19results about How to "Strong timeliness" patented technology

This invention relates to a method and system for recalling recommended products in a second-hand e-commerce scenario. The method includes: acquiring real-time user behavior data; extracting the user's identity identifier and the identity identifier of the object product in the user's current behavior based on the real-time user behavior data; acquiring co-occurring products that form a co-occurring product pair with the object product from a data source; calculating the similarity between the object product and each co-occurring product; and sorting the multiple co-occurring products in descending order of similarity, selecting a predetermined number of co-occurring products at the top of the sorting as the recall products. This invention achieves real-time updates of the similarity of each product by storing the user's real-time behavior and promptly deleting expired user behavior, ensuring that the recommendation results are more relevant to the user's current or recent interests.

The invention provides a mountain road flood disaster identification method and equipment based on a low-altitude digital image, and relates to the technical field of disaster identification. The method comprises the following steps: collecting a current low-altitude digital image and current point cloud data of a target mountain road; extracting a preset detection index and a water damage area of the current low-altitude digital image, and calculating a membership degree of the preset detection index and a preset water damage disaster situation level; inputting the current point cloud data into a pre-constructed three-dimensional terrain model to obtain a point cloud identification index; and according to the point cloud identification index, the membership degree and the water damage area, determining water damage disaster information of the target mountain road. According to the method, the low-altitude digital image and the point cloud data are cooperated to realize identification of water damage and hidden dangers, and the problem that hidden structure damage cannot be detected in a traditional method is solved. Moreover, the membership degree is calculated and combined with the point cloud identification index to determine the flood disaster information of the target mountain road, so that the error caused by single information identification can be avoided, and the identification accuracy is improved.

The invention discloses a defect severity prediction method based on developer modification history, and belongs to the technical field of software development. Comprising the steps of extracting modified historical data and defect report data, constructing an original data set, preprocessing the original data set, establishing a standard data set according to a file-defect-developer mapping relation, and constructing a developer-file weighted directed network based on the standard data set. Obtaining a file-file weighted directed network through weighted projection based on the developer-file weighted directed network, calculating a structure entropy score of a file node based on the file-file weighted directed network, and extracting a software measurement index of each source code file; and constructing a comprehensive feature set based on the structure entropy score, the software measurement index and the defect label combination, and training and verifying a machine learning model based on the comprehensive feature set to realize the defect severity prediction of the file. According to the invention, the accuracy and stability of defect severity prediction are improved from the perspective of history modification by developers.

The application discloses a filtering method for pulse width measurement, which firstly determines whether the pulse signal frequency in a first specified time period is stable, if stable, corrects the pulse width value according to the real-time cycle width, real-time high level width and real-time low level width obtained by real-time measurement, and determines whether the signal state of the current measurement pulse is full high, full low or normal according to the corrected pulse width value, then determines the number of the three signal states in a second specified time period, and determines the real-time signal state as the state with the largest number; if unstable, determines the real-time signal state according to the level for capturing the input pin in a third specified time period; finally, determines the output pulse width value according to the real-time signal state.

The invention discloses a common transaction behavior identification method and system based on dynamic association strength, and belongs to the field of big data analysis and financial science and technology. Aiming at the problems of single dimension, low performance and lack of timeliness of the existing financial common transaction identification method, the method comprises the following steps: firstly, extracting frequency, amount and time multi-dimensional features of a transaction entity pair based on a sliding time window and standardizing the features; clustering coarse screening data through an improved DBSCAN, and mining a frequent item set in a clustering cluster by using an FP-Growth algorithm; then constructing an association strength scoring function to screen significant common transaction groups; and finally, realizing dynamic updating of the model through incremental updating, intensity attenuation judgment and trigger type local recalculation. According to the invention, efficient, accurate and real-time identification of common transaction behaviors in financial mass transaction flow data is realized, and the intelligence and timeliness of financial risk control are improved.

The application relates to a device and a method for monitoring the anchor chain tension of a floating wind turbine mooring system, and belongs to the technical field of floating ocean engineering structures. The device solves the problems of large direct measurement error and high underwater measurement cost. The device comprises an inclinometer, a signal acquisition communication board, a signal processor, a central control room host, a display instrument and a strain sensor. The strain sensor, the inclinometer and the signal acquisition communication board are electrically connected. The signal acquisition communication board, the signal processor, the central control room host and the display instrument are sequentially electrically connected. The device has the advantages of simple structure, convenient installation and use, easy maintenance, overall replacement in case of failure, cost saving, comprehensive monitoring of the anchor chain tension and the inclination motion change, and small error.

The invention belongs to a mining activity monitoring method, and particularly relates to a satellite remote sensing monitoring method for mining activity of an in-situ leaching sandstone type uranium mine, which comprises the following steps of: 1, acquiring and preprocessing remote sensing data; 2, preliminarily screening mining active areas based on soil spectral characteristics; step 3, well site range fine extraction fused with image texture features; 4, identifying a central control room based on morphological characteristics; 5, comprehensively judging the range of the well site; and 6, mining activity dynamic monitoring. According to the method, the unique remote sensing features of the in-situ leaching sandstone type uranium mine mining activity are fully excavated, accurate recognition and dynamic change monitoring of the mine mining area are achieved, and efficient and reliable technical support is provided for monitoring of the in-situ leaching sandstone type uranium mine mining activity.

This invention relates to the field of low-carbon operation and carbon emission accounting technology in power systems, specifically a method for tracing carbon emissions from electric vehicles based on contract paths. The method involves collecting charging data from electric vehicles and identifying the charging process; matching and associating the charging process with power trading contracts; classifying the matched power trading contracts by transaction type, constructing contract paths, and attaching transaction traceability identifiers to each contract path to form a path-power source mapping table; decomposing the contracted electricity volume according to the contract type and contract path; and calculating the carbon emissions from the charging process based on the contracted electricity volume decomposition results and the carbon emission factors of various power sources. Compared with existing technologies, this invention achieves higher accuracy in traceability and improves carbon management efficiency. By combining data analysis and carbon emission control with the characteristics of power system carbon flow and contract trading, it efficiently realizes carbon footprint calculation and dynamic tracking, providing technical support for achieving carbon peaking targets in the transportation sector.

The invention discloses a sewage treatment system abnormity early warning method, system and equipment and a storage medium, and relates to the technical field of sewage treatment. The variety, concentration and time-space distribution of VOCs and malodorous gas in each process unit are monitored in real time through a multi-modal sensing technology, dynamic characteristic parameters are extracted, and a dynamic association model of coupling of an LSTM neural network and a random forest is constructed in combination with inflow water quality and process operation data. The model can accurately identify fault types such as water inlet toxicity impact and aeration abnormity, output graded early warning signals and position an abnormal source. According to the invention, traditional monitoring hysteresis is broken through, early warning is carried out 4-6 hours in advance, intrusive sampling is not needed, the operation and maintenance cost is reduced, and the impact resistance and operation stability of a sewage treatment system are improved.

The application discloses a kind of high similarity electric power tool identification method and system based on deep learning, belong to target detection technical field, its method includes, obtains high similarity electric power tool image dataset and carries out pretreatment;The image dataset after pretreatment is input to improved YOLOv8 model and is trained;The improved YOLOv8 model includes sequentially connected main network, neck network and detection head, wherein the detection head is used to receive the feature map of different scales output by neck network, and utilizes shared convolution layer output detection and classification result;The improved YOLOv8 model trained is used to detect the electric power tool image to be detected by the above-mentioned method and system, and the target detection efficiency and precision are improved.

The application discloses a kind of fastener fatigue life prediction methods based on machine learning, comprising the following steps: S1, acquisition and pre-processing fastener operation monitoring data;S2, sliding window and extract fusion feature vector;S3, construct time series correlation diagram based on feature difference;S4, perform multi-branch graph feature aggregation to generate embedding tensor;S5, expand graph embedding and perform recursive modeling to output degradation representation;S6, piecewise fitting degradation curve to predict remaining life;S7, align predicted value to generate fatigue warning result.The application realizes high-precision dynamic prediction and real-time early warning of fastener fatigue life, with the technical advantages of accurate modeling, fast response and wide operating condition adaptation.

This application discloses an abnormal data acquisition method and system based on a behavior-aware model. The method includes: acquiring multi-source behavior-aware data of a target application scenario and extracting features from the multi-source behavior-aware data to obtain behavioral feature time-series data corresponding to the multi-source behavior-aware data, wherein the multi-source behavior-aware data is acquired through multiple behavior-aware devices; judging the behavioral feature time-series data based on an event classification model and a preset rule engine to determine whether an acquisition event is triggered, wherein the preset rule engine is used to determine whether the behavioral feature time-series data matches the rule for triggering the acquisition event, and the event classification model is used to predict the corresponding acquisition event based on the behavioral feature time-series data; when an acquisition event is triggered, determining the behavior-aware device used to perform the acquisition operation and driving the behavior-aware device used to perform the acquisition operation to acquire behavioral data.

This invention relates to the field of electric vehicle driving technology, specifically to a method and device for active intervention to prevent fatigue driving in electric vehicles. The method uses a flexible dry electrode array pre-installed on the left and right handlebars of the electric vehicle to collect surface electromyography (EMG) signals from the driver's forearm in real time. At preset intervals, multidimensional feature values ​​are extracted from the EMG signals to determine the driver's state. When mild fatigue is detected, linear resonant actuators inside the left and right handlebars are activated to emit pulse vibrations. When severe fatigue is detected, the maximum speed limit is smoothly reduced to a preset limit within a preset time period, while the dead zone damping of the throttle is increased. This technical solution utilizes microscopic changes in EMG signals as a fatigue criterion, achieving high-precision early warning with good timeliness. The two-level intervention strategy significantly improves safety and enables non-intrusive monitoring, resulting in high user compliance.

The present application relates to a kind of Java program dead code detection method based on abstract syntax tree and iteration, which is the process of determining dead method in the analysis of abstract syntax tree of Java project code.Specific steps include cleaning project, obtaining abstract syntax tree, obtaining method call information and reference information, determining dead method, etc.When obtaining abstract syntax tree, Java Parser tool needs to be used, and the complete path name list and related information of the method are obtained by traversing nodes.Then, according to the call relationship between methods, dead methods are iteratively determined.The final result is a set of dead methods.The present application can solve the detection of dead methods in projects with method overriding and overloading, the detection of dead methods in projects with multi-thread programming, and the detection of dead methods in projects with cross-file method calls, while maintaining good timeliness.

The utility model relates to battery technology field, concretely provides cell baking temperature detection frock and cell baking device, including support seat, regulator and contact type temperature detector, support seat is used for connecting tray, is equipped with first guide portion on support seat, regulator connects support seat, and the regulator includes first adjusting part, second adjusting part and third adjusting part, is equipped with second guide portion on first adjusting part, is equipped with third guide portion and fourth guide portion on third adjusting part, third guide portion and second guide portion along first direction sliding fit, fourth guide portion and first guide portion along second direction sliding fit, second adjusting part is configured as can along second direction remove relative to first adjusting part, first direction is vertical direction, and second direction is perpendicular with first direction, and the contact type temperature detector includes first detection piece, and first detection piece establishes at second adjusting part, with adjusting device synchronous movement to adjust detection position, the utility model improves the reliability and detection efficiency of temperature data effectively.

This invention discloses a two-way authentication method for AB dual-lock cabinets based on SM4 and RF-ID, belonging to the field of electronic authentication technology. The invention includes: inserting an electronic key and activating the control board; the control board verifying the electronic key's identity information; collecting and encoding the mechanical key's operating status; generating a temporary session key based on the mechanical status; the control board and the electronic key performing two-way authentication; releasing the electronic lock pin upon successful authentication; recording and uploading the operation log and enabling permissions; and executing locking and alarm strategies upon authentication failure. This invention integrates the real-time operating status of the mechanical key as a dynamic seed into the SM4 session key derivation and two-way challenge-response authentication process, achieving deep real-time coupling and interlocking between electronic authentication and mechanical operation.

The application discloses a kind of multi-signal fusion's permanent magnet motor turn-to-turn short-circuit fault diagnosis method, to solve the prior art from motor current to diagnose turn short, especially for early turn short diagnosis, the probability of false negative is big technical problem, belongs to motor turn-to-turn short-circuit fault diagnosis technical field.The application respectively real-time acquisition motor three-phase current, three-phase line voltage, bearing end cap load area radial vibration acceleration signal in the process of motor operation, specific method includes the following steps Step one, current fault feature extraction;Step two, vibration fault feature extraction;S21, vibration spectrum fault feature extraction;S22, vibration envelope spectrum fault feature extraction;S23, vibration fault feature flag calculation;Step three, turn short online feature flag calculation;Step four, judge whether to open counter electromotive force feature calculation;Step five, counter electromotive force fault feature extraction;Step six, final diagnosis of motor turn short.

The application discloses an elevator operation button failure analysis and elimination method based on the Internet of Things and big data, collects operation signals of elevator operation buttons by using a data acquisition device, and uploads the signals to a cloud platform after a series of processing such as data filtering, cleaning, screening and analysis, so that the function state of the elevator operation buttons is obtained by statistical calculation of the data by the cloud platform. The application records the instruction information of the elevator operation buttons by letting the data acquisition device work independently, formats the information uniformly, uploads the information to the cloud platform for statistics, calculates the statistical data, judges the actual state of the elevator operation buttons according to the calculation result, and issues corresponding work orders to the maintenance system; the application can independently monitor and judge whether the elevator operation buttons are invalid, and execute different processing according to the state, so that the invalid elevator operation buttons can be maintained in time and effectively, and the safety and reliability of elevator operation are improved.

The application provides a wheel trajectory sight for assisting driving, which is used for helping the driver to quickly aim directly by relying on naked eyes, accurately predicting the ground trajectory and trajectory band of the wheel without affecting safe driving, and extending the trajectory band to the key position in the forward field of view according to the actual road condition, so as to win sufficient response time for the driver to calmly deal with complex situations and make active decisions to choose to avoid or pass. The application has the characteristics of accurate positioning, strong timeliness, convenient installation, flexible adjustment, wide application, low cost and the like, has excellent use and promotion value, and can be widely applied to scenes such as narrow road passing, meeting prediction, emergency risk avoidance and stunt driving under various road conditions. The rapid improvement of the driver's related skills is also helpful for the traffic management department to improve the passing efficiency of the road and reduce various traffic accidents from collision to scratching, and is a powerful assistant for cost reduction and efficiency increase.