27 results about "Black list" patented technology

The invention relates to the technical field of artificial intelligence, and discloses a method and a system for realizing multi-round questions and answers under security consultation based on a large model, and the method comprises the steps: receiving security consultation data of a target user to generate a dynamic user portrait of the target user, analyzing a first-level intention label and a first-level intention confidence coefficient of the security consultation data, and generating a second-level intention confidence coefficient; determining a second-level intention label of the target user; outputting structured query parameters of the target user; outputting dual-channel data of the security consultation data, and identifying a keyword blacklist, implicit commitments and undisclosed risks of the dual-channel data to filter the dual-channel data to obtain filtered multi-modal knowledge fragments; and generating a multi-dimensional answer of the target user to generate a multi-strategy model and a multi-strategy adjustment parameter and a user portrait update parameter of the dynamic user portrait, and executing multi-round questions and answers of the target user based on the multi-strategy adjustment parameter and the user portrait update parameter. According to the invention, the suggestion accuracy and personalization under security consultation can be improved.

The embodiment of the invention discloses an article circulation alarm information generation method and device, electronic equipment and a readable medium. A specific embodiment of the method comprises the following steps: in response to received missed scanning prompt information of unmarked personnel, obtaining a missed scanning personnel video frame sequence and article information, and executing the following generation steps: obtaining a personnel feature vector data set; pedestrian re-identification processing is carried out on the video frame sequence of the scanning-missing persons to obtain human body feature vector information of the scanning-missing persons; performing matching and binding processing on the human body feature vector information of the missing scanning personnel to obtain information of the missing scanning personnel; adding the scanning missing person information to a preset blacklist library; acquiring a person face image of the visited authorized user; performing facial feature extraction processing on the person facial image to obtain facial feature vector information; generating risk level information; and generating alarm information and sending the alarm information to a preset receiving end. According to the embodiment, the user experience is improved.

The application relates to the technical field of software development, in particular to a non-invasive dynamic burying point system for an application, an implementation method and a storage medium. A starting class is defined for a burying point application, the starting class is connected to a process after the burying point application is started, and a transformer module encapsulated into a java agent is loaded. Compared with an existing annotation-based invasive burying point scheme and a static instrumentation burying point scheme, the application is effective for any jvm application, meanwhile, targeted burying point optimization of common framework and tool class libraries and progressive burying point processing of specific scenes are considered; a classification process is generally mainly aimed at methods of the application itself, and existing classification can be generally adopted for widely used library classes. When the application needs to change or cancel burying point logic, only the black and white list classification and the processor associated with the white list need to be changed, and the server side is re-calculated according to the black list to obtain the bloom filter parameter, so that the operation is simple, the burying point is flexible, and the application is not aware of the burying point.

The invention discloses a software uninstalling method and device of a Windows operating system, a storage medium and a terminal, relates to the technical field of software engineering, and mainly aims to solve the problem of low software uninstalling efficiency under the existing Windows operating system. The method mainly comprises the following steps: monitoring a system event in real time to capture process event information of a software installation process; in response to a process ending event of the software installation process, performing multi-dimensional feature matching on the process event information and software matching information of blacklist software, so as to take the installed software as target software under the condition of the installed software corresponding to the process event information; and performing reverse cleaning operation on the target software according to the software matching information and the process event information of the target software so as to unload the target software. The method is mainly used for automatically uninstalling software.

The invention provides an information system red-black list intelligent evaluation method and system based on multi-dimensional data. The method comprises the following steps: acquiring data, namely acquiring multi-dimensional data including performance indexes, log data, user behavior data and business data from a target information system in real time; preprocessing the data, and cleaning the collected multi-dimensional data, including abnormal value processing, missing value completion and data standardization; feature engineering is carried out, evaluation features are extracted and screened from the preprocessed data, and feature weights are dynamically distributed based on business scenes; performing intelligent model evaluation, classifying the information system by utilizing a trained machine learning classification model and combining the dynamically distributed feature weight, and outputting a red list representing a high-performance system and a black list representing a low-efficiency system; and optimization execution: according to the red list and the black list, automatically triggering optimization processing for the black list. According to the method, comprehensiveness, dynamic self-adaptability and automation of the evaluation process are realized.

The invention provides an operation activity multi-account cheating identification method and system based on graph propagation. The method comprises the steps that a client side collects user behavior data and reports the data, a server side constructs a heterogeneous user relation graph based on the data and gives a cheating risk weight to an edge, the cheating probability is calculated by using a PageRank algorithm based on a seed blacklist user and is subjected to hierarchical disposal, and the client side dynamically adjusts operation activity interaction logic according to a hierarchical result; according to the system, the method is realized through cooperation of the client SDK module, the server graph construction and propagation module, the hierarchical decision module and the client anti-cheating execution module. According to the method, the defects that an existing supervision model is high in labeling cost, large in recognition delay and incapable of achieving front-end real-time intervention are effectively overcome, and the method is scientific, efficient and good in transverse transplantation capacity.

This invention discloses an intelligent generation method and apparatus for well logging acquisition construction design. The method includes: determining a matching template from a pre-established well logging acquisition construction design template library based on the well logging task category, well type, and well shape in the well logging acquisition task notification; using a well logging acquisition construction information knowledge graph based on a pre-established well logging team information library to screen qualified well logging teams that match the well logging task category, well type, and well shape in the notification and whose relevant information is not on a set blacklist; and using the knowledge graph to extract the information of qualified well logging teams required for the template from the well logging task notification and the well logging team information library, and filling this information into the template to generate the well logging acquisition construction design. This method enables intelligent requirements analysis for well logging acquisition construction design, improving the quality and timeliness of design preparation.

The invention provides a dynamic adaptive multilevel federated learning backdoor attack defense method, which comprises the following steps of: 1, feature extraction and clustering: extracting client model update features from cosine distance and normalized update energy dimensions, and clustering by using a DBSCAN algorithm; 2, model output classification: marking clients as possible malicious or benign clients according to model output; 3, dynamic weight aggregation: scoring the screened client model according to the classification statistics and the client aggregation result, and determining the weight of the client model in the global model according to the score; 4, directional rollback detoxification: after the global model is aggregated, analyzing the parameter update quantity of the client to identify abnormal update, and adaptively adjusting towards the direction of the initial model to eliminate the back door influence; and 5, self-adaption blacklist: quantifying historical malicious behaviors of the client through the scoring dictionary, and pre-filtering model parameters transmitted by the client in an aggregation preparation stage. According to the method, a multi-level defense system is constructed, and the confrontation adaptability and the security defense capability of the federal learning framework are remarkably improved.

The invention relates to the technical field of matrix multiplication, in particular to a sparse matrix-dense matrix multiplication acceleration system and method. The system comprises a sparse structure analyzer, a structure weighting scheduler, a plurality of PE rows and a runtime feedback controller. The sparse structure analyzer extracts features of each row of the sparse matrix and calculates a structure cost score; the structure weighting scheduler is combined with the PE row real-time load to distribute the PE row with the minimum comprehensive cost for the matrix row; executing scalar-vector multiply-accumulate by the PE array in parallel; and during operation, the feedback controller generates a blacklist bitmap and a splitting signal through performance monitoring, and dynamically adjusts task allocation. Parallel data reading and load balancing processing and calculation are carried out through a three-stage assembly line. According to the method, the calculation efficiency and the energy efficiency ratio of sparse-dense matrix multiplication are effectively improved, and then the data processing speed of the CPU / GPU is improved.

The present disclosure provides a blacklist generation method and device, electronic equipment and storage medium, relates to the technical field of computers, and particularly relates to the technical field of artificial intelligence such as automatic driving, data processing and simulation testing. The specific implementation scheme is: in response to a simulation task initiation request, the current target blacklist is called; machines in the cluster located in the target blacklist are shielded; the simulation task is submitted to the cluster, and running result data of the simulation task is obtained; the running result data is mined to obtain a fault machine that has a problem in the simulation task; and the target blacklist is updated based on the fault machine. According to the scheme of the present disclosure, the blacklist can be automatically generated in resource scheduling, and then the machines in the current latest target blacklist can be shielded for next time resource scheduling, so that the stability and efficiency of the simulation task running are improved.

The present invention relates to systems and methods for query-based analysis of optimization of a supply chain. In some embodiments, a query is received within a trusted environment of an optimization system, wherein the query includes a natural language expression. The query is supplied to an Artificial Intelligence (AI) transformation system, wherein the AI transformation system converts the query into a function and parameter statement. The function and parameter statement are translated into at least one API call and function, which are executed to generate results. The results are then outputted. The AI transformation system is locally trained on the optimization system. In some embodiments, the query may be augmented with information. The information includes at least one of query source information and query intent information. Additionally, sensitive data in the query may be identified. The sensitive information is identified by blacklist, whitelist or semantic analysis.

The invention provides a multi-source task-oriented material flow synthesis method and device and a storage medium, and relates to the technical field of automatic material management. The method comprises the following steps: monitoring a towed form and verifying a drop point, correcting a node connection line, and receiving node attribute configuration information; a supplier blacklist is monitored based on a Rete algorithm, budget is monitored by means of a Boolean algebraic optimization algorithm, and an alarm is generated when abnormity occurs; based on the request import form, receiving configuration information, and updating and storing the configuration information; calling data connector configuration parameters, and synchronizing the form data and the parameters to a material platform. According to the method, a full-process automatic material management system is constructed, form node visual arrangement is realized, the operation threshold is reduced, the risk is accurately identified and early warning is performed, the data barrier is broken through, the construction efficiency is improved, the risk control capability and cross-platform collaboration are improved, and the problems of much manual intervention, slow response and data island in the traditional process are solved.

The invention discloses an ash silo logistics anomaly detection and anti-cheating management and control method, and relates to the technical field of logistics safety management and control, and the method comprises the steps: collecting the historical business data of a vehicle in an ash silo logistics system, and constructing a logistics association map with a cargo owner, a vehicle and a driver as vertexes; inputting the association graph into a graph neural network model to obtain a vehicle node anomaly association degree score and generate a cross-factory dynamic blacklist; performing risk scoring on the vehicle requesting to enter the parking lot and comparing with the blacklist to realize access control release or rejection control; the process is predicted and early warning or control is executed based on a digital twinborn model before loading operation; and aggregating each factory model through federal learning to generate a global risk scoring model and dynamically updating the blacklist. According to the invention, the abnormity identification accuracy can be improved, and cross-factory collaborative anti-cheating management and control are realized.

The invention discloses an identity verification method and device based on an SIM card, equipment, a medium and a product, and the method comprises the steps that a called SIM card sends an identity verification request message to a calling SIM card by receiving a call request of the calling SIM card; wherein the identity verification request message at least comprises the identity information of the called SIM card; receiving and analyzing an identity verification response message sent by the calling SIM card to obtain identity information of the calling SIM card; wherein the identity verification response message is sent by the calling SIM card after the identity information verification of the called SIM card is passed; and verifying the identity information of the calling SIM card. By adopting the embodiment of the invention, the problem of error verification of a traditional blacklist mechanism can be avoided, and the verification effect is improved.

The invention discloses a blacklist text matching method and device based on a twin network. The blacklist text matching method and device are used for improving matching effectiveness for blacklist texts. According to the scheme, the method comprises the steps of obtaining a to-be-matched text; performing local feature extraction on the to-be-matched text through a convolutional neural network module of the twin network to obtain local features of the to-be-matched text; performing global feature extraction on the local features of the to-be-matched text through a multi-head self-attention module of the twin network to obtain global features of the to-be-matched text; and determining a blacklist text meeting a preset similarity condition with the global feature of the to-be-matched text from a preset blacklist text feature library, the preset blacklist text feature library comprising a plurality of blacklist text features, and any blacklist text feature being obtained by performing feature extraction on the preset blacklist text through a twin network.

The application discloses a credit anti-fraud blacklist area identification system and method based on grid division, which comprises a data acquisition and preprocessing module for acquiring and preprocessing GPS coordinate data and face recognition images to obtain preprocessed GPS coordinate data and preprocessed image features, and acquiring business data; a gridding division and coding module for performing spatial discretization and grid coding according to the preprocessed GPS coordinate data to obtain GPS grids, and according to the business data, counting a plurality of indexes in each GPS grid; a feature engineering construction module for determining a plurality of feature vectors based on the plurality of indexes and the preprocessed image features; and a black grid identification model training and execution module for inputting the plurality of feature vectors into a trained black grid identification model to obtain a black grid identification result. The application can improve the accuracy of identifying credit anti-fraud blacklist areas.

The invention discloses an instruction monitoring method and device, equipment, a storage medium and a program product. The method comprises the steps of obtaining a user instruction in response to an instruction input operation of a user, and collecting biological characteristics of the user; identifying an emotional state of the user based on the biological characteristics, and determining a first risk level corresponding to the user instruction according to the emotional state and a preset risk mapping relationship; according to the reversibility of the system operation indicated by the user instruction, the influence range of the system operation indicated by the user instruction, the authority of the user and the instruction blacklist, performing security assessment on the user instruction, and determining a second risk level corresponding to the user instruction; and determining a risk level of the user instruction according to the first risk level and the second risk level, and executing a system operation corresponding to the risk level. By adopting the method, the risk level of the instruction can be determined from the two aspects of the instruction and the user emotion, and the accuracy of an instruction risk level judgment result is effectively improved.

An object of a system according to an embodiment is to effectively prevent telephone fraud for elderly people.SOLUTION: A system includes a context analysis unit, a black list filter unit, and an in-terminal AI processing unit. The context analysis unit analyzes a context. The blacklist filtering unit performs filtering using an existing telephone number blacklist. The in-terminal AI processing unit performs AI processing in the terminals.SELECTED DRAWING: Figure 1

The invention relates to the technical field of communication, and provides an order cancel-after-verification intelligent control method and device, a server and a storage medium. On the premise of configuring the keyword mode for the recharging channel side, when the recharging result information of the order returned by the recharging channel side is received, the target keyword mode is determined according to the recharging frequency record value by obtaining the recharging frequency record value of the order, and the target keyword mode is used for recharging the order. And determining a target key field value according to the recharging result information and the target keyword mode, and executing a cancel-after-verification action according to a classification reason corresponding to the target key field value and the black pulling field value. The method can automatically cancel the orders after verification, improves the order processing efficiency, does not need manual intervention, and reduces the possibility of human errors.

The invention discloses an air unmanned cluster accurate tracking and track prediction method, and relates to the technical field of unmanned cluster collaborative awareness and target tracking, and the method comprises the following steps: obtaining a current to-be-tracked target set and observation data snapshots from a plurality of unmanned aerial vehicle nodes; determining whether first task configuration for the target set exists in a pre-stored task configuration library or not; constructing a candidate observation axis list for the target set; determining a preferred observation axis of the target set; dividing and scheduling the observation data of the target set to form a plurality of parallel observation subtasks; and controlling the unmanned cluster to execute target tracking and track prediction according to the parallel observation subtasks. Through cache multiplexing of the task configuration library, dynamic updating of the node blacklist and real-time monitoring of load balancing, a complete adaptive optimization mechanism is established, and the tracking precision and long-term task execution efficiency of the unmanned cluster in a complex environment are remarkably improved.

This invention relates to a vehicle-to-everything (V2X) traffic information consensus system and a route planning method based on the consensus system. The system includes a dynamic consensus group selection module for selecting a coordinator from edge nodes and generating a consensus group through dynamic election; a first-stage competitive block-producing module for competing for the right to produce blocks and report traffic information based on effective capacity and reputation; a second-stage competitive block-producing module for competing for the right to produce blocks and report traffic information again after excluding the winner of the first stage; a two-stage differentiated reward module for giving different reputation rewards to the winners of the first and second stage competitive block-producing stages; and a reputation management and self-healing module for managing the reputation values ​​of all edge nodes in the network and realizing fault recovery based on the established two-layer blacklist-self-healing logic. It not only maintains energy-saving characteristics but also significantly improves transaction processing speed, has anti-attack capabilities, and can self-heal in large-scale networks, providing reliable and consensus-based traffic data.

The application discloses a method and system for preventing DHCP spoofing, which comprises the following steps: firstly, obtaining a DHCP packet based on a controller, and extracting a source MAC address according to the obtained DHCP packet; secondly, judging whether the source MAC address matches a MAC address in a DHCP Snooping binding table; if yes, setting an uplink port as a trusted port; if no, deleting the DHCP packet, and marking a source MAC address corresponding to the DHCP packet with a discarded time number as 1; finally, judging whether the discarded time number of the source MAC address corresponding to the DHCP packet is greater than N; if yes, listing the source MAC address in a blacklist; if no, ending the process.

The application provides a task processing method and device, computer equipment and a computer readable storage medium. The method comprises the following steps: receiving task processing information of a Spark task, wherein the task processing information comprises a task processing node blacklist; determining a plurality of first task processing nodes required for processing the Spark task according to the task processing information; checking each first task processing node based on the task processing node blacklist; determining a second task processing node from the first task processing nodes that pass the check, and determining a task process corresponding to the second task processing node according to the task processing information; and executing the task process based on the second task processing node. The application improves the calling flexibility of the task processing node and the processing efficiency of the Spark task.

An automation production program error correction method and device, a storage medium and a computer program, the method is implemented by a program management system RMS, and the method comprises the following steps: obtaining the name of an automation production program to be checked; obtaining a key parameter combination of the automation production program based on the name as a first key parameter combination; obtaining a key parameter combination configured for the automation production program on a device capable of executing the automation production program as a second key parameter combination; comparing the second key parameter combination with the first key parameter combination to determine whether the second key parameter combination matches the first key parameter combination; when the second key parameter combination does not match the first key parameter combination, adding the automation production program to a blacklist so that the automation production program cannot take effect and thus cannot be used for production, and outputting the blacklist. The method can prevent large-area product defect problems caused by program configuration errors and reduce factory losses.

The application discloses a pure hardware vehicle regulation level key system, all core security logic is realized by pure hardware circuit, without central processing unit, without software, without firmware participation. The system comprises a hardware security chip, at least one wireless communication module and a one-time programmable storage unit, and is paired with a vehicle only when leaving the factory, and the identity identification is physically solidified and cannot be modified. The time-of-flight ranging circuit solves the relay attack threat from the physical layer, the quantum computing risk is solved by using the national secret algorithm hardware engine, a multi-scene emergency unlocking system composed of a near field communication passive chip, a mechanical keyhole, a physical emergency interface and a super capacitor is constructed, and the hierarchical permission management and the blacklist mechanism of the hardware solidification are supported. The application eliminates software vulnerabilities and remote attack risks from the root, prolongs the battery life to more than ten years, and significantly improves the safety and reliability of the automobile key system.

The invention discloses a network security management method, system and device based on an array server and a storage medium, and the method comprises the steps: enabling a client device to be connected to a corresponding computing node, obtaining the login information of a client through a BMC system, and constructing a protocol text token based on the login information, the format of the protocol text token comprises a function type, a timestamp, an IP address, an on-off state and a verification value, the IP address is the IP address of the client equipment, then the protocol text token is transmitted to a corresponding computing node, the computing node performs timing verification on the protocol text token, the client equipment after successful verification is allowed to be accessed, and the protocol text token is transmitted to the corresponding computing node. The black list and the white list of the client equipment ip are precisely controlled through the self-defined protocol text token, and the attack surface of the computing unit board card in an open network environment is effectively reduced, so that the security protection capability of the computing unit board card is remarkably improved.

The invention provides a freight driver credit evaluation method and system based on a block chain and an intelligent contract, and belongs to the technical field of credit evaluation. Comprising the following steps: preprocessing credit pre-evaluation data of a freight driver, packaging the credit pre-evaluation data according to a timestamp sequence to generate a block, and uploading the block chain through a consensus mechanism to carry out distributed evidence storage; calculating a freight driver credit score based on a multi-dimensional credit evaluation index system, and generating a credit portrait; event triggering conditions related to behaviors of the freight driver are automatically monitored through an intelligent contract, the credit rating and score of the freight driver are adjusted, and a credit portrait is updated; and meanwhile, executing a corresponding reward and punishment mechanism, and carrying out cross-platform sharing and blacklist synchronization of the credit rating of the freight driver in the alliance chain architecture. According to the invention, the credit of the freight driver can be automatically evaluated on the basis of ensuring the safety and credibility of the personal data of the freight driver.