474 results about "Assessment data" patented technology

The invention relates to the technical field of data acquisition, and discloses a multi-protocol extensible data acquisition system architecture and method with a unified interface standard, and the architecture comprises a data source management module which is used for storing data source configuration information and distributing protocol identifiers; the protocol adaptation module comprises an analysis unit and a matching unit and is used for realizing data format conversion and protocol compatibility verification; the data conversion module is used for generating target data through format conversion and semantic mapping; the data verification module and the anomaly detection module are used for comparing and monitoring data integrity based on an integrity algorithm and a threshold value; the data quality monitoring module and the early warning module are used for comparing and evaluating data quality through a consistency model and a threshold value; and the result output module transmits the early warning information to a target system. The method comprises the steps of data source configuration, protocol adaptation, data conversion, integrity and quality monitoring, early warning and the like. The system expansibility and the data reliability are improved, and the method is suitable for multi-scene data acquisition.

The invention relates to the technical field of water and fertilizer control, in particular to a water and fertilizer integrated control system and method based on the Internet of Things. The method comprises the following steps of obtaining environment measured data, historical environment data and regional geographic information of a farmland region, performing spatial modeling and time sequence learning on the farmland region, and constructing a farmland digital twinborn model; simulating the state of an area without sensors by using a farmland digital twinborn model to obtain virtual sensing node data; collecting multispectral image data of crops, and extracting physiological feature data of the crops; and constructing a crop physiological state model by utilizing the environment measured data, the virtual sensing node data and the crop physiological feature data, and generating crop physiological state evaluation data. Through multi-source data fusion and three-dimensional visualization integration, data-driven accurate regulation and control and whole-process intelligent management of the water and fertilizer integrated system are realized, and the intelligent level of irrigation and fertilization and the resource utilization efficiency are comprehensively improved.

The invention provides a reservoir water ecological health diagnosis method and system, and relates to the technical field of reservoir ecological monitoring, and the technical scheme is characterized in that the method comprises the steps: obtaining water quality parameters, biological indexes and environmental data collected by distributed sensors in a reservoir; according to the water quality parameters, the biological indexes and the environmental data, generating reservoir ecological health assessment data through a data fusion algorithm; calculating a reservoir ecological health assessment index according to the reservoir ecological health assessment data; according to the reservoir ecological health assessment index, generating a reservoir ecological health state assessment result; judging whether the ecological state of the reservoir is abnormal or not according to the evaluation result of the ecological health state of the reservoir; and when the ecological state of the reservoir is abnormal, generating an early warning signal. The reservoir water ecological health diagnosis method and system provided by the invention have the advantages that multiple ecological pressure factors are comprehensively considered, and a complex water ecological system is rapidly and accurately evaluated.

The invention discloses an electrophysiology and hemodynamics multi-mode intelligent wearable system and a data evaluation method, and belongs to the technical field of biomedical engineering, the system comprises an optical module, a flexible stretchable electric signal module and a central module; the optical module is used for collecting hemodynamic signals including an oxyhemoglobin change signal, a deoxygenated hemoglobin change signal and a pulse wave signal by combining a near-infrared light signal with hemodynamics; the flexible stretchable electric signal module collects electric signals, including electroencephalogram signals, electromyographic signals, electrocardiosignals and electro-oculogram signals, of all parts through electrodes; the central module is used for digitalizing and preprocessing the hemodynamic signal and the electric signal of each part, fusing multi-modal data, evaluating data quality and predicting a dominant external event influencing the data quality; and meanwhile, data quality and dominant external events are output to the terminal to feed back and adjust acquisition parameters of the optical module and the flexible stretchable electric signal module, so that the effectiveness and practicability of monitoring are remarkably improved.

The invention relates to the technical field of financial management, in particular to a financial management system based on a block chain, which comprises a data collection module, a data processing module, an intelligent reconciliation module and an intelligent adjustment module, the data processing module carries out data standardization and data verification on the transaction data, packages the transaction data passing the verification into blocks according to a time sequence, verifies the blocks, carries out verification by calculating a hash value and using a block consensus mechanism, and takes the successfully verified blocks as a block chain together; the intelligent reconciliation module determines an optimal reconciliation period through time sequence analysis, and identifies abnormal transactions by using an anomaly detection algorithm; the intelligent adjusting module adopts a three-time standard deviation method to set a difference threshold value, automatic adjustment or marking of manual assessment data is achieved, and the problems that in traditional financial management, data is prone to being tampered, the account checking efficiency is low, and abnormal recognition is inaccurate are solved.

The invention provides an energy control method and system for a wireless sensor network. The method comprises the following steps: acquiring working states and state conversion time of a plurality of wireless sensors of the wireless sensor network; inputting the working state and the state conversion time into a pre-constructed energy consumption prediction model to obtain an energy consumption prediction result output by the energy consumption prediction model; according to the energy consumption prediction result, determining a data quality demand, evaluating a data acquisition demand, and obtaining a work demand evaluation result; based on the work demand evaluation result, dynamically adjusting the sleep periods and data transmission parameters of the plurality of wireless sensors; and monitoring the adjusted energy consumption levels of the plurality of wireless sensors, identifying abnormal nodes, and adjusting the connection paths of the plurality of wireless sensors. According to the invention, energy consumption of the sensor can be controlled in real time, abnormal nodes can be identified in time, network connection is optimized, and operation efficiency, stability and reliability of the sensor network are improved.

The invention discloses a dynamic weight aggregation federated learning method and system for credit risk assessment, and the method comprises the steps: firstly, calculating the cosine similarity of each client model and the last gradient of a global model, so as to assess the consistency of model parameters; secondly, the distribution difference is quantified by calculating the data statistic difference between each client data and the global data, so that the diversity of the data is evaluated; further, a contribution degree comprehensively considering model parameter similarity and data distribution difference is obtained by combining a hyper-parameter with cosine similarity and data distribution difference; and finally, dynamically adjusting the weight of each client in global model updating according to the contribution degree of each client, so that the model pays more attention to the client with higher data quality, thereby improving the overall model performance. The federated learning method is especially suitable for processing non-independent identically distributed data, optimizes the generalization ability of the model, and is suitable for credit risk assessment in the financial field.

The invention discloses an intelligent breeding method for commercial crops based on big data analysis, and relates to the technical field of agricultural breeding, the method comprises the following specific steps: seed multi-modal data acquisition and processing: acquiring physical characteristic data of seeds, acquiring physiological indexes and genome information data through biochemical experiments and gene sequencing, and determining the seed multi-modal data according to the physiological indexes and the genome information data; carrying out pretreatment on the raw materials; according to the method, after multi-modal data are integrated and preprocessed, a seed vigor accurate evaluation model is constructed by using deep learning and data fusion technologies, the seed vigor level can be measured more accurately, and meanwhile, the electric signal change of the early growth stage of crops is monitored in real time by using high-precision plant electric signal acquisition equipment, so that the accuracy of the seed vigor evaluation is improved. And a correlation model of the electric signal characteristics and multiple traits of crops is established, and fusion analysis is performed on seed vigor evaluation data and plant electric signal data, so that the breeding efficiency is greatly improved, and powerful support is provided for cultivation of high-quality varieties.

The invention discloses an intelligent safety inspection system for a construction site, and relates to the technical field of construction inspection, multiple data sources of the construction site are obtained through a data acquisition module, and a data fusion and processing module dynamically optimizes heterogeneous data through time synchronization, format standardization and noise filtering in combination with a self-adaptive weighted fusion algorithm, so that the construction site safety is improved. The intelligent analysis and early warning module evaluates the data difference degree based on machine learning, adopts clustering analysis to mine an abnormal mode for high-difference data, carries out safety risk identification on low-difference data, and dynamically adjusts an early warning strategy. The terminal interaction module pushes the inspection result and the early warning information to the mobile terminal APP, the monitoring center display screen and the voice broadcast device, so that a manager can remotely check the construction condition and quickly make a decision, the intelligent level of construction site safety inspection can be remarkably improved, efficient and accurate potential safety hazard recognition and dynamic early warning are achieved, and the safety inspection efficiency is improved. And the safety accident risk is effectively reduced.

The invention provides a data quality detection and improvement system for a large language model, and relates to the technical field of code quality management. The data analysis and characteristic extraction module is used for extracting structured and semantic characteristics of codes and constructing a code distribution diagram; the multi-dimensional quality detection module comprehensively evaluates the data set quality from three aspects of semantic diversity, structural consistency and error mode; the code optimization and verification module generates an optimization suggestion based on a detection result, and verifies the effectiveness of an optimization scheme through dynamic evaluation and unit testing; the feedback and self-adaptive optimization module is combined with developer feedback and runtime performance test to dynamically adjust a repair strategy, and the complex code repair efficiency is improved through quantum computing auxiliary path search; according to the method, full-process automation from quality detection to optimization verification is achieved, the method is suitable for multi-dependency and multi-module complex code repair tasks, and the quality and efficiency of the code life cycle are improved.

The invention discloses an automatic DCMM evaluation system based on a multi-source heterogeneous data integration mechanism, and relates to the technical field of data quality management.The automatic DCMM evaluation system comprises a data source management module, a data integration module, a data quality evaluation module, a data quality diagnosis module and a data quality monitoring module which are connected through electric signals; and the data source management module is used for accessing various types of data sources and collecting original multi-source heterogeneous data from a plurality of data sources. By comprehensively monitoring and evaluating the data quality, the accuracy and integrity of the data are improved, the abnormal early warning coefficient is calculated, the potential data quality risk is recognized in time, and when it is detected that the data quality is abnormal, an early warning mechanism is automatically triggered, and an alarm is sent to inform related personnel, so that the data quality is ensured to be abnormal. Therefore, the data quality problem can be found and solved at the first time, so that the reliability and credibility of the data are improved.

Computer-based techniques for performing systematic literature reviews are provided. Some embodiments include an SLR system including various components that interoperate to generate regulation-compliant SLR reports regarding an issue based on user-defined parameters. In several embodiments, the SLR system may integrate various artificial intelligence techniques to facilitate generation of SLR reports. For example, various SLR systems disclosed hereby may utilize generative pre-trained transformers to assist in aspects of systematic literature reviews, such as literature database queries, literature screening, data question generation, level of evidence classification, qualitative assessments, data extraction, and report generation.

The invention relates to the technical field of power equipment monitoring, and discloses a hydropower station high-altitude equipment fault intelligent identification system based on multi-sensor fusion, which collects multi-modal data in real time and evaluates data quality by deploying multi-source sensors at key parts of high-altitude equipment. Extracting multi-scale features of each modal, performing normalization processing, calculating a fusion weight based on feature saliency and data credibility, and performing weighted fusion and dimension reduction on the multi-modal features; based on the fusion feature vector, intelligent matching analysis of fault features and intelligent identification of fault types are carried out; a fault identification result is obtained; in addition, the system also comprises safety monitoring of overhead working personnel, and realizes closed-loop management from fault identification to safety maintenance. According to the invention, early weak faults can be accurately identified, and the safe operation level of equipment and the intelligent degree of operation safety management are improved.

The invention relates to the technical field of industrial safety management, in particular to an intelligent security monitoring and responding method for a power generation enterprise. The method comprises the following steps: acquiring environmental data of a power generation enterprise; performing enterprise abnormal event detection on the power generation enterprise environment data to obtain enterprise environment abnormal event data; performing multi-source video linkage monitoring on the enterprise environment abnormal event data to obtain multi-angle video data; and performing abnormal event analysis according to the enterprise environment abnormal event data and the multi-angle video data to obtain abnormal event risk assessment data so as to perform intelligent security monitoring and response auxiliary operation of the power generation enterprise. According to the method, the abnormal event detection and analysis capability is improved, and the safety and the operation efficiency of an enterprise are also remarkably improved.

The invention discloses a new energy weather forecast method based on numerical weather forecast and AI revision, and particularly relates to the technical field of weather forecast, and the method comprises the steps: obtaining the historical actually measured weather data of a new energy power station, correcting the numerical weather forecast data of multiple sources through employing an artificial intelligence algorithm, and building a correction model; inputting the current numerical weather forecast data into the correction model for correction, extracting the internal structure characteristics of the correction data in combination with a high-dimensional geometric mapping and manifold learning method, and evaluating the matching degree between the manifold distribution of the correction data and the historical data; analyzing and evaluating the data complexity by using multi-scale entropy, and ensuring that the data meets a preset standard; on the basis of matching degree and complexity evaluation, selecting suitable numerical weather forecast data for new energy short-term and medium-term meteorological parameter forecast; the method improves the prediction accuracy and applicability, and is suitable for the generation power prediction and optimization scheduling of the new energy power station.

Described here are systems and methods for generating microvessel images from image data acquired with an ultrasound system while analyzing the image data in realtime, or retrospectively, to generate a performance descriptor that can be used to assess data quality and / or motion correction quality; to adaptively suppress noise in the data; or both.

The invention relates to the technical field of power supply system health assessment, in particular to a health assessment method and system of a power supply system based on artificial intelligence. The method comprises the following steps: acquiring data of a power supply system, estimating initial performance parameters of the power supply system, evaluating an initial running state of the power supply system, and acquiring initial state data of the power supply system; performing power supply equipment load operation simulation based on the initial state data, and predicting a fault evolution trajectory; the transmission quality attenuation degree of the power supply system is estimated by analyzing the fault evolution trajectory of the power supply system, the loss increase trend of a transmission line is detected, and the performance decrease trend is evaluated in combination with the attenuation degree; evaluating health attenuation data of the power supply system according to the performance reduction trend; transmitting the data to an artificial intelligence model for deep learning training to obtain accurate power supply system health state evaluation data; according to the invention, health analysis is carried out on the power supply system, so that the power supply system is safer and more stable.

The invention discloses a multifunctional safety helmet management system and method based on the Internet of Things, relates to the field of safety production, and solves the problems that a traditional safety helmet is single in safety management function, insufficient in data processing precision and the like. A multi-dimensional state data sequence is continuously collected through a three-axis acceleration sensor, a temperature and humidity sensor, a positioning sensor, a vital sign sensor and the like integrated on the safety helmet terminal; terminal monitoring characteristics including an acceleration module value, a position drift distance and a physical sign fluctuation index are constructed, data reliability is evaluated through weighted fusion of dynamic fluctuation values, and an abnormal terminal is isolated. And carrying out space-time calibration on the reliable data to generate a unified coordinate system trajectory, analyzing and identifying abnormal behaviors through group trajectory consistency, and constructing a regional safety risk map in combination with a signal blind area coverage rate, thereby realizing safety risk level prediction based on historical data and real-time trajectory matching. According to the invention, comprehensive real-time monitoring of the state of the operator is realized, and the intelligent level of safety production management is improved.

The invention discloses a BIM application method and system in cost management. The method comprises the steps of BIM model construction, cost data integration, engineering quantity list and cost report generation, collaborative management, AI real-time optimization, digital twinborn early warning management, design change and contract text analysis, edge calculation cost management, block chain data guarantee and the like. The system comprises a plurality of modules such as a BIM model construction module and a cost data integration module. According to the method, software is utilized to generate a BIM model, cost data is integrated, data quality is evaluated before a report is generated, multi-specialty and multi-participant authority is cooperatively managed, AI real-time optimization, digital twinborn early warning, a natural language processing engine analyzes a change document, cost is processed through edge calculation, safety protection is achieved, and data is guaranteed in combination with a block chain. Compared with the prior art, the construction cost calculation accuracy is improved, personal errors are reduced, the association between the component and the construction cost data is enhanced, the change response efficiency is improved, and the data transmission delay is reduced.

The invention discloses a course recommendation method and system for intelligently building an education platform, and relates to the technical field of intelligent education informatization, and the method comprises the steps: building a knowledge graph, and completing the multi-level association of tasks and knowledge points; analyzing the assessment data, identifying weak knowledge points of the trainees, and setting score threshold fusion post weights; generating a course recommendation result on the basis of recognizing weak knowledge points of students, and optimizing recommendation logic in real time by adopting feedback; according to the method, a knowledge graph model associated with three layers of tasks, knowledge points and posts is constructed, student examination performance and post skill requirements are fused, and a course recommendation path construction mechanism driven by a multi-dimensional weight is realized; a dynamic feedback and real-time scoring algorithm is adopted, closed-loop logic of recommendation, assessment and re-recommendation is established, recommendation individuation and post matching degree are effectively improved, and the problems that knowledge points in an existing platform are not traceable, post adaptability is weak, and a recommendation strategy is single are solved.

The invention relates to the technical field of training simulation, and discloses a VR-based gas safety emergency training system, which comprises a three-dimensional scene construction module, an interactive data acquisition module, an interactive data processing module, an examination data analysis module and an examination evaluation module, entity drilling is replaced, the same scene can be repeatedly trained for infinite times, and the training cost is remarkably reduced; the VR technology provides multi-sensory stimulation, the intuitive cognition of students on danger is enhanced, the safety awareness and the emergency response capability are effectively improved, and no danger exists; by collecting operation data of students, the system automatically calculates key coefficients such as equipment operation precision, process compliance, disposal timeliness and path planning rationality, and scientific quantification and accurate scoring of training effects are achieved; and automatic grading evaluation is carried out according to a grading result, and a customized remedy module is triggered for students with insufficient abilities, so that a'training-assessment-intervention 'closed loop is formed, and the overall training efficiency is improved.

The invention discloses a BIM-based bridge construction monitoring method and system, and belongs to the technical field of bridge construction. Construction time deviation characteristics and structure response deviation characteristics are extracted, a drift index is calculated to evaluate the binding error degree between data and components, and a differential processing strategy is implemented based on the drift level; and for high-risk data, a similarity scoring mechanism is introduced, an intelligent matching algorithm is adopted to identify an optimal component and complete rebinding, and finally accurate storage of the data and dynamic updating of a BIM model state are realized, so that the binding accuracy of the construction monitoring data, the risk identification capability and the model linkage efficiency are effectively improved, and the construction quality is improved. And the intelligent management and decision support level of bridge construction is enhanced.

One embodiment of a method comprises continuously collecting cross-sector data related to geopolitical risk from a worldwide network to update a risk assessment dataset the comprises a series of risk assessment states for a plurality of geopolitical entities in time increments; selecting forecasting risk assessment data from the risk assessment dataset; processing the forecasting risk assessment data using an inquisitive artificial intelligence engine to identify a pattern and trend in the forecasting risk assessment data and use the pattern and trend to forecast risk assessment variable values for an first entity; and generating a user interface comprising an interactive map for a user, the interactive map embodying the forecasted risk assessment variable values to display a forecasted risk for the first entity to the user in association with the first entity in the user interface.

The invention discloses a data quality management method and system, belongs to the technical field of data management, and aims to solve the technical problems of how to improve the data quality management effect and improve the data service quality. Comprising the following steps: during data acquisition, performing quality detection through a corresponding quality detection rule, and performing data partitioning and data labeling according to an initial data quality detection evaluation result; performing corresponding preprocessing on the data of the to-be-used data partition according to the initial data quality detection and evaluation result; evaluating data quality according to the data preprocessing result to obtain an evaluation result, and adjusting and optimizing a quality detection rule according to the evaluation result; a user is supported to evaluate data quality through a data quality evaluation service, a data problem report rate of the user is formed, the data quality is evaluated according to the data problem report rate of the user, and data acquisition quality detection and data integration preprocessing operation are improved.

The invention discloses a water feed pump fault diagnosis system, which belongs to the field of data analysis and is characterized in that real-time operation data of a water feed pump are acquired by adopting a synchronous acquisition strategy, an operation database is constructed, and when a data record is newly added, detection and self-adaptive judgment are performed to determine an early warning decision; when abnormal early warning of the water feeding pump is detected, screening a fault assessment set for fault identification based on the operation database, and performing information fusion on fault assessment data to obtain a fault assessment feature set; and performing comprehensive time sequence analysis on the preliminary fusion data of each fault evaluation subset to obtain a key part fault evaluation score so as to judge a fault, for the key part judged as the fault, obtaining a fault source by constructing a time axis, and constructing a fault propagation path in combination with a correlation coefficient matrix. The technical problems of synchronization and exception handling during collection of multi-source information, targeted screening and effective integration during fusion, and discrimination and traceability during fault diagnosis are solved.

The invention belongs to the technical field of video quality evaluation, and particularly relates to a video quality evaluation method based on spatio-temporal feature fusion. Comprising the following steps: acquiring a video quality evaluation data set, preprocessing the video quality evaluation data set, and processing preprocessed video data by adopting a time feature extraction module to obtain short video features and long video features; processing the preprocessed video data by adopting a spatial feature extraction module to obtain high-level semantic features and distortion features; fusing the short video features, the long video features, the high-level semantic features and the distortion features by using a feature fusion module to obtain fused features; inputting the fusion feature into a multi-layer perceptron for processing to obtain a video quality score; calculating the total loss of the model and continuously adjusting model parameters according to the total loss of the model to obtain a trained video quality evaluation model; performing video quality evaluation by using the trained model; according to the method, the correlation between the spatial-temporal features and the inter-frame features is effectively utilized, so that the accuracy of quality evaluation is improved.

The invention discloses a design and construction scheme optimization method and system for subway double-side extension heaving and sinking control, and the method comprises the steps: collecting design parameters, monitoring data and construction management data in the extension construction process of a subway station, and forming a standardized data set; and performing multi-dimensional fusion analysis on the standardized data set, evaluating the effects of earth excavation, a supporting system and deformation control, and generating target evaluation data. And based on the evaluation results, optimizing the subway double-side extension construction scheme by using an optimization algorithm to generate an optimized sub-scheme. The system analyzes the construction progress through a critical path algorithm, forms an adjustable progress plan, monitors the construction state in real time, dynamically updates the progress plan and control measures, and ensures the safety and high efficiency of construction. The system can effectively control the deformation problem in the construction process, the construction precision and the management level are improved, and the system is suitable for improving the quality and safety of subway double-side extension construction.

The invention discloses an employee work ability assessment method based on multi-modal health data fusion. The method comprises the steps of obtaining a plurality of health index values of physical examination data of an employee to be assessed, a plurality of self-assessment scale scores of psychological assessment data and a plurality of physiological monitoring index values of wearable device data; performing standardization processing on the acquired data, and converting the standardized data into feature vectors respectively; respectively inputting the feature vectors corresponding to the physical examination data and the psychological evaluation data into a trained multi-layer perceptron for feature extraction to obtain a physical examination feature vector and an evaluation feature vector; inputting the feature vector of the wearable device data into a trained long-short-term memory network for feature extraction to obtain a wearable feature vector; and inputting the physical examination feature vector, the evaluation feature vector and the wearing feature vector into a fusion module of a trained multi-modal learning model to perform feature fusion, and inputting the fused features into a DNN model of the multi-modal learning model to generate a work ability score.

A system, method, and computer programmable product for assessing a virtual interaction is provided. The system is configured to retrieve assessment data, wherein the assessment data comprises at least one of: a plurality of assessment parameters, and a weightage score associated with each of the plurality of assessment parameters. The system is further configured to retrieve response data of a candidate associated with the assessment data. The system is configured to generate, using a trained AI model, a score for the response data based on the assessment data. Further, the system is configured to obtain user input based at least on the score of the response data and update the weightage score associated with at least one of the plurality of assessment parameters based on the user input, wherein the AI model is re-trained based on the updated weightage score.

The invention discloses a black land water and soil resource bearing capacity evaluation system based on big data, which relates to the technical field of agricultural production and comprises a data acquisition module, a data preprocessing module, an evaluation index calculation module, a bearing capacity evaluation model module and a result analysis and display module. The data acquisition module is used for acquiring soil data, water resource data and social economic data of black land. According to the evaluation system for the bearing capacity of the water and soil resources of the black land, through integration of multi-source heterogeneous data, comprehensive and accurate evaluation of the bearing capacity of the water and soil resources of the black land is achieved, high-density deployment and real-time updating of the data acquisition module are achieved, the temporal-spatial resolution and reliability of an evaluation result are ensured, and the evaluation accuracy is improved. The problem of insufficient data acquisition density in the prior art is effectively solved, a data preprocessing and evaluation model construction technology is adopted, the dynamic adaptability and prediction precision of an evaluation model are improved, and visual and understandable chart display and decision support information are provided.