606 results about "FARR assay" patented technology

The invention discloses an intelligent measurement and calculation method and system for forest carbon reserves, and relates to the technical field of carbon reserve measurement and calculation, and the system obtains a forest electronic distribution diagram through a GIS technology and surveying and mapping data, collects various carbon reserve related data, and then carries out the correction and denoising of the collected data through a FLAASH algorithm and other preprocessing technologies; the AI sampling optimization model extracts forest features based on a convolutional neural network CNN, and dynamically adjusts a sampling area; secondary acquisition is carried out, data preprocessing is carried out, the total carbon reserve of the forest is estimated by calculating the carbon reserve of each grid, and the forest succession stage, the carbon sink change and the ecological carbon cycle are further analyzed; and finally, in combination with the dynamic influence coefficient of the carbon reserves and a preset threshold value Q, judging the change trend of the carbon reserves, triggering early warning and giving a coping strategy.

The invention provides a time sequence fusion and health state prediction method and system for multi-dimensional physiological data, and relates to the technical field of electrical digital data processing.The method includes the steps that high-precision sensing equipment and a parameter calibration model are adopted to calibrate data, time synchronization and error compensation are achieved through a weighted fusion algorithm, and the accuracy of physiological parameters is improved; meanwhile, a fatigue index and a pressure index are corrected in real time by using a closed-loop feedback mechanism, and a comprehensive state index is generated based on an evaluation result for further analysis and prediction; the change trend of the comprehensive state index is predicted through a machine learning technology, and a targeted dynamic rehabilitation scheme including training intensity optimization, diet adjustment, rest cycle planning and the like is provided in combination with physiological data of the user; and a timing feedback mechanism is established, so that the user can obtain health state change and adjustment suggestions in real time, thereby improving scientificity and timeliness of a rehabilitation scheme, remarkably optimizing the rehabilitation effect and efficiency, and realizing intelligent health management at the same time.

Systems and methods are provided for stabilization of cells and retention of biomolecules therein for further analysis. Block copolymers are inserted into membranes of biological particles, cell membranes or organelles (e.g., organelle membranes) and polymerized to form an intra-biological particle (e.g., intracellular) polymer network.

The technology disclosed relates to a system for constructing a probeable output generative space of an agent-under-test (AUT) for a target input probe. An agent sampling logic is configured to induce an agent-under-test (AUT) to disclose a plurality of outputs in response to processing a target input probe. A space construction logic, having access to the plurality of outputs, is configured to construct a probeable output generative space based on the plurality of outputs. A space probing logic, having access to the probeable output generative space, is configured to probe the probeable output generative space for a query, and to make available results of the probing for further analysis.

The invention discloses a construction monitoring system for a horizontal warehouse arched roof based on multi-modal data fusion, relates to the technical field of construction monitoring, and is used for solving the problem of poor structural construction anomaly recognition. The method comprises the steps of collecting multi-modal data and completing synchronous preprocessing, setting a sampling frequency according to modal characteristics, dividing construction stages, constructing a stage response function, dynamically updating during stage switching, fitting an actual observation value and a prediction value, extracting a modal response deviation degree, constructing a credibility function and evaluating data reliability. Further analyzing a residual trend, adjusting an abnormal score in combination with credibility, identifying a collaborative abnormal behavior between modals, systematically constructing a modal causal graph, identifying an abnormal propagation path, extracting modal conduction characteristic information, and triggering a risk response strategy according to the modal causal graph, so that dynamic tracking of the structural abnormality in the construction process of the horizontal warehouse arched roof is realized; and the accuracy of construction process monitoring identification is improved.

The standardized IFC entity data processing method comprises the following steps that 1, an original BIM model is obtained and preprocessed, and an IFC model data set is obtained; 2, aligning the geometric expression precision and the measurement unit, and performing sensitive information desensitization processing on the IFC model; 3, splitting the IFC model to obtain a single building component entity; step 4, performing standardization processing on the geometric expression information of the single building component entity to obtain standardized geometric expression information; step 5, format conversion and deduplication processing; step 6, generating a multi-view rendering strategy and visual data to obtain a multi-format data set which can be used for further analysis; according to the method, through format unification and geometric accuracy alignment of the multi-source BIM model, the problem that errors occur during combination or comparison of the IFC model due to the fact that different software uses different unit systems in the prior art is effectively solved.

The invention discloses an emergency decision deduction system and method, and relates to the technical field of emergency management and intelligent decision, and the method comprises the steps: receiving flood-related data, generating environment data and social data, building a space-time disaster matrix and a social vulnerability map based on the environment data and the social data, a coupling data body is determined based on analysis of a space-time disaster matrix and a social vulnerability map, and a disaster-social coupling network is determined based on the coupling data body. The disaster-social coupling network is further analyzed to construct an emergency decision-making row-known body comprising a candidate strategy set, a space-time knowledge graph, a multi-dimensional strategy evaluation matrix and an adaptive rule set, and the emergency decision-making row-known body is used for deduction. According to the invention, the method takes the construction of the emergency decision knowledge as a deduction core, and improves the dynamic adaptability and intelligent decision-making capability of a system to cope with complex disaster scenes.

The invention discloses a three-dimensional scene reconstruction and monitoring method based on multi-view fusion and deep learning. The method comprises the following steps: acquiring image data from different viewpoints through a plurality of cameras, and carrying out geometric calibration; denoising, correcting and enhancing the image; constructing a multi-scale convolutional neural network and a variational auto-encoder model, and extracting multi-level features; carrying out weighted fusion on the features, and carrying out three-dimensional reconstruction through sparse coding and a graph neural network; performing feature optimization by applying a dynamic graph convolutional network and a double attention mechanism, and performing model updating based on adversarial gradient descent; anomaly detection is carried out through multi-scale analysis and an adversarial variational auto-encoder, preliminary processing is carried out at a camera end by adopting an edge computing technology, and further analysis is carried out at a central server end through a heterogeneous graph neural network and sparse subspace clustering. According to the method, high-precision and intelligent three-dimensional scene reconstruction and monitoring are realized, and the method has a wide application prospect.

The invention provides an identification detection method and system for a radio frequency connector, and the method comprises the steps: obtaining the initial appearance image data of the surface of the radio frequency connector, and carrying out the processing of the image data, and obtaining a first appearance image; extracting an appearance feature set and an identification area image from the first appearance image; performing character segmentation and feature extraction on the identification area image, obtaining a preliminary manufacturing information text in combination with a pre-established text recognition model, verifying the preliminary manufacturing information text and associating the preliminary manufacturing information text with the appearance feature set to generate an associated data set; comparing the appearance feature set with a preset standard appearance database to generate defect marking data, fusing the associated data set with the defect marking data to generate comprehensive analysis data, and generating a visual chart according to the comprehensive analysis data; and further analyzing performance index change and defect severity according to the visual chart, generating risk assessment data, and generating a final detection analysis file according to the risk assessment data.

The invention provides an intelligent training system and method for a portable control console, and relates to the technical field of man-machine interaction and intelligent training, and the method comprises the steps of original trajectory data collection, operation feature extraction, capability score vector construction, user capability vector generation, capability trend analysis and personalized training path construction. The method comprises the following steps: acquiring attitude angular velocity, displacement acceleration and key data of a user in a training process through a portable console, extracting multi-dimensional operation characteristics, performing alignment and evaluation in combination with a scoring rule and a dynamic time warping method, and generating ability scores and user ability vectors; and further analyzing a capability development trend, identifying a capability short board, and matching a training task to generate a personalized path. The technical problems that an existing training system does not have portability, data acquisition dimensions in the training process are insufficient, capability evaluation is rough, and trend analysis and personalized training path recommendation are lacked are solved.

The invention relates to the technical field of artificial intelligence, in particular to a network fault root cause automatic diagnosis method and device and electronic equipment. The method comprises the following steps: S1, receiving a network alarm event, and extracting a prompt word and a termination word from the network alarm event; s2, a pre-training large model is triggered for analysis according to the cue word, analysis of the pre-training large model is stopped according to the termination word, and the pre-training large model is obtained according to network field general knowledge big data training; s3, obtaining an analysis result of the pre-training large model, calling a sub-function according to the analysis result of the pre-training large model, and finding historical interaction data; s4, sending the sub-function calling result and the historical interaction data to a pre-training large model, and further analyzing the pre-training large model to obtain an analysis result; and S5, repeating the steps S3-S4 until the large model finds the network fault root cause of the network alarm event. Therefore, rapid and accurate diagnosis and positioning of network fault root causes in a dynamic network environment are realized.

The invention relates to the technical field of atomization rate control, in particular to an intelligent atomization compliance monitoring method and system for children. The method comprises the following steps: firstly, acquiring expiration flow velocity, inspiration flow velocity and multi-direction motion acceleration time sequence data of a child in a historical monitoring period, and reflecting breathing and behavior action conditions; dynamically analyzing the numerical characteristics of the expiration flow velocity and the inspiration flow velocity to divide a respiratory cycle, and analyzing the cycle length, the inspiration flow velocity cumulant and the like to construct a respiratory characteristic vector; screening a target monitoring period based on the breathing feature vector and the period number similarity; further analyzing the motion acceleration change and accumulation characteristics in the target monitoring period, and quantifying the behavior compliance expression degree; the behavior compliance expression degree and the breathing feature vector are deeply fused, a'breathing state-behavior cooperation 'joint evaluation model is established, the breathing and behavior states of children are dynamically recognized, the medicine atomization rate is accurately adjusted, and effective deposition of lung medicine is improved.

The invention relates to the technical field of medical health monitoring, in particular to a portable intelligent breathing mask and a remote monitoring method thereof. The method comprises the steps that airflow, humidity, blood oxygen and temperature data in the mask wearing process of a user are obtained, and time sequence alignment and feature fusion are carried out; inputting the fused physiological feature sequence into an embedded neural network model to recognize respiratory behaviors, and generating an abnormal probability value; carrying out compression coding based on the parameters, setting an uploading period according to a self-adaptive formula, and uploading the data to a cloud end; the cloud model further analyzes and generates health assessment parameters and a risk trend sequence; and dynamically adjusting an early warning threshold according to an evaluation result, and updating a local model and a sampling strategy in combination with user feedback. The portable breathing mask has the functions of fusing multi-modal data analysis, edge-cloud collaborative recognition, self-adaptive uploading scheduling, personalized early warning regulation and control and the like, and the intelligent level and practicability of the portable breathing mask in remote health monitoring are improved.

The invention discloses a disaster emergency system based on big data, which comprises a data collection module, a fire-fighting early warning module and a fire-fighting emergency module, and is characterized in that the data collection module is used for collecting visual data and environmental comprehensive data in a monitored target area in real time, and inputting comprehensive information of all people entering a building into the system; the fire-fighting early warning module is used for detecting whether a fire occurs in a target area or not, further analyzing the spreading trend and the danger degree of the fire and adjusting the operation mode of the spraying system according to an analysis result, and the fire-fighting emergency module is used for obtaining the positions of trapped persons in a building, shunting the trapped persons and sending the trapped persons to the fire-fighting early warning module. The system comprises a data collection module and a distribution module, an evacuation route is planned for each distribution group, the data collection module comprises a data input module, a visual module and a sensor module, the data input module is used for inputting comprehensive information of personnel entering the building into the system, and the system has the advantage of improving evacuation efficiency and accuracy.

The invention provides an intelligent management system for encrypted archives, and relates to the technical field of intelligent archive management.The intelligent management system comprises the steps that operation data of a user and encoding data of an encoder are obtained and analyzed to obtain reconstruction errors, and then the reconstruction errors are further analyzed to obtain abnormal operation evaluation coefficients; dividing the users into normal, suspicious and abnormal types based on the abnormal operation evaluation coefficient, and obtaining historical access data of the suspicious users; analyzing to obtain a historical behavior judgment coefficient; and further judging the suspicious user through the historical behavior judgment coefficient, and finally judging the suspicious user as a normal or abnormal user, thereby breaking through the limitation of the traditional single judgment, realizing the intelligent association of the cross-cycle behavior track, more accurately judging the access behavior mode of the user, and greatly reducing the misjudgment rate.

The invention discloses a warehousing identification and storage management system based on an intelligent refrigeration house, and relates to the technical field of refrigeration house goods identification and storage, and the system comprises a goods identification and storage module, a real-time dynamic adjustment module and a storage planning management module. According to the method, the storage location is screened through the preset rule, and the storage scheme is intelligently determined according to the matching condition of the storage space of the storage location and the cargo quantity, so that the utilization rate of the storage location is improved, and the suitability of cargo storage is guaranteed; meanwhile, the storage planning management module calculates the cargo use frequency by using historical data, classifies the cargos, particularly further analyzes the storage demand conflict condition for high-frequency storage cargos, optimizes the storage planning by calculating the selection value of the screened storage location, more reasonably arranges the storage location according to the cargo characteristics and the use rule, and improves the storage efficiency. And the overall utilization efficiency of refrigeration house resources is improved.

In multimedia object tracking and merging of tracked objects, an object is tracked through frames of multimedia content until a frame appears in which the tracked object is not detected. A first track is designated as one or more consecutive frames in which the tracked object is detected, the first track ending at the first frame. Tracking continues to try to detect the tracked object in a second frame subsequent to the first frame. If the tracked object is not again detected, information about the first track is output. If the tracked object is detected subsequently, a second track of consecutive tracked object detection is designated. The tracked objects in the two tracks are then compared with the aid of trained data models, and a matching score is determined to reflect the degree of match. If the matching score meets or exceeds a first threshold, the compared tracks are merged using the same identifier assigned to both tracks. If the matching score does not exceed a second threshold that is less than the first threshold, the tracks may be discarded as showing no match. If the matching score falls between the first and second thresholds, an indication is output for further analysis of the compared tracked objects.

The invention discloses an AUV submarine petroleum pipeline inspection risk identification system based on deep learning, and relates to the technical field of ocean engineering, and the system comprises an inspection risk management center, and the inspection risk management center is in communication connection with the following modules: an AUV autonomous inspection module, which is used for deploying an AUV to carry out an inspection task of a submarine petroleum pipeline, and the control instruction of the AUV is dynamically adjusted according to the seabed environment and the pipeline state, and the inspection path and speed are optimized. The abnormal pipeline identification model is constructed by combining the deep learning algorithm, the pipeline image data can be efficiently classified, the submarine petroleum pipeline area in the abnormal state can be identified, the identified abnormal state area is further analyzed, the pipeline risk trend index is calculated, and the potential risk level of the pipeline is evaluated. Potential safety hazards can be found and treated in time, accidents are prevented, and safe operation of the submarine petroleum pipeline is guaranteed.

The invention discloses a digital twin high-voltage motor-based strong current operation state monitoring and maintenance system, which comprises the following steps: collecting current, temperature rise and vibration signals in motor operation, carrying out high-precision fitting processing on the signals by using a Chebyshev polynomial approximation algorithm, carrying out trend offset detection on a fitting result based on a CUSUM algorithm, and carrying out high-precision fitting processing on the fitting result based on a CUSUM algorithm; therefore, tiny abnormal changes are identified. And meanwhile, a prediction signal is generated in combination with a digital twinborn model, residual calculation is performed on the prediction signal and the fitting signal, fault features are further analyzed, and early warning information is output. Real-time sensing, trend judgment, fault prediction and intelligent early warning of the running state of the high-voltage motor are achieved, and the system is suitable for motor monitoring and maintenance in an industrial scene.

Methods and systems for identifying a region of interest in breast tissue utilize artificial intelligence to confirm that a target lesion identified during imaging the breast tissue using a first imaging modality (e.g. x-ray imaging) has been identified using a second imaging modality (e.g. ultrasound imaging). A computing system operating a lesion matching engine utilizes a machine learning classifier algorithm trained on cases of x-ray images and corresponding ultrasound images in which lesions were identified for further analysis. The lesion matching engine analyzes a target lesion identified with x-ray imaging and a potential lesion identified with ultrasound imaging to determine a likelihood that the target lesion is the same as the potential lesion. A confidence level indicator for the lesion match is presented on a display of a computing device to aid a healthcare provider in locating a lesion in breast tissue.

The present invention relates to the field of fuel cell technology, and in particular to a method for identifying the attenuation of the operational stability of a fuel cell vehicle stack. The method comprises the following steps: obtaining a first data set of the fuel cell stack of the fuel cell vehicle, processing the data set to obtain an effective operational data set; determining first and second operational characteristic time intervals and corresponding first and second effective operational data sets, performing fitting calculations to obtain respective voltage fitting values, and further analyzing the method to obtain a fuel cell stack fitting determination coefficient; calculating the relative deviation values ​​of the fuel cell stack fitting determination coefficients of the first and second operational characteristic time intervals, and determining whether the operational stability of the fuel cell stack has attenuated based on the relative deviation values. The present invention does not require the fuel cell stack to be disassembled from the vehicle for bench testing by traditional means, thereby significantly saving test resource requirements. At the same time, the method can also obtain the operational stability changes of the fuel cell stack in all time periods, providing technical support for the overhaul and maintenance of the stack product.

The invention relates to the technical field of remote sensing image analysis and processing, in particular to a complex natural scene-oriented land cover remote sensing information acquisition method. The method comprises the following steps: firstly, acquiring all tree clusters, further acquiring a difference separation weight of each environment index in each tree cluster, and adjusting the distance between different tree areas by combining the difference between corresponding environment information of different tree areas under the same environment index to acquire all sub-tree clusters; and finally, carrying out image enhancement, labeling and classification on the tree areas in the sub-tree clusters. The method comprises the following steps: firstly, carrying out preliminary clustering on tree areas based on morphological characteristics of trees, further analyzing differentiation performance of environments where different trees are located in the same cluster, determining the distinguishing capability of each environment index on tree species, then, carrying out reclassification on the trees in the cluster, and carrying out enhancement on the tree areas to different degrees; and the acquisition effect of the remote sensing information of the land cover is improved.

Methods, apparatuses, and systems for transcribing and performing analysis on patient data are disclosed. Data is collected from one or more medical professionals as well as sensors and imaging devices positioned on or oriented towards a patient. An analysis is performed on the patient data and the data is presented to a medical professional via a verbal interface in a conversational manner, allowing the medical professional to provide additional data such as observations or instructions which may be used for further analysis or to perform actions related to the patient's care.

The invention relates to the technical field of voice interaction, in particular to a dialogue type AI low-delay response control method and system based on semantic analysis. The method comprises the following steps: firstly, generating a driving intensity correlation factor of a current time window according to the change of CAN bus data; further monitoring the output of the NLU, analyzing the acoustic intonation raising characteristics, and obtaining an interaction suppression coefficient in combination with the driving intensity correlation factor; further acquiring a time pressure coefficient based on the mute duration after the user stops sounding; further comparing the interaction suppression coefficient and the time pressure coefficient of the current time window to obtain a response judgment value; further analyzing the change trend of the interaction inhibition coefficient, and updating an environment improvement flag bit; and finally, according to the driving intensity correlation factor, the environment improvement flag bit and the response decision value of the current time window, carrying out multi-level response decision control, and carrying out selective reset of an interaction state, thereby solving the problems of wrong truncation and response delay of voice interaction under dynamic driving.

The invention discloses a Chinese patent medicine prescription intelligent assistance method and system, and the method comprises the steps: determining a possible traditional Chinese medicine diagnosis range through disease information / patient information, carrying out the further analysis, obtaining the disease feature information / patient feature information which needs to be obtained for determining the traditional Chinese medicine diagnosis, and determining the traditional Chinese medicine diagnosis according to the related information. The Chinese patent medicine prescription intelligent auxiliary system comprises a demand acquisition module, a feature information analysis module, an information acquisition module and a traditional Chinese medicine diagnosis analysis module. According to the Chinese patent medicine prescription intelligent assistance method and system, doctors and patients can be helped to accurately select Chinese patent medicines, and damage caused by misuse of the medicines is prevented.

The invention discloses a hydroelectric generating set rotor acoustic diagnosis method and device based on a space-time joint feature map and a medium, and belongs to the technical field of hydroelectric machines. Generating an acoustic signal with a complete spatio-temporal information mark; forming a preliminary acoustic feature vector; further analyzing the preliminary acoustic feature vectors, and determining phase differences among the acquisition nodes; generating a space-time synchronization acoustic signal subjected to coupling alignment; further feature extraction is carried out on the space-time synchronous acoustic signals subjected to coupling alignment, and a space-time joint feature map used for describing the operation state of the hydroelectric generating set rotor is constructed; and performing automatic analysis on the space-time joint feature map to generate a final fault diagnosis report. According to the method, weighted focusing is carried out on the high-energy disturbance characteristics in the frequency domain and the time sequence dimension in combination with a self-attention mechanism, and the recognition resolution of complex multiple fault modes is improved.

The invention relates to the technical field of network communication, in particular to an intelligent visual edge gateway control method based on a 5G network. According to the method, real-time transmission and timely control of edge gateway information are achieved through the 5G network, meanwhile, the operation state of the edge gateway is visually and comprehensively mastered through the information, timely finding and processing of problems are facilitated, the visual quality and the data transmission quality are ensured along with channel communication quality evaluation and analysis of the 5G network, and the service life of the edge gateway is prolonged. The resource allocation is dynamically adjusted in an information progressive mode, the transmission timeliness of priority data is guaranteed during channel load fluctuation, the problem that a fixed scheduling strategy is poor in adaptability is solved, meanwhile, the operation state information of the edge gateway is further analyzed, and the service life of the edge gateway is prolonged. According to the method, reasonable fault management can be carried out according to hardware or software faults of the edge gateway, namely, the hardware faults and the software faults are analyzed, so that the faults of the edge gateway can be found and processed in time, manual intervention is reduced, and the reliability of the system is improved.

The invention discloses a fault diagnosis method for a series energy storage lithium battery pack under a charging condition, and the method comprises the following steps: (1), monitoring and collecting the single voltage and temperature of each cell in real time, and uploading the single voltage and temperature; (2) taking the single voltage of the battery cell as the characteristic of the battery cell and expanding; (3) expanding the characteristics of the battery cells, and introducing a voltage correlation coefficient between the battery cells as a new characteristic dimension; (4) calculating a correlation coefficient change rate of the current time window and the previous time window as a new feature; (5) calculating a Z-Score score of the information entropy as an expansion feature; (6) determining the characteristic vectors of the single cells to obtain a final characteristic matrix; (7) the feature matrix is put into an isolated forest algorithm for abnormal point identification; and (8) further analyzing and screening the battery cell data points marked as abnormal, and determining an abnormal reason and a fault type. The method does not need battery modeling, is suitable for various different types of battery cells, and is simple, convenient and rapid.

The invention discloses an artificial intelligence-driven thermal paper production waste intelligent identification method, and relates to the technical field of thermal paper production waste identification, and the method comprises the following steps: obtaining a first normal reference value and a second normal reference value based on a first amount of normal thermal paper drawing; obtaining an initial abnormal region based on the real-time thermo-sensitive paper graph, the first normal reference value and the second normal reference value; acquiring three-dimensional point cloud charts above and below the thermo-sensitive paper in the initial abnormal region part, and marking the three-dimensional point cloud charts as real-time point cloud charts; constructing a real-time detection angle based on the real-time point cloud atlas; acquiring a historical normal angle based on the second quantity of normal thermal sensitive paper; acquiring a normal angle reference value based on the historical normal angle; based on the real-time detection angle value and a normal angle reference value, judging whether the thermo-sensitive paper is a waste material or not; the method is used for solving the problem that in an existing thermo-sensitive paper production waste recognition technology, whether interference exists or not cannot be further analyzed to cause recognition error defects, and consequently the accuracy of thermo-sensitive paper production waste recognition is low.

A mobile camera-based system and method provides real-time analysis and feedback on baseball swing performance using computer vision and machine learning techniques. The system comprises a mobile application that captures high-frame-rate video of a hitter's swing using one or two smartphone cameras. A custom YOLO-based pose estimation model detects and localizes key points on the bat in each video frame. The extracted bat trajectories are then processed and input into an XGBoost machine learning model to predict critical swing metrics like bat speed, attack angle, and time to contact. The predicted metrics are displayed to the user through intuitive visualizations in the app's interface within seconds of the swing, enabling instant feedback and adjustment. Swing data is stored locally on the device and can be uploaded to a central server for further analysis, aggregation, and reporting.