571 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 relates to the technical field of public security monitoring, in particular to a public security situation prediction method and system based on multi-modal data fusion. The method comprises the following steps: performing continuous video monitoring and non-inductive discovery on a public place to obtain non-inductive discovery data of the public place; performing multi-modal data feature extraction on the public place non-inductive discovery data to obtain security threat multi-modal feature data; performing situation assessment on the security threat multi-modal feature data to obtain security threat event level data, and performing event level division on the security threat multi-modal feature data according to the security threat event level data to obtain security threat risk event data; and performing automatic response control according to the security threat risk event data to obtain response control early warning data, and performing multi-view evaluation optimization according to the response control early warning data to obtain response control early warning optimization data. According to the invention, the potential security situation can be predicted, and the level of public security management is improved.

The present disclosure relates to a system to proactively detect in real time one or more threats in crowded areas. The present disclosure presents a proactive system for real-time threat detection in crowded areas. Utilizing a network of imaging sensors and advanced machine learning algorithms, the system identifies suspicious individuals, objects, and behavioral patterns within a predefined area. The system detects potential threats such as individuals on watch lists, suspicious objects like unattended bags, and abnormal behaviors indicative of security risks, by continuously monitoring and analyzing images and video feeds. Upon detection, the system promptly notifies authorities, providing detailed information on threat location, suspected individuals, and behavioral analysis. Privacy-preserving measures, including encryption of facial recognition data, ensure compliance with privacy regulations. The present disclosure offers a scalable, efficient, and automated solution to enhance security measures, reduce response times, and safeguard public safety in dynamic urban environments.

The invention discloses an alarm data generation method and device and electronic equipment, and the method is applied to the field of public safety protection, and the method comprises the steps: collecting the multi-modal data of each region through a sensor, extracting the feature data of the multi-modal data, and obtaining a feature data set; performing feature fusion on the feature data set, converting fused feature data into graph structure data, and inputting the graph structure data into a target graph model to establish a target relation graph; adopting a quantum search algorithm to match abnormal objects in the target relation graph to obtain a matching result; and generating alarm data based on the matching result and the target processing request. Through application of the method and the device, the problems that data processing is delayed and public security risks are increased especially under the condition of large data volume or poor network conditions due to the fact that data collected by each area needs to be sent to a central server for processing and analysis when the public security condition is monitored and analyzed in real time in related technologies are solved.

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 discloses a multi-source text-based dynamic hotspot clue accurate extraction method, which realizes real-time adaptation and efficient training of a sensitive intelligence recognition model through dynamic label configuration and Prompt design in combination with a lightweight fine tuning technology (RsLoRA), and can accurately recognize in a changeable intelligence scene without re-training. Through human feedback reinforcement learning (PPO) and sorting training, clue texts output by the generative large model are optimized, event subjects and contents are accurately extracted, and a generated result is structured and highly summarized. A semantic vector model and a dynamic multi-layer DBSCAN clustering algorithm are combined, the problem that an existing clustering algorithm is insufficient in adaptability to semantic density difference scenes is solved, and high-precision clue aggregation and popularity statistics are achieved. The overall scheme reduces resource consumption and training time through the RsLoRA technology, comprehensively improves intelligence processing efficiency and analysis ability, and has significant technical advantages and wide application prospects in the fields of intelligent public security, government management and the like.

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 glass breaking device, and relates to the field of public security and antiriot. The glass breaker comprises a glass breaking structure and a load releasing device, the glass breaking structure comprises a shell and a tip part, and a plurality of through holes are formed in the shell; the load releasing device comprises an excitation mechanism and a load mechanism, the output side of the load mechanism communicates with the through hole, and the excitation mechanism is used for exciting the load mechanism so that the load mechanism can output an acting agent to be diffused and released to the outside through the through hole. In a specific operation process, in an emergency occasion, public security personnel can throw or launch the glass breaking device to directly break a window by using the sharp head part and force a target person to lose action ability by using released action load so as to stop adverse behaviors of the target person, and in this way, the window breaking function and the load releasing function are integrated into the glass breaking device, so that the window breaking function and the load releasing function are integrated into the glass breaking device, and the service life of the glass breaking device is prolonged. A mode of directly hitting the target person in the vehicle through one-time operation is provided, and an effective and reliable countering means is provided for public security personnel to flexibly implement disposal according to occasions.

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 relates to the technical field of water area public safety, in particular to a cross-modal intelligent perception fusion early warning method and system, and the method comprises the steps: respectively constructing a dam leakage recognition model, a dam deformation recognition model and a dam water pressure recognition model through collecting the sound wave data, image data and water pressure data of a dam in real time; and fast Fourier transform is carried out on the sound wave data through the dam leakage identification model, frequency domain features are extracted, and dam leakage abnormal coefficients are obtained. Recognizing the image data according to the dam deformation recognition model, obtaining dam image contour features and dam image region features, and obtaining a dam deformation abnormal coefficient. And recognizing the water pressure data according to the dam water pressure recognition model to obtain a dam water pressure abnormal coefficient. And obtaining a comprehensive dam abnormal coefficient according to the three abnormal coefficients, comparing the comprehensive dam abnormal coefficient with a preset dam abnormal threshold value, and performing real-time early warning. Through cross-modal data fusion analysis, the intelligent sensing and real-time management and control capabilities of dam public safety are improved.

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 provides a situation analysis method, system and device for public security and a medium, and the method comprises the steps: determining a target keyword for collecting situation data according to real-time preference, and obtaining a situation data set; constructing sentiment analysis and situation classification sub-models for the pre-trained large language model through knowledge distillation, and identifying a target event in the situation data set by adopting the sub-models; performing event cluster classification on the target event through streaming clustering to update an event cluster; and defining an early warning index to divide a target event in the event cluster, and outputting an event title and an event description of the target event through a large language model.

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 relates to a big data-driven public security situation awareness and evaluation system and model, which can comprehensively integrate various information such as social media, surveillance videos, sensors, government databases and the like through a multi-source data dynamic weighted fusion algorithm, and adopts an improved hidden Markov model (IHMM) and the like to construct a public security situation awareness model. The dynamic change process of the public security event can be effectively processed, and the occurrence probability and the possible development state of the public security event can be accurately predicted through learning of the state transition probability matrix and the observation probability matrix. Compared with a traditional method based on experience or simple statistics, the method has higher accuracy and adaptability in the face of complex and changeable public security situations.

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 check forbidden article detection method, device and equipment and a storage medium, and relates to the technical field of target detection, the security check forbidden article detection method comprises the steps that a target image is acquired, and the target image comprises a security check forbidden article image data set; and verifying the target detection model through the target image to obtain a verification result of security check prohibited article image detection. According to the method, the YOLO v5 algorithm is applied to X-ray security check image prohibited article detection, and dynamic convolution, a large-kernel separable attention mechanism and an optimized bounding box loss function are combined, so that the accuracy and efficiency of prohibited article detection are remarkably improved, meanwhile, the robustness of the model is enhanced, false detection and missing detection conditions are reduced, and the detection efficiency of the prohibited article is improved. Therefore, the efficiency of the security check process is improved while the public security is ensured.

The invention relates to the field of public security police affair command systems, and discloses a police affair processing method and system based on large model technology collaboration, and the method comprises the steps: collecting multi-channel alarm information, carrying out the data preprocessing of the collected alarm information through a police affair command large model, carrying out the automatic recognition of the preprocessed alarm information, and carrying out the automatic recognition of the alarm information. Key data are obtained, and alarm information is automatically filled and updated; analyzing the key data by using a police service command large model to generate a disposal plan and an instruction set aiming at the current alarm condition, and issuing the disposal plan and the instruction set to a next-level execution unit after confirming that the disposal plan and the instruction set are correct, so as to dispatch proper police force to an alarm condition site; using the police service command large model to combine with the police service knowledge base to identify the processing key points of the corresponding alarm condition, synchronizing the processing key points to the target terminal, receiving the alarm receiving and processing information of the case, and updating the processing key points in real time based on the alarm condition feedback information; and alarm feedback information is continuously monitored, and whether abnormity and potential risks exist or not is judged.