33results about How to "Strong timeliness" patented technology

The invention discloses a satellite networking constellation-based space-ground cooperative remote sensing agent system, which comprises a ground station and a satellite networking constellation, and is characterized in that the satellite networking constellation is divided into link establishment satellites and non-link establishment satellites according to link establishment conditions with the ground station; in the system, in response to a user input or sudden disaster event, a ground station generates an instruction file by using a ground large model and uploads the instruction file to a link establishment satellite; the link building satellite analyzes the instruction file and judges whether the link building satellite itself is a main responsibility satellite or not, if not, the link building satellite transmits the instruction file to the main responsibility satellite, and the main responsibility satellite generates a corresponding task plan through a satellite-borne large model; the main responsibility satellite generates a corresponding calculation request and / or shooting request based on the actual conditions of the main responsibility satellite and the other satellites, sends the calculation request and / or shooting request to the corresponding other satellites, receives returned calculation results and / or shooting results, generates a result file based on all the calculation results and / or shooting results, and downloads the result file to the ground station through the link building satellite; and the ground station analyzes and visualizes the result file.

The invention belongs to the technical field of power system data analysis, and relates to a power utilization habit-based power utilization abnormal behavior real-time analysis method and system, and the method comprises the steps: obtaining a historical power utilization data sample of a charging pile user, and carrying out the calculation to obtain a power utilization behavior habit interval; acquiring real-time power consumption data of the charging pile user according to a preset period, judging whether the real-time power consumption data is in the power consumption behavior habit interval, and if so, not giving an alarm; otherwise, giving an alarm; the advantages of wide coverage, high timeliness and high accuracy of power data are utilized, and the power utilization habit of the user is obtained through data analysis. Real-time power utilization data and historical power utilization habits are compared, abnormal power utilization behaviors are recognized, abnormal power utilization alarms are given out, and a charging pile operator is assisted in effective management, prevention and control; maintenance and upgrade plans of the charging piles are reasonably arranged, and usability of the charging piles is ensured. An electric power company can adjust an electric power supply strategy in advance, optimizes a power grid load, and avoids overlarge power grid pressure caused by a charging peak.

The invention relates to an offshore wind power plant reactive voltage regulation method based on deep learning, and the method comprises the steps: collecting the historical operation data of an offshore wind power plant, constructing an LSTM neural network model, training the LSTM neural network model through employing the preprocessed historical operation data of the offshore wind power plant, adjusting the weight and bias in a network, and obtaining the reactive voltage of the offshore wind power plant. And obtaining operation data of the offshore wind power plant in real time, inputting the operation data obtained in real time into the trained LSTM neural network model to obtain a predicted value of a reactive power demand and a grid-connected point bus voltage amplitude, and according to the predicted value of the reactive power demand and the grid-connected point bus voltage amplitude, combining an operation constraint and a control objective function of the wind power plant. And dynamically adjusting the reactive voltage by adopting a hybrid optimization algorithm. According to the invention, the time sequence characteristics and dynamic changes of the wind power plant can be accurately captured, so that the reactive power and voltage of the offshore wind power plant can be accurately predicted and adjusted.

The invention discloses a real-time electric power carbon emission metering method based on an enterprise electric power carbon emission metering platform, and the method comprises the steps: calculating the real-time electric power carbon emission of an electric terminal from the carbon emission of electric power flowing into a power plant and a province; comprising the steps of power plant direct carbon emission calculation, power plant direct carbon emission factor calculation, inter-provincial inflow power indirect carbon emission calculation, node carbon emission factor calculation, regional carbon emission factor calculation and terminal power utilization carbon emission. Real-time carbon emission measurement is carried out on the power utilization terminal, and more powerful underlying data is provided for government carbon supervision and enterprise carbon emission reduction.

The utility model belongs to the technical field of track detection, and particularly discloses a tunnel detection vehicle. The tunnel detection vehicle comprises a vehicle body, and a tunnel three-dimensional detection device, a tunnel appearance detection device, a tunnel internal structure detection device, a synchronous positioning device and a main control device which are arranged on the vehicle body, the tunnel three-dimensional detection device, the tunnel appearance detection device, the tunnel internal structure detection device and the synchronous positioning device are all in communication connection with the master control device. The tunnel detection vehicle not only can complete the inspection work of the tunnel of the urban rail transit line in a short time, is high in detection efficiency, but also can realize intelligent comprehensive detection on various diseases and faults, and is high in detection accuracy.

The invention belongs to the technical field of medicines, and particularly discloses a biomarker for detecting kidney fibrosis related diseases and application of the biomarker. The biomarker comprises serum copper cyanin, or the serum copper cyanin and serum creatinine. Compared with other methods for diagnosing renal fibrosis, the serum cupreuloplasmin has the following three advantages: 1, compared with a conventional diagnostic marker (such as serum creatinine), the cupreuloplasmin can detect up-regulation in the serum of a patient suffering from early chronic kidney diseases, and can be combined with an existing mature diagnostic marker for detection; secondly, compared with renal tissue pathologic biopsy, the use of the copper cyanin as the diagnostic marker has non-invasiveness, and only a small amount or even a trace amount of patient serum is needed, so that the harm to the body health of the patient is avoided; and thirdly, compared with a nuclear magnetic resonance method for analyzing the metabolic components of the urine, the method for detecting the markers in the serum is lower in cost, more flexible, high in timeliness and easier to popularize.

The invention provides an improved adaptive ant colony path planning method and system, and the method comprises the steps: S1, scanning a global map environment through a laser radar, carrying out the modeling, building a three-dimensional point cloud image, and carrying out the obstacle expansion and map rasterization; s2, initializing ant colony parameters, and performing grid search; s3, performing optimization processing on the state transition probability by utilizing the trained neural network model during searching; s4, performing path search planning according to the optimized state transition probability until the ant reaches a final target point; and S5, repeatedly executing the step S4 until the maximum number of iterations is reached, and completing all path planning. According to the method, the trained neural network model is utilized to optimize the state transition probability, the optimized algorithm can quickly find the optimal path in the iteration process, and the method has higher timeliness and better path planning capability.

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.

The present invention provides a wind-solar complementary fast frequency modulation control method, comprising the following steps: setting a system fast frequency modulation dead zone, and starting an energy storage device when the system frequency enters the fast frequency modulation dead zone; collecting the CT signal and PT signal of the power station to obtain the power station operation parameters including frequency, voltage, and active power; receiving the active power adjustment instruction forwarded by the AGC in real time, and combining the real-time frequency of the power system to perform frequency modulation according to a preset frequency modulation strategy. The present invention can perform high-frequency and high-precision measurement of the power station system operation parameters by adding a high-speed frequency measuring device, and analyze the real-time frequency value of the power system and the future output trend corresponding to the current state in combination with the active power adjustment instruction forwarded by the AGC, and adjust the system frequency. The present invention can enable the wind-solar system to have a frequency response characteristic, effectively and quickly participate in the system frequency modulation, reduce or avoid negative impacts on the system frequency modulation, and avoid sacrificing wind energy utilization and solar energy utilization.

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 present invention provides an interference base station positioning method and system, the method comprising: receiving information of a cell to which a frequency point scanned by a frequency scanning module belongs; wherein the RSRP of the frequency point is greater than a preset threshold, and the frequency scanning module is located at a target terminal position; when the information of the cell does not exist in a whitelist, determining the position of the interference base station according to the position of the frequency scanning module. The present invention improves the timeliness, accuracy and efficiency of interfering base station positioning.

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.

The present invention relates to the field of artificial intelligence technology, and in particular to a method, module and system for pushing scientific and technological intelligence information. The method and system for pushing scientific and technological intelligence information of the present invention utilize a ranking recommendation algorithm to intelligently screen, evaluate and sort the collected information, and put the information that users are more concerned about in the front, thereby helping users to screen out the most popular, most relevant and most timely information from a large amount of news information, and providing users with more efficient, more accurate and more professional energy science and technology intelligence services.

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 present invention proposes a new energy primary frequency modulation acquisition method and device based on autocorrelation frequency fitting. In the primary frequency modulation system of the new energy station, the three-phase current / voltage signal of the secondary side loop of the power grid is converted into digital and low-pass filtered. The current / voltage signal after low-pass filtering describes the autocorrelation function through a fitting model, and a loss function is constructed to represent the residual sum of squares between the autocorrelation function of the current / voltage signal and the fitting model. The loss function is minimized using a nonlinear least squares method; the frequency obtained by the fitting model is used as the estimated power grid frequency. The present invention uses the autocorrelation function for frequency fitting, which can quickly solve the power grid frequency with high accuracy, good robustness and strong timeliness.

The present application relates to the cross field of coal mine geological modeling and artificial intelligence vision application, and provides a coal mine gas occurrence three-dimensional geological modeling method and system based on AI vision, which comprises: acquiring multi-source data of a mining area in a coal mine, including geological basic data, engineering dynamic data, gas monitoring data and underground visual image data; performing low-light enhancement processing on the visual image data to obtain an enhanced image; based on the enhanced image, using a deep learning model to identify coal rock features, outputting semantic segmentation results containing coal rock interfaces, geological structures and fissures, and corresponding geological geometric parameters; taking the semantic segmentation results as spatial geometric constraints, combining the geological data, engineering data and gas monitoring data, and performing data fusion through a spatial interpolation algorithm to construct a three-dimensional geological model reflecting the gas occurrence state. The present application can realize high-precision modeling, dynamic updating and safety management and control of the gas occurrence state in the mining process in a coal mine.

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.