517 results about "Objective data" patented technology

The invention discloses a hierarchical optimization scheduling method for deep peak regulation of thermal power generating units, and the method comprises the following steps: system layering: dividing a power system into a plurality of subsystems, each subsystem comprising a thermal power generating unit cluster and differentiated load demands; subsystem-level prediction: generating a basic scheduling plan; dynamic preference-driven multi-target collaborative optimization: receiving multi-target data from each subsystem, carrying out multi-target collaborative optimization, modeling three targets of a power grid company, an environmental protection department and a terminal user as game participants, quantifying the priority of each party by adopting a fuzzy membership function, generating a dynamic game solution through a Nash equilibrium solver, and carrying out multi-target collaborative optimization; screening out a Pareto optimal solution giving consideration to interests of multiple parties; performing online verification on the dynamic security domain; virtual synchronous machine cooperative support: simulating operation characteristics of a synchronous generator; and global coordination and iterative optimization: uploading the optimal scheduling scheme of each subsystem to a scheduling center through a message queue telemetry transmission protocol, and performing global constraint verification.

The invention discloses a traditional Chinese medicine nursing management system and method based on a cloud follow-up visit management platform, and belongs to the technical field of intelligent nursing. The management system comprises the following modules: a dynamic data acquisition module for acquiring physiological parameters, living habits and environmental data of a patient in real time and performing primary processing; the dynamic health archive construction module is used for constructing and continuously updating an individualized health archive of the patient based on the data collected in real time; the traditional Chinese medicine knowledge map and reasoning module depends on the traditional Chinese medicine nursing knowledge map and combines an intelligent reasoning technology to realize accurate traditional Chinese medicine syndrome differentiation and treatment analysis and automatically generate a personalized nursing scheme; the multi-dimensional patient feedback module is used for constructing a feedback mechanism fusing objective data, patient subjective feeling and medical care evaluation so as to extract key nursing effect information; and the nursing scheme iterative optimization module dynamically adjusts and optimizes the traditional Chinese medicine nursing scheme based on the feedback data, and realizes personalized and self-adaptive continuous improved nursing.

The invention provides an indoor decoration digital management method. An initial BIM model is constructed based on target hidden engineering data. And carrying out data verification on the multi-source real-time decoration data, and carrying out spatial association on the verified data and the initial BIM model to generate a target BIM model so as to ensure the accuracy and availability of the data. And performing dynamic early warning auditing on the target BIM model based on a preset early warning threshold set, and generating comprehensive early warning level data. And according to feedback data sent by the construction party, the supervisor and the owner based on the comprehensive early warning level data, a quality acceptance report is constructed, and objective data support is provided for concealed project acceptance. And performing block chain encryption by using the construction data corresponding to the quality acceptance report and the target BIM model, constructing a collaborative management platform, and generating a decoration collaborative management platform. And through a block chain encryption technology, the security and non-tampering property of the data are ensured, and the data deviation is reduced.

The invention belongs to the technical field of personalized insole manufacturing, particularly relates to a personalized insole manufacturing method and system based on three-dimensional motion capture and 3D printing, and aims to solve the problem that most existing insoles are generally manufactured and lack targeted design for consumers. The method comprises the following steps: preprocessing foot data of a target to obtain preprocessed foot data; performing feature extraction on the preprocessed foot data to obtain each foot feature; constructing a comprehensive feature vector of each foot feature based on the data source feature of each foot feature; performing mechanical analysis on the target based on the data source features to obtain a mechanical analysis result; performing multi-modal fusion on each comprehensive feature vector by using an algorithm to obtain a foot biomechanical model; and performing 3D printing of the insole based on the mechanical analysis result and the foot mechanical model. According to the method, scientific and objective data support is provided for comprehensive quantitative analysis of the motion state of the foot, and manufacturing of personalized insoles is guided.

The invention belongs to the technical field of cloud computing and edge computing collaboration, and particularly discloses an intelligent scheduling and optimizing method for a cloud-edge collaboration network based on reinforcement learning. By constructing the state sensing matrix and generating the action decision vector, the problem that a traditional scheduling method is insufficient in correlation analysis of multi-dimensional operation state data in a complex network environment is solved, and the comprehensive sensing capability of the operation state of the network node is improved; a dynamic mapping mechanism among the running state, the resource limitation and the task allocation strategy is established, the task allocation and resource scheduling strategy is automatically and differentially adjusted according to the real-time state of the node, and the optimal matching between the task demand and the resource supply and the dynamic balance between the performance and the efficiency are realized; through performance index monitoring and closed-loop feedback optimization, the scheduling effect is mastered in real time, continuous iterative optimization is performed on the reinforcement learning model according to objective data, and resource waste and scheduling delay are reduced.

The invention discloses a multi-factor civil aviation communication link safety index evaluation method based on subjective and objective data source fusion. The method comprises the following steps: 1) establishing a communication link safety evaluation index; 2) constructing a communication link safety evaluation model considering a plurality of factors; 3) selecting different data types to carry out security assessment according to the difficulty level of factor index quantification; 4) according to the types of the factor indexes, different evaluation methods are selected to calculate the multi-layer index weight and the underlying factor index risk value of the evaluation system; and 5) fusing the index weight and the index risk value to calculate a comprehensive risk value of the communication link under a plurality of external factors, thereby achieving the purpose of evaluating the security of the communication link transmission process. According to the method, the security of the communication system is evaluated by using a subjective and objective combined method through multiple perspectives, and a scientific and effective evaluation means is provided for research, development and application of a communication technology.

The invention provides a cognitive ability training method and system based on a virtual reality technology, and the method comprises the steps: obtaining multi-data-dimension feature data used for evaluating the cognitive ability of a subject in the training process of each round; wherein the feature data comprises objective data collected by a sensor during training and subjective data counted after training; scoring the cognitive competence of the subject in each data dimension according to a preset rule, and performing weighted fusion of scores according to preset weight parameters for different data dimensions to obtain a comprehensive score; and taking the score of each data dimension and / or the comprehensive score as input, and utilizing a pre-trained artificial intelligence model to adaptively adjust the difficulty level suitable for the tested training task. According to the invention, the difficulty level suitable for the tested training task can be adaptively adjusted by using the pre-trained artificial intelligence model.

The invention discloses a crew workload evaluation method and system based on multi-source data fusion, and the method comprises the steps: 1, carrying out the collection of multi-source data, and enabling the multi-source data to comprise objective data and sensory monitoring data; 2, performing feature extraction on the multi-source data; 3, based on the physiological data and the sensory monitoring data, performing feature layer fusion and decision-making layer fusion; and step 4, visualizing the workload evaluation result to present the real-time workload level of the sailors, and when the workload index exceeds a preset threshold value, triggering an early warning prompt to remind the sailors and managers to pay attention to the fatigue risk. According to the method, multi-source heterogeneous data are comprehensively utilized, comprehensive and accurate evaluation and real-time visual presentation of the load state of the sailors are achieved, an effective early warning mechanism is constructed, and the method has important application value in the aspect of improving the working safety and the working efficiency of the sailors.

The invention provides a dynamic health monitoring system integrating artificial intelligence and wearable equipment and a method thereof, and relates to the field of health monitoring, and the method comprises the steps: collecting physiological parameters and environmental data of a user, eliminating time migration of multi-source data through a timestamp alignment technology, extracting dynamic coupling features of the physiological parameters and the environmental data, and obtaining a dynamic health monitoring result; and generating the health state characterization guided by the environment. The method comprises the following steps: synchronously acquiring user voice input information, and extracting and analyzing acoustic features to generate emotional state change features after performing Mel spectrum conversion; then, cross-modal semantic association between emotions and health states is established through a modal soft constraint mechanism, and personalized guidance suggestions are generated and displayed, so that the dynamic perception ability of health state assessment and the scene adaptability of suggestion generation are improved; therefore, the problems of environmental factor splitting, subjective and objective data isolation and insufficient dynamic nonlinear relation modeling in a traditional method are solved.

The invention discloses a wind and light energy storage power station capacity prediction system based on multiple targets, and relates to the technical field of new energy power generation. The multi-target data acquisition module determines a target area according to power grid topology, load and wind and light resource distribution, equipment is installed to acquire data, a reliable data basis is provided for subsequent modules, data is ensured to be accurate and effective, error and abnormal data interference is avoided, and the balance management module monitors a power grid in real time. Analyzing the wind-solar power generation and electrical load balance relationship, and adjusting the operation mode of the energy storage power station according to the relationship; the power prediction module constructs and optimizes a model, accurately predicts power, calculates energy storage charging and discharging power, ensures stable operation of a power grid and improves energy utilization efficiency, and the scheduling decision module formulates a scheduling scheme in combination with multi-aspect information to ensure safe, stable and economic operation of the power grid; the risk assessment module comprehensively considers various risk factors, assesses the operation performance of each power station, matches a coping strategy and an early warning mechanism, and enhances the operation reliability and safety of the power grid.

The invention relates to the technical field of operation training evaluation, in particular to a training quality evaluation method and system based on pilot attention distribution. The method comprises the following steps: collecting data required by pilot attention distribution training quality evaluation according to a specific flight mission scene; based on the collected data, calculating a standard attention distribution model of the pilot in a specific flight mission scene, and generating attention distribution and actual attention distribution of the standard attention distribution model; and comparing the attention distribution of the standard attention distribution model with the actual attention distribution, evaluating the training quality of the attention distribution of the pilot in the specific flight mission scene, and forming and displaying an evaluation result. According to the method, the attention distribution mode of the pilot is quantified, the eye movement tracking technology and task-related subjective and objective data are combined, the performance of the pilot in simulator training can be objectively and accurately evaluated, and the scientificity, fairness and safety of flight training are improved.

The invention belongs to the technical field of rehabilitation assessment, and provides a cerebral apoplexy rehabilitation assessment method and system based on multi-mode electroencephalogram and myoelectricity fusion. According to the method, 59-channel electroencephalogram signals and 14-channel electromyographic signals are preprocessed, mutual information between channels is calculated to construct a correlation matrix, spatial-temporal features are extracted in combination with a convolutional neural network (CNN) and a Transform self-attention mechanism, and rehabilitation level classification is achieved. According to the method, coherence between electroencephalogram / myoelectricity channels is quantified by adopting mutual information, and 59 * 59 and 14 * 14 dimensional feature matrixes are constructed; a CNN-Transform hybrid model is designed to optimize feature fusion, and the classification precision is improved; gradient weighted class activation mapping (Grad-CAM) is introduced to analyze feature saliency, and correlation between an electroencephalogram channel and a focus is revealed; and developing a visual evaluation system, and displaying the multi-modal data and the historical trend in real time. The system stores patient information and evaluation results through a MySQL database, and supports doctor-patient online interaction. Compared with traditional scale evaluation, the method has the advantages that the evaluation objectivity is improved by using multi-modal objective data, the model credibility is enhanced by combining interpretability analysis, and technical support is provided for formulating a personalized rehabilitation scheme.

The invention provides a multi-target data processing method and device, a model training method and device and equipment, and relates to the technical field of big data, in particular to the fields of data processing, service recommendation and machine learning in the field of big data. According to the specific implementation scheme, according to basic information of a user and data information of the user for a to-be-planned service, an initial planning path of the user about the to-be-planned service is generated, and the initial planning path comprises a plurality of process targets; monitoring progress data of the user for the plurality of process targets in real time, and adjusting the initial planning path based on the progress data to obtain a planning path; and synchronizing the planned path to a terminal device of the user.

The invention discloses a psychological support strategy generation engine based on multi-modal data fusion, and relates to the technical field of psychological health, the engine comprises a multi-modal data acquisition module, a multi-modal data fusion processing module, a dynamic recovery trajectory modeling module, a recovery process prediction and strategy generation module and a strategy evaluation and feedback adjustment module; multi-modal data such as physiology, behaviors and voice are deeply combined with core theories such as cognitive behavior therapy and positive psychology through an innovative mechanism, data processing is performed according to theoretical constraint cleaning, and data compliance is ensured; fusing the matching degree of the mid-quantized data and the theoretical dimension, and associating objective data with theoretical indexes; dynamic modeling strengthens sensitive features such as a critical period of wound recovery, so that evaluation reflects physiological and behavior changes and embodies intrinsic features such as psychological toughness, a real psychological state can be accurately captured, a recovery risk can be recognized in advance, a scientific basis is provided for accurate intervention, and pertinence and foresight of psychological support are effectively improved.

The invention relates to the technical field of education evaluation, and discloses a course teaching effect evaluation method, which comprises the steps of constructing a two-dimensional evaluation index system containing student ability and teacher teaching as a basic framework of evaluation; based on the two-dimensional evaluation index system, determining an initial weight by adopting an interval intuitionistic fuzzy analytic hierarchy process, and dynamically correcting the initial weight in combination with an entropy weight method to form a combined weight; collecting multi-source data corresponding to the two-dimensional evaluation index system through a triangular verification method, and preprocessing the multi-source data; combining the combined weight with the preprocessed multi-source data, and performing layered fuzzy comprehensive evaluation, thereby realizing quantitative evaluation and weak link diagnosis of the course teaching effect; according to the method, expert knowledge and objective data are fused, so that the evaluation weight is dynamically changed along with actual teaching data, and the problem that evaluation is separated from reality due to weight solidification is solved.

In one embodiment, a smart home modeling system receives objective data from a public data source. The system modifies baseline age information from the objective data based on indications from service and upgrade records, as well as smart home sensor measurements, to generate a corrected age model for the home. The system determines the effective ages of systems in the home in the corrected age model, and then uses the corrected age model to generate a risk model for the home. Based on the generated risk model, maintenance needs and statuses are re-evaluated. The system provides a virtual dashboard to view results from the smart home model. The dashboard displays alerts about particular systems of the home with elevated risk levels. Additionally, the dashboard generates price information based on the corrected age model and the risk model. The dashboard also notifies a user about service interventions for home systems.

The invention discloses an enterprise digital supply chain capability evaluation method and system, and relates to the technical field of supply chain evaluation, and the evaluation method comprises the steps: creating a digital supply chain capability evaluation index system; obtaining to-be-evaluated enterprise digital supply chain evaluation indexes and evaluation scores thereof, and forming an index score set; determining a combination weight of each evaluation index in the index score set; determining an evaluation cloud of each evaluation index in the index score set; determining a comprehensive evaluation cloud of the to-be-evaluated enterprise digital supply chain according to the combination weight and the evaluation cloud; identifying the maturity level of the enterprise supply chain capability; and matching the comprehensive evaluation cloud with the maturity level of the enterprise supply chain capability, and determining the maturity level of the to-be-evaluated enterprise. According to the method, an evaluation index system which comprehensively covers each key link of the digital supply chain is constructed, subjective judgment and objective data are combined, a weight determination method combining an OWA operator and an entropy weight method is adopted, and the reliability of an evaluation result is improved.

The invention discloses a data acquisition and management system for autism spectrum disorder child information, which comprises a puzzle game unit, a data acquisition unit, a data processing unit, a remote monitoring unit, a network transmission unit and a scheme customization unit, and belongs to the technical field of autism spectrum disorder. Intelligence game interaction is combined with various technical means of angle acquisition, sound wave acquisition, heart rate acquisition and response acquisition; comprehensive and objective data acquisition of expressions (such as correct rate and attention concentration degree), emotional fluctuation (through sound wave amplitude intensity), physiological status (such as heart rate change) and social reaction (such as response value) of children in the puzzle game is realized, and furthermore, the acquired data is scientifically analyzed through an algorithm formula, so that the data acquisition accuracy is improved. The accuracy and objectivity of evaluation are greatly improved, and multi-dimensional and all-around evaluation of the illness state of the child is achieved.

The invention discloses a talent management method and system based on objective data evaluation, and relates to the technical field of data processing, and the method comprises the steps: analyzing the entropy change condition of each class of reflection indexes, integrating all classes of reflection indexes, determining the comprehensive quality level, describing the overall ability level of different persons through the comprehensive quality level, and carrying out the evaluation of the overall ability level. The objectivity of talent evaluation is improved, and the influence of subjective factors on talent ability evaluation is avoided, thereby helping to optimize talent management. The post information is analyzed to determine the post difference coefficient of each group post, the hyperbola attenuation model of each group post is established, the time change content of multiple factors in the post information is considered, the weight of each piece of post information is determined, the historical value and timeliness are balanced through hyperbola characteristics, and the accuracy of the hyperbola attenuation model is improved. The attenuation speed of the capability value of the post is described, and the capability portrait of each enterprise staff is constructed, so that capability comparison between different posts can be realized, and enterprise talent management and post distribution are optimized.

The invention relates to the field of Internet of Things technology, data analysis and insurance claim settlement, in particular to a security Internet of Things and insurance claim settlement intelligent evaluation integrated system, which comprises a security state data acquisition module, a dynamic security portrait module, an accident traceability and responsibility intelligent division module and an automatic claim settlement evaluation module. According to the method, deep analysis of multi-source heterogeneous environment data and accurate representation of the safety state are achieved, the dynamic safety portrait module can capture the essential topological structure relation of the data by constructing the functions of topological feature extraction, state representation, multi-scale anomaly detection, risk prediction and the like, and high-precision recognition and risk prediction of gradient anomaly are achieved. After an accident occurs, a complete accident data chain can be constructed, the responsibility proportion of each party can be intelligently divided based on objective data, and the compensation amount can be automatically calculated, so that full-process automation from safety monitoring to insurance claim settlement is realized, the anomaly detection accuracy is greatly improved, and potential risks can be found in advance.

The invention relates to the technical field of tunnel engineering, in particular to a shield tunneling machine cutterhead cutting parameter decision-making method. According to the method, comprehensive and comprehensive stratum stress characterization and rock-soil cutting flow characterization are obtained by establishing cutterhead rock breaking stress models of various stratums, and final cutterhead parameters are intelligently obtained based on a multi-objective optimization model, a digital twin model and a neural network model in the form of establishing a database. According to the technical scheme, a reliable result can be obtained based on objective data, the representation capability of the finite element model can be expanded, and the credibility is improved.

The invention provides a collateral dredging instrument parameter optimization method based on subjective and objective data fusion, and relates to the technical field of collateral dredging instruments, and the method comprises the following steps: generating an initial electrical stimulation parameter according to an initial electrophysiological signal of the collateral dredging instrument for detecting a target acupoint, and starting the output; in an initial stage of starting output, receiving a subjective trigger signal which is fed back by a user and represents a tolerable critical point, and recording a stimulation intensity value corresponding to the subjective trigger signal as a current personal comfort threshold value of the user; performing continuous stimulation by taking the current personal comfort threshold as basic intensity, and simultaneously executing the following parallel steps: acquiring a continuous or discrete real-time subjective comfort feedback signal input by a user through an interactive interface in real time; monitoring a dynamic electrophysiological signal of the target acupuncture point in real time, and analyzing the change trend of the dynamic electrophysiological signal to generate an objective adjustment suggestion signal; and performing fusion analysis on the real-time subjective comfort feedback signal and the objective adjustment suggestion signal, and performing real-time dynamic adjustment on the current electrical stimulation parameter according to the fusion analysis.

A computer-implemented system and method for dynamic, explainable, and adaptive prioritization of cybersecurity controls is disclosed. The system ingests unstructured threat reports and employs a natural language processing (NLP) module to automatically extract adversary tactics, techniques, and procedures (TTPs). A scoring module applies a mathematical time-decay function to the extracted intelligence. A novel hybrid prioritization engine provides explainability-by-design by computationally integrating these objective, data-driven scores with organization-specific context within a transparent multi-criteria decision analysis (MCDA) model. Critically, the system establishes a self-optimizing closed feedback loop; it receives real-world control effectiveness metrics from the operational environment and uses this data as new ground-truth labels to continuously and automatically retrain internal machine learning models. This adaptive mechanism improves the computer's own predictive accuracy and resource allocation efficiency over time, representing a tangible technical improvement.

The invention belongs to the technical field of sandy ground grouting regulation and control, and relates to a dolomite sandy ground tunnel high-pressure curtain grouting regulation and control method and system. According to the method, the fuzziness and the uncertainty of single sensing information when complex geological conditions are judged and recognized are overcome by generating the sanding rate grade map and the splitting early warning signal, and the accuracy of stratum state sensing is improved; a dynamic linkage mechanism among stratum states, risk early warning, slurry parameters and grouting parameters is established, the slurry proportion and the grouting pressure are automatically and differentially regulated and controlled according to the sanding grade, and optimal matching of the slurry property and stratum requirements and dynamic balance of safety and efficiency are achieved; the formation condition of the curtain is mastered in real time through grouting effect real-time imaging and closed-loop feedback regulation and control, the construction scheme is continuously optimized according to objective data, the uniformity, continuity and overall quality of high-pressure curtain grouting of the sandy stratum tunnel are ensured, and meanwhile material waste and construction period delay are avoided.

The embodiment of the invention relates to the technical field of agricultural informatization and big data application, and provides a service information pushing method and system, electronic equipment and a medium, and the method comprises the steps: obtaining agricultural data; constructing a farmer portrait according to the agricultural data; matching to-be-pushed target agricultural service information according to the farmer portrait; and pushing the target agricultural service information to a corresponding farmer user side according to a preset information pushing rule. Therefore, existing massive objective data mining and analysis are fused, different types of peasant portraits are constructed, peasant demands are accurately grasped, personalized information and services are pushed to peasants in time, the information acquisition efficiency of the peasants and the timeliness and satisfaction degree of obtaining the services are improved, and a support is provided for improving the agricultural production efficiency and the peasant life quality.

The invention relates to the technical field of rehabilitation assessment, in particular to an orthopedic rehabilitation system and a rehabilitation assessment method based on data feedback, comprising the following steps: training a classifier by using a first single-mode data set formed by myoelectricity feedback data and corresponding rehabilitation status labels to obtain a first single data model of orthopedic rehabilitation assessment; a classifier is trained according to a second single-mode data set formed by the bone image data and the corresponding rehabilitation condition labels, and a second single-data model of orthopedic rehabilitation evaluation is obtained; and respectively carrying out transfer learning on the orthopedic rehabilitation assessment second single data model and the orthopedic rehabilitation assessment first single data model to obtain an orthopedic rehabilitation assessment multi-data model. According to the method, the orthopedic rehabilitation assessment multi-data model is constructed, objective assessment of orthopedic rehabilitation conditions is completed according to objective data such as the myoelectricity feedback data and the skeleton image data of the assessment object, the intelligent degree of assessment can be improved, and the assessment efficiency can be improved.

The invention provides a correlation analysis method for technological parameters in a wafer processing process and related equipment. The correlation analysis method comprises the following steps: determining target parameters needing correlation analysis in the wafer processing process; according to the target parameter, acquiring all other process parameters belonging to the same wafer batch as the target parameter and corresponding parameter values in the data warehouse, and packaging into a data set; inputting the data set into a machine learning algorithm model, and calculating the correlation between the target parameter and other process parameters through a machine learning algorithm; and determining key process parameters influencing the target parameters according to a correlation calculation result. The correlation between the target parameter and other process parameters is automatically calculated and identified through a machine learning algorithm. In this way, the key process parameters influencing the target parameters can be effectively screened out, objective data support and decision basis are provided for the process defect problem caused by the common influence of multiple factors, and the difficulty of fault analysis and problem positioning is reduced.

The invention relates to the technical field of computer data processing and mode recognition, and discloses an intelligent handwriting record analysis system supporting translation process traceability and capability assessment, which comprises the following steps: collecting multi-dimensional handwriting data generated when a user translates by using an intelligent pen; the Hidden Markov model decodes the handwriting into translation behavior primitive sequences such as smooth writing, hesitant pause and the like; automatically dividing semantic regions such as a main translation region and a draft region by a density clustering algorithm; performing space-time fusion attribution to generate a space-time attribution log for accurately recording behaviors, time and positions; and establishing a multi-dimensional capability index system containing translation efficiency, quality and strategies based on the log, and carrying out quantitative calculation and generating a visual user capability portrait. According to the method, the invisible translation cognition process is converted into traceable and quantifiable objective data, deep insight and comprehensive evaluation of the translation process are achieved, and a scientific and efficient analysis tool is provided for translation teaching and interpreter ability evaluation.

The invention relates to the technical field of dam safety detection, and discloses a dam hidden danger troubleshooting method and system.The dam hidden danger troubleshooting method comprises the steps that a visual dam image collected in a multi-mode mode is obtained, a target detection model is used for processing the visual dam image, information of at least one bounding box with a hidden danger target is output, and then the bounding box is cut; the method comprises the steps of obtaining a local image where a hidden danger target is located, processing the local image by using a semantic segmentation model, outputting a pixel-level segmentation mask corresponding to the hidden danger target, and calculating quantitative geometric parameters of the hidden danger target based on the pixel-level segmentation mask, thereby providing objective data support for subsequent risk study and judgment of dam hidden dangers. According to the method, subjective judgment errors of manual detection are avoided, automatic dam hidden danger investigation is achieved, manpower and cost of manual inspection are greatly reduced, automatic collection, detection and quantitative analysis of dam images are achieved, dam hidden danger can be found and rectified in time, and the risk of dam safety accidents is reduced.

The present application relates to the field of imaging effect testing, and discloses a three-dimensional imaging effect testing system and method, a device and a storage medium. The system (100) comprises: a head model (101), a binocular camera (102) provided at a binocular position of the head model (101), and a computing component (103) connected to the binocular camera (102). The binocular camera (102) is used for capturing a three-dimensional visual image (13) presented by a three-dimensional display device (104) to obtain a first left-eye image (111) and a first right-eye image (121). The computing component (103) is used for outputting imaging effect information of the three-dimensional display device (104) on the basis of the first left-eye image (111) and the first right-eye image (121). The solution can evaluate the imaging effect of the three-dimensional display device by means of objective data.