhole risk parameters, and determining the current drilling risk based on the downhole risk identification model, the current drilling parameters, and the current downhole risk parameters, so as to provide early warning for the current drilling risk based on a preset drilling risk early warning method. In this application, the drilling parameter prediction model and the downhole risk identification model are constructed using machine learning and big data technologies, thereby achieving comprehensive early warning for the current drilling risk.

The invention relates to the technical field of ecological monitoring, in particular to a wild animal community state monitoring method and system based on an unmanned aerial vehicle, and the method comprises the steps: firstly controlling a visible light camera, a thermal imaging camera and a multispectral camera carried by the unmanned aerial vehicle to carry out hardware synchronous collection, and obtaining time-space aligned multi-modal data; preprocessing and lightweight AI preliminary identification are carried out at the unmanned aerial vehicle end; edge calculation and cross-modal fusion analysis are carried out on a ground control station to realize species identification, quantity statistics, behavior analysis and vegetation index calculation; multiple spatio-temporal data are integrated at the cloud end for deep modeling, distribution density fine calculation, behavior pattern semantic understanding and habitat suitability comprehensive evaluation are completed, and finally a comprehensive monitoring report and early warning are generated. The problems that a traditional monitoring method is single in data dimension, poor in analysis timeliness and lack of habitat association evaluation are solved, and efficient, all-weather and automatic comprehensive monitoring and evaluation of the wildlife community state are achieved.

The invention discloses a stereoscopic warehouse multi-machine cooperative scheduling control method based on digital twinning, and relates to the technical field of intelligent warehousing, and the method comprises the steps: collecting stereoscopic warehouse three-dimensional layout and multi-machine sensing data, and obtaining a standardized data stream through consistency verification; binding an instruction of the compliance control instruction set with target equipment by adopting a time sequence coding method, and issuing the instruction to the target equipment to obtain an execution feedback and backspacing mark; and according to the execution feedback and the rollback mark, performing fusion and noise reduction processing to form a continuously optimized scheduling cycle. According to the method, the standardized data flow is processed by adopting a multi-source feature fusion method, vibration, temperature and current features are integrated, and a healthy feasible region is generated, so that comprehensive evaluation and monitoring of the state of the warehouse equipment are realized, a scheduling framework for dynamic adjustment is provided, the resource utilization rate is optimized, and the scheduling accuracy and adaptability are improved.

The invention discloses a specialized nurse-oriented blood purification personalized course recommendation method and system, and relates to the technical field of medical teaching. The method comprises the following steps: S1, collecting blood purification operation data, and preprocessing the blood purification operation data; s2, dividing the preprocessed blood purification operation data into four stages, and calculating corresponding operation characteristic values; S3, obtaining operation capability characterization parameters of each stage, constructing a nurse operation evaluation model, and outputting a blood purification operation evaluation value of a nurse; and S4, generating a personalized learning task package based on the hematopurification operation evaluation value and the weight coefficient of each stage, pushing the personalized learning task package to a nurse side, constructing an evaluation difference matrix before and after learning, and performing closed-loop updating of course recommendation. The problem that an existing nurse training system cannot achieve objective evaluation and precise course matching based on multi-dimensional data of the hematology operation process, and consequently learning content is general and insufficient in individuation is solved.

The application discloses a psychological state evaluation method and device, electronic equipment and computer program product, and relates to the technical field of psychological state evaluation. A psychological evaluation guide task is displayed; a voice signal during user voice interaction and a physiological detection signal of the user are collected; multi-dimensional feature extraction is performed on the voice signal; a physiological emergency index is extracted from the physiological detection signal; consistency verification is performed on a semantic emotional valence feature, an acoustic dynamics feature and a psychological state indicated by the physiological emergency index; a concealment penalty factor is generated based on the physiological emergency index when the consistency verification fails; a corresponding feature weight matrix is called to weight and fuse each dimensional feature to obtain an initial emotion score; the initial emotion score is corrected based on the concealment penalty factor to obtain a corrected emotion score, and a psychological state evaluation result is generated according to the corrected emotion score. The application can improve the accuracy of user psychological state evaluation.

This invention discloses a method, system, electronic equipment, and medium for monitoring and evaluating the degree of corrosion in pipelines. It relates to the technical field of monitoring and evaluating the degree of pipeline corrosion, particularly the technical field of monitoring and evaluating the impact of ultra-high voltage direct current (UHVDC) transmission systems on the corrosion of pipeline outer walls. This invention addresses the problem in existing technologies that cannot monitor the thinning of affected pipeline walls throughout their entire lifecycle, thus hindering the evaluation of the degree of pipeline outer wall corrosion and the effectiveness of drainage facilities. The invention includes: conducting pipe-to-ground potential detection along the entire pipeline; determining the distribution of cathodic and anodic zones along the entire pipeline; identifying the section of the pipeline most severely affected by external corrosion; calculating the outflowing current density; calculating the annual corrosion rate of the pipe wall; and evaluating the degree of influence of DC stray current on the pipeline. Based on the annual corrosion rate of the pipe wall, this invention can quantify the impact of UHVDC stray current on the pipeline, thereby simultaneously achieving the evaluation of the degree of pipeline outer wall corrosion and the effectiveness of drainage facilities.

The application discloses a search quality real-time evaluation method and device, equipment and a storage medium, and relates to the technical field of Internet, and comprises the following steps: a sample pool construction module is used for online search based on each preset search vocabulary and corresponding prediction category and entity recognition result to determine a corresponding number of positive and negative samples to obtain a target commodity sample pool; a vocabulary routing module is used for judging whether a target search vocabulary currently input by a user belongs to a preset search vocabulary, and if yes, the target search vocabulary is routed to the target commodity sample pool to perform a search operation; a data uploading module is used for storing detailed data of each search stage into a preset persistent database based on a preset burying point when the search operation is performed; and a quality evaluation module is used for determining a quality evaluation result based on the detailed data and each preset quality index formula. The application determines the search quality by evaluating the detailed data of each search stage when the preset search vocabulary is input, and effectively guarantees the real-time performance of the evaluation.

The invention discloses an equestrian competition performance prediction system based on multi-modal monitoring, and relates to the technical field of equestrian sport monitoring and intelligent prediction. The system comprises an acquisition module, a processing module, a prediction module, a display module and a data closed-loop optimization mechanism. The acquisition module is used for acquiring horse video and physiological and environmental multi-modal data through multiple types of equipment; the processing module is used for realizing data fusion processing, gait behavior recognition and facial expression emotion evaluation; the prediction module outputs a competitive performance quantitative score and risk early warning based on a machine learning model; the display module provides a visual interaction interface; and the data closed-loop optimization mechanism realizes model iterative optimization through competition result return. According to the method, the problems of single monitoring dimension, data splitting, lack of prediction capability and the like in the prior art are solved, comprehensive perception of horse states and accurate prediction of competitive performance are realized, the scientific level of training management is improved, and the method has wide practical prospects and industrialization values.

The invention belongs to the technical field of oil and gas field development, particularly relates to a high-water-cut stage seepage field potential utilization evaluation method, and aims to solve the problems that an existing evaluation method is incomplete in attribute parameters and inaccurate in evaluation of seepage field potential utilization. The method comprises the steps that parameters of a high-water-content oil field are collected and screened, and seepage field potential utilization space-time attribute characterization parameters are obtained; analyzing the seepage characteristics of the high-water-content oil field according to a streamline seepage field simulation method, and calculating the influence coefficient of the movable seepage potential in combination with the space-time attribute characterization parameters of the potential utilization of the seepage field; and according to each influence coefficient, obtaining the movable seepage potential of the high-water-content oil field in the development stage, namely an evaluation result. The problems that an existing seepage field potential utilization evaluation method is incomplete in attribute parameters and inaccurate in evaluation result are solved.

This invention belongs to the field of intelligent perception and simulation training technology for maritime navigation. It discloses a method and system for realistic training and evaluation of ship collision avoidance rules at sea. By constructing a hierarchical question bank, it generates scenario assembly lists and uniformly schedules ship motion simulation, 3D visual scenes, radar / AIS fusion display, and voice communication interaction. During training, it collects control inputs, motion states, target information, and communication records to form a traceable chain of evidence, and aligns sampling based on evaluation anchor points. An automatic scoring model is established based on result and process indicators, outputting an evaluation report with sub-scores, evidence location, a list of violations, and retraining suggestions. It is suitable for teaching, retraining, and assessment, improving the efficiency and achievement rate of collision avoidance decision-making training.

This invention belongs to the technical field of elevator car operation status monitoring and remaining life assessment, specifically involving a method for estimating the remaining life of an elevator car based on damage index trend prediction. A spatial reference coordinate system is established, sensors are deployed, and data is collected, including but not limited to vibration acceleration, sound pressure signal, and leveling accuracy. The car operation data is preprocessed to solve for characteristic parameters, including but not limited to swing angle, speed deviation, and leveling accuracy. A random forest algorithm is used to quantify the importance of features and screen core health indicators. Damage evaluation coefficients are constructed by normalizing and weighting the health of the indicators, and a time accumulation factor is introduced for correction, resulting in a quantified car damage index. The damage index is converted into a percentage of used life using an exponential mapping formula, achieving a quantitative estimate of the remaining life. This invention features multi-dimensional measurement, improving the accuracy of car status assessment; reasonable damage index quantification, taking into account cumulative effects; and reasonable estimation, providing strong guidance for operation and maintenance.

The invention provides a drinking water new technology risk identification method and system based on multi-source data, and the method comprises the steps: firstly constructing a multi-source data linkage sequence which comprises a multi-dimensional data time sequence linkage relation of a drinking water production process, water quality monitoring and the like; extracting technical, social and ethical dimension correlation characteristics causing risk conduction as multi-dimensional risk conduction trigger factors, constructing a risk dynamic conduction link presenting a risk conduction path and evolution correlation, constructing a dynamic evaluation model containing a weight self-optimization module based on a random forest algorithm, tracking risk evolution characteristics, and establishing a risk evaluation model containing a weight self-optimization module; and the risk type and the influence range are determined according to the characteristics and the model output result, so that the risk type and the influence range existing in the application process of the drinking water new technology are accurately determined, the accuracy and timeliness of risk identification of the drinking water new technology are effectively improved, and safe supply of drinking water and public health are guaranteed.

The application discloses a kind of sepsis multiple complications prediction method, system, storage medium and equipment, belong to intelligent medical technical field, method includes the following steps: S1. acquisition sepsis patient multidimensional time series physiological data;S2. medical perception feature vector is constructed to the time series physiological data;S3. sepsis multiple complications prediction model is constructed and introduced physiological logic space-time composite mask matrix;S4. the output multiple complications prediction probability using hierarchical expert hybrid network H-MoE;S5. the model is trained using dynamic weighted loss function based on task uncertainty;S6. risk output and feature contribution degree explanation;The model of the application is introduced by physiological logic mask and H-MoE architecture, realizes the accurate, synchronous prediction of sepsis complex complications, can effectively filter redundant features, and reveal the complex correlation therebetween, provide quantitative decision support for clinician, to realize early intervention and individualized treatment.