39 results about "Functional abnormality" patented technology

The invention relates to an intelligent equipment function reliability verification method and device, equipment and a storage medium, and the method comprises the steps: obtaining operation environment parameters and equipment state data of intelligent equipment, and carrying out the construction evaluation of a redundancy verification architecture, and obtaining an initial verification reference; acquiring real-time operation data of the intelligent equipment, and performing function association analysis on the real-time operation data and the initial verification reference to obtain function abnormality features; acquiring state information of associated equipment of the intelligent equipment, performing cross validation on the state information of the associated equipment according to the function abnormality characteristics, and performing fault analysis on the state information of the associated equipment and the function abnormality characteristics to obtain an evolution fault injection scheme; and carrying out abnormal behavior identification on the real-time operation data to obtain an equipment repair requirement, and carrying out feasibility evaluation on the equipment repair requirement based on the function abnormal characteristics to obtain a dynamic repair strategy. According to the invention, a comprehensive guarantee mechanism can be provided for reliable operation of the intelligent equipment.

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 relates to the technical field of health monitoring, in particular to a thyroid dysfunction detection system based on big data, which comprises a hormone data management module, a trend inflection point identification module, a section structure judgment module, a label dynamic updating module and a periodic evolution evaluation module. According to the method, continuous modeling is carried out on hormone concentration dynamic characteristics through multi-time-point data time sequence arrangement in hormone detection records, local extreme points of hormone values are recognized, key fluctuation inflection point positions are defined, an abnormal fluctuation range is recognized, and a reference interval ratio judgment mechanism is introduced to improve the abnormal recognition precision; the dynamic tracking of the function state change process is realized by recording the label evolution trajectory through the continuous state label comparison and updating process, the identification capability of potential function abnormality is enhanced by utilizing the quantitative evaluation of the individual state fluctuation stability, the identification efficiency and the monitoring timeliness of the key intervention individual are improved, and the monitoring accuracy is improved. And the continuity of thyroid function state change identification and the accuracy of intervention target identification are improved.

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.

The invention discloses a multi-index fusion-based biotechnology product risk dynamic identification method and system, and relates to the technical field of biological safety management, and the method comprises the steps: S1, obtaining experimental data and environmental data from each stage of a transgenic crop life cycle, constructing a dynamic data set containing a molecular structure sequence, a function performance index and an ecological impact factor, and carrying out the construction of a dynamic data set; extracting the mutation frequency of the molecular structure sequence along with the time change, the abnormal fluctuation of the function expression index and the dynamic change trend of the ecological influence factor; s2, according to molecular structure sequence mutation frequency and functional performance index abnormal fluctuation, extracting a nucleotide replacement mode and a functional abnormality data set of the gene editing product in a research and development stage and an application stage, and determining a nucleotide replacement mode and a functional abnormality association mode; according to the biotechnology product risk dynamic identification method and system based on multi-index fusion, accurate prediction and dynamic management of ecological risks of transgenic crops are realized, and the environmental release safety is remarkably improved.

The invention discloses a neurology index prediction system and method. The method comprises the following steps: firstly, collecting case data of a neurology patient; processing the collected case data to form test data; meanwhile, constructing a neurology index prediction model; inputting test data into the neurology index prediction model for training; and finally, inputting the case data which does not participate in training into the trained neurology index prediction model, and generating a patient neurology disease prediction result from the case data which does not participate in training through the neurology index prediction model. The prediction comprehensiveness is improved through multi-modal data fusion, the detection sensitivity of early functional abnormalities is remarkably improved, the prediction model is more accurate and stable through high-dimensional nonlinear correlation mining and model generalization optimization, non-invasive low cost and clinical interpretability can be considered, invasive detection or high-cost image equipment is not needed, and the method is suitable for large-scale popularization and application. The method is suitable for large-scale screening of basic medical institutions.

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 invention provides a domain controller exception handling method and system and a storage medium. The method comprises the steps that the working state of an SOC system is monitored based on a preset time period; wherein the working state at least comprises a GPIO state and SPI data; whether SPI communication abnormity exists or not is judged based on the SPI data, and if yes, a function abnormity type is obtained based on a preset time threshold level and the SPI data; otherwise, acquiring a function abnormality type based on the preset time threshold level and the current GPIO state; and executing corresponding exception handling work based on the function exception type. According to the method, the working state of the SOC system in the domain controller of the intelligent driver and the intelligent cabin is detected, and the abnormity is divided into different functional abnormity types based on different time threshold levels when the abnormity exists, so that the targeted abnormity processing is carried out, the requirements of the intelligent cabin and the intelligent driver on fault-tolerant time can be compatible, and the fault-tolerant efficiency of the intelligent driver and the intelligent cabin can be improved. And the safety and robustness of the system are ensured.

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.

An embodiment of the present application provides a method and device for handling functional abnormalities, which relates to the technical field of vehicles. The method includes: obtaining the network connection status between the vehicle and the terminal device; if it is determined that the network connection status switches from the connected state to the abnormal disconnection state, obtaining the vehicle status of the vehicle, where the vehicle status is the driving state or the stopped state; when the vehicle status is the stopped state, outputting a prompt message; the abnormal disconnection state is the disconnection state when the distance between the vehicle and the terminal device is less than or equal to a first preset threshold, and the prompt message is used to prompt that the automatic locking function is abnormal, and the automatic locking function is used to control the vehicle to automatically lock when the distance between the terminal device and the vehicle is greater than or equal to a second preset threshold. Through the above method, the safety of the vehicle can be improved.

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 invention discloses a method for establishing a concurrent cataract mouse model. The mouse model is obtained by specifically knocking out a Phb2 gene from the retina. The method comprises the following steps: constructing Phb2flox / flox; six3-Cre mice have phenotypes of degenerative retinopathy at 6-8 weeks old, retinal atrophy and thinning, structural disorder of each layer and disorder distribution of retinal pigment can be seen in fundus examination, and visual function abnormality of the mice is shown in electrophysiological examination. The 8-10-month-old mouse has lens opacity and shows the phenotype of concurrent cataract. Therefore, the model mouse can better simulate the phenotype of the clinical concurrent cataract, and can be used as an animal model for related research of the concurrent cataract.

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.