434 results about "Baseline data" patented technology

The invention relates to the technical field of bolt tightening, and provides an intelligent bolt tightening control method and system based on axial force measurement. Comprising the steps of collecting bolt identification and process parameter data, collecting temperature measurement data, and obtaining sound velocity compensation parameters and elastic modulus compensation parameters. And zero-load preloading data are collected and processed to generate zero-load baseline data, and a self-calibration model is obtained. Axial force data and ultrasonic echo data are collected, and an axial force fusion estimation result is obtained. And comparing an axial force fusion estimation result with target axial force data. And when in the target interval, fine twisting control is carried out based on model predictive control and a micro-stepping strategy, and the controllable damping unit is driven to carry out energy absorption and stopping. And acquiring an axial force fusion estimation result when the target axial force is reached, processing the data in the holding stage to obtain an axial force rebound check result, and if the axial force rebound check result exceeds the limit, performing secondary twisting control. According to the scheme, refinement and traceability of the bolt tightening process are achieved.

The embodiment of the invention discloses an equipment health assessment method and device based on multi-dimensional data fusion and dynamic weight, and relates to an intelligent monitoring and health assessment technology of an equipment complex system in the field of equipment health management. The method comprises the following steps: describing the change condition of health parameters along with the increase of equipment storage life from the angles of the regression trend of the health parameters along with time, the incidence relation among different health parameters and the probability distribution of health parameter data, and accurately reflecting the difference between health parameter monitoring data and baseline data. A dynamic weight updating method in the whole storage process is provided, and the weights of different health parameters in the process of calculating the health indexes are scientifically calculated. Therefore, the problem that health parameter monitoring data are scarce due to the fact that the equipment cannot be frequently powered on for testing in the actual storage and use process is effectively solved, and the assessment accuracy of the health state of the equipment is improved under the condition that the monitoring data are limited.

The invention discloses an underground pipeline three-dimensional deformation quantitative inspection method, which comprises the following steps: processing a multi-source sensing sequence to generate a baseline data packet; the method comprises the following steps of: performing first-stage on-line correction, acquiring a medium state agent quantity by applying micro-excitation sampling combined by a micro-amplitude angle and double power to laser, and performing joint inversion to obtain section geometric data subjected to medium correction by utilizing a forward model with endogenous refraction and scattering correction items; second-stage scale robustness processing is carried out, in a unified optimization framework, motor current-based dynamic consistency constraint, geometric conservation constraint and wellhead external reference strong constraint are fused, and a globally consistent pose track is solved; and performing three-dimensional reconstruction and deformation quantification according to the corrected section data and the pose track. According to the invention, the accuracy of geometric measurement of the pipeline and the global consistency of the pose track in a complex environment such as a humid environment can be improved.

The invention discloses a pneumatic joint sealing performance intelligent test method and system based on dynamic pressure spectrum and multi-parameter fusion, and belongs to the technical field of pneumatic element sealing detection. According to the method, a dynamic pressure spectrum simulating an actual working condition is applied to a tested joint, pressure, temperature and ultrasonic signals are synchronously acquired, environmental thermodynamic interference is eliminated by adopting a temperature real-time compensation algorithm, and pressure change purely caused by leakage is separated by deducting system baseline data; and finally, fusing double characteristic parameters of the pressure leakage rate and the ultrasonic energy, and comprehensively evaluating the sealing performance through an intelligent judgment model. The system comprises a gas circuit tool module, a multi-sensing module and a control processing module, can effectively solve the problems that a traditional static pressure maintaining method is greatly influenced by temperature, transient leakage cannot be detected and the like, has the advantages of being high in detection precision, high in anti-interference capability, capable of simulating real working conditions and the like, and remarkably improves the test reliability and efficiency.

The invention discloses a container anomaly detection method, electronic equipment and a storage medium. Acquiring a system call log, network flow data and a resource utilization rate in the container, and comparing the system call log, the network flow data and the resource utilization rate in the container with respective baseline data to obtain abnormal behavior alarm data; performing time-space alignment on the abnormal behavior alarm data and the process tree topological structure, and calculating an edge weight between process nodes by analyzing a mapping relationship between the abnormal behavior alarm data after time alignment and the process nodes in the process tree topological structure based on a triggering condition probability; a weighted causal graph is constructed through the mapping relation between the abnormal behavior alarm data and process nodes in the process tree topological structure and the edge weight between the process nodes; a root cause node with in-degree of 0 is extracted from a weighted causal graph, a root cause propagation path is backtracked along a causal edge, multi-modal data can be fused, the false alarm rate is reduced, and evolution of attack behaviors is reflected in real time.

The invention discloses a new energy charging pile remote control method considering a battery health state. The method comprises the following steps of obtaining battery full life cycle data, charging pile real-time working condition data and charging area power grid dynamic data, and performing preprocessing to generate battery health baseline data, pile body operation reference data and power grid dispatching reference data; extracting current health state parameters of the battery based on the health baseline data of the battery, calculating a charging adaptation coefficient in combination with the operation reference data of the pile body, analyzing the redundant capacity of the power grid, and constructing a charging control decision model; outputting an initial charging parameter combination based on the charging control decision model, performing health protection correction, and generating a basic remote control instruction; and issuing the basic remote control instruction to the target charging pile to calculate an instruction execution deviation degree, and performing dynamic iterative optimization to generate a new energy charging pile remote control scheme. According to the invention, collaborative optimization of safety and high efficiency of new energy vehicle charging and battery charging protection can be realized.

The invention discloses a sand and dust pollution health risk early warning terminal and early warning method, and the method comprises the steps: obtaining multi-source input data, such as meteorological parameters, sand and dust pollutant concentration and geographical environment, in real time, and carrying out the preprocessing through a data fusion algorithm, thereby obtaining a standardized data set; based on the data set, utilizing a trained time sequence prediction model to output a sand and dust pollution level prediction value of a future preset time length; meanwhile, an exposed crowd sensitivity index and regional health baseline data containing the disease incidence rate and the meteorological sensitive crowd proportion are constructed; calculating a comprehensive health risk index through a dynamic health risk assessment model in combination with the predicted value, the sensitivity index and the baseline data; and when the index exceeds a preset threshold dynamically adjusted based on the incidence peak range in the baseline data, generating a graded early warning signal and issuing the graded early warning signal in real time, thereby realizing accurate early warning of the dust pollution health risk.

The invention relates to the technical field of pipe network detection, in particular to a target detection method, system and device based on image recognition and a medium, and the method comprises the following steps: obtaining a data packet in a mechanical mining process; preprocessing the data packet to obtain a target point cloud; performing target positioning of the underground pipe network according to the preprocessed data to obtain a target area; a baseline database of the underground pipe network in the target area is obtained, target detection is conducted on the change trend of the baseline database along with the time sequence, and health data are obtained; according to the acquired health data and the data packet, acquiring detection data of the target area; through time synchronization packaging and semantic fusion of heterogeneous sensor data, a target point cloud containing space-time correlation characteristics is constructed, and progressive monitoring of the cable health state is realized in combination with time sequence analysis of a dynamic baseline database. The problems of leak detection and false detection of cable detection in a complex environment are effectively solved.

The invention discloses a machine vision emotion recognition and interaction system and method based on multi-modal fusion, and the method comprises the steps: obtaining the multi-modal emotion expression data of a user and the basic information data of the user, constructing a user emotion feature initial recognition model, carrying out the calculation through the user emotion feature initial recognition model, and obtaining a user emotion feature recognition result; obtaining an original emotion recognition result; based on the original emotion recognition result, historical recognition deviation data in the long-term interaction process of the user is obtained and preprocessed, and a processed individual emotion baseline data set is obtained; optimizing the user emotion feature initial recognition model by using the individual emotion baseline data set, and constructing a user feature adaptive emotion recognition model; and performing multi-modal emotion recognition prediction based on the user feature adaptive emotion recognition model. According to the method, the emotion discrimination accuracy in a multi-user recognition scene is remarkably improved, and the problem of misrecognition caused by expression differences among the users is solved.

The invention discloses a PLC behavior measurement method, device and equipment based on trusted 3.0 and national cryptographic algorithms, and relates to the field of safety control in an industrial control system.The method comprises the steps that a hardware trust root module installed on a PLC in advance is used for conducting trusted verification on the PLC during starting, and the module is constructed based on the trusted computing 3.0 standard; after verification succeeds, a behavior feature vector of the PLC operation state is obtained based on trusted computing 3.0, wherein the vector comprises at least one of an integrity verification parameter, a resource consumption parameter, an operation parameter and an associated parameter; generating behavior measurement data based on trusted computing 3.0, and measuring a vector through an Euclidean distance algorithm in combination with behavior baseline data; comparing the measurement result with a preset Euclidean distance threshold value, encrypting the measurement result by adopting a domestic key algorithm, and transmitting a comparison result to a security management platform; according to the method, trusted computing 3.0 (TC3.0) and the national cryptographic algorithm are combined, accurate behavior measurement is carried out on the operation state, and the equipment safety and the attack resistance can be enhanced.

The invention relates to the technical field of child cognitive function evaluation, and particularly discloses an intelligent child cognitive function evaluation method and device and a medium, and the method comprises the steps: collecting real-time behavior physiological coupling response data of a tested child under the stimulation of color and digital dual tasks through a multi-modal stimulation collection device, and obtaining a cognitive response chain dynamic model; according to the cognitive reaction chain dynamic model, performing performance quantification on the joint distribution of the reaction accuracy and the reaction time of the child under the stimulation of color and digital dual tasks to obtain a cognitive performance quantitative index; meanwhile, self-adaptive calibration of the individual cognitive baseline is carried out to obtain individual cognitive baseline data; and finally obtaining a personalized course recommendation matrix. The method not only is helpful for evaluating the current cognitive ability level of children, but also can provide reference for self-adaptive calibration of individual cognitive baselines. The method is crucial for recognizing cognitive deviation and formulating a personalized training plan. Adjustment can be carried out according to the specific cognitive requirements of children, and the effectiveness and pertinence of training can be improved.

The invention relates to the technical field of concrete detection, in particular to a concrete protective layer void detection method and system based on a detachable piezoelectric ceramic sensor and an electromechanical impedance resonance technology. The detection method comprises the following steps: temporarily fixing a detachable piezoelectric ceramic sensor on a detection point on the surface of a concrete structure; applying an alternating-current sweep-frequency voltage to excite the piezoelectric ceramic sensor, and obtaining an electromechanical impedance signal frequency spectrum; the electromechanical impedance signal frequency spectrum is analyzed, the frequency of a local defect formant is obtained, and according to the frequency of the local defect formant, geometric parameters of a void area are estimated through a defect size and frequency relation model; and moving the sensor and repeatedly detecting other detection points. The detection method has high efficiency and flexibility, and detection can be realized in any environment; the single-point detection speed is high, the efficiency is high, baseline data is not needed for detection, the anti-interference performance is high, and rapid positioning and quantitative evaluation of void damage can be achieved.

The present disclosure includes devices methods and systems for detecting draper belt tears in a work machine. The method may include receiving belt operation data from one or more sensors. The method may include receiving belt condition baseline data. The method may include includes determining a belt active condition based at least in part on the belt condition baseline data and belt operation data. The method may include determining a change in active condition based at least in part on the belt operation data and the belt condition baseline data. The method may include adjusting one or more belt operation parameters based at least in part on the determination of the change in active condition.

The invention relates to the technical field of optical current transformer detection, and provides an optical current transformer real-time calibration and health diagnosis method and system, and the method comprises the steps: carrying out the harmonic analysis of an initial digital signal sequence obtained through the conversion of a continuous modulation optical signal of a target optical current transformer, and obtaining harmonic component data; physical state evaluation is carried out according to the higher harmonic component data and preset health state baseline data to generate a physical state evaluation result, nonlinear disturbance compensation is carried out according to an error control signal generated based on the higher harmonic component data, and after a linear working point is reached, nonlinear disturbance compensation is carried out; and calculating an electrical measurement value according to the output target fundamental component data, and performing comprehensive analysis according to the physical state evaluation result and the electrical measurement value to generate a diagnosis report. According to the invention, through a measurement calibration and state diagnosis mechanism based on harmonic analysis, while the electrical performance measurement precision is effectively improved, reliable quantitative diagnosis and accurate traceability of different physical faults in the equipment are realized.

The invention relates to the technical field of agricultural product safety detection, in particular to an agricultural product veterinary drug residue high-throughput detection method and system based on spectrum peak analysis. The method comprises the following steps: acquiring multiple batches of spectral data, and performing cross-batch pre-alignment according to the multiple batches of spectral data to obtain cross-batch alignment data; performing background baseline estimation according to the cross-batch alignment data to obtain background baseline data; congestion detection area extraction is carried out according to the cross-batch alignment data to obtain congestion detection area data; performing common-peak decoupling according to the congestion detection area data to obtain common-peak decoupling data; performing residual image construction on the common peak decoupling data according to the background baseline data to obtain residual image data; performing image feature extraction according to the residual image data to obtain image feature data; and performing residual judgment according to the graph feature data to obtain residual judgment data. According to the method, the identification accuracy of spectrum peak pseudo-congestion and residual abnormity in a high-throughput mixed agricultural product detection scene is improved.

The invention provides a heart transplantation post-operation continuous physiological monitoring and anomaly recognition method and system based on wearable equipment, and relates to the technical field of physiological monitoring. According to the method, an individualized dynamic reference interval and a multi-index correlation coefficient matrix are constructed by collecting baseline data such as electrocardio, blood pressure and blood oxygen, abnormal state detection is achieved in combination with the Mahalanobis distance of a unified feature vector and a multi-task recognition model, and then a multi-physics field digital twin model is called to simulate hemodynamic evolution. And high-confidence abnormity is judged according to a coupling rule, so that the intellectualization and response efficiency of postoperative monitoring are improved.

The embodiment of the invention provides a photovoltaic power station fault rapid positioning method and system, and belongs to the technical field of electrical measurement and fault detection. The method comprises the following steps: acquiring electrical parameter data of each group string of a photovoltaic power station, performing normalization processing in combination with an environmental condition, generating baseline data, and screening suspicious group strings based on the baseline data; applying a preset power perturbation signal to the suspicious string and collecting corresponding voltage and current responses to form dynamic response data; determining impedance spectrum features based on the dynamic response data, extracting dynamic response fingerprints, and constructing feature vectors for representing the electrical state of the string; and comparing the feature vector with a health template, executing phase coupling analysis, generating a fault judgment result, and outputting corresponding string positioning information. According to the scheme of the invention, accurate identification and rapid positioning of the cascade fault of the photovoltaic power station group are realized, and high-robustness and high-confidence diagnosis effects can still be maintained under complex working conditions.

The invention relates to the technical field of SAR (Synthetic Aperture Radar) remote sensing imaging, in particular to a scattering heuristic SAR tomography method for structured three-dimensional reconstruction, which comprises the following steps of: S1, acquiring and preprocessing multi-baseline SAR data; s2, inverting scattering parameters based on an attribute scattering center model and orthogonal matching pursuit; s3, segmenting a building area according to coherence and morphological indication; s4, combining direction perception expansion with region updating, determining a continuous scattering region, and giving reference height prior; and S5, constructing a regional adaptive dictionary, sparsely solving the elevation and geocoding to generate a structured point cloud. According to the method, physical prior and regional statistics are cooperated, geometric continuity and anti-noise robustness are improved, pseudo peaks and calculation redundancy are reduced, and upgrading from sparse point cloud to a structured model is realized.

The invention relates to the technical field of computer information security, discloses a data security management and control method and system, and aims to solve the problems of security protection blind areas and response lag caused by management and control system splitting, strategy staticizing, passive risk identification and unclear sensitive data base number in the prior art. The method comprises the steps that a plurality of heterogeneous data sources are connected, operation logs and metadata are collected in real time, and a unified data blood relationship map representing data assets, user entities and processing activity circulation relations is constructed; when a data access request is received, multi-dimensional security features of a user behavior baseline, data sensitivity, and operating environment context are extracted. According to the scheme, panoramic tracking of cross-system data flow and risk assessment of context awareness are achieved, the recognition capability of abnormal behaviors is improved, accurate calculation of risk propagation is supported, automatic gradient response of security strategies is achieved, and service flexibility is considered while data security is guaranteed.

The invention provides an acoustoelectric multi-mode nerve regulation and control method and system for improving the language reading ability, and the method comprises the steps: obtaining brain function activity baseline data based on a positioned abnormal link of the language reading ability of a pupil in combination with resting state and task state electroencephalograms; based on the abnormal links and the brain function activity baseline data, determining a TES target spot and a tFUS target region; starting a TES module based on the TES target spot and starting a tFUS module based on the tFUS target region; a tUS module is started to perform focused ultrasound stimulation intervention on the TES target spot and the tFUS target region and prompt the pupil to start a training task, and real-time electroencephalogram signals of the pupil in the intervention process and behavioral performance data of the pupil in the training task process are obtained; based on the real-time electroencephalogram signals and the behavioral performance data, the stimulation intensity of the tUS module on the TES target spot and the tFUS target region is adjusted. According to the invention, the synergistic interaction capability and the intervention accuracy are improved.

The invention discloses a thoracic surgery complication risk intelligent pre-judgment and prevention and control system, and relates to the technical field of medical surgery, the system comprises a data acquisition module, an edge fusion module, a baseline storage module, a two-dimensional evaluation module and a prevention and control execution module; according to the invention, intraoperative multi-source dynamic data are comprehensively collected through the data acquisition module, after feature quantification is carried out on image data through the edge fusion module and normalization processing is carried out on other data, intraoperative real-time comprehensive data with unified dimensions are generated through fusion operation, and meanwhile, preoperative baseline data are called by relying on the baseline storage module; the two-dimensional evaluation module is used for correspondingly matching intraoperative comprehensive data with preoperative baseline data, and a pre-judgment result of a complication occurrence condition is formed through multi-step quantitative calculation, so that full-process collaboration from data acquisition, fusion processing to evaluation and pre-judgment is realized, the pre-judgment process depends on integration and multi-dimensional analysis of multi-source data, and the pre-judgment efficiency is improved. And a complete logic closed loop is formed.

The invention discloses an upper limb shoulder joint accurate evaluation and rehabilitation training system and method based on a remote sensor, and is applied to the technical field of rehabilitation medical engineering.The method comprises the steps that original multi-modal data is preprocessed, shoulder joint motion data and voice data are processed in combination with baseline data, and a multi-modal standardized data set is generated; multi-dimensional feature extraction is carried out on the multi-modal standardized data set to generate a feature set, and the feature set comprises time domain features, frequency domain features, nonlinear dynamic features and voice features; on the basis of the feature set, a comprehensive evaluation score of the shoulder joint function is calculated by using a linear and nonlinear combined comprehensive scoring model; inputting the feature set and the comprehensive evaluation score into a pre-trained LSTM time sequence prediction model, predicting a rehabilitation trend and abnormal fluctuation, and generating future score change and rehabilitation inflection point early warning information; and based on the comprehensive evaluation score, the prediction result and multi-modal feature fusion analysis, generating rehabilitation training feedback and adjustment information.

The invention belongs to the technical field of intelligent medical treatment, and provides a stretch sock for preventing lower limb venous thrombosis, comprising: a wrapping body having a pressure layer located on the outer side, a contact layer located on the inner side, and a functional layer located between the pressure layer and the contact layer; the physiological index sensor is arranged on the functional layer and used for collecting physiological index data of the user; the stimulating electrode is arranged on the contact layer, is in contact with the skin of the user and is used for stimulating muscles of the user; the controller is connected with the physiological index sensor and the stimulating electrode and used for receiving the physiological index data and generating a control signal of the stimulating electrode based on the physiological index data and the user baseline data so as to control the stimulating electrode to execute the stimulating parameters corresponding to the control signal. According to the stretch sock for preventing the lower limb venous thrombosis, personalized stimulation can be carried out based on the personalized physiological indexes of the current wearing user, and the prevention effect of the lower limb venous thrombosis is improved.

The invention relates to the field of computer systems based on specific calculation models, and discloses an intelligent water meter data driving leakage identification early warning system based on a neural network model, comprising a learnable decoupling module, parameters of which are determined by a collaborative adversarial training rule, and which is used for decomposing data into a baseline data stream and a transient data stream; the system is provided with a baseline channel and a transient channel in parallel; according to the system and the method, a behavior mode monitor, a transient mode analyzer and an abnormal arbitration module are arranged, through information cooperation of the three modules, accurate identification of different leakage states and normal mode drifting is achieved, through the architecture, the fundamental problem that a calculation model is shielded by a high-amplitude normal water use signal is solved, and the system and the method have the advantages of being high in practicability and high in practicability. According to the method, high sensitivity of the system to tiny baseline leakage is recovered, and meanwhile, active adaptation to user habit changes and effective compensation for a high-frequency leakage blind area are achieved through cross-channel information collaboration.

The invention relates to the technical field of computer simulation, and provides an ecological restoration measure carbon sink effect quantitative simulation prediction method and system based on digital modeling. The method comprises the following steps: collecting ecological element information of a target drainage basin, extracting a multi-dimensional spatial distribution feature set, and establishing an ecological environment response simulation model under engineering disturbance in combination with digital baseline data; constructing a carbon sink effect quantitative model based on construction operation situation information, and embedding the carbon sink effect quantitative model into associated nodes of the ecological environment response simulation model to realize coupling; the dynamic influence of engineering construction operation on the drainage basin carbon sink capacity is simulated based on the coupling model, the multi-target optimization decision matrix is generated, the pre-optimization decision suggestion is output, collaborative simulation of ecological environment response and carbon sink effect quantification is achieved, and the accuracy of hydropower engineering carbon sink influence prospective evaluation and the scientificity of decision suggestion are improved.

The invention discloses a chronic obstructive pulmonary disease dynamic risk prediction method based on a time sequence convolutional network, and the method comprises the steps: obtaining static baseline data and dynamic time sequence data of a patient through multi-source data collection, and achieving the data synchronization through timestamp alignment; carrying out one-hot coding on the static data, constructing a feature matrix for the dynamic data, and carrying out standardization processing; a personalized context vector is constructed based on the static features, and multi-source dynamic time sequence feature adaptive fusion is realized by using an attention mechanism; performing time sequence dependency feature extraction by adopting a causal expansion convolutional network, and capturing a long-term dependency relationship through residual block stacking and exponential expansion rate design; and inputting the extracted time sequence features into a classifier, and outputting the dynamic risk probability of acute exacerbation of the chronic obstructive pulmonary disease patient. According to the method, self-adaptive feature fusion is realized in combination with personalized context vectors and an attention mechanism, a long-term time sequence dependency relationship is captured by adopting a causal expansion convolutional network, and a high-precision chronic obstructive pulmonary disease dynamic risk prediction model is constructed.

The invention relates to the technical field of data security, in particular to an enterprise data leakage protection method and system based on behavior analysis. The method comprises the following steps: acquiring user keyboard dynamics data, terminal equipment micro-action data and data access flow data; according to the user keyboard dynamics data and the terminal equipment micro-action data, constructing a user biological behavior characteristic fingerprint, and obtaining user behavior characteristic baseline data; according to the data access flow direction data, establishing a data flow direction time sequence diagram to obtain user data access mode data; acquiring real-time user operation behavior data; and performing feature extraction processing on the real-time user operation behavior data to obtain real-time behavior feature data. According to the method, multi-dimensional user behavior characteristic fingerprints are established, more accurate identity confirmation is realized through biological behavior characteristics such as keyboard dynamics and terminal micro-actions, and the problem that the identity is reliable but the behavior is not reliable is effectively solved.

The invention discloses a longitudinal CT image-assisted rectal cancer neoadjuvant radiotherapy and chemotherapy treatment reaction evaluation device, which comprises a focus area segmentation module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module and a rectum focus area detection module, obtaining a standardized three-dimensional tumor area; a feature fusion module extracts deep learning features of a focus area after segmentation at two time points before neoadjuvant radiotherapy and chemotherapy and before an operation from the three-dimensional tumor area, and performs weighted fusion on the deep learning features at the two time points through a dynamic attention weight layer to obtain longitudinal comprehensive features representing tumor treatment response changes; and the prediction module fuses the longitudinal comprehensive characteristics and clinical baseline data to construct a multi-modal prediction model, and outputs a treatment response prediction result of the rectal cancer patient on neoadjuvant chemoradiotherapy, so that the prediction accuracy of pathological complete remission of the rectal cancer patient can be improved, and the prognosis of the patient and the utilization efficiency of medical resources can be improved.

The invention provides a remote data acquisition and analysis method and system for a baby stroller, and the method comprises the steps: synchronously acquiring environmental stimulation and passenger physiological reaction data through multiple modules on the baby stroller, locally processing the environmental data into cumulative stress indexes, and processing the physiological data such as sound production into state classification labels. And when the passenger has negative stress states such as crying, screaming and the like, the system automatically packs the accumulated stress index sequence in the previous period of time to form a stress event data packet. And the data packet and baseline data in a normal state are transmitted to a remote server together, and a specific environment mode causing the negative stress of the passenger is identified through comparative analysis. According to the method, through event-triggered data acquisition and comparative analysis, specific external factors causing discomfort of infants can be accurately identified and understood.

The invention relates to the technical field of business travel management, and discloses a business travel intelligent optimization management system comprising a data processing module used for collecting and preprocessing multi-source data related to business travel and integrating the multi-source data into a structured business travel data set; the optimization engine module is used for performing simulation-based solution based on input of the structured business travel data set and outputting a candidate business travel scheme set and corresponding baseline data; the evidence storage contract module takes the baseline data as well as transaction data, performance data, settlement data and abnormal data generated by the business travel process as input to generate a corresponding event abstract and perform uploading; a data learning module; a user interaction module; and an emergency simulation module. Monte Carlo simulation and event-driven simulation are combined and applied to business travel scheme generation, calculation can be carried out under the conditions of price fluctuation, flight delay and uncertain resource availability, multiple selectable schemes are formed, baseline data are determined according to the selectable schemes, and optimization processing of complex uncertain scenes is achieved.