72 results about "Human assessment" patented technology

The invention relates to the technical field of spine rehabilitation training, and discloses a computer vision recognition and guidance system for spine rehabilitation training actions. The system comprises a data acquisition module for acquiring user spine motion three-dimensional dynamic image data by using a multi-camera array; the action recognition module tracks bone joint points based on an improved algorithm to generate a real-time action sequence; the posture evaluation module is used for extracting spine curvature and joint angle features through a multi-scale convolutional neural network; the guidance instruction generation module inputs the features into a reinforcement learning model to generate a personalized instruction; and the feedback execution module constructs a fuzzy self-adaptive control model to correct the motion trail of the spine. According to the system, training actions can be accurately recognized, personalized guidance is provided, real-time correction is achieved, the spine rehabilitation training effect is effectively improved, subjectivity and errors of manual evaluation are reduced, and rehabilitation requirements of different patients are met.

The invention provides a vocational ability assessment system based on a skill map, and relates to the field of human resource technology and knowledge map application. The evaluation main system comprises a skill star map generation module, an occupational trajectory simulation module, a skill challenge module, a skill authentication chain module, a future skill navigation module, a skill collaborative ecological module, a skill evolutionary tree module, an occupational story generation module, a skill energy pool module, an occupational time capsule module and a control flow management module; according to the invention, the dynamic skill star map is constructed and the 5G network is combined to transmit data in real time, so that the occupational experience, learning records and test data of the user can be collected in time, and the node weight is dynamically adjusted through the semantic analysis algorithm and the weighted scoring algorithm; the problems that traditional manual evaluation and online testing are high in subjectivity and low in efficiency, and the capability cannot be accurately quantified are effectively solved.

The invention relates to the technical field of building structure health monitoring, in particular to an unmanned aerial vehicle laser radar-based building point cloud deformation quantitative extraction method, which comprises the following steps of: based on building corner structure characteristics in point cloud data, carrying out quantitative evaluation on the overall inclination state of a building through point cloud segmentation, straight line fitting and angle calculation; based on an ideal plane hypothesis, performing plane fitting and point-to-plane distance analysis on the building facade point cloud in the point cloud data, identifying a local concave-convex deformation area of the building facade, and performing quantitative evaluation; and roof plane detection and wall surface normal vector analysis are carried out based on the point cloud data, and accurate quantification is carried out on parapet wall inclination based on region growth segmentation and PCA principal direction analysis. Through automatic processing and quantitative analysis of an algorithm, subjective errors and inconsistency possibly occurring in a traditional manual evaluation method are avoided, and objectivity and repeatability of an evaluation result are enhanced.

The invention relates to the technical field of urban building management, and discloses an urban fifth facade roof space intelligent evaluation system and method. The system comprises a main control model, an image recognition agent, an index calculation agent and an Auto-Summary summary model. The image recognition agent realizes accurate semantic segmentation of roof elements through an image encoder, a prompt encoder and a mask decoder based on an SAM deep learning model; the index calculation agent adopts an RAG technology to dynamically retrieve an index library, and drives a space operation tool to calculate compliance indexes; the Auto-Summary summary model is fused with multi-modal data, and a visual report containing a thermodynamic diagram, a three-dimensional interaction model, rectification suggestions and roof layout suggestions is generated. According to the invention, a full-automatic roof is realized, the problems of low efficiency and large error of traditional manual evaluation are solved, and the intelligence and precision of urban fifth facade management are significantly improved.

The invention discloses a position searching method and device based on implicit intention recognition, and relates to the technical field of information retrieval and intelligent recommendation. The method comprises the following steps: sampling from a historical log and constructing prompt words, and performing weighted voting labeling on keywords and resume pairs by utilizing a plurality of large language models to generate an intention recognition training sample; and the cue word is iteratively optimized through manual evaluation, so that the labeling accuracy is ensured. And training an intention recognition model by using the samples. In the online search stage, structural features of search terms and resumes of a user are extracted and input into the pre-training model to recognize the implicit job hunting intention of the pre-training model. Finally, personalized retrieval conditions are dynamically generated according to the intention recognition result, and related positions are recalled and sorted from the position database. According to the method, the accuracy and the individuation degree of position searching are effectively improved, and the user experience of job seekers is remarkably improved.

Systems and methods for an artificial intelligence (AI) agent to perform self-learning, self-evaluation based on a rubric and then perform self-correction as needed is described. The methods generate a rubric. The rubric includes the AI agent's performance data and evaluations of the data and related feedback by a separate LLM and a human agent. Once a level of confidence is achieved that the AI agent is performing at a threshold confidence level of the human agent, or that the separate LLM is evaluating the AI agent's performance within a threshold confidence of the human agent's evaluation of the same, the rubric in which the AI agent's performance and evaluation data is inputted is determined to be complete for use in a self-evaluation. The AI agent may then use the rubric to self-evaluate and self-correct its performance without a need for human evaluation.

The invention discloses a psychological interview large model dialogue quality evaluation method and system, a medium and a product, and relates to the field of natural language processing, and the method comprises the steps: simulating and generating multiple types of interviewed objects based on a preset large language model and preset interview object cues; using a preset psychological interview large model to carry out at least one dialogue with the plurality of types of interviewed objects, and recording each dialogue; establishing a dialogue data set according to all recorded dialogues; constructing a dialogue quality evaluation system of the psychological interview large model; according to the dialogue data set, calculating to obtain a score of each type of indexes in the dialogue quality evaluation system; and performing weighted calculation according to the score of each index and a preset weight to obtain a comprehensive score of dialogue quality evaluation of the psychological interview large model. According to the method, comprehensive and objective automatic evaluation on the dialogue quality of the model from multiple dimensions is realized, the result consistency and credibility are ensured, and the problem caused by manual evaluation is solved.

The invention discloses a computer-aided old-age language erosion assessment method and assessment system, and relates to the technical field of old-age health assessment, and the method comprises the following steps: S1, collecting the voice data, text input data and interactive behavior data of an old-age user; s2, preprocessing the data, and extracting voice acoustic features, language structure features and cognitive behavior features; s3, inputting the extracted features into a pre-trained language erosion evaluation model, and outputting a language ability score and an erosion type classification result; and S4, generating a visual evaluation report, wherein the visual evaluation report comprises the language ability degradation degree, key obstacle points and intervention suggestions. According to the method, the score and classification result is automatically output through the multi-modal data acquisition and pre-trained deep learning model, so that the evaluation time is greatly shortened, the subjective deviation is eliminated, the result objectivity is ensured, the problems of low manual evaluation efficiency and high subjectivity are solved, and the effect of automatic evaluation is realized.

The invention discloses a service system configuration method, an electronic device and a storage medium, and relates to the technical field of storage systems, and the method comprises the steps: introducing an input and output tracking tool to automatically monitor the read-write performance data of each level of a service system, replacing a conventional manual collection and analysis process, and improving the efficiency. And the dependence on professional technicians and the time cost are greatly reduced. Meanwhile, through the application of the pre-training configuration model, the performance optimization experience of the business system in different preset scenes is solidified into the model, and the target configuration file can be quickly matched and generated according to the real-time analysis result, so that the hysteresis and subjectivity of manual evaluation are avoided, and the quick output of the optimization scheme is realized. Through continuous monitoring and analysis of full-link performance data, a bottleneck link can be accurately positioned, targeted adjustment is performed on a service system based on a target configuration file, and the performance bottleneck is fundamentally relieved instead of being temporarily avoided.

The present invention discloses a retinal laser photocoagulation surgery quality assessment system, electronic equipment, and computer-readable storage medium, belonging to the field of laser spot detection technology. The present invention establishes an assessment system based on three dimensions: laser spot level, laser spot spacing, and whether blood vessels are damaged. It also utilizes an artificial intelligence algorithm for automatic assessment, thereby resolving the large workload, subjectivity, and instability inherent in manual assessments. The system can accurately and rapidly assess the quality of retinal laser photocoagulation surgery, reducing reliance on manual labor and the influence of subjective factors. This resolves the existing technical issues of "large workload, subjectivity, and instability inherent in manual assessments of retinal laser photocoagulation surgery quality."

The invention discloses an engineering manpower informatization management scheduling method and system based on AI and big data, and the method comprises the steps: inputting the basic information and working state information of engineering personnel through employing a deep learning-based personnel capability evaluation model, precisely evaluating the fitness scores of the personnel for different tasks in a data-driven manner, and improving the working efficiency of the engineering personnel. Subjective and one-sided evaluation of manual evaluation is avoided, a scientific basis is provided for task allocation, and task efficiency and quality are improved; meanwhile, a task scheduling optimization model based on reinforcement learning is adopted, fitness scores, tasks and environment information are input, an optimal allocation strategy is learned through interaction with the environment, intelligent decision making is achieved, the method is more suitable for complex scenes, and allocation rationality and effectiveness are improved; besides, an initial allocation scheme is optimized by combining engineering real-time requirements and resource constraints, re-evaluation and dynamic adjustment are performed, and an optimal allocation state is kept on the premise that conditions are met, so that engineering manpower management scheduling is flexible and efficient, and engineering uncertainty is better dealt with.

This application relates to the field of construction monitoring and management technology, and in particular to a method, device, electronic device, and storage medium for monitoring construction tower cranes. The method includes: acquiring operational data and image data of the construction tower crane; performing three-dimensional mapping of the operational data in an established tower crane coordinate system to obtain the expression parameters of the operational data; calculating the swing data of the suspended object based on the image data; and acquiring Building Information Modeling (BIM) data and construction progress data of the construction site; importing the expression parameters of the operational data, the swing data of the suspended object, the BIM data, and the construction progress data into a three-dimensional simulation platform for visualization, thereby monitoring and managing the operational status and dynamic hazards of the construction tower crane. This solves the problems in related technologies where manual assessment of the tower crane's operational status is required, which can easily lead to low assessment accuracy, difficulty in providing timely and effective risk warnings, and low safety.

The present application provides a professional ability evaluation system based on a skill map, relates to the technical field of human resources and the application field of knowledge maps, and comprises an evaluation main system, which comprises a skill star map generation module, a professional track simulation module, a skill challenge module, a skill certification chain module, a future skill navigation module, a skill coordination ecological module, a skill evolution tree module, a professional story generation module, a skill energy pool module, a professional time capsule module and a control flow management module. The present application can collect user professional experience, learning records and test data in time by constructing a dynamic skill star map and combining 5G network real-time data transmission, and can effectively overcome the problems of strong subjectivity, low efficiency and inability to accurately quantify ability of traditional manual evaluation and online testing by dynamically adjusting node weights through a semantic analysis algorithm and a weighted scoring algorithm.

The invention provides a nursing evaluation method and electronic equipment. According to the method, pressure threshold ranges corresponding to a plurality of body parts are determined through an obtained to-be-matched physiological index group of a nursed person and a target nursing ending posture; and after a to-be-evaluated nursing video and a to-be-evaluated nursing audio corresponding to each target nursing sub-process are obtained, an action score and a language score are determined in combination with the obtained standard action feature vector sequence and the standard keyword set. After multiple pieces of pressure data are obtained, a pressure evaluation result is generated in combination with the target nursing ending posture and the pressure threshold range. And finally, generating a nursing assessment report according to a pressure assessment result, the action score and the language score. According to the scheme, the to-be-evaluated nursing video, the to-be-evaluated nursing audio and the pressure data are processed, so that the action, language and pressure of nursing are evaluated, manual evaluation is not needed, and the evaluation accuracy is improved.

The embodiment of the invention discloses a forward collision test analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: executing an active emergency braking simulation task of a vehicle active emergency braking system according to a preset test scene case and a preset test speed, and collecting test feedback data; determining a test index attribute value corresponding to the at least one test index dimension based on a preset test scene case, a preset test speed, the test feedback data and a pre-trained collision result analysis model; and determining a target test attribute corresponding to the active emergency braking simulation task based on the test index attribute value corresponding to each test index dimension, so that the transformation of the vehicle active emergency braking system test from traditional manual evaluation to intelligent and efficient analysis is realized on the whole. The test efficiency of the vehicle active emergency braking system and the accuracy of the test result are improved.

The application discloses a kind of based on big model's data security intelligent evaluation method, belong to data security and artificial intelligence cross technical field.The method includes the following steps: multi-source data asset acquisition and structured processing, extract metadata and construct data portrait;Classification and grading are carried out to data based on big model, and the classification and grading results are cross-validated in combination with rule engine;Management class data and technical class data are collected, and management dimension risk value and technical dimension risk value are respectively quantified;Weight coefficient is configured, and comprehensive risk value is calculated;Evaluation report is automatically generated and disposal suggestion is output.The method solves the problems of low efficiency, strong subjectivity and incomplete coverage in traditional manual evaluation, realizes the automation, precision and dynamic adaptation of data security evaluation, and provides reliable protection for organizational data asset security.

The invention provides a medical risk prediction system and method and a storage medium, and relates to the technical field of medical risk assessment, the system comprises a data entry device, a medical risk scoring device and a medical risk assessment alarm device; the data input device is used for inputting medical risk accident data; the medical risk scoring device is electrically connected with the medical risk assessment alarm device, and the medical risk scoring device is used for constructing a medical scoring model, assessing the medical risk accident data through the medical scoring model, and scoring an assessment result to obtain a risk score; and the medical risk assessment alarm device is used for carrying out risk prediction according to the risk score to obtain a medical risk prediction result and carrying out alarm prompt. By integrating the data entry device, the medical risk scoring device and the medical risk assessment alarm device, a complete automatic risk prediction process is constructed, tedious operation of traditional manual assessment is avoided, and medical risk assessment efficiency is remarkably improved.

The invention discloses a policy consistency assessment method and system based on a large model and iterative clustering. The method comprises the steps of obtaining multiple policy texts; performing semantic vectorization expression; based on the text vector, generating a candidate policy cluster with a moderate scale by adopting an iteration density clustering algorithm capable of dynamically adjusting a radius; extracting structured elements of the text in each cluster; and inputting the elements into a pre-trained large language model for multi-dimensional consistency reasoning, and generating an evaluation result. According to the scheme, iteration clustering and large model intelligent reasoning are combined, and automation of policy consistency evaluation is achieved. According to the process, firstly, potential conflict texts are focused through semantic vectorization and adaptive clustering, then comparative analysis is carried out by utilizing the deep semantic understanding capability of a large model, and an evaluation report is automatically generated. According to the method, the defects of low efficiency and incomplete coverage of traditional manual evaluation are effectively overcome, and efficient and objective consistency analysis can be carried out on massive multi-source policy documents.

The application discloses a kind of monitoring analysis evaluation methods applied to urban water supply, it is related to water supply evaluation technical field, and the present application establishes environmental factor library and characteristic index library based on historical evaluation record in water supply supervision platform;The operation data of characteristic index in historical evaluation record is collected, and is combined with the numerical value of artificial evaluation result of historical evaluation record, to generate score prediction model;The data of target environmental parameter is collected, and is combined with score difference, to generate score threshold factor model;The abnormal time interval of two adjacent abnormal records is calculated, and is combined with score threshold factor and prediction score, to generate prediction time interval model;Real-time acquisition data in environmental factor library and characteristic index library, calculate real-time prediction score and real-time score threshold factor, generate corresponding evaluation strategy, improve water supply system safety and management efficiency.

The invention relates to the field of highway tunnel engineering, and discloses a highway tunnel engineering construction safety risk intelligent assessment system and method, and the system comprises a data layer which serves as a data warehouse of the system and stores basic index rules, weight calculation rules and work order and index associated data required by assessment. Standardized data and rule support are provided for the whole evaluation; and the data layer is connected with an algorithm layer. Standardized evaluation rules are established through the index dictionary table of the data layer, a unified judgment basis is provided for risk evaluation, and the subjective deviation of'standard determination by experience 'in traditional manual evaluation is avoided; through combination of a dynamic scoring engine and a special risk engine of an algorithm layer, quantitative and hierarchical processing of risk assessment is realized, dependence on artificial experience is thoroughly eliminated, index determination logic consistency under different tunnels and different assessment scenes is ensured, and objectivity and reliability of an assessment result are improved.

The application relates to a tower safety evaluation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring the displacement of each tower leg of a tower in the direction of each coordinate axis of a three-dimensional rectangular coordinate system; processing the displacement of each tower leg by using a trained safety evaluation model to obtain the tower head displacement and the maximum stress value of the tower rod of the tower; the safety evaluation model is trained by using the tower leg sample displacement of each tower leg of the tower; and the safety state corresponding to the tower is determined based on the tower head displacement and the maximum stress value of the tower rod. The method uses the objective data of tower displacement to determine the tower safety, can reduce the influence of human subjective experience, can reduce the time required for evaluation by using the safety evaluation model instead of manual evaluation, can improve the efficiency of tower safety evaluation, can increase the reaction time of supervisors, and can reduce disaster events caused by tower accidents.

The invention discloses a matching evaluation method for putting a target agent to a current investigation road section and a related device. The method comprises the following steps: acquiring road information of a current reconnaissance road section and key parameters of a target agent, determining road characterization parameters based on image information related to point cloud in the road information, and performing parameter matching evaluation on the road characterization parameters of the current reconnaissance road section and the key parameters of the target agent; on the basis of image information related to RGB in the road information, verifying the matching evaluation result; and according to the verification result, evaluating the adaptation degree and the delivery risk of the target agent in the current investigation road section. Therefore, manual evaluation specialty does not need to be relied on, automatic intelligent matching evaluation is realized, evaluation cost and time consumption are reduced, and evaluation accuracy is high.

The invention relates to the technical field of aviation test flight, in particular to a multi-dimensional test flight playback and intelligent evaluation method. The method comprises the steps of firstly obtaining data of a plurality of sensors, completing synchronous acquisition and storage of the data, then integrating the data of the plurality of sensors to obtain a full-dimensional record of a test flight process, then outputting an intelligent scoring result according to the full-dimensional record, and finally performing deep analysis on the intelligent scoring result, outputting a targeted optimization suggestion scheme, and finally, obtaining a target optimization suggestion scheme. In the mode, the full-dimensional recording of the test flight process is realized by combining the multi-sensor data, the actual test flight condition can be reflected more comprehensively and accurately, rich data support is provided for subsequent evaluation and analysis, intelligent scoring is performed by using the AI algorithm, the full-dimensional recording of the test flight process is realized, the subjectivity of manual evaluation is reduced, and the evaluation efficiency is improved. The efficiency and accuracy of evaluation are improved, and the requirements of modern aviation test flight can be met.

The invention relates to the technical field of huge disaster data processing, is applied to the technical field of digital finance, and particularly relates to a huge disaster business processing method and device, computer equipment and a storage medium. According to the invention, the huge disaster risk level of the huge disaster area is predicted through the huge disaster risk prediction model, and the compensation capability level corresponding to the reinsurance company is predicted through the compensation capability prediction model, so that the target company is determined from the reinsurance companies based on the huge disaster risk level and the compensation capability level, and the reinsurance contract is signed with the target company. Compared with manual evaluation for selecting the target company, the risk matching efficiency can be effectively improved.

This invention discloses a method for automated evaluation of large-scale models based on multi-agent collaboration, comprising: First, automatically mining potential evaluation tasks from domain text through a task exploration module and constructing a task knowledge base; Second, using a teacher-student multi-agent structure to semantically expand and optimize task expression, generating multi-level task corpora; Third, introducing a teacher-student-reviewer multi-agent mechanism in the test case generation stage, generating high-quality test cases through iterative questioning and feedback; Finally, performing zero-human evaluation of the model's performance under each task based on an automatic scoring module, and outputting a comprehensive capability report. This invention enables automatic discovery and dynamic expansion of evaluation tasks, and can complete multi-dimensional capability assessment and weakness identification of large language models without human intervention, possessing advantages such as high automation, strong interpretability, and broad evaluation coverage.

The invention relates to the technical field of image processing, and particularly discloses an insurance object value evaluation method and device, computer equipment and a storage medium. Acquiring historical image data of the insurance object and performing feature extraction and fusion to generate a fusion feature vector; processing the fusion feature vector based on a space-time prediction model to obtain prediction state change information; and performing value evaluation based on the predicted state change information to obtain a value evaluation report. According to the method, the historical image data is subjected to feature extraction and fusion, state prediction is performed, and value evaluation is performed according to the prediction result, so that the future value of the insurance object can be accurately judged, automation of the whole process from data acquisition, feature fusion, state prediction to value evaluation is realized, subjective errors of manual evaluation are avoided, and the evaluation efficiency is improved. And the value evaluation accuracy of the insurance object is improved. The method is applied to the field of insurance businesses, the value of the insurance object can be accurately evaluated, and the rationality of insurance premium and insurance amount is further improved.

The invention relates to the technical field of medicine and rehabilitation, and discloses an automatic recognition-based whole body exercise quality evaluation and suggestion generation system, which comprises a data acquisition module, a data processing module, an exercise recognition module, an evaluation analysis module, a suggestion generation module and a user interaction module which are in communication connection in sequence. According to the method, data are collected through a camera, human body key point labeling and association are performed in combination with growth data, motion features are extracted by applying a growth association learning algorithm, a motion quality score is obtained based on a GMS standard evaluation method and growth data analysis, personalized rehabilitation suggestions are generated based on a big data expert knowledge base, and the personalized rehabilitation suggestions are displayed through a graphical interface. The defects that a traditional manual evaluation mode consumes manpower and time and is high in subjectivity are effectively overcome, the efficiency, accuracy and individuation degree of whole-body exercise quality evaluation and suggestion generation are improved, and the urgent requirement for exercise function evaluation in the field of medical treatment and rehabilitation is met.

The embodiment of the application provides a kind of database health degree evaluation method and device, it is related to operation and maintenance technical field, the method comprises: based on the type of the database to be evaluated, corresponding data collection plug-in is selected, to meet the individualized data collection and processing of different types of database in health degree evaluation, applicability is more extensive.Through data collection plug-in, the element data corresponding to multiple evaluation elements of the database to be evaluated is collected.Then based on the obtained element data, multiple evaluation elements are classified, to obtain multiple evaluation element categories;And respectively to the evaluation element in multiple evaluation element categories is discriminated, obtains the evaluation result of multiple evaluation element categories, and then the evaluation result of multiple evaluation element categories is integrated, to determine the target health degree of the database to be evaluated.Standardization, automation, periodicity and self-service of database health degree evaluation are realized, compared with artificial evaluation, the accuracy and efficiency of database health degree evaluation can be effectively improved.

The invention relates to the technical field of multi-source data processing, in particular to a foreign enterprise legal risk assessment method based on multi-source data, which comprises the following steps of: 1, inputting original data such as texts and pictures related to enterprises to be classified; 2, preprocessing the text data and the picture data respectively; text data usually contains a large amount of irrelevant information and noise, such as punctuation marks, HTML marks, stop words, abbreviation, spelling errors and grammar errors. The information and noise can interfere with the learning and performance of the model, so that the model prediction is inaccurate or inefficient; unnecessary information and noise can be removed through data preprocessing, the invention provides a model for intelligently evaluating the legal risk level, an intelligent algorithm is adopted to replace manual auditing, the time consumed by single evaluation is far less than that consumed by a manual method, meanwhile, the method is suitable for parallel calculation, and large-batch evaluation requirements are supported; the evaluation efficiency is greatly higher than that of a manual evaluation method in the prior art.

The invention discloses a data asset value evaluation method and system, a product, equipment and a storage medium, an evaluation method is intelligently recommended through a pre-training classification model, differential analysis is performed on structured and unstructured files to accurately extract key information, then value calculation is automatically completed based on a selected method, and the evaluation efficiency is improved. And finally, a standardized structured evaluation report is generated. A traditional manual evaluation process consuming several weeks is compressed to a minute level, the efficiency bottleneck of large-scale evaluation is solved, artificial experience judgment is replaced by AI driving, the problems of low efficiency, large subjective error and nonstandard report in the existing evaluation process are solved, full-process automation of data asset value evaluation is realized, the evaluation efficiency and accuracy are improved, and the evaluation efficiency and accuracy are improved. Meanwhile, a structured standard evaluation report is output, and reliable support is provided for data asset transaction and decision making.