129 results about "Continuous evaluation" patented technology

The invention discloses a building construction process risk assessment method and system based on a fault tolerance mechanism, and particularly relates to the field of building construction process risk assessment, and the method comprises the steps: building a measurement point hierarchical index of a building construction process, synchronously arranging time sequence displacement data, and executing difference calculation to obtain a space gradient evolution sequence; mapping a trend change interval set extracted by sliding window processing into a path node set, and constructing a topological connection relation of the path node set to form an initial risk path map; the data state of each path node in the risk path map is subjected to anomaly identification, the access control effect of the abnormal node in the path is analyzed, and whether the path fracture risk is formed or not is judged in combination with the sensitive monitoring area. By constructing a risk map structure based on a trend path and combining a path interruption node identification and alternative path dynamic deduction mechanism, continuous assessment and map self-correction of the risk trend under the condition of measuring point data failure are realized.

The invention discloses a multi-modal deep learning method for artistic work analysis, and relates to the technical field of computer vision, and the method comprises the steps: multi-modal data preprocessing: obtaining a visual image, text description and metadata of an artistic work, the method has the advantages that valuable feature vectors are extracted from visual images, text description and metadata of artistic works through multi-modal data preprocessing, the importance of all modal features is dynamically adjusted by means of an attention mechanism in the cross-modal dynamic fusion step, style information of different sources can be effectively integrated, and the effect of improving the artistic work quality is achieved. In the style migration stage, the multi-scale feature pyramid network is used for fusing feature information of different levels, real-time feedback optimization is continuously evaluated and parameters are adjusted through a discriminator, and compared with an existing neural style migration method, the method has the advantages that complex artistic styles are better processed, core elements of specific styles are accurately reserved in the migration process, and the method is suitable for popularization and application. And the accuracy of style migration and the artistic expressive force are improved.

The invention relates to the technical field of artificial intelligence, in particular to a cue word optimization method, an intelligent agent and a storage medium. According to the cue word optimization method provided by the invention, a plurality of cue word templates are generated, different cue word templates and different task sample combinations are input into different models, and through continuous evaluation and feedback optimization, a first cue word template adaptive to each task and model type is finally screened out; and setting a first label representing a corresponding task type and a second label representing a corresponding model type for each first cue word template to obtain a label cue word template and storing the label cue word template, so that searching in a task dimension and a model dimension is facilitated, rapid and accurate matching of the cue word templates can be realized, the adaptability of the cue word templates is improved, and the user experience is improved. Efficient operation of the intelligent agent in dynamic scenes of different tasks and models is supported, so that the intelligent agent can adapt to the cue word template with the most appropriate task and model type, and the generalization ability and execution effect of the intelligent agent in a multi-task scene and a multi-model are improved.

The invention discloses a large-model multi-dimensional automatic evaluation method based on dynamic adversarial evolution, which is characterized by comprising the following steps of: dynamic adversarial sample generation: generating a multi-modal adversarial sample including text, image and voice data through an adversarial evolution algorithm, simulating a hostile attack mode in a real environment, and generating a dynamic adversarial sample; triggering potential vulnerabilities of the large model; multi-dimensional evaluation index system construction: constructing a multi-dimensional quantitative evaluation system, automatically adapting to different industry requirements through a dynamic weight distribution algorithm, and generating a customized evaluation report; and automatic pressure test iteration: embedding a continuous evaluation module in a model development stage, and automatically generating an optimization strategy according to a test result in combination with a reinforcement learning technology.

The invention discloses a crop nitrogen fertilizer management system and method based on multi-agent reinforcement learning, and belongs to the technical field of intelligent agriculture. Comprising the following steps: collecting and preprocessing multi-source data of a target area, calibrating a crop growth-nitrogen cycle model based on the multi-source data, and constructing a dynamic simulation environment; constructing a nitrogen fertilizer application strategy model and a multi-target award function, and performing agent reinforcement learning training by adopting a centralized training-decentralized execution architecture; introducing a large language model, and updating a strategy network and / or a value network of each agent in a training process according to a reward adjustment signal and a decision constraint; when the index fluctuation ratio in the continuous evaluation period is smaller than a preset threshold value, it is judged that the nitrogen fertilizer application strategy model is converged, and an optimal nitrogen fertilizer application strategy model is obtained; and generating a nitrogen fertilizer application scheme based on the optimal nitrogen fertilizer application strategy model in combination with the real-time state data, and generating a natural language interpretation and risk assessment report based on a large language model.

An AI-powered data quality monitoring and automatic alerting system (100) for cloud-based data warehouses, comprising: a. an AI-driven data quality monitoring module configured to continuously assess data in the cloud-based data warehouse for accuracy, completeness, consistency, and timeliness; b. an anomaly detection and warning system configured to automatically detect data anomalies based on the assessment performed by the data quality monitoring module and to generate real-time alerts based on detected anomalies; c. an automated data cleansing and remediation workflow that triggers corrective actions in response to detected anomalies, including data imputation, record merging, and validation to maintain data integrity.

The invention discloses a wound healing degree evaluation method and system based on multi-data analysis, and relates to the technical field of multi-modal data analysis, and the method comprises the steps: collecting a multi-modal data set related to a wound, constructing a cross-modal extraction model, inputting the multi-modal data set, and obtaining a preliminary feature set; processing the preliminary feature set by using a dynamic time warping technology to obtain a cross-modal feature set; constructing a causal relationship graph based on the cross-modal feature set to identify an intervention path, correcting a causal effect through a backdoor adjustment method, and eliminating false correlation to obtain a causal purification cross-modal feature sequence; inputting the causal purification cross-modal feature sequence into the established wound healing prediction model, and outputting a wound healing prediction result according to time sequence characteristics; and according to a wound healing prediction result, generating a real-time wound visualization report and providing feedback. Through the wound healing prediction model, a breakthrough from a single time point to continuous evaluation is realized.

Certain aspects of the disclosure pertain to machine learning evaluation and selection in a streaming environment. A machine learning model can generate inferences based on real time streaming data. A plurality of machine learning models can be available for a particular domain or task. Performance of the plurality of machine learning models can be continuously evaluated. Based on evaluation results, at least one of the plurality of machine learning models can be selected to provide output. For example, the streaming data can be routed to a selected machine learning model. Further, a poor-performing model, as determined based on evaluation results, can be fine-tuned based on real time data to improve performance.

The invention provides a resource configuration and optimal scheduling method for supervision resources, and the method comprises the steps: building a cable laying three-dimensional visual model, enabling the attributes of supervision personnel to be matched with a supervision region, generating an initial configuration scheme, and carrying out the correlation analysis of a historical electrical engineering quality problem and the attributes of the supervision personnel, thereby achieving the optimal scheduling of the supervision resources. And dynamic adjustment of supervision personnel configuration is realized. And the construction process is monitored in real time by using the Internet of Things technology, and early warning and positioning are carried out on abnormal conditions. Quality of key processes is judged through image analysis, and professional supervisors are matched for disposal according to severity of problems. And skill training is carried out in combination with a virtual reality technology, and a personnel scheduling strategy is optimized. And through big data analysis, continuous evaluation and dynamic adjustment of supervision personnel configuration are carried out to form closed-loop optimization. According to the method, the supervisor and the supervision area are highly matched, the supervisor can quickly and efficiently complete the supervision work, the construction efficiency of the cable laying project is effectively improved, and the quality of the cable laying project is improved.

The invention relates to the field of electric power measurement, and provides a voltage transformer error evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting the voltage phasor information of a measurement node in a preset time window, and obtaining the reference technical parameters of a voltage transformer corresponding to the measurement node; feature extraction based on a sliding window is carried out, sequence diagram structural features are obtained in combination with an adjacent matrix, and the adjacent matrix is constructed based on network topology and line electrical parameters of a power system; error estimation is carried out through a trained error estimation model based on the time sequence diagram structure features, an error estimation value is obtained, and the error estimation model is obtained by training a graph neural network model based on historical voltage phasor information and corresponding error labels; and performing error state evaluation based on the error estimation value. According to the method, the device, the electronic equipment and the storage medium provided by the invention, online continuous evaluation of the voltage transformer error is realized, and the limitations of long period and high cost of a traditional offline verification method are broken through.

The invention discloses a teaching resource dynamic allocation method and system based on education software platform data, and relates to the technical field of informationization, and the method comprises the steps: S1, obtaining the geographic position information of education resources, and carrying out the standardization processing of the position data, and obtaining a structured spatial distribution frame; s2, acquiring logical relationship data according to the spatial distribution framework, performing association mapping on the logical relationship data and the geographical location information, and determining a multi-dimensional resource attribute set; s3, aiming at the multi-dimensional resource attribute set, integrating the geographic position information and the logical relationship data into a resource distribution presentation layer to obtain a dynamically updated resource distribution view; according to the teaching resource dynamic allocation method and system based on the educational software platform data, the resource balance is continuously evaluated according to the adjusted distribution state, the optimization process is repeated when necessary, finally, dynamic optimal allocation of the educational resources is achieved, the resource utilization efficiency is improved, and educational fairness is promoted.

Methods and systems for guiding interventional surgery are provided. The method includes receiving a plurality of intracardiac echocardiography (ICE) images of a target object acquired during a preset period, analyzing of the plurality of ICE images to obtain image analysis results, and generating an interventional surgical plan based on the image analysis results to guide the interventional surgery. This method conducts image analysis on ICE image before puncture to plan the interventional surgery, thus identifying the optimal puncture angle, depth, and timing for the interventional surgery. The method can help conduct real-time detection and tracking of the puncture process, identifying and mitigating potential risks during the surgical process to improve the success rate of the surgery. Furthermore, continuous assessment and management of possible complications can be carried out through postoperative evaluation to comprehensively reduce the risk of the interventional surgery and enhance its effectiveness and safety.

The invention discloses a rehabilitation nursing intelligent monitoring method and system based on image acquisition, and belongs to the technical field of rehabilitation nursing intelligent monitoring, and the method comprises the steps: collecting limb image data of a patient and a synchronous pain response value to generate a monitoring model; processing the current image data based on a monitoring model to evaluate and output a pain risk value; analyzing the fluctuation trend of a pain response curve formed based on the pain risk value to generate and output a nursing scheme; collecting subsequent image data in the process of executing the nursing scheme, monitoring a limb recovery state and outputting recovery feedback information to adjust the nursing scheme; according to the method, continuous assessment of pain risks is achieved by establishing quantitative association of visual features and pain responses, the nursing scheme is generated based on dynamic fluctuation trend analysis, a closed-loop management process is formed by recovering feedback information, and the efficiency and safety of rehabilitation nursing are improved.

The invention discloses a medical health assessment system and method for old people in the field of social group management and assessment, and the system comprises a data collection module, a data analysis module, a health assessment module, a user interaction module and a control module. The data analysis module is triggered to carry out data processing and analysis, the control module receives an analysis result, and the user interaction module confirms the analysis result with the old people; if the analysis result is inconsistent, the control module guides the user to collect data again; if it is confirmed that the analysis result is consistent with the monitoring data, the health assessment module comprehensively assesses the medical health condition of the old people based on the data and generates a health assessment report. According to the invention, automatic, real-time and continuous evaluation of the health condition of the old people is realized, the accuracy and reliability of the evaluation result are improved, the evaluation process is simplified, and the burden of medical personnel and the old people is reduced.

The invention discloses a dental and maxillofacial growth and development evaluation method and system based on intelligent image analysis, and relates to the technical field of dental and maxillofacial analysis. According to the dental maxillofacial growth and development evaluation method based on image intelligent analysis, a patient head side position color image is adopted as a data acquisition mode, professional X-ray imaging equipment and a standardized head positioning device are not needed, and the hardware investment cost of a medical institution is greatly reduced. Meanwhile, the deep learning modeling model is used for automatic key point labeling and measurement index calculation, dependence on professionals is reduced, personal errors are avoided, and automatic analysis is achieved. Besides, the method supports home use, high-frequency monitoring can be conveniently carried out, the requirement for continuous evaluation in the orthodontic treatment process is met, the whole evaluation process is simple, convenient and rapid, the period is short, the cost is low, efficient batch analysis can be achieved, and the efficiency and accuracy of dental maxillofacial growth and development evaluation are remarkably improved.

The invention relates to the technical field of attitude evaluation, and discloses an attitude evaluation method and system based on motion perception. A posture evaluation method based on motion perception comprises the following steps: S1, collecting basic static parameters of a user's body, setting optical mark points on main joint points of the user's body, and arranging the user to execute a target motion in a predetermined dynamic capture space; s2, capturing a three-dimensional space coordinate sequence of the optical mark point in the process that the user executes the target action by using a plurality of optical cameras arranged in the dynamic capture space; and S3, constructing a personalized three-dimensional skeleton model according to the human body basic static parameters. According to the method, the deviation integral value of the actual motion track and the standard motion track in the whole motion period is calculated, so that the process continuous evaluation of the motion quality is realized.

The invention discloses an intelligent network equipment management and optimization method based on artificial intelligence. The method comprises the following steps: S1, collecting and preprocessing real-time state information of network equipment; s2, performing dynamic analysis on the real-time state information based on a DBSCAN clustering algorithm to obtain a clustering result; s3, constructing a topological relation graph between the devices according to a clustering result; s4, learning the topological relation graph by adopting a graph neural network, and generating an update representation of each device; s5, performing adaptive adjustment on the network topology according to the update representation of the equipment; s6, redistributing the traffic among the devices based on the change of the device group and the optimized traffic routing strategy; s7, constructing a feedback mechanism, and continuously evaluating the network optimization effect; and S8, continuously optimizing the management strategy of the network equipment. According to the method, an efficient and scientific optimization scheme can be provided in network equipment management and optimization, and remarkable technical values and economic benefits are brought to practical application.

The invention relates to the technical field of charging pile management, in particular to a charging pile safety monitoring method and system based on diversified analysis, and the system comprises a charging pile safety monitoring center, a potential state division unit, a safety monitoring analysis unit, a multi-dimensional evaluation unit, a persistence evaluation unit and a state early warning unit. According to the method, analysis is preliminarily carried out from the state angle of the charging gun head of the charging pile so as to carry out early warning management on the charging gun head which is not completely installed, so that the safety of the charging pile is improved, analysis is carried out from the fire early warning angle of the charging pile, and through the whole process design of image recognition-dynamic verification-multi-source cross verification, the safety of the charging pile is improved. Real-time and reliable technical guarantee is provided for safe operation of the charging pile, multi-dimensional health comprehensive evaluation analysis is carried out in an information progressive mode, comprehensive, objective and quantitative evaluation of the safe state of the charging pile is achieved, and powerful support is provided for safe operation and intelligent management of the charging pile.

The invention discloses an intelligent control method for equipment for chemical production, and relates to the technical field of intelligent control, and the method comprises the following steps: collecting process variable data, an automatic control instruction, an operator intervention behavior and equipment health state information; based on the collected process variable data, constructing a causal association relationship model between the operator intervention behavior and the equipment health state; identifying abnormal points by combining sliding time window prediction and a dynamic tolerance threshold value, and removing polluted data; constructing a multivariable causal diagram based on process variable deviation, and distinguishing control error paths caused by equipment degradation and intervention; deploying a multi-version control strategy model in the control system, and evaluating and screening an optimal strategy; an operator is guided to record the intervention purpose and mode on a human-computer interface; continuously evaluating the performance of the control strategy, and triggering a model retraining and replacing mechanism; the control logic is always close to the real state.

The invention provides a game big data real-time processing method and system, and belongs to the technical field of computer software, and the method comprises the steps: obtaining operation comparison data of new and old version functions according to a version switching preparation state, and verifying the output consistency of a data cleaning conversion link to obtain a verification passing signal; a data flow is gradually switched to a new version function by adopting a progressive flow migration mode according to the unloading process of the old version function through the verification pass signal, and the flow migration completion state is determined; through operation data monitored in real time, the stability of a new version function is continuously evaluated, dynamic adjustment details are stored by adopting a log recording module, and a complete adjustment record is obtained; and according to the complete adjustment record, constructing a prediction model to analyze a data flow interruption risk, and determining an optimized adjustment strategy.

The invention discloses a subjective evaluation method for the sound quality of in-vehicle noise under all working conditions, and the method comprises the steps: constructing an in-vehicle noise sample database under all working conditions, and the database comprises a steady-state noise sample and an unsteady-state noise sample; evaluating the steady-state noise sample to obtain a first evaluation result, and mapping the first evaluation result to a continuous evaluation interval to generate a first evaluation result curve; evaluating the unsteady noise sample to obtain a time-varying second evaluation result which takes the first evaluation result as an initial reference to generate a second evaluation result curve; and performing dynamic correlation analysis on the first evaluation result curve and the second evaluation result curve, and establishing a corresponding relation between the subjective evaluation results of the steady-state noise and the non-steady-state noise by calculating an optimal matching path of the two curves. According to the method, the integrity and systematic coverage of an in-vehicle noise sound quality evaluation system are realized, and a unified data basis is provided for sound quality analysis under all working conditions.

This invention discloses a robot visual servo control method based on depth point clouds, belonging to the field of intelligent control technology. The method includes: statistically analyzing the local point density, depth continuity, occlusion ratio, and missing trend of a target point cloud, and attaching these parameters to the corresponding point cloud region to form a target point cloud package; constructing a set of point cloud confidence servo error output terms based on a viewpoint maintenance index, and performing dynamic weighting in conjunction with local point cloud region quality markers to generate point cloud confidence servo error data and active viewpoint adjustment commands; performing robot visual servo control based on the currently executed control package, and outputting the target alignment state and end effector positioning state when the point cloud confidence servo error data meets preset joint discrimination conditions. This invention, by generating viewpoint maintenance indices, continuously evaluates the coverage changes, boundary changes, and center position changes of the target object region in the current field of view, achieving a stable representation of continuous observation maintenance.

The invention relates to the technical field of intelligent energy control, in particular to a central air conditioner optimizing energy-saving regulation and control method based on an artificial intelligence algorithm, which comprises the following steps of: acquiring operation parameters of central air conditioner parts and indoor and outdoor environment data, obtaining a standard data set through abnormal value cleaning and normalization processing, and storing the standard data set into a database; the method comprises the following steps: extracting characteristic variables influencing the energy efficiency of an air conditioner, constructing an energy-saving regulation and control model through a neural network algorithm, capturing a nonlinear relationship between operation parameters and environmental conditions, taking equipment safety as a constraint condition, applying the parameters to the energy-saving regulation and control model in real time, establishing a feedback mechanism to continuously monitor the operation state, and optimizing the air conditioner operation control model. And a continuously improved air conditioner energy-saving regulation and control scheme is obtained. The method solves the problems that traditional central air conditioner regulation and control cannot accurately capture the non-linear relation between operation parameters and the environment, a real-time feedback dynamic adjustment mechanism is lacked, and the energy-saving effect cannot be continuously evaluated and optimized, so that energy saving is insufficient, and operation is unstable.

The present application relates to oil and gas geology evaluation technical field, specifically to a kind of construction method of resistivity model for evaluating the adsorbed oil content of shale oil reservoir.The method includes: based on effective medium symmetric conduction theory, establish the shale adsorbed oil resistivity model consisting of rock skeleton, adsorbed oil, free oil, free water and clay bound water five components;According to well logging data, determine the mineral skeleton content, argillaceous content and porosity in the model;Using the whole rock XRD of core sample, porosity determination and rock pyrolysis experimental data, the model geometric parameters are determined by using Powell optimization algorithm;Finally, the adsorbed oil component content is solved according to dichotomy iteration algorithm and converted into adsorbed oil mass.The present application overcomes the problem that traditional step pyrolysis experiment is limited by core sample and is difficult to realize continuous evaluation of whole well section, realizes continuous and quantitative evaluation of the adsorbed oil content of shale oil reservoir, and provides technical support for the evaluation of adsorbed oil content of shale oil reservoir.

The application discloses an airport clearance protection zone low, slow and small aircraft risk assessment method and system, belongs to the technical field of risk assessment of an airport clearance protection zone, and comprises the following steps: acquiring and analyzing airport clearance protection zone data, aircraft data, low, slow and small aircraft data and external environment data; establishing a static comprehensive risk assessment coordinate system; comparing the low, slow and small aircraft data with the static comprehensive risk assessment coordinate system to obtain a static comprehensive risk; on the basis of the static comprehensive risk result, a plurality of dynamic risk correction results are obtained, and a final low, slow and small aircraft risk assessment result is further obtained; and according to the low, slow and small aircraft data and the airport clearance protection zone data, a risk upgrade and downgrade trend result is obtained. The application realizes the functions of continuously assessing the risk of low, slow and small aircraft and warning the risk upgrade and downgrade, and has important significance for risk assessment and management of an airport clearance protection zone.

The invention provides a high-density storage article real-time positioning system and method based on 2.4 G wireless communication, and relates to the technical field of wireless communication.The system comprises a sensing layer, the sensing layer transmits collected wireless environment information to an active decision-making layer, the active decision-making layer analyzes the received information, generates a coping strategy and then transmits the coping strategy to an execution layer, and the execution layer sends the coping strategy to a server; according to the method, a'sensing-decision-execution-optimization 'closed loop is constructed, conversion from passive response to active prevention is realized aiming at 2.4 G frequency band interference, the system can prospectively detect and identify an interference source, an avoidance strategy is generated through intelligent decision before the interference takes effect, precision is synchronized, and communication parameters are seamlessly switched in the whole network; and meanwhile, the system has a self-learning capability, and the accuracy and robustness of interference resistance are continuously improved by continuously evaluating the effect and performing feedback optimization, so that the continuity and stability of a positioning signal and the overall efficiency of the system are ensured in a complex electromagnetic environment.

The application provides an experimental intelligent system self-evolution and knowledge sedimentation method and system based on experimental process data, and relates to the technical field of artificial intelligence. The method comprises the following steps: obtaining experimental process data generated in an experimental execution process; extracting experimental knowledge such as a successful path, a failure path and a key parameter combination based on the experimental process data; evaluating the experimental knowledge and generating corresponding labels, weights and confidence; storing the evaluated experimental knowledge as a structured knowledge unit containing a parameter set, an applicable condition and version information; updating a decision model or an experimental decision strategy based on the structured knowledge unit, and performing knowledge migration and reuse in a new experimental scenario. The application realizes the structured sedimentation, version management, continuous evaluation and update and cross-scene migration and reuse of experimental knowledge, so that the experimental intelligent system has the ability of continuous self-evolution based on experimental process data.

The invention discloses a robot pursuit game method based on a retrieval enhancement driving large language model. Firstly, a system model is established for a multi-agent pursuit game, and four core components of retrieval enhancement generation, a reward designer, a reward analyzer and evolution iteration are defined. Then, a large-scale language model is introduced to serve as a core engine of reward design and analysis, and a vectorization knowledge base containing domain expert knowledge is constructed; then, a mechanism for autonomously generating an initial reward function is provided; and finally, proposing a reinforcement learning framework which iteratively optimizes the weight, the structure and the component of the reward function by continuously evaluating the interaction performance of the intelligent agent in the environment and generating a feedback instruction until the reward function is converged to the optimal form. According to the method, a complex collaborative strategy can be autonomously found and designed, the learning efficiency and the final performance of the agents in various pursuit scenes are remarkably improved, and finally efficient and robust multi-agent collaborative pursuit is achieved.

The invention relates to the technical field of marine environment forecasting, in particular to a marine environment accurate forecasting method and system based on multi-scene dynamic matching, and the method comprises the steps: firstly constructing a dynamically updated scene feature library through unsupervised learning, and training a hierarchical management sub-model for each scene to balance the precision and efficiency; during forecasting, through a real-time scene identification and fuzzy matching mechanism, a corresponding model is called, and real-time data is used for dynamic correction and rapid learning; and finally, outputting a forecasting result with confidence, forming closed-loop optimization through continuous evaluation, and realizing precision and adaptive evolution of marine environment forecasting. According to the invention, the accuracy of marine environment accurate forecasting based on multi-scene dynamic matching is improved.

The present invention relates to a method and system for automatically sanctioning users in an online community based on risk scores derived from user-generated content and behavior data. The system includes a machine learning risk score engine that processes segmented sessions of user activity, assigning risk scores according to predefined violation categories.These risk scores are aggregated and compared to configurable thresholds specific to each violation category. If the aggregated risk score exceeds the threshold, the system automatically triggers a sanction, such as a warning, muting, temporary suspension, or permanent ban. The system continuously monitors user activity, recalculating risk scores with new data, ensuring ongoing evaluation and appropriate sanctioning. The invention offers flexibility through customizable thresholds and violation categories, enhancing the effectiveness and precision of automated moderation in online communities.