150 results about "Assessment data" patented technology

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 a wound nursing image intelligent evaluation system and a method thereof, which are applied to the field of medical image processing, and comprise an image acquisition module, a differential geometry-based feature extraction network module, a data analysis module and a user interaction module, the feature extraction network module based on differential geometry comprises a wound geometric characterization layer, a multi-scale feature analysis layer and a tissue classification decision-making layer, a two-dimensional image of the wound is mapped into a three-dimensional geometric surface, curvature features and differential invariants are extracted based on the differential geometry theory, accurate assessment of the wound is achieved, and the tissue classification decision-making efficiency is improved. The data analysis module generates nursing suggestions and evaluation results according to the extracted wound feature data, the differential geometry theory is innovatively applied to the field of wound evaluation, and the problem that the precision is limited when a traditional method is used for treating a wound with a complex shape and irregular edges is effectively solved; the system has the characteristics of high evaluation accuracy, strong anti-interference capability and high automation degree, and the efficiency and quality of wound evaluation and nursing are remarkably improved.

Described herein are methods and systems for evaluating datasets through a multi-faceted scoring approach that assesses data quality across multiple dimensions. A trust score for a dataset may be generated by a scoring engine and displayed on a user interface, providing a comprehensive assessment of the dataset's readiness for use in artificial intelligence applications. The trust score incorporates multiple dimensions including diversity, timeliness, accuracy, security, discoverability, and LLM-readiness, offering users quantitative insights into dataset quality. This scoring system enables organizations to identify high-quality datasets suitable for AI model training, reducing the risk of poor model performance due to inadequate data. The visualization of trust scores through intuitive interfaces allows data scientists, analysts, and other stakeholders to quickly assess and compare datasets, facilitating more informed decision-making in AI development processes.

The invention relates to the technical field of data processing, and discloses a full-life-cycle-oriented engineering technology data governance method and a full-life-cycle-oriented engineering technology data governance system. The method comprises the steps that project object attributes are divided into an anchor point attribute set and an extended attribute set, a locking mark is added to the anchor point attribute set, the extended attribute set is layered according to stages to obtain a data object meta-model, a dependency relationship mapping table is established according to the data object meta-model to achieve automatic verification when the attributes are changed, and the attribute change is completed. And establishing a spatial topological graph and a process graph to diagnose inconsistent defects, establishing a weighted graph to quantitatively analyze change influences, evaluating data quality according to scene types, and intelligently complementing missing attributes. According to the method, the integrity and consistency of cross-stage transmission of engineering data, the real-time performance and accuracy of multi-professional data collaboration, the depth and automation degree of data quality diagnosis, the scientificity and comprehensiveness of change impact analysis and the flexibility and efficiency of data utilization are improved.

The invention relates to the technical field of engineering digital management, in particular to an engineering project digital asset management method and management platform. The method comprises the following steps: performing engineering digital asset quality evaluation processing on engineering digital asset data to generate engineering digital asset quality evaluation data; performing engineering digital asset collaborative application heterogeneity feature analysis based on the historical engineering digital asset application data to generate engineering digital asset collaborative application heterogeneity feature data; carrying out the optimal distribution strategy analysis of the full-life-cycle engineering digital asset application on the full-life-cycle engineering digital asset application data through the engineering digital asset collaborative application heterogeneity feature data, and generating a full-life-cycle engineering digital asset application optimal distribution strategy; and based on the engineering digital asset quality data and full-life-cycle engineering digital assets, applying an optimal distribution strategy to execute engineering digital asset intelligent management operation. According to the invention, intelligent operation and maintenance optimization management of engineering project digital assets is realized.

The invention relates to the technical field of medical informatization, in particular to an intelligent rehabilitation assessment and data analysis method and system.The method comprises the steps that multi-source heterogeneous rehabilitation assessment data collected by a wearable sensor when a patient executes a preset rehabilitation action is obtained in real time; based on a preset time window, processing the multi-source heterogeneous rehabilitation evaluation data into a series of time sequence dynamic graphs; inputting the series of time sequence dynamic graphs into a pre-trained time sequence dynamic graph neural network model, and generating a quantized rehabilitation evaluation result based on the output of the time sequence dynamic graph neural network model. According to the method, deep fusion and time sequence calibration of the multi-source heterogeneous rehabilitation data are realized, the precision of an evaluation model and the recognition capability of a complex motion mode are remarkably improved, the robustness of the model to noise and data artifacts is enhanced through adaptive weighting of the data quality, and the accuracy of the evaluation model is improved. And the reliability and the stability of a rehabilitation evaluation result in a real clinical environment are ensured.

The invention belongs to the technical field of information fusion, and discloses a satellite and low-altitude facility data fusion method based on a space-time self-calibration module, a hierarchical feature compression module and a dynamic weight feedback module. GNSS original acquisition data and low-altitude facility data are sent into the space-time self-calibration module, and the space-time self-calibration module automatically constructs a cross-source space-time reference based on feature matching; the layered feature compression module performs bidirectional feature distillation on a feature layer, and compresses data dimensions while reserving complementary information; the dynamic weight feedback module constructs a double-loop feedback mechanism, evaluates data quality in real time and adjusts a fusion strategy, the double-loop feedback mechanism of the dynamic weight feedback module comprises feature weight feedback and confidence feedback, the feature weight feedback dynamically adjusts fusion weight, and the confidence feedback evaluates low-altitude facility data quality; and outputting a fusion result.

The invention discloses a livestock and poultry product full-chain traceability and slaughter supervision system based on a block chain, and belongs to the technical field of food safety traceability, and the system comprises a multi-source data fusion collection module, a block chain intelligent evidence storage module, an intelligent contract supervision module and a traceability credibility verification module. The multi-source data fusion acquisition module integrates RFID, IoT and video AI technologies to acquire full-dimensional traceability data and evaluate data quality, and the block chain intelligent evidence storage module adopts an alliance chain and an improved Hash algorithm to realize data credible evidence storage and dynamically adjust consensus weight. The intelligent contract supervision module automatically executes compliance check and generates a supervision adjustment instruction to form closed-loop feedback, the traceability credibility verification module adopts deep learning to perform authenticity verification and calculate a comprehensive risk score, and the four modules realize mutual promotion and superimposed synergy through a deep coupling closed-loop cooperation mechanism. And the efficiency and reliability of food safety supervision are obviously improved.

The invention relates to the technical field of data processing, can be applied to the field of science and technology finance, and discloses a credit evaluation method and device for multi-source data co-processing and related equipment. The method comprises the steps of receiving credit evaluation data of at least two types of preset data sources, and performing cleaning, time scale unification and numerical standardization processing on the credit evaluation data to form standardized multi-source feature data; constructing credit portrait data based on the standardized multi-source feature data; performing scoring reasoning on the credit portrait data through a pre-trained credit extension evaluation model, and generating credit extension evaluation result data through a limit mapping rule; receiving the use behavior data and the performance behavior data, performing time sequence analysis and deviation identification, and generating credit use behavior analysis data; and updating credit extension evaluation model parameters based on the credit use behavior analysis data, loading the credit extension evaluation model parameters to a model operation state, outputting credit extension evaluation result data, and recording model parameter updating information. According to the method, the dynamic adaptive capacity of multi-source credit assessment is improved.

The invention discloses a dam interior and exterior deformation monitoring method and system based on deep fusion of finite element and GNSS (Global Navigation Satellite System) monitoring. According to the method, surface displacement observation data are obtained through a GNSS receiver, a finite element model is constructed, and the mechanical response of the finite element model under hydrostatic pressure, temperature and seismic load is simulated. According to the first layer, parallel primary fusion is conducted on GNSS data and finite element output through Kalman filtering, particle filtering, a neural network and a Bayesian algorithm; in the second layer, weights are dynamically distributed on the basis of mean square errors of all algorithm results in a sliding window and reference data, and a high-precision displacement estimation value is generated through weighted integration. The system evaluates data quality and model confidence in real time, dynamically optimizes weight distribution, performs early warning based on multistage thresholds of displacement, speed and acceleration, and finally studies and judges structural risks through time sequence decomposition and abnormal mode recognition. According to the invention, the overall precision and reliability of dam deformation monitoring are significantly improved.

The invention discloses a health risk assessment method and system based on multi-source data fusion analysis, and belongs to the technical field of health risk assessment, and the method comprises the steps: S1, obtaining the physiological data and behavior data of a user in real time, and generating a multi-source data stream; s2, monitoring the multi-source data flow in real time by a preset terminal, and generating a first health assessment data set and a second health assessment data set; s3, performing dynamic analysis based on the first health assessment data set or the second health assessment data set to generate a health risk level; s4, generating an early warning signal based on the health risk level, and transmitting the early warning signal to preset user equipment; and S5, comparing the subsequently acquired multi-source data stream with the virtual data stream generated in the historical monitoring time period, and dynamically adjusting the parameters of the healthy digital mirror image model. The system comprises an acquisition module, a generation module, an analysis module, an early warning module and an optimization module. According to the method, the virtual data stream is generated in combination with the historical multi-source data stream, so that the integrity of health risk assessment is improved.

The present application provides a method for assessing the value of data assets. The method is applied to a scenario where a commercial bank performs service recommendation or service delivery. The method comprises: acquiring data asset data to be assessed inputted by a user, said data asset data comprising data assets to be assessed and an association relationship between said data assets, and said data assets comprising a plurality of data table assets; generating a corresponding data asset graph on the basis of said data asset data; determining an assessment indicator for said data assets on the basis of the data asset graph, the assessment indicator comprising at least one of the following indicators: an information content indicator for the data table assets, a data transmission efficiency indicator for the data table assets, and a data flow impact indicator for the data table assets; and determining the respective value of the plurality of data table assets on the basis of the assessment indicator for said data assets. The present solution can improve the accuracy of assessing the value of data assets.

The invention relates to the technical field of artificial intelligence and vehicle crossing, and provides a commercial vehicle maintenance strategy recommendation method and system.The method comprises the steps that multi-source loss assessment data are obtained, and the multi-source loss assessment data at least comprise damage information and vehicle attributes; generating a maintenance item list based on the damage information and the vehicle attributes, and inputting the multi-source loss assessment data and the maintenance item list into a reinforcement learning model, so that the reinforcement learning model constructs a state space, defines an action space, calculates a multi-target reward function, and generates a plurality of maintenance strategies; each maintenance strategy comprises maintenance action details and corresponding multi-dimensional quantitative indexes. According to the method, multi-source loss assessment data and a maintenance item list are processed through a reinforcement learning model, and a plurality of maintenance strategies including maintenance action details and multi-dimensional quantitative indexes are generated, so that the problems that an output scheme is single, the intelligent effect is poor, and a scheme conforming to an actual maintenance scene is difficult to generate are solved.

The invention provides a dynamic credit risk assessment and early warning system for a new citizen group, and relates to the technical field of risk early warning. The behavior sequence construction module is used for preprocessing the data and generating user behavior sequence data; the difference feature generation module is used for calculating a difference metric value between adjacent time slices according to the user behavior sequence data; the credit state calculation module is used for calculating credit variation data in combination with the historical credit evaluation record and generating initial credit state data; the dynamic correction module is used for determining a deviation correction parameter according to a matching result of the initial credit state data and an actual repayment record, and generating dynamic credit evaluation data; the risk early warning generation module is used for generating a risk early warning signal when the change rate or the change direction of the dynamic credit assessment data reaches a preset risk triggering condition; according to the invention, the autonomy and accuracy of dynamic credit risk assessment and early warning are improved.

The invention relates to the field of data asset value evaluation, and discloses a data asset value evaluation system and method based on a multi-period excess income method. The system comprises a data acquisition module, a dynamic discount rate calculation module, an excess income evaluation module and the like. Three innovative parameters including a data asset quality attenuation coefficient, a synergistic effect coefficient and a market environment adjustment coefficient are introduced into the system, and the time-varying characteristic, the synergistic value and the market sensitivity of the data assets are accurately quantified. The system can accurately evaluate the data asset value, provides a scientific basis for enterprise data asset management and investment decision, and is suitable for data asset value quantification demands of multiple types of enterprises such as the Internet.

The invention provides a pollution site multi-source heterogeneous data intelligent analysis and standardization system based on deep learning. The pollution site multi-source heterogeneous data intelligent analysis and standardization system comprises a site survey report intelligent analyzer, a risk assessment data structured processing unit, a national and local technical guide rule dynamic access module, a historical restoration case database and a data integration and quality control module. According to the system, a modular micro-service architecture is adopted, and automatic processing and standardization of heterogeneous data of a polluted site are realized by integrating a deep learning document understanding model, a multi-modal information extraction technology, a standardized risk characterization algorithm and a graph database storage architecture. The problems of low efficiency, non-uniform standard, high manual processing error rate, data islanding and the like in existing pollution site data processing are solved, the data analysis efficiency is improved by 5.8 times, the key information extraction F1 value reaches 0.85, the error rate is reduced by 75%, and a high-quality and standardized data basis is provided for pollution site restoration decision making.

The invention provides a shoulder joint subluxation evaluation data acquisition method and system. The method comprises the steps that first shoulder joint image data of a shoulder joint area on the affected side of a patient are collected through an imaging assembly, then the imaging assembly is controlled to rotate by a preset angle value, second shoulder joint image data are collected, and then according to the first position offset of the first shoulder joint image data and the second shoulder joint image data, the first shoulder joint image data and the second shoulder joint image data are obtained. Determining an imaging assembly compensation angle according to the first position offset of the first shoulder joint image data and the second position offset of the second shoulder joint image data and the standard shoulder joint image data of the first acquisition point, and finally adjusting the imaging angle of the imaging assembly according to the imaging assembly compensation angle. Therefore, the shoulder joint subluxation evaluation data acquisition of the first acquisition point is completed, so that the standardization degree and objective consistency of the shoulder joint subluxation evaluation data acquisition are effectively improved.

The invention belongs to the technical field of computer information processing and data security, and discloses a party construction assessment system based on a block chain technology, and the system comprises a data collection device which is used for obtaining party member attendance, organized life and task completion conditions and binding timestamps; the privacy protection mechanism carries out hierarchical encryption on identity information and assessment indexes and supports zero-knowledge verification; the distributed account book storage structure realizes corresponding storage of an on-chain abstract and an off-chain ciphertext; the intelligent contract execution mechanism calculates an assessment result according to a preset rule and has a rollback function; the permission control component defines a decryption range based on identity, hierarchy, use and time; the verification interface generates integrity proof through the block header and the Merkle path; the cross-organization sharing mechanism only transmits a summarizing result; and the audit tracing structure generates a log in the whole process and writes the log into the block chain. According to the system, credible storage, privacy protection and secure sharing of party building assessment data are realized.

This invention discloses a data security access control method based on an IoT platform, relating to the field of secure access technology. It includes a dynamic threat assessment step, which determines data risk by analyzing the behavioral sequence of the data parsing process. Specifically, for a specified data type, based on the parsing behavior of historical harmless data samples, a baseline of normal behavior sequence is constructed and its corresponding normal behavior ratio range is determined. Then, the current data to be assessed is parsed, and the real-time behavioral sequence generated during the parsing process is monitored and recorded. This invention overcomes the limitation of traditional access control that only manages who can access the data. By introducing a dynamic threat assessment engine into the access link, it performs in-depth behavioral analysis on the accessed data content itself, constructing a subject-object two-way security system that can effectively defend against data reverse intrusion attacks launched using legitimate data channels.

This application provides a method and system for processing on-site survey and damage assessment data, relating to the field of data processing technology. The method includes: acquiring equipment fault records from a power grid monitoring platform and extracting the voltage level and loss extent from the equipment fault records; determining the damage assessment calculation path based on the voltage level and processing the equipment fault records according to the damage assessment calculation path to obtain a preliminary damage assessment value; sending the preliminary damage assessment value to an insurance brokerage server and receiving the reviewed damage assessment value returned by the insurance brokerage server; calculating the damage assessment offset between the preliminary damage assessment value and the reviewed damage assessment value, and generating a damage assessment statistics table based on the damage assessment offset. In this application, the automatic selection of the damage assessment calculation path improves the damage assessment accuracy for equipment at different voltage levels; the quantitative calculation of the damage assessment offset allows for automatic identification of errors in the damage assessment calculation path selection; establishing a damage assessment consensus in advance during the review process avoids later disputes; and the distribution pattern of the damage assessment offset can be used to diagnose the rationality of the calculation rules.

This specification discloses an adaptive adjustment method, apparatus, device, and medium for a data quality assessment model, comprising: determining data quality assessment indicators corresponding to a data table to be assessed in multiple dimensions; obtaining metadata tables related to the data table to be assessed; analyzing the features of the data table to be assessed and the metadata tables to obtain feature analysis results; determining the weight values ​​of each data quality assessment indicator based on the feature analysis results; and adaptively adjusting a pre-generated data quality assessment model based on the weight values ​​to obtain a data quality assessment model that meets the requirements.

The invention provides a health degree assessment method and device for data quality and electronic equipment, and belongs to the technical field of big data. The method comprises the steps of obtaining to-be-evaluated data, and obtaining a plurality of evaluation score results of the to-be-evaluated data under a data quality evaluation index; based on the plurality of evaluation score results, constructing a relationship between the possibility distribution of the to-be-evaluated data in the data quality evaluation index and the possibility distribution of the to-be-evaluated data in the preset data quality grade; based on the to-be-evaluated data, determining a health value corresponding to the to-be-evaluated data in the data quality evaluation index; and determining health degree assessment information of the to-be-assessed data based on the relationship and a health value corresponding to the to-be-assessed data in the data quality assessment index.

The application provides a traffic signal intelligent control method and system, an electronic device and a storage medium, and belongs to the technical field of traffic signal control. The method collects intersection traffic state data through different types of detectors to obtain monitoring data of each control parameter, extracts traffic characteristic values after standardizing the monitoring data, performs correlation analysis on the traffic states to obtain the correlation degree between the parameters, evaluates the data quality by using a pre-trained signal control evaluation model, determines the control reliability in combination with the correlation degree, and generates a signal control scheme by optimizing the data according to the correlation degree when the reliability is lower than a threshold. The control parameters include vehicle flow, queue length and the like. The system comprises acquisition, processing and execution modules. The electronic device and the storage medium implement the execution of the method. The scheme improves the data reliability, optimizes the control scheme, improves the intersection passing efficiency and is suitable for the intelligent traffic signal control scene through multi-dimensional data acquisition, correlation analysis and quality evaluation.

The invention discloses a data quality evaluation method, device and equipment, and relates to the technical field of data processing, and a preset rule generation model can generate different data quality rules according to the difference of input data tables, so that adaptation to different business scenes is realized. A preset rule generation model is constructed based on a Reflection Agent framework, the preset rule generation model is iteratively optimized based on an reflection signal, so that a better data quality rule is generated, the data quality rule is dynamically generated based on structural metadata of different data tables, an index calculation task is dynamically generated according to the target data quality rule, and the index calculation task is executed. The index calculation result of the to-be-evaluated data table is obtained in a targeted manner; and according to a target data quality rule, evaluating the index calculation result to obtain a more accurate data quality evaluation result of the to-be-evaluated data table. Therefore, the data quality evaluation accuracy is improved.

This invention discloses a method and system for carbon emission compliance assessment based on multidimensional indicators, belonging to the field of carbon emission assessment technology. The method includes acquiring production and operation data, acquiring a basic operation dataset based on the production and operation data, and acquiring annual carbon emission forecast information based on the basic operation dataset. This invention achieves full-dimensional coverage of carbon emission compliance assessment data by constructing an operation dataset. By dividing the production and operation data into groups, it avoids the impact of data distortion on the compliance assessment results. Through the use of characteristic deviation thresholds for power generation and seasonally differentiated deviation thresholds for heating supply, it achieves precise adaptation of deviation judgment standards to different business characteristics, improving the accuracy of abnormal data identification. By introducing time weights and scale weights to construct a comprehensive weight calculation for unit carbon emission prediction intensity, it achieves multidimensional weighted accurate prediction of annual carbon emissions, ensuring the accuracy and reliability of carbon emission compliance assessment.

The invention provides a method for constructing a data quality evaluation model, and belongs to the technical field of quality evaluation, and the method comprises the steps: collecting a to-be-evaluated data set, and obtaining a business demand and an application scene of the to-be-evaluated data set; determining an assessment index vector of the to-be-assessed data set based on the business demand and the application scene of the to-be-assessed data set; based on the to-be-evaluated data set and the evaluation index vector, determining a training quality feature matrix and a test quality feature matrix; and constructing a quality evaluation model based on the training set and the training quality feature matrix, and evaluating and optimizing quality evaluation performance based on the evaluation index vector, the test set and the test quality feature matrix. The method can enhance the capturing capability of the model for the data quality characteristics, achieves the high-precision evaluation of dynamic weighting, improves the intelligence and stability of the quality evaluation model, and improves the adaptability of the model in the quality management and analysis scenes of complex data.

In the field of machine learning, there may be challenges associated with constructing a comprehensive and diverse training data set. For example, the data that is available may not be sufficiently diverse, which may cause issues such as overfitting in a model trained using the available data. A computer-implemented method and system are provided to use an embedding function as a tool in assessing the diversity of a data set. The embedding function may be employed in constructing a training data set having a high degree of data diversity for training a model.

The invention discloses a Unity-based cable accessory installation training system and a construction method thereof. The system comprises a basic knowledge learning module, a professional knowledge learning module, a training examination module, an examination evaluation module and the like. The basic knowledge learning module provides content such as identification graphs related to cable accessory installation, electrical instruments, tool use and safety specifications and the like, and teaching is assisted in a visual mode of images, texts, voice and structures. The professional knowledge learning module is used for displaying key installation processes and process standards in the forms of three-dimensional animation, video explanation, structure explosion diagram and the like; the training examination module is provided with basic examination and practical operation examination, and supports question bank import, interactive answering and action monitoring; and the assessment and evaluation module performs statistical analysis on assessment data and generates visual assessment reports such as a radar map and the like. According to the invention, the learning efficiency of trainees can be improved, the training cost is reduced, the training safety and flexibility are enhanced, and standardization, intelligence and high efficiency of cable accessory installation skill training and assessment are realized.

The invention discloses a multi-modal depression assessment system combining grade separation and migration regression, and belongs to the technical field of computer vision. Comprising a data acquisition and division unit which acquires and reads a multi-modal depression assessment data set and divides the multi-modal depression assessment data set into a training set, a verification set and a test set; the feature extraction unit is used for extracting video features from the video data in the multi-mode sentiment analysis data set; the model training unit is used for constructing a multi-modal depression evaluation model comprising an MDS module and an LDR module, and training and verifying the multi-modal depression evaluation model by using the training set and the test set; the evaluation unit is used for inputting the test set into the trained multi-modal depression evaluation model and outputting a predicted depression score; performing multi-modal fusion on the video features to obtain a multi-modal unified representation fs; a prediction level r'i is obtained through an MDS module, and an anchor feature fanchor corresponding to the r'i is retrieved; analyzing the difference between the fs and the fanchor, and obtaining a prediction deviation o'i; and finally, adding o'i to the central value of r'i to obtain a predicted depression score.

The invention discloses a data analysis method and system for power grid data exception, relates to the technical field of data analysis, is used for solving the problem that original exception features are weakened, and judges whether to introduce a repair analysis mechanism or not by monitoring the exception level and data retransmission features of power grid data and combining the number of exception values and retransmission behaviors. Under a repair analysis mechanism, analyzing a time sequence record of returned data after repair is completed, calculating repair time delay and repair aggregation frequency, describing the concentration degree of data repair behaviors from a time dimension, performing interval aggregation on repair type information in a feature analysis window, extracting repair repetition features and comprehensively evaluating a data repair state; and when the restoration state is abnormal, association detection is carried out in combination with restoration amplitude data and power grid operation data, an abnormal restoration behavior is identified, an alarm result is output, and a new technical means is provided for power grid data credibility evaluation and abnormal traceability under the condition that the original abnormality is covered by smoothing or interpolation.