51 results about "Device Evaluation Method" patented technology

An evaluation apparatus according to an embodiment includes processing circuitry. On the basis of performance information related to performance of a medical diagnostic imaging device and numerical information related to at least one evaluation item, the processing circuitry makes an evaluation of a first imaging plan used by the medical diagnostic imaging device, the numerical information conforming to a guideline related to imaging plans. On the basis of a result of the evaluation, the processing circuitry generates, from the first imaging plan, a second imaging plan conforming to the guideline. The processing circuitry outputs the second imaging plan.

The invention discloses an EEG signal quality real-time evaluation method and device, and the method comprises the following steps: 1, building an EEG signal quality evaluation database based on an EEG public database, marking the EEG signal quality based on a signal quality grade standard, and dividing EEG signal quality grades according to score scores; 2, performing feature extraction on the EEG signal quality evaluation database, and constructing an EEG signal quality time-frequency-space feature set; 3, taking the time-frequency-space feature fusion feature map as input, and constructing an EEG signal quality real-time evaluation model based on a deep learning algorithm; 4, performing model deployment and real-time evaluation; and 5, outputting and feeding back the model. According to the method, the signal quality in the EEG equipment acquisition process can be evaluated in real time, a signal quality real-time display interface is constructed for real-time visualization, a user feedback mechanism is established, and a user can be prompted to adjust the equipment or operate.

The invention relates to the technical field of rail bridge engineering, and particularly discloses a rail-bridge system residual traffic capacity assessment method, medium and equipment, and the assessment method comprises the steps: constructing a rail-bridge system seismic response model and a train-rail-bridge system coupling vibration model of a research object; constructing an earthquake-induced irregularity sample library; establishing a simplified calculation model of the shock-actuated irregularity equivalent coefficient; performing curved surface fitting to obtain a shock-induced composite unsmooth fitting curved surface; putting forward earthquake-induced irregularity thresholds in different driving performance index states, and establishing a linear mapping relation between the earthquake-induced irregularity thresholds and the driving speed; and establishing a mapping relationship among the seismic oscillation intensity, the structure natural vibration period and the post-earthquake driving speed limit value in different driving performance index states, and outputting a residual traffic capacity evaluation result according to the post-earthquake driving speed limit value. According to the method, the problem that in an existing method, the quantitative mapping relation between the current rail surface irregularity after the earthquake and the train operation performance is not clear is effectively solved.

The invention discloses a power transformation equipment evaluation method and system based on multi-dimensional parameters, and relates to the field of power transformation equipment safety management, and the method comprises a sensing module which is used for collecting the operation information and initial health characterization data of power transformation equipment, and carrying out the classification of the collected information and data; the screening module is used for receiving the classified information and data in the sensing module, screening key parameters strongly related to the health state of the equipment from the classified information and data, and labeling health influence dimensions corresponding to the parameters; according to the method, by collecting and classifying various types of operation and health data of the power transformation equipment, key parameters highly related to equipment health are accurately screened, meanwhile, health influence dimensions are marked, dimension weights are determined, core evaluation elements can be focused, and irrelevant data interference is reduced.

The invention provides an aluminum electrolysis process practical training platform based on multi-modal data, an evaluation method and equipment. The evaluation method comprises the following steps: acquiring log record data and video data in an aluminum electrolysis process practical training process, and acquiring sensor data of industrial equipment in an aluminum electrolysis workshop; an LSTM network is used for processing sequence data, the long-term dependency relationship in log record data can be captured, and the sequence and logic of aluminum electrolysis process operation can be understood; processing the video data by using a CNN network, and extracting key features in the video; processing sensor data using a full connection layer; and a full connection layer is used as a fusion layer, and outputs of the networks are fused. According to the method, different deep learning networks are adopted to process different data, the extracted features are more representative, and the reliability of a result obtained through fusion is higher; the actual operation effect of the aluminum electrolysis process can be reflected and evaluated more accurately, more valuable feedback is provided, and the aluminum electrolysis process is controlled and optimized.

The invention provides a power system source network load storage flexibility resource adjustment capability evaluation method and device, and the method comprises the following steps: constructing a flexibility resource adjustment model, according to the total load of the system, the variable coefficient, the new energy output, the nuclear power base load and the flexible resource adjustment model, a residual load interval, a maximum climbing demand, an adjustment power interval and maximum climbing power are obtained through calculation; comparing the residual load interval with the regulation power interval to evaluate the power regulation capability of the source network load storage of the power system; and comparing the maximum climbing demand with the maximum climbing power to evaluate the climbing ability of the power system source network load storage. The power system source network load storage flexibility resource adjustment capability assessment method and device are used for assessing the power adjustment capability and the gradeability of the power system, the operation risk of the power system can be identified, the assessment result is specific and quantitative, an optimization direction is provided for flexible resource configuration, and the applicability is high.

To provide an evaluation apparatus, evaluation method, and program for properly evaluating a producer for promotion.SOLUTION: In an evaluation system, an evaluation apparatus is connected to a plurality of storages over a network. The evaluation apparatus 200 of a producer who uses the storages for storing articles, as inventory goods, under high-humidity low-temperature conditions includes: a receiving unit which accepts inventory goods delivered from the producer; an order reception unit which receives an order for the inventory goods from a consumer; and an evaluation unit which evaluates the inventory goods or the producer of the inventory goods, based on at least one of receiving records and order reception records.SELECTED DRAWING: Figure 2A

The application relates to a device evaluation method and device, an apparatus, a readable storage medium and a program product. The method comprises the following steps: obtaining running state data corresponding to each of a plurality of configuration states of a target device; wherein the running state data comprises at least a compatibility data set and a security data set; for each configuration state, performing median aggregation and multi-dimensional weighting based on the compatibility data set in the running state data corresponding to the configuration state to obtain a compatibility score corresponding to the configuration state, and performing maximum aggregation and security failure evaluation based on the security data set in the running state data corresponding to the configuration state to obtain a security score corresponding to the configuration state; and weighting the compatibility scores and the security scores corresponding to the plurality of configuration states to obtain a device evaluation result. The method can improve the comprehensiveness of device evaluation.

The invention discloses a security inspection equipment evaluation method based on a security inspection platform, the security inspection platform, electronic equipment and a storage medium. The method comprises the following steps: acquiring first security check data uploaded by at least one security check device, wherein the first identification data comprises a security check image; performing secondary identification on the security check image based on the first security check image identification model to obtain second security check data; wherein the first security check image recognition model is obtained by training based on first security check data uploaded by multiple security check devices; and evaluating the security inspection equipment based on the first security inspection data and the second security inspection data to obtain an evaluation result of the security inspection equipment. According to the invention, the security check platform is introduced, so that the supervision department can effectively supervise the security check result, and the delivery safety is ensured; through secondary identification of the security check platform, the second security check data of the security check platform is used as the standard security check data to evaluate the first security check data uploaded by the security check equipment, so that evaluation of the security check equipment is realized, and consistency and accuracy of security check results are ensured.

An evaluation device according to the present technology includes an evaluation computing unit that learns to obtain deposit evaluation data as an output by machine learning using at least one of ocean topography data or fine particle component measurement data for a known seabed deposit as input data for learning, the evaluation computing unit calculating the deposit evaluation data for an evaluation target sea area by using at least one of the ocean topography data or the fine particle component measurement data for the evaluation target sea area as an input.

This application provides an AI device evaluation method, apparatus, device, and medium based on stepped pressure. The method includes: for each AI device to be evaluated, obtaining at least one type of test load model based on AI device domain requirements and AI device parameters; running the AI ​​device to be evaluated in each test load model; applying multiple levels of fixed stepped pressure to each test load model; when the test load model is stable under each level of fixed stepped pressure; collecting multi-dimensional performance parameters of the AI ​​device to be evaluated; determining cross-vendor multi-dimensional normalized reference values ​​of the test load model based on historical standard parameters of the reference AI device; calculating a comprehensive score for the AI ​​device to be evaluated based on the multi-dimensional performance parameters of each test load model, AI device domain requirements, and cross-vendor multi-dimensional normalized reference values ​​of each test load model; and evaluating the AI ​​device with the highest comprehensive score as the optimal device that meets the AI ​​device domain requirements, thereby improving the accuracy of the evaluation results.

To provide an evaluation apparatus, evaluation method, and program for granting a credit line based on inventory.SOLUTION: An evaluation apparatus in a storage that stores articles, as inventory goods, under high-humidity low-temperature conditions includes: a receiving unit which accepts inventory goods delivered from a producer; and a credit line granting unit which grants a credit line to the producer in accordance with the inventory goods.SELECTED DRAWING: Figure 1A

Evaluation device (100A) is a device that evaluates, by Bayesian optimization, an unknown characteristic point corresponding to a candidate experimental point based on a known characteristic point corresponding to an experimented experimental point, the evaluation device including: reception controller (10A) that acquires experimental result data (222) indicating the experimented experimental point and the known characteristic point, objective data (212) indicating an optimization objective, constraint condition data (213) indicating a constraint condition, and region reduction rule data (214); evaluation value calculator (12A) that calculates an evaluation value of the unknown characteristic point based on experimental result data (222), objective data (212), constraint condition data (213), and region reduction rule data (214); and evaluation value output unit (13) that outputs the evaluation value, in which evaluation value calculator (12A) gives a weighting according to a degree of conformity of the constraint condition to the evaluation value for at least one objective characteristic.

The embodiment of the invention discloses a power grid equipment evaluation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring an instrument image obtained by performing image acquisition on an equipment instrument of power grid equipment and an instrument type of the equipment instrument; determining an image identification mode corresponding to the instrument type, and performing image identification processing on the instrument image according to the image identification mode to obtain an image identification result; and determining an equipment evaluation result of the power grid equipment according to the image identification result. According to the technical scheme of the embodiment of the invention, the accuracy of power grid equipment evaluation can be improved.

The application relates to the technical field of equipment detection and equipment evaluation, and discloses an electromechanical equipment evaluation method and device based on large model intelligent agent cooperation, which comprises the following steps: collecting target equipment data, transmitting the target equipment data to a data buffer zone to store the target equipment data; reading the target equipment data and calling a model calculation intelligent agent to perform a data analysis operation to obtain a data analysis result; generating an equipment early warning result based on the data analysis result; performing a data encryption operation on the data analysis result and the equipment early warning result to obtain a data encryption result, generating an equipment evaluation result based on the data encryption result and the target equipment data; extracting evaluation feature data of the equipment evaluation result and sending the evaluation feature data to a large model cloud platform to generate intelligent agent analysis data, and generating an equipment evaluation report of the target equipment according to the intelligent agent analysis data. It can be seen that the application can improve the accuracy and reliability of electromechanical equipment evaluation and improve the efficiency of safety evaluation of electromechanical equipment.

The invention relates to the technical field of abnormal monitoring of power quality monitoring equipment, in particular to a power quality monitoring equipment evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: firstly obtaining a harmonic state space expression; then, according to a relation between a state variable of an electric power system evaluation object and harmonic waves, constructing a measurement model; constructing a Kalman filter according to the measurement model and the harmonic state space expression, and predicting an evaluation object state variable according to the Kalman filter to obtain an error vector and an error vector covariance matrix; and finally, according to the error vector, the error vector covariance matrix and the proportion of the error data in the total data, evaluating the measurement performance of the electric energy quality monitoring equipment. According to the embodiment of the invention, the measurement performance of the electric energy quality monitoring equipment is evaluated through the measurement data, the measurement performance of the electric energy quality monitoring equipment can be verified online, and the accuracy and reliability of the electric energy quality monitoring equipment are ensured.

The invention relates to the technical field of power system online monitoring, and particularly discloses an icing galloping monitoring device evaluation method based on fuzzy comprehensive evaluation. In order to solve the problems that an existing evaluation method depends on experience judgment, standards are not uniform, evaluation dimensions are single, and a scientific basis cannot be provided for device type selection and arrangement, the method comprises the steps that a three-level evaluation index system of a tree structure is constructed, and weights of indexes of all levels are determined in a grading mode; and then dynamically adjusting the weight by adopting multiple strategies according to the actual configuration condition of the device, and finally calculating a comprehensive evaluation value and grade by applying a multi-level fuzzy comprehensive evaluation method. According to the method, comprehensive and accurate quantitative evaluation of the icing galloping monitoring device from technical parameters to overall performance is realized, the accuracy and comparability of an evaluation result are remarkably improved, and a scientific and reliable decision basis is provided for an electric power department to perform device type selection, purchase and optimized layout.

The invention discloses an evaluation method and system for a comprehensive monitoring device of a transformer. The method comprises the steps of constructing a power grid simulation model of a power grid where a target transformer is located; determining each operation time of the power grid simulation model under different operation conditions according to different load requirements; screening to obtain each target time period; extracting each operation data of each target time period; obtaining an operation result of the target transformer corresponding to each operation condition based on each operation data; in the evaluation process, monitoring results corresponding to the different operation conditions are obtained; and generating an evaluation report of the to-be-evaluated comprehensive monitoring device based on a comparison result obtained by comparing the operation result of the target transformer determined by each operation condition with the corresponding monitoring result. According to the evaluation method for the comprehensive monitoring device of the transformer, the comprehensiveness and accuracy of evaluation of the comprehensive monitoring device are remarkably improved.

The invention relates to the technical field of multimedia data analysis, and discloses a video material contribution degree evaluation method and device and electronic equipment. The evaluation method comprises the steps of performing shot segmentation on a finished video to determine a plurality of query fragments; based on a pre-constructed material feature library, performing feature matching on each query fragment, and determining a plurality of original video materials adopted in the finished video; determining the effective reference duration of each original video material in the finished video; and for each original video material, determining the contribution degree of the original video material in the finished video based on the effective reference duration and the total duration of the finished video. The accuracy of evaluating the contribution degree of the video material in the finished video can be improved.

The invention discloses an equipment evaluation method and system based on digital twinning and multi-modal data fusion, and relates to the technical field of intelligent manufacturing and equipment health management, and the method comprises the steps: carrying out the integration based on data sequences corresponding to different types of monitoring data, and obtaining a multi-type monitoring data fusion sequence; performing feature fusion on the multi-category monitoring data fusion sequence to obtain a corresponding multi-channel tensor, a cross-modal weight and zoom dot product attention; adaptively adjusting health degree index thresholds corresponding to different types of monitoring data; and based on the multi-category monitoring data fusion sequence and the health degree index thresholds corresponding to the different categories of monitoring data, carrying out equipment evaluation. Through multi-source heterogeneous data fusion, a dynamic health assessment model and virtual-real collaborative decision optimization, an efficient and intelligent equipment health management system is constructed, a reliable basis is provided for equipment assessment work, and a guarantee is provided for the real-time performance and the working efficiency of assessment work.

This evaluation device comprises: an extraction unit that extracts, from time-series data obtained by measuring the temperature of a control target of a heat source device, an element included in a monitoring section from a prescribed start condition to an end condition of temperature control; an estimation unit that, on the basis of the extracted element, estimates a dead time estimation value of a response of the temperature with respect to the temperature control and a time constant estimation value of a first-order lag response; and an evaluation unit that creates evaluation information about the heat source device based on at least one of the dead time estimation value and the time constant estimation value.

An evaluation device disclosed herein comprises: a first acquisition unit that internally scans a device to be evaluated having a plurality of pieces of software each assigned a version and a plurality of resources used for the plurality of pieces of software, and thereby acquires the name and version of at least one piece of first software among the plurality of pieces of software; a second acquisition unit that externally scans the device to be evaluated, and thereby acquires the name and version of at least one piece of second software among the plurality of pieces of software; a collation and identification unit that collates the name and version of the first software and the name and version of the second software, and thereby identifies the name and version of common software; and a determination unit that determines to preferentially restore the contents of the common software and at least one of resources or paths corresponding to the software.

To provide an evaluation device capable of evaluating a privacy risk of a language model after assuming a realistic attacker.SOLUTION: An evaluation device 1 includes: a discriminator construction section 11 that constructs a membership discriminator using a proxy language model; a set creation section 12 that creates a set of N-grams from a data set; an initial input section 13 that inputs an N-gram selected from the set to a language model to be evaluated; a response acquisition section 14 that acquires a subsequent token and an internal state as a response to the input token; a state update section 15 that calculates a loss with respect to a class of training data by the membership discriminator with respect to the response, updates the internal state such that the loss becomes small, and then inputs the token input immediately before again; a character string acquisition section 16 that causes the response acquisition section 14 and the state update section 15 to repeatedly execute processing a predetermined number of times and repeats acquisition of a part of a character string until an end symbol is obtained; and an output section 17 that outputs the obtained character string.SELECTED DRAWING: Figure 1

This application relates to the field of ship interaction action assessment technology, and discloses an assessment method, assessment device, and electronic equipment for ship interaction actions. The assessment method includes: acquiring the regional characteristics and task logic of the target interaction action, and constructing a scenario tree spatiotemporal diagram of the target interaction action; calculating the scenario weight of each scenario in the target interaction action and the resource weight of the target interaction action based on the scenario tree spatiotemporal diagram; determining the comprehensive assessment index of the target interaction action based on the interaction parameters of the target interaction action; and using the scenario weight, resource weight, and comprehensive assessment index as the assessment result of the target interaction action. This application achieves a comprehensive evaluation of the interaction action as a whole, improving the comprehensiveness of the assessment of the effectiveness of ship interaction actions.

This evaluation device comprises: a depth information acquisition unit that acquires depth information indicating the distance between an observation means for observing a monitoring space and an object present in the monitoring space; an orientation information acquisition unit that acquires orientation information including the position and orientation of the observation means at the time when the depth information is acquired; a parameter acquisition unit that acquires a characteristic parameter of the observation means; and a calculation process unit that generates, for each of a plurality of voxels obtained by dividing the monitoring space, voxel data having registered therein attribute information indicating whether the voxel is in a dead angle or can be observed from the observation means on the basis of the depth information, the orientation information, and the characteristic parameter.

The invention discloses an equipment evaluation method and device, equipment and a storage medium. Simulated eyeball movement is controlled according to theoretical positions corresponding to a plurality of continuous test points, and an actual eye movement direction vector during simulated eyeball movement is acquired through to-be-tested eye movement tracking equipment; determining a theoretical rotation matrix between the adjacent test points according to the theoretical position, and determining an actual rotation matrix between the adjacent test points according to the actual eye movement direction vector; and calculating a relative angle error matrix between the theoretical rotation matrix and the actual rotation matrix, and evaluating the to-be-detected eye movement tracking device according to the relative angle error matrix. According to the method and the device, the relative angle error matrix between the theoretical rotation matrix and the actual rotation matrix of the adjacent test points is determined through the relative position difference matrix error calculation mode, so that the to-be-tested eye movement tracking equipment is evaluated according to the relative angle error matrix, and the precision of the to-be-tested eye movement tracking equipment is accurately evaluated.

To provide an evaluation device capable of evaluating a blur correction performance of an imaging apparatus based on a determination of whether or not an inputted oscillation waveform is different from a vibration waveform of the actually oscillated imaging apparatus.SOLUTION: An image evaluation device comprises: an imaging apparatus including a blur correction function; an oscillation control section which oscillates the imaging apparatus based on an input waveform; a vibration detection section which detects vibration of the imaging apparatus; comparison means which compares the input waveform to a waveform of a vibration detection result detected by the vibration detection section; and imaging evaluation means for performing an imaging evaluation which is an evaluation of the blur correction function based on an imaging result of a subject by the imaging apparatus. In a case where it is determined by the comparison means that a differential of the waveform of the vibration detection result with respect to the input waveform is within a predetermined range, the imaging evaluation means performs the imaging evaluation.SELECTED DRAWING: Figure 1

The tendency of classification related to the hazard of a chemical substance can be evaluated or predicted. A control unit of an evaluation support apparatus outputs information for evaluating or predicting the tendency of classification regarding the hazard of a chemical substance, based on the relatedness of a plurality of documents.

The present invention provides a device evaluation method, which mainly includes the training and testing of a probabilistic neural network and the specific evaluation process. Multiple different probabilistic neural networks are trained and tested based on the index data of the equipment. 85% of the data is selected as the training sample set and 15% as the test sample set. The output of the probabilistic neural network is transformed into evidence by improving the evidence theory, and a reliability factor is designed to correct each probabilistic neural network. The basic probability assignment decision method is used to determine the final evaluation result of the equipment to be evaluated. The present invention uses a probabilistic neural network to solve the evaluation result, avoiding the influence of subjective factors. Compared with other types of neural networks, the learning process of the probabilistic neural network is simple, the convergence speed is fast, and the output layer is replaced with a softmax layer to perform classification output in the form of probability, ensuring the intuitiveness of the output result. After each network is corrected by the evidence reliability factor, the reliability of the evaluation result is greatly improved.