44 results about "Security industry" patented technology

The invention relates to a system for realizing financial agent credible authentication based on distributed digital identity, which comprises a base layer, an identity layer, an authority layer and a business layer, and is characterized in that the base layer integrates distributed digital identity authentication service, subscription service and distributed storage service, and constructs a credible collaboration framework covering the whole life cycle; the identity layer is configured with a DID generation module, a DID verification module and a DID query module; the authority layer deploys a VC signing and issuing module, a VC verification module and a digital envelope module; the business layer comprises a client and a server between security institutions, and carries out cross-institution data calling through mutual trust verification and MCP message interaction. According to the system for realizing the credible authentication of the financial agent based on the distributed digital identity, through the fusion innovation of the distributed digital identity, the verifiable certificate and the block chain technology, the decentralization of identity authentication, the dynamic authority management and the full link of auditing and tracing are realized in a security industry agent cooperation scene.

The invention relates to the technical field of power systems, in particular to a method for constructing a general knowledge base in the power safety industry based on a large model. Performing data classification and sorting on the data source of the electric power safety industry to obtain classified data; constructing a plurality of agents based on the large model, and processing the classified data by using the plurality of agents to obtain structured data; and carrying out vectorization processing on the structured data to generate vector data, storing the vector data into a vector database, and constructing a universal knowledge base in the electric power safety industry. The data source comprises intrinsic safety analysis historical data, an intrinsic safety analysis report, electric power safety rules and regulations and operation specifications. The intelligent agents comprise a file analysis intelligent agent, an analysis report information extraction intelligent agent, a rule and regulation information extraction intelligent agent and an operation instruction information extraction intelligent agent. Through a multi-agent cooperative processing and vectorization storage mechanism, efficient organization and intelligent application of electric power safety knowledge are realized.

The invention discloses a security industry system application change level quantitative evaluation method, which comprises the steps of reading original change application data, historical change records and market transaction data, and respectively generating a change feature vector, a historical influence vector and a market feature vector; performing time window segmentation and weight calculation on the market feature vector to generate a time sequence feature matrix; carrying out nonlinear transformation on the change feature vector and carrying out dependency analysis on the change feature vector and a historical influence vector to generate a coupling feature tensor; decomposing the coupling feature tensor and performing feature mapping with the time sequence feature matrix to generate an evaluation score; and performing clustering analysis based on the evaluation score and the historical influence vector, generating a level threshold vector, and further obtaining a level change result. According to the method, through dynamic fusion and adaptive optimization of the multi-dimensional data, accurate quantitative evaluation of the system change risk is realized, and the accuracy and reliability of evaluation are improved.

The invention relates to a security industry large-computing-power multi-service artificial intelligence system and method, and the method comprises the steps: receiving configuration and task information issued by a security service platform through a platform interaction layer, and storing the information in a database; the task scheduling layer carries out judgment according to the task information content and schedules the task information content to a corresponding business service; and the business service works in the business implementation layer, executes tasks according to task information, combines intelligent result data, stores alarm pictures or audio and video videos, and returns execution results to the platform interaction layer. According to the invention, different types of services such as audio and video diagnosis, intelligent analysis, scene recognition, face recognition, trajectory tracking and the like can be supported on one server at the same time, algorithms and computing power combinations of different services are provided as required, and the service quality is improved. Therefore, the purpose of flexible configuration is achieved, and the requirements of different industries for multiple service scenes on artificial intelligence are met.

The invention relates to a multi-modal large model construction method for a security industry in a creative executable environment, and the method comprises the steps: a first agent module and a second agent module carry out the game playing of a first analysis result and a second analysis result obtained after the analysis of the same task based on long-term memory and short-term memory obtained from a memory module, if the first agent module and the second agent module are not consistent after gaming, a third agent module performs weighting processing on suggestions discussed by the first agent module and the second agent module based on a preset weight to obtain a gaming result; and the third agent module generates an initial solution based on the task for game playing, obtains suggestions for the initial solution from the first agent module and the second agent module, and carries out adjustment analysis based on the game playing result based on the received suggestions for the initial solution to obtain a decision result.

The present invention discloses an APT detection method that integrates cloud-network-end logs and threat knowledge. The cloud-network-end fusion logs of multiple platforms are collected and processed into a four-tuple sequence in process units and time order to construct an origin graph of the cloud-network-end. Attack entities and entity relationships are extracted from network threat intelligence to form an attack causal graph, which is used in cloud-network-end APT detection. The present invention collects cloud-network-end fusion logs of multiple platforms and extracts attack causal graphs from network threat intelligence. It does not rely on manual work, thereby avoiding the formulation of a large number of rules, helping security industry personnel to more accurately and efficiently identify cloud-network-end fusion APT attacks, thereby achieving effective detection of cloud-network-end fusion APTs.

1. The name of the design product: intelligent server. 2. The use of the design product: the design product is used in high-performance computing, intelligent analysis and security industry applications. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

1. The name of the product of this design: Retractable iris collector. 2. Application of this design product: Applied in the security industry, for iris collection and recognition. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the design points: three-dimensional diagram of the unfolded state.

The invention relates to the technical field of low-code platforms, and provides a low-code process generation method based on an AI large model, and the method comprises the steps: S1, obtaining a business demand description text through a natural language interaction interface; s2, inputting the business demand description text into the trained AI large model, training the AI large model based on the security industry historical process data, and outputting a Markdown format intermediate file comprising a form definition module and a process definition module; s3, analyzing the Markdown intermediate file through a format conversion program, and generating a process configuration file conforming to a target low-code platform specification; and S4, deploying the process configuration file to a self-developed low-code platform, and generating an executable business process application. According to the method, prototype design of a form and a process is converted into a format which can be identified by a low-code platform and is imported, and finally a formal annual authorization entrusting process is generated, so that digital and automatic management of the business process is realized.

The invention relates to the technical field of safety information intelligent processing, in particular to a robot question and answer service method and system based on safety industry information source data. The mixed information source collection strategy comprises collection of public internet data through an intelligent crawler, butt joint of closed information source data through an authorization interface and capture of regional real-time data through a localized edge node, and collection frequency is dynamically adjusted based on an information source type and event emergency degree; and carrying out real-time processing on the acquired data by adopting a stream processing framework, wherein entity extraction and format standardization are carried out on unstructured data. According to the method and the device, the global security domain data is acquired by adopting a mixed information source acquisition strategy, and the acquisition frequency is dynamically adjusted, so that the problem that information blind areas or lagging exist in question and answer responses due to the fact that traditional robot question and answer services mostly adopt single information source or fixed frequency acquisition is solved.

The invention discloses a risk assessment method for the public security industry, and belongs to the technical field of public security management. The method is cyclically executed in a preset time period, and comprises the following steps of: constructing a multi-dimensional dynamic risk map: gridding an activity site, converging multi-source real-time data associated with geographic grids, and calculating a basic risk value of each grid through a multi-factor risk contribution model; a risk exposure degree deduction step: acquiring a real-time police force position, calculating a police force response delay index of each grid, and performing coupling calculation to obtain a risk exposure degree index of each grid; and a police scheduling suggestion generation step of generating a police scheduling suggestion aiming at minimizing the total risk exposure degree of the whole field through a resource optimization allocation algorithm based on the risk exposure degree index. The technical problem that in the prior art, risk assessment and police dispatching are separated from each other, and on-site dynamic changes cannot be handled is solved.

1. The name of this design product: Desktop fingerprint scanner. 2. Application of this design product: Applied to the security industry, a fingerprint collector placed on the desktop. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

The invention relates to a novel security industry data resource analysis method, and belongs to the technical field of security, efficient processing and intelligent decision making of security data are realized through an end-side cloud cooperation architecture, real-time target detection is performed through deploying a lightweight YOLOv8 model through an edge end, and preliminary anomaly recognition is completed; an LSTM neural network is trained on the cloud platform based on historical alarm data, and high-risk area prediction in the next 24 hours is realized; a DBSCAN clustering algorithm is combined to carry out spatial correlation analysis on the personnel trajectory, and a suspicious gang is automatically identified; and finally, an entity relation network is constructed through the knowledge graph, and cross-system event reasoning is supported. According to the technical combination, the system has the capabilities of full space-time coverage, full factor fusion and full process intelligence, a closed-loop safety prevention and control system is formed, and the automation level and the actual combat capability of the security and protection system are effectively improved.

A target detection method and device, electronic equipment and storage medium are disclosed. The method comprises: processing a to-be-detected image to obtain a plurality of scale features; fusing the plurality of scale features to generate a fused feature map corresponding to a target scale feature; inputting the fused feature map into a prediction network model to obtain a width-height feature map, a category feature map, a center point deviation feature map and a target background feature map; wherein the prediction network model comprises a first branch, a second branch, a third branch and a fourth branch; determining a plurality of target detection boxes corresponding to the width-height feature map, the category feature map, the center point deviation feature map and the target background feature map; and determining a detection result of the to-be-detected image according to the plurality of target detection boxes. This makes the categories of different targets compete with each other, improves the accuracy of detection in the security industry, and avoids false detection of the background.

The invention discloses an AI native intelligent central operating system based on reinforcement learning, and relates to the technical field of reinforcement learning, and the AI native intelligent central operating system comprises the steps: determining a corresponding regulation action based on a preset business target and state representation; according to the invention, the plurality of sub-regulation items are determined based on the recognition of the regulation action, and the quantitative execution is carried out along each sub-regulation item, so that the change information of the regulated key performance indicator is predicted, and the accuracy of the change information of the regulated key performance indicator is improved. Determining a corresponding reward value according to the identification of the change information of the key performance indicator; determining to-be-optimized content according to reinforcement learning of the reward value; according to the method, each optimized sub-regulation item is determined according to the matching of the to-be-optimized content and each sub-regulation item, and the final operation content of the security industry AI native intelligent center is constructed in combination with the action constraint relationship of the security database, so that the accuracy of the final operation content of the security industry AI native intelligent center is improved.

The invention discloses a dynamic capacity expansion scheduling method, device and system for a security industry large model, and belongs to the technical field of security industry and large model capacity expansion, and the method specifically comprises the steps that a large model management platform sends a state calculation request carrying a copy resource demand to a node; the node calculates a time sensitive state score based on the real-time time sensitive parameter and replies the time sensitive state score; and the large model management platform calculates and ranks the final state scores of the nodes in combination with the scores replied by the nodes and the non-time sensitive parameters, and selects the optimal node for scheduling. The method effectively solves the problems of insufficient real-time performance of node parameters, low scheduling decision accuracy and high scheduling delay of a central platform in the prior art, and is particularly suitable for dynamic resource scheduling of large model applications such as real-time research report generation, intelligent customer service and abnormal transaction monitoring in the security industry.

The embodiments described herein include rapid change vertical shore used in the trench safety industry. Particular embodiments facilitate a rapid change between a single cylinder vertical shore and a double cylinder vertical shore.

The application discloses a securities industry large model factor mining and back testing method based on a Xinyuan executable environment, relates to the technical field of big data, and utilizes a large language model to identify a transaction factor from a financial research report and extract description information of the transaction factor; utilizes code generation to generate a large model to generate factor calculation code for the identified transaction factor; utilizes the factor calculation code and historical data in a historical market database to back test the transaction factor to obtain a factor value, utilizes a preset strategy testing method to evaluate the transaction factor based on the factor value to obtain an evaluation index for evaluating effectiveness of the transaction factor, and displays the transaction factor and the evaluation index through a visual interface on a client. The powerful natural language understanding capability and code generation capability of the large model realize automatic mining and back testing of the transaction factor in the research report, improve efficiency and accuracy of factor positioning, and automatically generate the factor calculation code, so that the back testing process is automated.

The invention belongs to the technical field of data processing, and particularly discloses a method for constructing a public safety industry knowledge base based on a large model, and the method comprises the following steps: firstly, obtaining data through an internal system and an external public channel of a public safety organ; cleaning and preliminarily integrating the collected data; then extracting entities, entity relationships and entity attributes from the cleaned and integrated data, and outputting a knowledge triple table; the extracted knowledge is fused; and finally, storing the fused clean knowledge into an internal table, and synchronizing the knowledge in the internal table to the GDB to form the public safety industry knowledge base. By means of data collection, data preprocessing, knowledge extraction and knowledge fusion and conflict resolution, public safety industry data storage and knowledge base construction are achieved, and the problems that due to the fact that public safety system data are dispersed in different service systems and are different in format, the data are difficult to achieve efficient integration and utilization, and the system reliability is poor are solved. Therefore, the working efficiency is influenced.

The present invention relates to a source code tracing method, which includes the following steps: establishing a standardized vulnerability knowledge base by crawling, obtaining open source code that meets the specifications, and storing the open source code results after preprocessing; building a Neo4j graph database; and performing business processing. Thus, based on the graph library association relationship between the content hash value of the source code and the directory hash value, the detection speed can be improved. The full-scale problem of detection through binary file and file collision is solved, and the speed is increased by more than 10 times, thereby providing engine support for further open source software supply chain security detection. Through multi-dimensional clustering, it is possible to distinguish whether a certain module or its subclass is used based on the calculated result score, and solve the problem of inaccurate supply chain detection results caused by the inclusion relationship between open source project modules. It can provide more accurate open source projects and versions, so that vulnerability detection in the software industry security industry can be more accurate.

The present application relates to a method for constructing a multimodal large model of the securities industry in an ICT executable environment, comprising: a first agent module and a second agent module, based on long-term memory and short-term memory obtained from a memory module, conduct a game between the first analysis result and the second analysis result obtained after analyzing the same task; if the first agent module and the second agent module fail to reach an agreement after the game, the third agent module performs weighted processing on the opinions discussed by the first agent module and the second agent module based on preset weights to obtain a game result; the third agent module generates an initial solution based on the task to be gambled, and obtains suggestions for the initial solution from the first agent module and the second agent module, and based on the received suggestions for the initial solution, performs adjustment analysis based on the game result to obtain a decision result.

The present invention relates to the technical field of stage safety performance, and discloses a stage safety performance control method, including a control module, an input module, an information processing MCU, a communication module, a comparison module, an alarm module and an equipment controller, wherein the input module is used to receive the operation signal data sent by the control module, and the input module also transmits the received operation signal data to the information processing MCU through a secure industrial Ethernet, and the comparison module stores pre-stored operation signal data and pre-stored performance pictures, and the information processing MCU packages the received operation signal data to the comparison module. The present invention accurately adjusts the moving distance of a small-range device controller through an encoder roller, two direction switches and a roller constitute a control combination key, and the control combination key can be flexibly configured as needed, and a safety switch can enable the operation of all combination keys at the same time. In order to improve safety, the safety switch key is designed as redundant.

1. The name of this design product: backup power box. 2. The purpose of this design product: used in the security industry, a battery box that provides backup power. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

The invention relates to the technical field of safety protection, in particular to a security integrated command and dispatch platform system. Various data such as monitoring videos and real-time personnel positioning are integrated, data sharing and cooperative processing are realized through a software architecture, and a data island is broken; by applying artificial intelligence and big data analysis technologies, various security events are predicted and analyzed, and a scientific basis is provided for command decision making; various events in the security industry are monitored in real time, early warning rules are set, problems are found in time, and early warning is triggered, so that disposal measures are taken quickly; unified commanding is carried out on the security unit and the resident unit, efficient resource allocation and cooperative combat are achieved, and the emergency response speed is increased; various types of data are presented in a visual mode, such as electronic map labeling and tree diagram display, so that a command decision maker is assisted to intuitively understand the situation and make a decision. The system has the advantages of being easy to install and maintain, stable, reliable and capable of guaranteeing stable operation.

The invention provides a security field large model construction method and system, and the method comprises the steps: S1, obtaining original security industry data, and carrying out the preprocessing of the obtained original security industry data; s2, constructing a data compliance detection model, and performing compliance detection on the preprocessed original security industry data by using the data compliance detection model to obtain compliance data; s3, building a model instruction fine tuning data set based on the obtained compliance data; and S4, performing supervised fine tuning training on the base large model by using the model instruction fine tuning data set to obtain a trained security field large model.

The invention discloses a network convergence-oriented AI-driven heterogeneous cooperative system and a data processing method. The network convergence-oriented AI-driven heterogeneous cooperative system comprises a routing control board card, an AI computing board card, a security board card, an internal and external network synchronous protection module, a software system and a high-speed interconnection bus. Through deep integration of hardware and software, intelligent, secure and highly reliable control of a data transmission process is realized. The system can complete intelligent link management, dynamic encryption protection, service priority scheduling and real-time data synchronization through multi-computing-power cooperation and multi-network fusion in a diversified environment so as to support high-security industry communication scenes of future railways / low altitude / satellites and the like.

The invention provides a security industry cross-institution insurance information sharing method based on a block chain, and belongs to the technical field of security industry data management. The method comprises the steps that S1, a sharing end obtains input sharing data and performs validity verification; s2, the sharing end performs validity verification on the block chain; s3, the sharing end calculates a signature value of the shared data and uploads the signature value to the block chain, and the block chain generates and stores a certificate number based on the signature value; s4, the sharing end splits the shared data into a plurality of shared sub-data, encrypts the shared sub-data into encrypted sub-data and sends the encrypted sub-data to the receiving end; s5, the receiving end decrypts the encrypted sub-data to obtain shared sub-data, and all the shared sub-data are spliced into shared data; and S6, the receiving end obtains the evidence storage number from the block chain, verifies the shared data based on the evidence storage number, and stores the shared data. The method has the advantage that the security industry data sharing security is greatly improved.

The present invention discloses a printing system with embossed printing and its process for security material and anti-counterfeit documents, in the printing of anti-counterfeit securities, various security features are used to increase security. The most important security element feature of security documents is embossed printing. Current technology has limitations in terms of durability, quality and number of colors in intaglio embossed printing. In addition, the equipment related to the security industry is large and expensive. In the present invention, a system for creating various security features in securities is introduced, in which a process including embossed printing is introduced into an integrated device. The current innovation not only eliminates the above-mentioned limitations of traditional printing plates, but also makes it possible to print low-quantity or low-issued documents and significantly reduces printing costs.

To reduce labor costs in the security industry and provide an efficient police reporting system.SOLUTION: A security system is capable of analyzing surveillance camera footage with AI and automatically notifying the police only when an anomaly is detected.SELECTED DRAWING: Figure 1