257 results about "Biological data" patented technology

The invention relates to a seat self-adaptive adjusting system and method based on multi-modal biological data, and the system comprises a multi-modal data collection module, a data processing and fusion module, a control decision module, and an execution mechanism. Physiological and posture information of a user is collected through a heart rate sensor, a myoelectricity sensor, a skin temperature sensor, a pressure distribution sensor, a three-dimensional posture sensor and other multi-mode sensors, a fatigue index and a posture deviation amount are calculated through feature extraction, normalization and weighted fusion, and the fatigue index and the posture deviation amount are compared with preset threshold values. The control decision module generates adjusting instructions of the backrest angle, the waist support height, the cushion inclination angle and the temperature control power based on fuzzy control and a prediction algorithm, and self-adaptive adjustment of the seat is completed through an execution mechanism. The method can achieve the precise recognition and active intervention of the fatigue and posture change of the user, is suitable for various scenes such as driving, working and rehabilitation, can remarkably improve the comfort, delays the muscle fatigue, and improves the posture health.

The invention belongs to the technical field of bioinformatics, particularly relates to a computer simulation and regulation method and system for beef cattle fat metabolism, and aims to solve the problems of excessive fat deposition, low feed conversion efficiency, difficulty in multi-objective optimization and the like in existing beef cattle breeding. A multi-scale biological network model is constructed, a computer simulation and prediction engine is developed, an intelligent intervention strategy optimization engine is designed, and implementation and feedback optimization are carried out. By reducing unnecessary fat deposition, improving the feed conversion efficiency and improving the beef quality, the economic benefit of beef cattle breeding is remarkably improved, the requirements of consumers for high-quality and safe beef products are met, and the method has important value for promoting intelligent upgrading and sustainable development of the beef cattle industry.

The invention relates to the technical field of biomedical engineering, in particular to a digital twinning-based biological fermentation reaction kettle process control modeling method, which comprises the following steps: step 1, multi-source heterogeneous data acquisition and treatment: acquiring physical, chemical and biological data of a fermentation reaction kettle, and carrying out cleaning, complementing and time sequence alignment; step 2, mapping knowledge domain driven biological mechanism modeling: constructing a cell metabolism network mapping knowledge domain, associating process parameters with a biological mechanism, and optimizing a feeding strategy; step 3, performing digital twinborn driven multi-physical field simulation: constructing a CFD-molecular dynamics coupling model, and simulating a fermentation tank flow field and a cell metabolism process; 4, quality prediction and control of mechanism-data dual-mode driving are carried out; and 5, deploying and optimizing a cloud edge-end collaborative model. By constructing a'mechanism-data 'dual-drive architecture, the problems of insufficient adaptability, weak multi-modal data fusion capability and the like of a traditional model are solved.

The invention provides a braking control method and a vehicle, and relates to the technical field of vehicle braking. The method comprises the following steps: acquiring braking parameters and driving parameters of a vehicle and biological data of a driver at the current moment; determining a driving style, a driving intention and a driving state of the driver at the current moment according to the driving parameters and the biological data; wherein the driving style is used for representing the preference of the driving behavior of the driver, the driving intention is used for representing the driving behavior of the driver in a preset scene, and the driving state is used for representing the physiological and psychological states of the driver; according to the driving style, the driving intention and the driving state, a braking control instruction corresponding to the braking parameters is determined; according to the technical scheme, braking control is conducted on the vehicle on the basis of the braking control instruction, braking control can be effectively adjusted according to the actual driving condition, and therefore the actual braking effect adapts to the driving behavior of a driver, the driving comfort is improved, and the driving safety is improved.

The invention belongs to the technical field of tumor clustering analysis, and particularly relates to a tumor clustering analysis system and method based on big data. Through deep fusion and space-time alignment processing of multi-dimensional biological data, the limitation of single-dimension or simple data integration in a traditional method is overcome, complex biological characteristics and space-time evolution laws in the tumor can be captured more comprehensively, and the tumor can be rapidly and accurately captured by constructing a low-dimensional association and independent characteristic space and combining a nonlinear dimension reduction technology. According to the method, low-dimensional feature vectors with biomarker features are effectively extracted, a high-quality data basis is provided for subsequent clustering analysis, meanwhile, based on sliding window density estimation and determination of a dynamic neighborhood radius, accurate spatial adjacency relation construction of the low-dimensional feature vectors is achieved, and the accuracy of the spatial adjacency relation is improved. And the potential clustering center of the tumor subtype is extracted through the multi-scale characteristic spectrum, so that the generated classification label can more accurately reflect the real distribution of the tumor subtype.

The invention discloses an intelligent pre-warning system for Parkinson / epilepsy recognition based on a multi-modal sensor and a bracelet, and relates to the technical field of biosensors. The intelligent early warning system for Parkinson / epilepsy recognition based on the multi-modal sensor comprises a multi-modal acquisition interference judgment module, a multi-modal interference processing optimization module and a multi-modal fusion input early warning module. According to the method, whether the multi-modal interference processing optimization is carried out or not is judged according to the multi-modal interference early warning result, if yes, the multi-modal fusion instruction is sent after the multi-modal interference processing optimization, and if not, multi-modal biological data fusion is directly executed, and the multi-modal fusion early warning result is obtained. Finally, whether multi-modal fusion input early warning is carried out or not is judged based on the multi-modal fusion early warning result, the effect of carrying out physical condition early warning more accurately is achieved, and the problem that in the prior art, in the process of carrying out physical condition early warning through wearable equipment, multi-modal biological data collection and fusion association are not sufficient is solved.

The invention provides a defect early warning method and device for multi-source heterogeneous data fusion of power equipment, and the method comprises the steps: collecting biological data, quantum data and operation data of the power equipment; performing biomolecular diagnosis on the biological data to obtain biological characteristic information; laying a quantum cobweb based on the quantum data to obtain quantum feature information; performing feature fusion on the biological feature information and the quantum feature information to obtain feature fusion information; performing space-time coding conversion on the feature fusion information and extracting bionic features to obtain space-time feature information; generating a space-time pulse characteristic flow based on the space-time characteristic information; constructing a holographic decision field through the space-time pulse characteristic flow; performing risk deduction by using the holographic decision field to generate a defect evolution cloud picture; and generating defect early warning information based on the defect evolution cloud atlas and the operation data. According to the method, potential defects of the equipment can be accurately found in advance, the equipment failure rate is reduced, and the reliability and stability of the power system are improved.

The invention relates to the field of medical artificial intelligence, in particular to a mucus-like brain tumor multi-source data auxiliary differential diagnosis system based on deep learning, and the system collects pathological images, medical images, immunohistochemical data and molecular biology data through a multi-source heterogeneous data collection and standardization module, and carries out standardization processing; a multi-scale feature extraction module extracts key features from the data to generate multi-modal feature data; a cross-modal correlation analysis module analyzes correlation among the feature data and generates attention mapping data and feature correlation data; the diagnosis reasoning and decision-making module identifies the tumor type by using the data, carries out differential diagnosis and calculates the uncertainty of a diagnosis result; the interpretability display module is used for generating a structured diagnosis report which comprises multi-level attention visualization data and positioning a key diagnosis area; and through multi-source data fusion and correlation analysis, the diagnosis accuracy is remarkably improved.

The invention provides a front-end high-frequency data security interaction method and system based on a Wasm, and relates to the technical field of network programming, network front ends and brain-computer interfaces, and the method comprises the following steps: securely storing protocol decoding parameters of Internet of Things equipment in a Wasm isolation environment; a composite authentication mechanism of JWT verification logic, domain name IP check and API authentication is provided in the Wasm through the closed characteristic of network assembly; parallel iterative processing is carried out on the multi-channel biological data in the wasm virtual environment, and analysis operation is executed; dynamically calculating a data time scale through a timestamp generation function, wherein the function comprises an index rewinding compensation mechanism; and each piece of parsed plaintext data is returned to the JavaScript calling end through an asynchronous data stream. According to the method, an authentication and authorization mechanism of the Wasm and the rear end, authentication and authentication in a Wasm isolation environment, analysis and a bridging architecture of the Wasm and the JavaScript are provided, so that the Wasm module is not called by the unauthorized Javascript.

The invention belongs to the technical field of water ecological protection and water resource management, and particularly provides a cascade reservoir operation ecological benefit evaluation method based on a habitat model, and the method comprises the steps: collecting the topographic data of a downstream fish spawning site of a cascade reservoir, the actual measurement hydrological data of a hydrological station, and the biological data of target fishes; performing unstructured triangular mesh division on the research river reach by adopting a finite volume method, and simulating flow velocity and water depth distribution of a research area through a continuity equation and a momentum equation; according to the biological data of the target fish, a suitability curve of the flow velocity and the water depth is constructed, and the habitat suitability index HIS of each grid is calculated in combination with the flow velocity and water depth data output by the hydrodynamic module; and calculating an effective available area WUA and an overall suitability index OSI based on the HSI value for ecological benefit assessment. The method comprehensively considers the influence of the cascade reservoirs on the correlation factors, accurately evaluates the ecological benefits, and provides a scientific basis for ecological scheduling of the cascade reservoirs and fish resource protection.

The invention relates to a digital twin construction method and system of a terrestrial ecosystem, and the method comprises the steps: obtaining animal and plant information and environment quality evaluation conditions according to biological and environmental data collected in an ecological protection area; processing animal and plant information, an environment quality evaluation condition, a biological feature vector generated based on biological data, an environment feature vector generated based on environment data and a comprehensive feature vector generated by fusing the two types of vectors based on the evaluation large model to obtain an ecological system service function evaluation result; generating a comprehensive characteristic matrix based on animal and plant information, environment quality evaluation conditions and ecological system service function evaluation results, and performing deep learning on the comprehensive characteristic matrix to construct a digital twinborn engine; and after a digital twinborn large model is constructed based on the digital twinborn engine and the model static substrate information, in response to real-time data input into the digital twinborn large model, rendering a linkage state of species, environment and ecosystem service functions on a visual interface.

Provided herein are systems, devices, and methods for processing, analyzing, and classifying biological data sets and generation of cell profiles. The data sets may include multi-omic data. Some embodiments may include the use of machine learning in training a classifier of raw multi-omic data and incorporating system biology knowledge to understand cellular behavior and cell status at the biomolecular level.

The present application relates to the technical field of emotion recognition, in particular to an asynchronous multi-modal emotion recognition method, device, equipment and medium. The present application respectively carries out pulse coding on each modality biological data of the user, obtains the pulse sequence of each modality biological data, applies cross-modal synchronous neurons to each pulse sequence based on the firing time of pulse coding, obtains the emotion feature of the user, and finally applies an emotion classifier to the emotion feature to obtain the emotion classification result. Since the pulse sequence formed by the pulse coding of the present application can record the time when the biological data changes, and the cross-modal synchronous neurons are applied to the pulse sequence according to the firing time of the pulse coding to analyze the emotion feature, the combination of the pulse coding and the cross-modal synchronous neurons of the present application can identify the time when each multi-modal biological data changes, and identify the emotion feature of the user according to the time sequence before and after the change of the multi-modal biological data, so as to improve the accuracy of emotion recognition.

The invention relates to the technical field of identity verification, in particular to a conference sign-in bimodal identity verification and data automatic matching system, which comprises a bimodal acquisition module for activating a face image sensor to continuously capture multiple frames of face images, synchronously starting a microphone array to directionally pick up voiceprint information in a voice instruction, and generating an original biological data stream. In the invention, through synchronous acquisition of the face image and the voiceprint information, continuous frame image capture and environmental noise reduction are utilized to ensure that high-quality biological characteristic data is obtained, and verification accuracy is effectively improved. The accuracy of identity recognition and risk assessment is enhanced by sensing contextual information such as conference importance level, sign-in time and physical security level in real time and comprehensively considering behavior characteristics of equipment operated by a user at the same time. In the feature matching stage, a facial key point geometrical relationship and a voiceprint Mel-frequency cepstrum coefficient are extracted, and biological feature comparison is carried out, so that the false recognition rate and the missed recognition rate are reduced.

The invention belongs to the technical field of biological information calculation, and discloses a pet feed additive formula calculation model optimization system, which comprises a data interface for acquiring raw material parameters and data containing environmental stress factors; the memory stores a heterogeneous neural network model, and comprises a first time sequence convolution subnet, a second cascade processing subnet and a global gating distribution unit which are arranged in parallel and are coupled through a lateral suppression channel; the processor operates the model, the global gating distribution unit is used for dynamically adjusting the computing power weight between the subnets according to the system energy state tensor, and back propagation iteration is carried out based on a nonlinear constraint penalty term. The problems of convergence difficulty and negative migration when a general model processes high-dimensional strong-coupling biological data are solved, and the prediction precision and safety of formula optimization are improved.

The invention discloses a tumor radiotherapy patient symptom management and prognosis evaluation method and system, and relates to the field of tumor radiotherapy, and the method comprises the steps: obtaining the age data, biological data, clinical data and symptom data of a tumor radiotherapy patient; calculating a comprehensive symptom score through the dynamic symptom evaluation model and the symptom data; when the symptom comprehensive score exceeds a preset threshold value, automatically matching a targeting scheme according to the symptom dominant factor; performing correction calculation on the biological data and the clinical data based on the age data to obtain a biological prognosis index and a clinical prognosis index; performing difference significance judgment on the biological prognosis index and the clinical prognosis index, and calculating a comprehensive prognosis index; dynamically adjusting the radiotherapy plan according to the biological prognosis index, the clinical prognosis index and the comprehensive prognosis index; the weight coefficient is updated after the radiotherapy plan is adjusted each time; according to the invention, dynamic symptom evaluation, multi-dimensional prognosis evaluation and adaptive radiotherapy plan adjustment can be realized.

The invention relates to an N-BEATSx plankton prediction method fusing a semantic model. The method comprises the following steps: acquiring an observation data set in a current environment; performing multi-scale interpolation processing on the observation data set according to interpolation strategies corresponding to different interval durations to obtain a target observation data set comprising target multi-source environment data and target plankton data; and in the improved N-BEATSx network fused with the semantic model, performing cross attention operation and residual operation on the target multi-source environment data and the target plankton data to obtain a prediction result based on the plankton variation in the current environment. By adopting the method, semantic fusion and dynamic prediction of the environmental data and the biological data can be realized.

The invention relates to the technical field of fish biological measurement, in particular to a fish biological data measurement method based on machine vision and a matched device thereof, and comprises the fish biological data measurement method based on machine vision and the matched device used for collecting fish biological data. The matching device comprises a control host, an image acquisition camera, a weighing device, a positioning device and a storage transmission device, and the control host cooperates with each module to realize semi-automatic and high-precision acquisition of fish biological data, the convenience and the accuracy of data acquisition are improved; the weighing equipment is used for synchronously acquiring weight data of fishes; the positioning equipment is used for obtaining position information of a shooting site; and the storage and transmission equipment is used for storing and transmitting the obtained information data.

The invention is suitable for the technical field of group teaching, and particularly relates to a group teaching strategy generation method and device based on a smart learning cloud platform, and the method comprises the steps: obtaining the teaching behavior data, biological data and teaching progress data of a plurality of classes through the smart learning cloud platform; according to the teaching progress data of the plurality of classes, carrying out space-time alignment processing on the teaching stages of the plurality of classes to obtain a teaching stage sequence; based on each teaching stage of the teaching stage sequence, performing group anomaly detection on a plurality of classes to obtain an anomaly detection result; and generating a group teaching strategy based on the teaching behavior data and the biological data of the teaching stage in the anomaly detection result. According to the method, group problems can be found and corrected in time, and teachers are helped to make more accurate teaching decisions, so that the overall teaching quality and learning effect are improved.

The invention provides a diabetes development stage identification method and device based on a bio-electricity signal, and relates to the technical field of biological data analysis, and the method comprises the steps: obtaining a bio-electricity signal triple set and diabetes data, the bio-electricity signal triple set comprises a plurality of bio-electricity signal triads, and the diabetes data comprises a diabetes development stage text; obtaining a mapping relation between the bioelectricity signal triple and the diabetes development stage text, and labeling the diabetes development stage text by taking the mapping relation as a label; performing iterative training on the initial diabetes development stage identification model through the bioelectricity signal triple set and the labeled diabetes development stage text set, and obtaining a final diabetes development stage identification model after training is completed; and inputting the to-be-measured bioelectricity signal triple into the final diabetes development stage identification model to carry out diabetes development stage identification so as to obtain a final prediction result of the diabetes development stage.

A personalized supplement formulation system integrates biological data, structured qualitative feedback, and machine learning algorithms to generate individualized nutritional protocols. The system receives biological inputs such as genetic single nucleotide polymorphism (SNP) data, blood biomarkers (e.g., ferritin, vitamin D, B12), lipid profiles, and urinary metabolites. A digital user profile is created by organizing these inputs and calculating derived indices relevant to supplementation. Structured qualitative feedback, including dietary restrictions, perceived wellness, and training goals, is normalized and processed alongside the biological data. A trained machine learning engine analyzes combined inputs to output a tailored supplement formulation specifying ingredient selection, dosage, delivery format, and timing. Instructions are transmitted to a manufacturing system capable of producing the custom formulation. The system supports periodic re-evaluation and iteration based on new biological samples or user-reported feedback, enabling dynamic personalization over time and improving efficacy, compliance, and outcome tracking in athletic and wellness domains.

A measuring device, a control method, and a control program are provided that can reduce the processing load of a processor and suppress processing delays, such as those occurring during measurement. A measuring device according to one aspect of the present invention includes a main MCU (18) configured to perform measurements based on biological data obtained from a sensor, a communication IC (14) configured to perform wireless communication with an information terminal (5), and a non-volatile memory (14a) connected to the communication IC (14).The main MCU (18) sequentially transmits the biological data acquired by the sensor during acquisition to the communication IC (14) without performing a transmission acknowledgment, and writes the biological data to the non-volatile memory (14a). Upon completion of acquisition, the communication IC (14) receives result information for writing the biological data to the non-volatile memory (14a). The communication IC (14) then transmits the biological data written to the non-volatile memory (14a) to the information terminal (5).

To prompt a user to exercise in consideration of the health of the user and power saving.SOLUTION: A generation section 33 generates, for each user, recommended exercise data related to an exercise to be recommended to the user on the basis of biological data. A calculation section 34 calculates, on the basis of total saved electric energy required to be saved for all the users and the generated recommended exercise data, user-specific saved electric energy that can be saved by electric energy generated by a power generation device that converts kinetic energy of each of the users into electric energy. A presentation section 35 presents, to each of the users, the recommended exercise data generated for each of the users and the user-specific saved electric energy calculated for each of the users.SELECTED DRAWING: Figure 4

This invention belongs to the field of biological signal processing and discloses a system and method for recovering and extracting neuronal signals from four-dimensional spatiotemporal data. The system includes a biological data acquisition module, a signal processing module, and a signal extraction module. The biological data acquisition module is connected to the signal processing module, and the signal processing module is connected to the signal extraction module. The biological data acquisition module is used to acquire image sequences of biological samples; the signal processing module is used to process the acquired biological image sequences; and the signal extraction module extracts the neuronal signal intensity from the data. Users can use this system to quickly and efficiently acquire, identify, extract, and analyze biological four-dimensional spatiotemporal data, significantly shortening the biological signal processing cycle and improving its efficiency. This invention flexibly utilizes various machine learning algorithms and designs a complete and reliable deployment scheme, obtaining highly accurate sample data while reducing the cost of manual intervention.

The application discloses a cross-industry data assetization management and circulation system, which comprises five core modules. A data block encryption module receives cross-industry biological data flow, performs sequence block and nested encryption, and generates an encrypted data flow; a blockchain section storage module generates a hash storage certificate for the data block and writes the hash storage certificate into a blockchain; a federal contribution attenuation evaluation module receives a federal learning gradient update signal, performs time attenuation and marginal benefit adjustment to generate a contribution evaluation signal; a cross-modal knowledge graph alignment desensitization module realizes cross-modal entity semantic alignment, calls a configurable desensitization rule chain to generate a desensitized data flow; and a data circulation gateway module integrates three types of signals to generate a data asset circulation control signal. The application solves the collaborative management problems of safe storage, privacy protection, contribution quantification and compliance desensitization in the circulation of cross-industry biological data.

The invention relates to a medical beauty implant material intelligent optimization method and system based on artificial intelligence, and the method comprises the following steps: S1, obtaining the performance parameters of different medical beauty implant materials, capturing the anatomical features of a patient through 3D scanning and MRI images, and carrying out the statistics of the personalized biological data of the patient; s2, constructing a multi-task learning model and training based on a multi-target supervised learning data set, wherein the target is to predict the performance of different implant material formulas in a medical beauty implant environment; s3, performing multi-objective optimization by adopting an NSGA-III algorithm to obtain an optimal implant material formula; s4, reconstructing a three-dimensional model of the patient by using a reverse modeling technology based on anatomical features of the patient; and S5, based on the three-dimensional model, according to the optimal implant material formula and the personalized biological data, simulating the biomechanical characteristics of the patient by utilizing finite element analysis, and outputting biocompatibility scores and suitability suggestions of the material in different implant environments. According to the method, the clinical requirements on customized design and application of the medical beauty implant material are effectively met.

The present application relates to computer vision and underwater detection technology, provide a kind of underwater small biological target detection method and system based on deep learning, the WLDA-YOLOv8 deep learning model constructed includes main network, neck network and detection head, main and neck embed light multi-scale module, detection head introduces dynamic detection head and adaptive sample selection strategy;Training stage uses improved WIoUv3s loss to carry out boundary box regression, the model is trained using underwater biological data set, finally realizes the accurate detection of small target in underwater image, enhances the capture and fusion ability to small target subtle features.

The invention provides a neural feedback training scheme automatic screening and effect suggestion method. The method comprises the following steps: acquiring biological data of a testee by using a brain wave collection device; wherein the brain wave collecting device comprises a household brain wave collecting device; transmitting the biological data to a brainwave database of a remote cloud system through a network; the brain wave database converts the biological data into a corresponding training parameter suggestion; and remotely feeding back the training parameter suggestions to a neural feedback training module for the testee to perform heart and brain training. Therefore, suggested training parameters are provided through a software interface, and superior and inferior brain area networks, digital therapy suggested training schemes and related health education are arranged, so that the effects of long-distance feedback and improvement of cognitive competence by brain training at home are achieved.

Provided is a low-noise infusion pump and an automated infusion system using the same, including: a wearable infusion bag device configured to be worn on an animal and to inject a fluid into a body of the animal, the wearable infusion bag device including a low-noise infusion pump operated by a low-noise driving motor; an in-body insertion sensing unit inserted into the body and configured to collect biological data; and an infusion control unit configured to control operation of the low-noise infusion pump based on operation data of the low-noise infusion pump and the biological data received from the low-noise infusion pump and the in-body insertion sensing unit via wireless communication.