115results about How to "Confidence" patented technology

The invention discloses a confocal laser microscope detection method for micro-plastic surface biofilm components and biomass, and belongs to the field of micro-plastic pollution monitoring. The method comprises the following steps: carrying out stratified sampling and polymer component identification on micro-plastic in an environmental water body; the method comprises the following steps: marking algae, blue-green algae, bacteria and extracellular polymeric substances in a biological membrane through specific fluorescent staining, carrying out high-resolution three-dimensional imaging by utilizing a confocal laser microscope, and realizing batch quantitative analysis of each component of the biological membrane by adopting ImageJ software. The technical problems that an existing detection method is insufficient in parameter dimension, evaluation result deviation is caused only based on partial component quantification, and the influence of the overall structure of the biological membrane on the micro-plastic migration behavior cannot be truly reflected are solved. According to the method, the biomass and spatial distribution of each component of the biological membrane can be comprehensively and accurately quantified, the accuracy of analyzing the vertical migration mechanism of the micro-plastic is improved, and multi-dimensional data support is provided for ecological risk assessment.

The application relates to the technical field of battery safety, and discloses a battery safety early warning method and system, an electric vehicle and a computer readable storage medium, wherein the battery safety early warning method comprises the following steps: when a battery is in a constant-voltage charging phase, voltage signals of each cell of the battery are acquired according to a preset time interval; based on the voltage signals of each cell, a voltage change rate sequence corresponding to each cell is calculated; if the voltage change rate sequence of any cell meets a preset abnormality judgment condition, the cell meeting the preset abnormality judgment condition is marked as a risk cell, and a voltage attenuation rate of the risk cell is collected; and in response to the voltage attenuation rate exceeding a preset threshold range of a reference discharge rate, a high-risk alarm is generated. Therefore, hidden failure signs are mined from voltage evolution trends before the battery enters an obvious overvoltage state, and high-confidence active safety early warning is realized.

This invention designs a multi-scale star surface impact crater detection method, system, and medium. The method includes the following steps: S1: Acquire star surface images and segment them into image patches of different scales; S2: Construct multiple impact crater detection sub-models; S3: Train each sub-model using star surface image patches of different scales; S4: Input the segmented star surface image to be detected into each pre-trained sub-model to obtain the detection results; S5: Select the bounding boxes with high confidence and large area among all candidate bounding boxes of each impact crater as the final detection result for that impact crater. This invention can simultaneously identify impact craters of different scales in star surface images with high accuracy, especially improving the accuracy and efficiency of identifying small impact craters, providing more accurate and comprehensive data support for lunar science research, and promoting the in-depth development of lunar exploration and geological research.

An oral care device includes a head for caring for a user's oral cavity, the oral cavity including a plurality of oral regions, and an inertial measurement unit (IMU) operable to output signals that depend on the position and / or movement of the head. The oral care device includes a controller configured to receive from the IMU signals indicative of the position and / or movement of the head relative to the oral cavity, and to process the received signals using a trained non-linear classification algorithm to obtain classification data. The classification algorithm is trained to identify the oral region in which the head of the oral care device is located from the plurality of oral regions. The controller is configured to use the obtained classification data to control the oral care device to perform an action.

The application provides a sewage plant safe capacity scheduling method and device for virtual power plant peak shaving, and the method comprises the following steps: load-process dynamic coupling modeling; when receiving a peak shaving task, calculating the maximum power adjustment amount that can be safely executed within a response time window T_win under the current working condition for each load object; in day-ahead scheduling, advancing the power adjustment start time of the load object to before the response time window T_win, and planning the maximum technical adjustment power to plan the reportable capacity; in the day-ahead scheduling, aggregating all load objects with P_dis>0 to form a safe schedulable resource pool, performing load object combination optimization, and generating scheduling instructions. The application has the beneficial effects that under the given peak shaving task of the power grid, the safe capacity DeltaP_max of each load object in the sewage plant under the current working condition is calculated and determined in real time, and safe scheduling is realized based on the safe capacity DeltaP_max, thereby solving the problems of untrustworthy scheduling instructions and process safety risks.

This specification provides a dynamic governance retrieval method based on generator-aware feedback and list-based rewards, comprising: performing multimodal preprocessing on input multi-format unstructured documents to establish a joint representation of text content and spatial coordinates, and outputting structured data; receiving mixed strings input by users, calling a large language model to perform semantic parsing and intent recognition, and outputting structured query parameters; recalling candidate data based on the structured query parameters, extracting semantic relevance features, timeliness features, data quality features, and multi-source consistency features of each candidate data, and generating a ranking list based on the features; inputting the ranking list into a generation model to generate answers, performing multi-dimensional quality assessment on the answers, and outputting assessment results; converting the assessment results into feedback signals, and dynamically optimizing the parameters and generation strategy of the ranking model based on the feedback signals.

The application discloses a data search and classification method based on eigenvalue index, comprising the following steps: obtaining a set based on original data through a regular semantic hybrid parser, wherein the set comprises feature names and feature values; obtaining a classification vector based on the set through mapping; obtaining classified index data based on the classification vector through two-level index search of multidimensional reverse arrangement and bitmap; obtaining a query data subset based on user query conditions through query rewriting, feature sorting and bitmap intersection; obtaining an updated query data subset based on the query data subset through sequential scanning and sorting; and obtaining updated classified index data based on the query data subset and new data through a bitmap merging strategy.

The present application relates to the field of engineering detection technology, in particular to a deep compaction degree nondestructive testing method and system for large-thickness water-stable base course. A multi-source heterogeneous dataset of a to-be-detected area is obtained using an integrated detection device; a construction process compliance index and a process uniformity index of the to-be-detected area are obtained; each data source in the multi-modal nondestructive testing data is deeply corrected to obtain a deep correction compaction degree estimation set of each data source; the deep correction compaction degree estimation set of each data source is input into an adaptive weight fusion model to obtain a preliminary compaction degree distribution map; a fusion compaction degree distribution map of the to-be-detected area and a dynamic confidence score of each spatial point are calculated; the compaction quality of the to-be-detected area is determined, and a detection strategy optimization instruction is generated for the area with a confidence score lower than a confidence threshold. The present application can realize high-precision, high-confidence and engineering-actual nondestructive testing of deep compaction degree of large-thickness water-stable base course.

The invention relates to the technical field of data transmission, and discloses a wireless self-calibration high-precision mine pressure sensor device and an accurate data transmission method thereof, and the method comprises the steps: carrying out the periodic or triggering correction of the zero point and sensitivity of a pressure sensitive part through an embedded self-calibration unit, so as to maintain the long-time measurement accuracy; in a data transmission link, a specific channel coding and error correction mechanism is used for packaging pressure data, and the pressure data is sent to a receiving end in an anti-interference modulation mode. According to the invention, negative effects of underground high-humidity and strong-vibration environments on sensor attributes and wireless signal transmission are overcome, and accurate acquisition and reliable return of mine pressure data are realized.

This invention relates to the field of coal quality testing technology, specifically to an online method and system for detecting coal quality entering the furnace; it includes: a multimodal data acquisition step: acquiring microwave scattering parameters and surface element characteristic spectra using a microwave tomography sensor array and a surface spectrometer, respectively; a physical field inversion and modeling step: reconstructing the complex permittivity distribution map based on the scattering parameters to establish a three-dimensional physical distribution model including density and moisture; a field-spectrum coupling deduction step: using the characteristic spectra as boundary constraints, and combining them with the complex permittivity distribution map to deduce the internal chemical element distribution; and a volume-weighted quantization step: performing volume-weighted integration on the three-dimensional physical model and chemical distribution to output comprehensive coal quality parameters; this invention eliminates the detection blind spots of single technologies and significantly improves the representativeness and safety of full-section detection of coal entering the furnace.

The present application relates to the technical field of image state recognition, in particular to a kind of based on pattern recognition's picking and replacing hook AI accurate identification and capture system and method, in the present application, the outline change of hook assembly in image sequence frame, boundary difference and gray dynamic are carried out node construction, and combined with edge displacement accumulation analysis, while through graph neural network, cosine value between nodes and coordinate difference are compared jointly, construct path jump sequence, to enhance the response sensitivity of state mutation, under the condition of complex background interference or local occlusion, still can stably extract the key path of morphological evolution, effectively improve the anti-interference and fault tolerance of spatial path recognition, further statistical modeling is carried out on state rate mutation point by hidden Markov model, by matching standard state mode, paragraph merging and invalid segment rejection operation are carried out to abnormal point section, construct state label sequence, realize the accurate division of high confidence, multi-section continuous state.

This invention discloses a pollen identification method based on dual-model decision arbitration and multimodal database association, belonging to the field of intelligent identification technology. The method includes: acquiring pollen images; inputting them into a first model based on Visual Transformer and a second model based on YOLO for parallel inference, outputting candidate categories and predicted probabilities; inputting the results into an arbitration engine; if the highest confidence categories match, a direct determination is made; otherwise, dynamic weighted arbitration is performed to obtain the final determination category; subsequently, the corresponding morphological feature text descriptions are retrieved in real time from the multimodal database, and the determination category, text description, feature heatmap, and alternative references are simultaneously pushed to the interactive interface. This invention overcomes the "black box" defect of models, achieves macro- and micro-feature complementarity and intelligent arbitration, constructs an "instant recognition and interpretation" auxiliary identification system, and improves the accuracy and interpretability of pollen identification.

A method, apparatus and computer program product are provided to identify a particular wireless communication node of one or more wireless communication nodes as a beamforming node. The method, apparatus and computer program product also restrict use of the particular wireless communication node for positioning purposes relative to another one of the wireless communication nodes that has not been identified as a beamforming node. In relation to restricting the use of a beamforming node for positioning purpose, the use of a beamforming node may be restricted in relation to the generation or updating of a radio map and / or in relation to the determination of a position of a mobile device.

The invention relates to the technical field of advanced manufacturing, in particular to a high-temperature and high-pressure metal three-dimensional component laser printing forming cabin which comprises a high-pressure welding and monitoring system used for providing a high-temperature and high-pressure environment for the workpiece forming process, monitoring a molten pool in real time and capturing the dynamic behavior of the molten pool; the environment control system is used for controlling the temperature and the pressure in the hyperbaric cabin assembly and simulating environment parameters of different working conditions; the electric control system is used for accurately controlling and adjusting power supply, signal transmission and operation parameters of the high-pressure welding and monitoring system and the environment control system to realize preset functions of the electric control system; the integrated cabinet body is used for installing the high-pressure welding and monitoring system, the electric control system and the environment control system and guaranteeing the safety of operators; the high-temperature and high-pressure environment can be simulated, and a multi-parameter in-situ monitoring function is integrated.

The application belongs to the technical field of vehicle information anonymization detection, and particularly relates to a multi-type image anonymization annotation dataset construction and target coverage rate determination method. The method is based on a face and license plate image dataset with double annotation of theoretical anonymization region and anonymization features, introduces an anonymization feature extraction branch in an improved YOLOv5-L model and performs cross-modal feature fusion, combines a plurality of loss functions with theoretical anonymization region positioning loss as the core and a two-stage progressive training and difficult example mining mechanism, and realizes precise learning of the anonymization features. After normalizing the input anonymization image, the model inference obtains the theoretical anonymization region coordinates and maps them back to the original size, and through non-maximum suppression and matching of the IoU threshold, the region coverage rate is calculated to determine the positive detection, missed detection and statistical false detection rate. The application effectively overcomes the feature dependency failure and model robustness problem, and realizes high-precision, low-misjudgment anonymization detection and evaluation under various anonymization conditions.

This invention discloses an intelligent monitoring and analysis system for standardized operation processes in restaurant chains, belonging to the field of data processing technology. It includes: a multi-dimensional data perception module that collects video streams, order data, and material weight data; a spatiotemporal semantic association module that synchronizes the time deviation of each device to within 10ms based on the IEEE 1588 protocol and extracts corresponding video slices according to order timestamps; an operation behavior recognition module that extracts human skeleton points and identifies atomic action sequences such as feeding and stirring; a material flow tracking module that triggers pressure sensors in response to feeding actions to record weight changes at a sampling frequency of no less than 100Hz, calculating the measured feeding amount when the standard deviation is less than 0.3g; and a process compliance evaluation module that compares the atomic action sequences and measured feeding amounts with standard operating procedures to generate a quantitative score. This invention solves the pain points of logical disconnect between video and order data and difficulty in tracing material losses, achieving refined closed-loop monitoring of kitchen operations.

The invention relates to the technical field of nondestructive testing, in particular to a copper bar coating lower substrate defect nondestructive testing device based on terahertz waves, which comprises a terahertz scanning acquisition module used for controlling a transmitting and receiving unit to perform two-dimensional scanning so as to acquire an original waveform; the flight time dynamic correction module is used for resolving the local coating thickness and carrying out dynamic time axis alignment; the substrate roughness deconvolution module is used for calling the reference model to carry out deconvolution filtering so as to strip background scattering noise; the interface dielectric feature analysis module is used for extracting a dielectric feature vector reflecting an interface dielectric constant mutation state; the defect risk assessment imaging module is used for constructing a three-dimensional chromatography map and outputting a detection result; according to the method, the physical characteristic decoupling of the signal source is realized, the coating thickness fluctuation and the real defect are effectively distinguished, the misjudgment rate is obviously reduced, and the evaluation accuracy is ensured.

The invention discloses a power distribution lightning arrester aging defect identification method and device considering environment confidence, electronic equipment and a storage medium, and belongs to the field of intelligent monitoring of power equipment, and the method comprises the steps: collecting the inspection image data of a power distribution lightning arrester and the time sequence synchronous environment parameters of the power distribution lightning arrester; mapping the environment parameters to defect display conditions, quantifying the adaptation degree of the environment to aging defect display, and generating an environment confidence index; visual features are extracted from the inspection image data, similarity calculation is carried out on the visual features and standard defect features, and an image similarity coefficient is obtained; and weighting the image similarity coefficient based on the environment confidence index to form a fusion defect probability value, and outputting a power distribution lightning arrester aging defect identification result according to the fusion defect probability value. Therefore, by implementing the method and the device, the problems of low identification confidence and missed detection and misjudgment caused by incapability of distinguishing'equipment defect-free 'and'environmental implicit' due to lack of quantitative evaluation on environmental suitability in the prior art can be solved.

This application relates to a network security testing method, apparatus, computer equipment, and storage medium. The method includes: receiving a network access request carrying a tester's identity token sent by a target terminal; aggregating traffic units corresponding to the network access request using the identity token as a reference to form multiple different logical session pools, and generating a behavior sequence corresponding to each logical session pool based on the correlation of traffic units within each logical session pool; for each behavior sequence corresponding to a logical session pool, determining a target feature vector corresponding to each logical session pool based on the behavior characteristics of the behavior sequence and a predefined attack cycle; and inputting the target feature vectors corresponding to each logical session pool into an intent determination model to obtain the test result of the network access request and the confidence level of the test result. This method can ensure the reliability of network attack and defense exercise test results.

The invention discloses an unmanned tricycle intelligent driving bag system based on visual navigation, and particularly relates to the technical field of intelligent driving, the unmanned tricycle intelligent driving bag system comprises a visual perception unit, a central control module and an execution control interface, and the central control module comprises an environment perception module, a positioning and mapping module and a path planning and decision module. According to the invention, by integrating the visual perception module, the central control module and the precise execution module, autonomous reverse driving of the tricycle in a complex environment is realized. By means of a high-precision vision and inertial measurement unit fusion technology, the system can complete three-dimensional environment perception, accurate positioning and mapping, accurate obstacle recognition and collision-free path planning, especially can perform look-ahead avoidance on dynamic obstacles, and remarkably improves operation safety and operation accuracy.

The invention provides a mass spectrum full-scanning mode-based qualitative and semi-quantitative integrated analysis method for environmental trace micro-pollutants, which comprises the following steps of: analyzing precursor ions without MS / MS secondary mass spectrum in a full-scanning MS mass spectrum, and forming a characteristic table by using data of the precursor ions, reserving candidate compounds corresponding to the precursor ions with high matching degree to generate a first candidate library; searching data of fragment ions with collision energy less than or equal to 20 eV corresponding to the candidate compound in an open source library, integrating the data of the fragment ions and the data of precursor ions in the first candidate library to construct a targeted screening library, performing targeted retrieval in a full-scanning MS mass spectrum, and then performing confidence evaluation and semi-quantitative analysis. According to the method, a large number of lost low-abundance fragment ions can be retrieved, trace micropollutants can be effectively identified, the lowest detection concentration can reach 0.5 ppt level or below, and the method has high confidence coefficient.

The invention provides a log analysis method and system based on a large language model, and belongs to the technical field of log analys.The method comprises the steps that anti-fact rewriting is conducted on a to-be-analyzed log, and multiple log variants are obtained; log parameters are extracted from the log variants through the large language model to serve as intermediate variables, and the confidence coefficient of each intermediate variable is determined; performing correctness scoring on each intermediate variable based on statistical information of a historical log corpus knowledge base; determining a comprehensive score of each intermediary variable according to the confidence coefficient of the intermediary variable and the correctness score of the intermediary variable; and screening the medium variable with the highest comprehensive score as a log parameter of the log to be analyzed, and generating a log template of the log to be analyzed according to the log parameter of the log to be analyzed. According to the log analysis method, the stable semantic structure of the log can be more emphasized in the log analysis process, and the accuracy and generalization ability of log analysis are improved.

The application discloses a forehead tactile point array blind guiding information processing method and system, and belongs to the technical field of barrier-free navigation and tactile man-machine interaction. The method comprises the following steps: acquiring navigation prompt information or environmental semantic information; parsing the information into direction parameters and environmental attribute parameters; according to a preset tactile mapping rule, mapping the direction parameters into forehead circumferential direction tactile codes and mapping the environmental attribute parameters into forehead central point array tactile codes; generating a tactile driving sequence according to a preset output logic, and driving a forehead tactile feedback module to output direction prompts and point array prompts. The application does not occupy the hearing channel, realizes the synchronous expression of direction guidance and road conditions, obstacles and action instructions through forehead double-region tactile hierarchical coding, has strong information carrying capacity, and the tactile semantics can be learned and extended, so that safe and continuous navigation assistance is provided for the visually impaired.

This invention relates to the field of connector appearance inspection technology, specifically to a connector appearance inspection device and its inspection process, including an inspection table and a first belt conveyor, a second belt conveyor, and a third belt conveyor respectively fixedly installed on the inspection table. It also includes: a top surface inspection unit, comprising a first inspection camera and a second inspection camera fixedly installed on the inspection table, the first inspection camera being located above the first belt conveyor and the second inspection camera being located above the second belt conveyor; and a position adjustment assembly, including at least three storage components for accommodating the connectors to be inspected and an adjustment structure for driving the storage components to move to a preset position and flip them over. This device can inspect all six sides of the connector, avoiding blind spots and improving the confidence and reliability of the overall inspection system. Furthermore, the multiple storage components reduce the time interval between flipping adjacent connectors, improving inspection efficiency.

The invention discloses a lung abnormal area accurate identification method and system based on cooperation of ultrasound and CT, and relates to the technical field of image identification, and the method comprises the steps: collecting the user features of a target user, and carrying out the lung abnormal feature prediction; performing parameter configuration of ultrasonic image acquisition and CT image acquisition; performing identification fitness evaluation on the initial ultrasonic acquisition parameters and the initial CT acquisition parameters to obtain initial identification fitness, performing iterative optimization to obtain optimal ultrasonic acquisition parameters and optimal CT acquisition parameters, and acquiring an ultrasonic image sequence and a CT image; and identifying the ultrasonic image sequence and the CT image to obtain an abnormal region identification result. The technical problem that an existing single image technology is insufficient in detail display in lung anomaly recognition, and consequently recognition precision is low is solved.

The application discloses a multi-modal perception-based intelligent terminal user identity identification method and system and a terminal, relates to the technical field of intelligent terminal user identity identification, and comprises the following steps: when it is detected that a mobile terminal meets the target local area network candidate user list and the current short-distance wireless signal strength meets the predetermined strength condition, it is determined that the mobile terminal owner meeting the condition is the current target user of the intelligent terminal; when it is detected that there is no mobile terminal meeting the target local area network candidate user list and the current short-distance wireless signal strength does not meet the predetermined strength condition, a secondary confirmation mechanism including non-inductive historical identity and active inquiry is controlled to be used to determine the current target user of the intelligent terminal; based on the determination of the current target user of the intelligent terminal, the entire interface of the intelligent terminal is seamlessly switched to the service interface and the recommended content corresponding to the current target user portrait. The application improves the confidence and reliability of intelligent terminal user identity identification.

The invention relates to the technical field of industrial control systems, in particular to a digital twinning-based self-adaptive control method for eye drop processing equipment, which comprises the following steps: acquiring a transient pressure attenuation sequence of an eye drop filling pipeline, a brake displacement sequence of a filling pump rotor and real-time temperature data; a rheological relaxation time constant is extracted according to the pressure sequence, a transient shear rate matrix in the pipeline is analyzed in combination with the displacement sequence, and a dynamic viscosity reference parameter is determined based on the temperature data; inputting the parameters into a digital twinborn model, and mapping and outputting a first normal stress difference dynamic evolution time sequence so as to predict a liquid bridge fracture moment and analyze residual normal tensile stress at the tail end of a needle; and analyzing the reverse resorption momentum compensation parameter based on the residual normal tensile stress and the predicted fracture moment, generating an asymmetric resorption control pulse sequence, and finally converting the asymmetric resorption control pulse sequence into a servo driving instruction and issuing the servo driving instruction to a control unit. According to the invention, through digital twin modeling, adaptive control is realized.

The invention discloses a sliding bearing fault diagnosis model construction method and device, equipment and a medium. The method comprises the steps of generating a training set based on historical operation data of a sliding bearing and corresponding multi-dimensional target features; constructing a convex quadratic programming problem of a support vector machine by using the training set according to an interval maximization strategy, and converting the convex quadratic programming problem into a second-order cone programming problem; and solving the second-order cone programming problem by using a solver to obtain an optimal sparse weight vector and offset, and obtaining a sliding bearing fault decision function based on the optimal sparse weight vector and offset to obtain a sliding bearing fault diagnosis model so as to carry out sliding bearing fault diagnosis. By fusing the multi-source operation data and the multi-dimensional target characteristics of the sliding bearing, the defect of single-dimensional data is overcome, and the accuracy of fault state diagnosis is improved; and moreover, the convex quadratic programming problem of the support vector machine is converted into the second-order cone programming problem for efficient solving, so that the solving efficiency is improved.

The application discloses a terminal consumption behavior verification method and system based on vacancy state recognition and adaptive verification, acquires a verification request submitted by a terminal user; the verification request comprises a package image and a package unique identification code; the state of the package is judged by using the package image; the state of the package comprises a vacancy state of content exhaustion and a non-vacancy state of content non-exhaustion; if the state of the package is the vacancy state, terminal consumption behavior data records responding to the package unique identification code are generated. The application improves the reliability and confidence of consumption verification.

The invention relates to the technical field of hydrological monitoring and computer vision, in particular to a hydrological station flow monitoring system and method based on deep learning, and the method comprises the steps: synchronously collecting the water level time sequence data of a river section and a video key frame flow containing water surface texture; converting the water level time sequence data into a corresponding water passing area numerical value and a hydraulic radius numerical value; outputting a flow state feature vector representing the dispersion degree of the water surface flow velocity; outputting a physical parameter vector changing along with time; calculating a moving average value of the river course roughness coefficient in a preset time window; judging whether the moving average value is greater than a preset siltation alarm threshold value or not; if the judgment result is yes, generating a river deposition early warning data packet and sending the data packet to a remote monitoring terminal; otherwise, generating a state log of normal operation of the river channel and storing the state log into the local database; the limit that a traditional flowmeter can only output numerical values is broken through, and a key diagnosis basis is provided for operation and maintenance and flood control decision making of a hydrological station.