20results about How to "Enable early detection" patented technology

The invention relates to the technical field of intelligent control of transformer substations, in particular to a standardized intelligent control system of a transformer substation, which realizes real-time response to the running state of equipment by acquiring the current, voltage, power factor, temperature and humidity, operation state, insulation state and environment temperature and humidity of a power transformation unit in real time. A gradient boosting decision tree is combined with a variational auto-encoder and a self-attention mechanism, state vector features are extracted, complex abnormal states are accurately recognized, potential faults are detected in an early stage, and the monitoring efficiency and the early warning precision are greatly improved. The future power demand is accurately predicted by combining the long-short-term memory network model with historical and real-time load data, and the limitation of traditional static load management is overcome. Based on the predicted power demand, the reinforcement learning model is adopted to dynamically adjust the operation number of the power transformation units, optimal configuration of resources is achieved while the demand is met, and the overall operation efficiency of the transformer substation is remarkably improved.

The invention relates to the technical field of battery detection, in particular to a new energy vehicle battery fault intelligent detection system which comprises a voltage change analysis module, an internal resistance micro-analysis module, a current response monitoring module and a fault diagnosis integration module. According to the invention, by refining voltage monitoring and internal resistance analysis, the capability of early recognition of battery performance degradation and faults is remarkably improved, the early warning of the faults is more accurate by recognizing abnormities through an advanced data processing technology and refining fluctuation analysis, the risk of false alarm and missing alarm is reduced, and the internal resistance data is analyzed by introducing a fractal theory. Microscopic changes of battery aging can be more accurately captured, early detection of the aging trend is realized, pulse current testing and current response data analysis are combined, evaluation of battery performance stability is further enhanced, comprehensiveness and depth of a detection result are ensured, and detection accuracy is improved. And the accuracy of battery fault detection and the efficiency of determining the fault type are greatly improved.

The invention relates to the technical field of immunodetection, in particular to a hybridoma cell strain secreting an anti-threonine phosphorylated tau protein monoclonal antibody. The hybridoma cell strain for secreting the p-tau181 monoclonal antibody is preserved in the China Center for Type Culture Collection, and the preservation numbers of the hybridoma cell strain and the hybridoma cell strain are respectively CCTCC NO: C2024272 and CCTCC NO: C2024273. The hybridoma cell strain can be used for preparing a monoclonal antibody capable of specifically recognizing p-tau181 and aggregate thereof, and the problem that a targeted p-tau181 antibody is lacked at present is solved. The invention has the advantages that the two monoclonal antibodies can be further applied subsequently to prepare a high-specificity and high-sensitivity p-tau181 detection kit, the passive immunological treatment of specific targeting p-tau181 is developed, the disease progress is slowed down, and the blank of the current immunological treatment of targeting p-tau181 is filled up.

This invention relates to the field of medical image processing, specifically to a multimodal medical image fusion system and method, comprising a data preprocessing module, a domain manifold embedding module, a hierarchical adaptive domain alignment network module, a multimodal feature fusion module, and a diagnostic decision module. The system establishes a topological correspondence between EBUS ultrasound, OCT tomography, and DWI functional images through a multidimensional domain manifold embedding structure; employs a hierarchical adaptive domain alignment network to achieve multi-scale feature extraction and precise alignment; designs a topology-preserving multimodal feature fusion mechanism to ensure the complete preservation of key diagnostic information from each modality during the fusion process; and utilizes a dual-path cross-validation decision strategy to improve the stability and reliability of diagnosis. The system inputs the domain-aligned multimodal features into a Transformer network and generates nodule detection and benign / malignant classification results through dual-threshold judgment, achieving accurate and non-invasive diagnosis of small lesions such as pulmonary nodules and early gastrointestinal lesions.

The invention discloses an intelligent acoustic positioning system for sick pigs in a hog house, and relates to the field of sound recognition and positioning. The problems that an existing sick pig acoustic positioning method is poor in voice recognition accuracy and large in positioning error in a complex acoustic environment are solved. According to the invention, a time difference and phase difference complementary positioning principle is adopted, and an adaptive beam forming technology is combined, so that the problem of inaccurate sound source positioning in a pig house environment with strong reflection and high noise is solved, multi-feature extraction and intelligent diagnosis are carried out on enhanced sound data through the sick pig sound recognition unit, and the recognition accuracy is improved. The method is mainly used for positioning sick pigs in a complex breeding environment.

The invention discloses a neck radiotherapy sequelae management and intervention system and method, and relates to the technical field of radiotherapy management, and the system comprises a symptom detection module, a data processing module used for analyzing data obtained by the symptom detection module, an intervention suggestion module used for generating personalized intervention suggestions, and a user interface module. Specifically, physiological parameters of a patient, images of neck skin and muscles and subjective symptom feedback are obtained in real time; integrating data from different sources to form a complete patient symptom database; processing the data by using a machine learning algorithm and a statistical analysis method, and identifying a symptom mode and trend; according to the analysis result, a patient symptom report is generated, and the patient symptom report comprises the severity, the change trend and the potential risk of the current symptom; and personalized intervention suggestions are provided for the patient. According to the invention, early detection, evaluation and personalized intervention of the neck radiotherapy sequelae can be realized, and the method has high clinical application value.

This invention discloses a method and system for identifying crop pests based on visual recognition, belonging to the field of pest identification technology. A crop pest identification system based on visual recognition includes a crop visual processing module and a crop pest identification module. This invention constructs a multi-source information collaborative judgment mechanism by integrating visual image features, volatile gas concentrations, and environmental parameters, effectively distinguishing between physiological abnormalities and pathogenic diseases, reducing misjudgments and missed judgments caused by environmental interference, and significantly improving the accuracy and reliability of identification results. By combining gas sensor data to analyze crop respiration characteristics and maturity status, it can identify crop physiological abnormalities before visual symptoms appear, achieving early detection and warning of pests. Dynamically adjusting the identification threshold and sensitivity according to different growth stages supports targeted prevention and control decisions, improving the timeliness of prevention and control.

This invention discloses a combination of methylation molecular markers for the diagnosis or prediction of nasopharyngeal carcinoma, consisting of three methylation molecular markers located in the human ZNF804A, FOXL1, and CASZ1 genes: chr2:184598880-184599080, chr16:86578884-86579084, and chr1:10642015-10642215, specifically located using hg38. This invention also discloses the application of this combination of methylation molecular markers, related kits, and analytical processing procedures recorded on a computer-readable storage medium. The methylation levels of the ZNF804A, FOXL1, and CASZ1 genes, as biomarkers for the early diagnosis or prediction of nasopharyngeal carcinoma, enrich the options available to those skilled in the art.

This invention relates to the field of social media content detection, and more specifically, to an early rumor detection method based on cross-modal meta-learning of social big data with small sample sizes. The method includes: acquiring multimodal information to be detected published on social media; extracting features using a multimodal feature extraction network; constructing a multimodal hidden information extraction network to extract hidden information, thereby obtaining multimodal hidden information; constructing a multimodal fusion processing network; using the multimodal fusion processing network to detect rumors; and optimizing the multimodal hidden information extraction network and the multimodal fusion processing network using a meta-learning algorithm to obtain the final detection result. This invention achieves rumor detection by extracting multimodal hidden information and performing deep fusion processing. Furthermore, this invention achieves early rumor detection with a small sample size by applying a meta-learning algorithm to the multimodal hidden information extraction network and the multimodal fusion processing network.

This invention discloses a structure-sensor integrated prefabricated transition connection component and monitoring method for offshore wind turbine steel-concrete towers. It relates to the field of building structural component technology, and includes: a lower flange and an upper flange respectively provided on the upper and lower ends of the transition connection component body; a sliding decoupling interface layer on the lower end face of the lower flange and an upper sliding decoupling interface layer on the upper end face of the upper flange; an inner ring bolt assembly for connecting the lower flange to the lower concrete section ring body; and an outer ring bolt assembly for connecting the upper flange to the upper steel tower section; an FBG sensing channel is disposed within the wall of the transition connection component body. This invention, a structure-sensor integrated prefabricated transition connection component and monitoring method for offshore wind turbine steel-concrete towers, can serve as a load-bearing structural component, and simultaneously integrates strain self-sensing, interface slip self-detection, bolt axial force self-monitoring, and preload active hydraulic-semi-passive SMA combined self-compensation functions within the component body.

The invention discloses a multi-parameter monitoring method for reservoir slope stability, which belongs to the technical field of reservoir slope safety. Before the reservoir is filled with water, a multi-parameter monitoring instrument that can simultaneously monitor the three-dimensional stress, pore water pressure and water temperature of the slope rock and soil body is pre-installed in the slope rock and soil body that will be submerged by the reservoir, and the three-dimensional stress, pore water pressure and water temperature of the slope rock and soil body are continuously measured 30 days before and 30 days after the reservoir is filled with water, respectively, and the variation coefficients of the three-dimensional stress, pore water pressure and water temperature of the slope rock and soil body before and after the reservoir is filled with water are calculated, and thus the variation coefficients of the three-dimensional stress, pore water pressure and water temperature of the slope rock and soil body when the slope is in a stable state are determined; then the three-dimensional stress, pore water pressure and water temperature of the slope rock and soil body are monitored in real time, and the dynamic variation coefficients of the three-dimensional stress, pore water pressure and water temperature are calculated, and compared with the reference value, so as to evaluate the stability of the reservoir slope, and the method is simple, convenient, practical and efficient.

This invention discloses an in-situ monitoring device for formation fluid invasion based on annular fluid density measurement. During drilling, drilling fluid flows from the annulus through a fluid sampling unit located on the logging-while-drilling tool. This unit is connected to the annulus via inlet and outlet guide pipes, and a portion of the annular fluid continuously flows through a built-in radiometric densitometer. The densitometer continuously measures the fluid mixing density at a set frequency and converts the density data into an electrical signal. The downhole logging-while-drilling tool simultaneously collects real-time temperature and pressure data, accurately calculates and corrects the fluid phase density, and thus calculates the invasion amount at that downhole location. By setting an invasion amount alarm threshold, it is determined whether a spill has occurred. If a spill is confirmed, the invasion amount data and alarm information are uploaded to the surface data processor in real time via a logging-while-drilling pulse generator. This invention enables early monitoring of spills, providing a more reliable technical guarantee for drilling safety.

The invention discloses a micro-fluidic chip, and an exosome miRNA detection kit and detection method based on the micro-fluidic chip, and belongs to the technical field of micro-fluidic chips. The micro-fluidic chip comprises a substrate and a cover plate, and a mixing channel area, an exosome capture area and an exosome detection area are sequentially connected to the cover plate; the exosome capturing area comprises an exosome capturing area inlet, a capturing area Y-shaped microcolumn array and an exosome capturing area outlet which are sequentially connected from left to right; the exosome detection area comprises an exosome detection area inlet connected with the exosome capture area outlet; an entrance of the exosome detection area is provided with two branches which are respectively connected with the first detection area Y-shaped micro-column array and the second detection area Y-shaped micro-column array. The microfluidic chemiluminescence biosensor based on multiple signal amplification of a Y-type array, HCR reaction and Poly-HRP disclosed by the invention can be used for early detection of breast cancer exosomes miR-21 and miR-155.

The invention discloses a molecular marker in a bovine KCNIP1 gene and application of the molecular marker. The molecular marker is an SNP (Single Nucleotide Polymorphism) site g.2487025Ggt in a bovine genome; a. The molecular marker or the substance for detecting the single nucleotide polymorphism of the cattle molecular marker provided by the invention can be applied to cattle genetic breeding, so that the number of red blood cells and the hemoglobin concentration of improved cattle offspring in Tibetan are reduced, and the problem of hypoxia faced by cattle breeding in a plateau environment is solved.

This invention relates to the field of machine tool processing technology, specifically to a gear-removing machine tool cutting tool and its electric spindle. The tool includes an electric spindle body, within which a rotary support module, a drive module, and a tool changing module are disposed. The rotary support module includes a rotary groove formed within the electric spindle body, a rotary sleeve rotatably connected within the rotary groove, and an air bearing disposed between the rotary sleeve and the inner wall of the rotary groove. The rotary sleeve is circumferentially arranged with several pneumatic displacement components, which generate displacement based on the air film pressure fluctuations generated by the air bearing on the rotary sleeve during tool processing. The invention also includes a monitoring mechanism and an alarm mechanism. The monitoring mechanism responds to the displacements generated by the pneumatic displacement components in various directions and performs displacement difference processing to obtain the displacement difference. The alarm mechanism triggers an alarm when the displacement difference exceeds a threshold. This invention enables real-time monitoring of the tool's operating status during gear-removing machining.

The present application provides a molecular marker in the ADAM18 gene of cattle and its application. The molecular marker is one or more of g.34873341T>A, g.34874222C>T and g.34877973C>G in the cattle genome. The molecular marker or the material used to detect the single nucleotide polymorphism of the molecular marker of cattle provided by the present application can be used in cattle genetic breeding to reduce the hemoglobin concentration of the offspring of Tibetan improved cattle and solve the low oxygen bottleneck problem faced by the breeding of plateau cattle.

The invention discloses a conveyor belt cigarette carton stacking collapse detection method, and relates to the technical field of image recognition, and the method comprises the following steps: S1, collecting a cigarette carton end image sequence on a conveyor belt, carrying out the positioning detection of each cigarette carton end in the cigarette carton end image sequence, and obtaining the coordinates of four vertexes of each cigarette carton end; s2, based on the coordinates of the four vertexes of each cigarette end, tracking the cigarette end head pixel points of the cigarette end head image sequence, and obtaining the coordinates of the cigarette end head pixel points in subsequent frames; s3, according to the tracking result of the cigarette end head pixel points, the motion direction of the same cigarette end head pixel point between adjacent frames is calculated; and S4, carrying out statistical analysis on the motion direction of the pixel points of the cigarette end head by utilizing the motion direction histogram, and identifying the collapsed cigarette in the cigarette end head image sequence. According to the method, through detection and identification of collapse of the cigarette pile, the position of the collapsed cigarette carton is adjusted in time before packaging, unpacking and secondary packaging are avoided, and the sorting efficiency is effectively improved.

The present invention discloses a gas turbine fault diagnosis method and system. Based on complex fault factors, typical fault modes and multi-source symptom information data are obtained to complete the construction of a fault diagnosis ontology knowledge base model; the functional requirements of ontology query and ontology-based data access are realized; the historical data of the gas turbine is automatically cleaned to obtain benchmark data that meets the training parameters; the benchmark data that meets the training parameters is processed to build a multivariate benchmark model of the unit; the multivariate benchmark model is processed to obtain early detection information of abnormal states of the gas turbine; based on the causal relationship and uncertainty between the early detection information of the abnormal state of the gas turbine and fault knowledge, a diagnostic decision model based on a structural causal model is constructed. Based on the diagnostic decision model, all candidate faults are compared, the cause of the fault is obtained, and a maintenance strategy is provided. The present invention can obtain the cause of the fault in complex machinery, save manpower and material resources, and improve work efficiency.

The invention discloses a method for identifying diseases of rice by using unmanned equipment, the unmanned equipment and a device, the unmanned equipment comprises an unmanned aerial vehicle, a connecting mechanism and an image collecting device, and the image collecting device and the unmanned aerial vehicle are connected through the connecting mechanism and the distance between the image collecting device and the unmanned aerial vehicle is controlled through the connecting mechanism; comprising the steps that a task instruction is obtained and sent to a corresponding unmanned device, the task instruction is used for enabling an unmanned aerial vehicle to be in a hovering state when the unmanned aerial vehicle reaches a task area above rice, a connecting mechanism is controlled to be prolonged, and an image collecting device moves downwards and penetrates through a leaf layer of the rice till the image collecting device is close to the water surface; the image of the rice close to the water surface is obtained; and inputting the image into the trained disease identification neural network to obtain an identification result. According to the technology, the image collecting device is controlled to penetrate through the rice leaf layer till the position close to the water surface for image collection, detection of diseases on the upper portion of the rice water surface is achieved, and early detection of the diseases such as banded sclerotial blight is ensured.