185 results about "Time response" patented technology

The invention relates to an intelligent operation and maintenance method and apparatus based on a knowledge graph and a large model, and an electronic device. The method comprises the steps of collecting multi-source runtime data of a Kubernetes cluster; constructing a knowledge graph with time dimension based on the resource change event, recording termination time in response to graph relationship failure, recording starting time in response to a newly added relationship and not setting the termination time, and associating the entity with the performance index and the log data; responding to the diagnosis request, scheduling a specialized agent by a coordination agent through multi-agent cooperation to retrieve associated information from multi-source data, and iteratively integrating to generate a structured context; and inputting the generated structured context information into a large model reasoning service to output a fault root cause diagnosis and solution. The technical problems that information dispersion and relevance are weak, root cause positioning is difficult and time-consuming, comprehensive context sensing ability is lacked and expert experience is excessively relied on are solved.

The invention relates to the technical field of electric data processing, and discloses a system and a method for hybrid processing of mass offline data and mass real-time data, and the system comprises a queue management module which receives task requests, identifies and marks task types, distributes tasks to a stream processing queue or a batch processing queue, and sorts the tasks according to priorities; the task types comprise flow tasks and batch tasks; the thread pool management module establishes a thread pool, sequentially processes tasks according to priorities and collects real-time parameters; the calculation module is responsible for calculating task priorities and calculating task jitter factors based on real-time parameters; the scheduling decision module compares the task jitter factor with a reference value, determines a weight adjustment coefficient, corrects the priority and formulates a scheduling strategy; the alarm module judges whether to trigger an alarm. According to the invention, by introducing the dynamic priority scheduling and preemptive processing mechanism driven by the jitter factor, the real-time response capability and the resource utilization efficiency of the system are improved.

The invention discloses an industrial database protection method based on multi-modal authentication and encryption, which belongs to the technical field of data processing and comprises the following steps: acquiring input data to be input into an industrial database; a multi-mode authentication gateway is adopted to verify user identity information, and a biological feature, a dynamic device fingerprint and a quantum random number token are fused to generate a third-level identity verification identifier; after the three-level identity verification is passed, determining an encryption algorithm according to the sensitive level of the industrial data transmitted in real time and the system load state, and encrypting the industrial data through the encryption algorithm; and for the encrypted industrial data, analyzing the function code of the Modbus message of the industrial data transmitted in real time, establishing a white list instruction set, and intercepting the industrial data of the function code which is not in the white list instruction set. According to the industrial database protection method based on multi-modal authentication and encryption, the problem of how to improve the real-time response capability of a system on the premise of ensuring the security of industrial data is solved.

The invention discloses a cloud storage data synchronization method and a cloud storage system, and relates to the technical field of data synchronization, and the method comprises the steps: calculating the priority Pi of each device based on a household appliance standard data set of household appliances through a synchronization priority calculation formula; and according to a comparison result with a preset priority threshold Pth, the system can flexibly generate a synchronization strategy. And when the priority of the equipment is higher, the system can improve the data synchronization frequency to ensure that the equipment is in a real-time response state. And for the equipment with lower priority, the system reduces the synchronization frequency and reduces unnecessary energy consumption through delay synchronization and an energy-saving strategy. Through the time sequence analysis of the user behavior mode, the system can predict the behavior of the user in advance and generate a linkage control instruction Ci, for example, when the user is about to go home, an air conditioner is automatically turned on, and the light brightness is adjusted. The intelligent linkage control not only improves the response speed of the equipment, but also enables different equipment to work cooperatively.

The invention relates to the technical field of electric power information security, in particular to an abnormal behavior detection method and system for electric power sensitive data flow. The method comprises the following steps: acquiring user operation behavior characteristics and data circulation track information through multi-source data acquisition; carrying out joint modeling on the behavior pattern by utilizing short time sequence Markov modeling and a long time sequence Informer structure; multi-dimensional features such as operation frequency, access paths and data types are integrated through a feature fusion module; a plurality of scoring mechanisms are introduced to judge the influence of data exception on the power system; further mining a data interaction relationship among multiple subjects by utilizing graph structure modeling; and a model structure search method is combined to carry out performance optimization on the whole detection process. The method can effectively improve the detection accuracy and real-time response capability of abnormal circulation of sensitive data in complex scenes such as power dispatching, power distribution automation and terminal access, and is suitable for data protection requirements of a power system in a high-safety-requirement environment.

The invention discloses a server-free workflow engine implementation method based on event orchestration, which relates to the technical field of server-free computing, and comprises the following steps: continuously monitoring a predefined event source; when an event is received, matching is performed according to a preset event triggering rule; if the event matching is successful, starting a corresponding predefined workflow according to an event triggering rule; mapping each task in the workflow to a server-free function according to the workflow definition, and triggering the server-free function to execute according to an event; recording the execution state of the workflow, updating the state, and performing flow control and error processing according to the execution state information. According to the method, the real-time response capability is improved, the execution efficiency is enhanced, and the reliability of process control and error processing is ensured, so that the requirements of an automatic complex business process are better met.

The invention relates to the technical field of distributed response, in particular to a distributed message dynamic response method based on websocket. The method comprises the following steps: constructing a distributed message service cluster to configure a websocket communication module, message middleware and a client for each service node, and providing a unified service access point to the outside through an Nginx load balancer, so as to automatically register and subscribe a global control theme to the message middleware and form a hierarchical theme subscription structure; when the client initiates a connection request, the connection is distributed and a secure channel is established; the client sends a service request message, packages the service request message into a standardized message format, and associates session information of the client at the same time; and receiving the session message, performing intelligent filtering, generating a response result, and pushing the response result to the client in real time. According to the invention, efficient processing and real-time response of messages in a distributed environment can be realized.

The invention relates to the field of geographic information systems, and discloses an agent-based automatic address governance method and device and a medium, and the method comprises the steps: obtaining a to-be-governed address through a data perception and preprocessing agent; inputting the address to be governed into a master control agent; the main control agent analyzes the address to be governed and gives an analysis result; according to the analysis result, selecting a tool execution agent to process the address to be treated; and outputting the governed address, and storing all data by adopting a memory and interaction agent. According to the method, the analysis accuracy, the semantic understanding depth and the robustness of the LLM in processing complex and non-standard addresses containing confusion information are greatly improved, meanwhile, the requirement for manual intervention is greatly reduced, the efficiency of processing mass address data is remarkably improved, and the real-time response speed is remarkably increased.

The invention provides a storage resource dynamic allocation method and system based on artificial intelligence, and relates to the technical field of storage resource dynamic allocation, and the method comprises the steps: obtaining the real-time response delay, data transmission bandwidth and node computing power data of each storage node in the current stage of a target task, and carrying out the cooperative processing of the data, thereby achieving the dynamic allocation of storage resources. Obtaining a storage resource real-time performance data packet containing the real-time load state of each storage node; analyzing the data packet based on an artificial intelligence model corresponding to the task stage to obtain a storage resource allocation target value; through a dynamic allocation optimization algorithm, a resource scheduling strategy is formulated according to a target value, a real-time control signal is generated, and dynamic allocation and scheduling of storage resources among storage nodes are executed accordingly, so that the dynamic allocation and scheduling of the storage resources among the storage nodes can be realized based on an artificial intelligence-based storage resource dynamic allocation method, and the dynamic allocation and scheduling of the storage resources can be realized through data co-processing, AI analysis adaptive to a task stage and the dynamic optimization algorithm. Accurate and dynamic allocation and scheduling of storage resources meeting target task requirements are realized.

The invention discloses an adjustable resource unified data external characteristic modeling, regulation and control method and system oriented to various transaction scenes. The method comprises the following steps: collecting real-time data of an internal adjustable resource of a virtual power plant; the climbing time, the duration, the response capacity and the responsible frequency are quantized and defined as a group of external characteristic parameters, an adjustable resource data external characteristic model is constructed, adjustable resources are classified according to the external characteristic parameters of the model, and an adjustable resource characteristic library is established; in combination with the response characteristic and the load state of each adjustable resource, generating an optimal regulation and control strategy meeting the regulation and control requirements, and allocating a corresponding regulation and control instruction to each adjustable resource; each adjustable resource adjusts power output according to the regulation and control instruction, and meanwhile, the calling cost of each adjustable resource is calculated; in combination with the real-time state and the adjustment cost minimization target, the regulation sequence and proportion are optimized; according to the method, the economy and flexibility of the virtual power plant in multi-scene regulation can be effectively improved, and meanwhile, the accuracy of power grid frequency modulation response and the system stability are enhanced.

The invention belongs to the technical field of information security, and particularly relates to a super-threshold data set intersection method and system for security information processing. The method comprises the following steps: S1, carrying out casual key value pair storage OKVS encoding and decoding between each participant and other participants; s2, each participant constructs a hash table according to a decoding result and elements of the participant; and S3, sending the constructed hash table to a specified party, summarizing information in the hash tables of all participants by the specified party, and finally obtaining a super-threshold intersection result by using a secret sharing scheme. The method has the characteristics that the hardware and operation and maintenance cost can be reduced, the data security is enhanced, and the calculation efficiency is improved, so that the industrial control requirement of short-time response is met.

A methodology is described for determining a time for responding to an initial encounter by a vehicle with a map error in a map segment, wherein the initial encounter is detected by a detector. A method includes identifying an error type associated with the map error, wherein the error type is one of a first error type and a second error type; determining at least one additional factor associated with the initial encounter; determining a response time for the map error based on the error type and the at least one additional factor; and initiating a mitigating action in response to the initial encounter within the response time, wherein the response time comprises an amount of time from the initial encounter with the map error to a time at which a mitigating action should be taken.

The invention discloses a multi-modal KV cache retrieval method and system based on a hybrid architecture, relates to the technical field of data processing, and aims to solve the technical problem that in the prior art, due to the adoption of a full-online computing architecture and lack of a unified cross-modal representation framework, the computing efficiency and the modal fusion effect are difficult to consider at the same time. By constructing a hybrid processing architecture of offline pre-calculation and online processing, multi-modal data is subjected to unified fragmentation coding and KV cache and semantic fingerprints of the multi-modal data are pre-calculated in an offline stage, and cache fragments are intelligently assembled and position codes are dynamically remapped based on a user query intention in an online stage; therefore, online repeated calculation of multi-modal content is fundamentally avoided, coherent alignment and efficient fusion of cross-modal semantics are achieved, the real-time response capability, the cache reuse rate and the multi-modal generation quality of the system are remarkably improved, and high-concurrency scenes can be dynamically adapted.

The invention discloses a training method and system for a fraud-related APP multi-modal feature vector embedder, and relates to the technical field of computer security, and the method comprises the steps: collecting basic information of an application and user behavior data after a user provides a protection authority; calculating a current fraud-related risk value, setting an initial fraud-related risk threshold, running a double-layer training embedding strategy when the current fraud-related risk value is greater than the initial fraud-related risk threshold, and forming a fraud-related APP judgment model by combining a shallow feedforward neural network model and a deep fraud-related APP feature classification model; when the current fraud-related risk value is smaller than or equal to the initial fraud-related risk threshold value, an adaptive data remigration strategy is directly operated, and a remigration period is obtained according to the APP active state and the fraud-related risk value; according to the method, the model training efficiency and the real-time response capability are balanced, the system can flexibly adapt to different data qualities, and the model performance is prevented from being influenced by noise data or low-quality data.

The invention relates to the technical field of network security, in particular to a computer network security analysis method and system based on big data, and the method comprises the following steps: firstly, collecting network flow data through multiple nodes, preprocessing the data, then extracting the time sequence characteristics of the flow, constructing a time sequence behavior model through a long and short-term memory network, and constructing a time sequence behavior model; the method comprises the following steps: acquiring a real-time traffic model, identifying a behavior mode of normal traffic based on the model, judging whether the traffic is abnormal by comparing a time sequence characteristic of the real-time traffic with the normal traffic model, performing deviation scoring, performing abnormal traffic clustering on the detected abnormal traffic by adopting a K-means clustering algorithm, identifying a potential attack type, and according to a deviation scoring result, determining whether the traffic is abnormal or not. Defense response is automatically triggered, and the security of the network is ensured; according to the invention, through the adaptive defense strategy and the efficient flow analysis method, the real-time response capability, accuracy and adaptability of network security are improved.

The invention provides an intelligent reply system suitable for a live broadcast matrix scene. The intelligent reply system comprises a receiving module, an algorithm processing module, a reply generation module, a distribution module and a feedback closed loop module. The receiving module collects user input data and dynamic scene parameters of the live broadcast room in parallel, wherein the parameters comprise audience activeness, content sensitivity and real-time response level. The algorithm processing module dynamically integrates a real-time sentiment analysis algorithm, a semantic association algorithm and a scene adaptation algorithm through a weight distributor to generate a real-time weight. The reply generation module generates self-adaptive reply content according to the output, the distribution module configures a resource allocation strategy, and the feedback closed loop module monitors user interaction indexes in real time and adjusts weight distributor parameters. The reply content highly matched with the live broadcast scene can be generated in real time, and the user interaction experience and the live broadcast efficiency are effectively improved.

The embodiment of the invention relates to the technical field of information, and discloses an interface verification and test method and device based on a large model, a medium and a product, and the method comprises the steps: obtaining a to-be-tested interface document, analyzing the interface document through a first large model, constructing a test request parameter based on a request parameter definition, acquiring a real-time response message by calling a real interface; performing semantic comparison on a response example in the document structure information and the real-time response message by using the second large model to obtain a corrected interface document; comparing the parameter constraint condition in the corrected interface document with the verification logic in the business logic code snippet by using a third model to generate a logic verification passing result; and on the basis of the corrected interface document and a logic verification passing result, a Mock rule and an automatic test case script are generated by utilizing the fourth model, so that the maintenance cost and the false alarm rate of the automatic test script are reduced, and the automatic test efficiency is improved.

The invention relates to the technical field of information, in particular to a personalized search information recommendation method based on user behavior analysis, which mainly comprises the following steps: acquiring interactive behavior data of a user in real time; the method comprises the following steps: automatically extracting keyword tags of various contents through a natural language processing technology, and constructing a content keyword tag system; according to the content keyword tag system, extracting keyword tags associated with user behaviors from the interactive behavior data; in combination with the accumulated value of each type of behavior and the preset weight value, calculating the matching degree between the keyword tag and the user, and generating a matching degree set; performing variance operation on the matching degree set corresponding to the keyword tag to obtain sensitivity corresponding to the user content; and generating recommendation information of the search content of the user according to the sensitivity. According to the method, high-precision and real-time response personalized search content recommendation can be realized by analyzing user behaviors.

The invention discloses a few-sample increment website fingerprint identification method and system based on QUIC specific characteristics and a graph structure, a medium and a program product, and aims to solve the problem that QUIC encrypted traffic is difficult to effectively identify by a traditional method. According to the system, firstly, multi-dimensional features of QUIC traffic are extracted and constructed into a graph structure, and the global relevance between the traffic features is captured; then, deep mining is carried out on the features by utilizing a graph analysis technology, and the expression ability of the model to the traffic features is enhanced; and finally, through an incremental learning mechanism, the system can quickly adapt to newly appearing QUIC website traffic, and dynamic updating of a website recognition model is realized, so that the recognition precision and the real-time response capability are remarkably improved. According to the method and the device, through feature extraction and incremental learning for the QUIC traffic, efficient identification of the newly appearing website traffic is realized, and the accuracy and adaptability of website identification in an encrypted traffic environment are remarkably improved.

The invention provides an AI dialogue device and method for financial analysis, and belongs to the technical field of artificial intelligence. The device comprises an intention recognition module used for recognizing a financial intention queried by a user; the task scheduling module is used for activating the processing unit according to the intention; the tool calling module is used for calling an external tool to obtain information; the concurrent execution module is used for executing calling of a plurality of external tools in parallel; the result fusion module is used for integrating multi-source information; the reply generation module is used for generating a final reply; and the streaming output module is used for gradually transmitting the final reply to the client in the form of data stream. According to the method, the total data acquisition time is shortened by concurrently executing multiple tool calls, and part of content is displayed to the user in advance through streaming output before the final reply is completely generated, so that the waiting time perceived by the user is remarkably shortened, and the real-time response experience of the system is optimized while the professional property of financial analysis is ensured.

The invention discloses a transaction monitoring method, device and equipment and a storage medium, and relates to the technical field of information processing, and the transaction monitoring method comprises the steps: obtaining the request time, response time and real-time performance index value of a transaction interface; inputting the interface type of the transaction interface, the request time, the response time and the real-time performance index value into a transaction response duration prediction model to obtain an actual business processing duration and an expected processing duration range output by the transaction response duration prediction model; and based on the interface type, the actual business processing duration and the expected processing duration range, determining whether to carry out transaction alarm. According to the method and the device, the accuracy of transaction response monitoring is improved by realizing the functions of real-time monitoring of the response duration, customized alarm and the like, and meanwhile, all transaction interfaces are monitored, so that the incomprehensiveness of sampling inspection is avoided, the false alarm and the missing alarm are reduced, and personalized alarm is realized.

The invention discloses a network attack protection method integrating a large model and a honeypot technology, and belongs to the field of information security. The problems that an existing network attack protection method lacks dynamic response capability and is poor in network attack protection performance when facing complex attacks are solved. The method comprises the following steps: collecting interaction data generated in an operation process of a to-be-protected system; performing data desensitization to generate a desensitized data set; based on the desensitization data set, utilizing a LoRA fine tuning method to perform fine tuning on a pre-trained large model to obtain a generative honeypot large model; the method comprises the following steps: deploying a honeypot environment in a Docker container, and constructing a honeypot system based on large model service in combination with an Ollan platform; creating a honeypot system configuration file according to the configuration information of the real system, and simulating system services and ports; attacks are attracted, when an attack instruction is received, the honeypot system based on the large model service generates a false response, and one-time response to an attack request of an attacker is completed. The method is suitable for network protection.

The embodiment of the invention provides a power station material management method, device and equipment based on intelligent storage, relates to the technical field of computers, is suitable for a power station material management system, and comprises the steps: employing multi-dimensional real-time monitoring data to generate a monitoring data set; the multi-dimensional real-time monitoring data comprises time parameter data, behavior information data and material information data, the time parameter data represents a time response parameter generated in system operation, and the behavior information data represents the authority level of a post role of a user and the use condition of the system; the material information data represents access control strength of different categories of materials in the system, a dynamic permission model is adopted to perform permission allocation calculation on the monitoring data set, permission allocation parameters are generated in combination with environmental fluctuation parameters, and the permission allocation parameters are adopted to update a permission allocation mechanism in the power station material management system; and managing power station materials based on the updated authority distribution mechanism in the system. The flexibility, the accuracy, the response speed, the safety and the like of the system are improved.

The invention discloses a safety protection method and device based on cross-cloud cooperative verification and terminal equipment, and the method comprises the steps: collecting the safety event data of a plurality of public clouds in real time, and generating an environment threat coefficient; dynamically generating feature weight vectors of different security feature dimensions in the service data flow based on the environmental threat coefficient and the real-time response time; calculating a basic threat score based on the feature weight vector, and superposing a vulnerability compensation value calculated based on a machine learning model gradient magnitude to generate a final threat score; when the final threat score exceeds a security threshold, an antagonism test sample is generated in a sandbox environment according to a highest attack level standard, and a real-time defense strategy is generated and executed based on the test sample. The global threat situation perception and defense can be realized by constructing a ''cross-cloud dynamic scoring-sandbox game verification-model real-time feedback'' framework, and the technical problem that the information security protection system in the prior art cannot realize the perception and defense of the global threat situation is solved.

The invention discloses a high-concurrency time series data processing method and device for an anti-unmanned aerial vehicle system. According to the method, a lock-free hash table is adopted in a cache space to cache time sequence data, lock overhead is reduced through lock-free cache design and CAS atomic operation, the batch writing throughput is high, the conflict probability is low, multi-thread high-concurrency reading and writing are supported, thread competition and blocking caused by a traditional cache mechanism are thoroughly eliminated, and the concurrent processing capacity is improved; meanwhile, the expenditure of dynamic memory allocation is reduced through batch writing, and the memory utilization rate is increased; through quick positioning of a compound key, priority query of a hotspot data index structure and a lock-free reading mechanism, the query delay of hotspot data is extremely low, the delay of related query in the future is further reduced through a data preloading mechanism, and the real-time response capability is improved; based on a dynamic elimination mechanism of an elimination score, hotspot / recent data can be reserved preferentially, the resource utilization rate is ensured, the cache hit rate is remarkably improved, and the access frequency of the TSDB is greatly reduced.

The invention provides an interface joint debugging analysis method and system for data privacy security, and the specific analysis method comprises four aspects: dynamic encryption and intelligent key management, dynamic access control based on behaviors, intelligent log monitoring and real-time threat detection, and data sensitivity automatic identification and intelligent protection. Wherein the dynamic encryption and intelligent key management comprise two directions of dynamic encryption algorithm selection and intelligent key management, and the behavior-based dynamic access control comprises two directions of dynamic role and authority distribution and behavior anomaly detection and limitation. The intelligent log monitoring and real-time threat detection comprise two directions of log intelligent analysis and a real-time response mechanism, and the data sensitivity automatic identification and intelligent protection comprise two directions of data sensitivity automatic identification and intelligent protection and intelligent data protection. And the data security is improved.

The application discloses a dynamic real-time transaction scheduling method and system, which breaks the limitation of pre-defined transactions and periodic scheduling, and enables real-time transactions to be dynamically scheduled according to actual requirements without pre-defined transactions through twice priority sorting, ensures that the transactions are completed before the deadline, and significantly improves the flexibility and real-time performance of the system; on the basis of the double loop structure of the database working thread, a multi-version concurrent access strategy and a shadow node strategy based on priority inheritance are introduced, so that the real-time database system can operate more efficiently under complex transaction load, and the real-time response capability is further enhanced; priority allocation and blocking management of the transaction are performed by using the characteristics of the real-time operating system, so that the real-time performance of the real-time operating system is not damaged in the transaction scheduling process, and the efficient response capability of the system is maintained; and the application also provides a system for implementing the method.

The invention relates to the technical field of power system dispatching, and discloses a power load dispatching optimization method considering dynamic response reliability, which comprises the following steps: collecting and classifying user historical and real-time load data; based on the response proportion, the peak clipping contribution degree and the forgetting factor, establishing a reliability coefficient model for interruptible, transferable and reducible loads; in combination with real-time response performance, a user dynamic response reliability calculation model is constructed, and quantitative indexes are obtained; establishing a scheduling optimization model which takes optimal response reliability and lowest scheduling cost as multiple targets and considers capacity and capability constraints; and finally, solving by adopting a hybrid intelligent algorithm to obtain an optimal scheduling scheme. According to the method, the uncertainty of user response is dynamically quantified, and the reliability index is introduced into a decision, so that economy can be considered on the premise of ensuring scheduling reliability, priority calling of high-reliability resources is realized, and the flexibility and stability of power grid operation are improved.

The invention provides a multi-agent collaborative decision-making and disambiguation method and system. The method comprises the following steps of: registering a dependency relationship between a task context and a concerned event triggering condition to the collaboration center, updating a dynamic dependency view through the collaboration center, analyzing knowledge updating time through the collaboration center, and generating a task context identifier set of the intelligent agent; constructing a fact set based on the task context identifier set, and inputting the fact set into a rule inference engine to execute rules in a collaboration rule base to generate a strategy so as to obtain a collaboration strategy; according to the method, the problem of efficiency bottleneck of event routing in the distributed intelligent system is solved from the system architecture level, and the real-time response capability and expansibility of the system in a high-concurrency scene are greatly improved on the premise of ensuring decision consistency.

The application relates to the technical field of big data statistics, and specifically discloses a short video recommendation method based on big data statistics, which comprises the following steps: S1: obtaining the total number of short videos watched by a user in a sampling period, and dividing the same short videos according to short video labels; S2: obtaining the number of times of logging in a platform and the single online duration in the sampling period, and calculating the mean value and the dependence coefficient of the single online duration; S3: dividing the sampling period into a front interval and a rear interval, and sequentially calculating the trend coefficients of the same short videos in the front interval and the rear interval; S4: correcting the trend coefficients to obtain corrected coefficients; and S5: calculating the trend difference values of the same short videos, and analyzing the watching trend of the same short videos by the user based on the trend difference values. The application can analyze the preferences of the user and predict future trends, establish a flexible and real-time response mechanism for the change of the preferences of the user, realize the long-term development of the platform, and maintain the market competitiveness.