834 results about "Security check" patented technology

The invention relates to the technical field of automatic guided vehicles, in particular to an AGV cluster scheduling method and system, and the method comprises the steps: converting multi-source order information into a standard order format, generating an order priority based on the time urgency, order source weight, material importance and historical distribution evaluation, and creating a distribution task; meanwhile, obtaining a distribution constraint condition, performing feasibility analysis on the distribution task, and generating an executable task; according to the AGV state information, the most suitable AGV is selected to execute an executable task through a multi-dimensional attribute matching mechanism; monitoring the execution state of the AGV in real time, and when an abnormal state is detected, storing the context of the task and reselecting the AGV to continue to execute the task; a scheduling instruction is generated, the scheduling instruction is safely packaged and sent to the target AGV, and the target AGV executes a task after executing multi-level safety verification on the received scheduling instruction; and through a multi-source order intelligent fusion mechanism, unified processing of different channel orders is realized, and the service adaptability of the system is improved.

The invention relates to the technical field of operation and maintenance automation, and provides an operation and maintenance field professional question and answer and diagnosis Agent design method and system, and the method comprises the steps: receiving and structurally analyzing an original query request of a user, carrying out the parameter validity check and safety verification, and extracting the query content and a session identifier; identifying task types through an intention classification algorithm based on the pre-training language model and performing content risk assessment; performing semantic extension on the query to generate an extended query word, performing similarity matching in the operation and maintenance knowledge base by using a hybrid retrieval algorithm, and fusing related knowledge fragments; inputting the enhanced query information and the task type into an inference engine for intelligent inference to obtain a diagnosis result; the reasoning result is stored in a historical memory library, and session context state information is updated; and performing formatting processing and security check on the reasoning result, packaging the result and context information, and outputting a standard response result. According to the method, the accuracy and the intelligent level of operation and maintenance professional question answering and diagnosis are improved.

The invention discloses a security check door security check process optimization scheduling system and method based on big data, and belongs to the technical field of intelligent security check, a whole security check area is divided into a plurality of functional areas and mapped into network nodes according to security check process links and physical space distribution, directed edges among the nodes represent passenger flow paths, and the flow paths of passengers are distributed in the network nodes. A weight matrix of edges is calculated through historical flow data, and a security check area network topology model is constructed; fusing ticket business, historical security check and real-time sensor data, constructing a load evaluation model, and calculating a node load index and an influence conduction coefficient; based on passenger ticket business time and historical behavior records, establishing a time urgency index and risk level model, and forming a two-dimensional decision matrix to dynamically divide priorities; constructing and training a congestion propagation model, predicting a future congestion path in combination with priority distribution, and identifying bottleneck nodes; and according to a prediction result, cooperatively implementing a grooming strategy from four dimensions of personnel, channels, equipment and processes.

A network device may receive one or more first user inputs in a security gateway creation user interface (UI) provided by a controller. A network device may query, by the controller, the CSP using Application Programming Interfaces (APIs) to retrieve information about applications within the at least one virtualized network environment including any services deployed within the at least one virtualized network environment. A network device may present a security status user interface that identifies the at least one virtualized network environment applications configured in the CSP account and a respective status indicating whether the at least one virtualized network environment is protected by the security gateway. A network device may receive a second user input within the security status user interface, the second user input is effective to enable protection of the at least one virtualized network environment by the security gateway.

The invention discloses an AI agent data protection method and device based on a controlled execution environment, and the method comprises the steps: verifying the integrity of a safety decision monitoring module in each service node kernel of a cloud side through a remote authentication service in a trusted execution environment, and verifying whether the service nodes of the cloud side are all operated in the trusted execution environment or not; the starting environment is safe and credible; the cloud side receives the security configuration from the user side and distributes the security configuration to the security decision monitoring module of each service node; firstly, security check is executed on a service request to be sent by an AI agent, the service request is received by a cloud side service node after compliance, and a security decision monitoring module of a cloud side gateway service node intercepts a received network packet and judges whether the service request is normal traffic; when other service node services of the cloud need to be called, continuing to judge whether the traffic meets the security configuration configured by the user; and the security decision monitoring module is established based on an eBPF and is used for intercepting a received or sent network packet and realizing verification and auditing functions.

The invention discloses an airport terminal security check passenger number hourly prediction method comprising the following steps: 1) preprocessing security check passenger flow characteristic data and terminal historical security check passenger data, and merging the preprocessed security check passenger flow characteristic data and terminal historical security check passenger data into a passenger security check number prediction data set; 2) constructing an NBMT-OneNet-based hourly security check passenger number prediction model; 3) training the hourly security check passenger number prediction model by using the passenger security check number prediction data set; and 4) obtaining security check passenger data and security check passenger flow characteristic data of the terminal to be predicted in the past T time period, and inputting the data into the hourly security check passenger quantity prediction model to obtain security check passenger data in the future T'time period. According to the characteristics of hourly airport security check data, the N-BEATS model and the MTGNN model are used for capturing the complex correlation between the time sequence trend and the variable, and the accuracy of the prediction result is effectively improved.

Various embodiments relate to integrated key revocation with a field loading process and / or related safety and security checks related to an asset system. In an implementation, a data loading request to store a data file via a target storage device of an asset is received. The data file can be signed based on a first key. In response to the data loading request, a key identifier for the first key associated with the data file is compared against (i) a list of key identifiers stored in a key revocation list and (ii) a one-time programmable (OTP) memory of the asset. Additionally, in response to a determination that the data file is successfully authenticated against the key revocation list and the OTP memory, a key revocation process is performed with respect to a second key for the data file associated with the data loading request.

The invention discloses a heterogeneous graph neural network-based microgrid cooperative scheduling prediction and security check method. The method comprises the steps of constructing a heterogeneous graph model; different micro-grid operation scene graphs are generated through analogue simulation; solving an optimal collaborative scheduling strategy vector for each scene graph to serve as an optimal scheduling label, generating a training data set of a'scene graph-optimal scheduling label 'data pair, and completing offline training of the graph neural network model; dynamically generating an instantiated heterogeneous graph; taking the instantiated heterogeneous graph as the input of the trained graph neural network model, carrying out first forward reasoning, and outputting a predicted collaborative scheduling strategy vector; and injecting a virtual fault, generating a virtual fault heterogeneous graph, inputting the virtual fault heterogeneous graph and the predicted cooperative scheduling strategy vector into the trained graph neural network model, carrying out second forward reasoning, predicting the state of the faulty micro-grid system, obtaining the predicted fault node voltage, and carrying out safety check on the predicted cooperative scheduling strategy vector.

The invention relates to the technical field of data processing, in particular to a mobile terminal data intelligent backup method and device, a mobile terminal and a storage medium. The mobile terminal data intelligent backup method comprises the steps that when a preset triggering condition is monitored, such as an NFC label approaching event and / or at least one additional triggering condition, safety verification is executed, and a backup task is triggered if verification is passed. Wherein the NFC label is only used for waking up the backup application program of the mobile terminal. And after a backup task triggering instruction is received, writing the to-be-backed-up data into the external storage device successfully connected with the mobile terminal according to the intelligent backup strategy. According to the data backup method and device, intelligent triggering is achieved through the NFC near field sensing characteristic, the expansion requirement of the external storage device is considered, backup can be conducted according to the intelligent backup strategy, the personalized backup requirement under the complex scene is met, and the convenience and applicability of data backup are improved.

The invention provides a quick start and security verification method and equipment for an embedded device, a medium and a program product, in the method, firmware is divided into a core start module and a non-core function module in advance, and the core start module and the non-core function module are respectively stored in a high-speed access area and a common storage area of a nonvolatile memory; after the embedded device is powered on, initialization of the key peripheral is completed; when firmware is started or updated for the first time, security verification based on a digital signature is executed on the core starting module, if verification is passed, a mirror image of the core starting module is written into a running cache partition of the nonvolatile memory, and an integrity label is generated; in the subsequent starting process, the mirror image is loaded from the running cache partition, and the integrity label is used for executing rapid verification; after loading of the core starting module is completed, the non-core function module is loaded, and lightweight check is executed. According to the method, the security of the core module is ensured, the performance and security requirements of the non-core module are considered, and the calculation and storage overhead is reduced.

The invention relates to the technical field of intelligent security inspection equipment, in particular to a self-learning metal detection security inspection door capable of being intelligently adjusted. The signal acquisition module is used for acquiring magnetic field change information of the total magnetic field; the signal amplification module is used for amplifying the electric signal; the signal filtering module is used for filtering the amplified electric signal; the alarm module is used for analyzing whether a metal early warning standard is met or not based on the amplitude of the filtered electric signal; the recording module is used for recording a time node for sending the alarm signal and a processing log aiming at the alarm signal; the analysis module is used for analyzing whether operation of the security check door is qualified or not based on the number proportion of the passing time nodes and determining the disqualification reason based on the variance of the amplitude of the electric signals of the passing time nodes, the traditional metal detection technology and the intelligent analysis algorithm are combined, the security check efficiency is improved, and the operation and maintenance cost is reduced.

The invention discloses a lightweight container X-ray intelligent security check method and system based on anti-distillation, and belongs to the technical field of image recognition. The method comprises the following steps: acquiring qualified image data, and respectively extracting metadata of the qualified image data to carry out hash verification marking for subsequent block chain evidence storage; generating a confrontation sample and a qualified image to form an enhanced data set; constructing a double-teacher confrontation distillation framework combined with a self-supervision pseudo-label generation mechanism, and after determining an optimal framework, completing model self-supervision training by using the enhanced data set as a training sample to obtain a target model; generating a model compression strategy of structured pruning and mixing precision quantification collaborative optimization, completing lightweight deployment of a target model, and executing an actual container security check task; a pixel-level risk heat map is generated according to X-ray image information obtained by an actual container security check task, and a risk assessment result is obtained; and generating a unique digital fingerprint record to the block chain by using a chain type evidence storage mechanism constructed based on the block chain to complete evidence storage.

The invention provides an Internet of Vehicles multi-protocol dynamic analysis method based on a protocol description model. The Internet of Vehicles multi-protocol dynamic analysis method comprises the following steps: constructing a multi-level PDM comprising a protocol metadata layer, a dynamic adaptation layer, a security verification layer and a resource scheduling layer; generating a dynamic analysis engine integrating protocol identification, parameter adjustment and result standardization functions based on PDM; after receiving multi-source data, calling an engine to analyze, distributing resources by combining a resource scheduling layer through PDM safety verification, and outputting standardized analysis data; when protocol updating is detected, PDM is updated through edge-cloud collaboration, and a new protocol can be adapted without reconstructing an engine. According to the invention, the analysis flexibility and precision are improved through the PDM dynamic adaptation to the Internet of Vehicles scene; double security check and Nash equilibrium resource scheduling guarantee security and real-time performance; edge-cloud collaborative updating gives consideration to efficiency and privacy, and is suitable for a multi-protocol dynamic analysis scene of the Internet of Vehicles.

The invention discloses a medical record large model privacy enhancement fine tuning method based on a federated learning framework. The method comprises the following steps: S01, constructing a three-level medical exclusive federated learning framework of a client, an edge node and a federated server; s02, the client performs privacy enhancement preprocessing on the medical record, generates a medical exclusive feature vector and adds differential privacy noise; s03, a three-level privacy enhancement mechanism of a data layer, a transmission layer and a training layer is designed, dynamic differential privacy noise injection is adopted in the data layer, homomorphic encryption and secure aggregation are adopted in the transmission layer, and gradient mask and federated distillation technologies are adopted in the training layer; s04, adopting a federal fine tuning strategy adaptive to medical characteristics; and S05, constructing privacy security verification, clinical compliance auditing and a model iterative optimization closed loop. Therefore, the privacy protection intensity is obviously improved, the medical data adaptability is optimized, the heterogeneous deployment flexibility is enhanced, the privacy and performance are dynamically balanced, the clinical compliance is guaranteed, and the cooperation efficiency is improved.

The embodiment of the invention discloses a database query optimization method and device based on a large model and a medium, and relates to the technical field of databases. The method comprises the steps that under triggering of a current query request, a query SQL text of the current query request is collected in real time, and real-time environment data of a current execution engine is obtained; vectorizing the query SQL text and the real-time environment data to determine a query reference vector; the query reference vector is input into a pre-trained space-time attention model, performance bottleneck positioning data for the current query is determined, a query optimization strategy is determined based on the performance bottleneck positioning data, and the query optimization strategy comprises an index optimization strategy and a query rewriting strategy; and optimizing the current query request according to the query optimization strategy, determining optimized query information, performing rewriting security verification on the optimized query information in a preset database copy environment, and executing database query based on the optimized query information after the rewriting security verification is passed.

A method to identify which safety leading indicators are not capturing hazards during safety inspections that later contribute to safety incidents and to identify blind spots in safety inspections. The method includes inputting a plurality of incident reports from the worksite and a plurality of inspection reports from the worksite into a system that uses machine learning (ML) and artificial intelligence (AI); identifying socio-technical factors influencing safety risks at the worksite using unsupervised clustering for topic modeling; evaluating semantic similarity between the plurality of incident report and the plurality of inspection reports conducted in a preceding time period to detect missing socio-technical factors not captured during safety inspections; and providing the identified socio-technical factors previously not captured during safety inspections to a stakeholder for development of modified inspection checklists tailored to capture the identified socio-technical factors previously not captured during safety inspection.

The invention discloses a message processing method and device and electronic equipment. The method comprises the steps that a message processing template is generated based on object information and service information, and the object information is attribute information of an object for message transmission; the template content of the message processing template is verified multiple times, a first verification result of the message processing template is obtained, multiple times of verification comprise integrity verification and safety verification, the integrity verification is used for verifying whether the message processing template comprises service information or not, and the safety verification is used for verifying whether transmission of the message processing template is safe or not; generating a target message based on a message processing template and a preset generation rule under the condition that the first verification result is successful verification; performing accuracy verification on the target message to obtain a second verification result of the target message; and processing the target message based on the second verification result. According to the invention, the technical problem of low message processing efficiency in related technologies is solved.

There are proposed methods, devices, and computer program products for safety management. In the method, in response to receiving a first query to a machine learning model, a first response to the first query is obtained by the machine learning model, the first query being represented in a natural language, and the machine learning model being a language model. A second query is determined based on the first query, the first response and a safety token, the safety token triggering a safety check on the second query. A second response to the second query is obtained by the machine learning model based a check result of the safety check.

The present invention discloses a safety inspection system and method based on a hidden danger database model, comprising the following steps: S1, establishing a hidden danger table, an inspection record table, and a rectification record table, and constructing a hidden danger rule base; S2, collecting hidden danger data, and storing it in the hidden danger database model after standardization; S3, constructing a hidden danger knowledge graph and generating a feature matrix; S4, inputting the data and the feature matrix into a time series model to generate a risk scoring matrix; S5, optimizing the hidden danger classification and updating the rule base based on the risk score and knowledge graph; S6, inputting a multimodal hidden danger identification model to classify the hidden dangers; S7, optimizing task allocation, updating the hidden danger status and storing it in the database; S8, integrating data to generate dynamic feature labels, optimizing the model, and outputting a heat map and trend report. Through multimodal data fusion and dynamic analysis, the present invention realizes intelligent management of hidden danger detection, risk assessment, task allocation, and model optimization, significantly improving the efficiency and accuracy of safety inspections.

The invention discloses a forklift graded anti-collision system control method. The method comprises the following steps that 1, the distance between an obstacle in a forklift safety inspection area and a forklift is obtained; 2, the anti-collision grade is judged according to the distance between the obstacle and the forklift, and the closer the distance is, the higher the anti-collision grade is; 3, forklift anti-collision control is triggered according to the anti-collision grade judged in the step 2, pedestrians and non-human obstacles around the forklift can be recognized by arranging the camera and the radar, and then the protection grade is judged according to the distances between the different obstacles and the forklift; according to different protection grades, an alarm can be given out, the forklift and people and objects around the forklift can be protected by controlling the forklift speed, and meanwhile normal running of the forklift is not affected.

The invention discloses a day-ahead and intra-day climbing resource checking and collaborative optimization scheduling method and system, and the method comprises the steps: carrying out the day-ahead climbing capability checking on the D-1 day, determining the starting capacity of a thermal power generating unit when D is the actual operation day of a power grid, and enabling the day-ahead maximum climbing demand of the D day to be smaller than the day-ahead minimum climbing capability of the D day; and on the day D, performing intra-day climbing ability checking, and performing iterative optimization on the output of each type of climbing resources by adopting the intra-day collaborative optimization upper and lower layer models of the climbing resources. According to the invention, through security check of day-ahead and day-by-day climbing resources, it is ensured that the system has sufficient static climbing ability during day-by-day real-time scheduling, and the operation security boundary requirement of the system is met; through intra-day climbing resource collaborative rolling optimization, economic scheduling of various climbing resources is realized on the premise of ensuring system frequency safety, and the dynamic climbing ability of the system is improved.

A computerized method for operating a digital twin representing a personal security checking system, comprising the following method steps: Obtaining influencing data that influence the operation of the personal security checking system; simulating the operation of the personal security checking system with the aid of the digital twin on the basis of the influencing data; generating output data on the basis of at least one simulation, wherein the output data are provided for operating the personal security checking system and / or for operating an entity separate from the personal security checking system.

Disclosed embodiments include technologies for improving safety mechanisms in computer assisted and / or automated driving (CA / AD) vehicles for protecting vulnerable road users (VRUs). Embodiments include various mechanisms to enable early Responsibility Sensitive Safety (RSS) checks for the CA / AD vehicles driving policy to protect in-danger VRUs. Embodiments also include controlled forwarding mechanisms to notify other CA / AD vehicles and roadside infrastructure when a potentially dangerous situation is detected. Other embodiments are described and / or claimed.

The invention relates to a security check X-ray image augmentation method, system and device and a medium, and the method comprises the steps: estimating high and low energy data after a prohibited goods pseudo-color image is inserted into a background pseudo-color image through a bottom layer physical model of security check machine imaging, and specifically inserting the prohibited goods pseudo-color image into a designated target region of the background pseudo-color image, and then mapping from the high and low energy data to the pseudo-color image is completed, and the pseudo-color image in which a specific contraband is inserted at a specified position is generated, so that TIP data enhancement based on the high and low energy data is realized, massive training data with labeling information can be quickly generated, and the purposes of directionally amplifying the data and enhancing the data are achieved.

The invention discloses a portable X-ray back scattering security check system based on AI image enhancement. The system belongs to the technical field of radiation imaging, and comprises a sensing unit used for sensing physical situation information in an imaging process so as to generate time-sequenced physical prior data; the prediction unit is used for predicting a future scanning behavior based on the physical prior data to generate a feed-forward control instruction and prediction confidence; the regulation and control unit is used for adaptively regulating and controlling scanning parameters based on the feedforward control instruction and recording execution information including regulation and control residual errors; and the AI reconstruction processor is used for performing enhanced reconstruction on the original X-ray image based on the physical priori data, the prediction confidence and the execution information. By constructing an intelligent closed loop of perception, prediction, regulation and control and reconstruction, physical uncertainty information in the imaging process is quantized and used for guiding AI reconstruction, so that the problem of information fault between physical acquisition and digital reconstruction is solved, and the quality and reliability of a final image are improved.

The invention discloses a thread scheduling method, electronic equipment, a storage medium and a program product, and relates to the technical field of computers, security check is performed on a source thread and a target thread through a thread switching request initiated by the source thread, an allocatable time period of the source thread allocated to the target thread is calculated according to a security detection result, and the allocatable time period of the source thread allocated to the target thread is sent to the electronic equipment. Bypassing a processing mechanism of a completely fair scheduler, updating the virtual running time of a source thread, distributing an allocable time period to a target thread, calling a context switching function to execute thread switching operation on the source thread and the target thread, and synchronously updating the virtual running time of the target thread when the target thread consumes the allocable time period. According to the thread scheduling method and device, the problem that in the related technology, when a complete fair scheduler processes high-frequency and collaborative thread tasks, due to the indirect handover mechanism and fair scheduling characteristics of the complete fair scheduler, the delay of thread scheduling is improved is solved, and the delay of thread scheduling is reduced.

The invention provides a security check method and system based on voice and smell fusion data recognition, and the method comprises the steps: collecting a smell signal and a voice signal through a signal collection device, and employing a filtering method to remove environment interference noise, and obtaining a preliminary smell molecule concentration sequence and a voice waveform sequence; after the comprehensive abnormal index vector is obtained, a time sequence analysis model is adopted to analyze the time sequence change of the comprehensive abnormal index vector so as to judge a potential abnormal mode sequence; for the potential abnormal mode sequence, similar modes are grouped through a data grouping algorithm, so that classified abnormal groups are obtained; determining a high-risk anomaly subset according to the classification anomaly group; and according to the high-risk anomaly subset, generating a fusion evaluation report so as to obtain a final threat level label.

The invention discloses a security check image recognition and early warning system based on a terahertz imaging technology, relates to the technical field of security check image recognition and early warning, and solves the technical problems that in the prior art, linear change processing cannot be carried out on a terahertz image, the availability of the image cannot be ensured, and consequently the subsequent article comparison efficiency is reduced. The terahertz imaging control unit is used for carrying out region division according to a real-time security check execution region and carrying out terahertz imaging mode control according to the divided region; the material analysis unit is used for performing material analysis on the object detected in real time in the terahertz image, and determining the material of the corresponding detected object in the terahertz image by combining terahertz image analysis with wavelength transmission process analysis of an imaging technology in an actual scene; and the terahertz image processing unit is used for carrying out linear change processing on the terahertz image, adjusting the gray level distribution condition of the image by establishing gray level mapping, and improving the definition of the image.

The invention discloses an electric energy metering error data security check method and system based on artificial intelligence, relates to the technical field of data security check, and aims to solve the problem of inaccurate fault point judgment. The method comprises the following steps: analyzing the similarity between an electric energy meter sub-meter fluctuation sequence and an electric energy meter power consumption difference sequence, obtaining the influence of an environment on electric energy meter sub-meters based on environment data, and analyzing the comprehensive damage condition of equipment based on the similarity of the sequence and the influence of the environment on the electric energy meter sub-meters. Judging whether the comprehensive damage of the equipment exceeds a preset equipment safety damage threshold or not; acquiring historical fault data and operation data of the damaged equipment to judge a fault similar condition; acquiring a fault point of the electric energy meter based on the fault similar condition; acquiring a line corresponding to the damaged equipment to judge as a fault line; and the efficiency and accuracy of fault inspection of the electric energy metering equipment are improved.

The present application relates to a kind of multi-protocol transmission text data monitoring and alarm method and system, method includes: based on dynamic authorization and hardware security check generates multi-source protocol transmission instance, collection and analysis text data;Through multithreading real-time monitoring network connection state, data accumulation and sensor numerical content parameter, generate transmission state and content abnormal event queue;Utilize bayesian network algorithm to learn the causal relationship of network congestion, equipment failure and alarm event, in combination with dynamic weight distribution strategy calculates root cause probability and generates priority-ordered comprehensive alarm list;Based on user feedback data adaptive update protocol weight and alarm strategy, realize abnormal early warning trigger, data snapshot binding and alarm log closed-loop optimization, solve the poor safety adaptability in multi-protocol hybrid transmission scene, monitoring dimension single, root cause analysis relies on static rule and strategy update lag problem, improve the real-time performance, accuracy and adaptability of industrial internet of things data transmission.