2140 results about "Evaluation data" patented technology

The invention discloses an artificial intelligence data privacy protection system based on a block chain and federated learning, and relates to the technical field of block chains and federated learning, and the system comprises a block chain module which employs a main chain-side chain double-chain architecture, a main chain stores a global model hash value and node reputation evaluation data, and a side chain module is used for storing node reputation evaluation data; the side chain stores the encrypted local model parameters through a fragmentation technology; the federated learning module comprises a dynamic difference privacy algorithm and a gradient ternary processing unit, and is used for adding noise to the gradient in a local training stage and converting the gradient into a ternary numerical format; the privacy protection module is used for integrating homomorphic encryption and zero-knowledge proof technologies and realizing ciphertext aggregation and verification of model parameters; and the malicious node detection module is used for identifying abnormal gradient update based on cosine similarity and Multi-Krum algorithm, and is linked with node reputation data in the block chain module. Through a system architecture and a privacy protection mechanism, the efficiency and performance of federal learning are improved while data privacy is ensured, and the method has a wide application prospect.

The invention discloses a mechanical welding seam defect identification method, and relates to the technical field of welding quality intelligent detection and defect identification, and the method comprises the steps: S1, collecting welding seam surface image data, obtaining a standardized image data set, carrying out the feature extraction of the standardized image data set, and obtaining a feature extraction result; the method comprises the following steps: S1, extracting edge contours, texture distribution and gray change features of defects, and generating a high-dimensional feature vector containing space position coordinates and morphological features of the defects, S2, generating a weld quality evaluation database containing defect distribution uniformity and defect severity scores according to edge continuity parameters and gray uniformity parameters in the high-dimensional feature vector; according to the mechanical welding seam defect identification method, accurate identification, classification and positioning of welding seam defects can be achieved, comprehensive evaluation of the welding seam quality is given, and the intelligent level and reliability of welding quality control are effectively improved.

The invention relates to the technical field of data processing, and discloses an automobile manufacturing industrial data distributed processing method and system based on digital twinning. The method comprises the following steps: monitoring and collecting production abnormal events, decision emergency degree parameters and simulation task types based on service states to generate a service priority evaluation data set, and performing priority ranking on a plurality of simulation tasks to form a dynamic priority queue and a resource demand matrix, the queue is input into a distributed simulation engine for CPU, memory and GPU load balancing processing to generate a node resource allocation scheme, and preemptive scheduling is executed to migrate low-priority tasks to idle nodes to form a task execution mapping table; and monitoring simulation progress and resource consumption through an adaptive scheduling mechanism to obtain scheduling performance feedback data, and updating the dynamic priority queue. According to the method and the device, the problem of lack of an intelligent scheduling mechanism during concurrent execution of multiple simulation tasks in the prior art is solved, and the response speed of the key simulation task and the utilization efficiency of computing resources are improved.

The invention relates to a cable online operation fault positioning, monitoring and early warning method and system based on a neural network algorithm, and the method comprises the steps: collecting the operation environment and state data of a cable through a multi-source sensor, and generating an original monitoring data set through noise reduction and time sequence alignment; aiming at the problem of periodic distortion caused by seasonal fluctuation of environment temperature and humidity, performing dynamic reference compensation to generate a reference data set without seasonal influence; aiming at a coupling effect of a dynamic load and temperature drift, calculating insulation performance evaluation data of temperature compensation through load-insulation correlation mapping; aiming at the problem of disconnection of waterproof sealing monitoring and fault positioning, a sealing degradation grade is generated based on regional high-humidity detection; aiming at the defect of a fixed threshold strategy, generating a fault coordinate and a risk probability in combination with spatial positioning and probability analysis; and finally, self-adaptive updating of the parameter library is realized through credibility verification.

The invention relates to the technical field of intelligent bid evaluation, and provides an intelligent multi-dimensional bid evaluation analysis and decision-making method based on big data, which comprises the following steps: acquiring original bid evaluation data from a multi-source heterogeneous data interface, and fusing through semantic role labeling and a timestamp alignment algorithm to generate a time-space association data set. And performing multi-level cleaning to generate a high-confidence bid evaluation data set. And extracting a multi-dimensional index based on the domain knowledge graph, generating a dynamic feature tensor, and dynamically allocating a weight by adopting a coupling attenuation weight model. And constructing a bidder association network, calculating a node influence score, detecting a potential bidding behavior and generating a risk correction coefficient. And injecting the real-time data stream into the dynamic feature tensor, updating the index weight, and generating a three-dimensional scoring vector through a multi-target aggregation decision algorithm. And performing Pareto optimization by using the asymmetric game equilibrium model, and outputting an optimal bid-winning party sequence and a risk early warning report. The bid evaluation efficiency and fairness can be improved, and the bid invitation risk is reduced.

The invention belongs to the technical field of three-dimensional measurement, and discloses a 3D laser line scanning system based on a point cloud positioning algorithm and a control method. The method comprises the following steps: acquiring environment initial point cloud data and scanning target information; carrying out feature extraction, constructing an environment feature description model, carrying out three-dimensional space partitioning processing, carrying out regional reflectivity feature analysis, and constructing a space reflection feature model; adaptive optimization configuration is carried out on the laser scanning parameters, and a scanning strategy is generated; scanning path dynamic planning is carried out on the scanning target information, scanning density distribution calculation is carried out, and a scanning path is generated; performing laser line scanning control by using the scanning path and the scanning strategy, and performing scanning data quality real-time monitoring to obtain scanning quality evaluation data; scanning parameter iterative optimization is carried out on the scanning quality evaluation data, point cloud data real-time fusion reconstruction is carried out, a high-precision three-dimensional model is generated, the modeling precision is improved, and meanwhile the system resource utilization efficiency is optimized.

The invention discloses a market supervision data asset management method and system based on compliance and privacy protection, and relates to the technical field of data asset management. According to the method, the privacy compliance strategy is generated according to the metadata label and is packaged into the data capsule, so that the binding of the data and the compliance strategy is realized; the privacy compliance knowledge graph is constructed, and semantic matching and compliance verification are performed in combination with a large language model, so that the semantic consistency and matching degree between a privacy compliance strategy and a supervision regulation are improved, and manual intervention is reduced; a privacy protection processing mechanism is called according to a compliance verification result, and a proper data protection technology is dynamically applied to reduce the risk of data leakage; anonymization processing parameters are optimized before data are published, data distortion processing is implemented, and the privacy disclosure risk is effectively controlled on the premise that data availability is guaranteed; and a data asset value dynamic evaluation model is constructed, the data comprehensive value and the risk level are evaluated, and refined and differentiated management of data assets is supported.

The invention relates to the technical field of data processing, and provides a trade big data processing and user behavior prediction method and system.The trade big data processing and user behavior prediction method comprises the steps that transaction data, browsing behavior data and feedback evaluation data of a user on a cross-border e-commerce platform are obtained to generate a user behavior pattern sequence, and then a user interaction graph is constructed; and carrying out edge weight updating and node label classification to obtain a high-dimensional relation graph, carrying out similarity partition processing to obtain a predicted popularity index graph, and predicting the future cross-border commodity interaction behavior of the target user group by using the predicted popularity index graph. By acquiring transaction data, browsing behavior data and feedback evaluation data of users and generating a prediction popularity index graph, multi-dimensional data are effectively and comprehensively processed, accurate prediction of cross-border commodity interaction behaviors of a target user group is realized, and the user experience is improved when a user intention deep relationship is processed and cross-time-period behavior evolution analysis is performed. The problems of low model dimension and insufficient prediction precision exist.

The invention provides an interactive simulation teaching processing method and device for ship electromechanical guarantee, and relates to the technical field of interactive simulation teaching, and the method comprises the steps: building a ship electromechanical full-structure model in a virtual simulation platform, digitally mapping real equipment operation parameters into virtual parts with real-time response characteristics, and carrying out the real-time simulation of the virtual parts; synchronous acquisition and normalized calibration of operation instructions, equipment feedback and environment disturbance in a teaching scene are realized, and a unified teaching simulation data set is generated. State recognition, fault simulation and operation evaluation calculation are completed through multi-layer task scheduling, and real-time teaching feedback is generated and visually displayed on a terminal. Based on difference analysis of dynamic evaluation data and a standard operation template, operation deviation and abnormity are automatically identified, and a targeted guide prompt is generated. According to the invention, unified modeling and data linkage can be carried out on student operation behaviors, equipment dynamic responses and teaching feedback results, and the problems of teaching feedback lagging and lack of pertinence in operation correction are solved.

The invention relates to the technical field of safety simulation training drilling, in particular to an intelligent assessment and evaluation method for mine accident emergency risk avoiding, which comprises the following steps of: S1, acquiring multi-dimensional data in an emergency risk avoiding capability drilling process of a student; s2, performing data preprocessing on the multi-dimensional data, and constructing an evaluation data set; s3, constructing an emergency risk avoiding capability evaluation system, wherein the system comprises evaluation items and distribution weights of the evaluation items; s4, establishing a weight evaluation model, and dynamically adjusting the distribution weight of the evaluation item in combination with the environmental data; and S5, scoring is completed according to the emergency risk avoiding capability evaluation system, and a personalized evaluation report is generated. According to the invention, comprehensive, objective and real-time evaluation of the emergency risk avoiding capability of the miner is realized, and the influence of human factors on the evaluation result can be reduced; the method can flexibly adapt to different environments and scenes through dynamic adjustment of the evaluation model, provides personalized evaluation reports and improvement suggestions, and effectively assists students in improving the emergency risk avoiding capability.

The invention discloses an unmanned aerial vehicle assisted three-dimensional scanning well measurement system and method. The method comprises the steps of firstly collecting shaft information, adjusting equipment, planning a measurement path, enabling an unmanned aerial vehicle to collect data according to the planned path, then preprocessing the data, intelligently identifying and classifying the data, constructing a three-dimensional model, analyzing errors, evaluating the data, and finally generating a measurement report. The system comprises an unmanned aerial vehicle, a laser radar, a structured light camera, a depth camera, an IMU + UWB and a cloud processor. Compared with a traditional measurement mode, the method has obvious advantages. Through laser radar SLAM + UWB positioning, the unmanned aerial vehicle can be accurately navigated, high-precision scanning and image acquisition are realized in combination with multi-sensor fusion, and measurement blind areas are reduced. The intelligent identification and classification technology can automatically detect and classify hoistway facilities, detect wall damage and support dynamic obstacle avoidance. Various algorithms are adopted for data processing to improve the model precision, the finally generated high-precision model can be directly used for various engineering scenes, and an efficient and intelligent solution is provided for well measurement.

The invention relates to the technical field of logistics order delivery management, in particular to a cross-border e-commerce logistics order management system based on big data, which comprises a data acquisition processing unit, an order clustering delivery unit and a delivery execution feedback unit, the data acquisition and processing unit collects orders, geographic information and real-time traffic data from a cross-border e-commerce platform and performs cleaning preprocessing, repeated orders are removed by using a Hash algorithm, the order clustering and delivery unit clusters the orders by using a density peak clustering algorithm, and the clustering effect is optimized by combining the order emergency degree and the customer loyalty, so that the service quality of the cross-border e-commerce platform is improved. A genetic algorithm is adopted to optimize the delivery sequence, the crossover and mutation probability is dynamically adjusted, a fitness function is calculated by considering the delivery distance, time cost and regional limitation penalty factors, a delivery execution feedback unit sends an optimized route to a delivery vehicle, client feedback evaluation data is collected, a delivery strategy is dynamically adjusted by means of an upper confidence bound algorithm, and the delivery efficiency is improved. And the delivery efficiency and the customer satisfaction are improved.

The invention relates to a visual-language model image-text pair accurate evaluation data construction method based on an optimization algorithm, and the method comprises the steps: firstly constructing an original image set through a manner of public data set screening, real-time equipment collection or deep generation, and reversely generating an initial cue word based on a pre-trained visual-language model; in combination with the constructed cue word template, optimizing the initial cue word by using a large language model, and generating a cue word highly matched with the picture; and then, performing optimization processing on the image-text pair data through a multi-dimensional evaluation function, performing manual verification on an optimized data set, removing low-quality or repeated image-text pairs, and finally constructing a high-quality vision-language model evaluation data set. According to the method, the matching degree and diversity of the image-text to the data are iteratively improved by adopting the optimization algorithm, the accuracy and coverage range of the evaluation data are remarkably improved, and the method can be widely applied to model performance evaluation of tasks such as image generation, visual question and answer and cross-modal retrieval.

The invention discloses an evaluation result determination method and device, a storage medium and electronic equipment, and relates to the technical field of retrieval enhancement generation, the method comprises the following steps: obtaining an evaluation data set generated by a preset agent based on knowledge base data; obtaining a retrieval result generated by a target retrieval enhancement generation system based on the evaluation data set; obtaining a target dimension capability evaluation result obtained by performing target dimension capability evaluation on the target retrieval enhancement generation system by the preset intelligent agent based on the retrieval result; and determining a target evaluation result of the target retrieval enhancement generation system according to a plurality of capability evaluation results corresponding to different target dimensions. The technical problem of how to efficiently evaluate the comprehensive capability of the retrieval enhancement generation system is solved, and the technical effect of efficiently evaluating the comprehensive capability of the retrieval enhancement generation system is achieved.

The invention provides a learning task generation method based on historical answering and real-time evaluation data, and the method comprises the steps: firstly constructing an initial state vector of a student, extracting a knowledge point range, building an association network, and then evaluating a current mastering state of the student through a probability graph model; for weak knowledge points, a greedy strategy optimization algorithm is adopted to generate a task sequence, and grading is performed according to difficulty and content depth; dynamically adjusting task difficulty and a content presentation sequence in combination with learning preferences of students, and generating a customized task sequence; through a real-time feedback mechanism, student state vectors are continuously updated, the mastery degree is re-evaluated, and a task sequence is optimized; finally, a personalized task set is generated by considering time constraints. According to the invention, accurate evaluation of the learning state of the student and dynamic optimization of the task sequence are realized, and the effect of personalized learning is effectively improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a call service quality auditing method, device, equipment and medium, and the method comprises the steps: collecting call audio data, and translating the call audio data into call text data; identifying a client intention in the call text data by using an intention identification model, and generating a client intention result; using an emotion recognition model to recognize customer emotion, and generating an emotion recognition result; using the service response analysis model to identify service behavior performance, and generating a service response analysis result; and generating an audit report containing service quality analysis content based on the client intention result, the emotion recognition result and the service response analysis result. According to the method, the client intention, the emotional state and the service response behavior in the call content are processed in a unified manner, the unstructured call information is converted into the structured evaluation data, automatic, multi-dimensional and deep auditing analysis of the service quality is realized, and the auditing efficiency and accuracy are improved.

The invention relates to a learning condition analysis method and system based on a large language model, a terminal and a medium, and relates to the field of education system technology, the method comprises the following steps: collecting learning data of students, the learning data comprising structured learning data and unstructured learning data, the structured learning data comprising examination scores and homework completion conditions, and the unstructured learning data comprising examination scores and homework completion conditions; the unstructured learning data comprises classroom behavior data, learning process data, student self-evaluation and mutual evaluation data and teacher teaching records; preprocessing the learning data to generate standardized data which can be used for analysis; input interaction information is received, analysis demand parameters are generated, and the interaction information comprises analysis demands of teachers or feedback information of students; based on a large language model, semantic understanding and deep analysis are carried out on the standardized data and the analysis demand parameters, and a learning condition analysis result is obtained; and displaying the study condition analysis result in a visual mode. The application has the effects of improving the teaching quality and meeting the actual teaching demand integrating degree.

The invention discloses a VLA model evaluation method and device, a storage medium and a program product. The method comprises the steps that an evaluation data set containing visual data, language data and action data is acquired; inputting the evaluation data set into a VLA model to be evaluated to obtain a corresponding model output result; based on the model output result, determining an index evaluation result of the VLA model corresponding to at least one evaluation dimension index; and determining a performance evaluation result of the VLA model according to each index evaluation result. Therefore, the comprehensive performance of the VLA model in multiple aspects of perception, prediction, decision making, planning, reasoning, interaction and the like can be comprehensively reflected.

The invention relates to an intelligent CNC (computer numerical control) machining method which comprises the following steps: dynamically monitoring a tool-workpiece interaction interface in a CNC machining process through a multi-dimensional torque sensor array to obtain a machining process feature data set, and analyzing a tool motion track in real time to obtain a tool motion state feature quantity; secondly, performing multi-scale analysis by adopting a wavelet packet decomposition technology, and determining processing quality evaluation data; and if the data does not reach the preset standard, performing microstructure reconstruction on the surface of the workpiece based on the data, and generating a workpiece processing state characteristic spectrum. Then, comprehensively analyzing the atlas by utilizing a multi-objective optimization technology to obtain a processing parameter optimization sequence; and finally, implementing closed-loop feedback control on the numerical control system according to the optimized sequence, and generating a real-time processing control instruction. The key problem of how to improve the automation level and the machining precision in the CNC machining process through an intelligent means is solved.

The invention relates to the technical field of computers, and discloses a time series data prediction method and device based on a diffusion model, equipment and a medium, and the method can ensure the consistency of different modal data in time by performing time dimension alignment on target time series data and multi-modal evaluation data. And feature splicing is performed on the multi-modal data corresponding to each time step, so that the multi-modal information can be integrated, and the data features of each time step are enriched. And on the basis of a self-attention mechanism, weighted summation is carried out on the fused feature vectors to obtain a self-attention feature matrix, so that mutual correlation between a time sequence dependency relationship and multi-modal features in a feature sequence can be captured, and richer feature representation is generated. And diffusion simulation is carried out on the self-attention feature matrix by using the pre-training diffusion model, more accurate target prediction data is output, and the accuracy of a prediction result is improved. And the application efficiency of the diffusion model in time series data analysis scenes in the financial field and the medical field is expanded.

The invention provides a manufacturing and processing photoetching method and system of a display panel, and relates to the technical field of photoetching processing. According to the method, the display panel substrate is subjected to cleaning, prime coating and partition division to form an imageable micro-mark covering all exposure partitions, and the thickness and the uniformity are measured after lower-layer supporting glue and upper-layer imaging glue are sequentially formed; carrying out in-situ acquisition and modeling by taking the micro-mark as an imaging object, and generating an exposure correction instruction and an overlapping energy table; performing partition exposure and real-time correction by using a digital micromirror, and completing development to obtain an anti-etching window with a steep side wall; and setting an etching window according to the exposure record and the window quality data, and executing low-damage etching and defect recovery to form stability evaluation data. According to the method, comprehensive improvement of high-precision exposure, pattern consistency and electrical stability is realized.

The invention relates to the technical field of data processing, and discloses an old people health state assessment data processing system based on multi-modal data, and the system comprises a medical data integration module which obtains electronic medical record data and a medical examination report through an FHIR interface, and extracts a structured health index; the cross-modal causal fusion processing module is used for fusing the monitoring data and the medical text through an image, text and image interlayer architecture; the health state evolution modeling module is used for mapping the health feature vectors into physiological function, cognitive level and athletic ability three-dimensional state indexes; an evaluation report backtracking module; and a decision output module. Through an image, text and image interlayer architecture, deep semantic fusion of multi-modal features is realized under the constraint of medical pathology rules, feature weight adaptive distribution is dynamically guided based on a medical causal atlas, a high-dimensional fusion vector retaining key pathology information is generated, the semantic integration ability of health data is remarkably improved, and the health data fusion efficiency is improved. And the reliability of discrimination and decision making is improved.

The invention discloses a road disease intelligent identification and evaluation method based on a multi-modal large model and an instance segmentation algorithm, and belongs to the field of road health monitoring. The method comprises the following steps: acquiring disease image data of road appearance, and constructing an original data set; training a road apparent disease recognition model based on an instance segmentation (Mask-RCNN and the like) deep learning method; constructing a multi-modal prompt engineering framework based on parameter state transition, and generating a field adaptive question and answer data set; historical disease data and professional knowledge and evaluation standards related to road diseases are collected, a professional knowledge compiling framework is constructed, and a multi-modal large model disease evaluation data set is generated; optimizing a training result based on a collaborative optimization method of a multi-modal large model fusion specification conformity function to form a large model special for the field; and constructing an end-to-end road disease intelligent identification and evaluation system based on the fine-tuned special large model. By means of the method, the problems that a traditional road appearance detection method depends on artificial experience and is low in efficiency can be solved, and meanwhile a professional road condition evaluation system is built through a large model.

The invention relates to an electric power system operation state identification and prediction method, device and equipment based on multiple tasks and self-attention, and the method comprises the steps: obtaining electric power data of electric power system operation state identification and prediction, and constructing a feature matrix and an adjacent matrix according to the electric power data; constructing an identification and prediction model by adopting a multi-task combined self-attention mechanism network, and inputting the feature matrix and the adjacent matrix into the identification and prediction model for processing to obtain prediction and evaluation data; and determining a state prediction result and a transient stability evaluation result of the power system according to the prediction and evaluation data. According to the method, an identification and prediction model is constructed by combining multiple tasks with a self-attention mechanism network, so that two key and important tasks of state identification and state prediction of the power system can be processed at the same time, state prediction and transient stability evaluation of the power system are realized at the same time, and the efficiency and prediction precision are improved; and the real-time requirement of a power system is met.

The invention provides a communication network operation accident simulation drilling method and device, and solves the problem that a standardized simulation drilling process with automatic deduction and objective quantitative evaluation capabilities is difficult to construct in the prior art. The communication network operation accident simulation drilling method comprises the following steps: acquiring historical operation data of a communication network, and generating at least one scene element data set according to the historical operation data; the scene element data set comprises disposal evaluation data; determining a scene element data set corresponding to the target accident scene, and constructing an analog simulation environment of a communication network operation accident; determining a preset timeline according to the fault injection parameter, and driving a simulation environment to evolve; and acquiring drill process data, and generating a drill evaluation result by combining with analysis of the disposal evaluation data. The invention further provides a device corresponding to the method. According to the method, historical data is converted into a structured drilling process, and measurable, reproducible and continuous optimization of the emergency capability is realized.

The invention relates to a response performance test method and system of a microswitch. The method comprises the following steps: inputting a multi-pulse trigger sequence to a microswitch contact and collecting contact response original data; performing digital filtering processing on the contact response original data to obtain contact response time sequence data; comprehensive response time parameters including contact mechanical action starting time, electrical initial contact time and electrical stable contact time are calculated according to the contact response time sequence data; performing temperature compensation and contact rebound characteristic extraction on the comprehensive response time parameter to obtain contact rebound characteristic evaluation data; according to the contact rebound characteristic evaluation data and the comprehensive response time parameter, the response performance stability of the microswitch under the high-frequency operation condition is calculated, and a reliability evaluation result is generated. According to the invention, the problem of time sequence measurement error caused by the influence of contact jitter in the traditional test is effectively solved, and the accuracy and consistency of contact response time measurement are remarkably improved.

The invention relates to the technical field of computing power resource allocation, and particularly discloses a computing power resource allocation method and system based on multi-dimensional dynamic evaluation and a product. The method comprises the steps of obtaining evaluation index data of a to-be-scheduled task in multiple dimensions; dynamically configuring a weight value of each evaluation index according to a task attribute and a system state; the evaluation index data is fuzzified by using a preset membership function, a fuzzy evaluation matrix is constructed in combination with the weight value, and comprehensive calculation is performed through a fuzzy inference rule to obtain fuzzy comprehensive evaluation data of the task, so that an accurate evaluation result is generated; modeling a task execution process by adopting a multi-layer perceptron model, and predicting the execution performance of the task; and based on the evaluation result and the prediction result, dynamically selecting a proper strategy from a plurality of predefined task scheduling strategies, and scheduling the tasks to optimize computing power resource allocation. According to the method, the resource utilization efficiency is remarkably improved through multi-dimensional evaluation and a dynamic scheduling mechanism.

The invention provides a battery consistency evaluation method and device, equipment and a storage medium. The method comprises the steps of obtaining real-time operation data of a battery module and storing the real-time operation data in a time sequence database; extracting evaluation data from the time sequence database; for each key performance parameter, performing feature extraction on the evaluation data to obtain a curve similarity index, an intra-group variable coefficient index and an information entropy index; and determining the consistency score of the battery module according to the curve similarity index, the intra-group variation coefficient index and the information entropy index corresponding to the multiple key performance parameters. According to the method, multiple key performance parameters are comprehensively considered, and the consistency of the battery modules is evaluated from different angles by adopting the curve similarity index, the intra-group variation coefficient index and the information entropy index, so that the one-sidedness of single-index evaluation is avoided, the actual consistency condition of the battery modules can be reflected more comprehensively and accurately, and the evaluation accuracy of the battery modules is improved. And the accuracy and reliability of evaluation are improved.

The invention discloses a multivariate monitoring data fusion method and system for a slope tunnel model test, particularly relates to the technical field of geotechnical engineering physical model test, and aims to solve the problem that dynamic stress waves in an existing model test integrating static and dynamic measurement cause static monitoring signal distortion and further influence the model state evaluation accuracy. Coupling interference in static signals is identified by synchronously collecting dynamic excitation and static response signals; obtaining signal samples before and after a model state sudden change event to perform frequency domain comparative analysis, identifying characteristic frequency components related to sudden change, and evaluating a coupling interference dominant frequency band; separating an effective static response component from the original static signal based on the frequency band; and finally, fusing the effective component with a soil dynamic parameter derived from dynamic excitation to generate comprehensive stability evaluation data. Dynamic interference is effectively suppressed, the authenticity of static signals is improved, and the reliability of model stability state evaluation is improved.

The invention relates to the technical field of physiological information processing, and discloses a personalized burn rehabilitation path planning method and system, and the method comprises the steps: obtaining historical rehabilitation data and real-time evaluation data, constructing a state time sequence, extracting a score fluctuation vector and an emotion change vector, and carrying out the window adjustment processing of a fluctuation feature vector set, a rhythm factor and a training factor are calculated in each window range, so that path analysis has the capability of synchronously evaluating rhythm change and a training state, the phenomenon of excessive path adjustment caused by short-time abnormal fluctuation is avoided, a rhythm feature sequence is input into a whale optimization algorithm to execute path updating processing, and the path updating efficiency is improved. The method ensures that the path generation process has the dynamic adjustment capability, enhances the adaptability of the algorithm to the recovery rhythm change, performs grading processing on each stage according to the stage sequence set, can effectively output the rehabilitation path matrix, and realizes the unified expression of the multi-stage rhythm and the training grade.