672 results about "Early detection" patented technology

The invention discloses a chronic disease early detection method and system based on a multi-modal large model, and relates to the technical field of intelligent medical treatment and artificial intelligence, and the method comprises the steps: obtaining a multi-modal data stream of a target user in a target time window from a pathology database, and generating an original multi-modal data set; performing timestamp unification and numerical value standardization processing on the original multi-modal data set to obtain a time sequence feature sequence; inputting the time sequence feature sequence to the multi-modal large model to obtain an abnormal symptom feature; calculating the similarity between the abnormal symptom features and feature vectors of marked cases in a historical case library, and determining matched cases; a diagnosis result and a development process of the matched case are extracted, a disease risk level and a development trend corresponding to the original multi-modal data set are determined in combination with the medical knowledge graph, and a pathology assessment result is obtained; and generating an early warning signal containing the risk type and the intervention suggestion according to the pathological assessment result. By implementing the application, the accuracy of early detection of chronic diseases can be improved.

The invention discloses a GIS-based urban underground pipe network monitoring and early warning platform, and relates to the technical field of underground pipe network early warning. A data acquisition module is used for acquiring a pressure fluctuation signal in a pipeline, fluid flow data, a pipe wall vibration spectrum and surrounding soil moisture content change data in real time to form a multi-source time sequence data set; the feature extraction module is used for performing wavelet packet decomposition on the pressure signals, extracting high-frequency-band micro pressure pulsation features, processing vibration data by adopting empirical mode decomposition, and separating a normal operation mode and an abnormal disturbance component of a pipeline, so that the early detection capability of micro leakage is remarkably improved, the false alarm rate is reduced, and meanwhile, high-precision positioning is realized; reliable technical guarantee is provided for safe operation of the underground pipe network, and resource waste and safety accidents caused by tiny leakage are effectively avoided.

An exemplary system and method are disclosed that is configured to detect cognitive impairment (e.g., early cognitive impairment) or assess cognitive function by analyzing, via machine learning and artificial intelligence analysis, behavioral metadata collected from a smart app during the course when a subject is using a cognitive test instrument for cognitive tests that incorporate motor activity (e.g., drawing or writing). The machine learning and artificial intelligence analysis can execute features associated with the test taker's metadata (e.g., time spent on task or questions, changing answers, referring back to the previous question), drawing qualities (e.g., line straightness, completeness), among others.

The invention discloses a three-dimensional positron emission tomography data image classification method based on multi-stage progressive feature extraction, and is applied to the technical field of Alzheimer's disease auxiliary diagnosis. The auxiliary diagnosis method comprises the following steps: acquiring and preprocessing PET image data of an Alzheimer's disease patient; improving the reliability of the data set by using data enhancement; performing long-range dynamic modeling on the three-dimensional voxel sequence through a stacked Lmamba block; global context semantic adaptive fusion is realized through a layer-by-layer cross-scale channel attention fusion module (CSACF), and a channel spatial perception module (CSPM) is constructed to optimize spatial feature fusion; an inverted bottleneck module is mixed with long-distance space and position information to enhance the capturing capability of the model on detail features; and finally, predicting the disease category probability through global average pooling, full connection and softmax functions. According to the method, the precision of AD early diagnosis and MCI conversion risk prediction can be greatly improved, the defects of a medical image diagnosis method of a convolutional neural network (CNN) and Transform in long-range dependence on modeling and calculation complexity are overcome, and the method has good application prospects and is suitable for AD early detection and MCI conversion risk assessment.

The invention relates to a system for early detection and risk prediction of schizophrenia. The system comprises a feature extraction module which performs parallel processing on a multi-mode original signal; the fusion prediction module fuses the voice feature signal, the behavior feature signal and the clinical feature signal through a cross-modal attention weighting mechanism to generate a fusion feature vector, and outputs an initial risk prediction signal through a machine learning classifier based on the vector; the dynamic updating module receives an incremental characteristic signal triggered by newly added collected data of a user, generates an updating risk prediction signal through incremental learning, and feeds back the updating risk prediction signal to the fusion prediction module to optimize the weight of a classifier; and the visual output module converts the updated risk prediction signal into a dynamic risk trajectory diagram and an interpretable feature contribution thermodynamic diagram. The system for early detection and risk prediction of schizophrenia can solve the problems that early screening of schizophrenia lags behind and dynamic risk assessment is missing.

The invention discloses a power transformation equipment working condition detection system and method based on self-adaption, and relates to the field of power equipment state monitoring. The method comprises the following steps: collecting multi-source monitoring data of power transformation equipment and carrying out standardization processing to generate standard time sequence data; dividing a time window based on working conditions, extracting characteristic parameters, and constructing a characteristic track; establishing and updating an adaptive reference library containing standard reference points and health tolerance boundaries under different working conditions based on historical health data; comparing the real-time characteristic track with a standard reference point and a health tolerance boundary of a corresponding working condition in a self-adaptive reference library, calculating a deviation degree and generating an operation health index; and determining a dynamic early warning threshold value according to the historical operation health index data, and judging the state of the equipment by comparing the real-time operation health index with the dynamic early warning threshold value and a preset rigid alarm threshold value. And early discovery of weak degradation of the internal coordination relation of the power transformation equipment is realized by constructing a comparison mechanism of a self-adaptive health benchmark and a feature trajectory.

The invention discloses an older child infantile autism evaluation system based on multi-modal data analysis. The older child infantile autism evaluation system comprises a multi-modal signal acquisition module and a multi-modal feature extraction and fusion module. The multi-modal signal acquisition module is used for acquiring physiological signals, behavior signals and scale data; and the multi-modal feature extraction and fusion module is used for extracting and fusing the features of the modal signals, generating unified feature representation, and providing an analysis result including autism evaluation in combination with scale data so as to realize the autism evaluation of the older children. According to the invention, early detection of older children can be realized.

The invention relates to the technical field of medical clinical auxiliary diagnosis, in particular to an orthopedic postoperative complication early detection and early warning system based on data analysis, which comprises a multi-modal sign synchronous acquisition module, a sign dynamic difference quantification module, a complication feature association module, a grading early warning decision module and a dissection calibration visualization module. Wherein the multi-modal sign synchronous acquisition module is used for acquiring postoperative data of a patient; calculating a difference quantization parameter; the complication characteristic association module is used for identifying associated risk characteristics of the osteofascia ventricular syndrome, the deep venous thrombosis and the fat embolism; and the grading early warning decision module is used for generating a three-level early warning instruction based on the associated risk characteristics. According to the system and the method, through a multi-modal sign synchronous acquisition, difference quantification and grading early warning linkage mechanism, accurate recognition, risk grade division and visual intervention guidance of postoperative complications are realized, and the continuity of postoperative monitoring and the timeliness of early warning are improved.

The invention provides a scoliosis screening method and system, and relates to the technical field of medical artificial intelligence, human body image data on a low-light detection garment worn by a patient is collected, the human body image data is preprocessed and sent to an SSD module, the SSD module is used for predicting the position of a low-light light source, and a scoliosis detection result is obtained. The key point detection module is used for detecting human body scoliosis, high-precision detection frame information in the detection result is used as a basis, a human body posture result predicted by the key point detection module is restrained and corrected, then human body scoliosis grading is performed, a human body scoliosis detection result is displayed and fed back to a patient, high-precision detection of scoliosis is achieved, and the system has the advantages of high efficiency, non-invasiveness and light weight; the scoliosis early detection rate and grading intervention precision can be remarkably improved, and the problems that traditional medical diagnosis is low in efficiency, large in radiation risk, insufficient in precision and difficult to operate on embedded equipment with limited resources are solved.

The invention discloses a diabetic foot early-stage detection method combining infrared and visible light imaging, and relates to the technical field of diabetic foot medical imaging diagnos.The diabetic foot early-stage detection method comprises the steps that multi-mode image collection and standardization processing are conducted, infrared images and visible light images are synchronously collected, and standardization processing such as size normalization is conducted; image registration and space alignment are carried out, marking points are set based on foot anatomical features, and feature extraction, mismatching point elimination and transformation matrix calculation are carried out; extracting and screening multi-dimensional features, extracting temperature and structural features, and screening by using a Relief-F algorithm; feature lesion recognition and classification are fused, and lesion probability is output through a double-branch convolutional neural network; carrying out detection result verification and feedback optimization, and comparing a clinical diagnosis optimization model; and generating a detection report and storing data, and generating a report containing the fused image. The early lesion detection precision is improved through multi-modal fusion, individual and environment differences are adjusted and adapted in a personalized mode, and reliable technical support is provided for clinic.

The invention provides a cardiovascular trend prediction method and system based on time sequence medical health data, and relates to the technical field of medical health data analysis. The method comprises the following steps: collecting time sequence medical health data of a patient through a multi-source sensor, wherein the time sequence medical health data comprises dynamic physiological indexes such as heart rate, blood pressure and oxyhemoglobin saturation; performing time calibration and feature extraction on the acquired multi-source data; constructing a time sequence feature model based on the time sequence features, and fusing the time sequence feature model with the static information of the patient to generate a comprehensive feature vector; using a deep learning model to train historical data, and learning a dynamic change rule of cardiovascular health indexes; predicting the change trend of the cardiovascular health indexes in real time, and evaluating the risk level of cardiovascular diseases; the model parameters are optimized through online learning, the prediction precision is improved, the change trend of cardiovascular health indexes can be predicted in real time, and support is provided for early discovery and personalized medical treatment of cardiovascular diseases.

The invention provides a tumor real-time tracking and benign and malignant detection method based on a mammary gland ultrasonic video, and the method comprises the steps: collecting the mammary gland ultrasonic video in real time, and inputting the mammary gland ultrasonic video into an UltraNet basic detector to generate a detection result of each frame of ultrasonic image; calculating a similarity probability matrix Si, j to reflect the similarity between the ith target and the jth target; performing cascade matching on the target based on the similarity probability matrix Si, j to form a target track pipeline Ti; and carrying out confidence coefficient updating and bounding box position updating on the detection result in each target track pipeline Ti, and finally outputting a breast lesion detection result of each frame of ultrasonic image. The tumor real-time tracking and benign and malignant detection method based on the breast ultrasonic video has the advantages of high calculation efficiency, high precision and strong robustness, can assist a doctor in accurately positioning a focus and performing diagnosis, and is beneficial to early discovery of breast diseases.

The method for determining the myocardial fibrosis evaluation model comprises the following steps: acquiring an ultrasonic image of a target rabbit in an echocardiography; performing feature extraction on the ultrasonic image by using a plurality of preset feature extraction models to form a plurality of feature sets; performing representative feature screening on each feature set by using a plurality of preset feature screening models to form a plurality of feature representative sets; performing feature classification on each feature representative set by using various preset classifiers to form a plurality of classification results; and determining a myocardial fibrosis evaluation model according to the classification result corresponding to each feature set. The problem that in the prior art, no auxiliary diagnosis technology for myocardial fibrosis of living organisms through ultrasonic image analysis exists is solved, robust evaluation on the severity of myocardial fibrosis of people is promoted, and early detection and longitudinal monitoring become possible.

The invention discloses a radionuclide labeled molecular probe for detecting collagen structure denaturation and a preparation method and application thereof, and relates to the technical field of biology. The structural general formula of the radionuclide labeled molecular probe disclosed by the invention is as follows: K (chelating agent-nuclide)-K-Linker-CHP, wherein K (chelating agent-nuclide) is a lysine residue labeled by a side chain amino coupling chelating agent and a radionuclide; the Linker is a connection joint; cHP is a collagen hybrid peptide with a polypeptide sequence of (GfO) n or (GPO) n. The probe has excellent definition and signal-to-noise ratio, not only can detect pancreatic ductal adenocarcinoma in a pancreatic cancer model, but also can sensitively detect precancerous lesions (such as pancreatic intraepithelial neoplasia lesions) of the pancreatic ductal adenocarcinoma and pulmonary fibrosis lesions in a bleomycin pulmonary fibrosis model. And the kit has important significance on early detection of pancreatic duct adenocarcinoma and reflection of dynamic change of ECM remodeling.

The invention relates to the field of all-weather safety monitoring in the chemical industry, and discloses an all-weather chemical plant monitoring method, which comprises the following steps: dynamically acquiring quantum state measurement data of environmental parameters through a quantum dot array, capturing parameters such as temperature, pressure and gas concentration in real time by the quantum dot array through a tunneling effect, the sensing principle is based on the displacement effect of the quantum limited energy level; mapping the measurement data to a five-dimensional AdS spatio-temporal manifold and calculating an Einstein tensor; based on the Einstein tensor, solving topology invariant and curvature evolution of the space-time manifold; generating an algebraic decision instruction according to the topology invariant; and the encrypted control signal is fed back to the execution mechanism through the topology photon link. Through dynamic coupling of the quantum dot array and the five-dimensional AdS space-time manifold, cross-scale correlation monitoring of quantum state fluctuation and macroscopic deformation is achieved, the recognition blind area of a traditional method for micro-nano defects is broken through, and the early detection rate of pipeline microcracks is increased by two orders of magnitude.

The invention provides a teenager depression auxiliary diagnosis model training method and system based on multi-modal data, and relates to the technical field of mental health. According to the method, firstly, a VR intelligent interaction scene is constructed, and physiological data of juvenile depression and physiological data of a normal contrast subject in the VR intelligent interaction scene are synchronously collected; data preprocessing and feature extraction are conducted on the electroencephalogram data, the heart rate variability data and the eye movement data, a single-mode depression recognition model is obtained through classifier training, and then a multi-mode depression recognition model and a cross-mode depression recognition model are obtained. The teenager depression auxiliary diagnosis model obtained through machine learning is helpful for early discovery and timely treatment of teenager depression.

The invention relates to the technical field of power equipment fault detection, in particular to a fault detection method for a ring main unit, and aims to solve the problems that in the prior art, the ring main unit of an urban power distribution network and an industrial park faces complex working condition challenges for a long time, and the challenges include severe load fluctuation, electromagnetic interference superposition and multi-node collaborative abnormity. According to the method, the problem of multi-node collaborative anomaly detection under complex working conditions is effectively solved, and the risk of misjudgment caused by electromagnetic interference and load fluctuation is reduced. The position of an abnormal terminal is accurately positioned, the fault cause is judged, and operation and maintenance resource waste caused by blind inspection in a traditional method is avoided. Through the synergistic effect of multi-modal data fusion and topology learning, the early slight anomaly detection rate is remarkably improved, and progressive faults are prevented from developing into equipment damage accidents.

The present disclosure, in some aspects, provides one or more biomarkers (e.g., plasma biomarkers or circulating biomarkers) for Facioscapulohumeral muscular dystrophy (FSHD). In some embodiments, a biomarker (e.g., plasma biomarkers or circulating biomarkers) described herein is regulated by DUX4. Methods (e.g., non-invasive methods) of using the biomarkers, e.g., for detection (e.g., early detection) and monitoring patient as well as treatment efficacy are also provided. The present disclosure, in other aspects, provides one or more biomarkers (e.g., transcriptome biomarkers) for Facioscapulohumeral muscular dystrophy (FSHD). In some embodiments, a biomarker (e.g., transcriptome biomarkers) described herein is regulated by DUX4. Methods of using the biomarkers, e.g., for detection (e.g., early detection) and monitoring patient as well as treatment efficacy are also provided.

The invention relates to the technical field of medical treatment, and discloses a patient cognitive impairment recognition method and system and electronic device.The method comprises the steps that patient original data are obtained, and the patient original data comprise clinical biochemical data, image data and cognitive behavior data; preprocessing the original data of the patient to obtain preprocessed data; feature extraction is conducted on the preprocessed data, key features are obtained, and the key features comprise clinical biochemical features, image features and cognitive behavior features; fusing the clinical biochemical features, the image features and the cognitive behavior features to obtain fused features; and determining a cognitive impairment recognition result based on the fusion features and the cognitive impairment recognition function. According to the embodiment of the invention, the early detection rate of cognitive impairment of the pituitary adenoma patient is improved, missed diagnosis and misdiagnosis are reduced, and the early warning cognitive risk when obvious clinical symptoms do not appear in cognitive impairment recognition of the pituitary adenoma patient is reduced.

The invention relates to the field of medical artificial intelligence, in particular to a laryngeal cancer early intelligent diagnosis system and method based on multi-modal deep learning, and the system comprises a multi-modal data collection module, a cross-modal feature extraction module, a cross-modal attention network module, an expert-level knowledge distillation network module, and a focus evolution prediction and diagnosis decision and visualization module. Endoscope images, acoustic features and clinical data are collected, a system extracts high-dimensional feature vectors, a cross-modal attention network is used for feature fusion, an expert-level knowledge distillation network is combined with pathology and expert experience, neural network learning is guided, a lesion evolution prediction module tracks lesion changes, risk prediction is generated, and finally, the lesion evolution prediction module is used for predicting the lesion change. And the diagnosis decision and visualization module generates a diagnosis result and explanation. The system improves the early detection rate of laryngeal cancer through multi-modal data fusion.

The invention discloses a wearable scoliosis rehabilitation monitoring system, and aims to solve the problems that teenager idiopathic scoliosis screening is insufficient, monitoring is inconvenient, and the conservative treatment effect is difficult to quantify. The system comprises a wearable sensor module, a data processing and transmitting unit, a mobile terminal application program and a rehabilitation assisting module. The sensor module adopts a flexible angle sensor, an acceleration sensor and a gyroscope, collects three-dimensional data of the spine in real time, and calculates a Cobb angle and a trunk rotation angle ATR; the data processing unit optimizes data through Kalman filtering and supports cloud transmission; the application program provides real-time monitoring and personalized rehabilitation guidance; the rehabilitation module corrects the posture through vibration and voice feedback. Noninvasive dynamic monitoring can be achieved without X-rays, the device is light, thin, comfortable, easy and convenient to operate and low in cost, the AIS early discovery rate and treatment compliance are effectively improved, the posture of a patient is improved, spine health is promoted, and the device is suitable for schools, families and other scenes and has remarkable popularization value.

The invention belongs to the technical field of in-vitro diagnosis, and particularly relates to a detection kit and a detection method for a pretumor marker. The kit comprises an elisa plate coated with a captured antibody, an HRP labeled antibody, an antigen standard substance, a washing solution, a developing solution and a stop solution, the capture antibody and the HRP labeled antibody are both prepared from a monoclonal antibody of the anti-SLC31A1 protein. According to the detection method of the kit for detecting the pretumor marker, the SLC31A1 protein content in human serum is detected through the kit, the specificity is high, a large batch of samples can be rapidly detected in a high-throughput mode, the cancer risk of a to-be-detected crowd can be effectively judged, and therefore relevant patients can find and treat the pretumor marker in advance, treatment prognosis is improved, and the death rate is reduced.

The invention belongs to the technical field of biology, and particularly relates to application of hepcidin expression quantity in larimichthys crocea liver as larimichthys crocea visceral white-spot disease resistance evaluation index and a construction method. The hepcidin expression quantity in the larimichthys crocea liver is applied as the larimichthys crocea visceral white-spot disease resistance evaluation index. The construction method of the resistance evaluation index comprises the following steps: taking healthy small yellow croaker intraperitoneal injection pseudomonas proteinus strain bacterial liquid, and determining a half lethal dose in 96 hours; the method comprises the following steps: injecting a half lethal dose of bacterial liquid of pseudomonas proteinus strains into the abdominal cavity of healthy small yellow croaker for 96 hours, and after infection, taking different tissues to carry out hepcidin gene expression fluorescent quantitative PCR analysis; a regression model of gene expression quantity and bacterium loading quantity is established through fluorescence quantification, and the relationship between hepcidin expression quantity and disease resistance is constructed. Accurate disease-resistant phenotype information is provided for development of breeding of visceral white-spot disease-resistant improved varieties of the small yellow croakers, the accuracy of disease-resistant breeding is promoted, early detection of diseases is realized, and prevention and treatment of the diseases are effectively guided.

The invention belongs to the field of medical image analysis and artificial intelligence, and provides a COPD early screening method and application based on a dynamic dependency graph and self-supervised learning for the problems of insufficient sensitivity, insufficient feature representation and the like in the prior art, and the method comprises the steps: S1, preprocessing chest CT image data, and obtaining an enhanced image block; s2, generating an image block sequence of the enhanced image blocks; s3, inputting the image block sequence into a self-supervised learning Transformer encoder, and constructing a total loss function of self-supervised learning; and S4, determining a model for predicting the COPD risk probability, and constructing a loss function with a supervised classification task to optimize the model of the COPD risk probability. And S5, constructing a comprehensive loss function, and integrally optimizing the model for predicting the COPD risk probability. According to the method, multiple advanced technologies are fused, and early detection and risk assessment are carried out on COPD in an automatic, efficient and high-precision mode.

The invention discloses a new energy vehicle battery extremely-early-stage detection and early-warning system based on big data, and the system comprises a multi-source data collection module, a data transmission and preprocessing module, a big data analysis and modeling module, an abnormality diagnosis and early-warning module, a real-time monitoring and feedback module, and a self-adaptive learning and optimization module. The multi-source data acquisition module is connected with a data transmission and preprocessing module, the data transmission and preprocessing module is connected with a big data analysis and modeling module, and the big data analysis and modeling module is connected with an abnormality diagnosis and early warning module, a real-time monitoring and feedback module and an adaptive learning and optimization module. The method has the remarkable advantages in fault detection, diagnosis accuracy, model adaptability and use convenience, and safe and stable operation of the new energy vehicle battery can be effectively guaranteed.

The invention relates to the technical field of energy storage Internet of Things and digital twinning, in particular to a digital twinning model data processing method applied to the energy storage Internet of Things, which performs multi-modal data synchronous acquisition through a three-in-one sensor, optimizes storage management through a dynamic cache allocation mechanism, and improves the reliability of data acquisition through the three-in-one sensor. Micro short circuit and voltage unbalance detection is completed through high-frequency harmonic extraction and difference frequency voltage scanning, the event cross trigger establishes a bidirectional feedback mechanism, and the lightweight decision tree verifier outputs a fault code. According to the digital twinborn model data processing method applied to the energy storage Internet of Things, the early detection sensitivity of micro short circuit and voltage imbalance is high, outdated redundant calculation is avoided, parameter configuration is optimized through a periodic diagnosis report, different conditions are flexibly adapted, and support is provided for an intelligent health management system of energy storage equipment.

The subject matter of the present invention includes a fully automatic, early detection fire suppression method. The fire suppression method implements MEMS technology combined with artificial intelligence (AI) and machine learning (ML). The fire suppression method includes real-time monitoring, fire detection sensors, and diagnostics algorithms. A uniquely integrated application of technologies provides for distinguishing between safe and dangerously destructive fire events and the instantaneous extinguishing of a dangerous fire at its inception.

The invention discloses an early diagnosis marker for B-cell lymphoma, a cross-species screening method based on lamprey and application, and relates to the technical field of molecular diagnosis. In the prior art, tissue biopsy is strong in invasiveness, and a traditional marker is low in early detection rate; in order to solve the problems that a large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large-scale large- When the expression quantity of the gene in a sample is greater than or equal to 1.8 times of that of a normal B cell (HMY2. CIR), the lymphoma is judged to be positive, and the minimally invasive early diagnosis efficiency is remarkably improved.

The invention, which belongs to the technical field of medical image processing and computer vision, discloses a multispectral imaging-assisted post-radiotherapy skin injury early detection system comprising a multispectral data acquisition module, a deep tissue feature extraction module, a three-dimensional lesion segmentation module and a space-time tracking evaluation module. The subcutaneous 2.5 cm depth tissue information is obtained through multispectral imaging at the wave band of 400-1350nm, blood perfusion, melanin concentration, collagen structure and other physiological parameters are inversed based on the radiation transfer theory, a three-dimensional medical image segmentation algorithm is adopted to achieve three-dimensional accurate segmentation of an injury area, a spatio-temporal evolution model is established to predict the injury development trend, and the damage development trend is predicted. The radioactive skin injury can be detected in the subclinical period, the detection time window is advanced by 5.2 days on average, the occurrence rate of severe dermatitis is reduced by 65%, and a basis is provided for clinical timely intervention.

The invention relates to the technical field of patient management, and discloses a patient management method and system based on incubator traceability, and the method comprises the steps of information binding, data storage, expiration reminding and data verification. The system corresponds to the method. According to the application, the scanning device is utilized to realize accurate association of the incubator code and the patient identifier, and in combination with matching verification of the incubator function and the diagnosis and treatment stage, mistakes and omissions of manual binding are effectively avoided; equipment information, patient information and cleaning records are stored through an incubator database, multi-dimensional historical tracing query is supported, and full-life-cycle transparent management of an incubator use track and a cleaning process is achieved; a diagnosis and treatment association verification model is constructed, the accuracy of diagnosis and treatment data of a patient is intelligently verified based on multi-rule cross comparison such as spatial position, time logic and state change, and meanwhile, the early discovery capability of equipment abnormality and data contradiction is improved based on real-time monitoring of environmental parameters and linkage verification of physical sign data.