128 results about "Imaging brain" patented technology

The invention discloses a GNN-based multi-mode brain imaging infantile autism spectrum disorder detection method, which is characterized in that a function connection network which is different from traditional characteristics and takes an edge as a center is introduced, higher-order information interaction in a brain interval is mined, and a brain function abnormity mode of an infantile autism patient is accurately captured. Meanwhile, integrating brain imaging data and clinical text information into a unified multi-modal graph by utilizing the graph structure learning ability of GNN, capturing a complex nonlinear relationship, and realizing deep combination of multi-modal features; according to the method, the accuracy and robustness of autism detection are effectively improved, technical support is provided for personalized diagnosis and treatment, key technical problems in multi-modal data fusion and brain network analysis are solved, and the accuracy and reliability of ASD early diagnosis are improved.

Disclosed herein are systems and methods for training an AI language model for assessing and improving happiness and wellbeing of a human user, the method comprising receiving a pre-trained AI language model; receiving a first training data set comprising non-user-specific training data comprising brain imaging data; applying one or more supervised training protocols based on the first training data set to modify the pre-trained AI language model to generate a non-user-specific language model configured for happiness and wellbeing support of human users; receiving a second training data set comprising user-specific training data comprising brain-imaging data for a specific user; and applying one or more supervised training protocols based on the second training data set to modify the non-user-specific language model to generate a user-specific language model configured for happiness and wellbeing support of the specific user.

The invention discloses a white matter fiber bundle structure-function connection coupling method based on a distance function. The method comprises the steps that brain imaging data are collected and preprocessed; extracting white matter fiber bundle structure attributes and function signals of the preprocessed brain imaging data; constructing a structure connection matrix and an individualized function connection matrix according to the white matter fiber bundle structure attributes and the function signals; measuring the connection weight similarity between corresponding columns of the structure connection matrix and the function connection matrix through a distance function, and carrying out white matter fiber bundle structure-function connection coupling according to the connection weight similarity; through the method, the relationship of the white matter fiber bundle structure and function of the brain can be quantified, and the method is suitable for brain structure and function analysis based on the white matter fiber bundle and research on brain development and mental diseases.

The invention belongs to the field of autism spectrum disorder recognition, and particularly discloses an autism spectrum disorder recognition method based on a multi-mode graph convolutional network, and the method comprises the steps: collecting patient data, preprocessing the data, and constructing the graph convolutional network. The MPPCA method based on the threshold value is designed, prior information related to noise variance and the PCA denoising threshold value are combined, signals with dominant noise can be better distinguished, the function of the prior information related to the noise variance and the like is fully played, and a better data basis is provided for follow-up phenotype prediction; according to the method, a multi-modal graph convolutional network is used, phenotypes are predicted according to brain imaging data, behavior data and gene data of a patient, advantage complementation among different data types is utilized, and specific parameterization design is performed on the weight of a filter, so that the recognition accuracy of the autism spectrum disorder is improved.

The invention discloses a low-resource and low-energy-consumption remote sensing image brain-like computing system based on an FPGA (Field Programmable Gate Array), which belongs to the cross technical field of brain-like computing and remote sensing information processing, and comprises a data interaction module which is used for high-speed data transmission between a PC (Personal Computer) end and the FPGA and comprises a PCIe (Peripheral Component Interconnect Express) interface sub-module and a data transmission control sub-module; the storage control module is used for data interaction control of the off-chip DDR and the on-chip BRAM and providing efficient data supply for the processing engine; the processing engine module is used for simulating a brain-like pulse neural network to complete convolution calculation of the remote sensing image; comprising a convolution processing sub-module, a data storage sub-module, an input buffer sub-module and an output buffer sub-module. According to the low-resource and low-energy-consumption remote sensing image brain-like computing system based on the FPGA, the brain-like pulse neural network is adopted, the BRAM is allocated to store the intermediate computing result, and the computing unit is designed in combination with SNN characteristics, so that energy consumption and resource waste are effectively reduced, and computing efficiency is improved.

The invention discloses a cognitive impairment screening method and device based on a VR eye tracker and near-infrared brain imaging. The device comprises a virtual reality scene generation module, an eye tracker data acquisition module, a near-infrared brain imaging module, a data evaluation processing module and a cognitive impairment screening module. The eye tracker data acquisition module is used for acquiring an eye movement data information set of a user in a virtual reality scene in real time; the eye movement data information set comprises a fixation point information sequence, a pupil area information sequence and an eye hopping frequency information sequence; the near-infrared brain imaging module is used for acquiring a blood oxygen information set of cerebral cortex when a user completes a cognitive task; the blood oxygen information set of the cerebral cortex comprises a brain blood oxygen information sequence; the data evaluation processing module is used for carrying out evaluation processing on the collected eye movement data information set and the blood oxygen information set of cerebral cortex to obtain cognitive disorder information of the user; the cognitive impairment information comprises information about whether cognitive impairment exists or not and the degree of the cognitive impairment.

The invention provides a multi-mode nuclear magnetic resonance image glioma segmentation method and application thereof, and belongs to the field of medical image processing. The invention provides an M2ES-UNet network aiming at the problems of insufficient utilization of spatial features, single multi-modal fusion mechanism and cross-level semantic loss of an existing segmentation method. According to the method, multi-view anatomical information is extracted through a multi-plane feature collaboration module; utilizing an orthogonal dimension fusion convolution and modal introspection-collaboration module to respectively realize differential fusion of shallow and deep features; and the progressive jump transmission of the features is realized through a coding information smooth transmission module. According to the method, multi-plane and multi-mode complementary information can be effectively mined, the boundary precision and robustness of brain glioma segmentation are remarkably improved, and clinical diagnosis is assisted.

The present invention describes an artificial intelligence (AI) enabled electroencephalography (EEG) system that integrates Pathway Hierarchical Adaptive Referencing (PHAR) for localized signal detection, large language models (LLMs) for automated EEG reporting, and Internet of Medical Things (IoMT) connectivity for adaptive neuromodulation control. The system can also deliver transcranial electrical stimulation (tES) pulses and function as an electrical impedance tomography (EIT) system. PHAR employs a multi-layered multiplexer hierarchy and adaptive referencing topologies to optimize EEG signal acquisition and spatial resolution. LLM integration enables automated generation of human-readable EEG reports. IoMT connectivity allows closed-loop neuromodulation, where real-time EEG analysis guides the adjustment of stimulation parameters. The system can deliver tES pulses and perform EIT expands its functionality, allowing for targeted neuromodulation and impedance-based brain imaging. This integrated system revolutionizes EEG-based diagnostics, treatment, and research in neurology and neuroscience, offering a comprehensive and versatile tool for understanding and modulating brain function.

The invention discloses an obsessive-compulsive disorder diagnosis system based on brain multi-modal indexes, and belongs to the field of mental diseases. The problem of lack of a cross-modal feature fusion mechanism is solved. The system comprises an electroencephalogram signal acquisition unit used for acquiring an EEG signal of a testee under a preset stimulation normal form and executing preprocessing operation; the brain imaging data acquisition unit is used for synchronously acquiring brain structure imaging data and brain function imaging data of the testee; the multi-modal data fusion unit is used for extracting frequency band power spectrum density characteristics and event-related potential amplitude or incubation period characteristics from the EEG signals; performing standardization processing on the EEG features, the sMRI structural features and the fMRI functional features; integrating modal features by adopting a weighted average fusion algorithm; screening fused feature subsets through a recursive feature elimination method; and the diagnosis model unit is used for inputting the fusion feature vector into a trained SVM classification model and outputting an obsessive-compulsive disorder diagnosis result. Used in the medical field.

Diagnosis of dementia. A method for diagnosing a dementia subtype is described. The method includes acquiring brain imaging data relating to one or more patients, analyzing the data using a deep learning model, and classifying the one or more patients into a plurality of categories using the deep learning model, including a first category of patients having a first subtype of dementia and a second category of patients having a second subtype of dementia. The deep learning model has been trained using brain imaging data from a patient, the brain imaging data includes a first set of images showing evidence of temporal-apical hypometabolism and a second set of images showing evidence of hypometabolism in the brain in place of or in addition to temporal-and apical regions, where the first set of images is marked as associated with a first subtype of dementia, and where the second set of images is marked as associated with a second subtype of dementia. And the second set of images are marked as being related to a second subtype of dementia. Related systems and methods are also described.

The invention discloses a brain feature extraction method, a brain feature extraction system, brain feature extraction equipment and a medium for an MRI (Magnetic Resonance Imaging) image, and relates to the technical field of intelligent biomedical signal processing. Deep features of a plurality of MRI sequence images are fully excavated, and the extracted imaging features related to brain lymphatic system functions and brain structure morphologies include diffusion degree values when bilateral brain tissues in the brain of a patient are converted into fiber bundles and fiber bundle reference values causing fiber bundle deviation when the brain of the patient discharges. The reference value of the fiber bundle can reflect which parameters are diffusion parameters when bilateral brain tissues are abnormal, and the diffusion degree value and the diffusion parameters reflected by the imaging characteristics are closely related to the epilepsy language barrier function of the patient and can be used as standards for predicting the language barrier of the epilepsy patient.

The invention discloses an fNIRS functional optical brain imaging signal quality evaluation method and device, a storage medium and electronic equipment. The method comprises the steps that incident light intensity data and emergent light intensity data emitted by a light source are acquired; determining a variable coefficient based on the incident light intensity data, and determining a signal-to-noise ratio based on the incident light intensity data and the emergent light intensity data; processing the incident light intensity data to obtain the number of peak values in a preset frequency range and heartbeat signal characteristics; and determining a quality evaluation result of the fNIRS signal based on the variable coefficient, the signal-to-noise ratio, the number of peak values and the heartbeat signal characteristics. According to the evaluation method, the quality of the near-infrared signal can be measured, and the evaluation method can adapt to physiological features of different individuals so as to guide real-time signal quality detection and hardware iteration design.

The invention provides a fetal magnetic resonance image brain region segmentation method and system, computer equipment and a medium, and belongs to the technical field of automatic segmentation of fetal brain magnetic resonance imaging. The method comprises the steps that firstly, 2D low-resolution fetal magnetic resonance images collected in multiple directions are processed, and 3D high-resolution fetal brain images are generated; secondly, performing selective interlayer labeling on the 3D image, and generating a complete segmentation label through a contour interpolation algorithm; and finally, performing mutual supervision learning by using a double-independent initialized segmentation network, including the steps of data enhancement consistency constraint, cross pseudo label supervision, feature comparison learning and the like. According to the fetal magnetic resonance image brain region segmentation method and system, the computer equipment and the medium, a high-performance divider can be trained only through a very few labels, the labeling cost is remarkably reduced, the segmentation efficiency is improved, the method and system are suitable for actual clinical fetal brain magnetic resonance research, and efficient technical support is provided for fetal brain development evaluation.

The invention relates to a brain-computer interface interaction system and method for inputting Chinese characters based on handwriting imagination, and belongs to the technical field of brain-computer interfaces. The system comprises a user interaction module which is used for recording and storing information such as names, genders and ages of subjects and selecting and setting experimental paradigms of handwriting imagination tasks; according to the electroencephalogram data acquisition module, a subject is guided by a handwriting imagination experiment normal form to carry out a corresponding handwriting imagination task, and offline electroencephalogram signals are acquired for multiple times; the model training module is used for constructing an off-line classification model through the handwritten imaginary electroencephalogram signals collected for multiple times; the on-line identification module is used for classifying the signals through the model and carrying out on-line classification identification; and the Chinese character input method module is connected with a character API (Application Program Interface) in an open input method and generates Chinese characters according to the classified strokes or pinyin. The invention provides a novel brain-computer interaction mode based on noninvasive brain imaging for disabled people who lose a handwriting function.

The invention discloses a light field reconstruction brain image brain region boundary extraction method based on convex hull fitting. The method comprises the steps that the p percentile of a brain image is calculated, and image cutting, contrast stretching and Gaussian filtering smooth denoising are carried out; carrying out Canny edge detection on the filtered image to obtain a binary edge image; boundary enhancement and connection are carried out through expansion and closed operation, all connected regions are extracted, and the contour with the maximum area is selected as the contour of the brain region boundary; a datum point is determined based on the contour of the brain region boundary, the polar angle and distance of each point except the datum point are calculated and sorted, and convex hull fitting is carried out through Graham Scan scanning based on the datum point and the sorted point set; converting the convex hull into a closed boundary region, and obtaining a mask image of the brain image after light field reconstruction through a mask function; according to the method, the special boundary form of the light field reconstruction image can be accurately adapted, deep learning and data training are not needed, and the generated mask has high geometric consistency.

The invention discloses a whole brain kinetic parameter fitting method based on a Landau-Stewart oscillator network, which mainly comprises the following steps of: generating time sequence data for simulating a blood oxygen level dependent BOLD signal in real brain imaging data as simulation data; extracting data characteristics of the simulation data, and based on the data characteristics, selecting an initial value of a global coupling parameter # imgabs0 # and an initial value of a bifurcation parameter # imgabs1 #; implementing a gradient descent process and gradient correction based on data features; and fitting of a global coupling parameter # imgabs2 # and a bifurcation parameter # imgabs3 # is completed. According to the method, individual fitting can be carried out on the BOLD signal time sequence of each subject, the fitting performance of the model can be remarkably improved, and the method is used for evaluating and enhancing the applicability and accuracy of the model.

The invention discloses a design and use method of a deep brain imaging fixed base based on a miniaturized microscopic system, the main body of the base is a substrate with an opening in the center, the diameter of the opening is equivalent to that of a gradient refractive index lens commonly used by the miniaturized microscopic system, and the purpose is that the lens can penetrate through the base to reach a position near an observation part for imaging. The inner layer of the substrate is matched with the bottom of the miniaturized microscopic system in size and is used for fixing the miniaturized microscopic system. Rare earth magnets are embedded in the four corners of the substrate, have the same magnetic orientation and attract magnets fixed at the same position of the bottom of the microscope, so that the uniform level of the miniaturized microscopic system relative to the substrate during fixation is ensured, and the optimal imaging effect is realized. The base structure designed in the scheme realizes the fixation of the head miniaturized microscopic system for deep brain imaging under the condition of free movement of animals.

The invention relates to the technical field of intelligent medical adjuvant therapy, in particular to a depression targeted nerve regulation method and system based on fNI RS dynamic brain entropy, and aims to overcome the defects that an existing nerve regulation method is fixed in stimulation parameter, lacks real-time feedback, depends on subjective evaluation and the like. According to the method, a multichannel near infrared spectrum optical brain imaging device is adopted to collect a time sequence signal of prefrontal lobe resting hemoglobin; calculating the time sequence signal of the prefrontal lobe static hemoglobin to obtain a static entropy feature and a dynamic entropy feature; calculating a brain entropy ratio according to the static entropy feature and the dynamic entropy feature; and matching the brain entropy ratio with a preset brain entropy threshold value, and adjusting nerve regulation and control stimulation parameters according to the corresponding brain entropy threshold value. Through the brain entropy real-time feedback closed-loop TMS regulation and control technology, the quantitative relation between the brain entropy and the stimulation parameters is established, and a stimulation parameter dynamic adjustment method is provided for the nerve regulation and control technology.

The invention provides a brain metabolism network epicenter detection method and device. The method comprises the following steps: establishing a healthy brain network based on MRI brain imaging data of a healthy subject, wherein the healthy brain network comprises a structural network and a functional network which have the same brain network nodes; calculating a brain metabolism damage degree value L of each brain network node based on FDG-PET brain image data of a disease patient and a healthy subject; calculating probabilities P1 and P2 that each brain network node may be a brain metabolism network epicenter based on a healthy brain structure network and a functional network; comprehensively ranking the brain network nodes according to the sizes of L, P1 and P2, wherein several brain network nodes ranked at the front are brain metabolism network epicenter. The method can accurately detect the epicenter of the brain metabolism network, and has good robustness.

The present invention relates to the technical field of medical image processing, and particularly to a method and system for detecting brain tumors in nuclear magnetic resonance (NMR) images. The method includes: obtaining an initial NMR brain tumor image, using a double convolutional cross-stage division network as the backbone network, and using a partial visual state space network as the feature enhancement network to lightweight the YOLOv8 model while improving its performance; in the improved YOLOv8 model, constructing a reparameterized dynamic selectable large kernel convolutional network as the multi-scale feature interaction network in the neck structure, and further improving the performance of the YOLOv8 model using reparameterization to obtain an improved YOLOv8 model; using the initial image dataset to perform iterative optimization training on the improved YOLOv8 model to obtain an NMR image brain tumor detection model; inputting the initial image dataset into the NMR image brain tumor detection model for detection, and outputting the NMR brain tumor detection result. The present invention can improve the accuracy and efficiency of brain tumor detection.

The invention discloses a multi-mode nuclear magnetic image brain tumor segmentation system based on optical flow method pixel correlation, relates to the field of image processing, and solves the problems that complex physiological correlation among multiple modes cannot be captured and specific characteristics of a tumor area are difficult to reproduce in an existing missing mode completion method. The segmentation system comprises a data preprocessing module, a cross-modal optical flow estimation module, a pixel association weight calculation module, a missing modal complementation module and a fusion segmentation module. The system is simple in structure and reasonable in design, breaks through the limitation that a traditional optical flow model depends on a gray level consistency hypothesis, and adapts to the characteristic that the gray level difference of the multi-mode MRI is remarkable. By capturing the consistency of the gray gradient direction of the same anatomical structure in different modals, cross-modal pixel correlation mapping is accurately established, optical flow estimation deviation caused by gray mismatching is avoided, a reliable correlation basis is provided for subsequent deletion completion, and the accuracy of cross-modal information transmission is guaranteed.

The utility model provides a cerebral hemorrhage detection device based on radio frequency microwaves, which belongs to the technical field of non-invasive brain imaging and comprises a microwave transmitter and a microwave receiver. The microwave transmitter comprises a power setting unit, a signal generation unit, a signal excitation source, a frequency synthesis circuit, a power amplifier locking module and a first microstrip line half-wave symmetrical oscillator. The frequency synthesis circuit comprises a frequency oscillator, a multiplier, a building capacitor, a first divider, a second divider and an RC filter circuit; the microwave receiver comprises a second microstrip line half-wave symmetrical oscillator, a signal amplifier, a frequency synthesizer, a power detection unit, an analog-to-digital converter and a signal processing unit. Through the microwave transmitter and the microwave receiver, the cerebral hemorrhage condition of a patient is detected, the problems that in the prior art, the examination process is tedious, consumed time is long, and equipment portability is poor are solved, the examination efficiency of the patient is improved, and meanwhile medical staff can conveniently treat the patient with cerebral hemorrhage in time.

A method for assessing a patient's brain disease state from brain images includes acquiring the brain images, partitioning them into predefined regions based on anatomical landmarks, extracting disease-indicative features, using a pretrained model to generate a disease-associated biomarker from the features, and determining the patient's brain disease state based on the biomarker.

The present invention discloses a medical imaging brain tumor detection method and system based on an improved Faster R-CNN. The method comprises: obtaining a magnetic resonance image dataset from a database; preprocessing the images of the dataset using an optical flow method and a generative adversarial network to perform data enhancement; constructing an improved Faster R-CNN deep network model; using the obtained dataset to train the constructed improved Faster R-CNN deep network model; inputting the magnetic resonance image to be predicted into the trained network model, and outputting the brain tumor target detection result. The present invention adopts a data enhancement method based on an optical flow method and a generative adversarial network, and designs an improved Faster R-CNN target detection network model, adds an image feature pyramid structure and a convolutional gated recurrent unit (ConvGRU) module, modifies the data reading method during network training, enhances the continuity of tumor detection results, and improves the retrieval accuracy and recall rate of brain tumors.

The invention relates to a miniature brain imaging device for two-color fluorescence and an imaging method thereof, and relates to the technical field of biomedical imaging device.The miniature brain imaging device comprises a shell and a lens end cover which are connected, and an imaging light path and an illumination light path are sequentially arranged in the shell and the lens end cover along an imaging optical axis; the imaging light path comprises a lens group, a first dichroscope, a first optical filter and a fifth lens which are arranged in sequence, the lens group is composed of a first lens, a second lens, a third lens and a fourth lens which are arranged in sequence, and the illumination light path comprises a first light source, a second light source, a second dichroscope and a sixth lens. The first light source and the second light source are combined through the second dichroscope to form a collimated light beam, and the collimated light beam is shaped through the sixth lens and then projected to the object plane through the first dichroscope. The application has the effects that different types of neural signals or cell populations can be monitored at the same time, different states or functional modules of neurons can be distinguished, and the resolution and accuracy of data can be improved.

The present invention provides a PET attenuation correction method and system for a five-modality integrated brain imaging device, relating to the field of brain imaging technology. The method comprises: generating a first attenuation coefficient map for a fixed-position hardware component; generating a second attenuation coefficient map for the optode and optical fiber of a non-fixed-position functional near-infrared spectrometer; superimposing the first attenuation coefficient map with the second attenuation coefficient map to generate a third attenuation coefficient map of the PET image under scanning conditions; and performing registration and attenuation correction on the third attenuation coefficient map to obtain an attenuation-corrected PET image. The present invention can more accurately obtain an attenuation template under each actual acquisition environment, and maximizes the correction of the effects of hardware components in the imaging field of view on the attenuation and scattering of gamma photons in PET imaging.

The invention discloses a modeling and intervention simulation method and system for children with difficulty in reading and writing Chinese based on a digital twin brain technology. A data acquisition module acquires behavior data and brain imaging data of children with difficulty in reading and writing Chinese and normal contrast children; constructing a Chinese read-write digital twin base model by using a model construction module, and simulating function shunting and consciousness formation dynamics in a brain read-write process; the individualized modeling module adopts a double constraint optimization method to carry out individualized fine tuning on the Chinese read-write digital twinborn base model and establish an individualized digital twinborn model; the intervention simulation module carries out verification and mechanism analysis on the individualized digital twinborn model, simulates different intervention strategies, predicts the improvement effect of the intervention strategies on individual read-write behaviors and brain characterization, and recommends and outputs an individualized intervention scheme; aiming at individual digital twinning modeling with difficulty in reading and writing in Chinese, an exclusive individual digital twinning model is constructed for each tester, and individual heterogeneity with difficulty in reading and writing is analyzed.

The invention discloses a design and use method of a brain imaging movable base based on a miniaturized microscopic system, the miniaturized microscopic system is placed on a substrate, the center of the substrate is provided with a hole with the same size as a gradient index lens (GRIN lens), and the lens can extend out of the hole; the bottom surface of the substrate is provided with four magnets which are opposite to the four magnets on the bottom surface of the miniaturized microscopic system in magnetism and are used for ensuring uniformity and levelness during installation. The two sides of the substrate are provided with two fixing screws which are used for fixing the microscopic system. The base plate is connected with the axial displacement platform, and the axial position of the axial displacement platform is adjusted through a spring and a screw which are connected with the transverse displacement platform below the axial displacement platform. The transverse displacement table is placed on the hollow sliding rail, and the transverse position of the transverse displacement table is adjusted through a screw penetrating through the positioning hole and a spring connected with the top plate. The center of the axial displacement table and the center of the transverse displacement table are each provided with a hole allowing the GRIN lens to pass through. The hollow slide rail is provided with four bases which are fixed on the head of a test animal through glue. According to the scheme, fast and convenient imaging at different positions of the head of the same animal is achieved, and influences on test results and injuries to the animal caused by multiple operations and installation processes are avoided.

The invention relates to the technical field of neural regulation and control, in particular to a dynamic cognitive regulation and control system which is suitable for dynamic cognitive regulation and control of different types of task paradigms, and for the dynamic cognitive regulation and control of each type of task paradigm, the system comprises the following modules: a cognitive function task module, a cognitive function task module and a cognitive function task module, the mapping module is used for establishing a mapping relation between a task normal form and a cognitive function and a mapping relation between the cognitive function and a brain functional area; the data analysis module is used for analyzing the cognitive function behavior data and the cognitive function brain imaging data to obtain behavior indexes and brain imaging characteristics; the regulation and control module is used for executing cognitive regulation and control on the user according to regulation and control parameters set by the judgment result of the cognitive aging risk probability of the user; and the evaluation module is used for comparing the current training result of the user with the historical training result to obtain an evaluation result, and dynamically adjusting the regulation and control parameters of the next round of cognitive regulation and control according to the evaluation result to realize dynamic monitoring and regulation and control of cognition.

The invention discloses a brain imaging communication system and method, and the system comprises a main control module, a multiplexer, and a brain imaging device. The communication method comprises the following steps: the main control module sends an initial signal, a communication target address and a read / write command, judges whether the multiplexer carries out feedback or not, and receives control switch data or sends a stop signal according to a feedback result; the main control module sends a starting signal, a communication target address, a read / write command and a control register address, judges whether the brain imaging device carries out feedback or not, and receives collection state data or sends a stop signal according to a feedback result. According to the invention, one-to-many communication between one main control module and a plurality of brain imaging devices is realized, the problem of insufficient I2C interfaces of the main control module can be effectively solved, the communication efficiency is effectively improved, and the use cost is reduced.