51 results about "Potentially abnormal" patented technology

The invention provides an Internet of Things data acquisition and abnormity early warning method and system, and relates to the technical field of Internet of Things data processing, and the method comprises the steps: constructing a digital twin model for a physical entity at a cloud end, and issuing prediction data to an edge node; acquiring sensor data by an edge computing node at a basic frequency, and computing a residual error between real-time data and predicted data and an information entropy of a residual error sequence; dynamically adjusting the data acquisition frequency of the edge node based on a two-dimensional decision space formed by the residual error and the information entropy; and when the residual error and the information entropy both exceed the threshold values, potential abnormity is judged, an edge side abnormity analysis program is triggered, and abnormal event information is uploaded. According to the invention, through cloud edge cooperation and intelligent decision making, the acquisition frequency is reduced in a stationary period, resources are saved, the frequency is improved when an abnormal symptom appears, monitoring is enhanced, and optimal balance between resource consumption and monitoring precision is realized; double criteria are adopted to effectively filter noise interference, and the false alarm rate and the missing report rate are reduced.

The invention relates to the technical field of wastewater pipelines, in particular to a health state diagnosis method for an electrochemical energy storage prefabricated cabin wastewater collection pipeline, which comprises the following steps: collecting an operation signal of the pipeline, simulating the operation state of the pipeline based on a preset simulation model, obtaining an operation virtual signal of the pipeline, comparing the operation signal with the operation virtual signal, and determining the health state of the pipeline. Operation deviation is obtained; extracting the characteristics of the operation deviation to obtain a deviation characteristic vector; and inputting the deviation feature vector into a fault classification model obtained by training, identifying a potential abnormal type of the pipeline, and outputting a health state index. By collecting flow, sound and vibration signals, generating operation virtual signals in combination with a simulation model, comparing and analyzing deviation characteristics, inputting the deviation characteristics into a fault classification model, identifying potential anomalies and outputting health state indexes, quantitative evaluation and early warning of pipeline health are achieved, the pipeline operation state can be reflected in multiple dimensions, and the reliability of the system is improved. Diagnosis accuracy and system reliability are improved, and health grade division and operation and maintenance decision making are supported.

The invention relates to the field of big data analysis, in particular to a prediction method and system based on industrial big data. The method comprises the following steps: extracting an equipment data stream in a factory production process, and carrying out multi-cycle division and centralized visualization to generate a visual production data stream; performing deviation evaluation according to the visual production data flow, and marking potential abnormal equipment; calculating the change rate and the change amplitude of the potential abnormal equipment; positioning fault equipment according to the change rate and the change amplitude, and marking the fault equipment; performing multi-parameter linkage analysis and deviation attribution analysis on the fault equipment, and identifying equipment fault factors; and performing equipment failure shutdown prediction and equipment maintenance priority planning based on the equipment failure factors, and generating an equipment maintenance sequence. Based on factory big data analysis, the equipment fault time point is accurately predicted, and the accuracy of fault diagnosis and the shutdown maintenance efficiency are improved.

The invention relates to the field of deep learning, and discloses a heart failure patient home care intelligent monitoring method combined with deep learning, which comprises the following steps: continuously acquiring the heart rate, blood pressure, breathing and body movement states of a patient in a home environment; performing conjoint analysis of time and activity states on the data streams after priority sorting, and forming a dynamic health feature set in combination with historical health records of patients and environment variable information; comprehensively evaluating the dynamic health feature set by using a deep learning model, and identifying a potential abnormal mode of short-time change through the deep learning model; marking and grouping the physiological parameters and related behavior scenes corresponding to the candidate abnormal events, locally evaluating the current health state of the patient based on grouping information, and identifying a potential high-risk condition; and according to the local evaluation result and the overall health trend of the patient, generating real-time early warning and personalized nursing suggestions. The method has the advantage of improving the identification capability of short-time abnormity.

The application relates to the technical field of network security, in particular to an encryption scene detection method and system based on double-model adaptive switching, which comprises the following steps: loading an unsupervised detection model and an unsupervised data collection module; the unsupervised data collection module collects data for the unsupervised detection model to perform unsupervised preliminary screening and obtain a real-time anomaly score; an adaptive switching controller compares the real-time anomaly score with a switching threshold to determine whether the system is abnormal; if the system is normal, the unsupervised preliminary screening is continued; if the system is abnormal, supervised fine screening is performed; a supervised data collection module collects behavior data and inputs the behavior data into the supervised detection model to determine whether the attack is an encryption virus attack; if the attack is an encryption virus attack, corresponding strategies are executed; if the attack is not an encryption virus attack, the unsupervised preliminary screening is continued. The application performs coarse-grained threat preliminary screening in a normal state, and adaptively switches the model to perform fine screening when potential abnormalities are detected, thereby guaranteeing a high detection rate and reducing the influence on system performance.

The application discloses a digital thread driven intelligent management system for a clinical laboratory, which comprises a system body, and the system body is operated through the following method, and the specific method comprises the following steps: obtaining patient historical records and same batch sample data from a pre-established database by collecting current test result data, and obtaining multimodal information containing original images, detection curves and trend deviations; determining potential abnormal patterns by processing the original images and the detection curves through an abnormality detection algorithm according to the multimodal information; if the abnormal patterns are inconsistent with historical comparison results, comparing previous results of the patient through a trend analysis algorithm to judge the trend deviation degree; obtaining critical project specific threshold values according to the trend deviation degree to obtain key clue recognition results; and the application aims to solve the problem that in the prior art, when facing critical value projects, comprehensive verification and rapid release cannot be balanced, resulting in poor quality control and efficiency of the clinical laboratory.

The invention relates to the technical field of medical data processing, in particular to a urinary post-operation sign monitoring data processing method and system, and the method comprises the steps: obtaining original urinary post-operation sign time sequence flows of a plurality of monitoring devices, and carrying out the verification of data integrity and consistency. And creating a dynamic sign map for describing physiological state evolution, and injecting sign nodes and state transition edges into the dynamic sign map based on the verified time sequence flow. And performing staged partitioning on the dynamic sign map according to a postoperative rehabilitation stage division standard. And deploying independent abnormal probes matched with the stage characteristics in each partition, and performing parallel operation to scan and collect potential abnormal signals. And constructing an abnormal signal aggregation network, and carrying out fusion and priority ranking on the collected abnormal signals. And according to the network output, generating a differentiated graded early warning instruction, and calling a corresponding early warning response protocol for execution. According to the method, modeling of continuous evolution of the postoperative physiological status and staged adaptive anomaly monitoring are realized.

This invention discloses a data analysis-based method and system for early warning of anomalies in basic nickel carbonate production. The method includes: acquiring historical monitoring data within a preset time period; selecting key target data points by calculating data deviation; and extracting multiple historical monitoring data sequences with adjacent target data points as start and end points. During real-time monitoring, when the current monitoring data does not exceed a preset threshold, it is matched with the most similar historical sequence. By analyzing the subsequent data change rate trend of the sequence, it is determined whether there are potential anomalies in the current data. Combining static threshold early warning with dynamic trend early warning can identify risks that are still within the safe range but have abnormal development trends, realizing the transformation from post-event alarm to pre-event early warning. This significantly improves the timeliness and accuracy of early warning in the basic nickel carbonate production process, effectively ensuring production safety and stability.

The invention relates to the technical field of coal mining, and discloses a coal mining-based driving working face abnormal information detection method and system, and the method comprises the steps: obtaining an acoustic emission signal generated when a heading machine works on a driving working face, separating a cutting pick-coal rock impact signal and a coal rock mass fracture signal based on a propagation path and waveform characteristics of the acoustic emission signal; identifying the wear state of the cutting pick based on the frequency attenuation characteristic of the cutting pick-coal rock impact signal, and extracting a coal rock fracture dominant frequency band based on the spectrum mutation characteristic of the coal rock mass fracture signal; compensating the energy of the coal rock fracture dominant frequency band according to the wear state of the cutting pick, and establishing a co-evolution relationship between the compensated energy and the spectrum mutation characteristic in a time sequence; according to the method, the energy precursor abnormal fluctuation is sensitively captured, so that early-stage, accurate and targeted detection of potential abnormal information of the driving working face is realized, and the accuracy and timeliness of early warning are remarkably improved.

This invention discloses a method and system for measuring the ratio of long bones in human limbs to height based on images. The method includes steps such as acquiring image sequences, selecting target frames, estimating two-dimensional human pose, predicting the ratio of long bones in limbs to height, labeling potential anomalies, and iterative updates. By acquiring human experience data, the method simultaneously verifies the model detection results and the potential anomaly labeling results based on this data. If there is a significant difference between the model detection results and the human experience data, it indicates that the model accuracy is insufficient and needs updating. If the number of abnormal individuals in the potential anomaly labeling results exceeds a preset proportion threshold, it indicates that the weighting of the linear relationship between long bones in limbs and height is incorrect. This invention offers convenient measurement, high measurement accuracy, and is unaffected by factors such as age, gender, or race; it is suitable for obese individuals and has good versatility.

The application provides a kind of unknown threat intelligent determination method and system based on multi-modal knowledge graph, it is related to computer security detection technical field.Therein, the application first obtains system performance time series data and security log data;Second, construct network security knowledge graph;Then, system performance time series data is converted into the state attribute of asset node in topological structure layer, and simultaneously, security log data is converted into security event node in event sequence layer, to generate dynamic interaction subgraph;Next, based on dynamic interaction subgraph, graph reasoning is carried out, and the correlation confidence is calculated, when the correlation confidence is lower than the pre-set confidence threshold, it is determined that there is abnormal behavior;Finally, according to the difference between the abnormal behavior and the known threat mode, unknown threat is determined, the technical scheme of the application not only improves the depth and accuracy of potential abnormal behavior discovery, but also improves the cognition and defense ability of unknown threat.

The invention discloses a water conservancy facility inspection fault diagnosis method based on AI analysis, and relates to the technical field of pre-diagnosis, and the method comprises the steps: recognizing a dominant abnormal mode in a preliminary diagnosis report, calculating the attention of the dominant abnormal mode, and generating a dynamic inspection scheduling instruction; positioning a high-risk area according to the dynamic inspection scheduling instruction, collecting enhanced inspection data of the high-risk area, removing interference factors of the enhanced inspection data, extracting structural response, and obtaining a working condition data set; and performing anti-fact reasoning on the working condition data set, outputting an anti-fact diagnosis report, comparing the difference between the anti-fact diagnosis report and the preliminary diagnosis report, reasoning causes, and generating a fault diagnosis report. According to the method, the potential abnormal feature set is constructed and anti-fact reasoning is carried out, so that the causal-level disassembling capability of abnormal causes is realized, the misdiagnosis and missed diagnosis risks are reduced, and the accurate identification capability of the operation safety state of the water conservancy facility is enhanced.

The invention discloses a multi-mode attack protection method and device for a measurement switch and a medium. The method comprises the steps that S1, the opening and closing state and magnetic field change parameters of a lock are monitored and collected in real time through a magnetic induction sensor; s2, electrical parameters of the metering switch are monitored in real time, and when sudden abnormal step change of power occurs, a time sequence monitoring mechanism is adopted to identify potential abnormity; s3, integrating the physical lock state data and the electrical parameter data, and balancing the importance of different modes through a weighting mechanism; and S4, constructing a decision tree model, performing layer-by-layer splitting from a root node according to a feature threshold, identifying an abnormal type based on a power step amplitude, and classifying abnormal behaviors. And S5, generating an encryption alarm after the decision tree confirms the attack, and uploading the encryption alarm to a remote server. According to the invention, real-time identification and response to physical invasion and electricity stealing behaviors can be realized through multi-source data fusion and intelligent decision.

This application provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for predicting the health of tightening equipment. The method includes: obtaining a batch of tightening samples of a target tightening equipment from a preset sample trend library; obtaining target sample drift characteristics of the target tightening equipment based on the batch tightening samples and a preset standard template library; obtaining target equipment drift characteristics of the target tightening equipment based on a first normal sample of the batch tightening samples and the preset standard template library; when the number of abnormal samples in the batch tightening samples exceeds a preset threshold, performing a health prediction based on the target sample drift characteristics and the target equipment drift characteristics to obtain a health prediction result. Abnormal samples refer to workpiece samples with abnormal process inspection results. The health prediction result includes at least one of the following: abnormal component type, abnormal confidence level, and abnormal level. This application can predict potential abnormalities in tightening equipment in advance, improving the accuracy of equipment health prediction results.

Systems and methods for automatically detecting instrument anomalies in a sampling device during a medical procedure may include obtaining, via a transducer of a medical device, a sequence of ultrasound images of a target site of the medical procedure. The system or method may also include detecting, in the sequence of ultrasound images, an instrument protruding from the medical device and within the field of view of the transducer. A characteristic of an instrument extending from a medical device is determined for each ultrasound image in a sequence of ultrasound images including the instrument extending from the medical device. The system or method may also include generating an alert indicative of a potential anomaly of the instrument extending from the medical device based on the characteristic of the instrument extending from the medical device exceeding a threshold.

The invention discloses a hybrid shared bicycle system environment impact assessment method and system based on digital twinning, relates to the technical field of shared bicycle system assessment, and realizes denoising and stable modeling of station occupation data by introducing a long-term baseline and a standardized data set, so that abnormal oscillation stations can be accurately identified. Through an oscillation intensity index (i.e., an accumulated variation value normalization result), normal periodic fluctuation and abnormal large fluctuation can be effectively distinguished. And a hotspot cluster set is formed by further combining density clustering, and the future short-term fluctuation direction is predicted based on a trend quantity, so that a potential abnormal region is locked in advance in double dimensions of space and time. The mechanism ensures that scheduling triggering does not depend on single-point data any more, but improves timeliness and accuracy of vehicle supplementing operation based on double judgment of stability and tendency.

The application provides a river channel regulation engineering monitoring method and system, which belongs to the technical field of engineering monitoring. The method comprises the following steps: based on channel scene information, a matching monitoring parameter set, an abnormal diagnosis rule library and an abnormal threshold set are called from a pre-constructed knowledge base, a monitoring network device is cooperatively dispatched to collect multi-source monitoring data for abnormal identification, and potential abnormal problems and abnormal confidence are obtained; if the abnormal confidence is greater than the abnormal threshold, a drone is controlled to verify the potential abnormal problems on site, and a reliability evaluation result is generated; if the reliability evaluation result is greater than a reliability threshold, the multi-source monitoring data are input into a time series prediction model to obtain a trend prediction result, which is compared with a maintenance critical threshold to obtain a spatiotemporal position of future maintenance needs, a channel comprehensive monitoring index is obtained through a mechanism-data hybrid model, and if the comprehensive monitoring index is greater than a warning threshold, warning information is generated; finally, a cooperative scheduling strategy is generated through an optimization algorithm and is executed.

The invention discloses an active defense method and device for network attacks and electronic equipment. The method comprises the following steps: collecting network security monitoring data from a plurality of data sources; performing standardized preprocessing on the network security monitoring data to obtain network security monitoring standardized data in a unified format; carrying out anomaly perception on the network security monitoring standardized data, and screening out an abnormal event; wherein the abnormal event is a dominant abnormal event or a potential abnormal event; the dominant abnormal event is an event triggering a dominant rule, and the potential abnormal event is an event deviating from a dynamic behavior baseline but not triggering the dominant rule; generating an attack judgment and risk assessment report according to the dominant abnormal event and the potential abnormal event; and starting a cooperative defense matrix according to the attack judgment and risk assessment report so as to realize active defense of the network attack. According to the method, accurate identification, deep research and judgment and active defense of known and unknown network attacks can be realized.

The invention provides an unknown threat intelligent determination method and system based on a multi-modal knowledge graph, and relates to the technical field of computer security detection. The method comprises the following steps: firstly, acquiring system performance time sequence data and security log data; secondly, constructing a network security knowledge graph; converting system performance time sequence data into state attributes of asset nodes in the topological structure layer, and converting security log data into security event nodes in the event sequence layer to generate a dynamic interaction subgraph; then, map reasoning is carried out based on the dynamic interaction subgraphs, the association confidence coefficient is calculated, and when the association confidence coefficient is lower than a preset confidence coefficient threshold value, it is judged that an abnormal behavior exists; and finally, determining an unknown threat according to the difference between the abnormal behavior and the known threat mode. According to the technical scheme of the invention, the depth and accuracy of finding the potential abnormal behavior are improved, and the cognition and defense capability of the unknown threat is also improved.

The application discloses a rigid contact line abnormal data detection system and method based on artificial intelligence, and relates to the technical field of abnormal data detection.The system comprises a full-life-cycle data construction module, a wear law mining module, a real-time data acquisition and matching module, and an abnormality determination and early warning archiving module;the method constructs a full-life-cycle database, mines wear laws of the same section and the same working condition, matches real-time and historical data, adopts real-time speed increase comparison and future trend prediction to determine abnormalities in two dimensions and trigger graded early warnings.The application solves the problems of the prior art, such as scattered historical data, static determination, and passive operation and maintenance, realizes precise early warning of potential abnormalities, promotes the shift of operation and maintenance from passive remediation to active prevention, and meets the demand of intelligent preventive maintenance of rail transit.

The embodiment of the invention provides an unmanned station safety monitoring method and system based on edge computing, and the method comprises the steps: firstly, building the precise mapping of a three-dimensional physical space and two-dimensional video pixels, and fundamentally solving the problem that a video event and a sensor event are difficult to associate due to the space-time isomerism; therefore, accurate locking of potential abnormal events is realized. On the basis, the depth visual features and the temperature time sequence features are extracted, the difference between different modal data is effectively spanned, and fusion features with high information density and strong discrimination capability are generated. And finally, the fusion feature is efficiently researched and judged through a lightweight fusion judgment model, and a highly credible alarm can be quickly generated under the condition that the finite computing power of edge equipment is ensured, so that the accuracy and reliability of safety monitoring of the unmanned station are improved.

This application provides a sensor-based fan operation monitoring method and system, relating to the field of fan operation monitoring technology. It acquires various sensor data reflecting the fan's operating status in an industrial environment, preprocesses and extracts features from the data, identifies potential anomalies exceeding preset fluctuation ranges or instantaneous anomaly thresholds, initiates a multi-dimensional analysis process, quantitatively assesses the fault probability score, and further determines whether the potential anomaly is caused by external interference. If external interference is confirmed, misjudgment of the fan itself is avoided; if it is not external interference and the fault probability score reaches a preset confidence level, a real fault in the fan is confirmed. This effectively overcomes the problems of false alarms caused by environmental interference and missed or delayed reporting of intermittent and transient faults in traditional monitoring systems in complex industrial environments.

The application relates to the technical field of code detection, in particular to an abnormal code detection method and system based on artificial intelligence, which comprises the following steps: obtaining a code set to be analyzed, the code set to be analyzed comprising at least one code segment to be detected, the code set to be analyzed being obtained by segmenting an original code file, and a core function segment in the original code file becoming at least one code segment to be detected in the code set to be analyzed after being segmented; when the syntax features of the at least one code segment to be detected are expanded from basic syntax features to complex syntax features, determining syntax element sequences and semantic association data obtained at multiple feature levels between the basic syntax features and the complex syntax features. Through multi-level feature analysis of the code, the basic syntax features and the complex syntax features are combined, potential abnormalities can be captured more carefully, the layered feature vector set and the semantic association data enable more complex code abnormalities to be recognized, and therefore the detection precision is improved.

The application relates to the technical field of industrial quality inspection, in particular to an abnormal sample generation system and an abnormal detection device for pre-manufacturing quality control, which comprises a data acquisition module used for acquiring a historical abnormal data set and a normal image data set of a target product, the historical abnormal data set comprising a historical product category, corresponding abnormal text and an abnormal image, and the normal image data set comprising a target product normal image; an abnormal reference retrieval module used for acquiring potential abnormal text and corresponding reference abnormal images of the target product based on the target product category; and a unified abnormal synthesis module used for generating pre-manufacturing synthetic abnormal samples based on the reference abnormal images and the normal image. The application realizes the generation of unknown abnormalities of the target product before manufacturing to fill the early quality control data blank, improves the synthetic sample diversity, can accurately balance the generation of abnormal fidelity and rationality, efficiently reuses the historical abnormal data to reduce the complexity, and finally improves the downstream industrial abnormal detection performance.

The application relates to the technical field of solid state disks, and discloses a fault diagnosis method and system for a solid state disk master control chip. The method comprises the following steps: collecting operation logs, delay distribution and multi-source information of the master control chip, generating a data set through time series analysis and extracting dynamic change characteristics; determining an association mode of a health state based on the characteristics and a clustering algorithm; when the mode shows that delay fluctuation exceeds a threshold value, adjusting a standard by fusing the multi-source information to identify potential abnormal fluctuation; integrating abnormal fluctuation and operation logs by using a decision tree algorithm to obtain an early warning index, constructing an adaptive model to update a diagnosis strategy; if the update involves a high load environment, filtering normal fluctuation through an anomaly detection mechanism to realize fault positioning; classifying and integrating fault-related multi-source information, evaluating overall system performance risks and generating a health state report. The application effectively improves the accuracy of fault early warning and the adaptability of diagnosis strategies of the master control chip of the solid state disk.

The invention relates to the field of intelligent medical treatment, and discloses a pathological recognition analysis method and system based on a blood detection sample. The method comprises the following steps: acquiring a blood smear microscopic image and extracting a cell contour; performing texture analysis on the interior of the contour to obtain morphological characteristics; judging a potential abnormal region based on the features; carrying out local enhancement on the abnormal region and extracting subtle difference features; based on the difference features, obtaining quantitative pathological descriptors through a deep learning model; identifying abnormal cells according to a matching result of the descriptors and the feature library; and comprehensively analyzing according to an identification result to obtain pathological quantitative indexes. Through a mode of combining local enhancement, a deep learning model and feature library matching, the problems of low blood smear abnormal cell recognition precision and poor efficiency are solved, and the accuracy and automation level of pathological analysis are improved.

The invention discloses a network security supervision method, system and device, a storage medium and a program product, the network security supervision system comprises a data acquisition module, a security analysis module and an event response module, the data acquisition module is used for acquiring software operation data; the security analysis module is used for carrying out feature recognition and analysis on the software operation data to obtain data features, and matching the data features by utilizing a preset reference database to determine potential abnormal information; the event response module is used for determining event types and response priorities corresponding to the potential abnormal information, and triggering corresponding response measures according to the sequence of the response priorities for different types of events; wherein each event type corresponds to at least one response measure. By adopting the embodiment of the invention, potential safety hazards possibly existing in the software system can be quickly detected, so that external attacks of the software system are effectively prevented, and the risk that the software system is invaded is reduced.

The invention discloses an early warning method and device based on an audit log and electronic equipment, the method is applied to the field of big data, and the method comprises the following steps: obtaining operation behavior information from target storage equipment every first preset duration, and extracting target behavior characteristics from the operation behavior information based on multiple dimensions; screening potential abnormal features from the target behavior features according to the cluster features of the plurality of clustering clusters; analyzing an association relationship between the potential abnormal characteristics through an association rule algorithm, and determining abnormal behavior information according to the association relationship; and determining an abnormal risk level according to the abnormal behavior information, and triggering an early warning operation according to the abnormal risk level. Through the method and the device, the problem that part of abnormal operation behaviors do not trigger early warning due to the fact that the judgment dimension is single and the incidence relation between the operation behaviors is ignored when a user carries out early warning on the abnormal operation line in the audit log in the related technology is solved.

This application discloses a method and system for visualizing gene testing reports, relating to the field of information visualization technology. The method includes the following steps: acquiring user interaction data on information items and risk markers for those items; identifying unreviewed potential abnormal items based on the risk markers and interaction data, and generating an information omission risk indicator; providing guidance information to encourage the user to focus on unreviewed potential abnormal items based on the information omission risk indicator; recording potential abnormal items that the user has been guided to focus on as reviewed; upon receiving a user-triggered instruction representing a final diagnostic decision, checking whether unreviewed potential abnormal items exist in the gene testing report; if so, interrupting the instruction and presenting a prompt interface for the user to choose to continue the decision or return to the report. This application can reduce the user's cognitive load, decrease the probability of key variants being missed during decision-making, and ensure the accuracy of diagnosis and treatment.

The application provides a root cause model analysis method in a micro-service system, and belongs to the technical field of cloud computing. It solves the problems of low accuracy for developers and the like in the existing method. The root cause model analysis method in the micro-service system comprises the following steps: step S5: collecting distributed tracking log data of a target system, constructing through the distributed tracking data, and determining a potential abnormal node; step S6: collecting logs of the target system, processing the logs, extracting events and parameters, and determining an abnormal event; and step S7: performing fault root cause analysis processing based on the potential abnormal node and the abnormal event, and obtaining an analysis result. The application has the advantages of simplifying the operation and maintenance cost of positioning the root cause of faults in the micro-service system, enabling operation and maintenance personnel to more quickly find the root cause existing in the system and the like.