456 results about "Public security" patented technology

The invention provides an intelligent Internet of Things public security management and control system and method based on multi-source data fusion, and belongs to the technical field of Internet of Things security. According to the system, unified collection and standardized processing of multi-source data are achieved by recognizing a system operation scene and loading corresponding model parameters and response strategies, and a unified semantic representation structure is constructed. And the system executes anomaly detection at an edge node, completes risk scoring and grading alarm judgment in combination with a semantic structure and anomaly feature information, and generates a control response instruction based on strategy matching. Meanwhile, dynamic optimization of the scoring model and identity authentication, behavior auditing and data compliance export in the operation process are supported. The system has intelligent identification, adaptive analysis and response closed loop capabilities, and improves the precision and credibility of public security management in the Internet of Things environment.

The invention relates to a multi-modal public opinion risk early warning system and method based on a dynamic knowledge graph and federal reinforcement learning, and belongs to the technical field of public opinion analysis. The system comprises a data acquisition module, a modal fusion module, a knowledge graph construction module, a comparative learning module, a federal reinforcement learning modeling module and a response output module. The system is based on multi-source heterogeneous data, multi-modal semantic alignment of texts, images, videos and the like is achieved, entity relations and propagation paths are mined through a dynamically updated knowledge graph, collaborative modeling under privacy protection among terminals is achieved by fusing federal reinforcement learning, and then real-time sensing, level early warning and multi-level response strategy recommendation of public opinion risks are achieved. Based on the system, the method has the advantages of high fusion precision, high response speed and strong visual propagation path, and is widely applied to the fields of enterprise crisis management, government affair and public opinion monitoring and public safety.

The invention belongs to the technical field of public safety monitoring, and particularly relates to an intelligent monitoring system and method based on vision and language fusion. According to the method, multi-dimensional spatial analysis is carried out on a space-time sequence image, hierarchical feature information can be extracted, context association analysis is carried out on text data, semantic coding information is generated, then image features and text entity description are in butt joint through a multi-stage interaction channel of the hierarchical feature information and the semantic coding information, and the text entity description is extracted. And performing trend consistency calibration, eliminating phase deviation on a time sequence, predicting a future trend of hierarchical features and semantic coding, generating joint characterization data, dynamically correcting evaluation boundary parameters by combining environment feedback parameters on the basis, and finally evaluating the current joint characterization data according to the corrected evaluation boundary parameters. And determining the public safety state level in the target scene, thereby realizing the intelligent monitoring process of the public safety information in the target scene.

The invention particularly relates to an intelligent security and protection dynamic risk assessment and prevention and control system based on multi-source data fusion, and the system comprises a data collection module which collects corresponding data and carries out the preprocessing of the data; the data processing module is used for analyzing the data to obtain a risk assessment coefficient; the risk assessment module is used for obtaining a risk level based on the risk assessment coefficient and making a corresponding prevention and control strategy; and a prevention and control decision optimization module. According to the invention, the data acquisition module integrates sensor data, service system data and external data, and realizes omnibearing data coverage of a security scene; real-time data collected by a visual sensor, an environment sensor and the like are combined with business system data of access control, vehicle management and the like, then external information of meteorology, public security, public opinions and the like is fused, feature extraction and weight distribution are carried out by using technologies of deep learning, an entropy weight method and the like through a data processing module, and finally an accurate risk assessment coefficient is obtained. More risk factors are comprehensively considered, and potential safety hazards are effectively identified.

The invention discloses a case factor intelligent analysis and processing method based on intelligent semantic understanding, and belongs to the field of artificial intelligence, natural language processing and public security judicial informationization, and the method comprises the following steps: 1, preprocessing public security judicial document information, and supporting text analysis, format standardization and structured data conversion; 2, carrying out refined extraction on case elements based on a large language model, and identifying case constituent elements, hazard degrees and processing results; 3, fusing logical reasoning of a public security judicial knowledge graph, and enhancing cross-cause semantic understanding and reasoning accuracy; and 4, performing intelligent auxiliary processing, generating class case recommendation and penalty interval prediction, and performing interactive regulation question and answer. According to the method, efficient, accurate and automatic case factor analysis is successfully realized, and the public security judicial document processing efficiency and decision accuracy are remarkably improved.

The invention discloses a smart city public safety management system based on video monitoring, and relates to the technical field of public safety management, original video data streams and a real-time road data set D are collected in real time through an API interface, a time sequence feature matrix TSM and a road congestion coefficient Ck are generated, and the data integration efficiency is improved. The behavior feature extraction module identifies group behavior features through target detection and an optical flow algorithm, and encryption transmission guarantees privacy. The risk prediction module calculates a cross-regional risk index RRI based on federated learning, triggers a response mechanism and accurately early warns risks, and the dynamic decision module combines real-time data to generate a resource scheduling scheme and selects a proper path and callable resources. Through the combination of the execution feedback module and the driving iterative optimization module, the algorithm is dynamically adjusted, closed-loop management is formed, the problems of data splitting, response lag and model stiffness of a traditional system are solved, and the real-time performance, the accuracy and the scene adaptability of public safety management are remarkably improved.

The invention discloses a public security event emergency resource allocation method based on multi-agent collaborative optimization. The method comprises the following steps: S1, forming a three-dimensional state tensor field; s2, initializing an event agent, a resource agent and a scheduling agent based on the three-dimensional state tensor field, constructing a resource scheduling solution space, configuring an evolution hybrid perception model for each agent, and outputting a scheduling potential map; s3, path search based on target driving is carried out in the resource scheduling solution space, honey source position selection is carried out based on the scheduling potential map through a bee-bird search algorithm, and a resource matching path is generated; s4, establishing a bidirectional coupling mechanism between the hummingbird search algorithm and the evolution hybrid perception model, and realizing dynamic collaborative optimization; and S5, outputting a final resource scheduling scheme and executing the final resource scheduling scheme in real time by each agent to form a scheduling decision closed loop of the emergency response of the public security event. According to the method, the multi-agent evolution hybrid perception model and the hummingbird search algorithm are fused, and public security event resource scheduling optimization is realized.

The invention provides a knowledge graph-based public security multi-source risk factor association identification analysis method, which relates to the technical field of risk identification, and comprises the steps of obtaining multi-source risk factor data, extracting information from unstructured data, constructing an initial association network, performing feature analysis and calculating a similarity matrix; and the close association subgroups are identified through community discovery, a multi-level association network is constructed, a conduction path is analyzed, a weight is calculated, and finally risk early warning information is generated. According to the invention, the complex association between public security risk factors can be effectively identified, and the risk prediction accuracy is improved.

The invention discloses a public security data digital management system and method based on artificial intelligence, and relates to the technical field of public security management, and the management method comprises the following steps: carrying out the multi-dimensional data collection of any public security event; based on the processing feedback result, performing anomaly identification on the actual occurrence condition of any public security event; randomly selecting a public security event with a normal judgment result, and identifying a monitoring blind area existing in each dimension; based on the judgment result difference between different public security events, the area range of each monitoring blind area is corrected, and the characteristic value of each dimension is analyzed; based on the characteristic value difference of each dimension, a trigger mechanism is set for the existing evaluation conflict condition; performing multi-dimensional data acquisition on the target area in real time to obtain a characteristic value of each dimension at the current moment; and analyzing the triggering condition between the characteristic value of each dimension and the evaluation conflict mechanism, and judging whether a public security event exists at the current moment.

The invention relates to the technical field of artificial intelligence and the technical field of computers, and provides an abnormal behavior recognition and early warning method and related system based on a cloud-edge cooperation mechanism, a first edge Internet of Things intelligent agent obtains personnel image data and environment data, and intelligently analyzes the personnel image data and the environment data; obtaining a first analysis result; the cloud internet-of-things intelligent agent is used for preprocessing the personnel image data and the environment data to obtain first personnel image data and first environment data; driving a multi-modal large model to diagnose the first person image data, the first environment data and the first analysis result to obtain a first diagnosis result, and determining a first early warning parameter according to the first diagnosis result; and the first edge internet-of-things intelligent agent performs early warning operation and reinforcement learning according to the first diagnosis result and the first early warning parameter. According to the embodiment of the invention, the intelligence of an intelligent security scene can be improved, so that the public safety is ensured.

The invention belongs to the technical field of public safety, and particularly relates to a map-based focused person management and control large model visualization system and method, and the system comprises a multi-source data collection layer which is used for constructing a multi-modal data set comprising identities, tracks and biological characteristics; the large model analysis layer comprises a space-time trajectory prediction model, a risk integral calculation engine, a group event early warning model and data processing; the visual interaction layer dynamically renders personnel tracks, risk thermodynamic diagrams and police resource distribution based on a three-dimensional map engine, and supports hierarchical early warning display and emergency plan linkage; the monitoring and tracking layer is matched with the tracking and early warning layer, frequently-going / non-frequently-going areas are divided through an improved space-time ST-DBSCAN algorithm, dangerous behaviors of management and control personnel are monitored and tracked, and a differential early warning mechanism is triggered. The multi-modal data fusion technology is combined, real-time loading of ten thousand people-level track points is achieved, data collaborative training of public security organizations in multiple places is supported, and dangerous behaviors of management and control personnel can be monitored and tracked.

The invention discloses a group behavior recognition method and system based on attitude estimation and space-time modeling, and belongs to the field of behavior recognition, and the method comprises the steps: S1, carrying out the target detection of an input video frame, and obtaining a single-person image detection frame and the position information of an abnormal object; s2, based on a single-person image detection frame, generating a high-precision key point heat map through an attitude estimation network; s3, converting the high-precision key point heat map, performing weighted fusion on the converted high-precision key point heat map and re-identification features, and performing feature matching on the re-identification features after weighted fusion to obtain an identity label; s4, inputting the high-precision key point heat map sequence into the space-time modeling network for identification to obtain position information and behavior categories; and S5, based on the identity label, the position information, the behavior category and the abnormal object position information of each person, identifying the group behavior. By identifying and analyzing the group abnormal behavior in the public place, the public safety management efficiency is improved, and the dynamic evaluation of the group behavior is realized.

The invention provides a multi-feature trajectory prediction and cross-defense early warning method for edge and coast defense control objects, relates to the technical field of trajectory prediction and intelligent control, and solves various limitation problems of poor trajectory continuity, lack of environmental constraints and the like of an existing trajectory prediction technology in edge and coast defense scenes. The method comprises the following steps: firstly, acquiring multi-channel monitoring video data, performing environment adaptation preprocessing, and detecting each target and category; based on a detection result, splicing the track fragments in the single-path monitoring range, and obtaining global track data of the target after preset constraint inspection; extracting features of the global trajectory data from a plurality of preset dimensions, and performing feature fusion to obtain high-dimensional vector representation of the target trajectory; and constructing a multi-dimensional feature constraint trajectory prediction model, respectively modeling through time dependence and space interaction, and outputting a subsequent trajectory of the target under a preset constraint. The method can effectively realize prediction and cross-defense risk identification of future trajectories of key control objects, and is applied to the field of public security.

The invention provides a data quality rule automatic matching method, system and device for public security service data, and belongs to the technical field of computer information processing. The method comprises the following steps: structurally analyzing a public security data element standard document; constructing a basic rule and an association rule according to an analysis result; constructing a quality rule knowledge graph, creating four types of entity nodes including data element nodes, rule nodes, standard nodes and synonymous name nodes, and establishing three types of relation edges including a dependency relation, a synonymous name relation and a reference relation; extracting multi-modal features of the to-be-retrieved field, wherein the multi-modal features comprise semantic features and structured features; a rule is generated through a hierarchical matching engine, and matching processes of an accurate matching layer, a semantic matching layer and a statistical matching layer are executed in sequence; and executing online optimization. According to the method, the bottleneck problems of insufficient rule coverage, semantic understanding deficiency, poor dynamic adaptability and the like of a traditional method are solved, and the method has remarkable technical progress and practical value.

The invention relates to the technical field of public safety decision making, and particularly discloses a public safety decision-making auxiliary system based on artificial intelligence driving, which comprises the following steps of: firstly, extracting event elements from historical public safety events by utilizing a safety event element extraction module; and performing geographical division according to the target area planning map through an event space distribution clustering module. Then, the event time distribution sorting module sorts the event elements according to a time sequence to form a time sequence; then, the event semantic information analysis module adopts event semantic information organic propagation analysis to generate a plurality of historical area public security event semantic information transmission organic coding expressions, and dynamic semantic association between events is captured; and finally, the event risk discrimination module identifies the public security risk of the target area in combination with the coding representation and the planning graph. In this way, the information of time and space dimensions is effectively integrated, and the ability to understand and predict the complex dynamic interaction relationship of the public security event is enhanced.

The invention discloses a public security data collaborative analysis method based on differential privacy and a block chain, and the method comprises the following steps: S1, constructing a disturbance configuration table by a first collaborative party, and configuring a disturbance trajectory compression Laplacian mechanism for trajectory data; s2, a second collaboration party formulates an analysis task request and determines a data type; s3, the third collaboration party writes the disturbance configuration table and the task request into the block chain through the task declaration smart contract; s4, each data owner performs differential disturbance on local data according to the disturbance configuration table, and writes the local data into the block chain through a data declaration smart contract; s5, the second collaboration party collects disturbance data and constructs a joint model, and submits a training intermediate result abstract to the block chain; and S6, the second collaboration party sends an encryption result to the first collaboration party after completing joint model training, and a third collaboration party records a result abstract and supports initiation of an on-chain correction request. According to the invention, differential privacy and block chain technologies are combined, and a cooperative analysis method oriented to public security data is provided.

The invention relates to a large model question answering method and system based on domain business ontology enhancement and application. By constructing the domain ontology and integrating the domain ontology into the large model question-answering system, close interaction between the large model and the domain ontology is achieved. The method comprises the steps of domain ontology construction and optimization, large model adaptation adjustment, ontology-based question and answer interaction process design, answer generation and feedback and the like, and the question and answer accuracy and specialty of the large model in a specific field can be remarkably improved. According to the method, accurate and comprehensive domain business ontology is constructed and deeply fused with a large model technology, so that accurate analysis and intelligent answering of user questions are realized. According to the method, the accuracy and specialty of intelligent question answering can be remarkably improved, the requirements of users in the professional field are met, and intelligent development of all industries is promoted. The method can be widely applied to the fields of public security criminal investigation, medical treatment, law, supply chain management and the like, and has important practical value.

The invention discloses an urban sewage pipe network system operation and maintenance effect evaluation system and method, and belongs to the technical field of sewage pipe network operation and maintenance management. According to the method, through four core models of data quality scoring, improved AHP-entropy weight method combination weighting, pipe network toughness approximate quantification and public security risk Bayesian updating, robust evaluation of data incomplete and error scenes is realized; and in combination with a core-maneuvering-wide area three-layer three-dimensional perception network and a four-step progressive data governance process, outputting a system health index with a confidence interval, designing a double-layer performance payment function associated with data quality, and generating a data short board diagnosis report. According to the method, the problem that efficiency quantification is difficult in the landing of an effect-based payment mechanism is solved, an enhanced closed loop of evaluation, data improvement and accurate operation and maintenance is constructed, a scientific quantification basis is provided for performance management of the sewage pipe network, and the method is suitable for operation and maintenance evaluation of the urban sewage pipe network under various data conditions.

The invention discloses an unmanned aerial vehicle identification and tracking system, which can integrate multi-source visual information by calling to-be-identified images shot by a plurality of camera units, thereby determining accurate position information of a target unmanned aerial vehicle. The plurality of camera units can be monitoring camera networks which are arranged all over, the camera networks are arranged and execute tasks such as public security monitoring at ordinary times, and unmanned aerial vehicle identification and tracking can be executed, so that the hardware use cost is reduced, and the camera units can be quickly spread and implemented under the existing hardware condition; the visual field range of the camera shooting unit is matched with the low-altitude flight height of the target unmanned aerial vehicle, the blind area of the flyer detection radar can be effectively covered, and the target unmanned aerial vehicle can still be effectively recognized and tracked under the condition that the target unmanned aerial vehicle flies at a low altitude and the flyer detection radar cannot effectively detect. Supervision of the unmanned aerial vehicle is improved, and use safety of the unmanned aerial vehicle is guaranteed. The method is widely applied to the technical field of unmanned aerial vehicle management.

The invention discloses a community governance method based on digital twinning, and the method comprises the steps: carrying out the layering and household-splitting modeling of buildings in a community, and building a spatial layout model of roads, greening and Internet of Things equipment, so as to construct a community three-dimensional digital twinning pedestal; real-time data of population track, vehicle passing, elevator running state, dog behavior and high-altitude object throwing events are obtained through Internet of Things equipment and synchronized to community dynamic data in real time; the real-time data is associated with a digital twin base, an interactive three-dimensional visual interface is generated, and personnel flow, vehicle tracks, elevator states and event alarm information are dynamically displayed; and analyzing the collected data based on a machine learning algorithm, and generating a resource configuration optimization strategy, a risk early warning instruction and an emergency response scheme. The method can solve the problem that the existing community public security governance means is mainly based on webpage end disposal and lacks visual presentation and later prevention drill ability.

The invention discloses a security unmanned aerial vehicle system based on STM32, and relates to the technical field of security unmanned aerial vehicles. The strategic value of the unmanned aerial vehicle in the public safety field is verified, the technical iteration and application deepening of the unmanned aerial vehicle can continuously promote the modernization of social governance, and the novel 7 * 24-hour all-weather security unmanned aerial vehicle system is provided for solving the problems that an existing unmanned aerial vehicle is poor in endurance, single in application scene, low in working efficiency and the like. The problem of endurance is solved by adopting a new technology and a new thought, and the system has the advantages of low cost, low power consumption, high efficiency, energy conservation, environmental protection and the like; the YOLOv5 algorithm and the ArcFace algorithm can form complementation in function, complete more complex tasks together, and realize more efficient and accurate face recognition and pedestrian analysis; the Hungary tracking algorithm and the Kalman filter are combined and used, so that multi-target tracking can be effectively realized, and the tracking accuracy and reliability are improved.

The invention relates to the field of combat command and dispatch, in particular to a multistage linkage combat unit command and dispatch system. According to the invention, a basic-level minimum emergency unit element information base is constructed through a data acquisition module; generating an electronic map supporting 2D and 3D switching; an intelligent analysis early warning module identifies abnormal conditions and generates early warning information; a plurality of multi-level linkage combat unit topology networks are generated through the multi-level linkage command architecture module; generating a command data large screen for dynamically aggregating public security indexes and a global situation visualization electronic map through a global situation visualization module; an early warning area is circled and selected through the intelligent scheduling module; and associating the corresponding multi-stage linkage combat unit to generate a deployment instruction. According to the multi-level linkage combat unit command and dispatch system provided by the invention, the multi-level linkage combat unit is effectively combined with a dynamic visual geographical environment from data acquisition, analysis and early warning to command and dispatch through cooperation of multiple modules, so that the command and dispatch efficiency is remarkably improved.

The invention discloses a monitoring video processing method and device in a public security scene and a medium, and the method comprises the steps: obtaining the current network state information of a video transmission network in real time in the process of carrying out normal compression coding on a monitoring video stream collected by a monitoring device in real time and transmitting the monitoring video stream to a receiving control end in real time; when the current network state information satisfies a degradation coding condition, determining each monitoring video stream to be processed as a degradation video stream, and respectively identifying a target monitoring area and a non-monitoring area in each degradation video stream; and performing at least one round of degradation coding processing on each degradation video stream according to the target monitoring area and the non-monitoring area in each degradation video stream until a degradation processing ending condition is met, according to the technical scheme of the embodiment of the invention, a reliable video transmission guarantee scheme is provided for public security scenes such as urban security and protection through intelligent area identification and dynamic hierarchical coding technologies.

The invention discloses a multi-dimensional graph series-parallel and visualization method for telecommunication network fraud cases based on a graph database. According to the method, intelligent series-parallel analysis across time-space cases is realized by constructing a knowledge graph containing various entities such as cases, personnel, accounts and equipment and deeply fusing a hard association matching algorithm and a soft similarity graph algorithm. The system has the functions of white list filtering, time sequence behavior analysis, NLP information extraction and the like, and can effectively identify criminal gangs, track fund flow directions and mine crime modes. And finally, dynamic visual display is carried out through a plurality of interactive views such as a force-oriented graph, a time axis and a mulberry-based graph, and clue intelligent pushing and report automatic generation are supported. According to the method, the case association mining depth, the serial-parallel analysis efficiency and the interpretability of a complex network are remarkably improved, and an efficient technical support is provided for the public security organization to fight against telecommunication network fraud.

The invention relates to the field of social security management data processing, and discloses a social security information intelligent analysis system based on dynamic closed-loop regulation, and the system comprises an analysis prediction unit which calculates a risk score based on a feature weight; the subthreshold residual cutoff unit captures weak score data lower than a trigger threshold value; the space-time entropy aggregation unit calculates the Shannon entropy of the weak data; the agility bias unit reduces the trigger threshold according to the low-entropy aggregation state; the self-adaptive damping correction unit utilizes a predicted and feedback difference vector to reversely correct a feature weight, the environmental background pressure is sensed by calculating a space-time entropy value of discarded residual data, a trigger threshold value is dynamically biased according to the environmental background pressure, a damping mechanism correction model is introduced into a feedback closed loop, and a damping mechanism correction model is introduced into the feedback closed loop. The problem of weak risk report omission caused by a fixed threshold value is solved, and self-adaptive dynamic balance of system sensitivity and false alarm rate is realized.

The invention discloses an intelligent public opinion research and judgment method and system for public security actual combat, and the method achieves the high-throughput data capture and real-time public opinion inflow through multi-source crawler, breakpoint resume, incremental collection and asynchronous concurrent processing, and greatly reduces the workload of manual screening. A unified, quantifiable and structured public opinion risk grading standard is established, and risk grading standard unification is realized by constructing three types of structured public opinion dictionaries of event subjects, event properties and sensitivity grades; the grading rule is quantified and objective and adapts to the grading system of the public security actual combat, and the subjectivity and inconsistency of research and judgment are remarkably reduced; and the recessive public opinion recognition capability is improved through the deep semantic model. According to the method, the BERT model, keyword vector fusion and the police affair large model are combined, so that accurate analysis of complex semantics such as metaphor, black talk and irony mood is realized; high-robustness semantic matching and duplicate removal are carried out; through accurate extraction of risk factors and event identification, the problem of high missed judgment rate in the prior art is improved.

The invention discloses a voiceprint recognition and identity authentication method and system based on public safety management and a medium, and relates to the field of voiceprint recognition, and the method comprises the following steps: S1, carrying out the preprocessing of an original voice signal, extracting three types of features in parallel, and splicing the three types of features into a joint feature for representation; s2, coding the joint feature representation, outputting a high-level shared feature vector, and generating a voiceprint identification vector and a living body attribute vector through branches; s3, when high-security-level authentication is judged, dynamic challenge content is sent, response voice is received, and instant behavior characteristics are analyzed; and S4, calculating voiceprint matching similarity and living body attribute probability, fusing the voiceprint matching similarity, the living body attribute probability and the selectable dynamic evidence score, and generating a final authentication decision. By introducing the joint feature coding method based on the correlation constraint, the deep discrimination information can be better reserved, the defense capability on advanced speech synthesis and conversion attacks is enhanced, and the overall security level of the system is improved.

The invention discloses a multi-level data security access control and trusted exchange device, and relates to the technical field of data processing, and the technical scheme is characterized in that the multi-level data security access control and trusted exchange device comprises the following steps: constructing a large-scale sensitive data multi-level security sharing management and control architecture in a data layer, and realizing formalized representation of shared data and dynamic construction of a sensitive data cross-domain access interface; the authority layer realizes data approval and authority confirmation, service feature-oriented fine-grained extensible access control and a dynamic collaborative encryption and decryption strategy in a resource-constrained environment; the behavior layer constructs a multi-dimensional trust evaluation system, a behavior evaluation model and an indirect trust synthesis mechanism. According to complex characteristics of big data, full-process management and control of data sharing are realized, security and compliance of data sharing are improved, risks of data leakage and abuse are effectively prevented, meanwhile, cross-department collaboration and data utilization efficiency are promoted, organization intellectualization and informatization processes are promoted, and the method is suitable for large-scale popularization and application. And a powerful support is provided for improving the public safety level and the treatment efficiency.

The invention discloses an intelligent early warning system based on fusion of a large model and a professional algorithm in the security field, and relates to the technical field of public security early warning. Overall efficiency upgrading is realized by relying on the decision-making capability of the large model, combining the scheduling advantage of intelligent routing and the capability of deeply activating a professional algorithm library; according to the method, efficient and accurate early warning of public security hidden dangers is realized through precise distinguishing of complex scenes by a large model and secondary judgment of a professional algorithm recognition result in combination with the fine recognition capability of a professional algorithm in the security field, the false alarm rate of a traditional early warning system is greatly reduced, and the early warning efficiency is improved. And reliable technical support is provided for work such as fire control, industrial production safety guarantee and the like.

The invention belongs to the technical field of computer vision and target detection, and particularly relates to an X-ray security check image hazardous article detection system combining multi-scale attention to improve YOLOv8, and the system comprises the steps: firstly carrying out the data preprocessing and enhancement of an X-ray security check image data set, so as to improve the number of data set samples; according to the method, a dangerous article detection framework based on YOLOv8 is modified, a Multi-scale Enhancement Attention Module module is combined, a YOLOv8-Multi SEAM network is constructed, the improved model can focus feature information of targets of different scales more accurately, and therefore the detection precision of dangerous articles is effectively improved. According to the method, the dangerous goods in the X-ray security check image are detected by using the YOLOv8-MultiSEAM algorithm, so that the detection precision and efficiency are improved, the security check work can be better served, and the public security is guaranteed.