46 results about "Assessment evaluation" patented technology

Embodiments of the disclosed technologies are capable of evaluating content recommendations. The embodiments describe creating a prompt using a search query and a content recommendation output by a machine learning model in response to the search query. The embodiments further describe causing a LLM to generate an evaluation of the content recommendation and the search query using the prompt. The evaluation includes a relevance score of the content recommendation and the search query. The embodiments further describe training the machine learning model to generate an updated content recommendation in response to the search query. The training includes using the relevance score of the content recommendation and the search query.

The invention discloses an employee ability growth navigation and resource recommendation system and method, and relates to the technical field of employee growth navigation and resource recommendation. Comprising a learning map construction module, a multi-modal ability evaluation module, a dynamic navigation engine, a resource recommendation module, a digital twinborn training module and a user interaction module. A learning map construction module; the multi-modal fusion realizes' voice-text-image-equipment 'four-dimensional evaluation, the evaluation dimension is expanded from traditional 3 items to 8 items, and the evaluation accuracy of the cross-department cooperation capability and the fault decision-making capability is improved; the assessment weight is dynamically adjusted through reinforcement learning, the technical trend matching degree is improved, the assessment period is shortened to the hour level from the quarterly level, and the talent inventory efficiency is improved; a composite fault scene is dynamically generated, practical training pertinence is improved, and the processing success rate of employees in a multi-equipment linkage failure scene is improved.

The invention provides a reserve land development potential grading evaluation method and system based on pattern spot data, and relates to the technical field of reserve land development management, and the method comprises the steps: carrying out the pattern spot data mining of reserve land, and obtaining an original pattern spot data set with pattern spot attribute information; performing multi-level classification and layering according to a spatial planning hierarchical relationship, and extracting a potential constructable base range; performing pattern spot spatial topology processing, extracting pattern spot geometric feature parameters, and performing pattern spot scale division based on a preset area threshold value; establishing a pattern spot potential evaluation index system, and performing pattern spot development potential category division; and executing the refined extraction of the available space to form a visual board for the reserve land. The technical problems that in the prior art, the development potential of the reserve land is evaluated by depending on manual judgment and a rule-based simplified evaluation model, the evaluation precision is insufficient, the development potential of the land cannot be comprehensively reflected, and the scientificity and rationality of a development decision are further influenced are solved.

The invention belongs to the technical field of pilot ability assessment, and particularly discloses a pilot ability assessment method based on dynamic time warping and hierarchical clustering, which comprises the following steps of: acquiring eye movement data of a tested pilot in a flight simulation task process, preprocessing the eye movement data, extracting behavior indexes of each stage of a flight task, and calculating a pilot ability assessment result; generating a fixation area number sequence according to the fixation point position; in the selection target evaluation stage, a dynamic time warping algorithm is used for carrying out nonlinear alignment on the gaze sequences of all the pilots, and an eye movement difference degree matrix between the pilots is generated; based on the difference degree matrix, adopting a hierarchical clustering algorithm to group the pilots; and outputting an 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 and the teacher watching sequence. According to the method, structured comparison and capability grade evaluation of complex cognitive behaviors can be realized, and an evaluation result has relatively high objectivity and interpretability.

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.

The application provides a crane health assessment method and system, the method comprising collecting data of a working state of the crane; performing health assessment based on the collected data and a health assessment model; obtaining quantitative data based on a result of the assessment and performing maintenance guidance based on the quantitative data. The application can monitor the use of the crane in real time, analyze and assess the state of main structural parts and key components of the crane, assess the health of the crane, provide quantitative data guidance for equipment maintenance, and realize early prediction of the state of the equipment by the production and maintenance departments, so as to arrange maintenance or component plans in a targeted manner.

The embodiment of the invention provides a reliability evaluation method based on mechanical equipment component-level mechanism analysis. The method comprises the following steps: analyzing an equipment failure mode; key components are decomposed; evaluating the failure rate of the component; determining a multiplier factor; and summarizing equipment failure rates. According to the embodiment of the invention, dependence on equipment experiment data is eliminated, the influence of environmental conditions on the parts is determined by issuing the operation environment faced by the equipment to the part level, the reliability of the mechanical equipment is evaluated under the condition that the experiment data is incomplete and insufficient, the evaluation precision is improved, the early failure is early warned, the maintenance cost is reduced, and the reliability of the mechanical equipment is improved. By establishing a'material-component-system 'multi-scale model, the modeling barrier between a micromechanism and macroscopic performance of a traditional method is broken through, the component failure rate is expressed in a multiplier factor mode, and quantitative calculation of the component failure rate is achieved.

One embodiment proposes a brainwave-based cognitive function evaluation method. The brainwave-based cognitive function evaluation method includes deriving a predicted response intended by an evaluation subject based on evaluation data including brainwaves of the evaluation subject for a specific task, generating a response accuracy including voice clarity, whether an answer matches, and answer meaning accuracy based on the predicted response and a preset correct answer for the specific task, and evaluating a cognitive function of the evaluation subject based on the response accuracy.

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 present invention makes it possible to evaluate buckling of a panel component before assembly to another component through simple molding analysis. Provided is a method for evaluating buckling in which buckling prior to assembly of a panel component obtained by press molding a plate material into a three-dimensional form is evaluated through molding analysis using a computer. This method for evaluating buckling comprises: a press molding analysis step (20A) for performing press-molding analysis in which the plate material is formed into the panel component, and acquiring the panel shape after mold release as a reference panel shape (10); a self-weight analysis step (20B) for performing self-weight analysis with respect to the reference panel shape (10) in a condition in which the panel component is placed on a flat surface, and acquiring the panel shape after deformation resulting from the weight of the panel component as an evaluation panel shape (11); and a buckling evaluation step (20C) for evaluating buckling of the panel component having the evaluation panel shape (11).

The invention discloses an intelligent monitoring method and system for the safety state of an efflux pump, and relates to the technical field of industrial equipment safety, the system collects working condition data of the efflux pump in real time through a sensor group installed on the efflux pump, and pre-processes the working condition data to construct a fluid state data set and a load evolution data set; medium disturbance evaluation is carried out by calculating a medium disturbance leading edge index VPI and a medium disturbance response threshold TH, when shear disturbance is evaluated to be formed, a load evolution analysis instruction is executed, a load lag evolution index LEI is calculated based on structure response data, and then a joint instability risk index UDR is calculated in combination, so that the shear disturbance is evaluated to be formed. And performing comprehensive instability response evaluation with a set first joint instability boundary threshold UA and a set second joint instability boundary threshold UB, and executing a grading safety adjustment strategy according to a comprehensive instability response evaluation result. According to the system, early recognition, dynamic evaluation and closed-loop response of the abnormal state of the outer conveying pump can be achieved, and the safety and stability of operation of the conveying system are improved.

The invention discloses a house health intelligent physical examination method based on a large model, and the method comprises the following specific steps: (1) carrying out the fusion analysis of multi-source monitoring data, and extracting the features of the safety hidden troubles of a house; (2) formulating house health examination standards; (3) constructing a house health intelligent physical examination large model, and establishing an intelligent evaluation algorithm; (4) developing a mobile terminal APP; according to the method, fusion analysis is carried out on monitoring data, space features of potential safety hazards of the houses are extracted, mass house feature data collected by non-professionals in an area are subjected to batch, efficient, low-cost and intelligent analysis through a house health AI intelligent evaluation algorithm, and the house health state is judged; according to the invention, the health conditions of massive houses in the area can be evaluated, a unified house evaluation standard is provided during evaluation, and the convenience and efficiency of monitoring are improved.

To evaluate consistency between testing criteria described in natural language and a testing procedure or testing command described in natural language.SOLUTION: A testing evaluation system comprises: a first extraction AI model that extracts descriptions related to predefined evaluation criteria from descriptions of testing criteria input to a first input part; a second extraction AI model that extracts descriptions related to the evaluation criteria from descriptions of a testing procedure or a testing command input to a second input unit; an evaluation item integration part that integrates the descriptions of the testing criteria extracted by the first extraction AI model and the descriptions of the testing procedure and the like extracted by the second extraction AI model to obtain evaluation items in the evaluation criteria; and an evaluation AI model that evaluates consistency between the descriptions of the testing criteria and the descriptions of the testing procedure and the like for the evaluation items in the evaluation criteria integrated by the evaluation item integration part.SELECTED DRAWING: Figure 2

This testing evaluation system comprises: a first extraction AI model that extracts descriptions related to predefined evaluation criteria from descriptions of testing criteria input to a first input part; a second extraction AI model that extracts descriptions related to the evaluation criteria from descriptions of a testing procedure or a testing command input to a second input unit; an evaluation item integration part that integrates the descriptions of the testing criteria extracted by the first extraction AI model and the descriptions of the testing procedure or the like extracted by the second extraction AI model to obtain evaluation items in the evaluation criteria; and an evaluation AI model that evaluates consistency between the descriptions of the testing criteria and the descriptions of the testing procedure or the like for the evaluation items in the evaluation criteria integrated by the evaluation item integration part.

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 discloses a method, system and device for government affair large model detection, comprising obtaining a first answer set through a target to-be-tested model according to a first query text set; obtaining a second answer set according to the first answer set; obtaining a second query text set corresponding to the second answer set according to the second answer set; obtaining a second Prompt set corresponding to the second answer set according to the second query text set; obtaining a third answer set through the target to-be-tested model according to the second Prompt set; and obtaining an evaluation result of the target to-be-tested model according to the first answer set, the second answer set and the third answer set. At least the problem that the existing government affair large model evaluation method cannot evaluate the understanding ability of the query text and the evaluation result cannot provide help for the optimization of the government affair large model is solved.

The invention relates to a method for operating a vehicle (10), in particular a motor vehicle, wherein the vehicle (10) comprises at least one assistance system for lateral guidance (12), wherein the vehicle (10) comprises at least one actuable device for indicating a change in the direction of travel (14). In a method in which the activation of a direction-of-travel indicator (14) can be interpreted without having to passivate the driver assistance system, it is provided that a preceding section (16) is evaluated by an evaluation device (18) when the direction-of-travel indicator (14) is activated, the evaluation device (18) checking whether at least a first criterion is met, the lateral guidance assistance system (12) remaining activated if at least the first criterion is met, the first criterion being met if the evaluation device (18) has determined that the vehicle (10) is at a fork (20) with a priority segment (22) and the direction-of-travel indicator (14) indicates a change of direction in the direction of the priority segment (22).

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.

The invention discloses an automatic work efficiency evaluation method based on near-infrared brain imaging, and relates to the technical field of work efficiency evaluation. According to the automatic work efficiency evaluation method based on near-infrared brain imaging, by collecting near-infrared brain imaging data of a worker, normal work of the worker is guaranteed, meanwhile, the activeness of different brain areas of the worker is monitored, and work efficiency evaluation in the work process of the worker is achieved in cooperation with brain area activeness analysis corresponding to work related characteristics and in combination with the proficiency level of the worker; compared with the prior art, the method achieves the regular updating of the working proficiency level of a worker through the statistics of the time, the error rate and the task integrity quality, achieves the dynamic evaluation of the working efficiency through the judgment of the proficiency level, achieves the early warning of the reduction of the working efficiency through the analysis of the activeness, and achieves the early warning of the reduction of the working efficiency. And the working efficiency of workers in the industrial production process and the production quality of products are improved.

A probabilistic explanatory method and system probabilistically generates explanatory information comprising syntactical elements that are generated in accordance with vector-embodied affinities. The explanatory information may comprise, for example a rationale, a representation of a level of confidence, and / or a theme that is derived from the vector of affinities. The explanatory information is assessed, which may be based on a user's interaction with the explanatory information. Additional syntactical elements are probabilistically generated based on the assessment and are included in a communication to a user.

The invention relates to the technical field of disease prediction, in particular to an early warning detection method, device and equipment for severe diseases of a digestive system, and the method comprises the steps: obtaining multi-modal data information of a patient, the multi-modal data information of the patient comprises clinical symptoms, laboratory examination and iconography examination; constructing and training a clustering model to obtain a plurality of clustering sets, inputting the multi-modal data information of the patient into a target clustering set in the clustering model, judging whether the multi-modal data information of the patient is abnormal or not, if the multi-modal data information of the patient is abnormal, constructing a probability model, and if the multi-modal data information of the patient is abnormal, determining whether the multi-modal data information of the patient is abnormal; generating a severity label from the target cluster set, and inputting the severity label into a probability model to obtain a probability result of severity assessment of the severe disease; and performing graded early warning according to a probability result, and performing clinical decision making according to the graded early warning. Therefore, the problems that in the prior art, various digestive system diseases cannot be evaluated by using the same method, and results are different and not timely when the diseases are evaluated are solved.

The invention provides a virtual reality medical education system and method, computer equipment and a readable storage medium, and belongs to the technical field of clinical medical education. The central virtual workstation is constructed to integrate electronic medical records, medical advice processing, critical value alarm and a multidisciplinary consultation unit, a whole-process data link is opened, and the problem of training closed-loop breakage caused by traditional system module splitting is solved; meanwhile, a causal chain model is constructed based on a three-dimensional evaluation engine, a loss report fusing biomechanical damage, resource consumption and prognosis influence is generated, and the problems of evaluation dimension fault and system module splitting in traditional evaluation are solved.

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.

A control system (200) manages a plurality of mobile machines (100) each equipped with a visual perception system (150) to capture perception data. The control system (200) via an onboard controller (140) applies an object detection operation (304) to the perception data to detect a detected marker position (214) corresponding to a visual marker (154). The onboard controller (140) assesses an assessed marker health status (226) with respect to the detected marker position (214) and transmits that to a central worksite server (180). The central worksite server (180) aggregates the assessed marker health statuses (226) from a plurality of mobile machines (100) to determine an aggregate marker health status (236) for the visual marker (154).