103results about How to "Accurate discovery" patented technology

The invention relates to the technical field of monitoring and early warning of power equipment, in particular to a monitoring and early warning method and system for substation power equipment. Comprising a data acquisition module, an intelligent diagnosis module, a risk assessment module, a collaborative early warning module and an interaction control module. The data acquisition module acquires equipment operation data including current, voltage, temperature and humidity and vibration signals. And the intelligent diagnosis module identifies equipment abnormity through the generative adversarial network and the graph neural network. The risk assessment module generates a risk assessment curve based on a fractional order differential model and a reinforcement learning algorithm. And the collaborative early warning module generates a graded early warning scheme by utilizing edge calculation. And the interaction control module displays a state and optimizes a control strategy through a human-computer interface. The intelligent monitoring and management level of the power equipment of the transformer substation is improved, the service life of the equipment is prolonged, and the operation and maintenance cost is reduced.

The invention relates to the technical field of grain storage monitoring, in particular to a wharf granary grain condition monitoring method and system. The method comprises the following steps: acquiring data of wharf granary environment characteristics, grain pile size characteristics, temperature characteristics, humidity characteristics and gas concentration characteristics; based on the data, spatial-temporal characteristic models are constructed, and the spatial-temporal characteristic models comprise a temperature spatial-temporal characteristic model, a humidity spatial-temporal characteristic model and a gas concentration spatial-temporal characteristic model; according to the spatio-temporal characteristic model, spatio-temporal characteristic data are obtained; processing the spatio-temporal characteristic data to obtain optimized spatio-temporal characteristic data; constructing a grain safety risk assessment model through the optimized spatial-temporal characteristic data; and monitoring the grain condition of the wharf granary according to the grain safety risk assessment model. According to the invention, internal sensorless monitoring is carried out on the temperature, humidity and # imgabs0 # concentration of the infinite space point in the grain pile, so that the monitoring cost is greatly reduced, and high-precision assessment of the grain safety risk is realized.

The invention provides a multi-mode anomaly detection system based on digital twinning, and relates to the technical field of information technologies. Comprising seven main subsystems including a digital twin modeling system, a real-time data acquisition and sensor network, a multi-modal data fusion and processing system, a real-time data analysis and anomaly detection algorithm, a decision support and feedback system, a virtual simulation and visualization system and a safety and compliance management system. The multi-mode anomaly detection system based on digital twinning further comprises six additional subsystems, namely an automatic response and adjustment system, an extensible model updating and management system, an intelligent prediction and early warning system, a deep learning and mode recognition system, a real-time optimization and resource allocation system and an intelligent feedback and user interface optimization system. Through application of a real-time data analysis and anomaly detection algorithm and in combination with a decision support and feedback system, the system can quickly generate decision suggestions or responses, and a decision result is visually displayed through a virtual simulation and visualization system.

The invention discloses a working face earth surface rock movement digital intelligent observation and through-structure safety intelligent prediction method, and belongs to the technical field of mining industry and information, and the method comprises the following steps: determining a structure movement deformation safety early warning value; moving deformation data are collected and preprocessed; analyzing a dynamic change rule of surface movement deformation by applying finite element analysis; building a structure movement deformation prediction model; the structure movement deformation prediction model is trained and verified, and the trained model is used for structure movement deformation prediction; comprehensively evaluating safety and stability; and correcting parameters of the prediction model and continuously evaluating until the structure is safely pushed and mined. The invention provides a novel way with great potential for accurately predicting the rock migration trend and guaranteeing the safety of important facilities such as structures.

The invention discloses a wind power plant blade icing prediction method and device, equipment and a medium, and relates to the field of wind power generation. The method comprises the following steps: acquiring predicted meteorological data of a wind power plant in a future preset time period, wherein the predicted meteorological data comprises temperature and humidity; and inputting the predicted meteorological data into a pre-trained blade icing prediction model, processing the predicted meteorological data by the blade icing prediction model, and outputting a blade icing prediction result of the wind power plant, the blade icing prediction result representing a blade icing condition in a future preset time period. The blade icing prediction model is obtained through training according to historical sample data of the wind power plant, the historical sample data comprises meteorological data in historical icing time and meteorological data in historical non-icing time, the meteorological data comprises temperature and humidity, and the historical sample data has labels used for representing blade icing conditions corresponding to the meteorological data. According to the embodiment of the invention, the safety risk of the wind power plant can be reduced.

The invention discloses a method for establishing a pulmonary nodule malignant risk prediction model by integrating iconography indexes and expired gas VOCs (Volatile Organic Compounds) data, and belongs to the field of precancerous lesions of lung cancer and early prediction of lung cancer. In order to improve the accuracy of precancerous lesions of lung cancer and early prediction of lung cancer, the method comprises the following steps: acquiring a CT image of a patient with pulmonary nodules detected in lung CT, then processing the CT image of the patient with pulmonary nodules, and extracting image data of iconography indexes; collecting exhaled air VOCs of the pulmonary nodule patient, performing exhaled air VOCs index calibration and abundance analysis to obtain exhaled air data, and then performing multi-omics data arrangement in combination with image data to obtain a multi-omics data set; formulating a lung nodule malignant risk degree standard; and constructing a multi-omics deep learning model, and training the multi-omics deep learning model by using the data set of the pulmonary nodule malignant risk prediction model to obtain a pulmonary nodule malignant risk prediction model integrating iconography indexes and expired gas VOCs data.

The invention discloses a computer network information security monitoring method, which relates to the technical field of network information security monitoring, and comprises the following steps of: monitoring and collecting network flow data in real time by using a monitoring and collecting module, extracting and recording historical network flow data, judging the abnormal degree of the current flow by using a comprehensive evaluation and adjustment module, and judging the abnormal degree of the current flow. The method comprises the following steps: evaluating the overall risk level of network security, adjusting a network security policy, generating a policy adjustment report, executing the adjusted network security policy by using a security execution module based on the policy adjustment report, and continuously monitoring network traffic by using a monitoring acquisition module. The beneficial effects and optimization innovation of comprehensive assessment, dynamic risk assessment, flexible strategy adjustment and sensitivity improvement are realized, and the optimization and innovation not only solve the problems and defects in the prior art, but also improve the accuracy and reliability of the monitoring system. And a more comprehensive and effective guarantee is provided for computer network information security.

The invention discloses an intelligent nursing method, an intelligent nursing system, a storage medium and a program product, and relates to the technical field of medical treatment.The method comprises the steps that electronic medical record data and physiological index data of a patient are obtained; determining personalized physiological index criteria of the patient based on the historical medical data; when at least any one physiological index does not meet the personalized physiological index standard, early warning information is pushed to a nurse terminal, and the disease development trend of the patient is evaluated according to the physiological index data; and when it is determined that the disease development trend is worsening, an emergency consultation request is pushed to a doctor terminal, and the electronic medical record data and the physiological index data of the patient are sent to the doctor terminal. By implementing the method, the nursing efficiency and precision are improved.

The invention discloses an AI-based viral pneumonia auxiliary diagnosis method and system, and relates to the field of pneumonia diagnos.The method comprises the steps that abnormal state monitoring is conducted on target people in a target area, and when it is detected that a target human body is in the abnormal state within a preset time period and the occurrence frequency of abnormal behaviors exceeds a preset threshold value, the target human body is judged to be in the abnormal state; mapping the occurrence position, the moving track and the real-time people flow density into a three-dimensional building model to obtain space environment associated data; inputting the space environment associated data into a propagation prediction model to obtain a propagation risk probability of each target sub-region in the target region; and marking the target sub-region of which the propagation risk probability is greater than a preset risk threshold as a target risk region, and generating first early warning information containing the target risk region and the target human body. By implementing the method, the accuracy of the viral pneumonia early warning information can be improved.

The invention provides a fault detection method and system for a series diesel-electric hybrid power system. The fault detection method comprises the steps that a rotating speed signal is collected; decomposing the rotating speed signal once to obtain a plurality of IMF signals, performing data processing on the IMF signals to obtain corresponding Hurst indexes, and dividing the plurality of Hurst indexes to obtain IMF signals with noise as a main part and IMF signals with effective signals as a main part; deep decomposition processing is carried out on the IMF signals with noise as the main part, secondary reconstruction is carried out on the IMF signals after deep decomposition processing and the IMF signals with effective signals as the main part, and rotating speed signals after quality improvement are obtained; and inputting the rotating speed signal into a pre-constructed xLSTM deep neural network model to detect whether the current power system has a fault or not. The diesel engine rotating speed data of the system can be analyzed, the system problem can be found accurately and timely, and the safety of the series diesel-electric hybrid power system is guaranteed.

The invention relates to the field of astronomy transient source recognition combining astronomy and artificial intelligence, and when a transient source recognition task in mass data shot by a large-view-field high-sampling-rate telescope is carried out, an existing method generally uses a single classification model only depending on image data and is difficult to deal with complex and changeable data. The invention provides an astronomical transient source identification method for dynamically updating a multi-modal model. The method comprises the following steps: constructing the multi-modal model for classification by combining image data, a point spread function and physical information; and in combination with manual examination, selecting samples with wrong classification and possible transient source candidates, storing the samples and the possible transient source candidates as a new sample set, injecting the new sample set into the original training set, and loading the weight of the last training for retraining, so that dynamic adjustment and optimization of the model are realized. The method can effectively cope with mass data generated by a telescope with a large field of view and a high sampling rate, and ensures that the model always keeps efficient and accurate recognition performance under a complex background and weak signals.

The invention discloses an ammonia gas test method applied to detection of gas tightness of welding seams of a liquefied natural gas film storage tank and a marine film cabin. The ammonia gas test method comprises the following steps: 1) preparing detection equipment and chemical reagents; 2) cleaning a welding seam of the film storage tank and spraying a reaction type coating; (3) vacuumizing the main heat insulation layer, injecting a nitrogen-ammonia gas mixture, waiting for a certain reaction time, and periodically detecting the ammonia gas concentration and supplementing the ammonia gas during the period; 4) after the reaction time, emptying the nitrogen-ammonia gas mixture, observing from the inner side of the film storage tank, marking a weld leakage point, and repairing; and (5) after the leakage point is not found any more, post-processing is conducted on the heat insulation layer space of the storage tank. According to the gas tightness detection method, the precision and reliability of gas tightness detection of the welding seam are improved; the method is suitable for large-scale liquefied natural gas storage tank detection, and time and cost are saved; a detection blind area possibly existing in a traditional method is effectively avoided; and a detailed leakage repair and detection scheme is provided.

The invention discloses a bidding and tendering data screening recommendation method, and relates to the technical field of data governance and large model application. Comprising the steps of (1) collecting bidding and tendering data, (2) preprocessing the bidding and tendering data, (3) carrying out keyword extraction on the preprocessed bidding and tendering data, (4) constructing a business opportunity evaluation model, and (5) recommending bidding and tendering projects with higher scores to enterprises and providing project analysis reports according to scoring results of the business opportunity evaluation model. And 6, monitoring the found bidding and tendering project in real time, obtaining the latest progress and change information of the bidding and tendering project in time, and dynamically adjusting the evaluation result.

The invention belongs to the technical field of electricity charge management, and discloses an electricity charge business online management method and system. The method comprises the following steps: establishing a digital twin model based on a cloud data center, and constructing an electric charge business data analysis model and an electric energy scheduling model; based on the data transmission base station, a plurality of pieces of real-time electricity charge service data are collected and uploaded to the cloud data center; performing data analysis on a plurality of pieces of real-time electricity charge service data by using an electricity charge service data analysis model and a digital twin model; performing electric energy scheduling on the real-time data analysis result by using an electric energy scheduling model; and based on the cloud data center, a digital twin model is used to visualize a plurality of pieces of real-time electric charge business data, real-time data analysis results and real-time electric energy scheduling strategies of the electric charge business service subareas. According to the method, the problems of high cost investment, low efficiency, lack of information interaction, low practicability, poor intuition and poor data processing capability in the prior art are solved.

The invention discloses an intelligent contract vulnerability mining method based on feedback information fuzzy testing. Comprising the following steps: in a static analysis stage, converting a contract source code into an abstract syntax tree, extracting a control flow graph and function list information of a contract, and defining a function sequence priority calculation method; in the seed pool and test transaction construction stage, a seed pool is initialized, and a variation mode of a transaction sequence is defined. Then the system performs instrumentation in the EVM for collecting feedback information and defining judgment modes of different types of contract vulnerabilities; in the dynamic test stage, a mass fraction formula of a seed case is designed, whether the seed case is reserved or not is determined by comparing mass fractions, the seeds are varied according to a certain probability, and a seed pool is updated. According to the method, the fuzzy test technology based on the feedback information in the test process is constructed and is used for carrying out the vulnerability mining task for the smart contract, more deep states of the smart contract can be explored, and the efficiency and the accuracy of the vulnerability mining task are improved.

The invention discloses a network attack log processing method, a medium, electronic equipment and a program product. The method comprises the following steps: for each attack log in an attack log set, determining an attack type of the attack log according to a target attack mode corresponding to the attack log; and for each determined attack type, inputting the basic alarm information, the interface information and the target attack mode corresponding to each attack log belonging to the attack type in the set and the model cue word into a first large language model to obtain a first attack behavior analysis report corresponding to the attack type. Automatic aggregation analysis of attack logs can be realized, and the safety operation efficiency is improved. A large language model is utilized to aggregate and analyze knowledge of multiple dimensions, hidden attack events can be accurately found, and a readable attack behavior analysis report is output according to attack types in a classified mode. The method can be applied to various network firewalls, such as a Web application firewall, a large model security firewall and the like.

The invention discloses a segmented quantitative analysis method for uplift pressure of a gravity dam. The uplift pressure of the gravity dam is influenced by various factors, and safety monitoring data of the gravity dam often have the problems of multiple collinearity and small samples. The method comprises the following steps: introducing a BFAST time sequence decomposition method, decomposing original uplift pressure monitoring data into a trend component and a seasonal component, and obtaining trend segment points and seasonal segment points; a Bayesian model average modeling method integrating Bayesian model selection is introduced, and a gravity dam uplift pressure statistical model is established in a segmented mode; and finally, upstream and downstream water pressure components of the uplift pressure are separated according to the average multiple models, and quantitative analysis of the uplift pressure of the gravity dam is achieved. According to the method, more accurate fitting and component separation effects can be obtained, and accurate quantitative analysis of the uplift pressure of the gravity dam can be realized.

The utility model discloses a battery cell structure and a battery, and relates to the technical field of batteries. The battery cell structure comprises a naked battery cell, a battery cell cover plate, a battery management unit and a sensor assembly, the battery management unit comprises a wireless transmitting and receiving device; the battery management unit and the sensor assembly are arranged on the internal surface of the battery cell cover plate, and the internal surface is the surface, close to one side of the naked battery cell, of the battery cell cover plate; the sensor assembly is used for acquiring cell information of the bare cell; the battery management unit is used for acquiring cell information from the sensor assembly; and through the wireless transmitting and receiving device, the cell information is wirelessly communicated with the outside through the cell cover plate. Therefore, the battery cell information is monitored in real time, and the battery cell information is sent to the outside in a wireless communication mode, so that related technicians can timely and accurately find the abnormal condition of the battery based on the battery cell information.

A comprehensive supervision method and system for health management proposed in the present application involves the field of big data analysis, makes full use of big data to analyze health anomalies, and determines the information acquisition frequency of the monitored institution by comprehensively utilizing the average values ​​of the monitored institution and similar institutions with the highest degree of comprehensive relevance. The determined information acquisition frequency is more in line with actual needs, and information acquisition methods are separately formulated for different items to be monitored, thereby ensuring the accuracy of health management data. In addition, based on frequent item sets, the correlation between data can be accurately utilized to accurately discover abnormal problems.

The invention provides an unmanned aerial vehicle cluster collaborative inspection method and system for an ultra-high voltage power transmission line, relates to the technical field of power transmission line operation and maintenance, and solves the limitation problems of low collaborative efficiency, difficulty in data synchronization and the like of the current unmanned aerial vehicle cluster technology in line inspection. The method comprises the following steps: firstly, establishing a line three-dimensional digital twinborn model, dividing a to-be-patrolled line into a plurality of patrolling areas, and planning an initial patrolling path for each unmanned aerial vehicle in an unmanned aerial vehicle cluster; the unmanned aerial vehicles are classified into main unmanned aerial vehicles, detection unmanned aerial vehicles and operation unmanned aerial vehicles, the detection unmanned aerial vehicles collect environment information in advance according to the initial patrol path, and the main unmanned aerial vehicles dynamically adjust the flight path of the operation unmanned aerial vehicles accordingly; the operation unmanned aerial vehicle performs preprocessing and standardized packaging on the collected multi-source data, and a formed data packet is uploaded to the cloud platform by the main unmanned aerial vehicle to analyze and identify line defects. The system has the advantages and characteristics of full coverage, high efficiency and high precision of ultra-high voltage transmission line inspection.

The present invention provides a processing method, device, electronic device and storage medium for illegal countermeasures against unmanned aerial vehicles, which specifically include: monitoring whether a communication signal of a unmanned aerial vehicle occurs a specified abnormal event; the specified abnormal event can indicate that: the communication signal is interfered by a countermeasure device; if the specified abnormal event is monitored, then: according to the positions of multiple target unmanned aerial vehicles where the abnormal event occurs, determining the preliminary position information of the target countermeasure device; the target countermeasure device refers to: a countermeasure device that sends an illegal countermeasure signal and causes the specified abnormal event to occur; and locating the target countermeasure device according to the preliminary position information.

The invention provides a security vulnerability detection system based on an API interface, and relates to the technical field of security vulnerability detection.The system firstly analyzes the API interface through an analysis module to obtain calling information, and then analyzes calling of the API interface through a static analysis module and a dynamic analysis module to obtain calling information; according to the method, a calling graph capable of representing calling relations among different APIs is generated, a detection queue is generated according to the calling graph, and test parameters of the APIs are generated according to the calling graph, so that the generation efficiency of the test parameters is improved, the security vulnerability detection efficiency is improved, and finally, the method calling when the APIs process the test parameters is analyzed, so that the security vulnerability detection efficiency is improved. The API interface with the recursive structure with vulnerability suspicion is marked as possible security vulnerabilities, so that the system can more efficiently and accurately help testers to find related types of security vulnerabilities in the API interface, and then can timely optimize the API interface to defend attacks against the vulnerabilities.

The present application discloses a method and device for discovering new words in a domain. The method and device, based on obtaining high-frequency and low-frequency candidate word sets by performing N-gram word string extraction processing and word string classification processing, further determine the internal similarity of each word string corresponding to each word string included in the low-frequency candidate word set and the first comprehensive word string information used to measure the possibility that the word string is a new word, and determine the new words in the domain in the low-frequency candidate word set according to the N values, the first comprehensive word string information and the internal similarity of the word strings corresponding to different word strings in the low-frequency candidate word set, so as to realize the discovery of new words in the domain for the low-frequency candidate word set. Therefore, the present application does not directly filter low-frequency words based on thresholds, but realizes the extraction and discovery of low-frequency new words through secondary processing of low-frequency words, effectively avoiding the occurrence of low-frequency new word loss, making new word discovery more accurate and comprehensive, and improving the recall rate.

The invention provides an automatic checking method and system for an IDC (Internet Data Center) based on an image processing algorithm. The method comprises the following steps: step 1, deploying a plurality of inspection robots in an IDC data center, and enabling the inspection robots to autonomously move in a channel of the data center; the inspection robot is deployed to autonomously collect images, the tedious process of traditional manual checking is avoided, the checking efficiency is greatly improved, a large amount of time and labor cost are saved, meanwhile, a path planning algorithm, an automatic focusing algorithm and the like are utilized, it is ensured that the robot efficiently moves and collects clear images, and the overall working efficiency is further improved; feature extraction and asset information comparison are carried out based on an image processing algorithm, errors caused by human factors are reduced, the accuracy of asset identification is improved, meanwhile, the illegal moving-in and moving-out condition of equipment is detected by adopting an image difference algorithm, abnormity can be found timely and accurately, disorder of asset management is avoided, and intelligent management is realized.

This invention provides a method for identifying low-speed, small targets using radar echoes based on optical flow. The method includes receiving radar echo data in real time and redrawing the images to form a series of multi-frame radar echo images; using the pyramid LK optical flow method, sequentially performing layered analysis on the formed multi-frame radar echo images to obtain the optical flow vector of each frame; comparing the optical flow vectors of each frame to determine dynamic and static targets in each frame; and based on the dynamic and static target identification results fed back by the radar, determining whether one or more dynamic targets in each frame have not been identified by the radar. If so, the unidentified dynamic targets in each frame are considered low-speed, small targets before being acquired. Implementing this invention solves the problem of insufficient radar detection capability for low-speed, small targets and improves target acquisition efficiency.

The present invention relates to the field of family environment evaluation, and specifically to a family growth environment perception system for teenagers. The invention uses the feelings that teenagers have about their family members to select clothes and colors that can represent their personal feelings for family member models representing different personalities, based on the different connotations given by the system to clothes of different parts and the different scores given to the colors of different clothes. Thus, the real feelings of the teenagers about the family atmosphere and family members of different personalities can be distinguished through specific scores. This method can better help family members and therapists understand the teenagers, and improve the family atmosphere and correct the words and deeds of family members in a targeted manner. It solves the problem that some teenagers are unwilling to express or cannot accurately express the family atmosphere in words, which makes it difficult for psychotherapists to accurately understand the teenagers and their family situations, and thus difficult to carry out targeted treatment.

The invention relates to the technical field of application software anomaly detection, in particular to an artificial intelligence application software anomaly detection device and method, and the method is realized based on an artificial intelligence application software anomaly detection system. The system comprises a data collection module, a preliminary data analysis module, a three-number middle-taking method partition detection module, a recursion detection module, a double-shaft rapid sorting anomaly detection module and an anomaly diagnosis classification module. The method sequentially comprises the following steps of data acquisition, benchmark test, partition anomaly detection, recursion anomaly detection, double-shaft rapid sorting anomaly detection and anomaly diagnosis and repair, and can comprehensively detect whether the logic of an algorithm is correct or not, including a partition process and a recursion process, so that potential logic errors are found, and the accuracy of the algorithm is improved. And algorithm logic anomaly detection is quickly carried out on the artificial intelligence application software.

The invention discloses a fault tolerance method and device for an INS / GNSS loose integrated navigation system and a medium, and belongs to the technical field of integrated navigation, and the fault tolerance method specifically comprises the following steps: S1, INS data fault mark judgment and normal integrated navigation process starting; s2, calculating a measurement residual error and a residual error covariance matrix; s3, fault judgment based on a chi-square detection algorithm; s4, Kalman filtering measurement updating and INS data correction are carried out under the fault-free condition; and S5, processing under a fault condition. Compared with a traditional fault tolerance method, the method has the outstanding advantages that the fault mode of the INS data is considered, faults of the INS data and the GNSS data can be detected at the same time, the detection accuracy is higher, and the method comprehensively covers the fault detection range of the two kinds of data. The simultaneous detection capability greatly improves the accuracy of fault detection.

The invention discloses an optimal configuration method for an energy storage system in a power distribution area, and relates to the technical field of energy storage optimization, and the method comprises the steps: constructing an intelligent fault diagnosis strategy, carrying out the real-time monitoring of key parameters of the energy storage system, introducing a fault prediction algorithm, carrying out the system state analysis, recognizing a potential fault, and predicting a possible risk. The predicted risks are classified according to fault types and severity; an automatic fault handling and self-healing judgment mechanism is adopted, a handling strategy is automatically generated, operation parameters are adjusted or a standby system is switched to according to the diagnosed fault, and it is ensured that stable operation can still be maintained under the fault condition. According to the method, an intelligent fault diagnosis strategy is constructed, key parameters of the energy storage system are monitored in real time, and a fault prediction algorithm is introduced, so that potential faults can be effectively identified, and possible risks can be predicted. Compared with traditional manual monitoring, problems can be found earlier and more accurately, and further deterioration of faults is avoided.

The present disclosure provides a method, device, electronic device and storage medium for discovering express delivery and receiving organizations based on relationship mining. The method includes: obtaining customs declaration data including recipient addresses and recipients; forming address clusters according to the similarity between the recipient addresses; using the recipients and recipient addresses in the address clusters as nodes, and using the relationship between recipients, the relationship between recipient addresses, and the relationship between recipients and recipient addresses as edges to construct a graph; processing the nodes and edges in the graph according to the type of recipient addresses in the graph to obtain a connected subgraph; and determining the express delivery and receiving organization relationship network according to the connected subgraph. The method can quickly and accurately discover the express delivery and receiving organization relationship network.