24 results about "Assessment evaluation" patented technology

The invention provides an employee work assessment recommendation method and system based on a collaborative filtering algorithm, and relates to the technical field of employee work assessment, and the method comprises the steps: extracting a task dependency relationship and time consumption data from a project management system, and constructing an employee task dependency graph; secondly, collecting and analyzing work interaction information containing document operation and task marking features, and identifying task switching critical points to generate quantitative indexes; constructing a similar employee group and extracting an efficient working mode by means of a collaborative filtering algorithm after establishing an evaluation data set through space-time alignment of the two; by taking the task dependency graph as an environment state and the efficient mode as a reference, generating a task sequence optimization scheme by using a reinforcement learning model; according to the scheme, the staff group composition is adjusted according to the scheme execution effect, the optimization model is fed back, finally, a comprehensive evaluation result is generated based on the scheme and actual task sequence matching degree and the quantitative index stability, the task sequence optimization and physiological state adjustment ability can be accurately evaluated, and the work assessment scientificity and the task execution efficiency are improved.

Systems and methods for improving query creation and AQM are provided. A user interface is provided that allows an analyst to generate and test queries. After the analyst provides a query, one or more subsets of previous call center interactions are selected to test the provided query using methods such as a large language model or string vectorization. Once the subsets of interactions are selected, an LLM is used to evaluate the query against the subset. The evaluations may indicate interactions which were positive and which interactions were negative and may include quotes from the interactions that support the evaluations. The evaluations are presented to the analyst for review, and the analyst may revise the query in view of the evaluations. Once the analyst is satisfied with the performance of the query, the query may be used by the LLM to perform AQM for new interactions in the call center.

The invention discloses an intelligent networked automobile safety situation assessment method based on multi-source information fusion. The method comprises the following steps: 1) acquiring training parameters of a speed prediction model; 2) training a neural network by using the speed prediction model training parameters, and constructing a speed prediction model; 3) predicting the speed of the vehicle at the next moment by using the speed prediction model; 4) acquiring driving safety information, and processing the safety information to obtain a safety situation assessment parameter; and 5) processing the safety situation assessment parameter to obtain a current safety situation assessment value so as to determine the driving safety state of the vehicle. According to the method, the self-vehicle speed prediction model and the driving safety situation evaluation model are constructed, and the importance of the safety situation value on the driving safety of the intelligent networked vehicle is clarified by taking the prediction of the self-vehicle speed as a link, so that theoretical and technical support is provided for realizing the safe driving technology of the intelligent networked vehicle.

The application belongs to the technical field of large model security, and specifically discloses a large model threat sample library construction method, which comprises the following steps: step S1, embedding malicious instructions or malicious data into prompt words to construct threat samples; step S2, inputting the threat samples into a target large model as prompt words; step S3, using an evaluation large model to evaluate the output results of the target large model; and step S4, if the evaluation result is harmful content, adding the threat sample into a threat sample library, otherwise, discarding the threat sample. The application solves the problem of the lack of a threat sample library for security testing of an existing large model, and automatically tests and evaluates the threat samples during the construction of the sample library, so that the threat samples can be stored in the library only after the evaluation, thereby effectively improving the construction efficiency of the threat sample library. Meanwhile, the constructed threat sample library can support the security testing of the target large model, and can be used to improve the security of the target large model.

This disclosure provides a data processing method, apparatus, electronic device, and storage medium. The method includes: inputting a first row of data into a positive evaluation model to determine a first evaluation score for positively evaluating the recommendation effect; inputting a second row of data into a negative evaluation model to determine a second evaluation score for negatively evaluating the recommendation effect; and determining a final evaluation score based on the first and second evaluation scores. This disclosure combines the first evaluation score from the positive evaluation and the second evaluation score from the negative evaluation to assess the recommendation effect, resulting in a more comprehensive evaluation and better overall evaluation performance.

An information processing method is an information processing method for weighting an evaluation item for evaluating an evaluation target, and includes: obtaining, as text information, information expressed by one or more persons in an organization to which the one or more persons belong; obtaining one or more topics related to the text information by analyzing the text information; and weighting the evaluation item based on one or more similarity degrees between the evaluation item and the one or more topics.

An information processing method of the present disclosure includes acquiring an evaluation perspective in evaluating a motion of an evaluation target. The information processing method includes determining a configuration evaluating the motion of the evaluation target based on the evaluation perspective. The information processing method includes acquiring at least one of video information related to a motion and audio information. The information processing method includes generating content including a configuration and at least one of video information and audio information.

This invention relates to the fields of neuroscience and brain cognitive function assessment, specifically to a method and system for assessing motor and cognitive synergistic function based on electroencephalogram (EEG) signals. The method includes: collecting EEG signal data from subjects performing motor imagery and cognitive tasks; extracting motor imagery features, cognitive task features, and brainwave propagation features from the EEG signal data; inputting these features into an assessment model to evaluate the subject's motor function, cognitive function, and synergistic function; and outputting a motor function score, a cognitive function score, a brainwave propagation score, and a comprehensive assessment index from the assessment model. This invention enables synergistic assessment of motor and cognitive functions, improving the objectivity and accuracy of assessment results, and is applicable to rehabilitation assessments for stroke and neurodegenerative diseases.

The invention discloses a force value traceability method based on a lever type micro force value measuring device, which comprises the following steps: evaluating the uncertainty of a force value measured by the lever type micro force value measuring device, and the evaluation method comprises the following steps: acquiring an uncertainty component related to the force value generated at a laser displacement sensor on a lever; calculating the measurement uncertainty of the force value generated at the laser displacement sensor based on the uncertainty component; the uncertainty component comprises uncertainty caused by weight change of the weight; the calculation of the uncertainty caused by the weight change of the weight comprises the calculation of the uncertainty caused by the swinging of the weight, the uncertainty caused by the mass of the weight, the uncertainty caused by the gravitational acceleration of the weight and the uncertainty caused by the gravitational acceleration of the weight; on the basis, calculation; and tracing the force value based on the uncertainty. According to the invention, the technical effects of improving the evaluation completeness of the uncertainty caused by the weight and improving the accuracy of the metering result are achieved.

The application provides a train braking system performance evaluation method, device and storage medium, the method comprising: obtaining pressure data of a brake cylinder; the pressure data is periodically collected from the time when a braking instruction is issued; calculating evaluation values of each evaluation feature according to the pressure data; forming an evaluation data set according to the evaluation values; evaluating the performance of the train braking system according to the evaluation data set and a pre-trained evaluation model; the evaluation model maps the evaluation data set to a high-dimensional feature space, and evaluates according to the relationship between the mapped data set and the hyperplane in the high-dimensional feature space. The method provided by the application evaluates the performance of the train braking system based on the pressure data of the brake cylinder, realizes real-time monitoring of the train braking system, and improves the reliability and safety of the rail transit system.

An apparatus (1) and process for wear monitoring can include scanning a wearable component (5) and providing scan data for analysis and evaluation. An evaluation device (11) can evaluate the scan data to monitor the wear experienced by the scanned component and provide an indication identifying when the component may need to be replaced, providing an output via an output device that can indicate a projected replacement time period for replacement of the component and / or scheduling a replacement for the component.

The application belongs to the technical field of pilot ability evaluation, and specifically discloses a pilot ability evaluation method based on dynamic time warping and hierarchical clustering, which comprises the following steps: collecting eye movement data of a test pilot during a flight simulation task, pre-processing the eye movement data, extracting behavior indexes of each stage of the flight task, and generating a fixation region number sequence according to the fixation point position; selecting a target evaluation stage, using a dynamic time warping algorithm to perform nonlinear alignment on the fixation sequences of each pilot, and generating an eye movement difference degree matrix between the pilots; grouping the pilots based on the difference degree matrix using a hierarchical clustering algorithm; and outputting the ability evaluation result of the pilot according to the distribution of the pilot in the difference degree matrix and the deviation information of the pilot from the instructor fixation sequence. The method can realize the structured comparison and ability level evaluation of complex cognitive behaviors, and the evaluation result has strong objectivity and interpretability.

The invention discloses a power service intelligent evaluation method and system for medium and small enterprises, and relates to the technical field of power service evaluation, and the method comprises the following steps: multi-source data collection and preprocessing, construction of a special evaluation index system for the medium and small enterprises, multi-dimensional evaluation based on a deep learning model, and evaluation result output and service adaptation. According to the method, firstly, an exclusive evaluation system containing four types of first-level indexes such as cost suitability and electricity utilization stability and 12 special second-level indexes is constructed, and the characteristics of small and medium-sized enterprises are accurately matched; secondly, intelligent preprocessing is conducted on 7 types of systems by means of RESTfu 1API butt joint, evaluation is conducted in combination with CNN, BERT, LSTM + random forest fusion models, the error rate is reduced to 0.2% from 0.5%, and manual work is replaced to improve efficiency; thirdly, a visual report is output through a three-layer two-link framework, an adaptive scheme is pushed, exploration resources are efficiently scheduled, and the progress is synchronized; finally, cost reduction and efficiency improvement of small and medium-sized enterprises are facilitated, resource configuration optimization of power grid enterprises is facilitated, and fine management is realized.

The objective is to provide an evaluation system that can suitably collect and evaluate the data necessary for evaluation. [Solution] It is an evaluation system, It comprises a management information acquisition unit, a detailed information acquisition unit, and an evaluation unit. The aforementioned management information acquisition unit acquires history management information including the event type, The detailed information acquisition unit acquires detailed information corresponding to the history management information from the data management device that acquired the history management information, based on the history management information relating to the event to be analyzed, which is identified based on the event type. The evaluation unit outputs evaluation information based on the detailed information. Evaluation system.

The system according to the embodiment aims to promote diversity and creativity through collaboration and competition between generative AIs. [Solution] The system according to the embodiment comprises an idea sharing unit, a collaborative creation unit, a competition unit, an evaluation unit, and a collaboration unit. The idea sharing unit shares ideas between generation AIs. The collaborative creation unit causes the generation AIs to collaboratively create works based on ideas shared by the idea sharing unit. The competition unit evaluates the works created by the collaborative creation unit. The evaluation unit feeds back the evaluation results obtained by the competition unit to the generation AI. The collaboration unit supports collaboration between the generation AIs.

Systems and methods for real-time monitoring, adjustment, and safeguarding of generative artificial intelligence (“AI”) output. The systems and methods may include a security risk assessment algorithm rules engine (“rules engine”). The rules engine may include logic-based, language-encoded, rules for weighting queries involving security risks and for flagging security risks comprising queries and outputs (“rules”). The systems and methods may include an internal compliance database. The internal compliance database may include flagged queries, flagged outputs, and the rules. The systems and methods may include an adaptive feedback loop mechanism. The systems and methods may include a generative, pre-trained, transformer (“GPT”) processor. The GPT processor may receive a plurality of queries from a plurality of users. The GPT processor may forward the plurality of queries to the rules engine for evaluation. The evaluation may compare the plurality of queries to the flagged queries in the internal compliance database.

The application belongs to the technical field of air pollution assessment, and relates to a city air pollutant change range assessment and ranking method and system, the method comprising: 1) obtaining multiple city air pollutant concentrations in different time dimensions and standardizing the air pollutant concentrations; 2) performing multi-dimensional time series analysis on the air pollutant concentrations to obtain multi-dimensional time series analysis results; 3) calculating the comprehensive score of each city according to the air pollutant concentrations and the multi-dimensional time series analysis results; 4) calculating the dynamic assessment score of each city and dynamically ranking the multiple cities according to the dynamic assessment score; and 5) assessing the change range of the pollutant concentration of each city and ranking the multiple cities according to the multi-dimensional time series analysis results and the change range to obtain the risk level of each city. The application is evaluated based on multi-dimensional time series analysis results and dynamic weights, and the evaluation results are comprehensive and accurate.

A multilingual translation evaluation system, according to the present invention, comprises: a machine translation engine providing unit for generating machine-translated documents for a reference document by using different machine translation engines; an analysis option setting unit for receiving settings for evaluation weights and evaluation target documents generated on the basis of the reference document; a quality evaluation unit for evaluating quality differences among the evaluation target documents by comparing the evaluation target documents with each other; and a report generation unit for generating a report using analysis results evaluated by the quality evaluation unit.

Systems and methods for improving query creation and AQM are provided. A user interface is provided that allows an analyst to generate and test queries. After the analyst provides a query, one or more subsets of previous call center interactions are selected to test the provided query using methods such as a large language model or string vectorization. Once the subsets of interactions are selected, an LLM is used to evaluate the query against the subset. The evaluations may indicate interactions which were positive and which interactions were negative and may include quotes from the interactions that support the evaluations. The evaluations are presented to the analyst for review, and the analyst may revise the query in view of the evaluations. Once the analyst is satisfied with the performance of the query, the query may be used by the LLM to perform AQM for new interactions in the call center.

The invention discloses a nerve and blood vessel coupling evaluation method. The method comprises the following steps: data acquisition: acquiring basic information and a blood flow velocity curve of a subject; the basic information comprises age, gender and medical history; feature extraction: eight features are obtained according to the blood flow velocity curve and the normalized blood flow velocity curve; normalization processing: converting the gender and the medical history into 0-1 numeric type parameters; carrying out normalization processing on the age and the eight features; and inputting the normalized data into a machine learning model to obtain the nerve and blood vessel coupling damage probability of the evaluated person. Due to the adoption of the technical scheme, compared with the prior art, whether the neurovascular coupling function of a patient is damaged or not can be evaluated, auxiliary suggestions are provided for diagnosis of doctors, and efficiency and accuracy are improved. On the other hand, the nerve and blood vessel coupling function damage of the subject is evaluated by combining the basic information, the baseline blood flow velocity and the movement blood flow velocity of the subject, and the evaluation result is more comprehensive and accurate.

A model assessment service is disclosed. The model assessment service may evaluate model metrics for an evaluation dataset using ground truth data and model predictions. The model assessment service may compare model performance by, among other things, comparing metric values against threshold values or against metric values of other models. Using a customizable configuration file, the model comparison may comprise different ways to compare models and different ways to evaluate specific metrics. As an example, the model assessment service can assess whether a new candidate model is to replace a deployed production model.

The invention discloses an aircraft simulator training assessment method and device and a computer readable storage medium, and the method comprises the steps: obtaining at least one aircraft fault item selected by a teacher, injecting each aircraft fault item into a training task of a corresponding student, and transmitting the training task to the corresponding student; when each trainee carries out training according to each training item in the training task, generating a training condition corresponding to each training item in real time, and sending the training condition to the matched instructor; obtaining the assessment and evaluation of a teacher on the training condition of each training item of the trainee; after the trainee completes all the training tasks, generating a final assessment result of the trainee according to the assessment evaluation of all the training tasks; according to the invention, a teacher can flexibly adjust the training target according to the training condition of the student, and the fault processing capability of the student in controlling the unmanned electric aircraft is enhanced.

This invention provides a characterization system, an automated analyzer, and a characterization method capable of evaluating the characteristics of a sensor used for sample inspection in an automated analyzer. [Solution] In a detection process including an electrochemical reaction, parameters representing the relationship between the state of sensor 11 and the amount of change in electrical signal data due to a change in the state of sensor 11 are stored from the electrical signal data acquired by sensor 11. A state estimation index for sensor 11 is calculated using the electrical signal data and parameters, and statistical values ​​of the state estimation index are calculated. In the detection process, an evaluation target state estimation index for sensor 11a is calculated using the evaluation target electrical signal data and parameters output from the evaluation target sensor 11a. The evaluation target state estimation index and statistical values ​​are compared to evaluate the characteristics of the evaluation target sensor 11a, and the characteristic evaluation results are output.

To provide a working face surface evaluation method and a working face surface evaluation program capable of more accurately evaluating a progress degree of weathering deterioration of a working face surface.SOLUTION: A facing surface evaluation method includes an evaluation target image acquisition step of acquiring an evaluation target image included in a captured image of a facing surface of a bedrock in a tunnel pit, a color specification value acquisition step of acquiring, for each of a plurality of pixels constituting the evaluation target image, at least an a * value as a color specification value among an L value, the a * value, and a b * value indicating a color of the pixel, and a progress degree evaluation step of evaluating a degree of progress of weathering deterioration of the facing surface shown in the evaluation target image on the basis of the color specification value in each of the plurality of pixels.SELECTED DRAWING: Figure 10