24 results about "Joint analysis" patented technology

The invention discloses a network security event analysis method and system based on multi-agent decision, and a storage medium, and the method mainly comprises the steps: carrying out the deep investigation and judgment through a deep decision agent based on a reinforcement learning network after an abnormal alarm of a monitoring platform is received, triggering the deep investigation when the calculated deep investigation probability is greater than a threshold value, and carrying out the early warning of the network security event. Calling an LLM-based sub-expert agent to carry out deep analysis aiming at specific features; and according to the analysis result of the sub-expert agent and the risk information provided by the risk information platform, the LLM-based comprehensive risk decision assessment agent performs conjoint analysis decision to generate a network security situation comprehensive assessment report. According to the method, the processing analysis capability of massive and heterogeneous security data and the recognition capability of complex attack paths are remarkably improved, and the discovery capability of unknown threats and the intelligent level of security operation and maintenance are effectively enhanced by introducing reinforcement learning optimization decision and the deep analysis capability of LLM.

The invention discloses a flight data safety management method applied to low-altitude economy, and relates to the technical field of data management, and the method comprises the steps: obtaining heterogeneous monitoring data of an infrastructure and IT equipment in real time; calculating the health degree score and the fault risk probability of each logic area in the low-altitude economic flight data center in real time; based on the health degree score and the fault risk probability, root cause analysis is carried out in combination with a pre-constructed operation and maintenance knowledge graph, and an optimal operation and maintenance disposal plan is generated through matching; and converting the generated operation and maintenance disposal plan into a specific automatic instruction. The method has the advantages that through deep fusion and conjoint analysis of multi-modal data, accurate and panoramic perception of the health state of the low-altitude economic flight data center and prospective prediction of the fault risk are realized, and through automatic execution and a closed-loop feedback mechanism, the safety of the low-altitude economic flight data center is improved. A traditional operation and maintenance mode which is passive in response and highly depends on manpower is converted into a closed-loop autonomous mode which is active in early warning, intelligent in decision making and autonomous in execution.

The invention relates to the technical field of computers, discloses an astronomical sensitive data security processing method, device and system based on privacy calculation, and a storage medium, and aims to solve the problems that in astronomical data cross-mechanism joint analysis, original data is easy to leak, the cooperation efficiency is low, and a traditional desensitization or encryption method is difficult to consider security and availability at the same time. The method specifically comprises the following steps: each participant deploys a private computing agent node locally, and original data is not out of a domain; the task coordination center issues an analysis task; the proxy node extracts and preprocesses local data, and loads a corresponding secure multi-party computing protocol template; according to the method, share segmentation is carried out on multi-modal data such as images, spectrums and star catalogues by adopting addition homomorphic secret sharing, and distribution is carried out through a national secret SM4 encryption channel; and multiple parties cooperatively execute task-oriented security calculation in an encrypted state, and the result is aggregated and returned with the minimum information amount. The method has the effect of realizing high-precision safe collaborative analysis which is available and invisible.

The application belongs to the field of data security, and particularly relates to a time-sequencing encryption storage and expiration pulverization method of external device monitoring data, comprising: obtaining an external device monitoring data stream, segmenting according to a preset time window and deriving a unique encryption key for each segment; encrypting the corresponding window data with the key and storing to obtain a time-sequencing encryption data block; in response to a joint analysis request, distributing a specified window key to a calculation party according to a preset authorization strategy for it to perform secure multi-party computation on the window data block; simultaneously, monitoring the data block life cycle in real time, automatically erasing the data and destroying the corresponding key when expired, generating a pulverization verifiable evidence, and uploading to a storage chain constructed by a general data platform root key and each calculation party key combined with a blockchain; the application guarantees data storage security and joint analysis compliance, avoids residual key risk, ensures pulverization operation traceability, and improves multi-party cooperation credibility.

The invention discloses a passive intelligent vulnerability detection system and method based on Web2 and Web3 fusion, and belongs to the field of block chains, and the passive intelligent vulnerability detection system and method based on Web2 and Web3 fusion comprises a data acquisition layer, a processing analysis layer, a joint analysis module, an intelligent decision-making layer and a response processing layer. The vulnerability detection accuracy is remarkably improved, the real-time response capability of the system is greatly improved, the average detection response time is shortened from the minute level to the millisecond level, threats can be found and handled in time at the early stage of occurrence, and the safety risk is effectively reduced.

The application relates to the technical field of electric digital data processing, and particularly discloses a product information risk assessment method and system based on a large model, which comprises the following steps: simultaneously scanning source code and products by a scanner to generate a source dependency list and a product component list, performing main name normalization and alignment through a large model, locking a warehouse and a version according to the source dependency list, analyzing original code by a parser to generate a cross-language path logic group, combining the product component list to correct a real package version, converging into a unified intermediate representation, performing reachability and joint analysis on the unified intermediate representation to obtain a risk candidate path set and complete path scoring, finally, according to a preset weight and a running period evidence, modifying the score, screening out a risk path, matching a disposal strategy, and generating executable strategy code by the large model for risk control of a gateway or a service side.

This invention discloses a method, system, and electronic device for personnel operation assessment based on video temporal joint analysis, relating to the field of power safety technology. This application involves acquiring video from the work site and extracting frames; extracting the sequence of key human points from each frame; dynamically defining risk areas for the head, torso, and hands based on these key points; calculating hand movement speed, distance from the risk area, and movement consistency characteristics; triggering refined behavior recognition when hand movement characteristics indicate a tendency to enter the risk area; extracting multi-scale temporal features before and after the trigger; and judging specific risk behaviors using a pre-trained action recognition model. This invention, through a two-level mechanism of "trend triggering + refined recognition," achieves early warning and accurate identification of risk behaviors such as removing helmets and gloves, effectively improving the real-time performance and preventative capabilities of work safety monitoring. This application realizes a shift from post-event identification to pre-event warning, resulting in better safety monitoring performance.

The invention discloses an application dynamic configuration optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: in the running process of a target application program based on a Spring Boot framework, monitoring resource use data, and capturing running abnormity; when the resource use exceeds the limit or is abnormal, collecting related target log data in a preset time window, and generating structured first input data; when a user initiates a configuration optimization query, acquiring context data during current operation from a context object of a target application program, and generating structured second input data; a query instruction, first input data and second input data are input into an artificial intelligence analysis model, joint analysis is carried out in combination with historical configuration cases and log data, the problem can be accurately positioned in combination with runtime context and log data, the troubleshooting efficiency is improved, and dependence on artificial experience is reduced; the configuration parameters of the target Bean are dynamically updated through the configuration refresher, so that the configuration change takes effect under the condition that the target application program is not restarted.

The invention relates to the technical field of mine safety, in particular to a mine intelligent self-rescuer real-time evaluation system based on multiple modes, and aims to improve the dynamism, scene adaptability and regionalization management level of mine self-rescuer evaluation. The system comprises a multi-modal data acquisition module, a sensor data calibration module, an intelligent self-rescuer evaluation module and a regional joint analysis module. The multi-modal data acquisition module integrates physical, chemical and visual three-dimensional data; the sensor data calibration module corrects errors caused by environmental interference; the intelligent self-rescuer evaluation module innovatively combines a geographic position and environmental parameters to realize dynamic evaluation of the self-rescuer; the area joint analysis module divides similar sub-areas through K-means clustering, constructs a function exertion capability index and a miner wearing performance index, and quantifies the overall performance of the area. The system breaks through the limitation of traditional static evaluation, and provides comprehensive technical support for precise maintenance and regionalization optimization of the mine self-rescuer.

The invention discloses a table-based inspection report generation method and related products, and the table-based inspection report generation method comprises a table structure analysis stage which comprises the steps of identifying a header row of a table and judging the column type of each column, and the column type comprises an inspection object and an inspection item; a derivative inspection item generation stage including identifying a combination of inspection items having a conjoint analysis value, and generating a derivative column based on the combination and adding the derivative column to a table; the inspection content analysis stage comprises inspection background analysis, table column analysis and table row analysis of the table; a report unit generation stage including matching the analysis result of the inspection content analysis stage according to the report unit selected by the user to generate report unit content; and the report output stage comprises the steps of adjusting the content of the report unit according to user requirements and outputting a final inspection report. According to the method, large-scale table data can be efficiently processed in a limited context window, table structure semantics can be accurately understood, and report generation quality is remarkably improved.

The invention provides a data sharing security decision-making method and system applying artificial intelligence, and the method comprises the steps: receiving data sharing task description information and an associated talent original data set, inputting the data sharing task description information and the associated talent original data set into a context-aware mapping network, generating a task data association strength mapping table, and carrying out the strategy matching with a preset basic security label set, performing conflict coordination on the data sharing demand and the security constraint condition, generating a fine-grained data protection guidance document, performing structured desensitization conversion operation on corresponding data units in the talent original data set to obtain an intermediate data form, and storing the intermediate data form into a database; and generating a matched access verification rule set and a transmission encryption rule set according to the access authority rule and the transmission encryption rule, deploying the access verification rule set, the transmission encryption rule set and the controlled cooperative computing environment to perform data sharing conjoint analysis, performing authority correction and encryption compliance check on the data access behavior, and outputting a sharing analysis result after the conjoint analysis operation is completed. According to the invention, the compliance and reliability of the shared analysis result report are guaranteed.

The present application relates to the field of big data, and provides a big data task exception detection and repair method, system and medium.The big data task exception detection and repair method comprises the following steps: obtaining task information of a big data task with an abnormal task state, wherein the task information comprises a task log; obtaining execution code of the big data task via a decompilation tool, and analyzing the execution code and the task log via an exception analysis module to identify an exception problem; in response to the exception problem being a data source exception and a code exception, analyzing an abnormal code segment, data source information and an exception type via a joint analysis module to determine an exception root cause, wherein the abnormal code segment is identified by the exception analysis module, the data source information is determined by the execution code, and the exception type is determined according to the task log; and triggering abnormal code repair and / or data processing optimization according to the exception root cause.

The invention discloses a multi-modal data analysis method and device, equipment and a storage medium, and relates to the technical field of data analysis, and the method comprises the steps: obtaining a data analysis task and structured data and unstructured data of a to-be-analyzed document, performing cross-modal joint analysis and fusion on the structured data and the unstructured data through a cross-modal agent analysis engine to generate multi-modal analysis data; based on the data analysis task, performing dynamic context anchoring and fusion on the multi-modal analysis data and the financial ontology knowledge graph to obtain multi-modal context fusion information; and performing difference identification and traceability based on the multi-modal context fusion information and the financial ontology knowledge graph, and generating an interactive difference traceability graph and a data difference analysis report. According to the method, structured and unstructured data are integrated, data islands are broken, the financial semantic understanding precision is improved, and difference sources are traced, so that the comprehensiveness, timeliness and credibility of multi-modal data analysis are improved.

The invention provides an intelligent routing inspection detection method and system based on a large model and artificial cooperation, and solves the problems of high false alarm rate, poor interpretability, lack of sustainable evolution ability and the like in the prior art. The method comprises the steps of obtaining multi-source heterogeneous inspection data and performing standardized preprocessing, performing multi-modal data conjoint analysis through an initial inspection large model to generate a detection report, performing shunting processing based on confidence score, directly outputting a high-confidence result, and pushing a low-confidence result to an artificial collaborative verification platform for correction. And finally, optimizing the model based on a verification result.

The invention discloses a distribution box fault diagnosis system based on the Internet of Things, and relates to the technical field. The distribution box fault diagnosis system based on the Internet of Things comprises a time sequence data acquisition unit used for acquiring operation state time sequence data of a distribution box, distribution box upstream power supply equipment and distribution box downstream power consumption equipment based on a set time period; the conjoint analysis unit is used for performing conjoint analysis on the operation state time sequence data based on a pre-trained operation state conjoint analysis model to obtain operation state characteristics of the distribution box; the comprehensive analysis unit is used for analyzing the operation state index of the distribution box based on the operation state characteristics, and the fault diagnosis unit is used for judging whether the distribution box breaks down or not based on the operation state index. The sensing granularity and the diagnosis accuracy of the operation state of the distribution box can be effectively improved.

The application provides a building comprehensive management and control system and method based on artificial intelligence and multi-source data fusion, all subsystem data are fused and processed, cross-field and cross-system joint analysis and decision are realized, and the information island problem is solved.The application combines structure safety and environmental safety data, uses AI to perform risk assessment and prediction, changes passive alarm to active safety prevention and control, and significantly improves the building essential safety level.The application realizes on-demand supply and dynamic optimization based on the AI energy consumption model of multi-dimensional data, and achieves deep energy saving and consumption reduction.The application automatically creates a safe, healthy and comfortable building internal environment by sensing personnel activities and learning preferences, and meets the personalized human settlement environment demand.The application links with the smart city through a standard interface, makes the building an intelligent node responding to the city operation and contributing to the city data, and improves the fine level and overall efficiency of city management.

The application discloses a tunnel portal slope risk identification and analysis system based on artificial intelligence, relates to the field of data processing, and comprises a slope risk identification system, wherein a risk factor investigation and collection module, a portal risk simulation processing module, a tunnel risk simulation processing module, a risk factor joint analysis module and a slope risk identification and analysis module are arranged; the risk factor investigation and collection module is used for collecting data information of a tunnel construction area; the portal risk simulation processing module is used for constructing a slope damage model according to obtained data information; the tunnel risk simulation processing module is used for constructing a support damage model according to obtained data information; the risk factor joint analysis module is used for generating a risk influence database; and the slope risk identification and analysis module is used for obtaining corresponding risk identification data; and the application improves the accuracy in the risk identification and analysis process to a certain extent.

This invention discloses a cross-domain data and multi-dimensional joint analysis system and method based on a large model. The system includes: a model building module, used to intelligently model at least one corresponding functional model using natural language-driven methods and based on a preset large language model; a processing module, used to: transmit the data to be queried to the functional model and perform multi-source data retrieval using deployed multi-source databases to obtain retrieval results; then perform feature fusion and intelligent analysis calculations based on the retrieval results, and generate an assessment result based on the calculation results; and an information verification module, used to perform multi-modal intelligent analysis technology and combine multiple databases to conduct multi-dimensional deep information correlation analysis on the assessment result, automatically generating a structured investigation report containing risk scores, suspicious behavior feature annotations, and handling suggestions. This invention can realize multi-dimensional intelligent correlation analysis of cross-domain data, effectively improving the intelligent level of port investigation and control.

The invention provides a request processing method and device, equipment, a medium and a program product, and can be applied to the technical field of request processing. The method is applied to a joint name processing platform, and comprises the following steps: in response to a received joint name request about a target joint name partner from a joint name requester, obtaining joint name request information; determining target joint cooperation information related to the target joint partner from the multiple pieces of joint cooperation information; analyzing the joint name request information and the target joint name cooperation information to obtain a joint name analysis result; determining a target conversion relationship between a target first format related to the target joint partner and the target format; sending the joint name analysis result and the joint name request information to a target joint name partner in a target first format; and receiving a processing result of the target joint partner about the joint request so as to send the processing result of the joint request to the joint requester.

The application provides a GNSS precise clock difference product outlier elimination method, equipment and medium, and belongs to the technical field of satellite navigation. Each analysis center is paired two by two, and the clock difference data of each pair of analysis centers is used to generate a transient difference sequence by using a clock difference per epoch alignment method; a clock difference residual sequence is generated by using a linear transformation residual calculation method; a second difference sequence is generated by using a clock difference second difference method; outliers are eliminated based on the transient difference sequence, the clock difference residual sequence and the second difference sequence by using a weighted fusion algorithm, consistency verification is performed on the final clock difference product, and a precise clock difference product after the outliers are eliminated is output. The method combines linear transformation between analysis centers and the clock difference second difference method on the basis of the clock difference per epoch estimation method, comprehensively utilizes advantages and disadvantages of each method, and completes clock difference product outlier elimination through joint verification of multiple analysis centers.

This invention discloses a cross-industry data sharing method and system based on dynamic hierarchical classification and privacy computing, relating to the fields of blockchain and privacy computing technologies. The method includes: acquiring raw data from multiple industry entities, extracting feature information from their evaluation dimensions, determining sensitivity scores, and classifying sensitivity levels; generating protected data using corresponding privacy protection technologies based on the sensitivity levels; generating digital fingerprints for the protected data and storing them on the blockchain; simultaneously performing joint analysis using the protected data based on a federated learning framework and storing the results on the blockchain; monitoring contextual changes in the protected data, reassessing the sensitivity, and updating the protection strategy and on-chain storage. The system includes a data hierarchical module, a privacy protection module, a trusted analysis module, and a dynamic adjustment module. This invention achieves secure sharing and trusted collaborative analysis of cross-industry data.

The invention belongs to the technical field of geophysical exploration, relates to a quality evaluation method, and particularly discloses a three-dimensional data quality evaluation method based on conjoint analysis of attributes of adjacent shot gathers, which comprises the following steps of: 1, extracting attribute characteristics of three-dimensional shot gather data; 2, grouping the attribute basic data; step 3, identifying an abnormal value; 4, reconstructing an abnormal value and a missing value; and 5, forming a visual data quality evaluation map. According to the method, the attribute features of the data are divided into a plurality of adjacent areas according to the specified distance range through the clustering algorithm, the accuracy of the three-dimensional data and the processing efficiency of the three-dimensional data are improved through the methods of anomaly recognition, data reconstruction and the like, and the method is suitable for accurately, efficiently and comprehensively evaluating the quality of mass three-dimensional data.

The invention discloses an automatic electric charge returning and supplementing method and system based on multi-source data, and belongs to the technical field of computers. Attribution deviation caused by data splitting in the traditional technology is eliminated through systematic fusion of multi-source data, the timeliness and adaptability of operation state evaluation are ensured through a dynamic updating mechanism of an equipment state analysis model, and misjudgment caused by the fact that the equipment aging trend is not included in analysis is avoided; objective quantification of responsibility weight is realized through a joint analysis architecture of abnormal mode recognition and a return and supplement responsibility analysis model, so that influence degrees of different inducements such as equipment defects and environmental disturbance are distinguished, and the problem of high subjectivity of responsibility distribution is solved; the integration of the return and supplement strategy generator and the automatic execution link thoroughly replaces the manual coordination process, the return and supplement processing period is remarkably shortened, and meanwhile, the consistency and traceability of the processing result are guaranteed through the decision closed-loop design.