25 results about "Effect assessment" patented technology

The invention discloses an advertisement design effect evaluation method and system based on artificial intelligence, and belongs to the technical field of effect evaluation, and the method comprises the steps of data collection, data preprocessing, advertisement data balance processing, advertisement design effect evaluation model construction and advertisement design effect evaluation. According to the scheme, the number of data needing to be synthesized by each sub-cluster is obtained based on the average density, the importance of the advertisement data in the sub-cluster to which the advertisement data belong and the similarity of the advertisement data with other sub-clusters are comprehensively considered, the selection probability is obtained, new advertisement data is synthesized based on the cluster center and the random adjustment factor, and the balanced data set is constructed according to the spatial distribution entropy; the method comprises the following steps: extracting potential information by using one-dimensional convolution, adjusting an adjacent matrix, adding a self-connecting edge, calculating spatial information, carrying out feature updating based on a convolution layer, obtaining an output label, designing a self-adaptive division proportion to divide individual positions, and respectively updating the individual positions by using a sine and cosine algorithm. And the accuracy and practicability of advertisement design effect evaluation are improved.

The invention discloses a photovoltaic base ecological environment effect evaluation system, and relates to the technical field of effect evaluation, the photovoltaic base ecological environment effect evaluation system comprises a data acquisition module, a data processing module, an effect analysis module and a decision management module, and the effect analysis module comprises an ecological sensitive submodule, an environment bearing submodule and an effect synthesis submodule. According to the invention, multiple factors are integrated through the ecological sensitivity sub-module to quantify ecological sensitivity, surface soil crust shear strength is introduced to fill the blank of traditional evaluation, multi-dimensional evaluation is realized, the collaborative level of ecological protection and energy efficiency is quantified through the output result, and the environment bearing sub-module is used to introduce the overground wind direction and underground hydrology, so that the comprehensive evaluation of the ecological protection and energy efficiency is realized. According to the method, the influence of ecological sensitivity and installed capacity can be accurately adjusted to improve the matching degree of photovoltaic development and ecological capacity, calculation results and factors of multiple sub-modules are integrated through the effect integration sub-module, the problem that existing evaluation is inaccurate is solved, and the whole system is based on the artificial intelligence technology and has an excellent intelligent effect.

The invention discloses a massage effect evaluation method and system, and the method comprises the following steps: S1, data collection: obtaining a plurality of physiological parameters; s2, data preprocessing: carrying out filtering, normalization and feature extraction processing on the physiological parameters; s3, effect evaluation: inputting the data after feature extraction into an effect evaluation model, and adaptively outputting an evaluation result by the effect evaluation model according to the massage manipulation; and S4, optimization iteration: optimizing the effect evaluation model by adopting a gradient descent method so as to improve the evaluation effect. The massage effect is evaluated by fusing the EMG feature, the skin temperature feature, the pressure distribution feature, the HRV feature and other parameter data, and the evaluation effect is more accurate.

The invention discloses a quantitative reasoning-based model effect evaluation system and method, and relates to the technical field of quantitative reasoning. The evaluation system comprises a model loading and preprocessing module, a quantitative processing module, a reasoning process simulation module, a performance evaluation module and a result feedback module. According to the technical key points, dynamic adjustment and optimization strategies are implemented according to conditions, various test sets are simulated for multiple times, and whether the performance of a model is stable or not is accurately judged after calculation; once it is detected that the comprehensive effect estimation exceeds an error range, an adjustment and optimization process is triggered, optimization is carried out for weight distribution, and rationality of quantization parameters is ensured; if the problem still exists, the model precision is further adjusted, and the optimal performance point is gradually approached; in the process, a dynamic adjustment formula is also introduced, the automatic adjustment of the precision and the adjustment amplitude is realized, the timeliness of the adjustment is ensured, and the instability caused by excessive adjustment is also avoided.

The invention relates to the technical field of mental health management, in particular to a mental health intervention effect assessment method and system. Collecting demographic basic information, psychological health problem types, psychological state improvement evaluation values before and after intervention and intervention feature information of each intervention object; for any intervention object, screening a historical intervention object matched with the intervention object from a historical intervention database as a reference object according to the demographic basic information, the psychological health problem type and the intervention feature information of the intervention object; calculating a deviation difference value between the psychological state improvement evaluation value of the intervention object and the psychological state improvement evaluation value of the reference object, comparing and judging the deviation difference value based on a preset deviation threshold value, and judging whether the intervention object is a suspected effect abnormal object or not; and for any suspected effect abnormal object, obtaining intervention behavior data during the intervention period.

The invention discloses a patient rehabilitation effect evaluation system based on nursing information processing. The system comprises an evaluation data acquisition module, a data preprocessing module, an effect evaluation model construction module, a model hyper-parameter optimization module and a rehabilitation effect evaluation module. The invention relates to the technical field of intelligent patient rehabilitation effect evaluation, in particular to a patient rehabilitation effect evaluation system based on nursing information processing, and the system comprises the steps: obtaining an effect evaluation original data set through data collection; a data preprocessing method of data cleaning, feature extraction, time sequence alignment, data standardization and data set segmentation is adopted; a double-flow deep network model is adopted as an effect evaluation model, and it is ensured that an evaluation result conforms to clinical logic while feature collaboration of heterogeneous medical data is achieved; an improved leapfrog algorithm is adopted to optimize model hyper-parameters, dynamic balance exploration and development are achieved, meanwhile, the stability of the algorithm is enhanced through population division, and stable performance of an evaluation system in different clinical scenes is ensured.

The invention relates to the technical field of effect evaluation, in particular to a cross-platform advertisement putting effect attribution analysis system, which comprises a user behavior feature construction module for collecting behavior data of users in an advertisement putting platform and constructing a user feature map by using a user identity fuzzy matching algorithm; the user behavior feature analysis module is used for determining a user behavior path set after advertisement putting based on the constructed user feature map and a time sequence algorithm; the advertisement putting effect evaluation module is used for comprehensively evaluating the advertisement putting effect based on a hierarchical evaluation algorithm according to the determined user behavior path set; according to the method, a second-generation path set is generated by combining path disturbance through a path construction mechanism based on a time sequence, so that the effect of comprehensively restoring, optimizing and reconstructing a real conversion path of a user is realized; and the generated paths are screened by setting a constraint equation, so that the effects of removing the paths with unreasonable behavior structures and improving the effectiveness of the evaluation paths are achieved.

A process for determining direct effect modifiers (DEMs) for an exposure of interest can include determining pre-treatment variables corresponding to characteristics of different subsets of an observed population for the exposure of interest, each individual of the observed population having a respective exposure to the exposure of interest and a corresponding outcome. A predicted conditional average treatment effect (CATE) can be determined for the exposure of interest on the observed population. Each variable in a matrix of covariates selected from the pre-treatment variables can be orthogonalized to the remaining covariates. Regression can be performed on the predicted CATE to determine additional residuals for each pre-treatment variable. Variables with additional residuals significantly associated with CATE are inferred to be DEMs.

The invention provides a method for calculating the greenhouse gas emission flux of a sewage biochemical treatment process, which can quickly and accurately calculate the greenhouse gas emission amount of a biological tank, thereby providing basic data and effect evaluation before and after the low-carbon transformation of the biochemical process.

The present invention provides a measure effect estimation device, a measure effect estimation method, and a measure effect estimation program capable of estimating an effect of a measure. A measure effect estimation device according to the present disclosure comprises: a customer image specification means for specifying a customer image on the basis of prescribed information on a customer; a measure reception unit for receiving prescribed measure information; and an output means for outputting the degree of interest of the customer to a measure estimated on the basis of the customer image and the prescribed measure information.

The invention relates to the field of image processing, in particular to an effect evaluation method and system for hair care and hair growth. The method comprises the steps that a hair image is acquired, image preprocessing is carried out through a multi-dimensional gradient aggregation enhancement algorithm, and an enhanced image is obtained; performing feature extraction on the enhanced image through a multi-level adaptive texture analysis algorithm to obtain a comprehensive feature vector; based on the comprehensive feature vector, generating a binary feature map through an adaptive threshold method; based on the binary feature map, a phase transformation matrix is constructed through a dynamic phase transformation tracking algorithm, and the growth rate is calculated; based on the growth rate, the hair growth amount is calculated, and a comprehensive evaluation report is generated. The problem that a traditional evaluation method cannot provide systematic analysis on key indexes such as hair growth, density and glossiness is solved; the real-time tracking and analysis capability on the dynamic change of hair growth is lacked; the operation is complicated, the result is difficult to read, and the wide application is limited.

The invention discloses an immersive teaching and training system based on safety accident prevention, and relates to the technical field of safety teaching and training. The system comprises a teaching and training management module, a scene experience module, a behavior supervision module and an effect evaluation module. The teaching and training management module constructs a cross-scene accident chain through multi-source data acquisition and parameter adaptation and adopts a Bayesian network model to quantify risks; the scene experience module is connected with the function scene unit through a standardized interface, and realizes multi-scene dynamic linkage based on accident chain logic; the behavior supervision module synchronously collects physiological and operation data, establishes a correlation model and executes hierarchical intervention; and the effect evaluation module integrates the multi-dimensional data to generate an evaluation report, and reversely optimizes teaching and training parameters. According to the system, through the technical means of multi-module cooperation, a whole-process closed loop of data acquisition, risk assessment, resource allocation, behavior supervision and effect optimization is realized, the practical training performance, accuracy and systematicness are improved, and effective support is provided for safety accident prevention.

The invention relates to the field of intelligent motion control systems, in particular to an astronaut dynamic balance-oriented specific muscle training intelligent motion control system, which comprises a data acquisition and input sensing module, an analysis and evaluation module, a result display module and an effect evaluation module, through multi-modal data acquisition and fusion, pre-training encoder feature mapping and a muscle and balance dual evaluation mechanism, the problems that astronaut muscle atrophy and dynamic balance descending under microgravity and existing training depends on manpower and is tedious in equipment are solved, and intelligent and accurate training is achieved.

The invention provides a multi-label model selection and effect evaluation method and system, equipment and a medium. The method comprises the steps of obtaining a corresponding service label based on published content of a user; obtaining a service label weight of a multi-label model based on the propagation area of the service label; obtaining an effect evaluation index of the multi-label model based on the service label weight; and obtaining an effect evaluation result of the multi-label model based on the effect evaluation index. According to the method, the service label weight can be intelligently calculated, the inaccuracy of manually defining the model label weight can be avoided, the weights of dozens of hundreds of service labels can be efficiently and intelligently adjusted according to the change of the service form, and then the effect of the multi-label model is efficiently evaluated. Moreover, the method can accurately judge the advantages and disadvantages of the effects between different multi-label models through the effect evaluation indexes, and enables the capability of the model selected in the re-iterative optimization process to better meet the requirements of services.

The invention discloses a new media promotion optimization system and method based on the Internet, and relates to the technical field of information promotion. The system comprises a multi-dimensional data analysis module, an intelligent content creation module, a user positioning promotion module, a user definition secondary promotion module and an effect evaluation adjustment module. The technical key points are as follows: the system introduces a rule engine to define active users and inactive users, so that the system can be flexibly adjusted according to different service requirements and user behavior changes; according to the method, the effect of each promotion activity can be comprehensively and accurately measured by setting a clear evaluation index and an evaluation formula, the most representative index is selected for evaluation according to different promotion modes, and the accuracy of an evaluation result is ensured; according to the comparison between the effect evaluation index and the preset standard threshold value, the system can automatically adjust the promotion frequency in real time, so that the waste of resources is avoided, the continuous optimization of the promotion activity is ensured, and the promotion efficiency is improved.

The invention relates to the technical field of enterprise digital management and activity planning, and discloses a whole-process digital management method for enterprise annual meeting literature and art activities. A demand information collection unit, a data analysis unit, a resource management unit, a program planning unit, a process control unit, a visual monitoring unit, an emergency processing unit and an effect evaluation unit are arranged in the system. A digital management process which runs through demand acquisition, intelligent analysis, resource overall planning, program planning, process control, on-site monitoring, emergency processing and effect evaluation is constructed, so that enterprise annual meeting literature activities are transformed from an experience-dominated traditional mode to a data-driven, precise-collaboration and intelligent-early-warning modernized operation mode; and finally, the problems of unclear requirements, resource mismatching, disordered execution, out-of-control risk and difficulty in effect measurement in the traditional annual preparation are solved.

A process for determining direct effect modifiers (DEMs) for an exposure of interest can include determining pre-treatment variables corresponding to characteristics of different subsets of an observed population for the exposure of interest, each individual of the observed population having a respective exposure to the exposure of interest and a corresponding outcome. A predicted conditional average treatment effect (CATE) can be determined for the exposure of interest on the observed population. Each variable in a matrix of covariates selected from the pre-treatment variables can be orthogonalized to the remaining covariates. Regression can be performed on the predicted CATE to determine additional residuals for each pre-treatment variable. Variables with additional residuals significantly associated with CATE are inferred to be DEMs.

The invention provides an effect evaluation method and system for multidisciplinary fusion teaching and a medium. The method comprises the following steps: acquiring multidisciplinary fusion teaching effect monitoring data including learning achievement monitoring data, learning process monitoring data, learning interest investigation data and teaching quality monitoring data, and processing according to the learning achievement monitoring data to obtain a learning effect evaluation index, a learning process quality evaluation index is obtained according to learning process monitoring data processing, an interest motivation effect evaluation index is obtained according to learning interest investigation data processing, and a teaching quality evaluation index is obtained according to teaching quality monitoring data processing; processing according to the learning effect evaluation index, the learning process quality evaluation index, the interest excitation effect evaluation index and the teaching quality evaluation index to obtain a teaching effect evaluation index, and performing fusion teaching effect feedback according to the teaching effect evaluation index; therefore, the purpose of evaluating multidisciplinary fusion effect teaching is achieved.

The invention discloses an agent interaction and evaluation method and system and a related device, and the method comprises the steps: obtaining the inquiry content outputted by a target agent and the reply content outputted by a reference agent according to a behavior label in a plurality of rounds, and obtaining a dialogue set; wherein each behavior tag corresponds to a respective dialogue mode, and the behavior tag of a single round is determined based on dialogue content of at least a part of historical rounds or is set as a preset behavior tag of a target question included in inquiry content of a current round; traversing each round in the dialogue set, obtaining a behavior intention corresponding to the behavior label of the previous round, and evaluating the inquiry content of the current round based on the dialogue content and the behavior intention of at least part of historical rounds to obtain a single round score of the inquiry content of the current round; wherein the single-round scores of all rounds are used for evaluating the target agent. According to the scheme, the authenticity of agent interaction and the accuracy of interaction effect evaluation can be improved.

The invention discloses an intelligent psychiatry treatment effect evaluation system. The system comprises a data acquisition module, a data preprocessing module, a psychiatry treatment effect evaluation model establishment module and a psychiatry treatment effect evaluation module. The invention belongs to the technical field of effect evaluation, and particularly relates to an intelligent psychiatric treatment effect evaluation system, which is characterized in that an evaluation level is really improved by defining an estimation range, and uncertainty is buffered; a self-adaptive Lagrangian multiplier is introduced, so that the system is allowed to more strongly punish prediction with a relatively large error, and punishment is performed on a prediction interval in which a real treatment effect cannot be captured, so that wrong prediction of an evaluation result is reduced; random factors are combined with time parameters, diversified system parameter adjustment initialization strategies are constructed, global randomness is increased, convergence of system parameter adjustment is higher, prediction results with higher robustness are generated, and prediction efficiency of the system on evaluation results is improved.

The invention discloses a method for evaluating the effect of same-view-angle image generation before and after damage, and belongs to the technical field of damage effect evaluation. The method comprises the following steps: constructing data sets before and after damage according to a video shot by an unmanned aerial vehicle, wherein the data sets comprise images, image shooting poses and sparse point clouds; then, according to sparse point cloud registration, space conversion is carried out on the pre-damage model and the post-damage model; thirdly, taking a pose, a point cloud and an image which are obtained after space conversion and generated before damage as input, and training through a micro-renderable model to obtain a model before damage; the trained model can generate an image before the damage according to a shooting angle after the damage, and the assessment of the damage degree is obtained by comparing the difference between the two images before and after the damage and the point cloud.

The present invention provides a measure effect estimation device, a measure effect estimation method, and a measure effect estimation program capable of estimating an effect of a measure. A measure effect estimation device according to the present disclosure comprises: a customer image specification means for specifying a customer image on the basis of prescribed information on a customer; a measure reception unit for receiving prescribed measure information; and an output means for outputting the degree of interest of the customer to a measure estimated on the basis of the customer image and the prescribed measure information.

The invention relates to an activity effect evaluation method and system, and the method comprises the steps: obtaining multi-dimensional activity data which comprises activity attribute information, user attribute information and operation attribute information; establishing an association relationship among different dimensions in the multi-dimensional activity data; effect variables and feature variables are extracted from the multi-dimensional activity data, the contribution degree of each feature variable to each attribution group is determined, and the attribution groups are determined by the effect variables; and performing automatic assessment on the activity effect based on the association relationship and the contribution degree. The system comprises a data acquisition module, an association mining module, an attribution analysis module and a result display module, and is used for executing corresponding method steps. Therefore, the problems of high manual dependence, single analysis dimension, long period, attribution deficiency and other pain points in the prior art are solved, and efficient, accurate and closed-loop activity effect evaluation and operation optimization decision making are realized.

The present application belongs to the technical field of behavior correction and educational informatization, and specifically relates to a pre-school fusion education behavior correction system and method based on AI empowerment, which comprises a data acquisition module, an AI analysis and processing module, a correction content library, an interactive execution module, a physiological monitoring module, an effect evaluation module and a teaching feedback module, and each module cooperates to realize a complete behavior correction process.The present application realizes accurate identification and analysis of problem behaviors of pre-school children, formulates individualized correction schemes in combination with individual characteristics of children, introduces AI technology and physiological monitoring means, improves the scientificity and objectivity of the correction process, solves the subjective defects of artificial evaluation, builds an integrated platform of teaching and practice, provides real cases and data support for behavior correction courses, improves the teaching effect, realizes dynamic tracking and quantitative evaluation of correction effect, timely adjusts the correction strategy, and improves the correction efficiency.