281 results about "Outcome data" patented technology

In some embodiments, a configured artificial intelligence system includes a plurality of intelligence models; a scoring system configured to generate know-your-model scores that quantify suitability for specific tasks of each model; a model execution system configured to provide standardized execution environment for the plurality of intelligence models; a training and reinforcement system configured to monitor outcomes relating to decisions or predictions made by the plurality of intelligence models and use outcome data as feedback to reinforce model performance; and a governance and analysis system configured to ensure model operations comply with governance standards. The intelligence controller may be configured to receive task requests, analyze task complexity, decompose tasks into manageable subtasks, and dynamically select appropriate models from the plurality of intelligence models to execute each subtask based on model suitability and performance characteristics.

The invention relates to the technical field of intelligent decision making, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a decision making method, device, equipment and medium based on multi-modal data, comprising: collecting and cleaning multi-modal original data, executing format conversion and desensitization processing, and generating standardized data; multi-modal features are extracted and fused based on the standardized data, and multi-modal feature vectors are generated; performing reasoning on the multi-modal feature vector through a reasoning model to generate reasoning result data; pushing the reasoning result data to a review terminal, and receiving correction feedback data; and updating parameters of the reasoning model based on the corrected feedback data, and generating an updated reasoning model. According to the method, after data cleaning, format conversion and desensitization processing, multi-modal feature extraction and reasoning model reasoning processing are executed, effective fusion and accurate reasoning of data multi-modal features are achieved, and decision accuracy and data safety are improved.

The invention discloses a multi-label chronic disease risk prediction device based on multi-modality and a graph neural network, and belongs to the technical field of intelligent medical treatment, and the device comprises a data processing unit which is used for obtaining electronic case data and carrying out multi-modality data screening, cleaning and preprocessing, wherein the multi-modal data comprises numerical value type inspection result data and text type inspection result data; the model construction unit is used for constructing a multi-label chronic disease risk prediction model comprising a multi-modal feature learning module, a multi-modal fusion module, a multi-disease correlation extraction module and a prediction module, and the application prediction unit is used for performing multi-label chronic disease risk prediction based on the constructed multi-label chronic disease risk prediction model. In this way, more effective features are mined from multi-source heterogeneous multi-modal data, and the correlation among chronic diseases is considered, so that on one hand, the risk of suffering from various chronic diseases is predicted more comprehensively, accurately and highly interpretably;

Systems and methods implement algorithms for initializing medical devices, including by programming initial medical device settings. Further, systems and methods implement adaptive algorithms for changing medical device settings. Settings initialization algorithms can calculate the settings most likely to be applicable for the patient at-issue using databases of positive clinical outcomes and respective therapy settings for existing users. Settings adaptation algorithm are configured to update medical device therapy settings at one or more intervals including based on databases of positive clinical outcomes and respective therapy settings for existing users.

The invention provides a multi-source data management method based on machine learning and a related device, and the method comprises the steps: obtaining a multi-source data set in an emergency command rescue scene, carrying out the data preprocessing of the multi-source data set, obtaining a standardized data set with a unified data form, extracting a data element information set from the standardized data set, and carrying out the processing of the data element information set. And calling a pre-trained blood relationship modeling model to perform association analysis processing on the data element information set, generating a blood relationship description set among the data entries in the standardized data set, and generating a data blood relationship tracking result based on the blood relationship description set. The data consanguinity tracking result comprises a historical record of the full life cycle of the data and cross-data entry dependency graph information. According to the invention, the method can provide a governance support with a data source capable of being checked, a processing process capable of being traced and a clear dependency relationship for emergency command rescue, enables a rescue decision to be developed based on reliable data consanguinity information, and effectively improves the support capability of data governance for the rescue decision.

The invention discloses a die life prediction and maintenance decision system based on digital twinning. The system comprises a data acquisition module, a data fusion module, a digital twinning body construction and updating module, a residual life prediction module, a maintenance decision and optimization module and a closed-loop execution and feedback module. The system collects working condition data and production parameters of a physical mold in real time, generates a comprehensive health state index after fusion processing, constructs a dynamic digital twin, simulates a future production plan based on the digital twin, predicts the remaining service life of the mold, and combines a production schedule, a resource inventory and a cost model. And generating, issuing and executing an optimal maintenance decision scheme. The system continuously updates and optimizes the digital twin by using the maintained result data through a closed-loop feedback mechanism, so that the health state evaluation, the life prediction and the dynamic optimization of the maintenance strategy of the mold are realized, the mold management level is effectively improved, and the maintenance cost and the production shutdown risk are reduced.

The invention discloses a whole-course accompanying robot for disabled old people and an automatic control system of the whole-course accompanying robot, relates to the technical field of intelligent accompanying, and is used for solving the problems of inaccurate positioning, untimely task response and poor service continuity in the accompanying process of the disabled old people. The multi-modal perception and state analysis module divides the accompanying environment into a plurality of sub-regions based on region characteristics, and accurately perceives the position and state change of the disabled elderly. And the task generation and scheduling execution module intelligently judges service requirements of disabled old people, and dynamically screens optimal robot resources for task matching and execution. The task feedback closed-loop module collects and analyzes task execution process and result data, and timely rechecks the state of the old person according to feedback. And the self-control module adjusts the accompanying strategy and periodically reviews the priority of the task queue. Through cooperative operation of the above modules, the system can achieve precise perception, intelligent decision and dynamic optimization of accompanying of disabled old people, and remarkably improves continuity, intelligence and reliability of accompanying service.

The invention relates to the field of traditional Chinese medicine diagnosis, and discloses a traditional Chinese medicine intelligent diagnosis system based on multi-source data fusion and an AI technology, and the system comprises a tongue picture collection and classification unit which is used for collecting tongue picture data of a patient through imaging equipment, carrying out the feature extraction of the tongue picture data, and obtaining a tongue picture feature data set; performing tongue picture pathological mode classification on the tongue picture feature data set based on a deep convolutional neural network to obtain tongue diagnosis identification result data; and the face diagnosis feature correlation analysis unit is used for inputting the tongue diagnosis identification result data into a multi-modal data fusion engine and carrying out face diagnosis pathological feature correlation analysis based on a graph attention network to obtain face diagnosis identification result data. The deep semantic understanding is realized by introducing a plurality of data acquisition modes such as three-dimensional imaging, thermal imaging, micro-expression recognition, spectral information and pulse condition harmonic analysis and combining advanced models such as a graph neural network, U-Net and Bi-LSTM.

The invention belongs to the technical field of operation and maintenance of edge computing equipment in an industrial scene, and particularly relates to an edge computing terminal information query method based on LLM and MCP protocols and an intelligent agent. According to the method, a free question and answer mode and a timed query mode are supported at the same time, the free question and answer mode meets the temporary and interactive information acquisition requirement of a user, and the timed query mode is suitable for periodic and automatic index acquisition and monitoring, so that two scenes of real-time query and long-term operation and maintenance are considered, semantic analysis and context modeling are performed on a natural language request through LLM, and the query efficiency is improved. According to the method, the multi-target requirements and implicit intentions of the user can be understood, the mood, specialty and context consistency can be adjusted when the natural language answer is generated, the user experience is remarkably improved, bidirectional authentication and communication encryption of the client side and the server side are achieved by combining the MCP with the encryption token, and the safety of the query process and the reliability of result data are ensured.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a strategy generation method, device, equipment and medium based on data analysis. Extracting historical interaction features and generating an association relationship, constructing a knowledge base, receiving current interaction data and extracting current interaction features, performing feature matching through the knowledge base to generate a matching result, and generating a strategy result based on the matching result. According to the method, the features are extracted based on the historical interaction data, the knowledge base is constructed, and the current interaction features are matched with the historical interaction features, so that the accuracy and the dynamic adaptability of the recommendation strategy are effectively improved, and refined strategy recommendation can be realized according to the event result data; and a personalized strategy can be dynamically generated based on real-time interaction data, so that the recommendation effect and the user satisfaction are remarkably improved.

The invention relates to a medical health big data exploration system, and the system comprises a demand understanding module which is used for converting a data exploration demand into a structured exploration task; the compliance review module is used for performing compliance review on the probing task; the code generation module is used for generating a query code according to the probing task and the target database characteristics; the query execution module is used for querying query result data corresponding to the query code from a target database according to the query code; the query result analysis module is used for performing multi-dimensional analysis on the query result data to obtain a multi-dimensional analysis result; a report generation module; and the result presentation module is used for presenting the exploration report in an interactive mode in a user interface and supporting iterative improvement of the report by providing a feedback mechanism, so that the problems of data isomerism, difficulty in quality evaluation, low exploration efficiency, high professional threshold, insufficient privacy protection and the like in the existing medical data exploration process are solved.

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology, medical treatment and health, and discloses a data matching method based on security approval, which comprises the following steps: creating a data table and configuring access authority, inserting data in the table into an acquisition table and then synchronizing to an intermediate database and adding a field identifier; synchronizing the data of the intermediate database to a distributed storage structure partitioned according to dates through a timed task; receiving a data table name, a data type and identification information, determining a matching field based on the data type, matching the data value in the distributed storage with a target field, and generating a result set; and submitting a matching result as a security signature, triggering an approval process, and sending result data to the terminal according to permission mapping after approval is passed. According to the method, the data processing efficiency is improved and the sensitive data circulation risk is reduced by constructing the structured acquisition process, setting the automatic synchronization mechanism, executing data matching based on the field type and integrating the approval control logic.

The invention relates to the technical field of medical information, and discloses a dosage regulation and control data storage method and system based on multi-modal diagnosis and treatment data, and the method comprises the steps: obtaining historical diagnosis and treatment data and historical dosage data of a patient; matching the diagnosis result data with health diagnosis result data in the health diagnosis and treatment data, and matching the wearable device data with health wearable device data in the health diagnosis and treatment data; determining whether to adjust the historical dose data to obtain the current dose, and outputting a judgment result needing to be adjusted; and obtaining the adjusted dose data, and storing the diagnosis result data, the diagnosis result similarity, the wearable device data, the wearable data similarity, the historical dose data and the adjusted dose data into a dose adjustment unit in a patient database. According to the invention, multi-modal diagnosis and treatment data can be integrated and applied to dose regulation and control, and a complete medication decision data chain is constructed.

The invention relates to the technical field of software testing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an automatic verification and report generation method, which comprises the following steps of: obtaining identification value data of a target object, performing structured processing, executing accuracy verification containing multi-dimensional verification items to generate verification result data, and generating a verification result; and constructing a test case set based on the verification result, carrying out an automatic test to generate test result data, and generating an informatization verification result report containing states, reasons and suggestions based on the test result data. According to the method, the standardization degree of data organization is improved through structured processing, potential deviation is found in advance through accuracy verification, the verification coverage range is expanded through test case construction, the verification efficiency is improved through automatic testing, and a feedback closed loop is achieved through result report output; therefore, the accuracy, the efficiency and the intelligent level of the identification value verification process are remarkably improved.

The invention discloses a semi-supervised medical image segmentation method based on data and model collaborative learning. The method comprises the steps of preprocessing medical image data and dividing the medical image data into a training set, a verification set and a test set; constructing a data and model collaborative learning model, and inputting the training set and the verification set into the data and model collaborative learning model for training and verification to obtain an optimal weight; inputting the test set into the data and model collaborative learning model with the optimal weight, and outputting a medical image segmentation result; the data collaborative learning module performs bidirectional copying and pasting on the labeled data and the unlabeled data by using an N-fold local copying and pasting technology so as to generate mixed data; and a model collaborative learning module is constructed to generate reliable pseudo labels for the unlabeled data through pixel-by-pixel competition. The problems that in an existing semi-supervised medical image segmentation method, noise of label-free data interferes with model training, and empirical distribution between the label-free data and the label data is not matched are solved.

The invention discloses a title review material authenticity automatic verification method, system and platform based on deep network search and a large model. The method comprises the following steps: respectively generating and acquiring first data and second data corresponding to a to-be-processed material, and generating corresponding third data; creating a first model corresponding to the natural language, and generating fourth data corresponding to the search engine on the basis of the first model and in combination with the third data; based on the fourth data and in combination with a white list data source, performing search processing to generate fifth data corresponding to the to-be-processed material; constructing an auditing prompt word corresponding to the first data, and generating sixth data corresponding to the to-be-processed material according to the auditing prompt word in combination with a first model; wherein the sixth data are authenticity verification result data and reasoning basis data, and the system and the platform corresponding to the method can solve the problems of low manual auditing efficiency, insufficient retrieval automation degree, poor verification accuracy caused by semantic model closure and the like.

The invention relates to the technical field of data processing, can be applied to the field of science and technology finance, and discloses a credit evaluation method and device for multi-source data co-processing and related equipment. The method comprises the steps of receiving credit evaluation data of at least two types of preset data sources, and performing cleaning, time scale unification and numerical standardization processing on the credit evaluation data to form standardized multi-source feature data; constructing credit portrait data based on the standardized multi-source feature data; performing scoring reasoning on the credit portrait data through a pre-trained credit extension evaluation model, and generating credit extension evaluation result data through a limit mapping rule; receiving the use behavior data and the performance behavior data, performing time sequence analysis and deviation identification, and generating credit use behavior analysis data; and updating credit extension evaluation model parameters based on the credit use behavior analysis data, loading the credit extension evaluation model parameters to a model operation state, outputting credit extension evaluation result data, and recording model parameter updating information. According to the method, the dynamic adaptive capacity of multi-source credit assessment is improved.

The invention discloses a triage method and device based on a multi-modal large language model and electronic equipment, and relates to the technical field of artificial intelligence and internet medical treatment, and the method comprises the steps: obtaining multi-modal data uploaded by a user, and obtaining a data type contained in the multi-modal data; performing data segmentation on the multi-modal data according to the data types to obtain data elements corresponding to the data types; fusing the data elements to obtain fused data elements; extracting a key entity based on the fused data element; matching the key entity with triage department data in a knowledge base to obtain matching result data; responding to a non-unique department corresponding to the matching result data, and matching from the specified department data based on the key entity to obtain target department data; in response to the fact that the matching result data corresponds to the unique department, taking the matching result data as target department data; and performing triage operation on the user according to the target department data. And the accuracy of performing department triage analysis on the multi-modal data uploaded by the user is improved.

A method of displaying an operational parameter of a surgical system is disclosed. The method includes receiving, by a cloud computing system of the surgical system, first usage data, from a first subset of surgical hubs of the surgical system; receiving, by the cloud computing system, second usage data, from a second subset of surgical hubs of the surgical system; analyzing, by the cloud computing system, the first and the second usage data to correlate the first and the second usage data with surgical outcome data; determining, by the cloud computing system, based on the correlation, a recommended medical resource usage configuration; and displaying, on respective displays on the first and the second subset of surgical hubs, indications of the recommended medical resource usage configuration.

Provided is a policy information processing device capable of discriminating factors contributing to the effectiveness of a policy. 【Solution means】 A policy information processing device including a processor for processing information related to a policy, wherein the processor obtains first policy data related to one or more first policies, environmental data related to the environment in which the first policy was executed, person data related to the people present in the environment at the time of execution of the first policy, and evaluation data related to evaluation indicators of the execution result of the first policy, and obtains first policy result data including the same, and generates a first learning model generated based on at least a part of the first policy data and the first policy result data, and based on the second policy data related to the second policy, derives factor data related to factors for improving the evaluation indicators of the execution result of the second policy and contribution degree data related to the contribution degree of the factor data to the evaluation indicators of the second policy, to generate a first learning model, obtains the second policy data, and based on the second policy data and the first learning model, derives the factor data and the contribution degree data of the second policy.

The invention discloses an intelligent debugging method and system for an LC filter, and relates to the technical field of automatic control, and the method comprises the steps: generating an initialization parameter set suitable for a debugging task of the LC filter based on a meta-learning framework; constructing a quantization particle swarm hybrid optimization debugging model; and constructing a parameter change trajectory database by combining the concept of the psychological field and a knowledge distillation technology, performing time sequence modeling on the parameter change trajectory database through an LSTM network, and predicting an optimal initial guess. The LC filter does not need to depend on a large amount of historical initial data at the beginning of regulation and control, the complex program of manual debugging is deleted, the time for carrying out initialization operation on the LC filter is greatly shortened, the difficulty of local solutions in the intelligent debugging process is solved, the convergence speed is high, the debugging time is shortened, and the debugging efficiency is improved. In the debugging process, result data is quickly and efficiently obtained on the basis of combining a small amount of historical data, and the automatic intelligent debugging process of LC can be continuously optimized.

The invention provides a data access and use control method and system for a trusted data space. The method is applied to a data user terminal. A data user connector, a safety control area management module and a result output area management module which are connected pairwise are integrated in the data user terminal; the security control area management module supports deployment of a data processing application program in a security control area; through setting of the security control area, the result output area and the corresponding management module and the corresponding processing flow, the data user application program can directly access the original plaintext data of the specified data, but cannot take the plaintext data out of the security control area; the result data processed by the application program can only be written into the result output area, the data is prevented from being leaked from the channel through a mechanism of firstly forcibly encrypting the result data and then auditing the result data, and the safety problems of data leakage and the like during cross-mechanism, cross-body and cross-area sharing and circulation of the data of the trusted data space are thoroughly solved.

A computer-implemented method for determining a refractive error of an eye of a person includes the following steps: a) generating input data containing a distance between the eye of the person and a visual stimulus displayed to the eye of the person and b) generating outcome data containing a refractive error of the eye of the person determined by evaluating the input data. A position of the entrance pupil of the eye of the person is considered when the refractive error of the eye of the person is determined. A computer program, a field device, a remote device, a determining device, a method for producing a geometrical model of a spectacle lens, and a method for producing a spectacle lens are also disclosed. The present disclosure provides a fast, easy, versatile, reliable, and accurate approach for determining a refractive error of an eye of a person.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a document and attachment version processing method, device, equipment and medium, which comprises the following steps: generating version identifiers of documents and attachments, creating new versions, and respectively storing the new versions into a document historical version set and an attachment historical version set; converting the new version document into an image format and extracting a searchable text layer; based on the searchable text layer, executing recognition operation and generating recognition result data; and executing a process control operation according to the retrieval tag of the attachment, and updating a historical version set of the document and the attachment. Through version control, format conversion and intelligent identification of the automatic processing document and the attachment, the document management efficiency and accuracy are improved, and the risk of manual operation is reduced; by ensuring the real-time updating and compliance of the document and the attachment, the management process is optimized, the management cost is reduced, and the reliability and maintainability of the system are improved.

Sleep apnea prediction using electrocardiograms and machine learning is described. In one or more implementations, a wearable monitoring device produces electrical potential measurements of a heart of a user during an observation period spanning multiple days. A sleep apnea classification of the user is predicted by providing the electrical potential measurements to one or more machine learning models as input. The one or more machine learning models are trained based on historical electrical potential measurements and historical outcome data of a user population to correlate patterns in electrical potential measurements to sleep apnea classifications. The sleep apnea classification may then be output, such as in a health report, via a user interface, as notification on a computing device, and so forth.

The invention discloses a large-model intelligent question setting system and method based on fuzzy mathematics fine tuning, and belongs to the technical field of intelligent question setting. Comprising a fuzzification processing module, a multi-dimensional reward feedback module, a rule-driven attribute reasoning module, an attribute accurate quantification module, a self-adaptive membership degree adjustment module, a strategy optimization and intra-cluster standardization module and a question generation and structured output module. According to objective fields such as post specifications, post levels, technical stack depth and team scales, hard indexes are converted into soft boundary semantics through five membership functions, and then quantitative attributes of surface test questions on difficulty, openness, prejudice and distinction are reasoned by using N rules without subjective wording. And the business result data is used for driving rewards to be updated online, and finally, daily automatic fine tuning is realized on the 32B large model, so that each subjective surface test question generated by the AI not only meets the objective requirements of posts, but also has explainable, auditory and iterable closed-loop capabilities.

To support the monitoring of fraudulent accounting by switching account items.SOLUTION: A cluster generation unit assigns a plurality of pieces of recorded data, which are recorded for each task and include at least an account item and a recorded amount, to a cluster that is a plurality of groups, according to the similarity between pieces of the recorded data. A detection unit detects, for each cluster, rare cost items, which are account items whose amount composition ratio that is the ratio of the recorded amount of each recorded data to the total amount of the recorded amounts of the recorded data for the entire cluster, is below a cost determination ratio threshold. An abnormality determination unit determines, as an abnormality, the recorded amount for the rare cost item that is outside a predetermined amount range. A display control unit then controls the display of the abnormality determination result data, including the total amount determined to be abnormal, the task, and the cluster to which the work belongs, on a display unit. Thus, the monitoring of fraudulent accounting by switching account items can be supported.SELECTED DRAWING: Figure 1

The invention discloses a kalman filtering and sparrow search optimization-based fall detection method for elderly people living alone. The method comprises the steps of S1, obtaining calibrated original motion data; s2, inputting the calibrated original motion data into a Kalman filtering algorithm to obtain attitude feature data; s3, training an action recognition model by using a pre-established labeled reference data set and the attitude feature data, and outputting preliminary action category judgment sequence data; s4, collecting historical motion behavior data, judging sequence data in combination with the preliminary motion category, initializing a sparrow search optimization algorithm, and forming a self-adaptive motion recognition model; and S5, inputting the real-time posture feature data into the self-adaptive action recognition model, generating fall event judgment result data, and when the fall event judgment result data indicates that a fall event occurs, triggering an alarm mechanism. According to the method, the problem that the transition states of suspicious falling and slow falling cannot be recognized clearly in a traditional method is effectively solved.

A system may include a neurostimulator and a processing system configured to provide a stimulation effects map by testing stimulation parameter sets from a plurality of available stimulation parameter sets, acquiring clinical effect data indicative of a patient response to electrical energy delivered to the tissue using at least a first subset of the tested stimulation parameter sets, acquiring sensed data indicative of a sensed response to the electrical energy delivered to the tissue using at least a second subset of the tested stimulation parameter sets, and evaluating parameter sets from the plurality of untested stimulation parameter sets including for each of the evaluated parameter sets, determining an estimated response by estimating at least one of the patient response or the sensed response to the electrical energy using the acquired clinical effect data and the acquired sensed data. A stimulation parameter set may be chosen based on the map.

The invention discloses a medical aid decision-making method and system, a clinical decision-making support instrument and a storage medium. The method comprises the following steps: acquiring medical problem data; performing retrieval enhancement labeling on the medical problem data to obtain problem knowledge fragments; performing semantic fusion analysis on the medical problem data and the problem knowledge fragments according to a fully trained large language model to obtain auxiliary decision data of the medical problem data; wherein the auxiliary decision-making data comprises medical result data and question knowledge fragments; according to the method, professional knowledge fragments related to medical problems are obtained by retrieving enhanced labels, reliable knowledge support is provided for a large language model, illusion or errors possibly generated due to the fact that the large model only depends on training data of the large model are avoided, and medical result data obtained after semantic fusion analysis better conform to medical professional logic; as the auxiliary decision-making data further comprises question knowledge fragments, the output auxiliary decision-making data is associated with the corresponding professional basis, so that the source of the auxiliary decision-making data can be understood conveniently.