79 results about "Condition assessment" patented technology

This invention discloses a method and system for cable line condition assessment and maintenance decision-making, relating to the field of power system technology. The method includes: acquiring cable condition data and determining missing data information; the missing data information includes the location and type of the missing data; based on the missing data information, employing a corresponding data completion strategy to complete the cable condition data, obtaining completed values ​​and corresponding uncertainty indicators; inputting the completed values ​​and corresponding uncertainty indicators into a pre-constructed fault prediction model to obtain the cable's fault probability and confidence interval within a specified future time window; and determining the optimal maintenance strategy based on the fault probability. This invention enables accurate assessment and scientific decision-making regarding cable line conditions even with incomplete information.

This invention relates to the field of maintenance management decision-making technology and discloses an intelligent pavement condition assessment and resource allocation system integrated into a mobile platform. The system includes sequentially connected modules for mobile data acquisition, data preprocessing, pavement condition assessment, resource optimization allocation, and decision support and feedback. The mobile data acquisition module collects multi-source data through a sensor array and dynamic sampling strategy; the preprocessing module generates a high-quality dataset through cleaning, spatiotemporal calibration, and standardization; the assessment module calculates the Pavement Health Index (PHI) based on a three-level model to accurately classify pavement condition levels; the resource optimization allocation module aims to maximize overall maintenance benefits by solving for the optimal resource allocation using a multi-constraint linear programming model; and the decision support and feedback module provides visualized decision display and corrects model parameters based on post-maintenance re-inspection data. The system improves the accuracy of pavement assessment and the scientific nature of resource allocation, adapting to the needs of large-scale road network maintenance management.

This invention discloses an ultra-high frequency partial discharge and temperature monitoring system for use in power distribution network systems, belonging to the field of smart grid facility condition monitoring technology. The system includes: a signal acquisition module for acquiring mixed signals via a main ultra-high frequency channel and interference reference signals via a mirror channel; an inversion compensation module for establishing interference propagation relationships and progressively reducing interference to obtain candidate pulses; a thermal response construction module for constructing a partial discharge thermal excitation coding sequence and generating an event-triggered thermal response matrix; a thermal memory extraction module for constructing an exponentially decaying thermal memory kernel and extracting thermal memory spectral features; a mapping solution module for inverting heat source parameters and thermal resistance distribution based on a reversible Fourier neural operator model; and an assessment and early warning module for generating the spatial distribution of heat sources, analyzing the defect evolution sequence, and outputting condition assessment and early warning information. This invention combines mirror interference compensation and a reversible Fourier neural operator model to achieve internal defect location and early warning in power distribution facilities.

This invention discloses a visual recognition-based furnace condition detection system and method, relating to the field of smelting furnace detection. The method includes: obtaining registered brightness-normalized visual data; combining the components into a multi-basic wavelet decomposition feature tensor; inputting the multi-basic wavelet decomposition feature tensor into a dual-path Vision-KAN coding network to obtain latent space feature representations of the molten pool structure and dust disturbance; constructing a non-uniform dust density estimation model based on the dust disturbance latent space feature representations to obtain latent space feature representations of the molten pool structure and dust disturbance after gating; generating a three-phase interface enhancement feature tensor; combining inverse wavelet transform to output a reconstructed image of the pure molten pool, a segmented image of the molten pool boundary, a liquid level line, and an abnormal splash risk map; and generating a smelting condition assessment result based on the abnormal splash risk map. This invention improves the accuracy and robustness of molten pool structure recovery under complex operating conditions.

This invention discloses a method and system for identifying apparent damage to bridge concrete based on texture analysis, belonging to the interdisciplinary field of bridge engineering inspection and computer vision technology. The method includes: acquiring surface images of bridge components using a drone and generating orthophotos; extracting contrast, correlation, and entropy features based on the gray-level co-occurrence matrix; extracting histogram features based on local binary patterns; detecting corrosion and seepage areas based on the HSV color space; fusing multi-dimensional features and inputting them into a multi-label classifier, simultaneously outputting pixel-level segmentation masks for multiple types of damage such as cracks, spalling, exposed rebar corrosion, and seepage; generating a damage level score and maintenance recommendation report according to bridge technical condition assessment standards. This invention integrates complementary texture features to achieve simultaneous identification of multiple types of damage, outputs pixel-level segmentation results, and automatically generates an assessment report that conforms to engineering specifications.

This invention discloses a method, system, and computer-readable storage medium for detecting post-microneedling transdermal drug delivery skin recovery after cosmetic procedures, relating to the field of skin condition assessment technology after microneedling cosmetic procedures. The detection method sequentially performs primary macroscopic screening, secondary state mechanism analysis, and tertiary precise risk localization, simultaneously collecting multimodal skin data and fusing and analyzing it through an AI model. Based on the highest positive level, it outputs low / medium / high risk conclusions and corresponding cosmetic care plans. The detection system includes primary, secondary, and tertiary detection modules and a control and decision-making module, with each module working collaboratively to achieve comprehensive detection and process control. The computer-readable storage medium stores the corresponding computer program capable of implementing the above detection method. This invention solves the problems of subjectivity, simplification, and low efficiency in existing detection methods, improves detection sensitivity and specificity, clarifies the mechanism of post-operative pigmentation abnormalities, provides a basis for individualized cosmetic care, and facilitates its promotion in cosmetic settings.

This invention discloses an intelligent primary and secondary integrated pole-mounted circuit breaker, relating to the technical field of integrated pole-mounted circuit breakers, including a mechanism box. This invention utilizes a temperature sensor and a surface-mount thermistor to facilitate direct and accurate temperature data acquisition from the terminal block inside the terminal box, improving the accuracy and real-time performance of temperature monitoring, thereby enabling real-time detection of terminal block overheating faults. An anomaly detection module accurately distinguishes between various abnormal states such as natural temperature rise, sensor failure, short-term overload, and poor contact, significantly reducing false alarm rates and enabling early identification of potential faults. A condition assessment module quantifies and calculates the lifespan of seven influencing factors, including temperature difference cycling, condensation, dust and salt spray, electromagnetic interference, mechanical vibration, ultraviolet radiation, and instantaneous high-temperature shock, dynamically assessing the remaining lifespan and health status of the surface-mount thermistor.

The application provides a multi-sensor-based road surface state evaluation method and a vehicle. The method comprises the following steps: determining signal confidence of a plurality of sensors, and determining scene classification probability based on measurement signals output by the sensors respectively; the signal confidence represents the measurement reliability of the sensors; the scene classification probability represents the classification probability of the sensors in each low adhesion scene; based on the signal confidence and the scene classification probability, a fusion distribution probability corresponding to each low adhesion scene is determined; based on the fusion distribution probability, a trust degree corresponding to each low adhesion scene is determined; and based on the trust degree, a road surface state result of a road on which the vehicle travels is determined. The method can avoid the problem of weak scene adaptability caused by single-sensor perception dimension and isolated processing of different sensor data, and can improve the evaluation accuracy of a wet and slippery road to a certain extent.

This invention provides a method for monitoring the condition of transmission lines. The method decouples and quantifies multi-segment conduction and multi-physical quantity coupling by constructing independent inter-segment topological conduction parameter systems and intra-segment multi-physical quantity coupling parameter systems. After two-level normalization of operational monitoring data, a comprehensive degradation index and evolution trend are generated. The dynamic residual degradation barrier is obtained by weighted integration of the influence of neighboring area conduction and the local degradation rate. A collaborative amplification factor is determined based on the degree of degradation. The gradually changing base and the amplified transient impact are superimposed to form a comprehensive risk value. The ratio of this value to the residual degradation barrier is used as the risk ratio. The state type and risk quantification results are output in conjunction with neighboring area constraints. This invention solves the problem in existing technologies where it is difficult to comprehensively handle the coupling effects of multiple factors and their dynamic evolution relationships during transmission line operation, leading to insufficient accuracy and reliability in condition assessment.

This invention relates to a dynamic optimization and mold condition assessment method for the stamping process of radiators in the field of information technology. The method includes: acquiring real-time pressure distribution data and real-time strain data of the sheet metal in key areas of the mold during the stamping process; predicting the material deformation trend using a preset deformation prediction model based on the real-time pressure distribution data and the real-time strain data; determining whether the material deformation trend exceeds a preset tolerance threshold; if the material deformation trend exceeds the tolerance threshold, generating a new combination of process parameters through reverse calculation; sending the new combination of process parameters to the stamping equipment control system for dynamic adjustment; acquiring actual size measurement data of the processed product; associating the actual size measurement data with the process parameter adjustment record and inputting it into a mold wear assessment model; and outputting the current mold wear condition assessment result through the mold wear assessment model.

This invention discloses an integrated method and system for wound area recognition and recovery time prediction. Belonging to the interdisciplinary fields of medical image processing, artificial intelligence, and clinical nursing, this invention aims to solve the problems of low segmentation accuracy, subjective recovery time prediction, and low operational efficiency caused by the separation of wound recognition and recovery time prediction in traditional wound assessment methods. The key technical points are: employing optimized U-Net++ network technology to achieve accurate wound segmentation, then fusing multi-dimensional features with an XGBoost model to predict recovery time, and integrating image acquisition, processing, quantification calculation, and result output functions. This system is adaptable to various scenarios such as hospital clinical assessment and home nursing monitoring, providing objective and quantifiable assessment data for acute and chronic wounds such as trauma and pressure ulcers. Simultaneously, it achieves integrated processing of wound condition assessment and recovery prediction, significantly improving the accuracy and efficiency of nursing work.

This invention relates to the field of power equipment insulation condition monitoring technology, and discloses a multi-source intelligent fault judgment method for compact ring main units (ECT) and electronic current transformers (EVTs). The method includes: simultaneously acquiring transient voltage and current data of the electronic current transformer in response to a voltage step event triggered by the closing action of the grounding switch; selecting short-time window data at the moment of the step, calculating the ratio of accumulated charge to voltage drop amplitude based on the principle of charge conservation, and calibrating the parasitic capacitance parameters of the equipment; constructing a capacitive displacement component using the parasitic capacitance parameters and voltage change rate, and decoupling it from the total current to reconstruct the dielectric response current reflecting the polarization process of the insulation material; performing multi-timescale decay trend analysis on the dielectric response current to identify the mechanism switching moment, and generating diagnostic results for insulation dampness or interface degradation accordingly. This invention can effectively suppress structural capacitance interference and achieve low-cost, high-precision insulation condition assessment.

This invention discloses a method and system for detecting cable condition anomalies based on dynamic modeling, relating to the field of power system condition monitoring and fault diagnosis technology. The method includes acquiring signals synchronously collected by multiple sensors on the cable, detecting abnormal events using the root mean square value of a sliding window, extracting abnormal signal segments, and calculating the arrival time difference and intensity difference of the same signal between different sensors. These differences are compared with a pre-built database of external interference and internal fault propagation features to determine the signal source. If it is external interference, adaptive filtering is used to remove interference components, obtaining a purified signal. Finally, the purified signal, combined with cable body temperature and load current parameters, is input into a cable condition assessment model to output a health score. This method achieves accurate differentiation and adaptive filtering of external electromagnetic interference and internal faults. Combined with multi-parameter fusion assessment, it significantly reduces the false alarm rate and improves the accuracy and reliability of anomaly detection.

This invention relates to a method for evaluating the state of a sensor, wherein the sensor has a deflectable micromechanical sensor structure for detecting physical input parameters and converting the physical input parameters into electrical sensor signals, wherein the environmental medium of the sensor directly or indirectly affects the micromechanical sensor structure, and wherein the micromechanical sensor structure can be deflected by an excitation signal. The method includes the following steps: generating an excitation signal using a driver unit, wherein the excitation signal has at least an amplitude variation; outputting the excitation signal to the micromechanical sensor structure; deflecting the micromechanical sensor structure by the excitation signal; detecting the response behavior of the micromechanical sensor structure in response to the excitation signal; comparing the response behavior with reference behavior to determine a deviation measure of the response behavior relative to the reference behavior; and evaluating the state of the sensor in terms of the presence of deposits based on the deviation measure. The invention also relates to a sensor system and a method for operating the sensor system.

This invention discloses a method, apparatus, terminal equipment, and storage medium for power equipment condition assessment, belonging to the technical field of power equipment operation assessment. The method involves: acquiring structured and unstructured data of the power equipment; wherein the structured data includes: insulation resistance, oil gas concentration, dielectric loss factor, and DC resistance imbalance coefficient; and the unstructured data includes: infrared thermographic images and ultraviolet discharge images; generating a first membership degree of the structured data to each condition category based on the structured data; generating a second membership degree of the unstructured data to each condition category based on the unstructured data; calculating a comprehensive membership degree for each condition category based on the first and second membership degrees; and taking the condition category with the highest comprehensive membership degree as the power equipment condition assessment result. This invention, based on structured and unstructured data, improves the accuracy of power equipment condition assessment.

The present disclosure relates to technology configured to enable fault detection and condition assessment of underground stormwater and sewer pipes. Embodiments of the present disclosure have been developed to allow automated processing of video captured by pipe inspection robots and the like thereby to identify and categorize artefacts in pipes.

This invention provides a method, device, terminal, and storage medium for on-board vibration and noise monitoring. The method includes: acquiring raw vibration data and raw environmental noise data collected by an intelligent monitoring terminal; the intelligent monitoring terminal installed at different locations on the train includes a dual IMU sensor for monitoring train vibration and a noise sensor for monitoring environmental noise; fusing the first raw vibration data measured by the first IMU sensor and the second raw vibration data measured by the second IMU sensor in the raw vibration data; and generating a track condition assessment report based on the fused vibration data and raw environmental noise data corresponding to each intelligent monitoring terminal. This invention achieves real-time monitoring and analysis of vibration and noise during train operation through the collaborative work of the dual IMU sensor and the noise sensor, using multi-sensor data fusion technology, thereby improving the reliability and accuracy of monitoring.

This invention discloses a data analysis-based relay protection equipment condition assessment system and method, relating to the field of industrial data analysis technology. The system acquires historical inspection records of relay protection equipment in substations, establishes a fault database, collects current operating status data of target equipment, calculates the error between the status data and standard values ​​to obtain the operating status assessment value of the target equipment, combines the anomaly recognition results of the target equipment's panel pressure plate image to assess the current abnormal state of the target equipment, collects the target equipment's target semantic features, compares the target semantic features with a set of defective equipment, calculates the similarity between the target equipment and the defective equipment, and performs a weighted calculation with the current abnormal state assessment value to obtain the overall assessment value of the target equipment. Based on the sequence of overall assessment values, the system predicts the future overall assessment value of the target equipment and triggers corresponding early warnings and maintenance decision suggestions based on the overall assessment value.

This invention discloses a method and system for assessing the main insulation condition of high-voltage cables based on a large model, belonging to the field of power equipment condition assessment technology. The method includes: collecting mechanical, physicochemical, and electrical multi-dimensional characteristic parameters of the cable to be assessed and its batch of comparative samples to form an original data matrix and recording the service life; obtaining a normalized matrix through extreme value normalization; calculating the initial weights of each characteristic parameter using the entropy weight method; determining the corrected bathtub curve stage of the cable based on the service life and normalized values; generating temporary weights through reinforcement learning iterative optimization starting from the initial weights, and obtaining expert weight adjustment suggestions from the large model at preset intervals, obtaining optimized weights after revising the suggestions; calculating the Euclidean distance and cosine similarity between the sample to be assessed and the positive and negative ideal solutions based on the optimized weights, and fusing them to obtain the relative closeness as the condition score. This invention significantly improves the accuracy and reliability of high-voltage cable insulation condition perception.

The present application relates to the field of intelligent medical auxiliary decision-making technology, and discloses a ladder treatment scheme recommendation method based on asthma control level and drug response phenotype. The present application aims to solve the problem that the existing scheme cannot identify the deep reason of uncontrolled condition, leading to blind step-up treatment. The present application excludes non-compliance interference by checking the drug execution rate, and improves the authenticity of condition assessment. The present application uses environmental exposure characteristics to adaptively correct the exhaled nitric oxide determination threshold, and eliminates the deviation of smoking on inflammation recognition. The present application combines the logic tree voting of multi-dimensional biological indicators to lock the drug response phenotype. Finally, based on the double index of condition state and phenotype, the present application locates the upgrade or transformation treatment path and generates a recommended scheme. The present application realizes the precise typing and differentiated drug administration of asthma treatment, effectively reduces the drug exposure risk of hormone-insensitive patients while ensuring the clinical efficacy.

The application discloses a kind of comprehensive microcirculation diabetes condition assessment system and method based on multi-modal parameters, belong to medical detection technical field.The system includes: multi-modal data integration acquisition module, for synchronous acquisition nail fold microcirculation video image of the finger and the toe of diabetic patient, blood oxygen saturation data and temperature, humidity data;Data processing and analysis module is used to process the microcirculation video image, extracts microvessel morphology and dynamics parameters, and is fused with blood oxygen saturation, temperature, humidity data, forms multi-modal feature vector;And condition assessment module is used to input the multi-modal feature vector to pre-trained condition assessment model, and outputs comprehensive assessment report about user diabetes microcirculation disorder grade, condition progression risk and complication tendency.The application is through non-invasive, synchronous comparison finger and toe multidimensional physiological parameters, solves the single problem that diabetes extremity distal microcirculation detection lags behind in the prior art, realizes early, dynamic and accurate assessment to diabetes condition.

This invention discloses a method and system for condition assessment and early warning of low-voltage distribution boxes, belonging to the field of power equipment assessment and early warning technology. The invention acquires multi-dimensional operational monitoring data of the low-voltage distribution box and extracts features to construct several electrical nodes. Then, it combines device layout data and multi-dimensional operational monitoring feature vectors to perform link reasoning on the electrical nodes, obtaining the electrical state propagation link. Next, it initializes and iteratively updates the electrical state influence region to obtain the first electrical state influence region. Based on the first electrical state influence region, it determines the state weight of each electrical node. Then, it generates a real-time quantum random walk graph using the state weights, and obtains the real-time state condition mode through quantum walk evolution. Finally, it obtains the condition assessment and early warning result using the state weights of the electrical nodes and the real-time state condition mode, improving the accuracy of condition assessment and early warning for low-voltage distribution boxes.

A drill bit cutting edge wear condition assessment device, belonging to the field of intelligent equipment testing technology, is mainly used to accurately assess the wear condition of drill bit cutting edges. The device includes a main body, a position sensor, a torque sensor, and a speed sensor. The main body comprises a top plate and a bottom plate. A drive assembly is mounted on the top plate, connecting to the drill bit to be assessed and enabling it to move in lifting and rotation. A fixing assembly is mounted on the bottom plate to fix a rock sample. One end of the position sensor is fixedly mounted, and the other end is connected to the drill bit to be assessed, used to monitor the axial position of the drill bit in real time. The torque sensor is mounted on the shaft of the drill bit to monitor the torque value of the shaft during drilling in real time. A speed measuring gear is mounted on the shaft, and a speed sensor is fixed relative to the speed measuring gear to monitor the gear frequency signal in real time to obtain the speed parameters of the drill bit.

This invention relates to a temperature-compensated laser-ultrasonic online high-temperature stress detection system and method, belonging to the field of industrial process and equipment damage detection technology. The system integrates a pulsed laser, laser interferometer, and infrared thermometer on an air-bearing vibration isolation platform via an optical path control platform to achieve grid path scanning of complex-shaped workpiece surfaces. First, the invention establishes a three-dimensional mapping database of sound velocity, stress, and temperature by performing multi-dimensional tests on the tested parts. Then, for service workpieces with existing material data, the real-time temperature and ultrasonic signals of the tested parts are simultaneously acquired, and a stress calculation algorithm is executed based on the database. This invention eliminates multi-factor interference in complex service environments, accurately plots the surface stress distribution of the tested parts, and generates a condition assessment report, providing a high-precision in-situ online assessment method for industrial process and equipment damage detection.

This invention discloses a method and system for assessing the wear status of a pantograph, relating to the field of intelligent condition assessment technology for transportation equipment. The method includes: constructing a time-series window by performing time alignment and standardization processing on multi-source operational data; identifying abnormal change segments and extracting candidate abnormal fragments to generate event vectors; clustering the event vectors to obtain event cluster vectors and determining the hierarchical wear evolution intensity; updating the wear memory state based on this, generating wear adjustment actions and determining the wear increment, thereby obtaining the cumulative wear status and remaining lifespan. This method continuously characterizes the abnormal evolution process and combines multi-source information to achieve dynamic assessment of the wear status, improving the accuracy and stability of the assessment results.

This application discloses a method, apparatus, equipment, medium, and product for technical condition assessment based on matter-element theory and Hidden Models (HMM), relating to the field of equipment technical condition assessment. The method includes: acquiring technical condition parameter data of a marine centrifugal pump; determining a technical condition assessment sequence group based on the technical condition parameter data using a technical condition assessment model for the marine centrifugal pump; selecting a predetermined number of technical condition assessment sequences within the technical condition assessment sequence group to train and re-evaluate the HMM, and then selecting the remaining technical condition assessment sequences for optimization using the Viterbi algorithm to determine the optimal assessment path; the optimal assessment path is used to assess the technical condition of the marine centrifugal pump. This application effectively reflects the dynamism and stability in technical condition assessment, providing a new approach for realizing equipment technical condition assessment.

The application discloses an explosion injury condition assessment and image data analysis system based on deep learning, and relates to the technical field of image data processing.The system comprises a body surface image analysis module, a feature extraction and injury analysis assessment module and an injury analysis assessment and feedback module.The application extracts body surface injury features by processing the body surface images of each target, obtains corresponding emergency early warning determination results according to the body surface injury features, then dynamically assesses the injury conditions of all targets in the order of emergency grades, selects an injury analysis strategy based on the image features of each target to improve the injury condition assessment precision and efficiency, finally performs injury condition assessment based on the selected injury analysis strategy, obtains the injury parameters of the corresponding target, obtains the injury condition state determination results based on the physiological parameters and the injury parameters of each target, and performs feedback, thereby solving the problem of low effectiveness of explosion injury condition assessment in the prior art.

A self-learning cluster control system for supply chain computing infrastructures, comprising a supply chain data acquisition unit, a node profiling unit, a cluster formation unit, a condition assessment unit, a self-learning control unit, and a resource allocation unit, wherein the system is configured to dynamically group supply chain nodes into clusters based on continuously acquired condition data and to enable adjustment of cluster allocation based on demand trends, inventory levels, transportation capacities, delivery times, and disruption risks.

This invention relates to the field of cable inspection technology, and proposes a method and system for inspecting cable lead-sealed structures. The method involves applying an excitation signal to a sensor array deployed on the lead-sealed structure of the cable to be inspected, acquiring the differential induced signal generated by the cable lead-sealed structure under the excitation magnetic field, preprocessing it, and constructing an image of the conductivity distribution of the lead-sealed structure cross-section for defect location, quantitative identification, and condition assessment. Based on magnetic induction tomography, the internal conductivity characteristics of the cable lead-sealed structure are visualized, thereby completing defect location, quantitative identification, and overall condition assessment. Furthermore, the sensor array is configured as multiple differential coil units, with symmetrical anti-phase series induction coils on both sides of the excitation coil of each differential coil unit, effectively suppressing environmental common-mode interference and retaining only useful signals from defects, thus improving the ability to identify weak defect signals. This effectively improves the accuracy, reliability, and early warning capability of cable lead-sealed structure inspection.