119results about How to "Reduce false negatives" patented technology

The invention provides a rainfall monitoring method and system based on unmanned aerial vehicle multi-mode noise reduction in the technical field of environment monitoring and unmanned aerial vehicle application, and the method comprises the steps: S1, collecting a visible light image and a long-wave infrared image through a multispectral vision module after an unmanned aerial vehicle takes off, and collecting a rainfall audio from an acoustic cabin through a microphone; s2, carrying out noise reduction processing on the visible light image, the long-wave infrared image and the rainfall audio; s3, extracting a raindrop size distribution histogram and a spatial density thermodynamic diagram based on the visible light image and the long-wave infrared image, and extracting acoustic MFCC features based on rainfall audio; s4, performing feature fusion operation based on the raindrop size distribution histogram, the visual density thermodynamic diagram and the acoustic MFCC features to obtain rainfall intensity; and S5, carrying out cumulant dynamic compensation based on the rainfall intensity to obtain the cumulative rainfall. The rainfall monitoring method has the advantages that the precision, robustness and practical level of rainfall monitoring are greatly improved.

The invention discloses a multi-modal perception and agent collaborative urban operation event handling method, which belongs to the technical field of artificial intelligence, and adopts the technical scheme that multi-modal front-end equipment is deployed to collect data, a fusion confidence coefficient is generated based on multi-source data, and an event work order is generated when the fusion confidence coefficient reaches a threshold value; the method comprises the following steps: classifying work orders by utilizing an urban management knowledge graph, determining responsibility subjects and disposal guidance, and determining priorities by integrating risks and regions through a weight model; abstracting various resources as disposal resources, and realizing work order and resource matching assignment under the constraint of response time limit; when a complex event condition is met, disassembling into sub-work orders according to a knowledge graph and carrying out parallel processing; the processing resources upload processing feedback, automatic verification is carried out based on computer vision and multi-source evidence, closed loop is qualified, if not, processing or evidence supplementation is carried out, and a closed loop result is archived and used for continuously optimizing the model and scheduling. The beneficial effect of the invention is that the urban operation event handling method based on multi-modal perception and intelligent agent cooperation is provided.

The invention provides a semantic perception collaborative intrusion detection method for multiple vehicle-mounted networks, and relates to the field of vehicle-mounted network security, and the method comprises the steps: collecting communication data from a plurality of vehicle-mounted network domains such as a controller local area network, a vehicle-mounted Ethernet, a host system and the like; analyzing business features of each domain based on semantic information, and extracting feature representation from the time sequence dependency relationship; and a cross-domain attention mechanism is utilized to realize feature association among multiple network domains so as to identify potential multi-stage intrusion behaviors. Through multi-domain semantic fusion and cooperative detection, the method can effectively recognize complex attack behaviors which only show weak abnormal features, improves the intrusion detection precision and robustness of a vehicle-mounted network, and is suitable for a safety protection scene of an intelligent connected vehicle.

This invention relates to the field of electricity meter operation monitoring technology, specifically to an intelligent analysis system for electricity meter operation faults based on sensor monitoring. The system includes: an operation status acquisition module, which collects the voltage, current, active power, reactive power, and power factor of the electricity meter and obtains the operation status at each moment; a periodic analysis module, which uses active power as a load parameter and obtains a basic period based on the load parameters within a preset time period, then segments the preset time period to obtain different time periods and obtains the period fit at each moment; and a fault monitoring module, which obtains the comprehensive rationality of the load linkage deviation at each moment; and uses the comprehensive rationality of the load linkage deviation at a moment and the period fit to correct the squared Mahalanobis distance corresponding to the data points composed of voltage, current, reactive power, and power factor at that moment, and uses the corrected squared Mahalanobis distance for fault monitoring. This application can effectively monitor the operation faults of electricity meters.

The application relates to the technical field of fault monitoring of wind turbines, in particular to a doubly-fed wind turbine fault monitoring method and system based on frequency monitoring, which comprises the following steps: acquiring operation parameter data of a doubly-fed wind turbine, generating a similar working condition time period of a gearbox in the doubly-fed wind turbine according to the operation parameter data based on a clustering algorithm; extracting target vibration data; generating modal components and central frequencies corresponding to each modal component according to the target vibration data, and generating modal component energy; generating a fusion modal energy value according to the modal component energy, and generating a unit fault early warning result. According to the application, only vibration data corresponding to the similar working condition time period is extracted, so that the interference of signal differences under different working conditions on vibration characteristics is excluded, a comprehensive criterion for unit faults based on the fusion modal energy value is used, the gearbox fault risk is early warned as accurately as possible, and false negatives are reduced.

The invention provides a method for deploying a plant-level alarm system, which comprises the following steps that: a whole-plant-level plant model is constructed according to a plant department architecture or a business object node, the plant model comprises a plurality of levels, each level at least comprises one sub-model, and the sub-models of different levels have a mapping relationship; importing instrument equipment data and binding the instrument equipment data to the corresponding sub-models; newly establishing instrument position numbers and binding the instrument position numbers to the corresponding sub-models and instrument equipment, wherein the instrument position numbers and the collector position numbers are in one-to-one correspondence; and configuring an alarm platform based on the configuration, establishing a whole plant appearance map, and binding each component with a corresponding sub-model, instrument equipment and an instrument position number. According to the technical scheme, by establishing a core concept of'factory equipment objects', alarm information of a plurality of'instrument position numbers' bound with the'factory equipment objects' is aggregated upwards, so that the equipment becomes a comprehensive alarm state indicator, and an operator can quickly master the overall operation health condition of the equipment from a macroscopic level.

The application relates to the technical field of mine water disaster monitoring, in particular to a mine water disaster early warning method based on geoelectric field parameter change; the method comprises the following steps: acquiring multi-dimensional geoelectric field parameters in real time and synchronously, performing time sequence feature extraction and fusion calculation on the multi-dimensional geoelectric field parameters, and obtaining a global early warning index; generating a primary early warning signal based on the global early warning index; constructing a three-dimensional geological model based on existing geological data, and generating a high-risk area according to an iterative growth rule; determining the early warning level of the primary early warning signal based on the high-risk area and early warning thresholds at all levels, and performing corresponding early warning response; the application realizes efficient early warning and hazard source positioning of mine water disasters, reduces the false alarm rate and the missed alarm rate of water disasters, and improves the reliability of early warning and the pertinence of emergency response.

The invention provides an inter-application calling risk monitoring method and system under bypass flow monitoring, and belongs to the technical field of network security, and the method comprises the steps: firstly constructing a dynamically updated application IP library and an application asset list through the combination of active scanning and passive flow analysis; then, performing dual matching on the source IP and the target IP of the bypass flow and an application IP library, and accurately screening out inter-application calling flow; then, carrying out deep analysis on the screened traffic, extracting key information, associating the key information with an application asset list, and constructing a fine-grained calling relation unit and a global calling relation graph; and finally, defining a normal behavior baseline and a risk rule based on the atlas, and giving an alarm for an abnormal calling behavior. The inter-application calling risk monitoring method and device achieve accurate and efficient monitoring of the inter-application calling risk, do not need business invasion and are low in implementation cost.

The application relates to the technical field of circuit breaker detection, and specifically discloses a circuit breaker action characteristic detection system based on artificial intelligence, which comprises a data acquisition layer, a data preprocessing layer, a data analysis layer, a decision warning layer and a system management layer; the data acquisition layer is responsible for collecting the action characteristic parameters of the circuit breaker. Through filtering, normalization and feature extraction, the data quality and availability are improved, laying a foundation for subsequent analysis; the data analysis layer uses comprehensive calculation and analysis to evaluate multidimensional parameters in mechanical action, obtains a mechanical action state evaluation coefficient, and accurately grasps the operation state of the circuit breaker; the decision warning layer accurately determines the operation state and timely warns according to the evaluation result and a threshold value, helping operation and maintenance personnel to quickly respond; and the system management layer is used for dynamically optimizing a feedback determination threshold value, so that the overall scheme effectively improves the accuracy, reliability and intelligent level of circuit breaker detection, reduces fault risks, and guarantees the safe and stable operation of a power system.

The present application relates to the technical field of precipitation forecast, solves the problem that the prior art is prone to underestimate the peak value of precipitation or produce false alarm under strong convective weather, and particularly relates to a short-impending precipitation forecast method fusing GNSS and water conservancy rain measurement radar, GNSS observation data and water conservancy rain measurement radar base data in a monitoring area are acquired, pretreated, corresponding atmospheric precipitable water distribution field and radar reflectivity factor distribution field are generated, based on a preset time sliding window, time sequence characteristics of the atmospheric precipitable water distribution field and the radar reflectivity factor distribution field are extracted, and the two are superimposed in channel dimension to construct a multi-channel spatio-temporal fusion input tensor. The present application overcomes the defect that the traditional extrapolation method rapidly decays with the prolongation of the forecast time, significantly improves the prediction accuracy of the rainstorm center intensity and the falling area, reduces the missed report and underestimation of the precipitation event, and solves the problem of low prediction accuracy of the traditional linear extrapolation algorithm under strong convective weather.

The invention provides an intelligent medical illusion detection method and system based on multi-modal data enhancement, and relates to the technical field of medical illusion detection. The method comprises the steps of collecting multi-modal medical data, and constructing an original multi-modal data set; performing data preprocessing on the original multi-modal data set, and generating a plurality of enhanced modal samples through semantic alignment and information fusion among modals by adopting a cross-modal data enhancement technology; modal feature vectors of the modal samples are extracted; according to the modal feature vector, determining a multi-modal consistency index; fusing the modal feature vectors, and determining an abnormal score; obtaining a semantic distance between the modal sample and a knowledge graph; determining illusion confidence; and according to the illusion confidence, judging whether medical illusion appears or not. According to the method and the device, the illusion content in the medical data can be detected according to the consistency of the multi-modal data, the abnormal score and the semantic distance between the multi-modal data and the knowledge graph, and the accuracy and the reliability of the medical data are improved.

The application discloses an online evaluation method for a condensate pump cavitation state. The online evaluation method comprises the following steps: obtaining original vibration signal and working condition signal data of the condensate pump; performing sub-band decomposition processing on the original vibration signal to obtain different types of sub-band features; inputting the working condition signal data into a pre-trained regression prediction model to output a health background prediction value that should appear under the condition of no cavitation; constructing residual features according to the sub-band features and the output of the regression prediction model; performing mechanical fault pre-screening according to the residual features to determine whether it is a mechanical fault; if it is not a mechanical fault, the cavitation state discrimination step is continued; in the cavitation state discrimination step, migration features are calculated according to the residual features, a threshold is dynamically corrected according to the working condition signal data, and finally, the cavitation state is evaluated. The method can obviously improve the problem that the bubble features are hidden under the low signal-to-noise ratio condition, and reduce the false positives and false negatives in the variable working condition operation.

The invention relates to an intelligent online patrol comprehensive management platform, and belongs to the technical field of intelligent monitoring and management. The platform adopts a multi-layer distributed architecture and comprises a data acquisition layer, a transmission layer, a platform layer and an application layer. The platform layer integrates a video resource unified access module, an artificial intelligence behavior analysis module, a patrol task scheduling engine, a multi-stage patrol management module and an early warning disposal closed-loop management module. Multi-source video streams are integrated through a standard protocol, video streams of key areas such as examination rooms and test paper storage rooms are analyzed in real time by using a deep learning algorithm, and illegal behaviors such as abnormal number of people and going out with articles are automatically identified and intelligently early warned. Meanwhile, the system supports multi-stage collaborative patrol and disposal process closed-loop management, full-process automation and intelligentization from problem discovery, early warning pushing, task disposal to result feedback are achieved, the supervision efficiency, accuracy and response speed of examination patrol are remarkably improved, and manual dependence is reduced.

This application relates to a pipe pile tilt monitoring device. The device includes an installation mechanism and a monitoring module. The installation mechanism comprises two installation components and is detachably mounted on the outer wall of the pipe pile. The monitoring module includes a plurality of first fixing members and a plurality of sensing elements. The first fixing members are spaced apart on the installation components facing the pipe pile. The sensing elements are correspondingly disposed on the first fixing members and located on the side of the first fixing members away from the installation components. The sensing elements abut against each other, thereby surrounding the outer wall of the pipe pile. This pipe pile tilt monitoring device can achieve all-round, multi-dimensional tilt monitoring of the pipe pile, effectively reducing the problem of missed detections due to monitoring blind spots and significantly improving the accuracy and reliability of monitoring data. Simultaneously, timely feedback of tilt problems also improves correction efficiency and early warning effect.

This invention provides an esophageal cancer diagnostic kit based on multi-gene methylation level detection. It detects the methylation status of esophageal cancer gene methylation sites, including ZNF154, RNF126, C2ORF27A, OTOP2, TM4SF19, and P16, thereby enabling early screening of esophageal cancer samples. Furthermore, this kit, combined with digital PCR, offers advantages such as absolute quantification, high batch-to-batch stability, and high sensitivity, accurately distinguishing the relationship between sample gene methylation and esophageal cancer. Simultaneously, it employs fluorescently labeled probes for genotyping, demonstrating high specificity and applicability to esophageal cancer patients at different stages. Therefore, it provides a convenient, rapid, and accurate detection method for early screening, diagnosis, and prognosis of esophageal cancer, possessing significant clinical value.

The invention discloses a software uninstalling method and device of a Windows operating system, a storage medium and a terminal, relates to the technical field of software engineering, and mainly aims to solve the problem of low software uninstalling efficiency under the existing Windows operating system. The method mainly comprises the following steps: monitoring a system event in real time to capture process event information of a software installation process; in response to a process ending event of the software installation process, performing multi-dimensional feature matching on the process event information and software matching information of blacklist software, so as to take the installed software as target software under the condition of the installed software corresponding to the process event information; and performing reverse cleaning operation on the target software according to the software matching information and the process event information of the target software so as to unload the target software. The method is mainly used for automatically uninstalling software.

The application relates to the technical field of intelligent transportation and carbon governance, in particular to a passenger car carbon emission monitoring and regulation system and method based on multi-source spatio-temporal data. The application solves the problem of scattered and inconsistent data by preprocessing and uniformly mapping multi-source spatio-temporal data. A three-dimensional carbon emission calculation model of vehicle-space unit-time period is constructed to realize accurate calculation of single-trip carbon emission of a single vehicle and positioning of high-carbon emission hotspots. A clustering algorithm is used to complete differentiated division of user groups. A dynamic early warning threshold is generated based on a warning threshold prediction model, regional carbon emission targets and historical data to reduce false positives and false negatives. A warning type-policy tool-adaptation object mapping rule library is established, and a regulation strategy is generated by combining early warning signals and real-time data to realize automatic and accurate generation of regulation strategies from problem identification. The model parameter, early warning threshold and mapping rule are optimized by using policy implementation effect data to form a monitoring, early warning, regulation, optimization closed-loop management and realize accurate regulation.

The application discloses a ground TT&C station fault diagnosis system and relates to the technical field of space TT&C.The application aims at the problems that the existing fault diagnosis of the ground TT&C station under the environment of low signal-to-noise ratio, multi-node concurrency and strong interference depends on fixed threshold, is difficult to depict the space-time correlation of faults and is highly sensitive to the quality of spectral data, etc., and the application is characterized in that: a plurality of monitoring branches are arranged before and after a plurality of key devices of a link, and multi-modal data such as spectrum, temperature, voltage and environmental parameters are collected, so that the diagnosis system no longer depends on single spectral information, and other observation quantities can be used to maintain the discrimination of the device state when the signal is seriously attenuated or interrupted for a short time; in cooperation with a data preprocessing module, the missing value compensation and targeted normalization or interval scaling are performed on the data from different sources, so that the characteristics of the same node at each time point are kept continuous and dimensionally unified, and the numerical stability of the training and reasoning process of the graph attention network is significantly improved.

This invention belongs to the field of flight simulator maintenance technology, specifically relating to a machine learning-based predictive maintenance method for flight simulators. It addresses the problems of existing maintenance methods relying on periodic inspections and passive repairs, delayed fault warnings, and high false alarm / missing rate. This invention acquires historical operational data, sensor time-series data, fault records, and environmental parameters from the flight simulator, and performs preprocessing including cleaning, labeling, and feature fusion. It constructs a composite health feature set containing statistical features, dynamic health status values, and aircraft material reliability parameters. A hybrid prediction model (random forest feature selection + LSTM time-series prediction + adaptive correction reliability assessment) is used to train the model. Combining prediction result fusion analysis and multi-level decision rule post-processing, maintenance work orders and spare parts demand plans are output. This invention improves the accuracy and timeliness of fault prediction, realizing a shift from passive response to proactive prediction in maintenance, significantly reducing maintenance costs and the risk of unplanned downtime.

The invention discloses an electric welding machine intelligent operation and maintenance management method and system based on machine learning, and belongs to the technical field of operation and maintenance management, and the method comprises the steps of electric welding machine operation and maintenance data integration, electric welding machine fault detection, electric welding machine residual life prediction and electric welding machine intelligent operation and maintenance management. According to the scheme, the original importance score is calculated based on the Gini impurity reduction amount, the weighted importance score of the features is obtained in combination with the fault correlation coefficient, the key feature set is constructed based on the accumulated score, the comprehensive confidence of the decision tree is obtained, the class weight compensation factor is introduced for correction, and the accuracy and reliability of fault detection are improved; based on the calibrated degradation complexity, a dynamic window increment is calculated, a multi-scale window set is constructed, a multi-scale sample is generated, a full connection graph is constructed, and weighted frequency domain features are obtained in combination with the degradation stage, so that the prediction precision of the remaining service life is improved, and a reliable basis is provided for operation and maintenance management of the electric welding machine.

The invention discloses an industrial control system anomaly detection method based on digital twinborn and reinforcement learning, and aims to solve the problems that in the prior art, abnormal samples are scarce, the false alarm rate is high under a dynamic working condition, and the generalization ability of a single detection model is insufficient. Generating synthetic data by simulating anomalies in the twinborn body, and mixing the synthetic data with normal data to form a training set after space-time and distribution alignment; based on the real-time data, generating a time sequence prediction residual error, a distribution reconstruction error and classification confidence multi-source evidence in parallel; constructing a reinforcement learning environment, constructing a state through multi-source evidence and the like, and guiding an agent to learn a fusion strategy through an asymmetric reward function; and training the intelligent agent to obtain a detection model and performing online deployment. According to the method, the multi-source evidence is adaptively fused through the reinforcement learning agent, so that the robustness, the accuracy and the adaptive capability of anomaly detection under the dynamic working condition are remarkably improved, and missing report and false report are effectively reduced.

The invention discloses a highway slope settlement measurement method fusing GNSS and InSAR, and belongs to the technical field of geodetic survey and geological disaster monitoring, and the method comprises the steps: obtaining an earth surface dielectric characteristic fingerprint representing the electrical characteristics of a slope surface through calculation; calculating to obtain subsurface structure characteristic fingerprints representing slope subsurface structure characteristics; constructing a double-fingerprint coupling diagnosis model of a physical process for associating the earth surface dielectric characteristic fingerprint with the subsurface structure characteristic fingerprint, wherein the double-fingerprint coupling diagnosis model is configured to output a diagnosis result based on a change mode of the input fingerprint; the surface dielectric characteristic fingerprints and the subsurface structure characteristic fingerprints serve as coupling fingerprints to be input into a double-fingerprint coupling diagnosis model for calculation, and internal state parameters representing the loosening degree of the soil structure in the slope are obtained; the stability early warning information of the target slope area is generated, and the instability risk of the internal structure of the slope can be recognized in advance in a non-contact and large-area mode before macroscopic settlement of the slope occurs.

The invention relates to an intelligent leakage detection method based on a water supply pipeline sound signal, and relates to the technical field of water supply pipe network leakage monitoring. The method comprises the following steps: acquiring a sound signal of a water supply pipeline, and extracting an acoustic feature vector of the sound signal; the sound signals are manually labeled, a sound sample set with labels is obtained, and the labels comprise a first label representing leakage and a second label representing normal; and constructing a leakage probability model, and training the leakage probability model through the sound sample set. And inputting pipeline sound signals collected in real time into the leakage probability model, and judging whether the water supply pipeline leaks or is normal according to the predicted leakage probability output by the leakage probability model. The method has the effect of improving the recognition efficiency and accuracy of water supply pipeline leakage state detection.

This invention provides a method and system for compliance detection of wearable devices in industrial settings based on edge computing. The method includes: determining an adaptive search region containing the wearable parts of a target person based on the pose geometry information of the target person in the current video frame; extracting a local region image from the current video frame based on the adaptive search region; performing feature inference based on the local region image to obtain a texture confidence index and a spatial location index; generating an instantaneous compliance evidence value and a feature semantic matching index for the current frame based on the texture confidence index and the spatial location index; and performing temporal lag filtering based on the instantaneous compliance evidence value and compliance status information from historical frames to obtain a preliminary compliance status for the current frame. This invention achieves low-latency response and high-accuracy detection of wearable devices in complex and highly interfering industrial work environments.

The invention relates to a photovoltaic cluster climbing event prediction method based on a satellite cloud picture, in particular to the technical field of photovoltaic power generation power climbing prediction, and the method comprises the steps: obtaining corresponding photovoltaic power data, weather forecast data and satellite cloud picture data of a photovoltaic power station in a photovoltaic cluster within a preset collection time, the preset acquisition time is a time period before the prediction time of the photovoltaic cluster climbing event; determining a forecast meteorological factor corresponding to the photovoltaic cluster from the weather forecast data according to the association strength between the weather forecast data and the photovoltaic power of the photovoltaic power station; determining the extreme weather of the photovoltaic cluster corresponding to the prediction time according to the satellite remote sensing value of the photovoltaic cluster geographic area corresponding to the photovoltaic cluster in the satellite cloud picture data; determining the predicted power of the photovoltaic cluster corresponding to the prediction time based on the extreme weather and the forecast meteorological factor; and determining a photovoltaic cluster climbing event corresponding to the prediction time according to the climbing characteristic quantity corresponding to the prediction power.

The invention discloses an event identification method and system for pig house intelligent monitoring, a camera and a medium. The method comprises the following steps: collecting and sampling pig house video streams; performing target identification on the sampled image to obtain an identification result including categories and position coordinates of pigs, personnel, vehicles and the like; performing label matching on the identification result based on a pre-configured label rule, wherein the matching rule at least comprises a target appearance rule, a region intersection rule, a target continuous appearance rule and a target disappearance rule; matching a pre-configured event rule according to a label matching result, and generating hog house monitoring events such as entry of pigs into a corridor and entry of personnel into an effective area; and intercepting an event associated video clip, monitoring a 4G network signal state, and adaptively selecting to locally store or report the event data and the video clip to a cloud platform. According to the invention, accurate and flexible event identification aiming at a pig house scene is realized, and the reliability of monitoring data in an environment without a wired network or a weak network is guaranteed.

The application discloses a risk monitoring system for agricultural product supply chains based on big data analysis, and relates to the field of risk monitoring for agricultural product supply chains.The system comprises a data standardization module, a disturbance feature generation module, a path construction module, a vector field generation module, a state evolution module, a memory calibration module and an early warning output module.The data standardization module is used to generate standardized agricultural product supply chain operation data.The disturbance feature generation module is used to generate a cold chain disturbance feature set and a temperature decay rate parameter.The path construction module is used to generate a cold chain disturbance control path.The vector field generation module is used to generate a gated risk vector field.The state evolution module is used to generate a supply chain risk state representation.The memory calibration module is used to generate a batch calibration risk state representation.The early warning output module is used to generate an agricultural product supply chain risk monitoring result.The application uses a cold chain disturbance to drive a Neural CDE model, thereby achieving agricultural product risk early warning and having the advantages of continuous monitoring and accurate tracing.

The invention provides a hidden danger processing method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting heterogeneous data from a plurality of data sources in a to-be-recognized hidden danger region, carrying out the feature extraction of each kind of heterogeneous data, obtaining a feature map of each kind of heterogeneous data, and evaluating the reliability and accuracy of each kind of heterogeneous data; determining a fusion weight of each feature map based on reliability and accuracy, and performing weighted fusion on the feature maps based on the fusion weights to obtain a fused feature map; inputting the fusion feature map into a target detection network into which an attention mechanism and a multi-scale feature fusion network are introduced, and determining an identification result of the current hidden danger of the hidden danger area to be identified; inputting a recognition result into a task allocation model, and determining a target principal for processing the current hidden danger; and generating a processing score based on the processing of the current hidden danger by the target principal, and determining a final processing result of the current hidden danger based on the processing score, thereby improving the accuracy of hidden danger identification and the enthusiasm of employees to participate in rectification.

The application relates to the technical field of radar electronic countermeasure, in particular to a radar deceptive jamming dragging success rate monitoring method and system; the method comprises the following steps: obtaining target observation track data recorded by a radar and real track data of the target; for any moment in the jamming attempt process, judging whether the jamming dragging at the moment is effective; for any moment, in response to the effective jamming dragging at the moment, confirming the moment as a jamming moment; for any jamming attempt, counting the duration that the distance deviation corresponding to the effective jamming moment in the operation is greater than the spatial error threshold as a continuous duration, and in response to the continuous duration being greater than a preset duration threshold, considering that the jamming attempt dragging is successful; counting the number of times of dragging success in a preset evaluation period, and taking the proportion of the number of times of dragging success to the total number of jamming attempts in the evaluation period as the dragging success rate. The application has the effect of improving the monitoring accuracy of the dragging success rate.

The invention discloses a container repairing method and device and electronic equipment. The method comprises the following steps: acquiring load monitoring data of a container; the load monitoring data is analyzed by inputting a convex long short-term memory network IC-LSTM to obtain load prediction data, the IC-LSTM is provided with a non-negative weight constraint and a convex activation function, the non-negative weight constraint is used for performing non-negative constraint on the model weight of the IC-LSTM, and the convex activation function is used for performing convex activation on the model weight of the IC-LSTM; the convex activation function is used for maintaining a monotonic change relationship between the load prediction data and the load monitoring data; and determining an abnormal detection result of the container according to the load prediction data and a preset threshold value, and repairing the container according to the abnormal detection result. The technical problems of weak multi-dimensional data relevance, insufficient fault prediction capability and poor container scene adaptability existing in a container operation and maintenance method in related technologies are solved.