29 results about "Self evaluation" patented technology

The invention discloses a psychological health assessment system and method based on big data, and relates to the technical field of physical and psychological health assessment, the system comprises a data acquisition module, a data processing module, a psychological mechanism analysis module, an ability quality modeling module, a multi-modal detection module and an assessment result generation module; the technical key points are as follows: in the implementation process of the technical scheme, a data acquisition module synchronously acquires static, dynamic, self-assessment and real-time emotion data of an assessed person in multiple scenes of campus, security and protection and the like; with a campus scene as an example, static information such as homework documents and social platform records of students is collected, dynamic behavior data such as classroom interaction and extracurricular activities are collected through an intelligent camera and voice equipment, and meanwhile, real-time emotion data are obtained by means of a face video stream collection technology to form a multi-dimensional data source; the heterogeneous data are processed into standardized coded data in a unified mode, and the limitation that traditional evaluation depends on a single questionnaire or simple observation is broken through.

The invention discloses a fidelity-based iterative text refining method, system and equipment and a medium, belongs to the technical field of natural language processing and artificial intelligence, and aims to solve the technical problem of how to overcome the limitation of unidirectional generation, realize self-evaluation and iterative optimization of a generated text, and improve the reliability of the text. According to the technical scheme, the method comprises the following steps: collecting a group of structured source information sets; a target text sequence is initialized, the length of the target text sequence is determined according to the estimated target text length, and the target text sequence is completely filled with special [MASK] lexical elements; entering an iterative refining cycle, and specifically, performing de-noising processing on a target text sequence by utilizing a pre-trained gradual de-noising language model to generate an intermediate version text; and evaluating fragments in the intermediate version text through a content fidelity evaluation module.

The invention relates to the technical field of industrial equipment monitoring and defect diagnosis, and particularly discloses a data self-adaptive equipment production process defect identification method and system. By constructing a multi-dimensional delay dynamic reference and based on a joint decision of a delay gradient and a fluctuation entropy, intelligent classification and fast routing of sudden faults, environmental disturbance and composite hidden dangers are realized. According to the system, a three-layer nested diagnosis mechanism of abnormal dimension screening, causal chain tracing and defect mode matching is adopted, a root cause is accurately positioned, and a three-order adaptive response closed loop of instantaneous suppression, parameter compensation and model correction is driven so as to minimize production interruption. Meanwhile, the system periodically calculates the health index of the production line and realizes self-evaluation and collaborative optimization of key parameters, so that a monitoring model can continuously evolve along with the change of the equipment state and the environment, and finally, integration from real-time perception and intelligent diagnosis to adaptive optimization is formed, and the defect identification accuracy, the system stability and the overall operation and maintenance efficiency are remarkably improved.

The invention discloses an intelligent dialogue method and system with self-evaluation and error correction capabilities, and relates to the technical field of artificial intelligence. Comprising the following steps of: 1.1, detecting a question input by a user, and analyzing a historical dialogue record by utilizing a context filling full-time agent; a semantic completion full-time agent is utilized to analyze the user input problem according to the filled context information, the problem is reasonably completed, and a processed user input problem is generated; 1.2, the processed user input question is transmitted into a large model, and a preliminary answer is generated for the user input question; 1.3, evaluating the generated preliminary answer in four aspects, namely, fact accuracy, logic rationality, semantic consistency and intention integrating degree; and step 1.4, outputting a decision. Step 2.1, defect detection is carried out, answered defect types are diagnosed, and corresponding error correction measures are taken according to different defect types; step 2.2, carrying out iterative optimization; and step 2.3, outputting a final qualified answer to the user side, and displaying the answer to the user for the user to check and use.

The invention relates to the technical field of personalized education, and discloses a personalized learning monitoring system based on user behavior data, and the system comprises a data collection module which is used for collecting the learning behavior data and physiological data of a target user, and integrating the interaction log data in a learning platform; the learning target formulating module is used for formulating a personalized learning target system containing short-term and long-term learning; the self-evaluation and feedback module is used for guiding the target user to perform self-evaluation regularly and assisting the target user in knowing the current learning progress; the emotion monitoring module identifies the current emotion fluctuation of the target user; and the dynamic task adjustment module is used for dynamically adjusting the difficulty, the content and the rhythm of the learning task based on the personalized learning target system, the current learning progress and the current emotion fluctuation, and generating a real-time adaptive learning task flow. The state change is automatically identified, the matching task is pushed, and the cognitive burden and the management cost are greatly reduced.

A system and method are disclosed to test signal paths within sensor signal channels by providing an additional signal channel having testing means. The additional signal channel has a test signal generator, a test signal evaluator, and a signal path that is provided in parallel to existing signal paths within the sensor. Signal paths between each sensing element and each output are manipulated by input and output path couplers so that sense signals always reach the correct outputs but the intermediary signal path(s) may be changed according to a self-test protocol. In particular, the signal path(s) may be selected for cyclical testing. The signal path under test is subjected to test signals from the test signal generator which are then evaluated by the test signal evaluator for correctness, and faults signaled. The test signals may be selected to test each signal processing function in the signal path.

An object of a system according to an embodiment is to provide a hint and a way of thinking for a user to derive an answer by himself / herself.SOLUTION: A system according to an embodiment includes a hint provider, a concept presenter, and a self-evaluation promoter. The hint providing unit provides a hint. The concept presenting unit presents a concept. The self-evaluation promotion unit promotes the self-evaluation.SELECTED DRAWING: Figure 1

An object of a system according to an embodiment is to analyze a user's emotion or psychological state and propose personalized counseling or self-care activity.SOLUTION: A system includes a self-evaluation unit, an analysis unit, a proposal unit, and a tracking unit. The self-evaluation unit receives a self-evaluation of the user. The analysis unit analyzes the self-evaluation data received by the self-evaluation unit. The suggestion unit suggests a counseling session or a self-care activity based on the data analyzed by the analysis unit. The tracker tracks the user's progress and adjusts the plan as needed.SELECTED DRAWING: Figure 1

The invention provides a method and system for personal growth and accountability, addressing limitations of traditional therapy approaches. It teaches individuals to recognize cyclical patterns of victimhood, identify triggers, and analyze personal narratives. The method includes an Adult Circle Process for defining boundaries and acknowledging responsibility, and introduces “The Line,” a self-assessment tool for evaluating behaviors on a respect-disrespect scale. A Power Circle component encourages positive growth through recognizing positive triggers and developing empowering narratives. The invention can be implemented via digital platforms, workbooks, or guided sessions, offering concrete tools and measurable outcomes for users to foster accountability and shift focus from problem-solving to strength-building.

The application discloses a depression relapse risk intervention method, device, equipment and medium, and belongs to the medical technical field. The method comprises the following steps: integrating a gene risk score, a neural imaging brain region feature and clinical history data through a multi-modal fusion neural network to generate an individualized baseline risk score; dynamically correcting the baseline score based on self-evaluation data to enhance the real-time performance of risk assessment; analyzing the time sequence characteristics of physiological and behavioral data by using an LSTM time sequence model to output a short-term relapse early warning label; and combining real-time voice / text data emotion knowledge graph analysis and dynamic risk grading to trigger a hierarchical intervention strategy. The application solves the problems of single evaluation dimension and response lag of traditional methods through a multi-dimensional data fusion and dynamic calibration mechanism, realizes closed-loop management from risk warning to precise intervention on the premise of avoiding direct clinical diagnosis, and improves the comprehensiveness and timeliness of depression relapse prevention and control.

Systems and methods are provided for generating, managing, adapting, and delivering speech therapy to a user via a mobile device in an at-home or out-of-clinic setting. Users may be persons experiencing aphasia or other speech conditions. For example, prompts may be communicated to a user, and their spoken responses recorded and analyzed. Based on analysis of the responses, these systems and methods may determine a mode of response including but not limited to playback of the user's spoken response to allow the user to recursively self-assess and / or self-correct. User performance and improvement trends may be assessed, and utilized to adapt future therapy sessions.

The application belongs to the technical field of deep learning and computer vision, and discloses a small sample segmentation method and device based on fine tuning and self-evaluation. The method firstly initializes system parameters, then predicts pseudo labels for query images to obtain query prototypes, updates the query pseudo labels after combining with support prototypes, fine tunes the system parameters by using the pseudo labels, then repeats the process to realize multiple fine tuning of the system, finally determines the best fine tuning round by using the designed self-evaluation method, and obtains the final segmentation result. The application alleviates the problem that the existing system uses categories or support images in biased training, and improves the segmentation accuracy. The designed self-evaluation method can evaluate the optimal fine tuning round, and prevent the error accumulation in the fine tuning process from causing the problem of low accuracy. Compared with the existing method, the fine tuning process only needs about 1% of the time cost and computing power cost, and can more quickly and efficiently adapt to new categories and obtain high-quality segmentation results.

In accordance with one embodiment, a method can be implemented that includes enlisting via a computer network a plurality of participants to report one or more effects of their respective usage of a substance; confirming that a prospective participant is located in a state where use of the substance is legal; receiving from each of the one or more participants via the computer network one or more self-assessment reports of any physical, emotional, or mental responses or any combination thereof to the substance; and processing the one or more self-assessment reports with a computer processor to determine if the substance has an effect on a particular symptom or condition.

A system is provided.SOLUTION: The system includes a means for collecting data such as an operation history of a learner, a test result, and self-evaluation, a means for analyzing the collected data and identifying a thinking pattern and a cognitive style of the learner, a means for automatically generating individualized educational contents based on the analysis result, a means for delivering the automatically generated educational contents to the learner, and a means for dynamically adjusting an educational program according to the progress of the learner.SELECTED DRAWING: Figure 1

We provide a device that provides an integrated system that includes quantitative multiple-choice evaluation by category, combined self- and peer-evaluation, visualization of score trends, and action list presentation. [Solution] The organization comprises a display control means for displaying on a display unit, for each member of a plurality of categories, a screen for each member including one or more questions for each category and multiple choice answers to the questions; an input accepting means for inputting multiple choice answers by each member to each question; and an evaluation means for evaluating each member based on one or more questions for each of the plurality of categories and one or more answers corresponding to each of the one or more questions, wherein the categories include at least categories related to each member's skills, categories related to each member's condition, and categories related to engagement, and the display control means displays on the display unit a screen for each member including the results of the evaluation.

The application relates to the technical field of industrial equipment monitoring and defect diagnosis, and particularly discloses a data-adaptive equipment production process defect identification method and system. A multi-dimensional delay dynamic benchmark is constructed, and a joint decision based on a delay gradient and fluctuation entropy is adopted to realize intelligent classification and rapid routing of sudden failures, environmental disturbances and composite hidden dangers. The system adopts a three-layer nested diagnosis mechanism of abnormal dimension screening, cause-effect chain tracing and defect mode matching to accurately locate the root cause and drive a three-order adaptive response closed loop of instantaneous inhibition, parameter compensation and model correction to minimize production interruption. Meanwhile, the system periodically calculates a production line health index to realize self-evaluation and collaborative optimization of key parameters, so that the monitoring model can continuously evolve with changes in the equipment state and the environment, and finally form an integration of real-time sensing, intelligent diagnosis and adaptive optimization, thereby significantly improving the defect identification accuracy, system stability and overall operation efficiency.

The invention discloses a three-dimensional scene understanding and generating method and system combined with auto-reflection learning, and the method comprises the steps: obtaining a reference image, a task instruction and a visual angle transformation parameter, carrying out the geometric-semantic feature coding of the reference image, and obtaining a visual representation; outputting a task result according to the task instruction, the visual angle transformation parameter and the visual representation in combination with the three-dimensional scene or language-visual features; the task result is input to the auto-reflection space stage, and self-evaluation information is obtained; obtaining a consistency reward signal based on the self-evaluation information in combination with the objective evaluation index; based on the task result, the self-evaluation information and the consistency reward signal, an objective function is obtained, the objective function is used for optimizing the three-dimensional generation model, and an optimal three-dimensional generation model is obtained and used for obtaining the optimal three-dimensional generation model under the condition that a reference image and view angle transformation parameters are given. And new view angle synthesis, point cloud reconstruction and spatial relation question answering based on language-visual features of a three-dimensional scene are realized.

Systems and methods are provided for generating, managing, adapting, and delivering speech therapy to a user via a mobile device in an at-home or out-of-clinic setting. Users may be persons experiencing aphasia or other speech conditions. For example, prompts may be communicated to a user, and their spoken responses recorded and analyzed. Based on analysis of the responses, these systems and methods may determine a mode of response including but not limited to playback of the user's spoken response to allow the user to recursively self-assess and / or self-correct. User performance and improvement trends may be assessed, and utilized to adapt future therapy sessions.

The embodiment of the invention provides an image editing method, a virtual fitting method, a model training method and equipment. In the method, the image editing model can be called to edit the initial image to obtain the intermediate image, and whether the editing effect of the intermediate image is matched with the image editing instruction can be automatically determined, so that self-evaluation is realized; and when the editing effect of the intermediate image is not matched with the image editing instruction, the image correction instruction can be generated to correct the intermediate image until the target image conforming to the image editing instruction is obtained, so that self-correction is realized. The image editing model forms a closed loop of'editing-evaluating-correcting ', initial editing errors of the image editing model can be actively recognized and corrected, the image editing effect is improved, and therefore the accuracy of generating the target image through the image editing model is improved.

Implementations relate to using instruction decomposition, self-evaluation, and optional progressive perfection to improve instruction compliance of large language models (LLMs). A processor of the system may obtain a natural language (NL)-based input, generate a plurality of candidate responses using the LLM and evaluate the candidate responses based on instructions included in the NL-based input, and progressively perfect the candidate responses until it is determined that one or more termination criteria are met. In some implementations, NL-based input may be received from a client device. In these implementations, a progressively perfected given candidate response may be rendered for presentation at a client device, and the given candidate response is responsive to an NL-based input. In additional or alternative implementations, NL-based inputs may be obtained from a database. In these implementations, a given candidate response that is progressively perfected may be utilized in fine tuning of the LLM.

A system and method for using retrieval-augmented generation (RAG), in a large language model (LLM) having additional content to enhance relevance of LLM generated content. In operation, a user asks a question and the LLM determines whether additional content needs to be retrieved from a data management system to answer the question. If so, the LLM is provided with the user question and said additional content and asked to score its ability to confidently answer the question with the provided content. If the score is below a predefined threshold, the LLM is instructed to generate an alternate response using the retrieved additional content. Additionally, the system generates follow-up suggestions to assist the user in gaining more in-depth knowledge on the subject at hand. If the score is below a predefined threshold for a selected suggestion, the system will record this to assist to fill gaps in available content.

The application discloses a fidelity-based iterative text refining method, system, device and medium, and belongs to the technical field of natural language processing and artificial intelligence, and aims to solve the technical problem of how to overcome the limitation of one-way generation, realize self-evaluation and iterative optimization of generated text, and thus ensure that the final output is highly faithful, concise and coherent, and the technical scheme is that: a set of structured source information is collected; a target text sequence is initialized, the length of the target text sequence is determined according to the estimated target text length, and the target text sequence is filled with special [MASK] word elements; an iterative refining cycle is entered, and each iteration generation process is specifically as follows: a pre-trained step-by-step denoising language model is used to perform denoising processing on the target text sequence to generate an intermediate version text; and a content fidelity evaluation module is used to evaluate the segments in the intermediate version text.

The invention discloses an intelligent prediction and regulation and control method for lamination parameters of a multilayer board, and the method comprises the steps: standardized collection and synchronization of multi-source heterogeneous process data, space-time-structure-process combined modeling of a multi-level high-order tensor model, and local / global feature factor disassembly of a multi-scale tensor decomposition algorithm. And inputting the weighted fusion feature vector subjected to adaptive weight fusion into a physical constraint neural network model to generate a press fit parameter prediction result, executing dynamic monitoring of prediction precision according to the press fit parameter prediction result and real-time quality feedback information, and triggering model self-evaluation and adaptive learning to obtain a press fit parameter prediction result. According to the method, interpretability of lamination parameter prediction and process safety guarantee are achieved, and dynamic closed-loop optimization of the whole lamination process is promoted.

The application relates to the technical field of personalized education, and discloses a personalized learning monitoring system based on user behavior data, which comprises a data collection module, a learning target setting module, a self-evaluation and feedback module and an emotion monitoring module.The data collection module is used for collecting learning behavior data and physiological data of a target user and integrating interactive log data in a learning platform.The learning target setting module sets a personalized learning target system comprising short-term and long-term targets.The self-evaluation and feedback module guides the target user to regularly perform self-evaluation and helps the target user to understand current learning progress.The emotion monitoring module identifies current emotional fluctuations of the target user.A dynamic task adjustment module dynamically adjusts the difficulty, content and rhythm of learning tasks based on the personalized learning target system, current learning progress and current emotional fluctuations, and generates real-time adaptive learning task flows.The application automatically identifies state changes, pushes matching tasks, and greatly reduces cognitive burden and management cost.

An object of the system according to the embodiment is to provide learning content customized in accordance with interests and learning styles of children.SOLUTION: A system according to an embodiment includes an interaction unit, a content generation unit, a gamification unit, an evaluation unit, and a mobile application providing unit. The dialogue unit dialogues with the children and grasps their interests and learning styles. The content generation unit generates learning content customized based on the information grasped by the dialogue unit. The gamification unit introduces gamification elements into the learning content. The evaluator provides self-evaluation and feedback on the learning content. The mobile App providing unit provides learning content through the mobile App.SELECTED DRAWING: Figure 1

The embodiment of the invention discloses a large language model routing method, electronic equipment and a storage medium, the large language model routing method comprises the following steps: inputting a request into a cascade routing system, the cascade routing system is constructed by cascading a plurality of large language models which are ranked from small to large according to the cost, and the large language models are connected with the cascade routing system; each large language model is subjected to self-evaluation ability training; when a request is routed to a certain large-scale language model, the certain large-scale language model can evaluate the request and evaluate whether the large-scale language model itself has the capability of solving the request, if the large-scale language model itself has the capability of solving the request, the large-scale language model answers the request, otherwise, the large-scale language model rejects the request and routes the request to the next large-scale language model; and obtaining a processing result of the cascade routing system on the request.

The invention provides a valuation table processing method and an RPA robot, which can be used in the financial field or other fields, and the method comprises the following steps: receiving a valuation table processing request; if the processing mode in the valuation table processing request is valuation table reconciliation, reconciliation data are obtained according to the valuation table processing request, and the reconciliation data comprise a to-be-processed party valuation table, a to-be-processed local party valuation table, a local party subject name and to-be-compared elements; according to the reconciliation data, reconciliation is carried out on the to-be-processed party valuation table and the to-be-processed local valuation table, and valuation table processing corresponding to the valuation table processing request is completed. According to the invention, the automation degree and accuracy of valuation table processing can be improved.

This invention provides an intelligent operation and maintenance fault diagnosis method based on problem reconstruction and consensus reasoning. The method includes: constructing a fault description rewriting model based on Direct Preference Optimization (DPO) by leveraging the generation capabilities and self-evaluation feedback of a large language model; reconstructing the original fault description in diverse ways based on the fault description rewriting model, and filtering non-standard fault descriptions into a set of fault query statements through a rejection sampling mechanism based on perplexity level (PPL); performing multi-perspective hybrid consensus diagnostic decision-making based on the real-time collected fault descriptions and fault query statement set, and outputting intelligent operation and maintenance fault diagnosis results corresponding to the real-time collected fault descriptions, including root cause localization and remediation suggestions. This invention uses the PPL rejection sampling mechanism to filter high-quality candidate rewrites that conform to operation and maintenance specifications, and utilizes a hybrid majority voting strategy to eliminate comprehension biases from a single perspective, achieving highly robust automated fault localization and diagnosis.

Systems and methods include improved planning and reasoning capabilities of a web agent, which interacts with a real world website. The agent improves with autonomous experience and requires limited supervision. An MCTS-based search routine is deployed over web pages, a base model is used as an initial distribution, and a number of rationales and possible web actions are explored. AI feedback and self-criticism are used, and the model provides self-evaluation at each node.