75 results about "Evaluation algorithm" patented technology

This invention provides a method for determining the carbon dioxide huff and puff mechanism in fractured reservoirs, belonging to the technical field of determining the carbon dioxide huff and puff mechanism in fractured reservoirs. This invention establishes a unified digital core by selecting outcrop cores to create cast thin sections and employing multi-scale fusion reconstruction technology. Topological connectivity is evaluated based on a seepage topological invariant fracture network connectivity evaluation algorithm. During well shut-in, a diffusion-convection hybrid encoding / decoding model is used to predict migration in real time, and the model learning rate is adjusted by a dynamic learning rate adjustment function. After well shut-in, a feedforward-feedback composite control strategy is used to gradually reduce back pressure to complete the production stage. The experiment ends after cleaning the chip with anhydrous ethanol. Finally, the degree of crude oil recovery is statistically analyzed using image processing software. This invention solves the technical problem of not being able to predict the migration process in fractures and matrix of fractured reservoirs in real time during well shut-in.

The present application relates to the technical field of data processing, and provides a task execution optimization method and system based on an environmental protection law enforcement inspection intelligent terminal. The method comprises the following steps: obtaining core performance indexes of the environmental protection law enforcement inspection intelligent terminal, and calculating a comprehensive performance score by using a nonlinear dynamic evaluation algorithm; obtaining ambient light intensity and the temperature of the environmental protection law enforcement inspection intelligent terminal, and calculating a saturation degree dynamic adjustment amount of the screen; calculating a screen contrast dynamic gamma correction amount according to the comprehensive performance score and the ambient light intensity, so as to adjust the brightness and contrast of the screen of the environmental protection law enforcement inspection intelligent terminal; obtaining a task queue to be executed, and evaluating the estimated resource consumption, subsequent dependency degree and historical risk coefficient of each task to be executed, so as to calculate the comprehensive priority weight of each task; and preferentially executing tasks with high comprehensive priority weight.

This invention relates to a method for assessing the quality of single-cell sequencing data, which addresses the current difficulty in evaluating the differences in data quality after applying different single-cell sequencing data imputation algorithms without the participation of real samples. The method includes the following steps: First, two single-cell sequencing data imputation algorithms are prepared. Then, a synthesis matrix based on the statistical characteristics of real data is created and normalized preprocessed. The normalized gene expression matrix is ​​input into the two imputation algorithms to be evaluated, and the output feature vectors are extracted to set an optimization function. The gene expression matrix is ​​optimized using the optimization function, and then the optimized matrix is ​​denormalized to obtain the imputed gene expression profile. Finally, the obtained gene expression profile is input into the two algorithms to be evaluated to obtain two sets of predicted feature vectors. The data quality difference value can be calculated using these feature vectors. This invention can accurately assess the data quality difference between two data imputation algorithms without the participation of real samples.

The application discloses a novel AI classroom teaching evaluation system based on multi-modal and man-machine cooperation, which comprises a multi-modal data acquisition module, a multi-modal data AI analysis module, a research and judgment analysis module, a complex situation and multi-element representation deconstruction module and a cooperative evaluation module.The multi-modal data acquisition module is used for collecting multi-modal classroom data in real time.The multi-modal data AI analysis module is used for performing deep learning analysis on teaching rhythm, student participation and teaching target matching degree to obtain analysis results.The research and judgment analysis module is used for performing research and judgment and scoring in the human dimension according to the analysis results, and forming a final classroom evaluation analysis report according to the scoring.The complex situation and multi-element representation deconstruction module is used for realizing situation characteristic quantization, correlation learning algorithm and a bidirectional optimization model.The cooperative evaluation module is used for constructing a quantitative scoring matrix and a qualitative dimension evaluation algorithm, integrating the quantitative scoring matrix and the qualitative dimension evaluation algorithm into an evaluation result integration model, and generating visual evaluation.The application forms bidirectional optimization through situation labeling and AI algorithm iteration, and the evaluation result is highly targeted and has reference value.

The application discloses a kind of thinking chain enhancement method and device for RAG system, the method includes: after obtaining input question text set, relevant document set associated with question text set is obtained from corpus as enhancement information;Multiple candidate thinking chains and corresponding answer texts are generated in parallel;Each candidate thinking chain is automatically scored by multiple dimension evaluation algorithm, and comprehensive score is generated based on multidimensional score and is sorted;When determining that initial optimal thinking chain is less than preset threshold, the optimal thinking chain that does not reach threshold is progressively refined and optimized, and improved version thinking chain is obtained;Preference pair sample is constructed, and LoRA fine-tuning is carried out on model based on preference pair sample by preference optimization algorithm;The model after fine-tuning is loaded to execute input question text reasoning task.The scheme can enable model to directly generate high-quality thinking chain in subsequent reasoning, and can improve the accuracy of reasoning answer.

This invention discloses an automatic generation and evaluation method for machine tool fault solutions based on a large language model, relating to the field of equipment fault diagnosis technology. It performs multimodal parsing on structured fault logs and unstructured maintenance reports; extracts key fault information from the parsed historical fault data; and automatically generates fault cause analysis, step-by-step troubleshooting processes, and solutions based on the extracted key fault information, combined with prompt word engineering and a large language model. The method also automatically evaluates and iterates the solutions, employing an expert feedback enhancement system for feedback. This invention's automatic generation and evaluation method for machine tool fault solutions, by integrating historical data mining and large language model generation capabilities, establishes a mapping knowledge base of fault characteristics and solutions. Based on a dynamic evaluation algorithm-driven iterative optimization mechanism and an expert feedback-driven continuous learning architecture, it significantly improves fault handling efficiency and reduces reliance on human experience.

The application discloses a multi-dimensionally scalable adaptive equal-weight user credit evaluation algorithm, and belongs to the technical field of electronic commerce credit evaluation and platform economy user behavior analysis, and comprises data acquisition and preprocessing, multi-dimensionally scalable input and output model construction, data adaptive adjustment, equal-weight score calculation, and result output and storage; equal weight between dimensions and equal weight within dimensions are taken as cores, the improved CRITIC objective weighting method is combined to guarantee fairness, data dynamic adaptation is realized through a PID-ELM fusion algorithm, indexes can be flexibly added or reduced, the role difference between buyers and sellers can be adapted, dimensions can be automatically unified, and data intervals can be updated.The multi-dimensionally scalable adaptive equal-weight user credit evaluation algorithm is suitable for adaptation of e-commerce, finance, and operators in multiple scenes, solves the problems of rigid indexes, dimension inaccuracy, and subjective weight in the existing system, and outputs objective and real-time credit scores.

This invention relates to the fields of medical device technology, biosignal processing, and artificial intelligence-assisted diagnostic technology. Specifically, it relates to a non-invasive dynamic monitoring system and method for gastric function based on quantum sensing and multimodal artificial intelligence. The system includes a quantum dot biosensor array, a multimodal signal fusion processor, an artificial intelligence dynamic evaluation algorithm, and a cloud-based collaborative diagnostic platform. The quantum sensor uses graphene quantum dot material and achieves 128-channel parallel acquisition through capacitive coupling. In this invention, by setting up the quantum dot biosensor, the sensitivity reaches the single-photon level, the resolution is 0.1μV, the frequency response range is 0.01-100Hz, and the anti-interference capability is 90dB common-mode rejection ratio, thus improving signal acquisition accuracy and anti-interference capability.

The application discloses a kind of temperature test methods for integrated circuit using pipe type electric heating technology, the inlet and outlet of compressor are connected to temperature control circuit by manual valve, temperature control circuit includes instrument wind main road, refrigerated water circuit, compressed air circuit and steam circuit, refrigerated water circuit is in turn through and communicate mixed jar, reaction jar, condensate tank and constant pressure tank, form cooling medium circulation path, utilize the heat of integrated circuit test environment in reaction jar by refrigerated water absorption, and discharge waste heat by condensate tank;During temperature regulation process, integrated circuit is placed in reaction jar test station, and the current, voltage and frequency parameters of temperature point are recorded by data acquisition module, and thermal reliability evaluation report is generated.The application balances pressure fluctuation in real time by hierarchical compensation strategy, generates thermal reliability report by combining comprehensive data acquisition and professional evaluation algorithm.The scheme significantly improves test efficiency and result reliability.

The application provides an IC equipment alignment mark image enhancement method and an image enhancement device, wherein the method comprises: acquiring a process degradation mark image to be enhanced; performing real-time quality evaluation on the process degradation mark image to be enhanced by using a preset image quality evaluation algorithm; if the evaluation result is lower than a preset quality threshold, inputting the process degradation mark image to a trained cycle generative adversarial network to output an enhanced alignment mark image; the cycle generative adversarial network is trained in the following manner: training the network by using a non-paired training data set, and in the training process, optimizing the network by using a loss function comprising a structural similarity loss and an edge phase consistency loss, and training and reasoning by using a dynamic slicing strategy. By the application, the signal-to-noise ratio, edge sharpness and geometric position fidelity of the alignment mark image are improved without paired data, thereby improving the alignment accuracy.

This invention discloses a method and system for clustering distributed adjustable resource nodes in a power system. The method includes: performing global preprocessing based on the global load, topology, and node types of the power system to generate a basic feature matrix containing various load characteristics, a topological region ID defining the topological affiliation of nodes, and a node symbol vector defining the uncertainty direction of nodes; segmenting the topology of the power system based on the topological region ID and constructing a high-dimensional feature matrix within each region; running a first-stage evaluation algorithm based on the high-dimensional feature matrix to determine the upper limit of the number of clusters; and performing a second-stage clustering and evaluation based on the upper limit of the number of clusters to obtain the optimal clustering result. This invention achieves globally optimal matching of the "fluctuation direction" and "fluctuation amplitude" between nodes while ensuring clustering accuracy, providing a novel and refined modeling paradigm for the aggregation analysis of heterogeneous resources in distribution networks.

This invention belongs to the field of intelligent sleep state monitoring technology, and particularly relates to an intelligent sleep state monitoring and analysis device, pillow, and method. The device includes two sound sensors and a three-axis accelerometer, as well as a main control module with built-in signal processing. The two sound sensors acquire dual-channel snoring signals, and suppress environmental noise through time delay estimation and beamforming. The three-axis accelerometer is attached to the back of the neck to acquire three-dimensional vibrations caused by breathing and heartbeat, and separates the breathing waveform, heartbeat waveform, and motion artifact components through bandpass filtering and principal component analysis / independent component analysis. The intelligent evaluation algorithm integrates snoring characteristics, respiratory rate, heart rate, and heart rate variability to output sleep stages and apnea events. This invention also provides a pillow incorporating this device and an intelligent sleep state monitoring and analysis method. This invention has a simple structure, can be integrated into a pillow, does not interfere with natural sleep, and is suitable for long-term use in homes and rehabilitation centers.

The application provides a kind of fractured body oil reservoir carbon dioxide huff and puff mechanism determination method, belong to fractured body oil reservoir carbon dioxide huff and puff mechanism determination technical field, the application is prepared by selecting outcrop core cast body thin section and using multi-scale fusion reconstruction technology to establish unified digital core, based on seepage topological invariant fracture network connectivity evaluation algorithm to evaluate topological connectivity;During the period of soaking, the migration is predicted in real time by using diffusion-convection hybrid coding and decoding model, and the model learning rate is adjusted by a dynamic learning rate adjustment function;After soaking, the production stage is completed by gradually reducing the back pressure based on the feedforward-feedback compound control strategy;After cleaning the chip with anhydrous ethanol, the experiment is ended;Finally, the oil recovery degree is counted by image processing software, which solves the technical problem that the migration process in fractured body oil reservoir fractures and matrix cannot be predicted in real time during soaking.

The application provides a fault detection method for a European standard wall penetrating terminal, and belongs to the technical field of fault detection. In the application, a partial discharge characteristic component is input into an artificial intelligence model based on a sparse self-encoder to output a partial discharge characteristic vector and calculate an insulation degradation index. Meanwhile, a contact state observer based on a virtual impedance principle is constructed to identify contact resistance parameters online by using a recursive least square method. A double-layer game optimization model is used to dynamically adjust a detection threshold and a sampling strategy, and a power grid fluctuation robustness evaluation algorithm based on topology dynamics is started to identify energy weak points. Finally, a fault confidence adjustment factor is calculated according to the insulation degradation index, the contact resistance parameter and the system stability to realize a graded response, so that the technical problem that the European standard wall penetrating terminal is difficult to realize the cooperative real-time detection of insulation degradation and contact deterioration in a high-power charging environment is solved.

The application discloses a soil quality intelligent evaluation method and system based on big data analysis, relates to the technical field of wisdom agriculture and digital soil, and comprises the following steps: collecting and fusing multi-source soil data of a target region, and extracting a characteristic index set; dynamically screening a core evaluation index set through a characteristic importance evaluation algorithm; inputting the core evaluation index set into a pre-trained model, outputting a comprehensive soil health index by analyzing the nonlinear relationship between indexes; combining the index with historical time series data of the core index to construct an early warning model, predicting the risk probability of indexes exceeding a safety threshold; generating a spatialized early warning map according to the risk probability and plot information; and finally matching a knowledge base to generate differentiated management schemes for plots with different risk levels. The method realizes adaptive and dynamic determination of core evaluation indexes, and can probabilistically predict and prospectively warn soil degradation risks.

The application discloses a kind of fire-fighting Internet of Things remote monitoring method and system, by collecting environmental parameter to construct real-time monitoring sequence, using data stream analysis method accurately identifies abnormal point, and when threshold is triggered in abnormal, fusion mechanism is triggered, multidimensional index is integrated into vector, risk signal is extracted and grouped similar mode to judge hazard type, and then matching historical response record generates optimized response chain.The action sequence is determined by priority evaluation algorithm, and the disposal instruction is generated by combining system linkage judgment, and the execution effect is evaluated by simulating the application scenario, and finally the record and template are updated to form a closed-loop response mechanism.The application realizes intelligent management of the whole process from anomaly detection to accurate disposal through multi-index fusion and dynamic adjustment, significantly improves the accuracy and efficiency of environmental risk early warning and emergency response.

The application provides a full-link cooperative upper limb rehabilitation personalized training method, belongs to the technical field of upper limb rehabilitation training, and is used for solving the problems of training scheme solidification, insufficient individualization and precision, and lack of full-link cooperative closed loop in related technologies. The method integrates user basic attributes, historical training, clinical reference and real-time sensing data, generates an individualized basic scheme through nonlinear feature extraction and rehabilitation stage determination, dynamically adjusts training parameters through a lightweight prediction model, generates a doctor-patient dual version report combined with an individualized evaluation algorithm, and optimizes full-link parameters through multi-directional feedback closed loop. The method realizes full-link cooperative adaptation, improves the precision, safety and individualization of rehabilitation training, and adapts to the needs of patients at different stages.

The application discloses a resource utilization evaluation method and device, a storage medium and an electronic device. It relates to the field of artificial intelligence, and the method comprises the following steps: obtaining first parameter data associated with a communication site to be evaluated by a large language model, wherein the first parameter data is parameter data related to resource utilization evaluation in a current evaluation period; determining target parameter data according to the first parameter data and second parameter data, wherein the second parameter data is parameter data used for resource utilization evaluation in a previous evaluation period; adjusting a current evaluation model according to the target parameter data to obtain a target evaluation model; and evaluating the resource utilization of the communication site to be evaluated according to the target parameter data and the target evaluation model to obtain an evaluation result. The method solves the problem of low evaluation efficiency of the resource utilization of the communication site in the related art, which relies on manual identification of parameter changes and manual adjustment of evaluation algorithms.

The application discloses a kind of based on multistage evaluation's aviation pollution source priority supervision area determination method, belong to airport environmental supervision technical field, including: first acquisition multi-source data, construct the multistage evaluation index system of distinguishing aircraft type and flight phase of aviation pollution source and complete quantization;Using time-space-level three-dimensional dynamic weight distribution method gives index dynamic weight and correction, by simulating diffusion process, obtain pollutant concentration spatial distribution;The target area is divided into adaptive spatial unit grid, by adaptive evaluation algorithm coupling concentration data and dynamic weight, obtain each grid comprehensive evaluation score;According to regional environmental standard and bearing capacity, set different threshold, generate pollution evaluation spatial distribution map by spatial interpolation, demarcate priority supervision area boundary and complete internal priority ranking, realize aviation pollution source supervision area accurate determination, improve airport pollution control efficiency and pertinence.

This invention discloses a multimodal emotion intervention data protocol and a collaborative repositioning evaluation method based on neural mapping parameters. The method defines a standardized Emotional State Data Structure (ESDS), atomically binding Role ID, Embodied Action Code, and Narrative Tag based on self-distance theory, thus addressing the problems of unstructured intervention instructions and the disconnect between cultural prototypes and machine instructions in existing systems. Simultaneously, this invention constructs a multimodal collaborative repositioning evaluation algorithm, quantifying the degree of emotion calming using the formula Q_return=alpha × Norm(S_bio) + beta × Norm(S_audio) + gamma × Sync(S_visual) + delta × Decay(S_time), where the weight alpha of the biological signal derived from real physical electrical signals (HRV / GSR) is ≥ 0.5. This invention establishes an industrial-grade emotion intervention interface, achieving cross-device interoperability, and ensures the objectivity and scientific rigor of the repositioning evaluation through high-weighted physiological indicators.

The invention relates to a passivation efficacy test method, device and system, computer equipment and a storage medium, and the method comprises the steps: carrying out the dark-state volt-ampere test of a perovskite photovoltaic cell, obtaining the volt-ampere characteristic parameters, carrying out the fitting analysis through combining with the volt-ampere characteristic parameters, obtaining the diffusion current and generated current of the perovskite photovoltaic cell in a reverse bias state, and obtaining the passivation efficacy of the perovskite photovoltaic cell. Finally, the diffusion current and the generated current are combined for analysis and calculation, and passivation efficiency parameters representing the passivation efficiency of the perovskite photovoltaic cell in the current state can be obtained. Through the scheme, the passivation potency parameter of the perovskite photovoltaic cell can be analyzed and calculated by adopting a simple volt-ampere test mode without a complex algorithm, that is, the performance evaluation of the passivation material of the perovskite photovoltaic cell is realized, the evaluation algorithm is simple, and the calculation cost is greatly reduced.

Embodiments of the present disclosure provide a kind of based on airspace grid unmanned aerial vehicle detection equipment performance calculation method and device, it is applied to unmanned aerial vehicle technical field.The method includes obtaining the construction barrier grid set in multiple source data in the region to be processed;Obtain the construction equipment envelope grid set in unmanned aerial vehicle detection equipment parameters in the region to be processed;According to barrier grid set and equipment envelope grid set, determine the visibility state of each equipment envelope grid unit, and generate the visibility grid set corresponding to each equipment;Through preset performance evaluation algorithm, calculate equipment performance according to the visibility grid set of each equipment;Based on preset merging algorithm, the visibility grid set and equipment performance of all equipment are merged, and the airspace total equipment performance grid set in the region to be processed is generated.In this way, the performance analysis and efficient fusion calculation problem in the management of low-altitude unmanned aerial vehicle detection equipment can be solved, and the coverage effect and comprehensive performance of the detection equipment widely deployed in low-altitude area can be accurately evaluated.

This invention relates to the field of intelligent supervision and early warning management, proposing a work supervision and early warning management method and system based on multi-dimensional risk sources. A multi-dimensional risk entropy model is designed, and supervision and spot checks are conducted based on complex multi-dimensional risk data items. Through kernel density estimation of risk entropy calculation, dynamic spot check probability calculation, and cumulative probability stratified sampling, efficient extraction of high-risk items is achieved, while improving the coverage balance of sampling, reducing the time consumed in a single sampling, and improving the overall efficiency of supervision and spot checks. Furthermore, a multi-level fuzzy evaluation algorithm based on AHP-entropy weight combination is designed. Through AHP-entropy weight combination, the influence of subjective bias or data noise is avoided, and the weights are adaptively adjusted according to changes in data distribution, improving the accuracy and stability of early warning monitoring. This invention improves the efficiency and stability of work supervision and early warning management methods based on multi-dimensional risk sources.

This application discloses an AI-based experimental verification and evaluation method and related apparatus, relating to the field of computer technology. The method includes: structurally segmenting experimental evaluation documents based on a parent-child segmentation pattern to obtain a two-layer segmented document structure; vectorizing the content of its sub-blocks to generate a vectorized knowledge base; invoking a data extraction agent based on data extraction rules, using an experimental data management software database as the data source, and extracting experimental plan, implementation, and result information from the document using a large language model; vectorizing user evaluation needs based on an experimental verification and evaluation task planning agent, calculating semantic similarity using the vectorized knowledge base, and deriving relevant evaluation algorithm specifications and experimental cost accounting standards; and evaluating the sufficiency, progress, and cost of the experimental verification process based on the specifications and standards using an experimental verification sufficiency, progress, and cost evaluation agent to obtain the experimental verification evaluation results. This application can quickly obtain evaluation results, supporting leadership decision-making.

This invention provides a collaborative control method based on consciousness impairment assessment and monitoring and synchronous neural stimulation. The method includes: an eye-tracking monitoring and analysis system extracting eye-tracking feature data from eye-tracking signals using image preprocessing and eye-tracking feature extraction algorithms; a server-side management system combining scale assessments and eye-tracking feature data, utilizing a multi-source data fusion assessment algorithm to output the patient's consciousness state assessment results and stimulation parameter information; a neural correlation electrical stimulator receiving the stimulation parameter information and using a timing synchronization control algorithm to synchronously initiate neural electrical stimulation with the eye-tracking monitoring and analysis system; and the server-side management system dynamically adjusting the consciousness state assessment results and stimulation parameter information based on post-stimulation eye-tracking response characteristics and stimulation parameter feedback, reusing the multi-source data fusion assessment algorithm to form a closed-loop control. This invention achieves timing synchronization and command closed-loop through a collaborative linkage algorithm, ensuring efficient collaborative operation of multiple modules and adapting to complex clinical scenarios such as ICUs and rehabilitation departments.

The application discloses a kind of multi-energy coupling's comprehensive energy system fast multi-state reliability improvement method.For the comprehensive energy system, a multi-state reliability model considering multi-energy coupling is established;Gaussian approximation is used to obtain a multi-state Gaussian approximation reliability model by approximating the reliability of the multi-state reliability model; a multi-state sampling approximation reliability model is obtained by approximating the reliability of the multi-state Gaussian approximation reliability model using sampling approximation; the approximate reliability of the comprehensive energy system under different multi-energy load requirements is calculated using a fast reliability evaluation algorithm for the multi-state sampling approximation reliability model; the approximate reliability is used to establish a reliability constraint condition for comprehensive energy system structure planning and standby optimization scheduling, thereby improving the reliability of the comprehensive energy system.The application improves the traditional reliability evaluation method in terms of time and accuracy, reduces the calculation time, and effectively improves the system reliability.