18 results about "Functional abnormality" patented technology

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 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.

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.

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 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 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.

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 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.

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.