128 results about "Link analysis" patented technology

The invention discloses a reservoir safety intelligent inspection method and system based on YOLO and VLM fusion, and the method comprises the following steps: S1, multi-source data collection and preprocessing: employing an unmanned plane and ground equipment to collect image / video data, and carrying out the noise reduction, enhancement and space-time alignment processing of the data; s2, improving YOLO target detection: optimizing a network structure and a training strategy; s3, link analysis after VLM: target / scene association judgment is realized by adopting the VLM; and S4, report generation. The invention provides a reservoir safety intelligent inspection method and system fusing YOLO and VLM. Cross-modal semantic understanding, zero sample reasoning and video global analysis capabilities of a visual language large model are utilized, the visual language large model is used as a post-processing tool of YOLO and is fused with the post-processing tool to work in parallel, full-process intelligentization of target detection-semantic analysis-report generation can be realized, the existing technical problems are effectively solved, and the visual language large model has a wide application prospect. And the comprehensiveness of the hydraulic engineering safety monitoring system is enhanced.

The invention relates to the technical field of membrane control, and discloses a membrane control method and system based on multi-parameter intelligent perception, and the method comprises the steps: obtaining multi-parameter operation data of a membrane separation system, and calculating a transmembrane pressure difference change value, an inlet and outlet water quality change rate and membrane operation load duration according to the operation data; based on the transmembrane pressure difference change value, the inlet and outlet water quality change rate and the membrane operation load duration, a pollution trend early warning model based on an improved long and short term memory network is constructed and analyzed, and a first prediction result is obtained; and performing dynamic pollution risk score calculation based on the first prediction result to obtain a pollution risk comprehensive assessment value. Linkage analysis of the pollution development trend and the system risk level is achieved, the potential pollution risk level can be recognized in advance, cleaning intervention suggestions or alarm strategies are output in a graded mode, and the initiative and timeliness of cleaning decision making are effectively improved.

The invention relates to an AI-based service anomaly detection method, and belongs to the technical field of data processing. Comprising the following steps: acquiring runtime data of a production environment, including service layer data, hardware layer data, network layer data and link layer data; supplementing the collected data, carrying out classified marking on data information, and defining a hierarchical relationship among the data; sending the data to an AI model, carrying out correlation analysis through a time dimension and a space dimension, and carrying out link analysis; the AI large model updates the knowledge graph and generates the model according to each operation and maintenance; and displaying the fault knowledge graph by using a graphical front end. According to the AI-based service anomaly detection method provided by the invention, multi-dimensional data are comprehensively collected and correlation analysis is carried out, so that rapid positioning of a fault is realized. The fault discovery time is greatly shortened through the real-time detection capability, and the fault processing efficiency is further improved through incremental updating and intelligent optimization of the knowledge graph.

The invention discloses a water pollution source tracking and atmospheric particulate distribution analysis method based on machine learning, comprising the following steps: S1, collecting and preprocessing multi-source data to generate an environment data set; s2, pollution diffusion characteristics are extracted, pollution sources are reasoned, and a water pollution tracking result is generated; s3, constructing a deep Bayesian neural network model, and outputting particulate matter concentration prediction and a confidence interval; s4, introducing a zebra optimization algorithm, and jointly optimizing a model structure and hyper-parameters; s5, fusing the water pollution tracking result and the environmental characteristics, and outputting a prediction result; s6, collecting real-time monitoring data, calculating a prediction residual error and feeding back the prediction residual error to the optimization model; and S7, generating a pollution thermodynamic diagram and a trend prediction diagram, and outputting a pollution level and an early warning suggestion. According to the invention, by fusing water pollution source tracking and atmospheric particulate distribution modeling, intelligent linkage analysis of pollution propagation path identification and air quality accurate prediction is realized.

The invention discloses a six-dimensional judgment intelligent operation and maintenance diagnosis method and system for an oil field gathering and transportation station yard, and belongs to the technical field of oil field gathering and transportation intelligent operation and maintenance. Comprising a data acquisition and single point location model establishment module, a six-dimensional analysis and judgment module, a six-dimensional fusion processing module and a feedback updating module, comprising flow, pressure, temperature, liquid level, water content, pump frequency / current, valve position / state position, furnace gas amount / air door and station control alarm position data in an oil field gathering and transportation station. According to the six-dimensional judgment intelligent operation and maintenance diagnosis method and system for the oil field gathering and transportation station, by integrating multi-link analysis, dynamic threshold adjustment, a closed-loop optimization mechanism and safety guarantee design, the problems that current diagnosis is poor in stability, insufficient in precision, weak in decision support and the like are solved; and reliable technical support is provided for efficient and stable operation of an oil field gathering and transportation station.

The invention discloses a full-link analysis display method and device based on routing configuration, a medium and equipment. The method comprises the following steps: collecting and extracting equipment routing data; confirming the type of equipment routing data, and executing data cleaning processing according to the type of the equipment routing data; path acquisition is carried out based on the routing configuration information of the target assets, and three groups of network incoming link diagrams are respectively obtained; and sequentially splicing the Internet network inbound link diagram, the internal network inbound link diagram and the cluster network inbound link diagram to generate a hierarchical whole network link topological diagram, and performing front-end link display on the whole network link topological diagram. Visualization of various assets and internet access links in a virtualized network is realized, and in an actual application scene, problems can be quickly found based on the topological graph of the whole network links according to association of the collected equipment routing data and the security events, the influence range is positioned, and the security response efficiency is improved.

The invention discloses a position detection and cheating device and method for an unmanned aerial vehicle, and the device integrates a position information obtaining module, a GNSS signal receiving module, and a GNSS cheating signal generating and transmitting module, obtains the position of the unmanned aerial vehicle through a Wi-Fi network card or a Bluetooth receiver, and a USRP B210 device and related software collects and processes a GNSS signal and generates a cheating signal. The method comprises three processes of unmanned aerial vehicle position information detection, real GPS signal receiving and position deception signal generation, and the safety of the unmanned aerial vehicle is evaluated through multi-link analysis, signal receiving and simulation signal generation and emission. The unmanned aerial vehicle position and the GPS signal can be acquired in real time, the authenticity of the deception signal is improved, and the test effectiveness is enhanced; and the device has the advantages of high simulation degree, high precision, simplicity and convenience in operation, low cost and the like, and an efficient scheme is provided for unmanned aerial vehicle position safety testing and anti-deception capability evaluation.

The invention discloses a power grid weak link identification method, system and equipment, and the method comprises the steps: firstly, selecting each evaluation index of the operation state of a power grid, then determining the initial weight of each evaluation index through employing an analytic hierarchy process, and carrying out the updating of the initial weight of each evaluation index through employing a Bayesian network and a self-learning method, obtaining the weight of each evaluation index; generating a power grid blocking event sample by adopting an OPA model based on direct current power flow optimization, and performing power grid operation state evaluation by adopting the power grid blocking event sample according to each evaluation index and the corresponding weight to obtain an evaluation result; and if the evaluation result shows that the cascading failure exists in the power grid, identifying a weak link of the power grid by adopting a link analysis algorithm. The method can improve the accuracy and efficiency of weak link identification of the power grid, and can effectively predict and prevent the blocking event of the power grid.

The invention relates to the technical field of information security, and particularly provides a JavaScript component vulnerability reachability analysis and detection system based on call link analysis, which comprises an acquisition unit, a risk assessment unit, a feature analysis unit, an analysis unit, a control unit and a self-adaptive detection unit, the system can accurately describe the operation base line of the platform in a normal state by acquiring key characteristic parameters such as the length of a historical calling link of the target platform, the execution frequency and the proportion of branch nodes, and the analyzed key characteristic characterization factors can judge the security risk tendency of the platform. And comparing the risk characteristic parameters extracted by the test platform in real time, wherein the risk characteristic parameters comprise an unverified input node proportion, an error triggering rate and a calculated risk characteristic characterization value with a preset threshold value. Through'historical data-risk quantitative analysis-standard judgment-problem tracing ', the efficiency and precision of JavaScript component vulnerability accessibility detection are improved.

The invention relates to the technical field of financial auditing, and particularly discloses a business auditing fusion auditing system based on big data, which comprises a data acquisition and preprocessing module, a standardized feature extraction module, a transaction link analysis module, a risk comprehensive score calculation module and an intelligent auditing report generation module. Compared with the prior art, the technical problem that high-risk transactions and abnormal fund flow cannot be accurately identified in the prior art due to dependence on static rule matching and manual auditing, especially under the conditions of transaction data cross-platform, account identity hiding and transaction path complexity in a decentralized transaction environment is solved. According to the method, efficient, real-time and dynamic financial industry auditing is realized through real-time streaming data processing, multi-scale time sequence alignment, transaction path risk propagation analysis and reinforcement learning optimization risk scoring.

The present disclosure relates to a network security risk identification method and apparatus, a medium, an electronic device, and a program product. The method comprises: acquiring a target network topology map based on a target object, the target network topology map being used for representing the relationship between the target object and an asset related to the target object; and performing attack link analysis on the target network topology map on the basis of a service attribute and a security feature of the asset in the target network topology map, to identify a security risk in the target network topology map. In this way, a network topology map can be used, the risk is combined with the service attribute and security characteristic of the asset in the network topology map, and attack link analysis is performed on the basis of an attacker's perspective, so that a key risk can be identified more accurately, the number of service repair risks can be reduced, the risk that is most likely to cause asset damage can be focused on, the continuity of a key service process can be ensured, and service interruption caused by a security problem can be reduced.

The invention relates to the technical field of data analysis and decision optimization. The real-time contact collaborative decision-making optimization method based on behavior link analysis comprises the steps that attribution analysis is conducted on a user behavior sequence through a multi-task learning model, a contact attribution matrix is obtained, the user behavior sequence and the contact attribution matrix are jointly input into a sequence prediction model, a contact candidate set is output, and a contact collaborative decision-making result is obtained. And calculating a user experience quality score corresponding to each contact based on the contact candidate set, constructing a multi-factor priority queue according to the user experience quality scores, obtaining a sorting result corresponding to the contact candidate set, determining a target contact from the contact candidate set based on the sorting result, and generating a contact execution instruction according to the target contact. The multi-contact collaborative decision-making method has the effect of realizing efficient collaborative decision-making among multiple contacts.

The invention discloses an aviation 5G communication system fusing edge computing and multiple communication access modes, and belongs to the technical field of aviation communication, and the aviation 5G communication system comprises an airborne sensor network module which obtains the operation parameter information of an airplane; the link resource acquisition module is used for acquiring associated parameter information related to link switching in the flight of the aircraft; the edge processing analysis platform comprises a data processing module, a storage module, a fault detection module and a link analysis module; and the response module comprises a fault response unit and a switching execution unit. According to the method, a relatively stable link can be selected for communication according to the link switching interval corresponding to the airplane, then a plurality of detection and judgment points are set for the link switching interval, and comprehensive analysis is performed according to the link quality condition, the network performance condition, the flight environment condition and the like among the detection and judgment points; therefore, the link communication condition can be timely and accurately judged, the communication quality can be timely processed, and the personalized requirements of passengers can be met.

The invention provides an elastic monitoring alarm method and system based on multistage cooperative verification. The method comprises the step of remarkably improving the alarm accuracy through a three-level verification mechanism. The first-stage verification adopts intelligent anomaly detection, compares a current index with a historical baseline, and identifies a preliminary anomaly; in the second-stage verification, association index analysis is introduced, and pseudo anomalies caused by normal business fluctuation are filtered; and the third-stage verification performs cross-customer collaborative analysis to distinguish individual anomaly from global events. In addition, fault root causes are automatically positioned through dependency link analysis, and structured alarm information containing causes, confidence coefficients and processing suggestions is generated. And a closed-loop learning mechanism is established, operation and maintenance feedback is continuously collected, and alarm parameters are automatically adjusted by adopting Bayesian optimization. According to the method, the false alarm rate is reduced, the root cause positioning accuracy is improved, the average repair time is shortened, and the IT operation and maintenance efficiency is remarkably improved.

The embodiment of the invention discloses a network attack link analysis method and device, computer equipment and a storage medium. The method comprises the following steps: determining a key entity related to a network attack in a description text corresponding to a network security event; matching the key entity with each entity of a preset network security knowledge graph, and determining a plurality of target entities matched with the key entity; determining a network attack defense coverage degree corresponding to each target entity, and determining a network attack defense coverage degree threshold value corresponding to the network attack defense coverage degree; according to the network attack defense coverage degree corresponding to each target entity and a network attack defense coverage degree threshold value, combining at least part of the target entities to obtain at least one part of network attack link corresponding to the network security event; and inputting each partial network attack link into a pre-training network security model, and outputting a complete network attack link corresponding to the network security event, the pre-training network security model being obtained by training based on a preset network security knowledge graph.

The invention provides a monitoring information full-link analysis method and system based on a network message, and belongs to the technical field of network monitoring, and the method comprises the steps: collecting an original message flow through a non-intrusive probe, carrying out the analysis in combination with a dynamically updated protocol fingerprint database, and employing an online random forest model and a multi-dimensional matching rule for protocol recognition; a mixed clock source is utilized to generate a time sequence metadata pipeline, and a space-time diagram convolutional network constructs a service dependence topology containing throughput and delay jitter, and an interactive link map of a final network layer, a service layer and a security three-dimensional index. The corresponding system comprises an intelligent load balance acquisition module, a stream batch integrated analysis engine and a visual platform, and the visual platform realizes multi-level permission isolation based on an SGX technology. According to the method, the problems of protocol identification lagging and weak topological relevance of a traditional monitoring system are solved, the full-link view can be dynamically constructed in real time, the operation and maintenance analysis precision and efficiency are improved, and the method is suitable for service quality monitoring and safety management and control in a complex network environment.

The invention provides a shared link generation method and device, an analysis method and device, equipment and a storage medium, and relates to the technical field of data processing, in particular to the technical field of personal cloud and file sharing. The specific implementation scheme is as follows: acquiring a user identity identifier of a sharing user initiating a sharing request; generating a unique link identifier for a sharing link corresponding to the sharing request; constructing a data load based on the link unique identifier and the user identity identifier; a path portion of the shared link is generated based on the data load. According to the scheme provided by the invention, the problem that link positioning is difficult when a sharer and a visitor belong to different areas can be solved.

The invention discloses an urban planning effect evaluation method and system based on carbon emission, and the method comprises the steps: dividing urban space units, and constructing a first space unit division matrix in combination with information data; obtaining a real-time data score, and adjusting the space unit to obtain space adjustment efficiency; updating the space units based on the space adjustment efficiency to obtain a second space unit division matrix; based on the upstream and downstream industry chains, constructing a directed weighted graph, and obtaining an importance sequence through a link analysis algorithm; in combination with a multi-criterion decision analysis method, determining comprehensive weights of carbon emission indexes, and judging key nodes of an industrial chain; a low-carbon technology is introduced, the industrial chain node dependency degree is adjusted, carbon emission is optimized, and evaluation data is obtained. According to the method, the economic and carbon emission characteristics of each block of the city in different development stages are considered, the space units are flexibly allocated by using the space adjustment rate, the industrial chain layout can be automatically adjusted along with the change of data, and the implementation efficiency of the carbon reduction policy is improved.

The invention provides a full-link analysis early warning method based on transaction and grain depot data fusion, and relates to the technical field of artificial intelligence and big data analysis, and the method comprises the steps: forming a multi-dimensional data set based on grain depot data, transaction data and own data; through unique identifier matching, an analysis rule and a mapping table are self-defined, transverse and longitudinal association is carried out on the multi-dimensional data set, and a full-link service data view is constructed; and analyzing the full-link service data view according to different service scenes to obtain an early warning result.

The invention discloses a video lag diagnosis method and device, equipment, a storage medium and a program product. The method comprises the following steps: associating user side data with network side data through a pre-constructed global index under the condition that video lagging occurs in a target video to obtain a target data set; performing link analysis on the target data set to obtain at least one link transmission parameter; and performing jamming analysis on the link transmission parameters by using a preset target model rule to obtain a target jamming factor. And associating the user side data with the network side data through a global index, finding out link transmission parameters causing the lag, and analyzing a root factor causing the video lag in the link transmission parameters through holographic diagnosis as a target lag factor. According to the embodiment of the invention, the method can effectively diagnose the root cause of lagging, can judge the root cause through the automatic correlation extraction and the target model rule, and achieves the technical effect of improving the analysis efficiency and accuracy.

The invention discloses a bipartite graph-based software test range determination method and a related product. According to the scheme, a change file between two version codes and a corresponding change line number are obtained; and determining a change function to which the change line number belongs. Function points corresponding to the change function are determined by calling the link, and the affected frequency of each function point is determined. And constructing a bipartite graph model according to the incidence relation between the change function and the function point. And solving the bipartite graph model to generate a minimum function point set. And sorting the minimum function point set according to the affected frequency, and generating a target test set with a test priority. According to the technical scheme, the code change analysis is combined with the bipartite graph model, the affected frequency is quantified by calling link analysis, and the test priority is sorted according to the affected frequency. And the test efficiency is obviously improved on the premise of ensuring the test sufficiency.

The application discloses a kind of comprehensive data analysis system and method applied to intelligent audit, it is related to big data analysis technical field, the present application is by obtaining audit object whole-link business data, constructs full audit data set by standardization processing;Extract static and dynamic risk characteristics, build the comprehensive rule system containing static and dynamic required verification data type library;Analysis single risk and associated business link causality, generate audit associated link mapping table and ordered risk set;Calculate corresponding risk determination threshold;Real-time monitoring audit business based on threshold, data characteristics are intercepted by sliding window, after audit cycle ends, iteration updates threshold.The system includes data full collection, audit rule construction, associated link analysis, determination threshold calculation, audit threshold iteration module and subordinate unit.The present application realizes full data coverage and real-time monitoring, accurately traces risk transmission, improves the confidence and timeliness of audit compliance judgment.

An incident alert associated with a deviation in a metric is received. A plurality of correlated metrics is identified based on contextual data. A plurality of labels for the plurality of correlated metrics is generated in accordance with a plurality of categories of signals. A causal graph is constructed based on the plurality of labels and a topology graph of the plurality of correlated metrics. A root cause of an incident associated with the incident alert is determined using a link analysis algorithm.

The application provides a data blood relationship full-link analysis method based on a graph technology, characterized in that the data blood relationship full-link analysis method based on the graph technology comprises: converting data in a data source into graph data according to a preset structure, wherein the graph data comprises at least one vertex, and the vertex comprises a vertex parameter and an edge parameter; obtaining an analysis result according to a preset algorithm based on the vertex parameter and the edge parameter; and forming an analysis report according to the analysis result. The preset algorithm involved includes but is not limited to a minimum spanning tree algorithm, a DFS algorithm, a degree centrality algorithm, a Louvain algorithm and the like. In addition, the application further provides a system and a computer device. The technical scheme of the application effectively solves the problem that the existing data blood relationship analysis cannot be intelligently and efficiently analyzed.

The embodiment of the invention provides a code change influence evaluation method, device and system and a medium, and the method comprises the steps: carrying out call link analysis on a target project to obtain at least one function call relationship, and generating a project call link diagram through the at least one function call relationship; performing code change analysis on the target item, and determining at least one function subjected to code change; and for each function subjected to code change, retrieving at least one call link where the function subjected to code change is located in the project call link diagram as a corresponding influenced call link, and generating an analysis report according to all the influenced call links.

The invention discloses an index link detail analysis method and device based on data consanguinity. The method is characterized by comprising the following steps: receiving a link traceability query task, and identifying a target index and an index query condition corresponding to the link traceability query task; performing link query in an index processing logic link based on the target index and the index query condition, and determining a target processing logic link of the target index; performing iterative blood relationship tracing in a data blood relationship graph based on the target processing logic link and the index query condition, and determining data blood relationship detail information of the target index; and constructing a data consanguinity link detail map corresponding to the link traceability query task based on the data consanguinity detail information and the target processing logic link. In the index link analysis process, the detail condition of the most source of the selected data can be directly checked, the interpretability of the index is greatly improved, and the analysis efficiency is improved.

The invention discloses a network security system driven by artificial intelligence. The network security system is designed to detect, analyze and relieve wide network threats in real time. The network security system comprises a data receiving module which is responsible for gathering diversified data types, including images from various sources, text content and URLs (Uniform Resource Locator). The AI analysis module processes the received data using image recognition, optical character recognition, and semantic analysis, and identifies potential threats, such as phishing attacks, fraud, and propagation of malicious content. And a link analysis module evaluates the security of the URL through the dynamic white list and black list, and performs AI-based evaluation on unknown links. The audio interface expands threat detection to audio-based communication, integrates voice-to-text and text-to-voice functions, and enables the system to analyze fraud or malicious intentions that may exist in a telephone call.

The invention discloses a data consanguinity link determination method and device based on a large model, relates to the technical field of big data processing, and mainly aims to solve the problems of poor accuracy and low efficiency of existing data consanguinity link generation. Comprising the steps that a knowledge base is called, and the knowledge base comprises technical knowledge information and business knowledge information; a knowledge object is obtained from the knowledge base according to a preset verification strategy, the knowledge object is analyzed based on a large model, a blood relationship link analysis result is obtained, and the knowledge object is at least one piece of knowledge key information formed based on the technical knowledge and / or the business knowledge information; and performing service verification on the blood relationship link analysis result, and generating a data blood relationship link based on a verification result.

The invention discloses a power grid network attack countering method and system based on flexible gray level self-adaption, and the method comprises the steps: obtaining the multi-level gray point information of a power grid, carrying out the abnormal flow analysis, carrying out the shadow asset recognition processing based on an analysis result, and obtaining the shadow asset recognition data, obtaining known attack data of the power grid, carrying out attack mode analysis, constructing an attack link graph of a known attack mode to obtain an attack link analysis model, recognizing an abnormal access behavior corresponding to shadow asset recognition data through the attack link analysis model, carrying out target asset threat analysis to obtain asset threat analysis data, and sending the asset threat analysis data to the power grid. And attacker positioning is carried out according to the asset threat data, a corresponding attack countering strategy is called in combination with the known attack means similarity to carry out attack source blocking processing on the positioned attacker, and power grid network attack countering data is obtained. The power grid attack is accurately traced and attack countering is carried out from the source, so that data support of attack countering is provided for operation safety of the power grid.

The invention provides a notebook adaptation degree detection method and system for software ecology, and the method comprises the steps: firstly constructing a software ecology adaptation demand three-dimensional time sequence evolution graph, extracting the demand intensity and other data, and building a notebook hardware capability elastic mapping table; the method comprises the following steps: calling mapping table parameters of notebook computer hardware capability elastic mapping to construct a dynamic coupling space, simulating a coupling process to generate adaptive coupling nodes and links, analyzing and adjusting mapping relation parameters based on the links, and obtaining an optimized mapping table through iterative optimization; and a collaborative adaptation scheme is constructed and converted into an adjustment instruction to be sent to the notebook computer system to execute dynamic adaptation adjustment, so that the adaptation degree of the notebook computer and software ecology is improved, and the notebook computer performance and the user experience are improved.