29 results about "Functional abnormality" patented technology

The invention provides a heart valve regurgitation detection method and system and a medium. The method comprises the steps that regurgitation area identification caused by heart valve structure or function abnormity is obtained; performing principal component analysis on the regurgitation area, and determining the main extension direction of the regurgitation of the corresponding heart valve; determining at least one morphological characteristic parameter of the reflux region according to the main extension direction; and determining the regurgitation state information of the heart valve according to the morphological characteristic parameters. According to the method, the spatial distribution of a reflux region is subjected to characteristic decomposition through principal component analysis, a complex irregular blood flow form is converted into a main extension direction with definite physical significance, and form characteristic parameters are calculated based on the main extension direction; random errors caused by subjective view angle differences or insufficient experience when valve reflux parameters are manually measured can be eliminated, and valve reflux evaluation results obtained by different operators under different detection conditions have high consistency and repeatability.

The invention provides a mood disorder assessment system based on multi-level feature fusion, and the system comprises a data collection unit which is used for collecting electroencephalogram signals of a plurality of brain regions of a to-be-assessed patient; the electroencephalogram feature extraction unit is used for extracting electroencephalogram features corresponding to the electroencephalogram signals of the brain regions; the multi-level feature extraction unit is constructed on the basis of the symptom features of the multiple testees and the corresponding electroencephalogram features, and is used for performing multi-level electroencephalogram feature latent variable extraction on the electroencephalogram features of the brain regions of the patient to be evaluated to obtain multi-level electroencephalogram feature latent variables; and the feature fusion and classification unit is used for carrying out classification prediction based on the electroencephalogram feature latent variables to obtain a mood disorder assessment result of the patient to be assessed. The method solves the problem that a mood disorder assessment system in the prior art adopts a single feature extraction and learning strategy and has no constraint of symptom information, so that the recognition capability of a model for mood disorders of different functional abnormality types is limited.

The invention relates to the technical field of physiological parameter monitoring, in particular to a chronic wound healing real-time monitoring system which comprises a region division module, a cross trend recognition module, a linkage structure recognition module, a risk node positioning module and a response instruction module. According to the method, the mapping relation between the regional temperature extreme value and the acid-base change trend is constructed on the basis of continuous monitoring, accurate description of the dynamic state of the local microenvironment of the wound is achieved, the regional change sensitive section is captured through the cross combination mode of the trend direction pair, time sequence linkage recognition of inflammation development and tissue reaction is promoted, and the accuracy and accuracy of recognition are improved. A spatial correlation model is established according to a trend direction angle relation, functional abnormality blocks with significant trend departure are locked, the recognition granularity and response path classification capability of chronic wound abnormal development and intervention lag states is enhanced, the monitoring precision of the abnormal development stage is improved, and the pertinence of evaluation and the adaptability of intervention measures are recovered.

The invention belongs to the technical field of verification of an electric digital integrated module, and provides a verification method and system for a reusable storage module based on a UVM (Universal Verification Module), and the method comprises the steps: simulating a multi-host concurrent access scene through a UVM component, and generating and synchronously outputting an excitation signal to a to-be-verified storage module and a reference model; acquiring a reference operation result and an actual operation result generated by the to-be-tested storage module according to the excitation signal; according to the design function points of the to-be-verified storage module, the actual operation result and the reference operation result are compared and verified, and a verification result is obtained; if the verification result is that at least one design function point in the actual operation result and the reference operation result is inconsistent, it is judged that the function of the to-be-verified storage module is abnormal in the multi-host concurrent access scene, and key signal waveform data and key log data of the to-be-verified storage module are recorded and reported. According to the scheme, the comprehensiveness and the accuracy of verification are improved, and the function reliability and the verification efficiency of the storage module in a complex concurrent environment are improved.

The invention discloses a lung function evaluation method for a patient in a pneumonia rehabilitation period, and relates to the technical field of rehabilitation management. Extracting a lung function feature set based on the image data; analyzing based on multi-dimensional parameter association, and establishing a dynamic mapping relationship between the lung function feature set and associated physiological parameters; identifying abnormal associated parameters through a dynamic mapping relation between the lung function feature set and the associated physiological parameters, and when the deviation between the image feature recovery degree and the corresponding physiological parameter recovery degree exceeds a preset threshold value, judging that functional abnormal parameters exist; and matching a potential complication database based on the abnormal parameter type, analyzing the relevance between the abnormal parameter and a preset disease feature model, and outputting secondary disease risk early warning. The method has the advantages that through cross-modal fusion of the lung image features and the dynamic physiological parameters, the early recognition precision of the pneumonia convalescence complication is remarkably improved.

The invention relates to the technical field of medical image detection, in particular to a medical image analysis method and system based on image processing, and the method comprises the following steps: collecting a diffusion tensor image, carrying out the filtering preprocessing, normalizing the main diffusion direction, extracting an energy super-threshold voxel, calculating the node distance offset, and carrying out the correlation analysis to generate an atlas. And interpolating and aligning the brain return boundary to obtain a coupling graph, and outputting an abnormal probability distribution graph in a classified manner. According to the method, fiber details are ensured by adopting anisotropic diffusion filtering noise reduction of a diffusion tensor image sequence, three-dimensional normalization and wavelet energy analysis are coordinated, node detection specificity is enhanced by dynamic threshold screening, space-time coupling is quantified by combining displacement Euclidean distance and time sequence relevance, and an individualized coupling model is established by interpolating and fusing brain return boundaries; the template rigid constraint is broken through, the support vector machine recognizes an abnormal mode, structural distortion and functional abnormality cross-scale detection is achieved, and the early lesion image marker recognition efficiency is improved.

Anomaly determination device and anomaly determination system. An anomaly determination device cooperates with a robot to determine whether an elevator is abnormal. The elevator includes an elevator control device that controls the operation of each part of the elevator, an appearance device that is provided in a car or a landing and includes at least one of an input unit and a display unit, and an appearance device control section that controls the operation of the appearance device based on first reception data. The robot has a function of detecting the operation state of the appearance device by a detection section and generating operation state data indicating the detected operation state, and a second function of acquiring first copy data through communication with the appearance device control section. The anomaly determination device includes a data collection section that collects the operation state data and the copy data, and a determination section that determines whether the appearance device is abnormal by judging the matching between the collected operation state data and the first copy data.

The invention provides a method for solving the abnormal condition of a network function NF. The method is performed by a network entity and comprises: receiving NF anomalous behavior / function analysis data from an operation, management and maintenance OAM; and performing an action based on the received NF abnormal behavior / function analysis data. This can improve the efficiency of handling NF abnormal behaviors / functions.

To improve estimation accuracy of functional abnormality.SOLUTION: A medical diagnosis support method includes acquiring a medical image of a subject, acquiring variant information including an estimation result of variants of a plurality of genes of the subject by analyzing the medical image, classifying the plurality of genes into one or more co-occurrence groups based on co-occurrence information indicating co-occurrence of variants between genes, determining variants of one or more genes belonging to each of the co-occurrence groups based on the variant information, and estimating a functional abnormality of the subject based on a determination result of the variants of the genes.SELECTED DRAWING: Figure 1

The application relates to the field of circuit fault simulation and technical training, and discloses an optoelectronic instrument circuit system simulation and training platform, which comprises a basic circuit simulation panel, a micro-environment physical stress simulator, a dynamic parameter adjustment network and a fault evolution and injection control unit. According to a preset fault cause-effect chain script, the fault evolution and injection control unit cooperatively controls the micro-environment physical stress simulator and the dynamic parameter adjustment network, so that a complete main fault cause-effect chain triggered by a physical inducement, continuously drifted by circuit parameters and finally manifested as functional abnormality is reproduced with high fidelity at a physical level. By combining the dynamic evolution of the fault with the interactive response of the diagnosis behavior, a training closed loop from phenomenon observation to operation consequence feedback is constructed, the authenticity and seriousness of the training are significantly enhanced, and the system analysis ability and standard operation consciousness of learners are effectively improved.

The invention discloses a human-derived ampulla cancer cell line DPC-X2 and application thereof, and belongs to the field of microbial animal cell lines. The human-derived ampulla carcinoma cell line is named as the human-derived ampulla carcinoma cell line (homo sapiens) DPC-X2, and is preserved in the China Center for Type Culture Collection on October 30, 2025, and the preservation number is CCTCC NO: C202579. The human-derived ampulla cancer cell line DPC-X2 can be applied to establishment of a cell model for occurrence, development or metastasis of ampulla cancer. The human-derived ampulla cancer cell line DPC-X2 can be applied to cell models for researching differentiation mechanisms, cell morphology and dysfunction and tumor infiltration and metastasis mechanisms of ampulla cancer and guiding clinical comprehensive diagnosis and treatment. The human-derived ampulla cancer cell line DPC-X2 can be applied to research on the occurrence mechanism of the ampulla cancer and screening of drugs for preventing and treating the ampulla cancer. The human-derived ampulla cancer cell line DPC-X2 can be applied to establishment of an ampulla cancer animal model.

The invention relates to the technical field of database testing, in particular to a database testing method, system and equipment based on knowledge distillation and a medium, and the method comprises the following steps: constructing a multi-modal feature extractor, and performing multi-dimensional feature extraction on an input structured query statement and an execution context thereof; training the first network model based on a multi-modal feature sample generated by executing the historical test case in the reference database and execution behavior data corresponding to the multi-modal feature sample to obtain a teacher model; training the second network model according to the output probability distribution of the teacher model and the middle layer features to obtain a student model; and processing the structured query statement query of the to-be-tested database by the multi-modal feature extractor, respectively inputting the processed structured query statement query into the trained teacher model and student model, and judging whether functional abnormality exists in the current query or not according to the difference of output prediction results. The objective of the invention is to realize database function testing with high coverage rate, low manual intervention and interpretability.

The present invention discloses a method and system for dynamically identifying risks of biotechnology products based on multi-indicator fusion, which relates to the field of biosafety management technology, including: S1, obtaining experimental data and environmental data from various stages of the life cycle of genetically modified crops, constructing a dynamic data set including molecular structure sequences, functional performance indicators and ecological impact factors, and extracting the mutation frequency of molecular structure sequences over time, abnormal fluctuations in functional performance indicators and dynamic change trends of ecological impact factors; S2, based on the mutation frequency of molecular structure sequences and abnormal fluctuations in functional performance indicators, extracting nucleotide substitution patterns and functional abnormality data sets of gene editing products in the research and development stage and the application stage, and determining the correlation pattern between nucleotide substitution patterns and functional abnormalities; this method and system for dynamically identifying risks of biotechnology products based on multi-indicator fusion realizes accurate prediction and dynamic management of ecological risks of genetically modified crops, and significantly improves the safety of environmental release.

The application provides a mood disorder evaluation model construction method and equipment based on electroencephalogram and symptom-oriented fusion. The method comprises the following steps: obtaining multiple mood disorder related symptom characteristics of multiple subjects; collecting electroencephalogram characteristics of multiple brain regions of each subject to construct a first training sample set; based on the first training sample set, a multi-level symptom-oriented feature extraction module is constructed, and multiple levels of electroencephalogram characteristic latent variables oriented by symptoms are obtained; based on the electroencephalogram characteristic latent variables of each level, the corresponding mood disorder diagnosis label is labeled, and then the feature fusion module and the classification module are iteratively trained; based on the multi-level symptom-oriented feature extraction module, the feature fusion module and the classification module, a mood disorder evaluation model is obtained. The application solves the problem that in the prior art, a single feature extraction and learning strategy is adopted, and there is no constraint of symptom information, resulting in limited recognition ability of the model for mood disorders of different functional abnormal types.

The invention relates to the technical field of data analysis, in particular to a neural function feature analysis method and system based on multi-modal data, and the method comprises the following steps: obtaining multi-modal neural data, analyzing rhythm, reaction and motion features, calculating coupling and blocking parameters, and normalizing the coupling and blocking parameters into feature vectors to judge abnormality. According to the method, the cerebral cortex nerve potential and the limb movement track are aligned at high precision by constructing a unified time axis coordinate system, the signal phase is analyzed through Hilbert transform, and the nerve path block mapping parameter is calculated in combination with the reaction dispersion so as to quantify the conduction stability; determining a precursor nerve coupling parameter characterization motion preparation state aiming at a phase reverse drift phenomenon in a backtracking precursor potential segment at a motion fluctuation moment, and realizing automatic and accurate classification of neurological function abnormality categories based on comparison of a multi-dimensional feature vector and a standard baseline. The problems that a neural behavior coupling mechanism cannot be quantitatively analyzed, conduction block is difficult to distinguish and defect pain points are difficult to prepare are effectively solved.

The invention relates to the technical field of MCU fault early warning, in particular to an MCU fault early warning method and system based on predictive analysis. The method comprises the following steps: acquiring environmental parameters and operation characteristic parameters of an MCU, constructing a starting state characteristic vector group, and extracting a deviation index of the MCU; a state transition chain behavior graph of the MCU is constructed, a state transition chain consistency residual function is constructed, and a state transition chain consistency residual value is calculated; function response behaviors of the MCU are obtained, function response feature vectors are obtained through arrangement, a state function collaborative matching difference function is constructed, and whether the MCU is in a false operation state with abnormal functions or not is judged; and integrating MCU process faults and outputting fault early warning information. The invention provides an MCU fault early warning method based on starting state behavior modeling and functional response consistency comparison. A false operation state and an abnormal functional fault in the MCU starting process are recognized.

The first aspect of the technical scheme of the invention is to disclose a retina skiascopy optometry diopter prediction method. The second aspect of the technical scheme of the invention is to provide the electronic equipment. The third aspect of the technical scheme of the invention is to provide a computer readable storage medium. Based on traditional skiascopy optometry technology experience, a dynamic skiascopy analysis model system is preliminarily designed and constructed, skiascopy photo streams, patient historical data and biological parameters are fused, and a space-time convolutional network is constructed to extract dynamic features. The diopter is predicted through the light band motion trail, and the regulation function abnormality is diagnosed in an auxiliary mode in combination with the pupil reaction speed. And an AI pre-examination-physician re-examination dual-circulation mode is adopted, and the system automatically marks suspicious frames, so that the diagnosis efficiency is improved. And a transfer learning technology is introduced, and different age groups and pathological characteristics are adapted through a multi-center data iterative optimization model. And forming multiple risk judgments based on the multi-modal index system.

The application discloses a light adjustment method based on real-time neural safety monitoring and related equipment, and belongs to the technical field of data processing. Wherein, the method comprises: in the case of adjusting light to the user, collecting the user's retinal electrical response signal and visual cortex electrical response signal; extracting the electrical response parameter value from the retinal electrical response signal and the visual cortex electrical response signal; calculating the deviation degree value of the electrical response parameter value relative to the preset parameter value; determining the pathological waveform mode information of the functional abnormality according to the deviation degree value; according to the deviation degree value and the pathological waveform mode information, executing the hierarchical response measures, the hierarchical response measures including at least one of the following: outputting prompt information, reducing the output power of the adjustment light, suspending the light output of the adjustment light, and terminating the light output of the adjustment light.

The application discloses a human ampullary carcinoma cell line DPC-X5 and an application thereof, and belongs to the field of microbial animal cell lines. The human ampullary carcinoma cell line is named as a human ampullary carcinoma cell line (homo sapiens) DPC-X5, has been preserved in the China Center for Type Culture Collection on October 30, 2025, and the preservation number is CCTCC NO: C202580. The human ampullary carcinoma cell line DPC-X5 can be applied to the establishment of a cell model of ampullary carcinoma occurrence, development or metastasis. The human ampullary carcinoma cell line DPC-X5 can be applied to the research of a cell model of differentiation mechanism, cell morphology and functional abnormality, tumor infiltration and metastasis mechanism of ampullary carcinoma and guidance of clinical comprehensive diagnosis and treatment. The human ampullary carcinoma cell line DPC-X5 can be applied to the research of an occurrence mechanism of ampullary carcinoma and screening of drugs for preventing and treating ampullary carcinoma. The human ampullary carcinoma cell line DPC-X5 can be applied to the establishment of an animal model of ampullary carcinoma.

Disclosed is a method for assessing atrial fibrillation in a subject, said method comprising the steps of determining the amount of ESM-1 in a sample from the subject, and comparing the amount of ESM-1 to a reference amount, whereby atrial fibrillation is to be assessed. Moreover, a method for diagnosing heart failure and / or at least one structural or functional abnormality of the heart associated with heart failure is disclosed.

The invention discloses a lung function evaluation method and system combining ultrasound and electrical impedance tomography, and relates to the technical field of medical imaging, and the method comprises the steps: decomposing multi-frequency electrical impedance tomography data to obtain ventilation and perfusion impedance signals, and reconstructing the ventilation and perfusion impedance signals into a functional diagram; fusing the functional diagram with the ultrasonic image to generate a structure-function fused image; and calculating a ventilation-perfusion ratio, setting a threshold value according to data of healthy people, and performing classification evaluation. According to the method, the anatomical structure segmentation of the ultrasonic dynamic image is combined with the time sequence feature reconstruction of the electrical impedance tomography functional signal, so that the spatial continuity and the corresponding accuracy of the structural information and the functional information are improved; multi-frequency impedance signals are decomposed, and weighted fusion is realized under an anatomical reference system, so that the expression ability of the system on ventilation and perfusion differences in different areas and the recognition precision of functional abnormalities are enhanced; and high-precision ventilation / perfusion evaluation and classification of lung structure-function integration are realized.

The application discloses a small molecule degrading agent of aryl formamides and application thereof, and has a structure shown in general formula I or a pharmaceutically acceptable salt, an isotopically labeled compound thereof. The compound of the application has a targeted degradation activity on MYC protein, and has a significant proliferation inhibiting activity on various solid tumor cells such as neuroblastoma and rhabdomyosarcoma. The compound of the application can be used for preparing a degrading agent targeting MYC protein, and can also be used for preparing a drug for treating diseases related to MYC gene amplification, fusion, mutation, expression imbalance, conformational change and function abnormality.

The application belongs to the field of data model processing, and discloses a liver disease thyroid function abnormality early warning system based on machine learning; data is collected, and the collected data is marked through prior processing; potential noise data is subjected to self-cleaning processing to obtain verification data; a causal path is pre-constructed according to a false alarm signal scene, a liver-thyroid cross-organ causal network is formed by comprehensively integrating the causal path, and time sequence analysis of the verification data is matched with the causal path to form a generated causal feature vector; a denoising engine is deployed to re-optimize the causal feature vector, a fusion feature vector is obtained, and an edge device is updated through global learning; the contribution of the fused feature vector is quantified, hierarchical early warning is performed through judgment, and liver disease thyroid function abnormality early warning based on machine learning is realized.

The application discloses a diagnostic equipment abnormality positioning method and device, equipment and storage medium, relates to the vehicle diagnosis technical field, and includes the following steps: if a function failure prompt of a first diagnostic equipment is received, a diagnostic head interface on a preset three-pronged line is connected to a diagnostic head of a vehicle to be diagnosed; the preset three-pronged line further includes a first extension line interface and a first socket serving as a diagnostic equipment interface which are connected with the diagnostic head interface respectively; a data transmission link is established by connecting the first extension line interface and a second extension line interface on a preset extension line; the preset extension line further includes a second socket serving as a diagnostic equipment interface; the first diagnostic equipment is connected to the first socket, and a second diagnostic equipment with normal function is connected to the second socket; and function abnormality positioning of the first diagnostic equipment is performed by comparing log files recorded in a data collection operation process. Related workers can accurately locate the specific reason for diagnostic failure, and the problem elimination speed is improved.

The invention provides an electroencephalogram and symptom-oriented fusion mood disorder assessment model construction method and equipment. The method comprises the following steps: acquiring a plurality of mood disorder related symptom characteristics of a plurality of testees; collecting electroencephalogram characteristics of a plurality of brain regions of each testee, and constructing a first training sample set; based on the first training sample set, constructing a multi-level symptom-oriented feature extraction module, and obtaining a plurality of levels of symptom-oriented electroencephalogram feature latent variables; based on the electroencephalogram feature latent variables of each level, marking corresponding mood disorder diagnosis tags and then carrying out iterative training on the feature fusion module and the classification module; and obtaining a mood disorder evaluation model based on a multi-level symptom-oriented feature extraction module, a feature fusion module and a classification module. According to the invention, the problem that the model has limited ability to identify mood disorders of different functional abnormality types due to the adoption of a single feature extraction and learning strategy and no constraint of symptom information in the prior art is solved.

The invention discloses a culture method of a prostate cancer organoid, and relates to the technical field of biomedical engineering, the culture method comprises the steps of cell pretreatment, preparation of a metabolism-mechanics-morphological three-mode coupling probe, construction of an LDH-PSA double-signal self-response microsphere-matrix system, construction of a multi-mode characteristic weight AI decision, execution of a closed loop, and complete culture and passage. According to the method, metabolism-mechanical-form data are synchronously acquired by a common microscope through a three-mode coupling probe, and deviation caused by normal form but abnormal function is avoided; nutrition and rigidity are dynamically adapted by an LDH-PSA double-signal self-response system, so that the organoid passage reaches 8 times, and the genome stability and the heterogeneity retention rate are improved; aI decision-execution closed-loop linkage common equipment is utilized to realize autonomous regulation and control, and artificial passive circulation is broken; meanwhile, PSA-LDH regulates and controls a new dry target spot of the tumor stem cell, the signal amplification effect of the probe is reduced to the detection threshold, materials are easy to obtain and depend on conventional equipment, popularization is facilitated, and the clinical transformation value of organoids is greatly improved.

This invention discloses a rapid rehabilitation control system for shoulder surgery that integrates analgesia pathways. The system first acquires signals in real time through a multimodal physiological sensor network to construct a dynamic functional spectrum of the shoulder reflecting pain and functional status, identifying core areas of functional abnormalities. Then, it controls a targeted drug infusion system, combining a multiphysics coupling model and a model predictive control algorithm to generate a personalized analgesia pathway that matches the patient's physiological response. Next, by analyzing the spatiotemporal correlation between the functional spectrum and the analgesia pathway, a comprehensive rehabilitation confidence score quantifying rehabilitation potential is calculated. Finally, based on the comprehensive rehabilitation confidence score, a safe, effective, and patient-tolerable personalized rehabilitation training instruction set is generated using a multi-objective rehabilitation optimization model, driving the rehabilitation equipment to execute the instructions. This invention has the following advantages and effects: it achieves precise spatiotemporal matching and dynamic synergy between analgesia and rehabilitation training, thereby significantly improving rehabilitation efficiency and quality while ensuring safety.

The application relates to a chamber wall segment motion estimation method based on confidence weight analysis of optical flow tracking, belongs to the technical field of ultrasonic image processing, and aims to solve problems such as the need to rely on subjective judgment of doctors and quality of echocardiographic images for myocardial functional abnormalities. Through optical flow calculation based on confidence weight analysis, motion vector decomposition and ROI region dynamic tracking, the dynamic motion field of each segment is accurately calculated, and finally, the myocardial segment motion time curve is obtained. The optical flow calculation method based on confidence weight analysis corrects the motion amount from top to bottom through a Gaussian pyramid algorithm, combines image correction technology, adds an image "confidence weight matrix", greatly reduces the calculation proportion of the "shadow" place by using the confidence weight coefficient, and thus improves the accuracy of optical flow calculation. Through motion vector decomposition, ROI region dynamic tracking is realized, the motion characteristic information of the myocardial segment is more accurately reflected, and the calculation precision of the myocardial chamber wall motion is improved.

The invention relates to a power distribution terminal line loss function abnormity diagnosis and treatment method based on power distribution master station screening, and belongs to the technical field of power distribution network line loss management. The method comprises three stages of main station screening, field investigation and field reconstruction. Performing trend analysis and abnormal mode identification on the historical electric quantity data through a power distribution master station system, automatically screening out terminals with abnormal line loss functions, and generating a negative list; on-site operation and maintenance personnel perform on-site verification on the functional integrity of the terminal by adopting a power verification method according to the list; and finally, implementing a differentiated transformation strategy according to a troubleshooting result. According to the invention, through combination of the data trend analysis model and the abnormal mode identification mechanism, accurate positioning and intelligent diagnosis of line loss function abnormity are realized, and by means of on-site power verification and differential transformation, the accuracy and the disposal efficiency of line loss function investigation are improved, and the method is suitable for large-scale popularization and application. Therefore, the automation degree and the refinement level of power distribution network line loss management are remarkably improved.