545 results about "Online evaluation" patented technology

The invention relates to the technical field of building construction monitoring, and discloses a method for evaluating mechanical properties of a steel structure building construction whole process based on digital twinning. The method comprises the steps of establishing a digital twin model fusing multi-source information, and performing real-time linkage with a sensor network arranged on site. Collected data such as deformation, temperature and wind speed are processed through dynamic fusion and an anomaly recognition algorithm, model parameters are continuously corrected, and real-time dynamic high-fidelity mapping of the mechanical state in the construction process is achieved. And based on the updated model, the intelligent analysis module performs cooperative calculation, autonomously identifies construction abnormity, quantitatively predicts potential risks, generates a process optimization decision instruction and feeds back the process optimization decision instruction to a site. According to the method, a closed-loop regulation and control mechanism from data perception, model analysis to decision execution is constructed, and accurate online evaluation of construction mechanical properties and active prediction control of safety risks are realized.

The invention discloses an electric power system inertia online evaluation method, device and equipment and a readable storage medium, and relates to the technical field of novel electric power systems, a unified inertia analysis model comprising network construction type equipment, network following type equipment and a synchronous generator is established through theoretical derivation, then dynamic modeling is performed based on extended Kalman filtering, and the inertia of the electric power system is evaluated. Inertia solution is converted into a parameter identification problem, so that inertia levels of various generators in the system are obtained, continuous tracking of equivalent inertia of the system is realized by adopting a recursive least square algorithm with a forgetting factor, and accurate calculation of inertia of each unit can be realized. The local and system equivalent inertia of the new energy power system under various control strategies is effectively evaluated, and an important reference basis is provided for power grid operation.

The invention discloses a pilot manipulation ability evaluation method and system based on multi-modal data and online learning driving. The method comprises the steps of determining an input parameter set, establishing a flight vision time sequence fusion network, and extracting each feature of the parameter set. And completing feature fusion of the multi-modal data through an attention mechanism and global aggregation. And constructing a pilot maneuvering ability evaluation architecture, and generating an offline pilot maneuvering ability evaluation model by designing a loss function based on a comprehensive scoring error and sub-scoring consistency constraint. Flight data and eye movement data streams generated in real time are fragmented and preprocessed by using a sliding window mechanism, a lightweight model based on pruning and parameter quantification technologies is designed for real-time scoring requirements, and feature expression ability in an offline pilot manipulation ability evaluation model is migrated to a lightweight online evaluation model. The method can be used for improving a pilot training scheme, optimizing the man-machine interface design of a cockpit, and improving the efficiency and safety of flight operation.

The invention belongs to the field of battery monitoring, and particularly relates to a battery health state on-line evaluation system considering a temperature-current coupling effect, which comprises an acquisition module, a coupling module, an on-line evaluation module and an early warning module, the acquisition frequency and the window length of current and temperature data are adaptively adjusted through reinforcement learning and a federated frame in the acquisition module; the coupling module constructs a consistency interval based on the current and temperature slope sequence, calculates a segmented current thermal stress factor and a segmented temperature gradient factor, and further generates a temperature-current coupling feature vector; the online evaluation module performs health state evaluation and life decay rate calculation in combination with dynamic weight distribution and principal component analysis; the early warning module gives an alarm in a grading manner according to an evaluation result, and feeds back an attenuation rate to the front-end acquisition module and the online evaluation module to realize closed-loop optimization; according to the method, the accuracy and reliability of battery health state evaluation of the electric two-wheeled vehicle and the tricycle under complex working conditions are effectively improved.

The invention relates to the technical field of SRAM (Static Random Access Memory) memory anti-radiation testing, and discloses an SRAM memory anti-radiation testing method and system based on running state injection, and the method comprises the following steps: system initialization, mapping preparation and time sequence baseline establishment; carrying out model quantitative compiling, sensitivity analysis and physical bit candidate generation; generating a running state injection plan and loading a test case; performing operation state execution, disturbance injection and synchronous monitoring acquisition; multi-source triggering, weight read-back and fault decoupling judgment are carried out; and performing quantitative evaluation on anti-radiation performance, constructing a degradation curve and outputting a report. The system corresponds to the method. According to the invention, a hardware-in-the-loop operation state injection and online evaluation technology is constructed, and the technical problems of guidance and position limited disturbance / equivalent irradiation injection based on weight bit level sensitivity and logic-physical mapping are solved.

The invention relates to the technical field of data resource management, in particular to an online evaluation method for data resource value, which comprises the following steps of: constructing a multi-dimensional evaluation index set, and acquiring metadata and market dynamic information of to-be-evaluated data in real time through a distributed interface; carrying out weight distribution on the index set based on a dynamic weighting algorithm, generating a weight coefficient set and constructing a value calculation model; performing standardized preprocessing on the collected original data, inputting the data into the model, and calculating an initial estimated value; and dynamically correcting the initial value estimation value based on the timeliness attenuation factor and the data scarcity factor, and generating a final value estimation value and an estimation report. Through multi-dimensional index construction, a dynamic weighting algorithm, standardized preprocessing and two-factor correction, automatic evaluation of the data value is realized, the problems of single dimension, static weight and neglect of timeliness scarcity of a traditional method are solved, the real-time performance and scientificity are improved, and a quantitative decision basis is provided for data capitalization.

The invention relates to a task simulation method and device of a virtual simulation platform and computer equipment. The method comprises the following steps: a data synchronization management module performs data synchronization of each heterogeneous virtual simulation sub-platform according to a pre-controlled data synchronization frequency, and further needs to perform online evaluation and offline evaluation so as to optimize an autoregressive moving average model and a reinforcement learning model to perform simulation at the next moment. By adopting the method, the efficiency of data synchronization between heterogeneous simulation platforms can be improved.

The invention relates to the field of power system operation and maintenance, in particular to a power transformation equipment operation and maintenance risk online assessment method and system. A power transformation equipment operation and maintenance risk online evaluation system comprises a data acquisition module, a weight configuration module, a sequence risk evaluation module, a collaborative decision game module and a disposal scheme output module. According to the method, a multi-source data fusion and dynamic threshold mechanism is introduced, real-time state quantity, historical maintenance records, operation modes and external weather information are uniformly mapped to a convolution-long and short-term memory network, key features are adaptively amplified in a feature weighting layer, redundant features are weakened, and collaborative recognition of short-term fluctuation and long-term degradation is achieved; compared with a traditional fixed threshold value or single monitoring quantity model, the method can keep sensitive and steady risk early warning capacity under the complex working conditions of severe weather, heavy load operation and the like, the false alarm rate and the missing report rate are greatly reduced, potential faults are locked in advance, and sudden power failure events are avoided.

A hydroelectric generating set primary frequency modulation performance online evaluation method establishes a plurality of primary frequency modulation performance evaluation indexes, introduces an entropy weight method and an empowerment method, constructs a primary frequency modulation performance comprehensive evaluation system, and acquires and analyzes historical data and real-time data of a hydroelectric generating set. Real-time supervision and comprehensive evaluation of the primary frequency modulation performance of the hydroelectric generating set are realized, the problems of few evaluation angles, strong subjectivity, single data source and the like are effectively solved, weak links of the primary frequency modulation of the hydroelectric generating set can be found in time, the primary frequency modulation performance of the set is adjusted and optimized, the primary frequency modulation capability of the set is improved from management, and the efficiency is improved. A primary frequency modulation control system and system of a hydroelectric generating set are guided and helped to be perfected, so that random fluctuation of a power grid is quickly eliminated, the electric energy quality is improved, and safe, stable and economical operation of the set is guaranteed.

The invention discloses an AI-based depression digital comprehensive intervention system and method. The system comprises a patient end, a family member end, a doctor end and a community end. The patient end comprises a multi-dimensional evaluation module, a multi-dimensional intervention module and a management module, and is used for performing multi-dimensional evaluation on a patient and forming a personalized intervention scheme, so that the patient can perform multi-dimensional personalized intervention training, and the personal state is recorded. The family member end comprises a science popularization education module, an interaction module and a question answering module, and is used for carrying out science popularization education on family members, carrying out intervention training in cooperation with patients and checking personal information of the patients. The doctor end comprises a patient list module, an online multi-dimensional evaluation module and an online help module, and is used for online evaluation and online question answering. The community end is connected with the patient end and the doctor end and is used for carrying out manual intervention on high-risk patients. By means of intelligent and personalized means, a patient is helped to relieve the depressive symptom from multiple dimensions in the whole process of evaluation, treatment and prognosis.

The invention relates to the technical field of power equipment operation and maintenance and state monitoring, in particular to an optical fiber transformer health state assessment method and system based on feature fusion. According to the invention, sampling signals of a plurality of optical fiber current transformers are collected and preprocessed; constructing a historical data matrix for principal component analysis, establishing a principal component space and calculating a square prediction error control limit value; constructing a simulated fault data set and projecting the simulated fault data set to a principal component space to calculate a square prediction error time sequence; performing wavelet packet decomposition on the square prediction error time sequence to extract a normalized energy feature vector, and training a one-dimensional convolutional neural network model; projecting the real-time sampling signal to a principal component space to judge whether the real-time sampling signal exceeds the limit or not; if yes, the health state level is output through the trained model. According to the invention, accurate grading and real-time online evaluation of the health state of the optical fiber transformer are realized.

The invention discloses a one-stop intelligent recruitment method, device and equipment and a storage medium, and the method comprises the steps: analyzing an electronic resume uploaded by a user, and extracting personal basic information; pushing an online evaluation questionnaire to the user, obtaining evaluation result data of the user, and performing dimension alignment on the personal basic information and the evaluation result data to generate a user comprehensive information file; acquiring post demand data corresponding to a preset job fair, analyzing the post demand data, and generating post feature vectors; constructing a matching model corresponding to the user and the post based on a deep matching neural network, inputting the user comprehensive information file and the post feature vector into the matching model, and outputting a candidate post list containing a matching degree score; receiving a target post selected by the user from the candidate post list, constructing an AI virtual interview scene, and analyzing the answer content of the user in the virtual interview to generate an interview evaluation report; and analyzing the interview evaluation report to assist the user in participating in the preset recruitment fair.

The invention discloses a voltage transformer metering error evaluation method, which relates to the technical field of voltage transformer metering and comprises the following steps of: constructing a synchronous acquisition system of multiple groups of voltage transformers in a transformer substation, acquiring original voltage data, constructing a virtual reference voltage generation model and outputting a reference voltage sequence; the method comprises the steps of establishing a virtual reference voltage model, calculating error statistical characteristics, marking the operating state grade of the voltage transformer, evaluating health scores according to the error characteristics, outputting a scoring result, receiving inspection feedback comparison differences, updating the model, generating an analysis chart, and uploading the analysis chart to a client display and linkage operation and maintenance system. On-line error evaluation is realized, multi-source data are fused, error features are dynamically extracted, an operation state is marked, a health scoring and early warning mechanism is established, a feedback closed loop is introduced to optimize model precision, an evaluation result is displayed in a chart form, operation and maintenance scheduling is assisted, and the real-time performance, the accuracy and the self-adaptive capability of evaluation are improved.

The invention relates to the technical field of industrial process control, and discloses a digital twinning-based fermentation process temperature adaptive control system, which comprises a data acquisition and state representation module, a probability generation type digital twinning module, an adaptive risk management module, an opportunity constraint model prediction control module and a model iteration trigger module, according to the method, probability distribution of future states is predicted by constructing probability generation type digital twinning, and the uncertainty of the model is quantified in real time; the system can dynamically adjust the risk tolerance, drives the chance constraint model prediction controller, seeks an optimal control solution on the premise of ensuring the process safety probability, triggers the model adaptive update by means of an online evaluation mechanism, and realizes the optimal control solution on the basis of effectively managing the process uncertainty. The fermentation temperature is subjected to high-performance self-adaptive control considering safety and economy, and the problem that a traditional deterministic model is insufficient in control robustness is solved.

The invention discloses a power battery health degree online evaluation method and system based on a charging curve, and the method comprises the steps: dynamically dividing a characteristic interval of a constant-current charging stage and a constant-voltage charging stage through monitoring the voltage and current data in a charging process in real time; extracting time domain and frequency domain characteristic parameters in the characteristic interval; inputting the time domain and frequency domain characteristic parameters into a health degree evaluation model subjected to transfer learning optimization, and generating a fusion health degree index at least comprising a battery capacity attenuation coefficient and an internal resistance change vector; and outputting a final health degree evaluation result and residual service life prediction through an adaptive weighting algorithm based on the fused health degree index in combination with historical cycle data and operating environment parameters of the battery. According to the embodiment of the invention, high-precision and non-intrusive online evaluation and life prediction of the health degree of the battery can be realized, and the real-time performance, accuracy and engineering applicability of evaluation are improved.

The invention discloses a new energy station multi-band oscillation risk online evaluation and early warning method and a storage medium, and the method comprises the steps: carrying out the frequency sweeping to obtain the frequency domain impedance characteristics of fans and photovoltaic equipment of various models in a new energy station under different typical working conditions, and carrying out the frequency sweeping to obtain the frequency domain impedance characteristics of a power grid under different typical working conditions; the power flow level of the new energy station and the power grid side is monitored online, the working condition types of a new energy unit and the power grid are judged according to online monitoring data, and a new energy station equivalent impedance model is established according to the impedance characteristics of fans and photovoltaic equipment of various models of the new energy station and the station topology. And on-line evaluation of oscillation risks is carried out based on the Nyquist criterion. And selecting whether to send an oscillation alarm signal based on the evaluation result. According to the method, the multi-band oscillation risk of the new energy station can be effectively evaluated and early warned, and safe and stable operation of the new energy station is ensured.

The invention discloses a numerical control machining tool health state online evaluation system and method based on multi-sensor fusion. Comprising a data acquisition module, a noise data preprocessing module, a noise feature analysis and mode recognition module, a tool health state evaluation module and a multi-source data fusion module, and the data acquisition module, the noise data preprocessing module, the noise feature analysis and mode recognition module and the tool health state evaluation module are connected in sequence. The data acquisition module and the cutter health state evaluation module are both connected with the multi-source data fusion module, and the multi-source data fusion module is connected with the numerical control system. By integrating various sensor data, noise interference of a single sensor is reduced, the accuracy and stability of cutter health state evaluation are improved, and intelligent online evaluation of the cutter health state is realized.

The invention provides an online value evaluation system based on real estate, and belongs to the technical field of online evaluation, and the system comprises an online data obtaining module which is used for obtaining a basic feature vector and multi-modal evaluation data of a target real estate online; the feature extraction and mining module is used for carrying out space mapping on the explicit features and the defect features and carrying out potential association mining on the implicit features, and obtaining high-association implicit description in combination with the basic feature vector; the evaluation processing module is used for identifying real evaluation, false evaluation and fuzzy evaluation, and correcting the fuzzy evaluation after preliminary analysis according to a spatial mapping result and high-correlation implicit description in combination with social platform subject discussion data; and the value evaluation module is used for obtaining a comprehensive value according to the basic value reference line, the real evaluation and the correction evaluation, and outputting a value evaluation report. The problems that real estate evaluation is difficult to distinguish virtuality and reality and is fuzzy and difficult to solve are effectively solved, and the evaluation result can be comprehensive and accurate.

The invention relates to an SCR system self-adaptive flow guide regulation and control method based on real-time flow field feedback, and belongs to the technical field of flue gas denitration. The method comprises the following steps: acquiring inlet flue gas flow velocity distribution and outlet partition NOx concentration distribution; establishing a flow field and concentration field uniformity online evaluation rule, and constructing an initial knowledge base based on adaptive regulation and control logic; dynamically adjusting the angle of the adjustable guide plate according to the uniformity evaluation result, synchronously recording the state change of the flow field, and iteratively updating the regulation and control knowledge base; and an exclusive data interaction link of diversion regulation and control and partition ammonia spraying control is established, and flow field pre-judgment information sharing and ammonia spraying parameter presetting are achieved. The real-time collaborative optimization of the flow field and the concentration field in the SCR system is realized, the denitration efficiency and the ammonia escape control precision are improved, and the self-adaption and intelligent regulation and control capabilities of the system are enhanced.

The invention relates to a college student artificial intelligence course online evaluation method and related equipment, and the method comprises the following steps: carrying out the construction of a knowledge point dependency chain of a course outline knowledge point, and obtaining a knowledge point dependency chain; obtaining an online behavior log of a target user, and inferring a learning track of the target user in the knowledge point dependency chain based on the online behavior log through a preset hidden Markov model; based on the learning track and the online behavior log, predicting a mastering probability of a target user at each course outline knowledge point; performing weight adjustment on each course outline knowledge point based on the mastering probability to obtain an adjusted knowledge point weight; and evaluating the online condition of the target user based on the mastering probability corresponding to the knowledge points of the course outline and the adjustment of the weight of the knowledge points, thereby solving the technical problem that a traditional evaluation method mainly depends on an end-of-period test or a regular test and often cannot reflect the learning progress and the knowledge mastering condition of students in real time.

The invention discloses a crystallizer copper plate correction method based on digital twinning, which aims at solving the problem of deformation of a crystallizer copper plate caused by thermal stress and mechanical stress in the using process, realizes real-time monitoring and intelligent correction of the copper plate from a global perspective, and mainly comprises the following steps: constructing a digital twinning model of the crystallizer copper plate; performing multi-physics field coupling simulation by combining the structural characteristics and the use environment of the copper plate; on the basis of real-time data collected by a digital twin system, an improved genetic algorithm is used for online evaluation and pre-judgment of copper plate deformation; generating a correction scheme based on a self-adaptive control strategy, and performing intelligent correction on the copper plate in combination with an execution mechanism; and continuously updating the digital twin model through a feedback mechanism to realize closed-loop optimization of copper plate correction. Through the digital twinning technology, deformation of the crystallizer copper plate is accurately predicted and dynamically corrected, the service life of the copper plate is prolonged, the operation efficiency of the copper plate is improved, and the automation level and the production stability of metallurgical equipment are improved.

The invention is suitable for the technical field of data processing, and provides a new energy station voltage and frequency coupling cooperative control performance online evaluation method, which comprises the following steps: acquiring a three-phase voltage signal, a frequency signal, a unit output power signal and a power grid dispatching instruction signal of a new energy station grid-connected point, and calculating a voltage and frequency coupling characteristic matrix; performing collaborative quantitative analysis to generate a collaborative parameter matrix; performing control performance evaluation on the collaborative parameter matrix based on the collaborative evaluation network structure to generate collaborative evaluation parameters; constructing a dynamic coupling constraint function, and calculating to obtain a collaborative stability boundary representing the operation state of the station; and dynamically correcting the collaborative evaluation parameters to generate a station control performance optimization sequence. And the safety, the stability and the electric energy quality of the operation of the new energy station are effectively improved.

The invention relates to the technical field of online water quality intelligent monitoring scheme design, in particular to an online water quality intelligent monitoring method and system and electronic equipment. The method comprises the following steps: acquiring GPS data, water temperature data, PH data, dissolved oxygen content data and conductivity data of a target point location of a target water area within a preset duration according to a preset time interval through a GPS sensor, a water temperature sensor, a PH sensor, a dissolved oxygen sensor and a conductivity sensor which are arranged at the target point location of the target water area, and acquiring timestamp data; the online water quality intelligent monitoring model is trained, whether the water quality data of the target water area is abnormal or not is judged, overall online evaluation and judgment on the overall water quality data of the target water area are achieved, whether the overall water quality data of the target water area is abnormal or not is accurately judged, and the safety, reliability and intelligent degree of water quality detection are improved to a great extent. The intelligent degree and usability of the system are improved to a great extent, and the application scenarios of the system are greatly expanded.

The invention relates to a new energy power plant black-start capability evaluation method, which comprises the steps of determining evaluation indexes for evaluating the black-start capability of a new energy power plant, including a continuous output capability index, a black-start voltage regulation capability index, a frequency modulation capability index, a frequency modulation time index and a virtual inertia index; obtaining the value of each evaluation index of the new energy power plant to be evaluated, and constructing a standardized evaluation matrix; and based on the standardized evaluation matrix, adopting an entropy weight fuzzy comprehensive evaluation method to evaluate the black-start capability of the to-be-evaluated new energy power plants, and realizing sorting of the black-start capabilities of the plurality of to-be-evaluated new energy power plants. According to the invention, on the basis of seasonal historical data of the new energy power plant, a black-start capability evaluation index system based on seasonal characteristics of the new energy power plant is constructed. And the lack of research on the black-start capability evaluation scheme of the new energy power plant in the prior art is made up. The method can meet the demand of off-line evaluation, and is suitable for industrial application.

The embodiment of the invention provides an online evaluation method and device applied to a product. The method comprises the steps of obtaining a to-be-admitted product; for the to-be-admitted product, determining a plurality of first indexes of the to-be-admitted product under the inherent risk assessment dimension and a plurality of second indexes of the to-be-admitted product under the control measure effectiveness dimension; determining a first evaluation result according to the plurality of first indexes and the corresponding first risk attribute information, and determining a second evaluation result according to the plurality of second indexes and the corresponding second risk attribute information; determining a target risk level of the to-be-accessed product according to the first assessment result, the second assessment result and a predetermined risk assessment table; and determining an online evaluation result of the to-be-admitted product according to the target risk level. According to the technical scheme of the embodiment of the invention, the online evaluation result of the to-be-admitted product is obtained according to the inherent risk evaluation dimension and the control measure effectiveness dimension, whether the product has non-compliant transactions or not is evaluated more comprehensively, and the supervision efficiency and accuracy are improved.

The invention relates to the technical field of power system inertia evaluation, in particular to a random mode switching driven power distribution network equivalent inertia online evaluation method and system, and the method comprises the steps: carrying out the priori definition of a group of hidden operation modes based on the equivalent inertia of a power distribution network, and constructing an unobservable Markov chain; a double-layer hidden Markov jump system model is constructed, the bottom layer is an unobservable Markov chain, and the upper layer is a continuous dynamic behavior model for describing the power distribution network; on the basis of PMU high-resolution time sequence disturbance data, deducing the probability of a system maximum probability dominant operation mode and the probability of each hidden operation mode; and carrying out multi-mode probability adaptive weighting on each equivalent inertia estimation value based on the probability of the dominant operation mode and each hidden operation mode so as to obtain the equivalent inertia estimation value of the power distribution network on line. Through the method, the problem that the equivalent inertia of the power distribution network is difficult to quickly, accurately, dynamically and adaptively assess online under the conditions of high permeability of new energy and frequent switching of working conditions is effectively solved.

The invention discloses a method for dynamically evaluating the ultimate bearing capacity of a layered damage composite material cylindrical pressure-resistant shell in the field of deep sea equipment structure health monitoring, which comprises the following steps of: establishing a nonlinear relationship between a critical buckling load of an unlayered composite material cylindrical shell and a critical buckling load of a layered composite material cylindrical shell, and calculating an attenuation coefficient of each group of samples; measuring inner and outer layer strain values of the to-be-evaluated composite cylindrical shell, detecting extreme value statistics and cumulative sum based on change points of the inner and outer strain values, judging that the position of the point is layered, constructing a layering point set, calculating five layering characteristic parameters of the to-be-evaluated composite cylindrical shell based on the layering point set, and substituting the five layering characteristic parameters into an attenuation coefficient formula, obtaining a critical load of the to-be-evaluated composite cylindrical shell; according to the method, through combination of dual-surface strain cooperative monitoring and a regression model, prediction of critical buckling loads in a service state is realized, environments such as shell thickness fluctuation, salt spray corrosion, interface fracture parameter measurement errors and impact loads are compatible, strain gauge monitoring and limit load online evaluation are fused, and a dual evaluation system is formed.

The invention belongs to the technical field of energy internet, and particularly relates to a regional energy internet toughness online evaluation method based on model-data hybrid driving. According to the method, firstly, a comprehensive energy network physical model is constructed, a toughness evaluation index system is established, the physical model covers energy nodes and line models, and energy activities are simulated through the law of conservation of energy and the like; and then carrying out real-time monitoring and risk early warning, collecting data by using an intelligent sensor, and predicting the risk in combination with machine learning. Then, based on physical model output and real-time data, a deep learning algorithm is adopted to evaluate network toughness, and a CNN-RNN model, adversarial training and Bayesian reasoning are fused to determine a weight; and finally, according to an evaluation result, applying a hybrid optimization algorithm and a reinforcement learning optimization strategy, and considering market factors to adjust energy operation. The advantages of the model and the data are integrated, the toughness evaluation accuracy, the risk early warning timeliness and the strategy optimization effectiveness are effectively improved, and the method is of great significance to energy internet development.

The invention discloses an ocean engineering structure extended Kalman filter parameter estimation method based on attitude angle virtual observation, and relates to the technical field of ocean engineering, and the method comprises the following steps: S1, initializing a system process noise covariance matrix and an observation noise covariance matrix, and obtaining an extended Kalman filter attitude angle estimation value; s2, establishing a high-precision angular velocity-attitude angle dynamic conversion model, generating attitude angle virtual observed quantity and using the attitude angle virtual observed quantity for online evaluation of a Kalman filtering result; s3, setting a multi-dimensional performance evaluation index system, and quantitatively evaluating the amplitude error and trend stability of the extended Kalman filter estimated attitude angle; s4, adjusting scale factors of a system process noise covariance matrix and an observation noise covariance matrix by using a gradient descent algorithm based on the attitude angle virtual observation quantity and the error measurement of the multi-dimensional performance evaluation index, and carrying out real-time iteration on the scale factors of the system process noise covariance matrix and the observation noise covariance matrix; the problem that attitude parameters cannot be accurately measured and predicted in real time in the prior art is solved.

The invention discloses an old people health risk assessment method based on artificial intelligence. The method comprises the following steps: collecting and preprocessing multi-source health original data of target old people; constructing a feature set, and generating a multi-modal feature vector; performing parameter training on the improved MTGP model to obtain a trained improved MTGP model; generating an online evaluation feature vector and inputting the online evaluation feature vector into the trained improved MTGP model to obtain a health risk prediction result; calculating a health risk score; performing time sequence analysis and trend identification analysis, and outputting a health risk level; generating risk early warning information; and the health risk archives are displayed and called, so that fusion of multi-source health data, joint evaluation of the multi-task health risk of the old people and dynamic identification of the change trend of the health risk are realized.